diff --git a/.ipynb_checkpoints/README-checkpoint.md b/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..3def13d --- /dev/null +++ b/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,116 @@ +# Tiny Language Models Framework + +This repository contains the implementation and resources for the Tiny Language Models Framework project. In this project, we developed small-scale language models to facilitate detailed research into various aspects of large language models (LLMs), particularly in the domain of code. + +## Project Structure + +- `data/` + - `meta.pkl` : Metadata for the dataset. + - `prepare.py` : Script to prepare data for training. + - `sample_data.txt` : Sample data used for testing and demonstration. + - `test.bin` : Binary file containing test data. + - `test.txt` : Text file containing test data. + - `tinypy_generator.py` : Script to generate TinyPy data. + - `train.bin` : Binary file containing training data. + - `train.txt` : Text file containing training data. + - `val.bin` : Binary file containing validation data. + - `val.txt` : Text file containing validation data. + +- `generalization/` + - `data/` : Contains tokenized data to fine-tune and evaluate Code LLaMa model. + - `models/` : Stores fine-tuned Code LLaMa models. + - `results/` : Holds results from the evaluation. + - `demonstration.ipynb` : Jupyter notebook demonstrating fine-tuned Code LLaMa capabilities. + - `evaluate.py` : Script to evaluate fine-tuned Code LLaMa. + - `finetune.py` : Script for fine-tuning Code LLaMa model. + - `tokenizing.py` : Handles tokenization for Code LLaMa model. + +- `models/` + - `arithmetics_level1_696K.pth` : Pretrained model for arithmetic operations at level 1 with 696K parameters. + +- `results/` + - Directory to store results of model evaluations and tests. + +- `demonstration.ipynb` : Jupyter notebook demonstrating the usage of the models and scripts. + +- `eval.py` : Script to evaluate the trained models. + +- `model.py` : Contains the model architecture and related functions. + +- `README.md` : This file. + +- `train.py` : Script to train the models. + +## Requirements + +To install the required packages, you can use the following: + +```bash +pip install -r requirements.txt +``` + +## Usage + +### Data Generation +Generate the data using TinyPy Generator by running : + +```bash +cd data/ +python tinypy_generator.py --num_programs 1000 --level 1.1 --filename sample_data.txt --deduplicate +``` + +### Data Preparation +Prepare the data by running: + +```bash +python prepare.py +``` + +This generation command is just an example to get you started. If you want to train your own model, you'll likely need to generate significantly more data. + +### Training +Train the model using the following command: + +bash +```bash +cd .. +python train.py --batch_size 64 --max_iters 35000 --learning_rate 0.01 --miles 0.7 0.8 0.9 --eval_interval 10000 --eval_iters 500 --data_dir data +``` + +### Evaluation +Evaluate the trained model by running: + +```bash +python eval.py --dataset_dir data --model_name arithmetics_level1_696K +``` + +### Demonstration +To see a demonstration of the model's capabilities, open the demonstration.ipynb notebook and follow the instructions within. + +### Generalization +This section aims to generalize the results obtained from training tiny language models to large language models. This can be achieved through fine-tuning Code LLaMa. + +#### Fine-tuning +Fine-tune Code LLaMa model using the following command: + +```bash +cd generalization/ +python finetune.py --train_dataset_path data/tokenized_train --val_dataset_path data/tokenized_val --output_dir models/code-llama-finetuned-demo +``` + +#### Evaluation +Evaluate the fine-tuned Code LLaMa model by running: + +```bash +python evaluate.py --checkpoint_dir models/code-llama-finetuned-level1 --test_file data/test.txt --output_file results/result_llama.txt --csv_file results/results_llama.csv +``` + +#### Demonstration +To see a demonstration of the model's capabilities, open the generalization/demonstration.ipynb notebook and follow the instructions within. + + +# License +This project is licensed under the MIT License. + +# Acknowledgements +This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. diff --git a/.ipynb_checkpoints/code_execution-checkpoint.py b/.ipynb_checkpoints/code_execution-checkpoint.py new file mode 100644 index 0000000..3c82637 --- /dev/null +++ b/.ipynb_checkpoints/code_execution-checkpoint.py @@ -0,0 +1,164 @@ +import os +import pickle +import torch +import numpy as np +import pandas as pd +import re +from tqdm import tqdm +import argparse +from model import GPT + +class ScriptEvaluator: + """ + Class to evaluate a GPT model on a dataset and save the results. + """ + + def __init__(self, dataset_dir, model_name): + """ + Initialize ScriptEvaluator with dataset directory and model name. + + Args: + - dataset_dir (str): Directory where the dataset is stored. + - model_name (str): Name of the pre-trained model (without .pth extension). + """ + self.dataset = dataset_dir + self.model_name = model_name + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + torch.manual_seed(1337) + self.test_data, self.meta = self.load_dataset() + self.m = self.load_model() + + def load_dataset(self): + """ + Load test dataset and metadata. + """ + test_data = np.memmap(os.path.join(self.dataset, 'test.bin'), dtype=np.uint16, mode='r') + meta_path = os.path.join(self.dataset, 'meta.pkl') + meta_vocab_size = None + if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"Found vocab_size = {meta_vocab_size} (inside {meta_path})") + + return test_data, meta + + def load_model(self): + """ + Load pre-trained model based on the provided model name. + """ + model_path = os.path.join('models', f"{self.model_name}.pth") + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file '{model_path}' not found.") + + model = GPT() + print("Compiling the model...\n") + try: + model = torch.compile(model) # requires PyTorch 2.0 + except Exception as e: + pass + model.load_state_dict(torch.load(model_path)) + m = model.to(self.device) + return m + + def encode(self, s): + """ + Encode string `s` into token IDs. + """ + return [self.stoi[c] for c in s] + + def decode(self, l): + """ + Decode token IDs `l` into a string. + """ + return ''.join([self.itos[i] for i in l]) + + def evaluate_example(self, example, max_new_tokens=30): + """ + Evaluate an example using the loaded model. + """ + # Split example and determine maximum new tokens allowed + splited_example = example.split("# output") + if not ("for" in splited_example[0]): + max_new_tokens = 22 + + # Encode prompt and prepare for evaluation + encoded_example = torch.tensor(self.encode(splited_example[0] + "# output"), dtype=torch.long).unsqueeze(0).to(self.device) + prompt_text = splited_example[0] + "# output" + result_example = splited_example[-1] + + # Extract real results from example + real_results = [float(match.group()) for match in re.finditer(r"(?<=# )-?\d+(\.\d+)?", result_example.split('\n\n')[0].replace("\n", ""))] + + # Generate response from model and extract generated results + response = self.decode(self.m.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist()) + splited_response = response.split("# output") + result_response = splited_response[-1] + generated_results = [float(match.group()) for match in re.finditer(r"(?<=# )-?\d+(\.\d+)?", result_response.split('\n\n')[0].replace("\n", ""))] + + return prompt_text, real_results, generated_results + + def write_results_to_file(self, output_file, prompt, real_results, generated_results): + """ + Write evaluation results to a CSV file. + """ + df = pd.DataFrame({ + 'Prompt': prompt, + 'Real_Results': real_results, + 'Generated_Results': generated_results + }) + df.to_csv(output_file, index=False) + + def main(self): + """ + Main evaluation function. + """ + # Extracting stoi and itos from meta + self.stoi = self.meta['stoi'] + self.itos = self.meta['itos'] + + # Split examples and initialize lists for results + examples = self.decode(self.test_data).split("\n\n") + examples = [example for example in examples if example] + + # Start evaluation process + print(f"Starting evaluation for model '{self.model_name}' on dataset '{self.dataset}'...") + prompt = [] + real_results = [] + generated_results = [] + + # Iterate through examples and evaluate each one + for example in tqdm(examples): + prompt_text, real_result, result = self.evaluate_example(example) + prompt.append(prompt_text) + real_results.append(real_result) + generated_results.append(result) + + # Calculate and print accuracy + correct_count = sum(1 for real, generated in zip(real_results, generated_results) if real == generated) + accuracy = correct_count / len(generated_results) + print(f"Accuracy: {accuracy * 100:.2f}%") + + # Store accuracy in a file + accuracy_file = os.path.join('results', f"{self.model_name}_accuracy.txt") # Saving in 'results' folder + with open(accuracy_file, 'w') as f: + f.write(f"Accuracy: {accuracy * 100:.2f}%\n") + print(f"Accuracy saved to {accuracy_file}") + + # Store results in a CSV file + results_file = os.path.join('results', f"{self.model_name}_results.csv") # Saving in 'results' folder + self.write_results_to_file(results_file, prompt, real_results, generated_results) + print(f"Results saved to {results_file}") + +if __name__ == "__main__": + # Argument parsing + parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on a dataset.') + parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') + parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + + # Parse the command-line arguments + args = parser.parse_args() + + # Create ScriptEvaluator instance and run main function + evaluator = ScriptEvaluator(args.dataset_dir, args.model_name) + evaluator.main() diff --git a/.ipynb_checkpoints/line-level_code_completion-checkpoint.py b/.ipynb_checkpoints/line-level_code_completion-checkpoint.py new file mode 100644 index 0000000..530c982 --- /dev/null +++ b/.ipynb_checkpoints/line-level_code_completion-checkpoint.py @@ -0,0 +1,99 @@ +import os +import pickle +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import pandas as pd +from tqdm import tqdm +import re +from model import GPT + +# Argument parsing +parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on token-level code completion.') +parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') +parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + +# Parse the command-line arguments +args = parser.parse_args() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +torch.manual_seed(1337) + + +# Constants for dataset and file paths +MODEL_FILE = f"models/{args.model_name}.pth" +ACCURACY_FILE = f"results/{args.model_name}_acc_line-level_code_completion.txt" +RESULTS_FILE = f"results/{args.model_name}_line-level_code_completion.csv" + + +data_dir = args.dataset_dir +test_data = np.memmap(os.path.join(data_dir, 'test.bin'), dtype=np.uint16, mode='r') + + +# attempt to derive vocab_size from the dataset +meta_path = os.path.join(data_dir, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})") + +stoi = meta['stoi'] +itos = meta['itos'] +encode = lambda s: [stoi[c] for c in s] +decode = lambda l: ''.join([itos[i] for i in l]) + +model = GPT() +print("Compiling model...") +model = torch.compile(model) # pytorch 2.0 +model.load_state_dict(torch.load(MODEL_FILE)) +m = model.to(device) + +examples = decode(test_data).split("\n\n") +examples = [example for example in examples if example] + +correct_predictions = 0 +total_predictions = 0 + +results = [] + +for code_snippet in tqdm(examples): + + lines = code_snippet.split('\n') + for i in range(1, len(lines)): + + context_lines = lines[:i] + actual_next_line = lines[i] + + context_tokens = torch.tensor(encode('\n'.join(context_lines) + '\n'), dtype=torch.long).unsqueeze(0).to(device) + actual_next_line_tokens = torch.tensor(encode(actual_next_line), dtype=torch.long).unsqueeze(0).to(device) + + n = actual_next_line_tokens.shape[1] # Limit to length of actual next line + predicted_next_line_tokens = m.generate(context_tokens, max_new_tokens=n) + predicted_next_line_tokens = predicted_next_line_tokens[:, -n:] + is_correct = torch.equal(predicted_next_line_tokens, actual_next_line_tokens) + + if is_correct: + correct_predictions += 1 + results.append({ + 'context': context_tokens.cpu(), + 'actual_next_line': actual_next_line_tokens.cpu(), + 'predicted_next_line': predicted_next_line_tokens.cpu(), + 'is_correct': is_correct + }) + + total_predictions += 1 + +df = pd.DataFrame(results) +df.to_csv(RESULTS_FILE, index=False) + +accuracy = (correct_predictions / total_predictions) * 100 + +# Store accuracy in a file +with open(ACCURACY_FILE, 'w') as f: + f.write(f"Accuracy: {accuracy:.2f}%\n") + +print(accuracy) \ No newline at end of file diff --git a/.ipynb_checkpoints/token-level_code_completion-checkpoint.py b/.ipynb_checkpoints/token-level_code_completion-checkpoint.py new file mode 100644 index 0000000..4bb6211 --- /dev/null +++ b/.ipynb_checkpoints/token-level_code_completion-checkpoint.py @@ -0,0 +1,96 @@ +import os +import pickle +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import pandas as pd +from tqdm import tqdm +import re +from model import GPT + + +# Argument parsing +parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on token-level code completion.') +parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') +parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + +# Parse the command-line arguments +args = parser.parse_args() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +torch.manual_seed(1337) + + +# Constants for dataset and file paths +MODEL_FILE = f"models/{args.model_name}.pth" +ACCURACY_FILE = f"results/{args.model_name}_acc_token-level_code_completion.txt" +RESULTS_FILE = f"results/{args.model_name}_token-level_code_completion.csv" + + +data_dir = args.dataset_dir +test_data = np.memmap(os.path.join(data_dir, 'test.bin'), dtype=np.uint16, mode='r') + +# attempt to derive vocab_size from the dataset +meta_path = os.path.join(data_dir, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})") + +stoi = meta['stoi'] +itos = meta['itos'] +encode = lambda s: [stoi[c] for c in s] +decode = lambda l: ''.join([itos[i] for i in l]) + +model = GPT() +print("Compiling model...") +model = torch.compile(model) # pytorch 2.0 +model.load_state_dict(torch.load(MODEL_FILE)) +m = model.to(device) + +examples = decode(test_data).split("\n\n") +examples = [example for example in examples if example] + +correct_predictions = 0 +total_predictions = 0 + +results = [] + +for code_snippet in tqdm(examples): + + tokens = torch.tensor(encode(code_snippet), dtype=torch.long).unsqueeze(0).to(device) + + for i in range(1, tokens.shape[1]): + + context = tokens[:, :i] + actual_next_token = tokens[:, i].item() + predicted_next_token = m.generate(context, max_new_tokens=1) + predicted_next_token = predicted_next_token[:, -1].item() + is_correct = (predicted_next_token == actual_next_token) + + if is_correct: + correct_predictions += 1 + results.append({ + 'context': context.cpu(), + 'actual_next_token': actual_next_token, + 'predicted_next_token': predicted_next_token, + 'is_correct': is_correct + }) + + total_predictions += 1 + +df = pd.DataFrame(results) +df.to_csv(RESULTS_FILE, index=False) + + +accuracy = (correct_predictions / total_predictions) * 100 + +# Store accuracy in a file +with open(ACCURACY_FILE, 'w') as f: + f.write(f"Accuracy: {accuracy:.2f}%\n") + +print(accuracy) \ No newline at end of file diff --git a/README.md b/README.md index 73ceaa2..7e682eb 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,12 @@ # Tiny Language Models Framework -This repository contains the implementation and resources for the Tiny Language Models Framework project. In this project, we developed small-scale language models to facilitate detailed research into various aspects of large language models (LLMs), particularly in the domain of code. +This repository contains the implementation and resources for the Tiny Language Models Framework project. In this project, we developed small-scale language models to facilitate detailed research into various aspects of large language models (LLMs), particularly in the domain of code. + +

+ +

+ +We've also prepared a [TinyLM Starter Notebook on Kaggle](https://www.kaggle.com/code/nairmarwa/tinylm-starter-notebook). This notebook is designed to help you get started quickly with our project. It guides you through training a tiny language model from scratch using our dataset and evaluating its performance on code execution tasks. ## Project Structure @@ -33,7 +39,11 @@ This repository contains the implementation and resources for the Tiny Language - `demonstration.ipynb` : Jupyter notebook demonstrating the usage of the models and scripts. -- `eval.py` : Script to evaluate the trained models. +- `code_execution.py` : Script to evaluate the trained models on the code execution task. + +- `token-level_code_completion.py` : Script to evaluate the trained models on the token-level code completion task. + +- `line-level_code_completion.py` : Script to evaluate the trained models on the line-level code completion task. - `model.py` : Contains the model architecture and related functions. @@ -42,6 +52,7 @@ This repository contains the implementation and resources for the Tiny Language - `train.py` : Script to train the models. ## Requirements +We've used Python 3.11.7. To install the required packages, you can use the following: @@ -59,15 +70,15 @@ cd data/ python tinypy_generator.py --num_programs 1000 --level 1.1 --filename sample_data.txt --deduplicate ``` +This generation command is just an example to get you started. If you want to train your own model, you'll likely need to generate significantly more data. + ### Data Preparation -Prepare the data by running: +Prepare (tokenize and split) the data by running: ```bash python prepare.py ``` -This generation command is just an example to get you started. If you want to train your own model, you'll likely need to generate significantly more data. - ### Training Train the model using the following command: @@ -78,10 +89,22 @@ python train.py --batch_size 64 --max_iters 35000 --learning_rate 0.01 --miles 0 ``` ### Evaluation -Evaluate the trained model by running: +Evaluate the trained model on code execution by running: ```bash -python eval.py --dataset_dir data --model_name arithmetics_level1_696K +python code_execution.py --dataset_dir data --model_name arithmetics_level1_696K +``` + +Evaluate the trained model on token-level code completion by running: + +```bash +python token-level_code_completion.py --dataset_dir data --model_name arithmetics_level1_696K +``` + +Evaluate the trained model on line-level code completion by running: + +```bash +python line-level_code_completion.py --dataset_dir data --model_name arithmetics_level1_696K ``` ### Demonstration @@ -108,9 +131,14 @@ python evaluate.py --checkpoint_dir models/code-llama-finetuned-level1 --test_fi #### Demonstration To see a demonstration of the model's capabilities, open the generalization/demonstration.ipynb notebook and follow the instructions within. +# Contact + +- **Kamel Yamani**: [mky2023@nyu.edu](mailto:mky2023@nyu.edu) +- **Marwa Naïr**: [mn3620@nyu.edu](mailto:mn3620@nyu.edu) + # License This project is licensed under the MIT License. # Acknowledgements -Special thanks to all contributors and the community for their support and contribution +This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. diff --git a/__pycache__/model.cpython-311.pyc b/__pycache__/model.cpython-311.pyc new file mode 100644 index 0000000..3491bc4 Binary files /dev/null and b/__pycache__/model.cpython-311.pyc differ diff --git a/code_execution.py b/code_execution.py new file mode 100644 index 0000000..3c82637 --- /dev/null +++ b/code_execution.py @@ -0,0 +1,164 @@ +import os +import pickle +import torch +import numpy as np +import pandas as pd +import re +from tqdm import tqdm +import argparse +from model import GPT + +class ScriptEvaluator: + """ + Class to evaluate a GPT model on a dataset and save the results. + """ + + def __init__(self, dataset_dir, model_name): + """ + Initialize ScriptEvaluator with dataset directory and model name. + + Args: + - dataset_dir (str): Directory where the dataset is stored. + - model_name (str): Name of the pre-trained model (without .pth extension). + """ + self.dataset = dataset_dir + self.model_name = model_name + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + torch.manual_seed(1337) + self.test_data, self.meta = self.load_dataset() + self.m = self.load_model() + + def load_dataset(self): + """ + Load test dataset and metadata. + """ + test_data = np.memmap(os.path.join(self.dataset, 'test.bin'), dtype=np.uint16, mode='r') + meta_path = os.path.join(self.dataset, 'meta.pkl') + meta_vocab_size = None + if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"Found vocab_size = {meta_vocab_size} (inside {meta_path})") + + return test_data, meta + + def load_model(self): + """ + Load pre-trained model based on the provided model name. + """ + model_path = os.path.join('models', f"{self.model_name}.pth") + if not os.path.exists(model_path): + raise FileNotFoundError(f"Model file '{model_path}' not found.") + + model = GPT() + print("Compiling the model...\n") + try: + model = torch.compile(model) # requires PyTorch 2.0 + except Exception as e: + pass + model.load_state_dict(torch.load(model_path)) + m = model.to(self.device) + return m + + def encode(self, s): + """ + Encode string `s` into token IDs. + """ + return [self.stoi[c] for c in s] + + def decode(self, l): + """ + Decode token IDs `l` into a string. + """ + return ''.join([self.itos[i] for i in l]) + + def evaluate_example(self, example, max_new_tokens=30): + """ + Evaluate an example using the loaded model. + """ + # Split example and determine maximum new tokens allowed + splited_example = example.split("# output") + if not ("for" in splited_example[0]): + max_new_tokens = 22 + + # Encode prompt and prepare for evaluation + encoded_example = torch.tensor(self.encode(splited_example[0] + "# output"), dtype=torch.long).unsqueeze(0).to(self.device) + prompt_text = splited_example[0] + "# output" + result_example = splited_example[-1] + + # Extract real results from example + real_results = [float(match.group()) for match in re.finditer(r"(?<=# )-?\d+(\.\d+)?", result_example.split('\n\n')[0].replace("\n", ""))] + + # Generate response from model and extract generated results + response = self.decode(self.m.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist()) + splited_response = response.split("# output") + result_response = splited_response[-1] + generated_results = [float(match.group()) for match in re.finditer(r"(?<=# )-?\d+(\.\d+)?", result_response.split('\n\n')[0].replace("\n", ""))] + + return prompt_text, real_results, generated_results + + def write_results_to_file(self, output_file, prompt, real_results, generated_results): + """ + Write evaluation results to a CSV file. + """ + df = pd.DataFrame({ + 'Prompt': prompt, + 'Real_Results': real_results, + 'Generated_Results': generated_results + }) + df.to_csv(output_file, index=False) + + def main(self): + """ + Main evaluation function. + """ + # Extracting stoi and itos from meta + self.stoi = self.meta['stoi'] + self.itos = self.meta['itos'] + + # Split examples and initialize lists for results + examples = self.decode(self.test_data).split("\n\n") + examples = [example for example in examples if example] + + # Start evaluation process + print(f"Starting evaluation for model '{self.model_name}' on dataset '{self.dataset}'...") + prompt = [] + real_results = [] + generated_results = [] + + # Iterate through examples and evaluate each one + for example in tqdm(examples): + prompt_text, real_result, result = self.evaluate_example(example) + prompt.append(prompt_text) + real_results.append(real_result) + generated_results.append(result) + + # Calculate and print accuracy + correct_count = sum(1 for real, generated in zip(real_results, generated_results) if real == generated) + accuracy = correct_count / len(generated_results) + print(f"Accuracy: {accuracy * 100:.2f}%") + + # Store accuracy in a file + accuracy_file = os.path.join('results', f"{self.model_name}_accuracy.txt") # Saving in 'results' folder + with open(accuracy_file, 'w') as f: + f.write(f"Accuracy: {accuracy * 100:.2f}%\n") + print(f"Accuracy saved to {accuracy_file}") + + # Store results in a CSV file + results_file = os.path.join('results', f"{self.model_name}_results.csv") # Saving in 'results' folder + self.write_results_to_file(results_file, prompt, real_results, generated_results) + print(f"Results saved to {results_file}") + +if __name__ == "__main__": + # Argument parsing + parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on a dataset.') + parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') + parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + + # Parse the command-line arguments + args = parser.parse_args() + + # Create ScriptEvaluator instance and run main function + evaluator = ScriptEvaluator(args.dataset_dir, args.model_name) + evaluator.main() diff --git a/data/.ipynb_checkpoints/prepare-checkpoint.py b/data/.ipynb_checkpoints/prepare-checkpoint.py new file mode 100644 index 0000000..cb2189e --- /dev/null +++ b/data/.ipynb_checkpoints/prepare-checkpoint.py @@ -0,0 +1,83 @@ +import os +import pickle +import requests +import numpy as np + +input_file_path = os.path.join(os.path.dirname(__file__), 'sample_data.txt' ) + +with open(input_file_path, 'r') as f: + data = f.read() +print(f"length of dataset in characters: {len(data):,}\n") + + +# get all the unique characters that occur in this text +chars = sorted(list(set(data))) +vocab_size = len(chars) +print("all the unique characters:", ''.join(chars)) +print(f"vocab size: {vocab_size:,}") + +# create a mapping from characters to integers +stoi = { ch:i for i,ch in enumerate(chars) } +itos = { i:ch for i,ch in enumerate(chars) } +def encode(s): + return [stoi[c] for c in s] # encoder: take a string, output a list of integers +def decode(l): + ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string + + +# save the meta information as well, to help us encode/decode later +meta = { + 'vocab_size': vocab_size, + 'itos': itos, + 'stoi': stoi, +} +with open(f'meta.pkl', 'wb') as f: + pickle.dump(meta, f) + + +# split by examples using "\n\n" +examples = data.split("\n\n")[:-1] +n = len(examples) +print(f"total number of examples: {n:,}\n") +# shuffle the examples +np.random.shuffle(examples) + +# split into train, val, and test sets +train_examples = examples[:int(n*0.8)] +val_examples = examples[int(n*0.8):int(n*0.9)] +test_examples = examples[int(n*0.9):] + +# join the examples back into strings +train_data = "\n\n".join(train_examples) +val_data = "\n\n".join(val_examples) +test_data = "\n\n".join(test_examples) + + + +# Save train, val, and test sets to separate files +with open(os.path.join(os.path.dirname(__file__), 'train.txt'), 'w') as f: + f.write(train_data) +with open(os.path.join(os.path.dirname(__file__), 'val.txt'), 'w') as f: + f.write(val_data) +with open(os.path.join(os.path.dirname(__file__), 'test.txt'), 'w') as f: + f.write(test_data) + + + + +# encode both to integers +train_ids = encode(train_data) +val_ids = encode(val_data) +test_ids = encode(test_data) +print(f"train has {len(train_ids):,} tokens for {len(train_examples):,} examples") +print(f"val has {len(val_ids):,} tokens for {len(val_examples):,} examples") +print(f"test has {len(test_ids):,} tokens for {len(test_examples):,} examples\n") + +# export to bin files +train_ids = np.array(train_ids, dtype=np.uint16) +val_ids = np.array(val_ids, dtype=np.uint16) +test_ids = np.array(test_ids, dtype=np.uint16) +train_ids.tofile(os.path.join(os.path.dirname(__file__), 'train.bin')) +val_ids.tofile(os.path.join(os.path.dirname(__file__), 'val.bin')) +test_ids.tofile(os.path.join(os.path.dirname(__file__), 'test.bin')) + diff --git a/data/.ipynb_checkpoints/sample_data-checkpoint.txt b/data/.ipynb_checkpoints/sample_data-checkpoint.txt new file mode 100644 index 0000000..dd6e285 --- /dev/null +++ b/data/.ipynb_checkpoints/sample_data-checkpoint.txt @@ -0,0 +1,6098 @@ +d = 0 +b = d * d +print(b) +# output +# 0 + +c = 5 +d = 5 +d = c - 4 +print(d) +# output +# 1 + +d = 5 +a = 0 +print(a + d) +# output +# 5 + +b = 2 +print(b) +# output +# 2 + +a = 8 +e = a + 1 +print(a / a) +# output +# 1.0 + +e = 8 +print(e) +# output +# 8 + +d = 0 +e = 7 +c = d / 1 +print(e / e) +# output +# 1.0 + +d = 6 +c = 4 + 1 +print(d + 2) +# output +# 8 + +d = 9 +d = 7 +print(d) +# output +# 7 + +c = 2 +e = 8 +print(e) +# output +# 8 + +e = 5 +c = 8 +print(c) +# output +# 8 + +c = 2 +c = 7 + c +print(c * c) +# output +# 81 + +a = 2 +print(a / a) +# output +# 1.0 + +c = 5 +e = 1 +print(c) +# output +# 5 + +d = 6 +d = 0 +b = d * 5 +print(d * d) +# output +# 0 + +e = 1 +c = 8 +print(c * 2) +# output +# 16 + +e = 4 +b = 8 +d = 4 + b +print(d) +# output +# 12 + +d = 9 +print(d) +# output +# 9 + +a = 8 +print(a - a) +# output +# 0 + +d = 5 +a = 0 * d +print(a) +# output +# 0 + +d = 5 +print(d) +# output +# 5 + +a = 8 +print(a) +# output +# 8 + +c = 8 +d = 4 +print(d / 9) +# output +# 0.4444444444444444 + +e = 6 +c = 4 +b = c + c +print(e / e) +# output +# 1.0 + +b = 8 +a = 5 - b +print(a) +# output +# -3 + +a = 4 +e = 4 +print(e) +# output +# 4 + +b = 3 +a = 0 +print(a) +# output +# 0 + +d = 4 +b = 6 / 2 +print(b) +# output +# 3.0 + +d = 9 +b = 6 +print(b - b) +# output +# 0 + +e = 7 +print(e * 9) +# output +# 63 + +e = 4 +e = 8 * 8 +print(e) +# output +# 64 + +a = 5 +a = 7 +a = a + a +print(a) +# output +# 14 + +e = 8 +print(e * e) +# output +# 64 + +e = 9 +e = 9 +print(e + e) +# output +# 18 + +a = 0 +b = a + a +print(a / 9) +# output +# 0.0 + +d = 6 +a = d / d +print(a) +# output +# 1.0 + +e = 2 +b = 0 + e +print(e - 4) +# output +# -2 + +e = 6 +c = 1 / e +print(e + e) +# output +# 12 + +d = 7 +c = 7 +print(c) +# output +# 7 + +b = 7 +print(b) +# output +# 7 + +c = 3 +a = 0 +e = c / 3 +print(a + c) +# output +# 3 + +e = 5 +print(e - 0) +# output +# 5 + +e = 8 +d = 0 - e +print(e / e) +# output +# 1.0 + +a = 5 +b = 5 +e = 9 - 2 +print(a * a) +# output +# 25 + +e = 4 +print(e + e) +# output +# 8 + +e = 2 +b = e / e +print(e + 8) +# output +# 10 + +b = 7 +a = 1 +print(a) +# output +# 1 + +a = 4 +a = 2 +print(a) +# output +# 2 + +b = 2 +a = 7 +print(a) +# output +# 7 + +e = 6 +b = 5 +c = 4 / 9 +print(e * 6) +# output +# 36 + +a = 4 +c = 0 +d = 9 / a +print(d) +# output +# 2.25 + +c = 6 +d = 9 - 5 +print(c / 1) +# output +# 6.0 + +d = 5 +d = 9 - 7 +print(d) +# output +# 2 + +b = 3 +a = 0 +c = 6 - 1 +print(c) +# output +# 5 + +a = 8 +print(a * a) +# output +# 64 + +d = 8 +d = 4 * 5 +print(d) +# output +# 20 + +b = 1 +a = 1 + b +print(a) +# output +# 2 + +c = 8 +print(c / 3) +# output +# 2.6666666666666665 + +c = 0 +b = 1 +c = 4 - 0 +print(b + b) +# output +# 2 + +e = 7 +b = 2 +print(e) +# output +# 7 + +a = 9 +b = a * 0 +print(a / a) +# output +# 1.0 + +d = 8 +a = 5 +print(d) +# output +# 8 + +e = 4 +b = 0 + e +print(e + 1) +# output +# 5 + +e = 9 +a = 1 +c = 9 * a +print(e + 9) +# output +# 18 + +b = 3 +c = 1 +e = 6 - c +print(e) +# output +# 5 + +e = 3 +e = e / 1 +print(e) +# output +# 3.0 + +e = 4 +e = 5 +print(e) +# output +# 5 + +c = 9 +print(c) +# output +# 9 + +a = 9 +print(a) +# output +# 9 + +a = 3 +print(a) +# output +# 3 + +a = 0 +d = a * 9 +print(d) +# output +# 0 + +b = 8 +b = 2 / 5 +print(b / b) +# output +# 1.0 + +b = 4 +b = 8 +d = b - 0 +print(d) +# output +# 8 + +d = 4 +d = 4 +print(d) +# output +# 4 + +b = 1 +print(b * 6) +# output +# 6 + +c = 1 +e = c / c +print(c + 5) +# output +# 6 + +a = 5 +print(a) +# output +# 5 + +a = 9 +c = 2 +a = c - 2 +print(a + a) +# output +# 0 + +d = 3 +c = 7 / 2 +print(d - d) +# output +# 0 + +b = 6 +b = 4 +b = b / b +print(b - 4) +# output +# -3.0 + +c = 6 +a = 0 +d = a - a +print(c / 2) +# output +# 3.0 + +a = 7 +c = a * a +print(a / a) +# output +# 1.0 + +d = 4 +d = 1 +a = 1 - d +print(d / d) +# output +# 1.0 + +d = 2 +e = d + 1 +print(e) +# output +# 3 + +e = 1 +print(e + 3) +# output +# 4 + +b = 6 +c = 4 +print(b / b) +# output +# 1.0 + +b = 7 +d = 4 +print(b) +# output +# 7 + +a = 5 +c = 8 +print(a) +# output +# 5 + +b = 6 +b = 5 +b = b - b +print(b / 3) +# output +# 0.0 + +a = 3 +print(a + a) +# output +# 6 + +b = 3 +print(b) +# output +# 3 + +e = 3 +d = 8 +a = d / d +print(d - e) +# output +# 5 + +c = 3 +d = 5 +a = c - c +print(c / d) +# output +# 0.6 + +c = 4 +d = 5 +d = c / 1 +print(d + c) +# output +# 8.0 + +e = 6 +print(e) +# output +# 6 + +d = 6 +c = 3 +a = 9 + d +print(a) +# output +# 15 + +b = 1 +e = b * b +print(e) +# output +# 1 + +e = 6 +print(e - e) +# output +# 0 + +e = 6 +print(e - 6) +# output +# 0 + +d = 9 +c = 5 +e = c / 7 +print(e) +# output +# 0.7142857142857143 + +e = 9 +c = 0 +print(e) +# output +# 9 + +c = 8 +print(c * c) +# output +# 64 + +e = 2 +c = 3 +print(c) +# output +# 3 + +d = 1 +b = 8 +print(d * 1) +# output +# 1 + +d = 0 +print(d) +# output +# 0 + +e = 9 +print(e) +# output +# 9 + +d = 7 +b = 3 +print(b) +# output +# 3 + +d = 0 +c = 6 +e = 7 * c +print(e) +# output +# 42 + +c = 2 +print(c) +# output +# 2 + +b = 7 +d = 1 +print(d - b) +# output +# -6 + +e = 3 +d = e - 2 +print(e / e) +# output +# 1.0 + +b = 2 +a = b / b +print(a) +# output +# 1.0 + +d = 8 +e = d + d +print(e) +# output +# 16 + +a = 3 +b = 2 +c = b * 7 +print(c) +# output +# 14 + +b = 3 +print(b - b) +# output +# 0 + +b = 6 +c = 1 +print(b) +# output +# 6 + +a = 4 +e = 0 * 7 +print(a + 2) +# output +# 6 + +b = 0 +b = 1 +a = 1 + b +print(b - 7) +# output +# -6 + +a = 7 +e = a + 9 +print(a * 9) +# output +# 63 + +a = 9 +d = 9 + 5 +print(d) +# output +# 14 + +d = 9 +b = 9 +a = b - d +print(a) +# output +# 0 + +e = 3 +print(e / 6) +# output +# 0.5 + +c = 6 +print(c * 2) +# output +# 12 + +b = 1 +e = 7 +e = b * 4 +print(e + e) +# output +# 8 + +c = 5 +print(c - c) +# output +# 0 + +d = 2 +d = 2 + d +print(d) +# output +# 4 + +e = 7 +b = 6 +print(b * 6) +# output +# 36 + +b = 2 +print(b * 6) +# output +# 12 + +e = 7 +d = e + e +print(d) +# output +# 14 + +c = 0 +print(c / 2) +# output +# 0.0 + +b = 6 +a = 5 +e = b / 8 +print(e) +# output +# 0.75 + +c = 1 +c = 9 +d = 0 - 5 +print(d) +# output +# -5 + +a = 3 +c = 8 +d = 5 + 3 +print(d) +# output +# 8 + +d = 2 +a = d + d +print(a) +# output +# 4 + +a = 9 +a = 2 + 7 +print(a) +# output +# 9 + +d = 1 +print(d) +# output +# 1 + +e = 7 +print(e / 6) +# output +# 1.1666666666666667 + +c = 4 +d = 6 +e = d / d +print(c / c) +# output +# 1.0 + +c = 3 +print(c - 0) +# output +# 3 + +b = 1 +e = 0 +b = 4 + e +print(b / 3) +# output +# 1.3333333333333333 + +e = 3 +e = e / e +print(e / e) +# output +# 1.0 + +c = 1 +a = 7 +print(a) +# output +# 7 + +d = 3 +d = 8 +print(d) +# output +# 8 + +e = 9 +d = 2 - e +print(e - 1) +# output +# 8 + +b = 5 +b = 5 +print(b + b) +# output +# 10 + +b = 8 +print(b * 1) +# output +# 8 + +c = 4 +print(c) +# output +# 4 + +e = 9 +b = 1 +print(b) +# output +# 1 + +c = 0 +c = c - c +print(c) +# output +# 0 + +a = 7 +d = 5 +b = 3 / 3 +print(b) +# output +# 1.0 + +d = 5 +b = 2 +b = d * d +print(b) +# output +# 25 + +d = 8 +print(d - 5) +# output +# 3 + +e = 3 +a = e * e +print(e * 0) +# output +# 0 + +e = 2 +print(e * e) +# output +# 4 + +d = 2 +b = 4 +e = 8 + 3 +print(d / 5) +# output +# 0.4 + +d = 7 +d = 9 +print(d + 5) +# output +# 14 + +b = 9 +b = 1 / b +print(b * 0) +# output +# 0.0 + +b = 3 +d = 2 +d = 6 / 6 +print(d) +# output +# 1.0 + +b = 0 +print(b + b) +# output +# 0 + +e = 2 +c = 3 +e = 1 * 5 +print(e * c) +# output +# 15 + +a = 1 +b = 4 +e = 2 + 7 +print(a * b) +# output +# 4 + +a = 0 +e = 4 * a +print(a / 1) +# output +# 0.0 + +a = 9 +print(a - 6) +# output +# 3 + +a = 2 +a = 1 +print(a) +# output +# 1 + +a = 8 +d = 2 +a = d - 8 +print(a) +# output +# -6 + +d = 1 +b = 2 / d +print(b) +# output +# 2.0 + +c = 7 +print(c) +# output +# 7 + +e = 0 +e = 3 +print(e / 6) +# output +# 0.5 + +c = 1 +b = 1 + 4 +print(b) +# output +# 5 + +c = 9 +print(c / c) +# output +# 1.0 + +d = 4 +c = 6 +print(d + c) +# output +# 10 + +c = 4 +d = 3 +print(d + 6) +# output +# 9 + +c = 0 +a = 1 +print(a) +# output +# 1 + +d = 7 +c = 2 + 8 +print(d * 1) +# output +# 7 + +a = 9 +print(a / a) +# output +# 1.0 + +d = 1 +d = 0 +b = 2 * d +print(b) +# output +# 0 + +b = 2 +c = 7 +c = c + c +print(c) +# output +# 14 + +c = 5 +d = 3 +e = 2 + 6 +print(d / 8) +# output +# 0.375 + +a = 6 +e = a - 9 +print(e) +# output +# -3 + +e = 6 +b = 5 +a = 4 / b +print(a) +# output +# 0.8 + +d = 4 +b = 6 +a = 0 / d +print(a) +# output +# 0.0 + +c = 9 +e = 8 +b = 9 * e +print(b) +# output +# 72 + +d = 4 +d = d / d +print(d / 3) +# output +# 0.3333333333333333 + +c = 9 +e = 9 +print(c / c) +# output +# 1.0 + +a = 9 +d = a + 1 +print(a * a) +# output +# 81 + +b = 5 +a = 1 +b = a - a +print(b) +# output +# 0 + +d = 9 +print(d * 9) +# output +# 81 + +d = 4 +d = 3 +e = d * d +print(e) +# output +# 9 + +d = 6 +e = 0 +print(d * d) +# output +# 36 + +a = 4 +e = a / 2 +print(a + 1) +# output +# 5 + +b = 8 +print(b * b) +# output +# 64 + +d = 2 +a = 4 +c = d * 0 +print(c) +# output +# 0 + +c = 3 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +c = 3 +print(c / c) +# output +# 1.0 + +e = 4 +d = 7 +d = e * 8 +print(e / 1) +# output +# 4.0 + +d = 8 +print(d / d) +# output +# 1.0 + +c = 3 +d = 7 +b = d / 8 +print(b) +# output +# 0.875 + +b = 2 +e = b - 5 +print(e) +# output +# -3 + +c = 8 +print(c - 2) +# output +# 6 + +e = 7 +a = e + e +print(a) +# output +# 14 + +b = 3 +e = 6 +print(b) +# output +# 3 + +b = 3 +a = 3 +print(a) +# output +# 3 + +b = 8 +d = 9 +a = d + 5 +print(a) +# output +# 14 + +a = 6 +b = 7 +d = a - 0 +print(d) +# output +# 6 + +a = 5 +print(a / 5) +# output +# 1.0 + +b = 7 +c = b * 6 +print(b * 9) +# output +# 63 + +b = 0 +print(b) +# output +# 0 + +a = 1 +print(a) +# output +# 1 + +c = 6 +a = 5 +d = c + 1 +print(d) +# output +# 7 + +d = 7 +a = 0 +print(a) +# output +# 0 + +c = 9 +e = 4 +print(c - 5) +# output +# 4 + +d = 7 +print(d) +# output +# 7 + +e = 6 +a = 4 - e +print(a) +# output +# -2 + +b = 7 +b = 8 +print(b) +# output +# 8 + +e = 3 +print(e) +# output +# 3 + +c = 9 +c = c + 7 +print(c) +# output +# 16 + +a = 1 +c = a + 9 +print(a * 4) +# output +# 4 + +c = 0 +b = 8 +print(c) +# output +# 0 + +d = 3 +d = 5 +print(d + d) +# output +# 10 + +b = 4 +d = 4 +e = 3 / 9 +print(d + 6) +# output +# 10 + +c = 0 +a = 4 +d = c / 4 +print(a / a) +# output +# 1.0 + +c = 5 +e = 0 +print(c) +# output +# 5 + +a = 6 +c = 8 +c = 6 + a +print(c) +# output +# 12 + +c = 0 +d = 8 +d = d - 9 +print(d) +# output +# -1 + +b = 5 +e = 7 - 8 +print(e) +# output +# -1 + +d = 7 +a = d * d +print(a) +# output +# 49 + +a = 4 +a = 4 +print(a) +# output +# 4 + +d = 4 +print(d * 6) +# output +# 24 + +b = 5 +b = 8 +c = b - b +print(c) +# output +# 0 + +d = 5 +c = 9 + d +print(c) +# output +# 14 + +e = 1 +print(e - e) +# output +# 0 + +b = 9 +print(b / 6) +# output +# 1.5 + +e = 5 +d = 0 * e +print(e + e) +# output +# 10 + +c = 1 +print(c) +# output +# 1 + +e = 7 +print(e) +# output +# 7 + +d = 6 +print(d + d) +# output +# 12 + +b = 8 +a = 2 +c = b + 1 +print(b * a) +# output +# 16 + +c = 8 +print(c / 1) +# output +# 8.0 + +e = 8 +b = 6 / e +print(e / 2) +# output +# 4.0 + +a = 5 +b = 2 +print(b / a) +# output +# 0.4 + +a = 1 +e = 7 - a +print(e) +# output +# 6 + +e = 3 +e = 4 +c = 6 - e +print(e + 3) +# output +# 7 + +b = 4 +e = b - 0 +print(b - 9) +# output +# -5 + +a = 6 +d = 0 +d = d + 0 +print(d) +# output +# 0 + +e = 5 +a = 0 - e +print(a) +# output +# -5 + +e = 6 +c = 3 / e +print(c) +# output +# 0.5 + +c = 8 +e = 8 +print(e * c) +# output +# 64 + +e = 1 +print(e / e) +# output +# 1.0 + +a = 0 +c = 3 +c = 2 + 5 +print(c) +# output +# 7 + +d = 1 +d = d * 2 +print(d) +# output +# 2 + +a = 3 +e = 9 +b = e - a +print(b) +# output +# 6 + +c = 5 +a = c + 9 +print(a) +# output +# 14 + +e = 5 +c = 1 +print(c - e) +# output +# -4 + +d = 7 +d = 9 +e = 3 * d +print(d - 9) +# output +# 0 + +d = 9 +c = 3 +e = d - 5 +print(d + c) +# output +# 12 + +d = 1 +b = 2 +print(b) +# output +# 2 + +e = 5 +print(e - e) +# output +# 0 + +a = 2 +a = 4 +print(a) +# output +# 4 + +b = 0 +c = 3 +print(b) +# output +# 0 + +e = 6 +b = 1 +d = e / e +print(d) +# output +# 1.0 + +e = 4 +print(e) +# output +# 4 + +e = 3 +b = 4 +a = e - 6 +print(a) +# output +# -3 + +c = 2 +c = c * c +print(c - 5) +# output +# -1 + +e = 1 +print(e) +# output +# 1 + +d = 6 +d = 3 / 7 +print(d * 7) +# output +# 3.0 + +d = 5 +d = d - d +print(d) +# output +# 0 + +e = 8 +a = 3 +print(e / a) +# output +# 2.6666666666666665 + +e = 9 +print(e + 9) +# output +# 18 + +b = 8 +e = b + b +print(e) +# output +# 16 + +c = 5 +e = 8 + c +print(e) +# output +# 13 + +c = 3 +c = 0 +a = c + 6 +print(c + 8) +# output +# 8 + +b = 5 +print(b + b) +# output +# 10 + +d = 2 +a = 6 +e = 2 - 6 +print(e) +# output +# -4 + +b = 8 +print(b) +# output +# 8 + +e = 7 +a = 2 +d = e / 3 +print(d) +# output +# 2.3333333333333335 + +a = 4 +e = 9 +print(e + a) +# output +# 13 + +a = 7 +e = 0 +d = 5 * e +print(a * a) +# output +# 49 + +c = 6 +a = 0 / 5 +print(a) +# output +# 0.0 + +e = 2 +c = 8 / e +print(e * 9) +# output +# 18 + +e = 1 +d = 0 +d = 4 - d +print(d - 7) +# output +# -3 + +a = 5 +a = 6 +c = 1 + 9 +print(a * 3) +# output +# 18 + +b = 3 +b = 5 +d = b * 2 +print(d) +# output +# 10 + +e = 1 +c = 3 +print(e - 3) +# output +# -2 + +c = 6 +b = 6 +print(b) +# output +# 6 + +d = 7 +c = 0 + 7 +print(c) +# output +# 7 + +c = 2 +e = 6 +b = c * 6 +print(b) +# output +# 12 + +a = 6 +e = a + a +print(a + a) +# output +# 12 + +e = 1 +b = 0 +print(b) +# output +# 0 + +d = 8 +c = d - 6 +print(c) +# output +# 2 + +e = 9 +a = 5 +print(e) +# output +# 9 + +a = 2 +c = 3 * 5 +print(c) +# output +# 15 + +d = 8 +print(d) +# output +# 8 + +c = 4 +b = 5 +c = 0 / c +print(c) +# output +# 0.0 + +c = 8 +b = 3 +print(b) +# output +# 3 + +e = 4 +e = 4 * e +print(e / 8) +# output +# 2.0 + +c = 3 +print(c) +# output +# 3 + +a = 2 +a = 5 +d = a + a +print(d) +# output +# 10 + +a = 7 +b = 1 +a = a / 5 +print(a) +# output +# 1.4 + +b = 7 +print(b / 4) +# output +# 1.75 + +a = 5 +e = 1 +print(a) +# output +# 5 + +c = 8 +b = c / 7 +print(c - c) +# output +# 0 + +e = 0 +a = 3 +print(a) +# output +# 3 + +e = 5 +e = e - e +print(e - e) +# output +# 0 + +a = 9 +e = 7 +print(e - e) +# output +# 0 + +a = 3 +c = a / 3 +print(c) +# output +# 1.0 + +d = 2 +a = 0 +e = 7 / 8 +print(d + a) +# output +# 2 + +d = 0 +e = 8 +print(e * e) +# output +# 64 + +d = 2 +a = 6 +print(d) +# output +# 2 + +d = 3 +a = 2 +print(d - a) +# output +# 1 + +e = 4 +a = 5 - e +print(e - 7) +# output +# -3 + +e = 7 +e = 9 +print(e) +# output +# 9 + +e = 1 +print(e * e) +# output +# 1 + +e = 5 +d = 2 - e +print(d) +# output +# -3 + +b = 1 +print(b * 9) +# output +# 9 + +e = 0 +c = 6 +print(e) +# output +# 0 + +c = 5 +print(c / c) +# output +# 1.0 + +e = 4 +c = 2 +e = e - 0 +print(c * c) +# output +# 4 + +e = 6 +d = 9 +print(d) +# output +# 9 + +a = 0 +b = a + a +print(b) +# output +# 0 + +c = 8 +b = 5 +print(c - 8) +# output +# 0 + +e = 8 +b = e / 6 +print(b) +# output +# 1.3333333333333333 + +d = 0 +b = 4 +print(b) +# output +# 4 + +c = 4 +d = 9 +b = 6 + 9 +print(b) +# output +# 15 + +e = 9 +print(e / 1) +# output +# 9.0 + +d = 1 +c = 0 +d = d / 7 +print(d * 2) +# output +# 0.2857142857142857 + +a = 9 +b = 0 +d = a + a +print(d) +# output +# 18 + +a = 4 +a = 5 +a = a * 7 +print(a / a) +# output +# 1.0 + +a = 3 +c = 6 +d = c / 7 +print(c - 1) +# output +# 5 + +d = 2 +d = d - d +print(d) +# output +# 0 + +c = 7 +b = 9 / c +print(b) +# output +# 1.2857142857142858 + +c = 4 +e = 4 +print(c * c) +# output +# 16 + +e = 8 +a = 0 +c = a + a +print(a * 7) +# output +# 0 + +a = 9 +d = 9 +b = a + 4 +print(a + 0) +# output +# 9 + +c = 4 +e = 3 +print(c * e) +# output +# 12 + +d = 8 +print(d + 9) +# output +# 17 + +d = 6 +d = 6 +d = d + d +print(d) +# output +# 12 + +a = 0 +print(a - 9) +# output +# -9 + +a = 6 +c = 7 +b = c + c +print(a - c) +# output +# -1 + +d = 4 +a = 8 +print(a * a) +# output +# 64 + +e = 4 +b = 3 +print(b) +# output +# 3 + +e = 9 +d = 6 / 4 +print(e / 1) +# output +# 9.0 + +a = 4 +print(a / a) +# output +# 1.0 + +a = 8 +c = 2 / 7 +print(a * a) +# output +# 64 + +d = 9 +b = 2 +c = 3 / d +print(b - b) +# output +# 0 + +d = 6 +b = d / d +print(b) +# output +# 1.0 + +c = 3 +b = 0 +d = c + b +print(d) +# output +# 3 + +c = 6 +e = 2 +d = 6 + 1 +print(c * 1) +# output +# 6 + +c = 4 +a = 4 +print(a) +# output +# 4 + +e = 9 +print(e / 3) +# output +# 3.0 + +c = 6 +c = 2 +print(c + c) +# output +# 4 + +c = 7 +print(c - c) +# output +# 0 + +b = 2 +c = 1 +print(b + 2) +# output +# 4 + +c = 0 +d = c + 6 +print(c * c) +# output +# 0 + +b = 9 +c = 4 + 6 +print(c) +# output +# 10 + +a = 6 +print(a) +# output +# 6 + +a = 6 +print(a - 3) +# output +# 3 + +e = 3 +a = 9 +a = 8 - a +print(a) +# output +# -1 + +a = 1 +b = 3 +print(a) +# output +# 1 + +b = 2 +a = 8 +d = b / 6 +print(a / 5) +# output +# 1.6 + +a = 6 +b = 0 +a = 3 - 3 +print(b / 9) +# output +# 0.0 + +e = 8 +a = 1 +d = 1 - 9 +print(e * e) +# output +# 64 + +c = 6 +e = 9 +d = 4 * c +print(c - 8) +# output +# -2 + +d = 1 +b = 6 +print(d) +# output +# 1 + +d = 0 +c = 7 +print(d - d) +# output +# 0 + +b = 6 +a = 4 / 9 +print(b - b) +# output +# 0 + +d = 5 +d = 9 +e = 0 - d +print(d * d) +# output +# 81 + +a = 4 +print(a * a) +# output +# 16 + +b = 5 +b = 0 - 7 +print(b) +# output +# -7 + +d = 5 +c = 8 +b = c + c +print(c + 6) +# output +# 14 + +e = 4 +d = 1 +print(e) +# output +# 4 + +a = 3 +c = a / a +print(c) +# output +# 1.0 + +e = 2 +print(e) +# output +# 2 + +b = 8 +b = 8 +print(b) +# output +# 8 + +e = 1 +b = 7 +print(b - 0) +# output +# 7 + +d = 9 +c = 7 - d +print(d - d) +# output +# 0 + +b = 8 +d = 3 +print(d / d) +# output +# 1.0 + +b = 2 +print(b + 3) +# output +# 5 + +b = 9 +a = 2 +b = a - 9 +print(a - 4) +# output +# -2 + +c = 7 +print(c / c) +# output +# 1.0 + +c = 8 +print(c) +# output +# 8 + +b = 8 +b = b / 8 +print(b) +# output +# 1.0 + +a = 0 +c = 8 +print(c) +# output +# 8 + +d = 9 +b = 6 +a = 6 + 5 +print(a) +# output +# 11 + +e = 6 +a = 8 - e +print(e + 3) +# output +# 9 + +c = 6 +a = 4 +print(a) +# output +# 4 + +e = 1 +c = e + 5 +print(e + 7) +# output +# 8 + +d = 8 +b = 7 +print(b) +# output +# 7 + +a = 1 +print(a * 0) +# output +# 0 + +b = 4 +print(b * b) +# output +# 16 + +e = 9 +a = 9 +print(a * e) +# output +# 81 + +e = 8 +a = 6 / e +print(e * e) +# output +# 64 + +b = 7 +d = b - 4 +print(b + 8) +# output +# 15 + +d = 4 +e = 3 +b = 0 + 8 +print(b) +# output +# 8 + +a = 2 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +d = 9 +print(d - 9) +# output +# 0 + +e = 8 +e = 0 +print(e + e) +# output +# 0 + +b = 9 +b = 9 +print(b / 9) +# output +# 1.0 + +d = 4 +e = 3 +a = 9 - 9 +print(d * 4) +# output +# 16 + +c = 9 +d = 5 +print(d) +# output +# 5 + +b = 6 +b = 3 +print(b) +# output +# 3 + +d = 2 +e = d - d +print(d - d) +# output +# 0 + +c = 7 +e = 5 +d = 1 + c +print(c - 7) +# output +# 0 + +d = 0 +a = 9 +print(a * 7) +# output +# 63 + +d = 2 +print(d + d) +# output +# 4 + +e = 1 +a = 3 +e = e / a +print(e) +# output +# 0.3333333333333333 + +b = 2 +b = 3 +print(b * 7) +# output +# 21 + +d = 0 +a = 1 +print(a) +# output +# 1 + +a = 2 +e = 9 +c = 7 * a +print(a * e) +# output +# 18 + +c = 1 +e = 9 +d = 6 * 8 +print(e - e) +# output +# 0 + +a = 7 +b = a / a +print(a * a) +# output +# 49 + +e = 1 +a = 6 +print(a / 6) +# output +# 1.0 + +d = 3 +b = 4 +print(b) +# output +# 4 + +e = 3 +b = e * 6 +print(e / e) +# output +# 1.0 + +c = 4 +print(c + 3) +# output +# 7 + +e = 5 +print(e) +# output +# 5 + +d = 5 +d = d * d +print(d) +# output +# 25 + +d = 4 +a = 1 +print(d) +# output +# 4 + +d = 7 +e = 5 +e = e + d +print(e) +# output +# 12 + +d = 5 +print(d - d) +# output +# 0 + +d = 2 +print(d + 9) +# output +# 11 + +b = 6 +a = 2 +d = 9 - b +print(b - 0) +# output +# 6 + +d = 6 +b = d / 4 +print(d - 3) +# output +# 3 + +c = 7 +a = c - c +print(a) +# output +# 0 + +b = 0 +d = 0 +print(d) +# output +# 0 + +d = 5 +c = d * d +print(d / d) +# output +# 1.0 + +c = 8 +b = 7 +c = c + c +print(c - c) +# output +# 0 + +a = 2 +d = 1 +print(d) +# output +# 1 + +d = 4 +c = 0 + 1 +print(c) +# output +# 1 + +b = 1 +c = b + 3 +print(c) +# output +# 4 + +a = 1 +print(a / 2) +# output +# 0.5 + +b = 6 +c = 5 +print(b / b) +# output +# 1.0 + +a = 8 +a = 8 +print(a) +# output +# 8 + +d = 4 +c = d - d +print(c) +# output +# 0 + +c = 6 +b = 2 +c = b - 4 +print(b + c) +# output +# 0 + +b = 2 +e = 2 +print(e) +# output +# 2 + +e = 5 +print(e + e) +# output +# 10 + +e = 2 +a = 6 +print(e) +# output +# 2 + +a = 8 +a = a / 8 +print(a * a) +# output +# 1.0 + +d = 7 +print(d * d) +# output +# 49 + +d = 9 +b = 5 +print(d) +# output +# 9 + +c = 3 +e = 7 +print(c) +# output +# 3 + +b = 3 +d = b * b +print(d) +# output +# 9 + +b = 9 +c = 6 +a = c * 6 +print(c / 6) +# output +# 1.0 + +a = 2 +c = 1 +b = 7 * c +print(b) +# output +# 7 + +b = 6 +a = 5 +d = b + 9 +print(d) +# output +# 15 + +d = 2 +e = 2 +print(e) +# output +# 2 + +e = 3 +c = e + 2 +print(e + e) +# output +# 6 + +e = 8 +d = e - 9 +print(d) +# output +# -1 + +b = 8 +d = 6 +print(b / d) +# output +# 1.3333333333333333 + +a = 7 +e = 1 +print(a + 3) +# output +# 10 + +c = 9 +c = 9 +print(c - 4) +# output +# 5 + +d = 0 +d = d + 7 +print(d) +# output +# 7 + +a = 1 +e = 6 + a +print(a * 4) +# output +# 4 + +d = 5 +b = 7 +print(d) +# output +# 5 + +c = 9 +d = c / 3 +print(d) +# output +# 3.0 + +c = 6 +d = 1 +print(d) +# output +# 1 + +c = 7 +e = 3 +e = 0 + 0 +print(e) +# output +# 0 + +a = 5 +a = 9 +print(a) +# output +# 9 + +b = 3 +d = 8 +a = d - 6 +print(a) +# output +# 2 + +b = 1 +d = 5 * b +print(d) +# output +# 5 + +e = 8 +a = 9 +print(e) +# output +# 8 + +c = 1 +b = 9 + 5 +print(c / 4) +# output +# 0.25 + +b = 0 +a = 0 / 5 +print(a) +# output +# 0.0 + +a = 9 +d = a * a +print(d) +# output +# 81 + +d = 2 +print(d / 6) +# output +# 0.3333333333333333 + +e = 0 +b = 8 +c = b * b +print(c) +# output +# 64 + +a = 8 +b = 6 / a +print(b) +# output +# 0.75 + +e = 9 +e = e * 2 +print(e) +# output +# 18 + +a = 1 +d = 3 +print(a * 2) +# output +# 2 + +c = 0 +c = 1 * c +print(c - c) +# output +# 0 + +a = 3 +b = 7 +d = a + a +print(d) +# output +# 6 + +a = 7 +b = 8 +e = 9 - 5 +print(a * a) +# output +# 49 + +c = 5 +c = 8 +print(c / 5) +# output +# 1.6 + +b = 6 +e = b + b +print(b + 1) +# output +# 7 + +e = 6 +c = e + e +print(e + e) +# output +# 12 + +b = 4 +b = 3 - b +print(b) +# output +# -1 + +d = 4 +e = 5 +a = 6 * e +print(e / e) +# output +# 1.0 + +b = 9 +print(b * 1) +# output +# 9 + +e = 2 +b = 7 +print(e) +# output +# 2 + +c = 7 +c = c + 2 +print(c - 1) +# output +# 8 + +e = 9 +e = 3 +b = e / 8 +print(e + e) +# output +# 6 + +c = 3 +e = 4 +print(e) +# output +# 4 + +b = 1 +print(b) +# output +# 1 + +a = 6 +b = 0 +e = 5 / a +print(b * b) +# output +# 0 + +e = 9 +d = 6 +print(e) +# output +# 9 + +c = 0 +a = 7 +print(a * a) +# output +# 49 + +d = 5 +a = 6 +a = d / 7 +print(a) +# output +# 0.7142857142857143 + +a = 2 +c = a - 6 +print(a / a) +# output +# 1.0 + +b = 4 +print(b) +# output +# 4 + +b = 3 +a = 5 +print(a + a) +# output +# 10 + +c = 5 +b = c * c +print(b) +# output +# 25 + +d = 4 +d = 7 * 4 +print(d * d) +# output +# 784 + +b = 6 +e = 1 +e = 5 / 1 +print(e - 2) +# output +# 3.0 + +e = 6 +b = 9 +print(e) +# output +# 6 + +d = 7 +d = 8 +e = 7 * 5 +print(e) +# output +# 35 + +d = 3 +print(d) +# output +# 3 + +e = 2 +d = 9 +a = e - 2 +print(a) +# output +# 0 + +a = 0 +a = 0 +print(a) +# output +# 0 + +e = 5 +b = 6 +print(e) +# output +# 5 + +b = 6 +print(b) +# output +# 6 + +b = 8 +d = 3 +a = 4 - 9 +print(d + b) +# output +# 11 + +a = 9 +d = a / a +print(a + 6) +# output +# 15 + +a = 7 +e = 6 +print(a + a) +# output +# 14 + +d = 3 +d = 6 +print(d + d) +# output +# 12 + +d = 6 +a = 6 +print(a) +# output +# 6 + +e = 4 +b = e * e +print(e * 7) +# output +# 28 + +d = 8 +e = 4 +print(e + d) +# output +# 12 + +d = 2 +print(d) +# output +# 2 + +b = 3 +print(b - 6) +# output +# -3 + +d = 3 +b = 5 +print(d) +# output +# 3 + +a = 9 +b = 7 +print(b - b) +# output +# 0 + +c = 3 +a = 3 +print(c / 7) +# output +# 0.42857142857142855 + +b = 9 +print(b) +# output +# 9 + +a = 7 +c = 1 +a = a / 1 +print(a * c) +# output +# 7.0 + +b = 9 +c = b - 8 +print(c) +# output +# 1 + +b = 1 +print(b - 9) +# output +# -8 + +a = 0 +c = 8 +print(a) +# output +# 0 + +a = 2 +e = a + a +print(a * 5) +# output +# 10 + +b = 9 +d = b - b +print(b - b) +# output +# 0 + +d = 0 +a = 4 +print(a) +# output +# 4 + +c = 6 +d = 0 +a = 9 - 0 +print(a) +# output +# 9 + +c = 9 +b = 8 / c +print(b) +# output +# 0.8888888888888888 + +c = 0 +d = 2 +b = 0 / d +print(b) +# output +# 0.0 + +a = 9 +a = a - 6 +print(a - a) +# output +# 0 + +d = 2 +e = 4 +a = d / 2 +print(e / 2) +# output +# 2.0 + +e = 0 +a = 2 +c = e + e +print(c) +# output +# 0 + +a = 0 +d = 8 +b = a * 5 +print(b) +# output +# 0 + +c = 9 +a = 4 +a = 8 - 5 +print(a + 8) +# output +# 11 + +c = 3 +d = 9 +print(c * 3) +# output +# 9 + +a = 6 +d = 8 +print(a) +# output +# 6 + +e = 5 +c = 4 +c = c - e +print(e / c) +# output +# -5.0 + +e = 2 +c = 7 +d = 7 + 4 +print(d) +# output +# 11 + +a = 3 +e = 4 * 7 +print(a - a) +# output +# 0 + +a = 2 +e = 0 / 8 +print(a * a) +# output +# 4 + +e = 0 +d = 6 +a = 0 + 0 +print(e * d) +# output +# 0 + +b = 6 +e = 5 +print(b) +# output +# 6 + +d = 0 +a = 1 * d +print(d + 6) +# output +# 6 + +b = 7 +d = 8 * 6 +print(b - 5) +# output +# 2 + +d = 3 +b = 2 +a = d * b +print(d / 6) +# output +# 0.5 + +d = 9 +d = 8 * d +print(d * d) +# output +# 5184 + +b = 9 +c = 6 / b +print(b * 2) +# output +# 18 + +d = 8 +a = d + d +print(d - 3) +# output +# 5 + +a = 0 +b = 7 +print(b) +# output +# 7 + +e = 7 +c = 4 +print(e) +# output +# 7 + +e = 8 +e = 4 +d = 5 * 3 +print(d) +# output +# 15 + +d = 9 +a = 2 +b = 7 - d +print(a / d) +# output +# 0.2222222222222222 + +b = 7 +a = 2 +print(a) +# output +# 2 + +c = 7 +c = 4 +d = 6 * c +print(c / c) +# output +# 1.0 + +a = 1 +print(a * a) +# output +# 1 + +e = 8 +d = 6 +print(e + d) +# output +# 14 + +c = 4 +print(c * 8) +# output +# 32 + +d = 6 +a = 1 + d +print(a) +# output +# 7 + +d = 3 +a = 4 +a = d * d +print(a * 4) +# output +# 36 + +b = 8 +a = 4 +d = 5 - b +print(b * 3) +# output +# 24 + +c = 5 +d = 2 +print(d + 4) +# output +# 6 + +e = 2 +print(e - 3) +# output +# -1 + +d = 4 +print(d - d) +# output +# 0 + +d = 9 +c = 7 +print(c - 2) +# output +# 5 + +b = 9 +c = 6 +a = c + c +print(c * 5) +# output +# 30 + +d = 7 +e = 7 +a = 3 - d +print(a) +# output +# -4 + +e = 4 +c = 5 +print(c * e) +# output +# 20 + +a = 6 +print(a - 9) +# output +# -3 + +d = 6 +b = 2 +print(d) +# output +# 6 + +d = 7 +c = 9 +a = 0 + 9 +print(c * 4) +# output +# 36 + +d = 1 +b = 1 +a = 2 / b +print(d + 9) +# output +# 10 + +b = 4 +e = 8 - b +print(e) +# output +# 4 + +a = 3 +d = 4 +print(a) +# output +# 3 + +a = 3 +c = a * a +print(c) +# output +# 9 + +a = 8 +c = 4 +b = c / a +print(b) +# output +# 0.5 + +d = 6 +a = 5 +a = 2 + a +print(d - a) +# output +# -1 + +b = 6 +a = 5 +print(a) +# output +# 5 + +d = 6 +print(d) +# output +# 6 + +b = 2 +c = 3 +print(b / b) +# output +# 1.0 + +c = 5 +print(c * c) +# output +# 25 + +c = 5 +a = 9 / c +print(a) +# output +# 1.8 + +d = 8 +d = 0 +c = 7 - 1 +print(c) +# output +# 6 + +e = 7 +c = e + e +print(c) +# output +# 14 + +b = 9 +b = 9 +print(b - 5) +# output +# 4 + +a = 9 +b = 7 +print(b / 5) +# output +# 1.4 + +d = 8 +print(d - d) +# output +# 0 + +b = 2 +b = 6 +print(b - 6) +# output +# 0 + +c = 8 +b = 6 +print(c) +# output +# 8 + +a = 5 +d = 7 +print(d) +# output +# 7 + +c = 9 +a = c * 1 +print(c / 1) +# output +# 9.0 + +e = 3 +b = 5 +print(b * 9) +# output +# 45 + +c = 5 +e = 3 +d = 8 * 3 +print(d) +# output +# 24 + +c = 4 +e = 1 +e = 4 / e +print(e) +# output +# 4.0 + +a = 3 +d = a - a +print(d) +# output +# 0 + +d = 9 +a = 5 +print(a) +# output +# 5 + +e = 1 +c = e / 7 +print(e - e) +# output +# 0 + +d = 6 +a = 6 +print(a / a) +# output +# 1.0 + +a = 0 +b = 4 +b = a / 2 +print(b) +# output +# 0.0 + +a = 4 +b = a / a +print(b) +# output +# 1.0 + +a = 2 +d = 1 +b = a / d +print(b) +# output +# 2.0 + +e = 1 +b = 8 +print(b) +# output +# 8 + +a = 8 +d = a - 7 +print(d) +# output +# 1 + +d = 7 +b = 2 +print(b) +# output +# 2 + +d = 9 +print(d / d) +# output +# 1.0 + +b = 6 +d = 6 +c = 5 - 4 +print(c) +# output +# 1 + +a = 7 +b = 0 +e = 7 - a +print(b / 7) +# output +# 0.0 + +b = 3 +c = 2 / b +print(b * 8) +# output +# 24 + +d = 3 +d = 7 +print(d) +# output +# 7 + +a = 0 +print(a + a) +# output +# 0 + +e = 3 +d = 5 +print(e - 1) +# output +# 2 + +b = 3 +d = 5 +d = b - 7 +print(b - b) +# output +# 0 + +d = 0 +e = 3 +print(e) +# output +# 3 + +b = 3 +e = 1 +e = 9 + e +print(e) +# output +# 10 + +c = 7 +a = 9 +print(a - 2) +# output +# 7 + +c = 1 +b = 8 +a = 7 / 1 +print(a) +# output +# 7.0 + +a = 4 +b = 4 +print(b * b) +# output +# 16 + +d = 6 +d = 8 +e = d - 9 +print(d / 4) +# output +# 2.0 + +c = 3 +a = 0 +print(c - 4) +# output +# -1 + +b = 0 +e = 2 / 1 +print(b - 2) +# output +# -2 + +e = 9 +b = 8 +a = 8 + 3 +print(b * 4) +# output +# 32 + +b = 4 +a = 1 +a = 2 / b +print(a) +# output +# 0.5 + +b = 9 +c = 4 +print(b) +# output +# 9 + +c = 8 +d = c + 1 +print(d) +# output +# 9 + +b = 6 +b = 5 +print(b) +# output +# 5 + +e = 0 +e = e + e +print(e) +# output +# 0 + +d = 3 +c = 0 +a = c * 5 +print(c / 5) +# output +# 0.0 + +d = 8 +b = 2 + d +print(b) +# output +# 10 + +d = 8 +a = 7 +print(d) +# output +# 8 + +a = 2 +print(a + a) +# output +# 4 + +e = 6 +print(e * 4) +# output +# 24 + +d = 8 +e = 2 +print(d + 2) +# output +# 10 + +d = 1 +a = 3 +d = 7 - a +print(d / d) +# output +# 1.0 + +e = 9 +d = 5 +d = e * d +print(e / d) +# output +# 0.2 + +c = 3 +e = 7 +print(c * c) +# output +# 9 + +d = 8 +b = d * d +print(d * d) +# output +# 64 + +e = 4 +b = 8 +e = e - 6 +print(b / 6) +# output +# 1.3333333333333333 + +a = 9 +a = 5 +e = a / 7 +print(e) +# output +# 0.7142857142857143 + +d = 2 +c = 5 +print(d * d) +# output +# 4 + +d = 1 +a = 7 - 4 +print(a) +# output +# 3 + +a = 1 +d = a / 4 +print(d) +# output +# 0.25 + +d = 9 +b = 1 +d = d / 9 +print(b * b) +# output +# 1 + +a = 8 +print(a + a) +# output +# 16 + +a = 5 +e = 8 +b = a / a +print(e - e) +# output +# 0 + +d = 2 +d = 6 * d +print(d) +# output +# 12 + +d = 2 +b = 5 +print(b) +# output +# 5 + +b = 9 +c = 6 +print(c) +# output +# 6 + +e = 3 +c = 0 +print(c) +# output +# 0 + +d = 4 +b = 8 * d +print(d * d) +# output +# 16 + +b = 7 +e = b - b +print(b - 5) +# output +# 2 + +e = 8 +e = 4 +print(e) +# output +# 4 + +a = 8 +a = 4 - a +print(a) +# output +# -4 + +a = 9 +d = 1 +d = 5 + 5 +print(a / 9) +# output +# 1.0 + +d = 5 +b = 6 +print(d) +# output +# 5 + +c = 8 +e = 7 +print(e * 3) +# output +# 21 + +e = 4 +a = e + 9 +print(a) +# output +# 13 + +e = 7 +a = 2 +d = 5 * 7 +print(a * 7) +# output +# 14 + +e = 1 +d = 5 +c = d * d +print(d / 2) +# output +# 2.5 + +c = 6 +a = 6 +print(a) +# output +# 6 + +b = 4 +print(b + b) +# output +# 8 + +b = 8 +a = 2 + b +print(a) +# output +# 10 + +b = 3 +c = 7 +print(c / 3) +# output +# 2.3333333333333335 + +c = 3 +a = c - 9 +print(a) +# output +# -6 + +b = 5 +d = 8 +print(b - 0) +# output +# 5 + +e = 3 +a = 4 +print(e) +# output +# 3 + +c = 6 +b = 0 +c = 0 * 4 +print(c) +# output +# 0 + +a = 9 +c = 7 / 9 +print(c) +# output +# 0.7777777777777778 + +b = 7 +d = 4 +print(d) +# output +# 4 + +c = 1 +b = 1 +print(c) +# output +# 1 + +a = 1 +a = a + a +print(a + a) +# output +# 4 + +b = 1 +c = 3 +print(c) +# output +# 3 + +b = 5 +e = 8 +b = e / e +print(b * 0) +# output +# 0.0 + +b = 1 +b = 9 +print(b) +# output +# 9 + +e = 0 +b = 2 +print(b * b) +# output +# 4 + +b = 7 +d = 6 +a = 0 / d +print(b - 3) +# output +# 4 + +b = 5 +d = b - b +print(d) +# output +# 0 + +c = 6 +print(c / c) +# output +# 1.0 + +c = 3 +e = 0 +d = e + e +print(d) +# output +# 0 + +c = 4 +d = 4 +e = c - c +print(e) +# output +# 0 + +d = 6 +d = 2 +c = d - 0 +print(c) +# output +# 2 + +a = 9 +b = a + a +print(a / a) +# output +# 1.0 + +e = 7 +b = 7 +c = 7 - e +print(b + 2) +# output +# 9 + +e = 7 +print(e + e) +# output +# 14 + +d = 9 +print(d * d) +# output +# 81 + +a = 7 +print(a / 2) +# output +# 3.5 + +b = 4 +b = 4 +b = b / 8 +print(b + 3) +# output +# 3.5 + +b = 0 +d = 9 * 1 +print(d) +# output +# 9 + +b = 3 +e = 8 +print(e) +# output +# 8 + +d = 3 +print(d * 7) +# output +# 21 + +b = 0 +e = 1 +print(b) +# output +# 0 + +a = 6 +e = 7 +b = e + a +print(a / 2) +# output +# 3.0 + +d = 1 +b = 3 +b = b / 8 +print(b / d) +# output +# 0.375 + +a = 7 +c = a / a +print(c) +# output +# 1.0 + +d = 6 +c = d - d +print(c) +# output +# 0 + +a = 7 +print(a) +# output +# 7 + +e = 7 +a = 7 +a = 7 - e +print(a * 0) +# output +# 0 + +a = 4 +print(a) +# output +# 4 + +c = 4 +d = 3 +print(c) +# output +# 4 + +d = 2 +b = 4 +c = d * 9 +print(c) +# output +# 18 + +c = 6 +a = 4 +print(c) +# output +# 6 + +d = 8 +b = 0 * d +print(d - 7) +# output +# 1 + +d = 6 +e = 2 +c = 2 - d +print(c) +# output +# -4 + +c = 1 +c = c * 8 +print(c) +# output +# 8 + +b = 6 +c = 4 +e = c + c +print(e) +# output +# 8 + +b = 1 +c = 1 +d = c / 5 +print(b - 0) +# output +# 1 + +b = 4 +b = 9 +b = b + 4 +print(b) +# output +# 13 + +c = 9 +print(c * c) +# output +# 81 + +b = 1 +a = b + b +print(b / b) +# output +# 1.0 + +b = 5 +a = 1 +print(b) +# output +# 5 + +c = 4 +a = 7 +a = c + a +print(a + 5) +# output +# 16 + +b = 1 +c = 5 +print(c) +# output +# 5 + +a = 5 +a = 2 +print(a * a) +# output +# 4 + +c = 1 +e = 6 +print(e / 7) +# output +# 0.8571428571428571 + +b = 0 +d = 1 +print(d) +# output +# 1 + +a = 3 +b = 7 - a +print(b) +# output +# 4 + +e = 9 +print(e / e) +# output +# 1.0 + +e = 2 +b = 3 +e = 1 - e +print(b + 6) +# output +# 9 + +d = 2 +a = 3 +e = 1 + a +print(e) +# output +# 4 + +a = 2 +e = 0 +a = a * a +print(a) +# output +# 4 + +a = 0 +c = 5 +print(c * 0) +# output +# 0 + +e = 1 +a = 7 +print(a * 3) +# output +# 21 + +c = 8 +a = 4 +b = c / c +print(c + a) +# output +# 12 + +e = 4 +c = 9 +e = 8 - e +print(e / 3) +# output +# 1.3333333333333333 + +c = 3 +a = 7 +b = 3 * 9 +print(a + 6) +# output +# 13 + +e = 5 +print(e / e) +# output +# 1.0 + +a = 2 +print(a / 4) +# output +# 0.5 + +a = 4 +a = 4 +a = 7 / a +print(a) +# output +# 1.75 + +c = 2 +print(c - c) +# output +# 0 + +a = 2 +print(a) +# output +# 2 + +e = 5 +a = 5 +a = e / 4 +print(a) +# output +# 1.25 + +c = 9 +a = 3 +print(a * 4) +# output +# 12 + +a = 8 +b = 5 +print(b / 2) +# output +# 2.5 + +e = 7 +b = 0 +c = b - b +print(e * 4) +# output +# 28 + +b = 0 +d = 7 +e = b + b +print(d + b) +# output +# 7 + +e = 0 +print(e) +# output +# 0 + +a = 1 +print(a + a) +# output +# 2 + +c = 6 +d = 8 +print(d / 4) +# output +# 2.0 + +a = 6 +e = a / 7 +print(a + 4) +# output +# 10 + +e = 6 +c = 4 +print(e / 9) +# output +# 0.6666666666666666 + +d = 3 +b = 6 + 3 +print(b) +# output +# 9 + +e = 4 +b = 6 + e +print(b) +# output +# 10 + +a = 8 +e = 2 +print(a) +# output +# 8 + +b = 9 +a = 3 +d = b / 7 +print(d) +# output +# 1.2857142857142858 + +b = 3 +e = 6 +print(b / 3) +# output +# 1.0 + +c = 3 +a = 5 +a = 4 * 3 +print(a) +# output +# 12 + +a = 5 +c = a / a +print(a + a) +# output +# 10 + +b = 8 +a = 9 +e = 5 / 2 +print(b * a) +# output +# 72 + +e = 6 +b = 3 +e = e - 6 +print(e) +# output +# 0 + +c = 7 +a = 3 +a = 2 / c +print(a) +# output +# 0.2857142857142857 + +a = 2 +d = 8 * 2 +print(a * a) +# output +# 4 + +a = 8 +c = 3 +e = 4 + 1 +print(a / a) +# output +# 1.0 + +c = 4 +b = 2 +d = 6 + 3 +print(b + 9) +# output +# 11 + +a = 5 +d = 5 +print(d) +# output +# 5 + +c = 3 +b = 7 +b = c + c +print(b / 1) +# output +# 6.0 + +d = 2 +b = 1 / 8 +print(d - d) +# output +# 0 + +a = 5 +e = 5 * 9 +print(e) +# output +# 45 + +c = 6 +print(c - c) +# output +# 0 + +d = 0 +e = d * 1 +print(e) +# output +# 0 + +a = 2 +a = 1 +print(a / 2) +# output +# 0.5 + +d = 7 +d = 2 +print(d) +# output +# 2 + +a = 9 +print(a - a) +# output +# 0 + +c = 9 +d = 7 * c +print(d) +# output +# 63 + +e = 4 +b = e + e +print(e / e) +# output +# 1.0 + +d = 8 +d = 1 + 6 +print(d - 2) +# output +# 5 + +d = 5 +print(d + 6) +# output +# 11 + +a = 9 +d = 8 +print(a) +# output +# 9 + +a = 4 +d = 7 + a +print(a - 9) +# output +# -5 + +d = 6 +b = d * d +print(b) +# output +# 36 + +b = 0 +a = b * b +print(b + b) +# output +# 0 + +a = 7 +print(a * a) +# output +# 49 + +d = 9 +c = 7 +print(c + c) +# output +# 14 + +b = 3 +a = 6 +a = 8 - 0 +print(a) +# output +# 8 + +c = 8 +print(c / 4) +# output +# 2.0 + +c = 0 +b = 3 * 6 +print(c - 1) +# output +# -1 + +a = 4 +print(a - a) +# output +# 0 + +e = 4 +d = 2 / e +print(d) +# output +# 0.5 + +c = 6 +a = 4 +print(c / c) +# output +# 1.0 + +b = 2 +d = 5 * b +print(b * 0) +# output +# 0 + +c = 4 +d = 8 +c = 7 + 6 +print(c) +# output +# 13 + +e = 7 +e = 8 +c = 3 / 7 +print(c) +# output +# 0.42857142857142855 + +d = 7 +d = 3 +e = d - d +print(d * 0) +# output +# 0 + +d = 2 +c = 1 +print(d) +# output +# 2 + +d = 4 +print(d) +# output +# 4 + +a = 1 +a = 3 +print(a / 1) +# output +# 3.0 + +a = 7 +e = 4 +d = a / e +print(d) +# output +# 1.75 + +e = 2 +c = 5 +a = e / 3 +print(e / e) +# output +# 1.0 + +b = 7 +b = 3 / b +print(b) +# output +# 0.42857142857142855 + +d = 0 +e = d + 4 +print(d * d) +# output +# 0 + +b = 5 +e = 9 +print(b * b) +# output +# 25 + +c = 3 +e = 2 +print(c) +# output +# 3 + +e = 5 +print(e * 5) +# output +# 25 + +b = 1 +c = 5 +e = c * b +print(c - b) +# output +# 4 + +e = 6 +d = 3 + e +print(d) +# output +# 9 + +d = 4 +c = 6 +d = 1 - 6 +print(d) +# output +# -5 + +d = 2 +b = 2 * 8 +print(d - d) +# output +# 0 + +e = 1 +a = e + 8 +print(a) +# output +# 9 + +d = 9 +c = 2 +print(c) +# output +# 2 + +a = 2 +a = 4 + a +print(a) +# output +# 6 + +d = 1 +c = 7 +print(d) +# output +# 1 + +b = 8 +d = 5 +e = 8 - b +print(d / 3) +# output +# 1.6666666666666667 + +b = 7 +a = b - 9 +print(b - 6) +# output +# 1 + +c = 2 +b = 0 * c +print(c + c) +# output +# 4 + +d = 4 +e = 0 +b = e * 0 +print(b) +# output +# 0 + +b = 9 +a = 8 +c = b + a +print(b / 4) +# output +# 2.25 + +b = 0 +d = 8 +print(b * b) +# output +# 0 + +d = 2 +print(d / d) +# output +# 1.0 + +a = 6 +c = 3 +a = 4 / 7 +print(a) +# output +# 0.5714285714285714 + +e = 7 +b = 1 +print(b) +# output +# 1 + +e = 5 +e = 6 +print(e) +# output +# 6 + +a = 4 +d = 2 +print(a) +# output +# 4 + +e = 6 +b = e * e +print(e / 8) +# output +# 0.75 + +e = 9 +e = e - 7 +print(e) +# output +# 2 + +d = 5 +print(d * d) +# output +# 25 + +b = 1 +c = 0 +print(c / 2) +# output +# 0.0 + +e = 3 +c = 2 +d = 1 + 5 +print(c * c) +# output +# 4 + +e = 8 +b = e / 5 +print(e / 2) +# output +# 4.0 + +a = 7 +print(a + 4) +# output +# 11 + +d = 2 +a = 7 +a = 6 + d +print(a) +# output +# 8 + +d = 0 +b = 4 +print(d) +# output +# 0 + +c = 7 +c = 7 +print(c / 8) +# output +# 0.875 + +a = 2 +e = 0 +b = 5 - 4 +print(b) +# output +# 1 + +a = 2 +b = 2 / 8 +print(a / 8) +# output +# 0.25 + +c = 1 +a = 2 +c = 2 * 3 +print(c) +# output +# 6 + +b = 4 +d = 9 +print(d + 6) +# output +# 15 + +e = 9 +print(e * e) +# output +# 81 + +e = 2 +a = 1 +print(a + a) +# output +# 2 + +d = 2 +b = 4 * d +print(b) +# output +# 8 + +b = 5 +d = 5 +print(d - 6) +# output +# -1 + +b = 7 +e = 4 +d = 0 - b +print(b - e) +# output +# 3 + +d = 6 +b = 5 +print(d - d) +# output +# 0 + +a = 9 +e = 9 +d = 8 + 1 +print(a * 0) +# output +# 0 + +b = 8 +e = 9 +d = 0 - b +print(e - b) +# output +# 1 + +b = 8 +b = 5 +print(b) +# output +# 5 + +d = 8 +b = 2 +e = 2 + b +print(e) +# output +# 4 + +d = 1 +d = 5 / 7 +print(d / d) +# output +# 1.0 + +d = 7 +a = 4 * d +print(d - 3) +# output +# 4 + +c = 6 +b = 3 + 1 +print(b) +# output +# 4 + +d = 3 +d = 8 - d +print(d + d) +# output +# 10 + +d = 2 +d = 7 +d = 7 + 4 +print(d / 9) +# output +# 1.2222222222222223 + +b = 2 +a = b - b +print(b - 6) +# output +# -4 + +e = 6 +d = 1 - 3 +print(d) +# output +# -2 + +c = 3 +e = 9 +e = 0 * c +print(e) +# output +# 0 + +a = 9 +e = 1 - a +print(a - 1) +# output +# 8 + +d = 9 +e = 9 +b = e + 2 +print(e - e) +# output +# 0 + +c = 3 +print(c + 3) +# output +# 6 + +a = 3 +c = 0 +print(c) +# output +# 0 + +a = 1 +d = 5 * 3 +print(a + a) +# output +# 2 + +b = 4 +e = 3 +a = b - 7 +print(e / e) +# output +# 1.0 + +b = 4 +c = 4 +print(b + 5) +# output +# 9 + +a = 5 +print(a / 3) +# output +# 1.6666666666666667 + +a = 0 +e = a + a +print(a + a) +# output +# 0 + +e = 6 +b = 1 +print(b - 2) +# output +# -1 + +b = 7 +e = 2 +b = 0 * 7 +print(b) +# output +# 0 + +d = 7 +print(d / d) +# output +# 1.0 + +a = 3 +print(a + 3) +# output +# 6 + +d = 9 +e = 5 +print(d) +# output +# 9 + +b = 6 +b = 4 - b +print(b + 0) +# output +# -2 + +b = 6 +c = 1 +print(c / 3) +# output +# 0.3333333333333333 + +a = 2 +e = 4 +a = a / 7 +print(a + a) +# output +# 0.5714285714285714 + +a = 4 +c = 0 +print(c - c) +# output +# 0 + +a = 6 +a = 6 +c = 9 - 7 +print(a * 4) +# output +# 24 + +c = 1 +a = 4 / 3 +print(c - 5) +# output +# -4 + +e = 8 +d = 2 - 0 +print(d) +# output +# 2 + +a = 2 +c = a * 6 +print(a / a) +# output +# 1.0 + +a = 3 +c = 5 +e = 2 + a +print(e) +# output +# 5 + +a = 5 +b = 7 +print(b / 2) +# output +# 3.5 + +d = 1 +b = 4 +print(b) +# output +# 4 + +d = 9 +c = d / d +print(c) +# output +# 1.0 + +e = 0 +c = e / 4 +print(e + 6) +# output +# 6 + +c = 0 +a = 3 +print(c + a) +# output +# 3 + +b = 7 +c = 9 +print(b) +# output +# 7 + +b = 8 +c = 3 +print(b) +# output +# 8 + +e = 1 +e = 6 / 6 +print(e) +# output +# 1.0 + +e = 7 +print(e - e) +# output +# 0 + +c = 4 +print(c * 4) +# output +# 16 + +c = 6 +print(c / 4) +# output +# 1.5 + +c = 5 +print(c + c) +# output +# 10 + +c = 4 +b = 5 +print(c + c) +# output +# 8 + +e = 2 +print(e - 7) +# output +# -5 + +c = 2 +d = 9 +print(d) +# output +# 9 + +b = 5 +c = 4 +print(b) +# output +# 5 + +c = 0 +b = 3 +a = 0 + 4 +print(b * 6) +# output +# 18 + +d = 9 +c = 4 +e = d / c +print(e) +# output +# 2.25 + +b = 8 +b = 1 +d = b - 6 +print(d) +# output +# -5 + +e = 7 +d = e - 1 +print(e + e) +# output +# 14 + +e = 8 +c = 7 +e = e - c +print(e) +# output +# 1 + +d = 2 +e = 6 +print(e - 2) +# output +# 4 + +c = 2 +d = 6 +print(c / d) +# output +# 0.3333333333333333 + +d = 2 +print(d - 0) +# output +# 2 + +b = 2 +d = 4 +print(b / 1) +# output +# 2.0 + +a = 2 +e = 7 +print(e) +# output +# 7 + +a = 4 +c = 7 +print(a) +# output +# 4 + +a = 7 +a = 7 +print(a + a) +# output +# 14 + +b = 8 +b = 5 * 4 +print(b) +# output +# 20 + +d = 0 +print(d * d) +# output +# 0 + +d = 7 +b = 7 + d +print(b) +# output +# 14 + +d = 4 +a = 2 * d +print(a) +# output +# 8 + +d = 4 +d = 3 +b = d + 7 +print(b) +# output +# 10 + +e = 2 +a = 6 +c = a / 4 +print(c) +# output +# 1.5 + +c = 0 +a = c - c +print(a) +# output +# 0 + +e = 5 +c = 0 +print(e + 1) +# output +# 6 + +e = 2 +b = e - e +print(e / e) +# output +# 1.0 + +d = 7 +d = 5 +print(d) +# output +# 5 + +c = 8 +e = c + c +print(e) +# output +# 16 + +c = 4 +print(c - c) +# output +# 0 + +e = 9 +e = 7 * e +print(e + 7) +# output +# 70 + +c = 0 +d = 4 +e = c + 6 +print(e) +# output +# 6 + +a = 7 +d = 2 +print(a) +# output +# 7 + +b = 8 +c = 9 +d = c - b +print(d) +# output +# 1 + +d = 7 +e = 5 + d +print(e) +# output +# 12 + +d = 8 +d = 1 +e = d + d +print(d / 5) +# output +# 0.2 + +b = 8 +b = 2 +a = 3 / b +print(a) +# output +# 1.5 + +d = 4 +a = 2 * 5 +print(d / 6) +# output +# 0.6666666666666666 + +a = 3 +a = a * a +print(a) +# output +# 9 + +a = 9 +e = 3 +print(a + 1) +# output +# 10 + +e = 2 +d = e / e +print(d) +# output +# 1.0 + +d = 0 +a = 3 - d +print(a) +# output +# 3 + +d = 7 +a = 6 +print(a) +# output +# 6 + +d = 1 +b = 5 +print(b - 6) +# output +# -1 + +c = 9 +e = 3 - 8 +print(c + 9) +# output +# 18 + +e = 9 +e = e / e +print(e - 0) +# output +# 1.0 + +d = 4 +print(d + 4) +# output +# 8 + +b = 7 +c = 1 +print(c + 7) +# output +# 8 + +a = 2 +b = 1 +print(a / 9) +# output +# 0.2222222222222222 + +e = 6 +a = 7 +d = a - 1 +print(d) +# output +# 6 + +d = 1 +a = 6 +print(d) +# output +# 1 + +c = 0 +d = 5 +print(c) +# output +# 0 + +c = 6 +e = 0 +print(e + 1) +# output +# 1 + +e = 8 +c = 9 +c = 5 - 3 +print(c) +# output +# 2 + +c = 8 +c = 0 +b = 9 * c +print(b) +# output +# 0 + +d = 8 +e = 8 * d +print(d + d) +# output +# 16 + +d = 0 +d = 7 +print(d + 3) +# output +# 10 + +e = 8 +a = 1 +c = 5 * 9 +print(a * 9) +# output +# 9 + +e = 7 +d = 0 +e = 9 - 1 +print(e - 7) +# output +# 1 + +a = 2 +print(a * 6) +# output +# 12 + +c = 3 +a = 2 * 7 +print(c - c) +# output +# 0 + +c = 8 +c = 5 / c +print(c + c) +# output +# 1.25 + +e = 8 +c = 7 +print(c) +# output +# 7 + +b = 8 +a = 6 +print(b) +# output +# 8 + +e = 9 +b = 3 +print(b) +# output +# 3 + +e = 2 +b = 4 +c = e + 0 +print(b * 1) +# output +# 4 + +b = 6 +c = 3 +b = 3 - 3 +print(c - c) +# output +# 0 + +e = 0 +d = e - 4 +print(d) +# output +# -4 + +d = 9 +print(d * 6) +# output +# 54 + +a = 8 +a = 9 +d = a / 8 +print(d) +# output +# 1.125 + +d = 6 +a = d - 2 +print(a) +# output +# 4 + +d = 5 +d = 3 +a = d * d +print(d - 8) +# output +# -5 + +a = 9 +d = 2 +d = a + d +print(d) +# output +# 11 + +e = 2 +c = 2 +print(c * 6) +# output +# 12 + +d = 9 +d = 6 +b = d - 0 +print(b) +# output +# 6 + +d = 9 +b = 4 +a = d + 3 +print(a) +# output +# 12 + +c = 1 +print(c - c) +# output +# 0 + +b = 7 +b = 7 +print(b) +# output +# 7 + +e = 1 +c = e + e +print(c) +# output +# 2 + +b = 5 +d = 1 +e = d - 7 +print(d + b) +# output +# 6 + +e = 2 +a = 6 +print(a) +# output +# 6 + +e = 6 +e = 7 +print(e) +# output +# 7 + +e = 7 +print(e / 7) +# output +# 1.0 + +c = 6 +c = 1 * 4 +print(c) +# output +# 4 + +d = 8 +e = 3 +a = d / d +print(e / e) +# output +# 1.0 + +d = 0 +b = 2 +print(d * d) +# output +# 0 + +b = 2 +print(b - b) +# output +# 0 + +c = 5 +c = 0 +print(c) +# output +# 0 + +e = 7 +e = 6 +b = e * 5 +print(e * e) +# output +# 36 + +b = 8 +a = 8 +print(b - 4) +# output +# 4 + +e = 3 +c = e + e +print(e - e) +# output +# 0 + +b = 0 +a = 4 +print(a * 1) +# output +# 4 + +b = 9 +b = 9 +d = 7 + b +print(d) +# output +# 16 + +e = 3 +b = 7 +print(b) +# output +# 7 + +c = 7 +print(c * 4) +# output +# 28 + +a = 0 +print(a) +# output +# 0 + +c = 0 +c = 6 - 8 +print(c) +# output +# -2 + +b = 9 +e = 1 +print(e) +# output +# 1 + +b = 8 +b = 9 - 6 +print(b) +# output +# 3 + +b = 4 +c = 7 +c = c / c +print(c) +# output +# 1.0 + +e = 3 +print(e + 8) +# output +# 11 + +e = 2 +d = e + e +print(e * e) +# output +# 4 + +e = 3 +e = e + 5 +print(e) +# output +# 8 + +c = 2 +b = c * c +print(c / 1) +# output +# 2.0 + +d = 5 +d = d / d +print(d - 4) +# output +# -3.0 + +d = 3 +b = 4 +d = 4 / 3 +print(b * b) +# output +# 16 + +e = 6 +print(e + e) +# output +# 12 + +b = 8 +c = 7 / 7 +print(b + b) +# output +# 16 + +b = 8 +c = 7 +print(b) +# output +# 8 + +c = 2 +e = 2 +print(c - 1) +# output +# 1 + +c = 4 +b = 1 +print(c + c) +# output +# 8 + +a = 6 +e = 6 +a = e * a +print(a) +# output +# 36 + +e = 1 +a = 0 - 8 +print(a) +# output +# -8 + +c = 1 +print(c + c) +# output +# 2 + +e = 3 +print(e - e) +# output +# 0 + +d = 6 +e = 6 +print(e) +# output +# 6 + +d = 8 +d = 5 +e = 9 - d +print(e) +# output +# 4 + +d = 3 +d = d / d +print(d - 8) +# output +# -7.0 + +a = 8 +a = 9 +print(a) +# output +# 9 + +a = 6 +b = 6 +a = 3 - 3 +print(a) +# output +# 0 + +c = 5 +print(c) +# output +# 5 + +c = 5 +e = 6 +e = e + 2 +print(e) +# output +# 8 + +a = 9 +d = 1 +print(a * d) +# output +# 9 + +b = 3 +e = 1 +d = e * 6 +print(d) +# output +# 6 + +e = 2 +e = 6 +a = e - e +print(e + e) +# output +# 12 + +e = 4 +a = 7 - 0 +print(e * e) +# output +# 16 + +d = 9 +e = 6 +print(e) +# output +# 6 + +d = 7 +e = 8 +print(e / d) +# output +# 1.1428571428571428 + +d = 1 +a = 4 + 6 +print(a) +# output +# 10 + +d = 7 +b = 9 / d +print(d - d) +# output +# 0 + +e = 8 +d = 5 +a = e - d +print(e / e) +# output +# 1.0 + +b = 2 +a = 0 +print(a * 2) +# output +# 0 + diff --git a/data/.ipynb_checkpoints/tinypy_generator-checkpoint.py b/data/.ipynb_checkpoints/tinypy_generator-checkpoint.py new file mode 100644 index 0000000..81b36e7 --- /dev/null +++ b/data/.ipynb_checkpoints/tinypy_generator-checkpoint.py @@ -0,0 +1,332 @@ +from anytree import Node, RenderTree +import random +from io import StringIO +from contextlib import redirect_stdout +import argparse +import time +from tqdm.auto import tqdm +import hashlib +import os +import psutil + + +class CodeGenerator: + def __init__(self): + """ + Initialize the CodeGenerator object with the given context-free grammar rules. + + """ + + self.init_count = 0 + self.max_init = 0 + + # Dictionary containing context-free grammar rules. + self.cfg_rules = { + # Variables and digits + "VARIABLE": ["a", "b", "c", "d", "e"], + "DIGIT": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + + # Operators + "ARITHMETIC_OPERATOR": ["+", "-", "*", "/"], + "RELATIONAL_OPERATOR": ["<", ">", "<=", ">=", "!=", "=="], + "LOGICAL_OPERATOR_INFIX": ["and", "or"], + "LOGICAL_OPERATOR_PREFIX": ["not"], + "LOGICAL_OPERATOR": ["LOGICAL_OPERATOR_INFIX", "LOGICAL_OPERATOR_PREFIX"], + "OPERATOR": ["ARITHMETIC_OPERATOR"], + + # Formatting + "NEW_LINE": ["\n"], + "TAB_INDENT": ["\t"], + "BRACKET_OPEN": ['('], + "BRACKET_CLOSE": [')'], + "EQUALS": ["="], + "COLON": [":"], + "COMMA": [","], + + + # Keywords + "IF": ["if"], + "ELIF": ["elif"], + "ELSE": ["else"], + "FOR": ["for"], + "IN": ["in"], + "RANGE": ["range"], + "WHILE": ["while"], + "PRINT": ["print"], + + # Terms and expressions + "TERM": ["EXPRESSION_IDENTIFIER", "DIGIT"], + "EXPRESSION": ["TERM SPACE OPERATOR SPACE TERM"], + "ENCLOSED_EXPRESSION": ["BRACKET_OPEN EXPRESSION BRACKET_CLOSE"], + "DISPLAY_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE EXPRESSION_IDENTIFIER" , + "EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE DIGIT"], + + # Initializations and assignments + "IDENTIFIER_INITIALIZATION": ["IDENTIFIER_INITIALIZATION INITIALIZATION", + "INITIALIZATION"], + + "INITIALIZATION": ["VARIABLE SPACE EQUALS SPACE DIGIT NEW_LINE"], + + "SIMPLE_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , ""], + "ADVANCED_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE SIMPLE_ARITHMETIC_EVALUATION NEW_LINE", + "VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , + ""], + + "SIMPLE_ARITHMETIC_EVALUATION": ["SIMPLE_ARITHMETIC_EVALUATION ARITHMETIC_OPERATOR ENCLOSED_EXPRESSION", + "ENCLOSED_EXPRESSION", + ], + + # Conditions + "SIMPLE_IF_STATEMENT": ["IF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_IF_STATEMENT": ["IF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "SIMPLE_ELIF_STATEMENT": ["ELIF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_ELIF_STATEMENT": ["ELIF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "ELSE_STATEMENT": ["ELSE SPACE COLON NEW_LINE"], + + "CHAIN_CONDITION": ["CHAIN_CONDITION SPACE LOGICAL_OPERATOR_INFIX SPACE ENCLOSED_CONDITION", + "LOGICAL_OPERATOR_PREFIX SPACE ENCLOSED_CONDITION", + "ENCLOSED_CONDITION"], + "ENCLOSED_CONDITION": ["BRACKET_OPEN CONDITION BRACKET_CLOSE"], + "CONDITION": ["OPTIONAL_NOT CONDITION_EXPRESSION", "CONDITION_EXPRESSION"], + "CONDITION_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE EXPRESSION_IDENTIFIER", + "EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE DIGIT"], + "OPTIONAL_NOT": ["LOGICAL_OPERATOR_PREFIX SPACE", "SPACE"], + + # Loops + "FOR_HEADER": ["FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL COMMA SPACE STEP BRACKET_CLOSE SPACE COLON", + "FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL BRACKET_CLOSE SPACE COLON"], + "INITIAL": ["DIGIT"], + "FOR_LOOP": ["FOR_HEADER NEW_LINE TAB_INDENT DISPLAY"], + "ADVANCED_FOR_LOOP": ["FOR_LOOP", + "FOR_HEADER NEW_LINE TAB_INDENT ADVANCED_DISPLAY"], + + + # Displaying + "DISPLAY" : ["PRINT BRACKET_OPEN DISPLAY_IDENTIFIER BRACKET_CLOSE"], + "ADVANCED_DISPLAY" : ["DISPLAY", + "PRINT BRACKET_OPEN DISPLAY_EXPRESSION BRACKET_CLOSE"], + + + "LEVEL1.1": ["IDENTIFIER_INITIALIZATION SIMPLE_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL1.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL2.1": ["IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE SIMPLE_ELIF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY"], + "LEVEL2.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ADVANCED_ELIF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY"], + "LEVEL3.1": ["IDENTIFIER_INITIALIZATION FOR_LOOP"], + "LEVEL3.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_FOR_LOOP"], + + "ALL": ["LEVEL1.1", "LEVEL1.2", "LEVEL2.1", "LEVEL2.2", "LEVEL3.1", "LEVEL3.2"], + + } + + + + def generate_code(self, symbol, assigned_identifiers, last_variable, for_init_step, parent=None ): + """ + Generate code recursively based on the context-free grammar rules. + + Parameters: + - symbol (str): The symbol to generate code for. + - assigned_identifiers (set): Set of assigned identifiers. + - last_variable (set): Set of the last used variables. + - parent (Node): Parent node in the syntax tree. + + Returns: + - str: The generated code. + """ + node = Node(symbol, parent=parent) + + if symbol in self.cfg_rules: + if symbol == "IDENTIFIER_INITIALIZATION": + if self.init_count < self.max_init: + self.init_count += 1 + else: + symbol = "INITIALIZATION" + + rule = random.choice(self.cfg_rules[symbol]) + symbols = rule.split(" ") + + generated_symbols = [self.generate_code(s, assigned_identifiers, last_variable, for_init_step, node) for s in symbols] + + if symbol == "INITIAL": + init = generated_symbols[0] + for_init_step["initial_value"] = init + + + if symbol == "INITIALIZATION": + assigned_identifiers.add(generated_symbols[0]) + + if (symbol == "SIMPLE_ASSIGNMENTS") or (symbol == "ADVANCED_ASSIGNMENTS"): + if generated_symbols[0]: + last_variable.add(generated_symbols[0]) + + return ''.join(generated_symbols) + + elif symbol == "FINAL": + + initial_value = for_init_step.get("initial_value", "0") + + # Generate valid step_value and execution_count + valid_values = [(1, 2), (2, 1), (2, 2), (2, 3), (3, 2)] + step_value, execution_count = random.choice(valid_values) + for_init_step["step"] = str(step_value) + + final_value = step_value * execution_count + int(initial_value) - 1 + return str(final_value) + + + + elif symbol == "STEP": + + return for_init_step.get("step", "0") + + elif symbol == "EXPRESSION_IDENTIFIER": + identifier = random.choice(tuple(assigned_identifiers)) if assigned_identifiers else random.choice(self.cfg_rules["DIGIT"]) + return identifier + + elif symbol == "DISPLAY_IDENTIFIER": + try: + return f"{tuple(last_variable)[0]}" + except: + return f"{random.choice(tuple(assigned_identifiers))}" + else: + return symbol + + + + def print_tree(self, root): + """ + Print the syntax tree using the RenderTree utility from the anytree module. + + Parameters: + - root (Node): The root node of the syntax tree. + """ + for pre, _, node in RenderTree(root): + print(f"{pre}{node.name}") + + def generate_program(self, level): + """ + Generate a program based on the specified level. + + Parameters: + - level (str): The level of the program. + + Returns: + - Tuple[Node, str]: The syntax tree root node and the generated program. + """ + assigned = set() + last_variable = set() + for_init_step = {} + root = Node("ROOT") + + self.init_count = 0 + if level == "1.1": + self.max_init = 2 + elif level == "1.2": + self.max_init = 3 + elif level == "2.1": + self.max_init = 2 + elif level == "3.1": + self.max_init = 2 + elif level == "3.2": + self.max_init = 4 + else: + self.max_init = 5 + + if level == "ALL" : + level_passed = level + else : + level_passed = "LEVEL" + level + + program = self.generate_code(level_passed, assigned, last_variable, for_init_step, root) + + return root, program.replace("SPACE", " ") + + def memory_usage(self): + process = psutil.Process(os.getpid()) + mem_info = process.memory_info() + return mem_info.rss + + def generate_and_write_programs(self, num_programs, level, filename='data.txt', deduplicate=True): + """ + Generate and write a specified number of programs to a file. + + Parameters: + - num_programs (int): Number of programs to generate and write. + - level (str): The level of the programs. + - filename (str): Name of the file to write the programs (default is 'data.txt'). + - deduplicate (bool, optional): Whether to perform deduplication of generated programs (default is True). + """ + start_time = time.time() + start_mem = self.memory_usage() + max_tries = 1000 + num_tries = 0 + + with open(filename, 'w') as file: + + generated_programs = 0 + hashes = set() + pbar = tqdm(desc="Generation", total=num_programs) + while generated_programs < num_programs: + try: + root, program = self.generate_program(level) + code = program + "\n# output" + + SIO = StringIO() + with redirect_stdout(SIO): + exec(code) + output = SIO.getvalue().strip() + + output = '\n'.join([f'# {line}' if line else f'# ' for line in output.split('\n')]) + result = f"""{code}\n{output}""" + + program_hash = hashlib.sha256(result.encode('utf-8')).hexdigest() + + if deduplicate: + if program_hash not in hashes: + hashes.add(program_hash) + file.write(result + '\n\n') + generated_programs += 1 + pbar.update(1) + num_tries = 0 + else: + num_tries += 1 + if num_tries >= max_tries: + print("Hit max tries in deduplication, stopping generation.") + break + else: + file.write(result + '\n\n') + + generated_programs += 1 + pbar.update(1) + + except Exception as e: + continue + + pbar.close() + end_time = time.time() + end_mem = self.memory_usage() + deduplication_info = "with deduplication" if deduplicate else "without deduplication" + print(f"Code generation completed in {end_time - start_time:.2f} seconds.") + print(f"Memory used during code generation: {end_mem - start_mem} bytes") + print(f"Generated {generated_programs} {'unique ' if deduplicate else ''}programs {deduplication_info}.") + print(f"Programs are saved to {filename}.") + + +def main(): + parser = argparse.ArgumentParser(description='Generate and write programs based on a specified level. ') + parser.add_argument('--num_programs', type=int, default=3000000, help='Number of programs to generate and write (default is 1000)') + parser.add_argument('--level', default="3.1", help='The level of the programs (1.1, 1.2, 2.1, 2.2, 3.1, 3.2, ALL)') + parser.add_argument('--filename', default='3.1.txt', help='Name of the file to write the programs (default is data.txt)') + parser.add_argument('--deduplicate', action='store_true', default=True, help='Perform deduplication of generated programs (default is True)a') + + args = parser.parse_args() + + code_generator = CodeGenerator() + code_generator.generate_and_write_programs(num_programs=args.num_programs, level=args.level, filename=args.filename, deduplicate=args.deduplicate) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/data/meta.pkl b/data/meta.pkl new file mode 100644 index 0000000..a5db975 Binary files /dev/null and b/data/meta.pkl differ diff --git a/data/prepare.py b/data/prepare.py new file mode 100644 index 0000000..cb2189e --- /dev/null +++ b/data/prepare.py @@ -0,0 +1,83 @@ +import os +import pickle +import requests +import numpy as np + +input_file_path = os.path.join(os.path.dirname(__file__), 'sample_data.txt' ) + +with open(input_file_path, 'r') as f: + data = f.read() +print(f"length of dataset in characters: {len(data):,}\n") + + +# get all the unique characters that occur in this text +chars = sorted(list(set(data))) +vocab_size = len(chars) +print("all the unique characters:", ''.join(chars)) +print(f"vocab size: {vocab_size:,}") + +# create a mapping from characters to integers +stoi = { ch:i for i,ch in enumerate(chars) } +itos = { i:ch for i,ch in enumerate(chars) } +def encode(s): + return [stoi[c] for c in s] # encoder: take a string, output a list of integers +def decode(l): + ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string + + +# save the meta information as well, to help us encode/decode later +meta = { + 'vocab_size': vocab_size, + 'itos': itos, + 'stoi': stoi, +} +with open(f'meta.pkl', 'wb') as f: + pickle.dump(meta, f) + + +# split by examples using "\n\n" +examples = data.split("\n\n")[:-1] +n = len(examples) +print(f"total number of examples: {n:,}\n") +# shuffle the examples +np.random.shuffle(examples) + +# split into train, val, and test sets +train_examples = examples[:int(n*0.8)] +val_examples = examples[int(n*0.8):int(n*0.9)] +test_examples = examples[int(n*0.9):] + +# join the examples back into strings +train_data = "\n\n".join(train_examples) +val_data = "\n\n".join(val_examples) +test_data = "\n\n".join(test_examples) + + + +# Save train, val, and test sets to separate files +with open(os.path.join(os.path.dirname(__file__), 'train.txt'), 'w') as f: + f.write(train_data) +with open(os.path.join(os.path.dirname(__file__), 'val.txt'), 'w') as f: + f.write(val_data) +with open(os.path.join(os.path.dirname(__file__), 'test.txt'), 'w') as f: + f.write(test_data) + + + + +# encode both to integers +train_ids = encode(train_data) +val_ids = encode(val_data) +test_ids = encode(test_data) +print(f"train has {len(train_ids):,} tokens for {len(train_examples):,} examples") +print(f"val has {len(val_ids):,} tokens for {len(val_examples):,} examples") +print(f"test has {len(test_ids):,} tokens for {len(test_examples):,} examples\n") + +# export to bin files +train_ids = np.array(train_ids, dtype=np.uint16) +val_ids = np.array(val_ids, dtype=np.uint16) +test_ids = np.array(test_ids, dtype=np.uint16) +train_ids.tofile(os.path.join(os.path.dirname(__file__), 'train.bin')) +val_ids.tofile(os.path.join(os.path.dirname(__file__), 'val.bin')) +test_ids.tofile(os.path.join(os.path.dirname(__file__), 'test.bin')) + diff --git a/data/sample_data.txt b/data/sample_data.txt new file mode 100644 index 0000000..dd6e285 --- /dev/null +++ b/data/sample_data.txt @@ -0,0 +1,6098 @@ +d = 0 +b = d * d +print(b) +# output +# 0 + +c = 5 +d = 5 +d = c - 4 +print(d) +# output +# 1 + +d = 5 +a = 0 +print(a + d) +# output +# 5 + +b = 2 +print(b) +# output +# 2 + +a = 8 +e = a + 1 +print(a / a) +# output +# 1.0 + +e = 8 +print(e) +# output +# 8 + +d = 0 +e = 7 +c = d / 1 +print(e / e) +# output +# 1.0 + +d = 6 +c = 4 + 1 +print(d + 2) +# output +# 8 + +d = 9 +d = 7 +print(d) +# output +# 7 + +c = 2 +e = 8 +print(e) +# output +# 8 + +e = 5 +c = 8 +print(c) +# output +# 8 + +c = 2 +c = 7 + c +print(c * c) +# output +# 81 + +a = 2 +print(a / a) +# output +# 1.0 + +c = 5 +e = 1 +print(c) +# output +# 5 + +d = 6 +d = 0 +b = d * 5 +print(d * d) +# output +# 0 + +e = 1 +c = 8 +print(c * 2) +# output +# 16 + +e = 4 +b = 8 +d = 4 + b +print(d) +# output +# 12 + +d = 9 +print(d) +# output +# 9 + +a = 8 +print(a - a) +# output +# 0 + +d = 5 +a = 0 * d +print(a) +# output +# 0 + +d = 5 +print(d) +# output +# 5 + +a = 8 +print(a) +# output +# 8 + +c = 8 +d = 4 +print(d / 9) +# output +# 0.4444444444444444 + +e = 6 +c = 4 +b = c + c +print(e / e) +# output +# 1.0 + +b = 8 +a = 5 - b +print(a) +# output +# -3 + +a = 4 +e = 4 +print(e) +# output +# 4 + +b = 3 +a = 0 +print(a) +# output +# 0 + +d = 4 +b = 6 / 2 +print(b) +# output +# 3.0 + +d = 9 +b = 6 +print(b - b) +# output +# 0 + +e = 7 +print(e * 9) +# output +# 63 + +e = 4 +e = 8 * 8 +print(e) +# output +# 64 + +a = 5 +a = 7 +a = a + a +print(a) +# output +# 14 + +e = 8 +print(e * e) +# output +# 64 + +e = 9 +e = 9 +print(e + e) +# output +# 18 + +a = 0 +b = a + a +print(a / 9) +# output +# 0.0 + +d = 6 +a = d / d +print(a) +# output +# 1.0 + +e = 2 +b = 0 + e +print(e - 4) +# output +# -2 + +e = 6 +c = 1 / e +print(e + e) +# output +# 12 + +d = 7 +c = 7 +print(c) +# output +# 7 + +b = 7 +print(b) +# output +# 7 + +c = 3 +a = 0 +e = c / 3 +print(a + c) +# output +# 3 + +e = 5 +print(e - 0) +# output +# 5 + +e = 8 +d = 0 - e +print(e / e) +# output +# 1.0 + +a = 5 +b = 5 +e = 9 - 2 +print(a * a) +# output +# 25 + +e = 4 +print(e + e) +# output +# 8 + +e = 2 +b = e / e +print(e + 8) +# output +# 10 + +b = 7 +a = 1 +print(a) +# output +# 1 + +a = 4 +a = 2 +print(a) +# output +# 2 + +b = 2 +a = 7 +print(a) +# output +# 7 + +e = 6 +b = 5 +c = 4 / 9 +print(e * 6) +# output +# 36 + +a = 4 +c = 0 +d = 9 / a +print(d) +# output +# 2.25 + +c = 6 +d = 9 - 5 +print(c / 1) +# output +# 6.0 + +d = 5 +d = 9 - 7 +print(d) +# output +# 2 + +b = 3 +a = 0 +c = 6 - 1 +print(c) +# output +# 5 + +a = 8 +print(a * a) +# output +# 64 + +d = 8 +d = 4 * 5 +print(d) +# output +# 20 + +b = 1 +a = 1 + b +print(a) +# output +# 2 + +c = 8 +print(c / 3) +# output +# 2.6666666666666665 + +c = 0 +b = 1 +c = 4 - 0 +print(b + b) +# output +# 2 + +e = 7 +b = 2 +print(e) +# output +# 7 + +a = 9 +b = a * 0 +print(a / a) +# output +# 1.0 + +d = 8 +a = 5 +print(d) +# output +# 8 + +e = 4 +b = 0 + e +print(e + 1) +# output +# 5 + +e = 9 +a = 1 +c = 9 * a +print(e + 9) +# output +# 18 + +b = 3 +c = 1 +e = 6 - c +print(e) +# output +# 5 + +e = 3 +e = e / 1 +print(e) +# output +# 3.0 + +e = 4 +e = 5 +print(e) +# output +# 5 + +c = 9 +print(c) +# output +# 9 + +a = 9 +print(a) +# output +# 9 + +a = 3 +print(a) +# output +# 3 + +a = 0 +d = a * 9 +print(d) +# output +# 0 + +b = 8 +b = 2 / 5 +print(b / b) +# output +# 1.0 + +b = 4 +b = 8 +d = b - 0 +print(d) +# output +# 8 + +d = 4 +d = 4 +print(d) +# output +# 4 + +b = 1 +print(b * 6) +# output +# 6 + +c = 1 +e = c / c +print(c + 5) +# output +# 6 + +a = 5 +print(a) +# output +# 5 + +a = 9 +c = 2 +a = c - 2 +print(a + a) +# output +# 0 + +d = 3 +c = 7 / 2 +print(d - d) +# output +# 0 + +b = 6 +b = 4 +b = b / b +print(b - 4) +# output +# -3.0 + +c = 6 +a = 0 +d = a - a +print(c / 2) +# output +# 3.0 + +a = 7 +c = a * a +print(a / a) +# output +# 1.0 + +d = 4 +d = 1 +a = 1 - d +print(d / d) +# output +# 1.0 + +d = 2 +e = d + 1 +print(e) +# output +# 3 + +e = 1 +print(e + 3) +# output +# 4 + +b = 6 +c = 4 +print(b / b) +# output +# 1.0 + +b = 7 +d = 4 +print(b) +# output +# 7 + +a = 5 +c = 8 +print(a) +# output +# 5 + +b = 6 +b = 5 +b = b - b +print(b / 3) +# output +# 0.0 + +a = 3 +print(a + a) +# output +# 6 + +b = 3 +print(b) +# output +# 3 + +e = 3 +d = 8 +a = d / d +print(d - e) +# output +# 5 + +c = 3 +d = 5 +a = c - c +print(c / d) +# output +# 0.6 + +c = 4 +d = 5 +d = c / 1 +print(d + c) +# output +# 8.0 + +e = 6 +print(e) +# output +# 6 + +d = 6 +c = 3 +a = 9 + d +print(a) +# output +# 15 + +b = 1 +e = b * b +print(e) +# output +# 1 + +e = 6 +print(e - e) +# output +# 0 + +e = 6 +print(e - 6) +# output +# 0 + +d = 9 +c = 5 +e = c / 7 +print(e) +# output +# 0.7142857142857143 + +e = 9 +c = 0 +print(e) +# output +# 9 + +c = 8 +print(c * c) +# output +# 64 + +e = 2 +c = 3 +print(c) +# output +# 3 + +d = 1 +b = 8 +print(d * 1) +# output +# 1 + +d = 0 +print(d) +# output +# 0 + +e = 9 +print(e) +# output +# 9 + +d = 7 +b = 3 +print(b) +# output +# 3 + +d = 0 +c = 6 +e = 7 * c +print(e) +# output +# 42 + +c = 2 +print(c) +# output +# 2 + +b = 7 +d = 1 +print(d - b) +# output +# -6 + +e = 3 +d = e - 2 +print(e / e) +# output +# 1.0 + +b = 2 +a = b / b +print(a) +# output +# 1.0 + +d = 8 +e = d + d +print(e) +# output +# 16 + +a = 3 +b = 2 +c = b * 7 +print(c) +# output +# 14 + +b = 3 +print(b - b) +# output +# 0 + +b = 6 +c = 1 +print(b) +# output +# 6 + +a = 4 +e = 0 * 7 +print(a + 2) +# output +# 6 + +b = 0 +b = 1 +a = 1 + b +print(b - 7) +# output +# -6 + +a = 7 +e = a + 9 +print(a * 9) +# output +# 63 + +a = 9 +d = 9 + 5 +print(d) +# output +# 14 + +d = 9 +b = 9 +a = b - d +print(a) +# output +# 0 + +e = 3 +print(e / 6) +# output +# 0.5 + +c = 6 +print(c * 2) +# output +# 12 + +b = 1 +e = 7 +e = b * 4 +print(e + e) +# output +# 8 + +c = 5 +print(c - c) +# output +# 0 + +d = 2 +d = 2 + d +print(d) +# output +# 4 + +e = 7 +b = 6 +print(b * 6) +# output +# 36 + +b = 2 +print(b * 6) +# output +# 12 + +e = 7 +d = e + e +print(d) +# output +# 14 + +c = 0 +print(c / 2) +# output +# 0.0 + +b = 6 +a = 5 +e = b / 8 +print(e) +# output +# 0.75 + +c = 1 +c = 9 +d = 0 - 5 +print(d) +# output +# -5 + +a = 3 +c = 8 +d = 5 + 3 +print(d) +# output +# 8 + +d = 2 +a = d + d +print(a) +# output +# 4 + +a = 9 +a = 2 + 7 +print(a) +# output +# 9 + +d = 1 +print(d) +# output +# 1 + +e = 7 +print(e / 6) +# output +# 1.1666666666666667 + +c = 4 +d = 6 +e = d / d +print(c / c) +# output +# 1.0 + +c = 3 +print(c - 0) +# output +# 3 + +b = 1 +e = 0 +b = 4 + e +print(b / 3) +# output +# 1.3333333333333333 + +e = 3 +e = e / e +print(e / e) +# output +# 1.0 + +c = 1 +a = 7 +print(a) +# output +# 7 + +d = 3 +d = 8 +print(d) +# output +# 8 + +e = 9 +d = 2 - e +print(e - 1) +# output +# 8 + +b = 5 +b = 5 +print(b + b) +# output +# 10 + +b = 8 +print(b * 1) +# output +# 8 + +c = 4 +print(c) +# output +# 4 + +e = 9 +b = 1 +print(b) +# output +# 1 + +c = 0 +c = c - c +print(c) +# output +# 0 + +a = 7 +d = 5 +b = 3 / 3 +print(b) +# output +# 1.0 + +d = 5 +b = 2 +b = d * d +print(b) +# output +# 25 + +d = 8 +print(d - 5) +# output +# 3 + +e = 3 +a = e * e +print(e * 0) +# output +# 0 + +e = 2 +print(e * e) +# output +# 4 + +d = 2 +b = 4 +e = 8 + 3 +print(d / 5) +# output +# 0.4 + +d = 7 +d = 9 +print(d + 5) +# output +# 14 + +b = 9 +b = 1 / b +print(b * 0) +# output +# 0.0 + +b = 3 +d = 2 +d = 6 / 6 +print(d) +# output +# 1.0 + +b = 0 +print(b + b) +# output +# 0 + +e = 2 +c = 3 +e = 1 * 5 +print(e * c) +# output +# 15 + +a = 1 +b = 4 +e = 2 + 7 +print(a * b) +# output +# 4 + +a = 0 +e = 4 * a +print(a / 1) +# output +# 0.0 + +a = 9 +print(a - 6) +# output +# 3 + +a = 2 +a = 1 +print(a) +# output +# 1 + +a = 8 +d = 2 +a = d - 8 +print(a) +# output +# -6 + +d = 1 +b = 2 / d +print(b) +# output +# 2.0 + +c = 7 +print(c) +# output +# 7 + +e = 0 +e = 3 +print(e / 6) +# output +# 0.5 + +c = 1 +b = 1 + 4 +print(b) +# output +# 5 + +c = 9 +print(c / c) +# output +# 1.0 + +d = 4 +c = 6 +print(d + c) +# output +# 10 + +c = 4 +d = 3 +print(d + 6) +# output +# 9 + +c = 0 +a = 1 +print(a) +# output +# 1 + +d = 7 +c = 2 + 8 +print(d * 1) +# output +# 7 + +a = 9 +print(a / a) +# output +# 1.0 + +d = 1 +d = 0 +b = 2 * d +print(b) +# output +# 0 + +b = 2 +c = 7 +c = c + c +print(c) +# output +# 14 + +c = 5 +d = 3 +e = 2 + 6 +print(d / 8) +# output +# 0.375 + +a = 6 +e = a - 9 +print(e) +# output +# -3 + +e = 6 +b = 5 +a = 4 / b +print(a) +# output +# 0.8 + +d = 4 +b = 6 +a = 0 / d +print(a) +# output +# 0.0 + +c = 9 +e = 8 +b = 9 * e +print(b) +# output +# 72 + +d = 4 +d = d / d +print(d / 3) +# output +# 0.3333333333333333 + +c = 9 +e = 9 +print(c / c) +# output +# 1.0 + +a = 9 +d = a + 1 +print(a * a) +# output +# 81 + +b = 5 +a = 1 +b = a - a +print(b) +# output +# 0 + +d = 9 +print(d * 9) +# output +# 81 + +d = 4 +d = 3 +e = d * d +print(e) +# output +# 9 + +d = 6 +e = 0 +print(d * d) +# output +# 36 + +a = 4 +e = a / 2 +print(a + 1) +# output +# 5 + +b = 8 +print(b * b) +# output +# 64 + +d = 2 +a = 4 +c = d * 0 +print(c) +# output +# 0 + +c = 3 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +c = 3 +print(c / c) +# output +# 1.0 + +e = 4 +d = 7 +d = e * 8 +print(e / 1) +# output +# 4.0 + +d = 8 +print(d / d) +# output +# 1.0 + +c = 3 +d = 7 +b = d / 8 +print(b) +# output +# 0.875 + +b = 2 +e = b - 5 +print(e) +# output +# -3 + +c = 8 +print(c - 2) +# output +# 6 + +e = 7 +a = e + e +print(a) +# output +# 14 + +b = 3 +e = 6 +print(b) +# output +# 3 + +b = 3 +a = 3 +print(a) +# output +# 3 + +b = 8 +d = 9 +a = d + 5 +print(a) +# output +# 14 + +a = 6 +b = 7 +d = a - 0 +print(d) +# output +# 6 + +a = 5 +print(a / 5) +# output +# 1.0 + +b = 7 +c = b * 6 +print(b * 9) +# output +# 63 + +b = 0 +print(b) +# output +# 0 + +a = 1 +print(a) +# output +# 1 + +c = 6 +a = 5 +d = c + 1 +print(d) +# output +# 7 + +d = 7 +a = 0 +print(a) +# output +# 0 + +c = 9 +e = 4 +print(c - 5) +# output +# 4 + +d = 7 +print(d) +# output +# 7 + +e = 6 +a = 4 - e +print(a) +# output +# -2 + +b = 7 +b = 8 +print(b) +# output +# 8 + +e = 3 +print(e) +# output +# 3 + +c = 9 +c = c + 7 +print(c) +# output +# 16 + +a = 1 +c = a + 9 +print(a * 4) +# output +# 4 + +c = 0 +b = 8 +print(c) +# output +# 0 + +d = 3 +d = 5 +print(d + d) +# output +# 10 + +b = 4 +d = 4 +e = 3 / 9 +print(d + 6) +# output +# 10 + +c = 0 +a = 4 +d = c / 4 +print(a / a) +# output +# 1.0 + +c = 5 +e = 0 +print(c) +# output +# 5 + +a = 6 +c = 8 +c = 6 + a +print(c) +# output +# 12 + +c = 0 +d = 8 +d = d - 9 +print(d) +# output +# -1 + +b = 5 +e = 7 - 8 +print(e) +# output +# -1 + +d = 7 +a = d * d +print(a) +# output +# 49 + +a = 4 +a = 4 +print(a) +# output +# 4 + +d = 4 +print(d * 6) +# output +# 24 + +b = 5 +b = 8 +c = b - b +print(c) +# output +# 0 + +d = 5 +c = 9 + d +print(c) +# output +# 14 + +e = 1 +print(e - e) +# output +# 0 + +b = 9 +print(b / 6) +# output +# 1.5 + +e = 5 +d = 0 * e +print(e + e) +# output +# 10 + +c = 1 +print(c) +# output +# 1 + +e = 7 +print(e) +# output +# 7 + +d = 6 +print(d + d) +# output +# 12 + +b = 8 +a = 2 +c = b + 1 +print(b * a) +# output +# 16 + +c = 8 +print(c / 1) +# output +# 8.0 + +e = 8 +b = 6 / e +print(e / 2) +# output +# 4.0 + +a = 5 +b = 2 +print(b / a) +# output +# 0.4 + +a = 1 +e = 7 - a +print(e) +# output +# 6 + +e = 3 +e = 4 +c = 6 - e +print(e + 3) +# output +# 7 + +b = 4 +e = b - 0 +print(b - 9) +# output +# -5 + +a = 6 +d = 0 +d = d + 0 +print(d) +# output +# 0 + +e = 5 +a = 0 - e +print(a) +# output +# -5 + +e = 6 +c = 3 / e +print(c) +# output +# 0.5 + +c = 8 +e = 8 +print(e * c) +# output +# 64 + +e = 1 +print(e / e) +# output +# 1.0 + +a = 0 +c = 3 +c = 2 + 5 +print(c) +# output +# 7 + +d = 1 +d = d * 2 +print(d) +# output +# 2 + +a = 3 +e = 9 +b = e - a +print(b) +# output +# 6 + +c = 5 +a = c + 9 +print(a) +# output +# 14 + +e = 5 +c = 1 +print(c - e) +# output +# -4 + +d = 7 +d = 9 +e = 3 * d +print(d - 9) +# output +# 0 + +d = 9 +c = 3 +e = d - 5 +print(d + c) +# output +# 12 + +d = 1 +b = 2 +print(b) +# output +# 2 + +e = 5 +print(e - e) +# output +# 0 + +a = 2 +a = 4 +print(a) +# output +# 4 + +b = 0 +c = 3 +print(b) +# output +# 0 + +e = 6 +b = 1 +d = e / e +print(d) +# output +# 1.0 + +e = 4 +print(e) +# output +# 4 + +e = 3 +b = 4 +a = e - 6 +print(a) +# output +# -3 + +c = 2 +c = c * c +print(c - 5) +# output +# -1 + +e = 1 +print(e) +# output +# 1 + +d = 6 +d = 3 / 7 +print(d * 7) +# output +# 3.0 + +d = 5 +d = d - d +print(d) +# output +# 0 + +e = 8 +a = 3 +print(e / a) +# output +# 2.6666666666666665 + +e = 9 +print(e + 9) +# output +# 18 + +b = 8 +e = b + b +print(e) +# output +# 16 + +c = 5 +e = 8 + c +print(e) +# output +# 13 + +c = 3 +c = 0 +a = c + 6 +print(c + 8) +# output +# 8 + +b = 5 +print(b + b) +# output +# 10 + +d = 2 +a = 6 +e = 2 - 6 +print(e) +# output +# -4 + +b = 8 +print(b) +# output +# 8 + +e = 7 +a = 2 +d = e / 3 +print(d) +# output +# 2.3333333333333335 + +a = 4 +e = 9 +print(e + a) +# output +# 13 + +a = 7 +e = 0 +d = 5 * e +print(a * a) +# output +# 49 + +c = 6 +a = 0 / 5 +print(a) +# output +# 0.0 + +e = 2 +c = 8 / e +print(e * 9) +# output +# 18 + +e = 1 +d = 0 +d = 4 - d +print(d - 7) +# output +# -3 + +a = 5 +a = 6 +c = 1 + 9 +print(a * 3) +# output +# 18 + +b = 3 +b = 5 +d = b * 2 +print(d) +# output +# 10 + +e = 1 +c = 3 +print(e - 3) +# output +# -2 + +c = 6 +b = 6 +print(b) +# output +# 6 + +d = 7 +c = 0 + 7 +print(c) +# output +# 7 + +c = 2 +e = 6 +b = c * 6 +print(b) +# output +# 12 + +a = 6 +e = a + a +print(a + a) +# output +# 12 + +e = 1 +b = 0 +print(b) +# output +# 0 + +d = 8 +c = d - 6 +print(c) +# output +# 2 + +e = 9 +a = 5 +print(e) +# output +# 9 + +a = 2 +c = 3 * 5 +print(c) +# output +# 15 + +d = 8 +print(d) +# output +# 8 + +c = 4 +b = 5 +c = 0 / c +print(c) +# output +# 0.0 + +c = 8 +b = 3 +print(b) +# output +# 3 + +e = 4 +e = 4 * e +print(e / 8) +# output +# 2.0 + +c = 3 +print(c) +# output +# 3 + +a = 2 +a = 5 +d = a + a +print(d) +# output +# 10 + +a = 7 +b = 1 +a = a / 5 +print(a) +# output +# 1.4 + +b = 7 +print(b / 4) +# output +# 1.75 + +a = 5 +e = 1 +print(a) +# output +# 5 + +c = 8 +b = c / 7 +print(c - c) +# output +# 0 + +e = 0 +a = 3 +print(a) +# output +# 3 + +e = 5 +e = e - e +print(e - e) +# output +# 0 + +a = 9 +e = 7 +print(e - e) +# output +# 0 + +a = 3 +c = a / 3 +print(c) +# output +# 1.0 + +d = 2 +a = 0 +e = 7 / 8 +print(d + a) +# output +# 2 + +d = 0 +e = 8 +print(e * e) +# output +# 64 + +d = 2 +a = 6 +print(d) +# output +# 2 + +d = 3 +a = 2 +print(d - a) +# output +# 1 + +e = 4 +a = 5 - e +print(e - 7) +# output +# -3 + +e = 7 +e = 9 +print(e) +# output +# 9 + +e = 1 +print(e * e) +# output +# 1 + +e = 5 +d = 2 - e +print(d) +# output +# -3 + +b = 1 +print(b * 9) +# output +# 9 + +e = 0 +c = 6 +print(e) +# output +# 0 + +c = 5 +print(c / c) +# output +# 1.0 + +e = 4 +c = 2 +e = e - 0 +print(c * c) +# output +# 4 + +e = 6 +d = 9 +print(d) +# output +# 9 + +a = 0 +b = a + a +print(b) +# output +# 0 + +c = 8 +b = 5 +print(c - 8) +# output +# 0 + +e = 8 +b = e / 6 +print(b) +# output +# 1.3333333333333333 + +d = 0 +b = 4 +print(b) +# output +# 4 + +c = 4 +d = 9 +b = 6 + 9 +print(b) +# output +# 15 + +e = 9 +print(e / 1) +# output +# 9.0 + +d = 1 +c = 0 +d = d / 7 +print(d * 2) +# output +# 0.2857142857142857 + +a = 9 +b = 0 +d = a + a +print(d) +# output +# 18 + +a = 4 +a = 5 +a = a * 7 +print(a / a) +# output +# 1.0 + +a = 3 +c = 6 +d = c / 7 +print(c - 1) +# output +# 5 + +d = 2 +d = d - d +print(d) +# output +# 0 + +c = 7 +b = 9 / c +print(b) +# output +# 1.2857142857142858 + +c = 4 +e = 4 +print(c * c) +# output +# 16 + +e = 8 +a = 0 +c = a + a +print(a * 7) +# output +# 0 + +a = 9 +d = 9 +b = a + 4 +print(a + 0) +# output +# 9 + +c = 4 +e = 3 +print(c * e) +# output +# 12 + +d = 8 +print(d + 9) +# output +# 17 + +d = 6 +d = 6 +d = d + d +print(d) +# output +# 12 + +a = 0 +print(a - 9) +# output +# -9 + +a = 6 +c = 7 +b = c + c +print(a - c) +# output +# -1 + +d = 4 +a = 8 +print(a * a) +# output +# 64 + +e = 4 +b = 3 +print(b) +# output +# 3 + +e = 9 +d = 6 / 4 +print(e / 1) +# output +# 9.0 + +a = 4 +print(a / a) +# output +# 1.0 + +a = 8 +c = 2 / 7 +print(a * a) +# output +# 64 + +d = 9 +b = 2 +c = 3 / d +print(b - b) +# output +# 0 + +d = 6 +b = d / d +print(b) +# output +# 1.0 + +c = 3 +b = 0 +d = c + b +print(d) +# output +# 3 + +c = 6 +e = 2 +d = 6 + 1 +print(c * 1) +# output +# 6 + +c = 4 +a = 4 +print(a) +# output +# 4 + +e = 9 +print(e / 3) +# output +# 3.0 + +c = 6 +c = 2 +print(c + c) +# output +# 4 + +c = 7 +print(c - c) +# output +# 0 + +b = 2 +c = 1 +print(b + 2) +# output +# 4 + +c = 0 +d = c + 6 +print(c * c) +# output +# 0 + +b = 9 +c = 4 + 6 +print(c) +# output +# 10 + +a = 6 +print(a) +# output +# 6 + +a = 6 +print(a - 3) +# output +# 3 + +e = 3 +a = 9 +a = 8 - a +print(a) +# output +# -1 + +a = 1 +b = 3 +print(a) +# output +# 1 + +b = 2 +a = 8 +d = b / 6 +print(a / 5) +# output +# 1.6 + +a = 6 +b = 0 +a = 3 - 3 +print(b / 9) +# output +# 0.0 + +e = 8 +a = 1 +d = 1 - 9 +print(e * e) +# output +# 64 + +c = 6 +e = 9 +d = 4 * c +print(c - 8) +# output +# -2 + +d = 1 +b = 6 +print(d) +# output +# 1 + +d = 0 +c = 7 +print(d - d) +# output +# 0 + +b = 6 +a = 4 / 9 +print(b - b) +# output +# 0 + +d = 5 +d = 9 +e = 0 - d +print(d * d) +# output +# 81 + +a = 4 +print(a * a) +# output +# 16 + +b = 5 +b = 0 - 7 +print(b) +# output +# -7 + +d = 5 +c = 8 +b = c + c +print(c + 6) +# output +# 14 + +e = 4 +d = 1 +print(e) +# output +# 4 + +a = 3 +c = a / a +print(c) +# output +# 1.0 + +e = 2 +print(e) +# output +# 2 + +b = 8 +b = 8 +print(b) +# output +# 8 + +e = 1 +b = 7 +print(b - 0) +# output +# 7 + +d = 9 +c = 7 - d +print(d - d) +# output +# 0 + +b = 8 +d = 3 +print(d / d) +# output +# 1.0 + +b = 2 +print(b + 3) +# output +# 5 + +b = 9 +a = 2 +b = a - 9 +print(a - 4) +# output +# -2 + +c = 7 +print(c / c) +# output +# 1.0 + +c = 8 +print(c) +# output +# 8 + +b = 8 +b = b / 8 +print(b) +# output +# 1.0 + +a = 0 +c = 8 +print(c) +# output +# 8 + +d = 9 +b = 6 +a = 6 + 5 +print(a) +# output +# 11 + +e = 6 +a = 8 - e +print(e + 3) +# output +# 9 + +c = 6 +a = 4 +print(a) +# output +# 4 + +e = 1 +c = e + 5 +print(e + 7) +# output +# 8 + +d = 8 +b = 7 +print(b) +# output +# 7 + +a = 1 +print(a * 0) +# output +# 0 + +b = 4 +print(b * b) +# output +# 16 + +e = 9 +a = 9 +print(a * e) +# output +# 81 + +e = 8 +a = 6 / e +print(e * e) +# output +# 64 + +b = 7 +d = b - 4 +print(b + 8) +# output +# 15 + +d = 4 +e = 3 +b = 0 + 8 +print(b) +# output +# 8 + +a = 2 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +d = 9 +print(d - 9) +# output +# 0 + +e = 8 +e = 0 +print(e + e) +# output +# 0 + +b = 9 +b = 9 +print(b / 9) +# output +# 1.0 + +d = 4 +e = 3 +a = 9 - 9 +print(d * 4) +# output +# 16 + +c = 9 +d = 5 +print(d) +# output +# 5 + +b = 6 +b = 3 +print(b) +# output +# 3 + +d = 2 +e = d - d +print(d - d) +# output +# 0 + +c = 7 +e = 5 +d = 1 + c +print(c - 7) +# output +# 0 + +d = 0 +a = 9 +print(a * 7) +# output +# 63 + +d = 2 +print(d + d) +# output +# 4 + +e = 1 +a = 3 +e = e / a +print(e) +# output +# 0.3333333333333333 + +b = 2 +b = 3 +print(b * 7) +# output +# 21 + +d = 0 +a = 1 +print(a) +# output +# 1 + +a = 2 +e = 9 +c = 7 * a +print(a * e) +# output +# 18 + +c = 1 +e = 9 +d = 6 * 8 +print(e - e) +# output +# 0 + +a = 7 +b = a / a +print(a * a) +# output +# 49 + +e = 1 +a = 6 +print(a / 6) +# output +# 1.0 + +d = 3 +b = 4 +print(b) +# output +# 4 + +e = 3 +b = e * 6 +print(e / e) +# output +# 1.0 + +c = 4 +print(c + 3) +# output +# 7 + +e = 5 +print(e) +# output +# 5 + +d = 5 +d = d * d +print(d) +# output +# 25 + +d = 4 +a = 1 +print(d) +# output +# 4 + +d = 7 +e = 5 +e = e + d +print(e) +# output +# 12 + +d = 5 +print(d - d) +# output +# 0 + +d = 2 +print(d + 9) +# output +# 11 + +b = 6 +a = 2 +d = 9 - b +print(b - 0) +# output +# 6 + +d = 6 +b = d / 4 +print(d - 3) +# output +# 3 + +c = 7 +a = c - c +print(a) +# output +# 0 + +b = 0 +d = 0 +print(d) +# output +# 0 + +d = 5 +c = d * d +print(d / d) +# output +# 1.0 + +c = 8 +b = 7 +c = c + c +print(c - c) +# output +# 0 + +a = 2 +d = 1 +print(d) +# output +# 1 + +d = 4 +c = 0 + 1 +print(c) +# output +# 1 + +b = 1 +c = b + 3 +print(c) +# output +# 4 + +a = 1 +print(a / 2) +# output +# 0.5 + +b = 6 +c = 5 +print(b / b) +# output +# 1.0 + +a = 8 +a = 8 +print(a) +# output +# 8 + +d = 4 +c = d - d +print(c) +# output +# 0 + +c = 6 +b = 2 +c = b - 4 +print(b + c) +# output +# 0 + +b = 2 +e = 2 +print(e) +# output +# 2 + +e = 5 +print(e + e) +# output +# 10 + +e = 2 +a = 6 +print(e) +# output +# 2 + +a = 8 +a = a / 8 +print(a * a) +# output +# 1.0 + +d = 7 +print(d * d) +# output +# 49 + +d = 9 +b = 5 +print(d) +# output +# 9 + +c = 3 +e = 7 +print(c) +# output +# 3 + +b = 3 +d = b * b +print(d) +# output +# 9 + +b = 9 +c = 6 +a = c * 6 +print(c / 6) +# output +# 1.0 + +a = 2 +c = 1 +b = 7 * c +print(b) +# output +# 7 + +b = 6 +a = 5 +d = b + 9 +print(d) +# output +# 15 + +d = 2 +e = 2 +print(e) +# output +# 2 + +e = 3 +c = e + 2 +print(e + e) +# output +# 6 + +e = 8 +d = e - 9 +print(d) +# output +# -1 + +b = 8 +d = 6 +print(b / d) +# output +# 1.3333333333333333 + +a = 7 +e = 1 +print(a + 3) +# output +# 10 + +c = 9 +c = 9 +print(c - 4) +# output +# 5 + +d = 0 +d = d + 7 +print(d) +# output +# 7 + +a = 1 +e = 6 + a +print(a * 4) +# output +# 4 + +d = 5 +b = 7 +print(d) +# output +# 5 + +c = 9 +d = c / 3 +print(d) +# output +# 3.0 + +c = 6 +d = 1 +print(d) +# output +# 1 + +c = 7 +e = 3 +e = 0 + 0 +print(e) +# output +# 0 + +a = 5 +a = 9 +print(a) +# output +# 9 + +b = 3 +d = 8 +a = d - 6 +print(a) +# output +# 2 + +b = 1 +d = 5 * b +print(d) +# output +# 5 + +e = 8 +a = 9 +print(e) +# output +# 8 + +c = 1 +b = 9 + 5 +print(c / 4) +# output +# 0.25 + +b = 0 +a = 0 / 5 +print(a) +# output +# 0.0 + +a = 9 +d = a * a +print(d) +# output +# 81 + +d = 2 +print(d / 6) +# output +# 0.3333333333333333 + +e = 0 +b = 8 +c = b * b +print(c) +# output +# 64 + +a = 8 +b = 6 / a +print(b) +# output +# 0.75 + +e = 9 +e = e * 2 +print(e) +# output +# 18 + +a = 1 +d = 3 +print(a * 2) +# output +# 2 + +c = 0 +c = 1 * c +print(c - c) +# output +# 0 + +a = 3 +b = 7 +d = a + a +print(d) +# output +# 6 + +a = 7 +b = 8 +e = 9 - 5 +print(a * a) +# output +# 49 + +c = 5 +c = 8 +print(c / 5) +# output +# 1.6 + +b = 6 +e = b + b +print(b + 1) +# output +# 7 + +e = 6 +c = e + e +print(e + e) +# output +# 12 + +b = 4 +b = 3 - b +print(b) +# output +# -1 + +d = 4 +e = 5 +a = 6 * e +print(e / e) +# output +# 1.0 + +b = 9 +print(b * 1) +# output +# 9 + +e = 2 +b = 7 +print(e) +# output +# 2 + +c = 7 +c = c + 2 +print(c - 1) +# output +# 8 + +e = 9 +e = 3 +b = e / 8 +print(e + e) +# output +# 6 + +c = 3 +e = 4 +print(e) +# output +# 4 + +b = 1 +print(b) +# output +# 1 + +a = 6 +b = 0 +e = 5 / a +print(b * b) +# output +# 0 + +e = 9 +d = 6 +print(e) +# output +# 9 + +c = 0 +a = 7 +print(a * a) +# output +# 49 + +d = 5 +a = 6 +a = d / 7 +print(a) +# output +# 0.7142857142857143 + +a = 2 +c = a - 6 +print(a / a) +# output +# 1.0 + +b = 4 +print(b) +# output +# 4 + +b = 3 +a = 5 +print(a + a) +# output +# 10 + +c = 5 +b = c * c +print(b) +# output +# 25 + +d = 4 +d = 7 * 4 +print(d * d) +# output +# 784 + +b = 6 +e = 1 +e = 5 / 1 +print(e - 2) +# output +# 3.0 + +e = 6 +b = 9 +print(e) +# output +# 6 + +d = 7 +d = 8 +e = 7 * 5 +print(e) +# output +# 35 + +d = 3 +print(d) +# output +# 3 + +e = 2 +d = 9 +a = e - 2 +print(a) +# output +# 0 + +a = 0 +a = 0 +print(a) +# output +# 0 + +e = 5 +b = 6 +print(e) +# output +# 5 + +b = 6 +print(b) +# output +# 6 + +b = 8 +d = 3 +a = 4 - 9 +print(d + b) +# output +# 11 + +a = 9 +d = a / a +print(a + 6) +# output +# 15 + +a = 7 +e = 6 +print(a + a) +# output +# 14 + +d = 3 +d = 6 +print(d + d) +# output +# 12 + +d = 6 +a = 6 +print(a) +# output +# 6 + +e = 4 +b = e * e +print(e * 7) +# output +# 28 + +d = 8 +e = 4 +print(e + d) +# output +# 12 + +d = 2 +print(d) +# output +# 2 + +b = 3 +print(b - 6) +# output +# -3 + +d = 3 +b = 5 +print(d) +# output +# 3 + +a = 9 +b = 7 +print(b - b) +# output +# 0 + +c = 3 +a = 3 +print(c / 7) +# output +# 0.42857142857142855 + +b = 9 +print(b) +# output +# 9 + +a = 7 +c = 1 +a = a / 1 +print(a * c) +# output +# 7.0 + +b = 9 +c = b - 8 +print(c) +# output +# 1 + +b = 1 +print(b - 9) +# output +# -8 + +a = 0 +c = 8 +print(a) +# output +# 0 + +a = 2 +e = a + a +print(a * 5) +# output +# 10 + +b = 9 +d = b - b +print(b - b) +# output +# 0 + +d = 0 +a = 4 +print(a) +# output +# 4 + +c = 6 +d = 0 +a = 9 - 0 +print(a) +# output +# 9 + +c = 9 +b = 8 / c +print(b) +# output +# 0.8888888888888888 + +c = 0 +d = 2 +b = 0 / d +print(b) +# output +# 0.0 + +a = 9 +a = a - 6 +print(a - a) +# output +# 0 + +d = 2 +e = 4 +a = d / 2 +print(e / 2) +# output +# 2.0 + +e = 0 +a = 2 +c = e + e +print(c) +# output +# 0 + +a = 0 +d = 8 +b = a * 5 +print(b) +# output +# 0 + +c = 9 +a = 4 +a = 8 - 5 +print(a + 8) +# output +# 11 + +c = 3 +d = 9 +print(c * 3) +# output +# 9 + +a = 6 +d = 8 +print(a) +# output +# 6 + +e = 5 +c = 4 +c = c - e +print(e / c) +# output +# -5.0 + +e = 2 +c = 7 +d = 7 + 4 +print(d) +# output +# 11 + +a = 3 +e = 4 * 7 +print(a - a) +# output +# 0 + +a = 2 +e = 0 / 8 +print(a * a) +# output +# 4 + +e = 0 +d = 6 +a = 0 + 0 +print(e * d) +# output +# 0 + +b = 6 +e = 5 +print(b) +# output +# 6 + +d = 0 +a = 1 * d +print(d + 6) +# output +# 6 + +b = 7 +d = 8 * 6 +print(b - 5) +# output +# 2 + +d = 3 +b = 2 +a = d * b +print(d / 6) +# output +# 0.5 + +d = 9 +d = 8 * d +print(d * d) +# output +# 5184 + +b = 9 +c = 6 / b +print(b * 2) +# output +# 18 + +d = 8 +a = d + d +print(d - 3) +# output +# 5 + +a = 0 +b = 7 +print(b) +# output +# 7 + +e = 7 +c = 4 +print(e) +# output +# 7 + +e = 8 +e = 4 +d = 5 * 3 +print(d) +# output +# 15 + +d = 9 +a = 2 +b = 7 - d +print(a / d) +# output +# 0.2222222222222222 + +b = 7 +a = 2 +print(a) +# output +# 2 + +c = 7 +c = 4 +d = 6 * c +print(c / c) +# output +# 1.0 + +a = 1 +print(a * a) +# output +# 1 + +e = 8 +d = 6 +print(e + d) +# output +# 14 + +c = 4 +print(c * 8) +# output +# 32 + +d = 6 +a = 1 + d +print(a) +# output +# 7 + +d = 3 +a = 4 +a = d * d +print(a * 4) +# output +# 36 + +b = 8 +a = 4 +d = 5 - b +print(b * 3) +# output +# 24 + +c = 5 +d = 2 +print(d + 4) +# output +# 6 + +e = 2 +print(e - 3) +# output +# -1 + +d = 4 +print(d - d) +# output +# 0 + +d = 9 +c = 7 +print(c - 2) +# output +# 5 + +b = 9 +c = 6 +a = c + c +print(c * 5) +# output +# 30 + +d = 7 +e = 7 +a = 3 - d +print(a) +# output +# -4 + +e = 4 +c = 5 +print(c * e) +# output +# 20 + +a = 6 +print(a - 9) +# output +# -3 + +d = 6 +b = 2 +print(d) +# output +# 6 + +d = 7 +c = 9 +a = 0 + 9 +print(c * 4) +# output +# 36 + +d = 1 +b = 1 +a = 2 / b +print(d + 9) +# output +# 10 + +b = 4 +e = 8 - b +print(e) +# output +# 4 + +a = 3 +d = 4 +print(a) +# output +# 3 + +a = 3 +c = a * a +print(c) +# output +# 9 + +a = 8 +c = 4 +b = c / a +print(b) +# output +# 0.5 + +d = 6 +a = 5 +a = 2 + a +print(d - a) +# output +# -1 + +b = 6 +a = 5 +print(a) +# output +# 5 + +d = 6 +print(d) +# output +# 6 + +b = 2 +c = 3 +print(b / b) +# output +# 1.0 + +c = 5 +print(c * c) +# output +# 25 + +c = 5 +a = 9 / c +print(a) +# output +# 1.8 + +d = 8 +d = 0 +c = 7 - 1 +print(c) +# output +# 6 + +e = 7 +c = e + e +print(c) +# output +# 14 + +b = 9 +b = 9 +print(b - 5) +# output +# 4 + +a = 9 +b = 7 +print(b / 5) +# output +# 1.4 + +d = 8 +print(d - d) +# output +# 0 + +b = 2 +b = 6 +print(b - 6) +# output +# 0 + +c = 8 +b = 6 +print(c) +# output +# 8 + +a = 5 +d = 7 +print(d) +# output +# 7 + +c = 9 +a = c * 1 +print(c / 1) +# output +# 9.0 + +e = 3 +b = 5 +print(b * 9) +# output +# 45 + +c = 5 +e = 3 +d = 8 * 3 +print(d) +# output +# 24 + +c = 4 +e = 1 +e = 4 / e +print(e) +# output +# 4.0 + +a = 3 +d = a - a +print(d) +# output +# 0 + +d = 9 +a = 5 +print(a) +# output +# 5 + +e = 1 +c = e / 7 +print(e - e) +# output +# 0 + +d = 6 +a = 6 +print(a / a) +# output +# 1.0 + +a = 0 +b = 4 +b = a / 2 +print(b) +# output +# 0.0 + +a = 4 +b = a / a +print(b) +# output +# 1.0 + +a = 2 +d = 1 +b = a / d +print(b) +# output +# 2.0 + +e = 1 +b = 8 +print(b) +# output +# 8 + +a = 8 +d = a - 7 +print(d) +# output +# 1 + +d = 7 +b = 2 +print(b) +# output +# 2 + +d = 9 +print(d / d) +# output +# 1.0 + +b = 6 +d = 6 +c = 5 - 4 +print(c) +# output +# 1 + +a = 7 +b = 0 +e = 7 - a +print(b / 7) +# output +# 0.0 + +b = 3 +c = 2 / b +print(b * 8) +# output +# 24 + +d = 3 +d = 7 +print(d) +# output +# 7 + +a = 0 +print(a + a) +# output +# 0 + +e = 3 +d = 5 +print(e - 1) +# output +# 2 + +b = 3 +d = 5 +d = b - 7 +print(b - b) +# output +# 0 + +d = 0 +e = 3 +print(e) +# output +# 3 + +b = 3 +e = 1 +e = 9 + e +print(e) +# output +# 10 + +c = 7 +a = 9 +print(a - 2) +# output +# 7 + +c = 1 +b = 8 +a = 7 / 1 +print(a) +# output +# 7.0 + +a = 4 +b = 4 +print(b * b) +# output +# 16 + +d = 6 +d = 8 +e = d - 9 +print(d / 4) +# output +# 2.0 + +c = 3 +a = 0 +print(c - 4) +# output +# -1 + +b = 0 +e = 2 / 1 +print(b - 2) +# output +# -2 + +e = 9 +b = 8 +a = 8 + 3 +print(b * 4) +# output +# 32 + +b = 4 +a = 1 +a = 2 / b +print(a) +# output +# 0.5 + +b = 9 +c = 4 +print(b) +# output +# 9 + +c = 8 +d = c + 1 +print(d) +# output +# 9 + +b = 6 +b = 5 +print(b) +# output +# 5 + +e = 0 +e = e + e +print(e) +# output +# 0 + +d = 3 +c = 0 +a = c * 5 +print(c / 5) +# output +# 0.0 + +d = 8 +b = 2 + d +print(b) +# output +# 10 + +d = 8 +a = 7 +print(d) +# output +# 8 + +a = 2 +print(a + a) +# output +# 4 + +e = 6 +print(e * 4) +# output +# 24 + +d = 8 +e = 2 +print(d + 2) +# output +# 10 + +d = 1 +a = 3 +d = 7 - a +print(d / d) +# output +# 1.0 + +e = 9 +d = 5 +d = e * d +print(e / d) +# output +# 0.2 + +c = 3 +e = 7 +print(c * c) +# output +# 9 + +d = 8 +b = d * d +print(d * d) +# output +# 64 + +e = 4 +b = 8 +e = e - 6 +print(b / 6) +# output +# 1.3333333333333333 + +a = 9 +a = 5 +e = a / 7 +print(e) +# output +# 0.7142857142857143 + +d = 2 +c = 5 +print(d * d) +# output +# 4 + +d = 1 +a = 7 - 4 +print(a) +# output +# 3 + +a = 1 +d = a / 4 +print(d) +# output +# 0.25 + +d = 9 +b = 1 +d = d / 9 +print(b * b) +# output +# 1 + +a = 8 +print(a + a) +# output +# 16 + +a = 5 +e = 8 +b = a / a +print(e - e) +# output +# 0 + +d = 2 +d = 6 * d +print(d) +# output +# 12 + +d = 2 +b = 5 +print(b) +# output +# 5 + +b = 9 +c = 6 +print(c) +# output +# 6 + +e = 3 +c = 0 +print(c) +# output +# 0 + +d = 4 +b = 8 * d +print(d * d) +# output +# 16 + +b = 7 +e = b - b +print(b - 5) +# output +# 2 + +e = 8 +e = 4 +print(e) +# output +# 4 + +a = 8 +a = 4 - a +print(a) +# output +# -4 + +a = 9 +d = 1 +d = 5 + 5 +print(a / 9) +# output +# 1.0 + +d = 5 +b = 6 +print(d) +# output +# 5 + +c = 8 +e = 7 +print(e * 3) +# output +# 21 + +e = 4 +a = e + 9 +print(a) +# output +# 13 + +e = 7 +a = 2 +d = 5 * 7 +print(a * 7) +# output +# 14 + +e = 1 +d = 5 +c = d * d +print(d / 2) +# output +# 2.5 + +c = 6 +a = 6 +print(a) +# output +# 6 + +b = 4 +print(b + b) +# output +# 8 + +b = 8 +a = 2 + b +print(a) +# output +# 10 + +b = 3 +c = 7 +print(c / 3) +# output +# 2.3333333333333335 + +c = 3 +a = c - 9 +print(a) +# output +# -6 + +b = 5 +d = 8 +print(b - 0) +# output +# 5 + +e = 3 +a = 4 +print(e) +# output +# 3 + +c = 6 +b = 0 +c = 0 * 4 +print(c) +# output +# 0 + +a = 9 +c = 7 / 9 +print(c) +# output +# 0.7777777777777778 + +b = 7 +d = 4 +print(d) +# output +# 4 + +c = 1 +b = 1 +print(c) +# output +# 1 + +a = 1 +a = a + a +print(a + a) +# output +# 4 + +b = 1 +c = 3 +print(c) +# output +# 3 + +b = 5 +e = 8 +b = e / e +print(b * 0) +# output +# 0.0 + +b = 1 +b = 9 +print(b) +# output +# 9 + +e = 0 +b = 2 +print(b * b) +# output +# 4 + +b = 7 +d = 6 +a = 0 / d +print(b - 3) +# output +# 4 + +b = 5 +d = b - b +print(d) +# output +# 0 + +c = 6 +print(c / c) +# output +# 1.0 + +c = 3 +e = 0 +d = e + e +print(d) +# output +# 0 + +c = 4 +d = 4 +e = c - c +print(e) +# output +# 0 + +d = 6 +d = 2 +c = d - 0 +print(c) +# output +# 2 + +a = 9 +b = a + a +print(a / a) +# output +# 1.0 + +e = 7 +b = 7 +c = 7 - e +print(b + 2) +# output +# 9 + +e = 7 +print(e + e) +# output +# 14 + +d = 9 +print(d * d) +# output +# 81 + +a = 7 +print(a / 2) +# output +# 3.5 + +b = 4 +b = 4 +b = b / 8 +print(b + 3) +# output +# 3.5 + +b = 0 +d = 9 * 1 +print(d) +# output +# 9 + +b = 3 +e = 8 +print(e) +# output +# 8 + +d = 3 +print(d * 7) +# output +# 21 + +b = 0 +e = 1 +print(b) +# output +# 0 + +a = 6 +e = 7 +b = e + a +print(a / 2) +# output +# 3.0 + +d = 1 +b = 3 +b = b / 8 +print(b / d) +# output +# 0.375 + +a = 7 +c = a / a +print(c) +# output +# 1.0 + +d = 6 +c = d - d +print(c) +# output +# 0 + +a = 7 +print(a) +# output +# 7 + +e = 7 +a = 7 +a = 7 - e +print(a * 0) +# output +# 0 + +a = 4 +print(a) +# output +# 4 + +c = 4 +d = 3 +print(c) +# output +# 4 + +d = 2 +b = 4 +c = d * 9 +print(c) +# output +# 18 + +c = 6 +a = 4 +print(c) +# output +# 6 + +d = 8 +b = 0 * d +print(d - 7) +# output +# 1 + +d = 6 +e = 2 +c = 2 - d +print(c) +# output +# -4 + +c = 1 +c = c * 8 +print(c) +# output +# 8 + +b = 6 +c = 4 +e = c + c +print(e) +# output +# 8 + +b = 1 +c = 1 +d = c / 5 +print(b - 0) +# output +# 1 + +b = 4 +b = 9 +b = b + 4 +print(b) +# output +# 13 + +c = 9 +print(c * c) +# output +# 81 + +b = 1 +a = b + b +print(b / b) +# output +# 1.0 + +b = 5 +a = 1 +print(b) +# output +# 5 + +c = 4 +a = 7 +a = c + a +print(a + 5) +# output +# 16 + +b = 1 +c = 5 +print(c) +# output +# 5 + +a = 5 +a = 2 +print(a * a) +# output +# 4 + +c = 1 +e = 6 +print(e / 7) +# output +# 0.8571428571428571 + +b = 0 +d = 1 +print(d) +# output +# 1 + +a = 3 +b = 7 - a +print(b) +# output +# 4 + +e = 9 +print(e / e) +# output +# 1.0 + +e = 2 +b = 3 +e = 1 - e +print(b + 6) +# output +# 9 + +d = 2 +a = 3 +e = 1 + a +print(e) +# output +# 4 + +a = 2 +e = 0 +a = a * a +print(a) +# output +# 4 + +a = 0 +c = 5 +print(c * 0) +# output +# 0 + +e = 1 +a = 7 +print(a * 3) +# output +# 21 + +c = 8 +a = 4 +b = c / c +print(c + a) +# output +# 12 + +e = 4 +c = 9 +e = 8 - e +print(e / 3) +# output +# 1.3333333333333333 + +c = 3 +a = 7 +b = 3 * 9 +print(a + 6) +# output +# 13 + +e = 5 +print(e / e) +# output +# 1.0 + +a = 2 +print(a / 4) +# output +# 0.5 + +a = 4 +a = 4 +a = 7 / a +print(a) +# output +# 1.75 + +c = 2 +print(c - c) +# output +# 0 + +a = 2 +print(a) +# output +# 2 + +e = 5 +a = 5 +a = e / 4 +print(a) +# output +# 1.25 + +c = 9 +a = 3 +print(a * 4) +# output +# 12 + +a = 8 +b = 5 +print(b / 2) +# output +# 2.5 + +e = 7 +b = 0 +c = b - b +print(e * 4) +# output +# 28 + +b = 0 +d = 7 +e = b + b +print(d + b) +# output +# 7 + +e = 0 +print(e) +# output +# 0 + +a = 1 +print(a + a) +# output +# 2 + +c = 6 +d = 8 +print(d / 4) +# output +# 2.0 + +a = 6 +e = a / 7 +print(a + 4) +# output +# 10 + +e = 6 +c = 4 +print(e / 9) +# output +# 0.6666666666666666 + +d = 3 +b = 6 + 3 +print(b) +# output +# 9 + +e = 4 +b = 6 + e +print(b) +# output +# 10 + +a = 8 +e = 2 +print(a) +# output +# 8 + +b = 9 +a = 3 +d = b / 7 +print(d) +# output +# 1.2857142857142858 + +b = 3 +e = 6 +print(b / 3) +# output +# 1.0 + +c = 3 +a = 5 +a = 4 * 3 +print(a) +# output +# 12 + +a = 5 +c = a / a +print(a + a) +# output +# 10 + +b = 8 +a = 9 +e = 5 / 2 +print(b * a) +# output +# 72 + +e = 6 +b = 3 +e = e - 6 +print(e) +# output +# 0 + +c = 7 +a = 3 +a = 2 / c +print(a) +# output +# 0.2857142857142857 + +a = 2 +d = 8 * 2 +print(a * a) +# output +# 4 + +a = 8 +c = 3 +e = 4 + 1 +print(a / a) +# output +# 1.0 + +c = 4 +b = 2 +d = 6 + 3 +print(b + 9) +# output +# 11 + +a = 5 +d = 5 +print(d) +# output +# 5 + +c = 3 +b = 7 +b = c + c +print(b / 1) +# output +# 6.0 + +d = 2 +b = 1 / 8 +print(d - d) +# output +# 0 + +a = 5 +e = 5 * 9 +print(e) +# output +# 45 + +c = 6 +print(c - c) +# output +# 0 + +d = 0 +e = d * 1 +print(e) +# output +# 0 + +a = 2 +a = 1 +print(a / 2) +# output +# 0.5 + +d = 7 +d = 2 +print(d) +# output +# 2 + +a = 9 +print(a - a) +# output +# 0 + +c = 9 +d = 7 * c +print(d) +# output +# 63 + +e = 4 +b = e + e +print(e / e) +# output +# 1.0 + +d = 8 +d = 1 + 6 +print(d - 2) +# output +# 5 + +d = 5 +print(d + 6) +# output +# 11 + +a = 9 +d = 8 +print(a) +# output +# 9 + +a = 4 +d = 7 + a +print(a - 9) +# output +# -5 + +d = 6 +b = d * d +print(b) +# output +# 36 + +b = 0 +a = b * b +print(b + b) +# output +# 0 + +a = 7 +print(a * a) +# output +# 49 + +d = 9 +c = 7 +print(c + c) +# output +# 14 + +b = 3 +a = 6 +a = 8 - 0 +print(a) +# output +# 8 + +c = 8 +print(c / 4) +# output +# 2.0 + +c = 0 +b = 3 * 6 +print(c - 1) +# output +# -1 + +a = 4 +print(a - a) +# output +# 0 + +e = 4 +d = 2 / e +print(d) +# output +# 0.5 + +c = 6 +a = 4 +print(c / c) +# output +# 1.0 + +b = 2 +d = 5 * b +print(b * 0) +# output +# 0 + +c = 4 +d = 8 +c = 7 + 6 +print(c) +# output +# 13 + +e = 7 +e = 8 +c = 3 / 7 +print(c) +# output +# 0.42857142857142855 + +d = 7 +d = 3 +e = d - d +print(d * 0) +# output +# 0 + +d = 2 +c = 1 +print(d) +# output +# 2 + +d = 4 +print(d) +# output +# 4 + +a = 1 +a = 3 +print(a / 1) +# output +# 3.0 + +a = 7 +e = 4 +d = a / e +print(d) +# output +# 1.75 + +e = 2 +c = 5 +a = e / 3 +print(e / e) +# output +# 1.0 + +b = 7 +b = 3 / b +print(b) +# output +# 0.42857142857142855 + +d = 0 +e = d + 4 +print(d * d) +# output +# 0 + +b = 5 +e = 9 +print(b * b) +# output +# 25 + +c = 3 +e = 2 +print(c) +# output +# 3 + +e = 5 +print(e * 5) +# output +# 25 + +b = 1 +c = 5 +e = c * b +print(c - b) +# output +# 4 + +e = 6 +d = 3 + e +print(d) +# output +# 9 + +d = 4 +c = 6 +d = 1 - 6 +print(d) +# output +# -5 + +d = 2 +b = 2 * 8 +print(d - d) +# output +# 0 + +e = 1 +a = e + 8 +print(a) +# output +# 9 + +d = 9 +c = 2 +print(c) +# output +# 2 + +a = 2 +a = 4 + a +print(a) +# output +# 6 + +d = 1 +c = 7 +print(d) +# output +# 1 + +b = 8 +d = 5 +e = 8 - b +print(d / 3) +# output +# 1.6666666666666667 + +b = 7 +a = b - 9 +print(b - 6) +# output +# 1 + +c = 2 +b = 0 * c +print(c + c) +# output +# 4 + +d = 4 +e = 0 +b = e * 0 +print(b) +# output +# 0 + +b = 9 +a = 8 +c = b + a +print(b / 4) +# output +# 2.25 + +b = 0 +d = 8 +print(b * b) +# output +# 0 + +d = 2 +print(d / d) +# output +# 1.0 + +a = 6 +c = 3 +a = 4 / 7 +print(a) +# output +# 0.5714285714285714 + +e = 7 +b = 1 +print(b) +# output +# 1 + +e = 5 +e = 6 +print(e) +# output +# 6 + +a = 4 +d = 2 +print(a) +# output +# 4 + +e = 6 +b = e * e +print(e / 8) +# output +# 0.75 + +e = 9 +e = e - 7 +print(e) +# output +# 2 + +d = 5 +print(d * d) +# output +# 25 + +b = 1 +c = 0 +print(c / 2) +# output +# 0.0 + +e = 3 +c = 2 +d = 1 + 5 +print(c * c) +# output +# 4 + +e = 8 +b = e / 5 +print(e / 2) +# output +# 4.0 + +a = 7 +print(a + 4) +# output +# 11 + +d = 2 +a = 7 +a = 6 + d +print(a) +# output +# 8 + +d = 0 +b = 4 +print(d) +# output +# 0 + +c = 7 +c = 7 +print(c / 8) +# output +# 0.875 + +a = 2 +e = 0 +b = 5 - 4 +print(b) +# output +# 1 + +a = 2 +b = 2 / 8 +print(a / 8) +# output +# 0.25 + +c = 1 +a = 2 +c = 2 * 3 +print(c) +# output +# 6 + +b = 4 +d = 9 +print(d + 6) +# output +# 15 + +e = 9 +print(e * e) +# output +# 81 + +e = 2 +a = 1 +print(a + a) +# output +# 2 + +d = 2 +b = 4 * d +print(b) +# output +# 8 + +b = 5 +d = 5 +print(d - 6) +# output +# -1 + +b = 7 +e = 4 +d = 0 - b +print(b - e) +# output +# 3 + +d = 6 +b = 5 +print(d - d) +# output +# 0 + +a = 9 +e = 9 +d = 8 + 1 +print(a * 0) +# output +# 0 + +b = 8 +e = 9 +d = 0 - b +print(e - b) +# output +# 1 + +b = 8 +b = 5 +print(b) +# output +# 5 + +d = 8 +b = 2 +e = 2 + b +print(e) +# output +# 4 + +d = 1 +d = 5 / 7 +print(d / d) +# output +# 1.0 + +d = 7 +a = 4 * d +print(d - 3) +# output +# 4 + +c = 6 +b = 3 + 1 +print(b) +# output +# 4 + +d = 3 +d = 8 - d +print(d + d) +# output +# 10 + +d = 2 +d = 7 +d = 7 + 4 +print(d / 9) +# output +# 1.2222222222222223 + +b = 2 +a = b - b +print(b - 6) +# output +# -4 + +e = 6 +d = 1 - 3 +print(d) +# output +# -2 + +c = 3 +e = 9 +e = 0 * c +print(e) +# output +# 0 + +a = 9 +e = 1 - a +print(a - 1) +# output +# 8 + +d = 9 +e = 9 +b = e + 2 +print(e - e) +# output +# 0 + +c = 3 +print(c + 3) +# output +# 6 + +a = 3 +c = 0 +print(c) +# output +# 0 + +a = 1 +d = 5 * 3 +print(a + a) +# output +# 2 + +b = 4 +e = 3 +a = b - 7 +print(e / e) +# output +# 1.0 + +b = 4 +c = 4 +print(b + 5) +# output +# 9 + +a = 5 +print(a / 3) +# output +# 1.6666666666666667 + +a = 0 +e = a + a +print(a + a) +# output +# 0 + +e = 6 +b = 1 +print(b - 2) +# output +# -1 + +b = 7 +e = 2 +b = 0 * 7 +print(b) +# output +# 0 + +d = 7 +print(d / d) +# output +# 1.0 + +a = 3 +print(a + 3) +# output +# 6 + +d = 9 +e = 5 +print(d) +# output +# 9 + +b = 6 +b = 4 - b +print(b + 0) +# output +# -2 + +b = 6 +c = 1 +print(c / 3) +# output +# 0.3333333333333333 + +a = 2 +e = 4 +a = a / 7 +print(a + a) +# output +# 0.5714285714285714 + +a = 4 +c = 0 +print(c - c) +# output +# 0 + +a = 6 +a = 6 +c = 9 - 7 +print(a * 4) +# output +# 24 + +c = 1 +a = 4 / 3 +print(c - 5) +# output +# -4 + +e = 8 +d = 2 - 0 +print(d) +# output +# 2 + +a = 2 +c = a * 6 +print(a / a) +# output +# 1.0 + +a = 3 +c = 5 +e = 2 + a +print(e) +# output +# 5 + +a = 5 +b = 7 +print(b / 2) +# output +# 3.5 + +d = 1 +b = 4 +print(b) +# output +# 4 + +d = 9 +c = d / d +print(c) +# output +# 1.0 + +e = 0 +c = e / 4 +print(e + 6) +# output +# 6 + +c = 0 +a = 3 +print(c + a) +# output +# 3 + +b = 7 +c = 9 +print(b) +# output +# 7 + +b = 8 +c = 3 +print(b) +# output +# 8 + +e = 1 +e = 6 / 6 +print(e) +# output +# 1.0 + +e = 7 +print(e - e) +# output +# 0 + +c = 4 +print(c * 4) +# output +# 16 + +c = 6 +print(c / 4) +# output +# 1.5 + +c = 5 +print(c + c) +# output +# 10 + +c = 4 +b = 5 +print(c + c) +# output +# 8 + +e = 2 +print(e - 7) +# output +# -5 + +c = 2 +d = 9 +print(d) +# output +# 9 + +b = 5 +c = 4 +print(b) +# output +# 5 + +c = 0 +b = 3 +a = 0 + 4 +print(b * 6) +# output +# 18 + +d = 9 +c = 4 +e = d / c +print(e) +# output +# 2.25 + +b = 8 +b = 1 +d = b - 6 +print(d) +# output +# -5 + +e = 7 +d = e - 1 +print(e + e) +# output +# 14 + +e = 8 +c = 7 +e = e - c +print(e) +# output +# 1 + +d = 2 +e = 6 +print(e - 2) +# output +# 4 + +c = 2 +d = 6 +print(c / d) +# output +# 0.3333333333333333 + +d = 2 +print(d - 0) +# output +# 2 + +b = 2 +d = 4 +print(b / 1) +# output +# 2.0 + +a = 2 +e = 7 +print(e) +# output +# 7 + +a = 4 +c = 7 +print(a) +# output +# 4 + +a = 7 +a = 7 +print(a + a) +# output +# 14 + +b = 8 +b = 5 * 4 +print(b) +# output +# 20 + +d = 0 +print(d * d) +# output +# 0 + +d = 7 +b = 7 + d +print(b) +# output +# 14 + +d = 4 +a = 2 * d +print(a) +# output +# 8 + +d = 4 +d = 3 +b = d + 7 +print(b) +# output +# 10 + +e = 2 +a = 6 +c = a / 4 +print(c) +# output +# 1.5 + +c = 0 +a = c - c +print(a) +# output +# 0 + +e = 5 +c = 0 +print(e + 1) +# output +# 6 + +e = 2 +b = e - e +print(e / e) +# output +# 1.0 + +d = 7 +d = 5 +print(d) +# output +# 5 + +c = 8 +e = c + c +print(e) +# output +# 16 + +c = 4 +print(c - c) +# output +# 0 + +e = 9 +e = 7 * e +print(e + 7) +# output +# 70 + +c = 0 +d = 4 +e = c + 6 +print(e) +# output +# 6 + +a = 7 +d = 2 +print(a) +# output +# 7 + +b = 8 +c = 9 +d = c - b +print(d) +# output +# 1 + +d = 7 +e = 5 + d +print(e) +# output +# 12 + +d = 8 +d = 1 +e = d + d +print(d / 5) +# output +# 0.2 + +b = 8 +b = 2 +a = 3 / b +print(a) +# output +# 1.5 + +d = 4 +a = 2 * 5 +print(d / 6) +# output +# 0.6666666666666666 + +a = 3 +a = a * a +print(a) +# output +# 9 + +a = 9 +e = 3 +print(a + 1) +# output +# 10 + +e = 2 +d = e / e +print(d) +# output +# 1.0 + +d = 0 +a = 3 - d +print(a) +# output +# 3 + +d = 7 +a = 6 +print(a) +# output +# 6 + +d = 1 +b = 5 +print(b - 6) +# output +# -1 + +c = 9 +e = 3 - 8 +print(c + 9) +# output +# 18 + +e = 9 +e = e / e +print(e - 0) +# output +# 1.0 + +d = 4 +print(d + 4) +# output +# 8 + +b = 7 +c = 1 +print(c + 7) +# output +# 8 + +a = 2 +b = 1 +print(a / 9) +# output +# 0.2222222222222222 + +e = 6 +a = 7 +d = a - 1 +print(d) +# output +# 6 + +d = 1 +a = 6 +print(d) +# output +# 1 + +c = 0 +d = 5 +print(c) +# output +# 0 + +c = 6 +e = 0 +print(e + 1) +# output +# 1 + +e = 8 +c = 9 +c = 5 - 3 +print(c) +# output +# 2 + +c = 8 +c = 0 +b = 9 * c +print(b) +# output +# 0 + +d = 8 +e = 8 * d +print(d + d) +# output +# 16 + +d = 0 +d = 7 +print(d + 3) +# output +# 10 + +e = 8 +a = 1 +c = 5 * 9 +print(a * 9) +# output +# 9 + +e = 7 +d = 0 +e = 9 - 1 +print(e - 7) +# output +# 1 + +a = 2 +print(a * 6) +# output +# 12 + +c = 3 +a = 2 * 7 +print(c - c) +# output +# 0 + +c = 8 +c = 5 / c +print(c + c) +# output +# 1.25 + +e = 8 +c = 7 +print(c) +# output +# 7 + +b = 8 +a = 6 +print(b) +# output +# 8 + +e = 9 +b = 3 +print(b) +# output +# 3 + +e = 2 +b = 4 +c = e + 0 +print(b * 1) +# output +# 4 + +b = 6 +c = 3 +b = 3 - 3 +print(c - c) +# output +# 0 + +e = 0 +d = e - 4 +print(d) +# output +# -4 + +d = 9 +print(d * 6) +# output +# 54 + +a = 8 +a = 9 +d = a / 8 +print(d) +# output +# 1.125 + +d = 6 +a = d - 2 +print(a) +# output +# 4 + +d = 5 +d = 3 +a = d * d +print(d - 8) +# output +# -5 + +a = 9 +d = 2 +d = a + d +print(d) +# output +# 11 + +e = 2 +c = 2 +print(c * 6) +# output +# 12 + +d = 9 +d = 6 +b = d - 0 +print(b) +# output +# 6 + +d = 9 +b = 4 +a = d + 3 +print(a) +# output +# 12 + +c = 1 +print(c - c) +# output +# 0 + +b = 7 +b = 7 +print(b) +# output +# 7 + +e = 1 +c = e + e +print(c) +# output +# 2 + +b = 5 +d = 1 +e = d - 7 +print(d + b) +# output +# 6 + +e = 2 +a = 6 +print(a) +# output +# 6 + +e = 6 +e = 7 +print(e) +# output +# 7 + +e = 7 +print(e / 7) +# output +# 1.0 + +c = 6 +c = 1 * 4 +print(c) +# output +# 4 + +d = 8 +e = 3 +a = d / d +print(e / e) +# output +# 1.0 + +d = 0 +b = 2 +print(d * d) +# output +# 0 + +b = 2 +print(b - b) +# output +# 0 + +c = 5 +c = 0 +print(c) +# output +# 0 + +e = 7 +e = 6 +b = e * 5 +print(e * e) +# output +# 36 + +b = 8 +a = 8 +print(b - 4) +# output +# 4 + +e = 3 +c = e + e +print(e - e) +# output +# 0 + +b = 0 +a = 4 +print(a * 1) +# output +# 4 + +b = 9 +b = 9 +d = 7 + b +print(d) +# output +# 16 + +e = 3 +b = 7 +print(b) +# output +# 7 + +c = 7 +print(c * 4) +# output +# 28 + +a = 0 +print(a) +# output +# 0 + +c = 0 +c = 6 - 8 +print(c) +# output +# -2 + +b = 9 +e = 1 +print(e) +# output +# 1 + +b = 8 +b = 9 - 6 +print(b) +# output +# 3 + +b = 4 +c = 7 +c = c / c +print(c) +# output +# 1.0 + +e = 3 +print(e + 8) +# output +# 11 + +e = 2 +d = e + e +print(e * e) +# output +# 4 + +e = 3 +e = e + 5 +print(e) +# output +# 8 + +c = 2 +b = c * c +print(c / 1) +# output +# 2.0 + +d = 5 +d = d / d +print(d - 4) +# output +# -3.0 + +d = 3 +b = 4 +d = 4 / 3 +print(b * b) +# output +# 16 + +e = 6 +print(e + e) +# output +# 12 + +b = 8 +c = 7 / 7 +print(b + b) +# output +# 16 + +b = 8 +c = 7 +print(b) +# output +# 8 + +c = 2 +e = 2 +print(c - 1) +# output +# 1 + +c = 4 +b = 1 +print(c + c) +# output +# 8 + +a = 6 +e = 6 +a = e * a +print(a) +# output +# 36 + +e = 1 +a = 0 - 8 +print(a) +# output +# -8 + +c = 1 +print(c + c) +# output +# 2 + +e = 3 +print(e - e) +# output +# 0 + +d = 6 +e = 6 +print(e) +# output +# 6 + +d = 8 +d = 5 +e = 9 - d +print(e) +# output +# 4 + +d = 3 +d = d / d +print(d - 8) +# output +# -7.0 + +a = 8 +a = 9 +print(a) +# output +# 9 + +a = 6 +b = 6 +a = 3 - 3 +print(a) +# output +# 0 + +c = 5 +print(c) +# output +# 5 + +c = 5 +e = 6 +e = e + 2 +print(e) +# output +# 8 + +a = 9 +d = 1 +print(a * d) +# output +# 9 + +b = 3 +e = 1 +d = e * 6 +print(d) +# output +# 6 + +e = 2 +e = 6 +a = e - e +print(e + e) +# output +# 12 + +e = 4 +a = 7 - 0 +print(e * e) +# output +# 16 + +d = 9 +e = 6 +print(e) +# output +# 6 + +d = 7 +e = 8 +print(e / d) +# output +# 1.1428571428571428 + +d = 1 +a = 4 + 6 +print(a) +# output +# 10 + +d = 7 +b = 9 / d +print(d - d) +# output +# 0 + +e = 8 +d = 5 +a = e - d +print(e / e) +# output +# 1.0 + +b = 2 +a = 0 +print(a * 2) +# output +# 0 + diff --git a/data/test.bin b/data/test.bin new file mode 100644 index 0000000..3e30074 Binary files /dev/null and b/data/test.bin differ diff --git a/data/test.txt b/data/test.txt new file mode 100644 index 0000000..dc9b283 --- /dev/null +++ b/data/test.txt @@ -0,0 +1,608 @@ +c = 3 +e = 9 +e = 0 * c +print(e) +# output +# 0 + +e = 2 +a = 6 +c = a / 4 +print(c) +# output +# 1.5 + +e = 6 +c = 3 / e +print(c) +# output +# 0.5 + +e = 8 +a = 3 +print(e / a) +# output +# 2.6666666666666665 + +a = 8 +e = a + 1 +print(a / a) +# output +# 1.0 + +e = 6 +a = 7 +d = a - 1 +print(d) +# output +# 6 + +d = 9 +print(d * d) +# output +# 81 + +c = 4 +print(c + 3) +# output +# 7 + +d = 7 +print(d / d) +# output +# 1.0 + +d = 5 +a = 0 +print(a + d) +# output +# 5 + +a = 6 +b = 0 +a = 3 - 3 +print(b / 9) +# output +# 0.0 + +a = 0 +a = 0 +print(a) +# output +# 0 + +c = 3 +e = 7 +print(c * c) +# output +# 9 + +a = 2 +e = 0 / 8 +print(a * a) +# output +# 4 + +e = 9 +a = 9 +print(a * e) +# output +# 81 + +d = 8 +e = 4 +print(e + d) +# output +# 12 + +a = 2 +c = a - 6 +print(a / a) +# output +# 1.0 + +a = 6 +b = 7 +d = a - 0 +print(d) +# output +# 6 + +b = 7 +e = 2 +b = 0 * 7 +print(b) +# output +# 0 + +c = 9 +e = 4 +print(c - 5) +# output +# 4 + +d = 4 +d = 7 * 4 +print(d * d) +# output +# 784 + +a = 4 +print(a / a) +# output +# 1.0 + +e = 3 +b = 4 +a = e - 6 +print(a) +# output +# -3 + +d = 5 +d = 9 - 7 +print(d) +# output +# 2 + +b = 7 +b = 3 / b +print(b) +# output +# 0.42857142857142855 + +a = 9 +b = a + a +print(a / a) +# output +# 1.0 + +a = 0 +d = a * 9 +print(d) +# output +# 0 + +e = 1 +c = e + e +print(c) +# output +# 2 + +e = 3 +c = e + 2 +print(e + e) +# output +# 6 + +a = 4 +e = 0 * 7 +print(a + 2) +# output +# 6 + +b = 6 +b = 5 +b = b - b +print(b / 3) +# output +# 0.0 + +b = 0 +d = 9 * 1 +print(d) +# output +# 9 + +a = 4 +a = 2 +print(a) +# output +# 2 + +e = 9 +b = 3 +print(b) +# output +# 3 + +d = 6 +c = 4 + 1 +print(d + 2) +# output +# 8 + +d = 8 +c = d - 6 +print(c) +# output +# 2 + +a = 9 +d = 1 +print(a * d) +# output +# 9 + +c = 4 +a = 4 +print(a) +# output +# 4 + +b = 5 +a = 1 +b = a - a +print(b) +# output +# 0 + +d = 2 +c = 1 +print(d) +# output +# 2 + +e = 3 +b = 5 +print(b * 9) +# output +# 45 + +e = 9 +d = 5 +d = e * d +print(e / d) +# output +# 0.2 + +c = 5 +print(c * c) +# output +# 25 + +e = 2 +c = 8 / e +print(e * 9) +# output +# 18 + +c = 0 +c = 6 - 8 +print(c) +# output +# -2 + +e = 7 +a = 2 +d = 5 * 7 +print(a * 7) +# output +# 14 + +e = 1 +print(e * e) +# output +# 1 + +d = 6 +b = d * d +print(b) +# output +# 36 + +e = 8 +b = e / 5 +print(e / 2) +# output +# 4.0 + +d = 6 +c = 3 +a = 9 + d +print(a) +# output +# 15 + +d = 4 +a = 8 +print(a * a) +# output +# 64 + +e = 6 +print(e + e) +# output +# 12 + +b = 3 +a = 3 +print(a) +# output +# 3 + +b = 9 +b = 9 +print(b - 5) +# output +# 4 + +a = 8 +print(a) +# output +# 8 + +b = 2 +e = 2 +print(e) +# output +# 2 + +e = 1 +c = e / 7 +print(e - e) +# output +# 0 + +e = 3 +c = 0 +print(c) +# output +# 0 + +a = 1 +a = 3 +print(a / 1) +# output +# 3.0 + +c = 3 +print(c / c) +# output +# 1.0 + +c = 0 +c = c - c +print(c) +# output +# 0 + +a = 7 +d = 2 +print(a) +# output +# 7 + +c = 3 +a = 0 +print(c - 4) +# output +# -1 + +e = 9 +a = 5 +print(e) +# output +# 9 + +d = 3 +b = 4 +d = 4 / 3 +print(b * b) +# output +# 16 + +c = 5 +d = 5 +d = c - 4 +print(d) +# output +# 1 + +e = 6 +b = 3 +e = e - 6 +print(e) +# output +# 0 + +d = 4 +c = 6 +print(d + c) +# output +# 10 + +b = 8 +a = 9 +e = 5 / 2 +print(b * a) +# output +# 72 + +e = 4 +print(e) +# output +# 4 + +b = 2 +c = 3 +print(b / b) +# output +# 1.0 + +d = 8 +d = 0 +c = 7 - 1 +print(c) +# output +# 6 + +e = 9 +print(e * e) +# output +# 81 + +e = 1 +a = 6 +print(a / 6) +# output +# 1.0 + +d = 5 +d = 9 +e = 0 - d +print(d * d) +# output +# 81 + +d = 5 +d = d * d +print(d) +# output +# 25 + +b = 7 +a = 2 +print(a) +# output +# 2 + +b = 3 +e = 1 +e = 9 + e +print(e) +# output +# 10 + +e = 9 +e = 7 * e +print(e + 7) +# output +# 70 + +e = 3 +c = 2 +d = 1 + 5 +print(c * c) +# output +# 4 + +d = 9 +b = 5 +print(d) +# output +# 9 + +d = 6 +e = 6 +print(e) +# output +# 6 + +e = 6 +e = 7 +print(e) +# output +# 7 + +b = 9 +a = 2 +b = a - 9 +print(a - 4) +# output +# -2 + +e = 2 +c = 3 +print(c) +# output +# 3 + +b = 4 +b = 3 - b +print(b) +# output +# -1 + +d = 0 +d = 7 +print(d + 3) +# output +# 10 + +d = 9 +print(d - 9) +# output +# 0 + +c = 4 +print(c) +# output +# 4 + +c = 3 +e = 2 +print(c) +# output +# 3 + +e = 6 +c = 4 +print(e / 9) +# output +# 0.6666666666666666 + +a = 4 +print(a) +# output +# 4 + +c = 8 +c = 0 +b = 9 * c +print(b) +# output +# 0 + +a = 8 +a = 9 +print(a) +# output +# 9 + +a = 0 +e = a + a +print(a + a) +# output +# 0 + +a = 7 +e = 6 +print(a + a) +# output +# 14 + +b = 8 +e = b + b +print(e) +# output +# 16 + +a = 3 +e = 9 +b = e - a +print(b) +# output +# 6 + +e = 4 +e = 4 * e +print(e / 8) +# output +# 2.0 + +d = 7 +c = 2 + 8 +print(d * 1) +# output +# 7 \ No newline at end of file diff --git a/data/tinypy_generator.py b/data/tinypy_generator.py new file mode 100644 index 0000000..542be31 --- /dev/null +++ b/data/tinypy_generator.py @@ -0,0 +1,332 @@ +from anytree import Node, RenderTree +import random +from io import StringIO +from contextlib import redirect_stdout +import argparse +import time +from tqdm.auto import tqdm +import hashlib +import os +import psutil + + +class CodeGenerator: + def __init__(self): + """ + Initialize the CodeGenerator object with the given context-free grammar rules. + + """ + + self.init_count = 0 + self.max_init = 0 + + # Dictionary containing context-free grammar rules. + self.cfg_rules = { + # Variables and digits + "VARIABLE": ["a", "b", "c", "d", "e"], + "DIGIT": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + + # Operators + "ARITHMETIC_OPERATOR": ["+", "-", "*", "/"], + "RELATIONAL_OPERATOR": ["<", ">", "<=", ">=", "!=", "=="], + "LOGICAL_OPERATOR_INFIX": ["and", "or"], + "LOGICAL_OPERATOR_PREFIX": ["not"], + "LOGICAL_OPERATOR": ["LOGICAL_OPERATOR_INFIX", "LOGICAL_OPERATOR_PREFIX"], + "OPERATOR": ["ARITHMETIC_OPERATOR"], + + # Formatting + "NEW_LINE": ["\n"], + "TAB_INDENT": ["\t"], + "BRACKET_OPEN": ['('], + "BRACKET_CLOSE": [')'], + "EQUALS": ["="], + "COLON": [":"], + "COMMA": [","], + + + # Keywords + "IF": ["if"], + "ELIF": ["elif"], + "ELSE": ["else"], + "FOR": ["for"], + "IN": ["in"], + "RANGE": ["range"], + "WHILE": ["while"], + "PRINT": ["print"], + + # Terms and expressions + "TERM": ["EXPRESSION_IDENTIFIER", "DIGIT"], + "EXPRESSION": ["TERM SPACE OPERATOR SPACE TERM"], + "ENCLOSED_EXPRESSION": ["BRACKET_OPEN EXPRESSION BRACKET_CLOSE"], + "DISPLAY_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE EXPRESSION_IDENTIFIER" , + "EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE DIGIT"], + + # Initializations and assignments + "IDENTIFIER_INITIALIZATION": ["IDENTIFIER_INITIALIZATION INITIALIZATION", + "INITIALIZATION"], + + "INITIALIZATION": ["VARIABLE SPACE EQUALS SPACE DIGIT NEW_LINE"], + + "SIMPLE_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , ""], + "ADVANCED_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE SIMPLE_ARITHMETIC_EVALUATION NEW_LINE", + "VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , + ""], + + "SIMPLE_ARITHMETIC_EVALUATION": ["SIMPLE_ARITHMETIC_EVALUATION ARITHMETIC_OPERATOR ENCLOSED_EXPRESSION", + "ENCLOSED_EXPRESSION", + ], + + # Conditions + "SIMPLE_IF_STATEMENT": ["IF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_IF_STATEMENT": ["IF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "SIMPLE_ELIF_STATEMENT": ["ELIF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_ELIF_STATEMENT": ["ELIF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "ELSE_STATEMENT": ["ELSE SPACE COLON NEW_LINE"], + + "CHAIN_CONDITION": ["CHAIN_CONDITION SPACE LOGICAL_OPERATOR_INFIX SPACE ENCLOSED_CONDITION", + "LOGICAL_OPERATOR_PREFIX SPACE ENCLOSED_CONDITION", + "ENCLOSED_CONDITION"], + "ENCLOSED_CONDITION": ["BRACKET_OPEN CONDITION BRACKET_CLOSE"], + "CONDITION": ["OPTIONAL_NOT CONDITION_EXPRESSION", "CONDITION_EXPRESSION"], + "CONDITION_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE EXPRESSION_IDENTIFIER", + "EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE DIGIT"], + "OPTIONAL_NOT": ["LOGICAL_OPERATOR_PREFIX SPACE", "SPACE"], + + # Loops + "FOR_HEADER": ["FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL COMMA SPACE STEP BRACKET_CLOSE SPACE COLON", + "FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL BRACKET_CLOSE SPACE COLON"], + "INITIAL": ["DIGIT"], + "FOR_LOOP": ["FOR_HEADER NEW_LINE TAB_INDENT DISPLAY"], + "ADVANCED_FOR_LOOP": ["FOR_LOOP", + "FOR_HEADER NEW_LINE TAB_INDENT ADVANCED_DISPLAY"], + + + # Displaying + "DISPLAY" : ["PRINT BRACKET_OPEN DISPLAY_IDENTIFIER BRACKET_CLOSE"], + "ADVANCED_DISPLAY" : ["DISPLAY", + "PRINT BRACKET_OPEN DISPLAY_EXPRESSION BRACKET_CLOSE"], + + + "LEVEL1.1": ["IDENTIFIER_INITIALIZATION SIMPLE_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL1.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL2.1": ["IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE SIMPLE_ELIF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY"], + "LEVEL2.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ADVANCED_ELIF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY"], + "LEVEL3.1": ["IDENTIFIER_INITIALIZATION FOR_LOOP"], + "LEVEL3.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_FOR_LOOP"], + + "ALL": ["LEVEL1.1", "LEVEL1.2", "LEVEL2.1", "LEVEL2.2", "LEVEL3.1", "LEVEL3.2"], + + } + + + + def generate_code(self, symbol, assigned_identifiers, last_variable, for_init_step, parent=None ): + """ + Generate code recursively based on the context-free grammar rules. + + Parameters: + - symbol (str): The symbol to generate code for. + - assigned_identifiers (set): Set of assigned identifiers. + - last_variable (set): Set of the last used variables. + - parent (Node): Parent node in the syntax tree. + + Returns: + - str: The generated code. + """ + node = Node(symbol, parent=parent) + + if symbol in self.cfg_rules: + if symbol == "IDENTIFIER_INITIALIZATION": + if self.init_count < self.max_init: + self.init_count += 1 + else: + symbol = "INITIALIZATION" + + rule = random.choice(self.cfg_rules[symbol]) + symbols = rule.split(" ") + + generated_symbols = [self.generate_code(s, assigned_identifiers, last_variable, for_init_step, node) for s in symbols] + + if symbol == "INITIAL": + init = generated_symbols[0] + for_init_step["initial_value"] = init + + + if symbol == "INITIALIZATION": + assigned_identifiers.add(generated_symbols[0]) + + if (symbol == "SIMPLE_ASSIGNMENTS") or (symbol == "ADVANCED_ASSIGNMENTS"): + if generated_symbols[0]: + last_variable.add(generated_symbols[0]) + + return ''.join(generated_symbols) + + elif symbol == "FINAL": + + initial_value = for_init_step.get("initial_value", "0") + + # Generate valid step_value and execution_count + valid_values = [(1, 2), (2, 1), (2, 2), (2, 3), (3, 2)] + step_value, execution_count = random.choice(valid_values) + for_init_step["step"] = str(step_value) + + final_value = step_value * execution_count + int(initial_value) - 1 + return str(final_value) + + + + elif symbol == "STEP": + + return for_init_step.get("step", "0") + + elif symbol == "EXPRESSION_IDENTIFIER": + identifier = random.choice(tuple(assigned_identifiers)) if assigned_identifiers else random.choice(self.cfg_rules["DIGIT"]) + return identifier + + elif symbol == "DISPLAY_IDENTIFIER": + try: + return f"{tuple(last_variable)[0]}" + except: + return f"{random.choice(tuple(assigned_identifiers))}" + else: + return symbol + + + + def print_tree(self, root): + """ + Print the syntax tree using the RenderTree utility from the anytree module. + + Parameters: + - root (Node): The root node of the syntax tree. + """ + for pre, _, node in RenderTree(root): + print(f"{pre}{node.name}") + + def generate_program(self, level): + """ + Generate a program based on the specified level. + + Parameters: + - level (str): The level of the program. + + Returns: + - Tuple[Node, str]: The syntax tree root node and the generated program. + """ + assigned = set() + last_variable = set() + for_init_step = {} + root = Node("ROOT") + + self.init_count = 0 + if level == "1.1": + self.max_init = 2 + elif level == "1.2": + self.max_init = 3 + elif level == "2.1": + self.max_init = 2 + elif level == "3.1": + self.max_init = 2 + elif level == "3.2": + self.max_init = 4 + else: + self.max_init = 5 + + if level == "ALL" : + level_passed = level + else : + level_passed = "LEVEL" + level + + program = self.generate_code(level_passed, assigned, last_variable, for_init_step, root) + + return root, program.replace("SPACE", " ") + + def memory_usage(self): + process = psutil.Process(os.getpid()) + mem_info = process.memory_info() + return mem_info.rss + + def generate_and_write_programs(self, num_programs, level, filename='data.txt', deduplicate=True): + """ + Generate and write a specified number of programs to a file. + + Parameters: + - num_programs (int): Number of programs to generate and write. + - level (str): The level of the programs. + - filename (str): Name of the file to write the programs (default is 'data.txt'). + - deduplicate (bool, optional): Whether to perform deduplication of generated programs (default is True). + """ + start_time = time.time() + start_mem = self.memory_usage() + max_tries = 1000 + num_tries = 0 + + with open(filename, 'w') as file: + + generated_programs = 0 + hashes = set() + pbar = tqdm(desc="Generation", total=num_programs) + while generated_programs < num_programs: + try: + root, program = self.generate_program(level) + code = program + "\n# output" + + SIO = StringIO() + with redirect_stdout(SIO): + exec(code) + output = SIO.getvalue().strip() + + output = '\n'.join([f'# {line}' if line else f'# ' for line in output.split('\n')]) + result = f"""{code}\n{output}""" + + program_hash = hashlib.sha256(result.encode('utf-8')).hexdigest() + + if deduplicate: + if program_hash not in hashes: + hashes.add(program_hash) + file.write(result + '\n\n') + generated_programs += 1 + pbar.update(1) + num_tries = 0 + else: + num_tries += 1 + if num_tries >= max_tries: + print("Hit max tries in deduplication, stopping generation.") + break + else: + file.write(result + '\n\n') + + generated_programs += 1 + pbar.update(1) + + except Exception as e: + continue + + pbar.close() + end_time = time.time() + end_mem = self.memory_usage() + deduplication_info = "with deduplication" if deduplicate else "without deduplication" + print(f"Code generation completed in {end_time - start_time:.2f} seconds.") + print(f"Memory used during code generation: {end_mem - start_mem} bytes") + print(f"Generated {generated_programs} {'unique ' if deduplicate else ''}programs {deduplication_info}.") + print(f"Programs are saved to {filename}.") + + +def main(): + parser = argparse.ArgumentParser(description='Generate and write programs based on a specified level. ') + parser.add_argument('--num_programs', type=int, default=1000, help='Number of programs to generate and write (default is 1000)') + parser.add_argument('--level', default="ALL", help='The level of the programs (1.1, 1.2, 2.1, 2.2, 3.1, 3.2, ALL)') + parser.add_argument('--filename', default='data.txt', help='Name of the file to write the programs (default is data.txt)') + parser.add_argument('--deduplicate', action='store_true', default=True, help='Perform deduplication of generated programs (default is True)a') + + args = parser.parse_args() + + code_generator = CodeGenerator() + code_generator.generate_and_write_programs(num_programs=args.num_programs, level=args.level, filename=args.filename, deduplicate=args.deduplicate) + +if __name__ == "__main__": + main() diff --git a/data/train.bin b/data/train.bin new file mode 100644 index 0000000..4c8911d Binary files /dev/null and b/data/train.bin differ diff --git a/data/train.txt b/data/train.txt new file mode 100644 index 0000000..add0fce --- /dev/null +++ b/data/train.txt @@ -0,0 +1,4870 @@ +c = 3 +a = 7 +b = 3 * 9 +print(a + 6) +# output +# 13 + +e = 2 +print(e - 3) +# output +# -1 + +e = 2 +b = 4 +c = e + 0 +print(b * 1) +# output +# 4 + +d = 8 +d = 1 + 6 +print(d - 2) +# output +# 5 + +a = 2 +print(a / a) +# output +# 1.0 + +c = 8 +e = c + c +print(e) +# output +# 16 + +d = 3 +d = 5 +print(d + d) +# output +# 10 + +e = 1 +print(e / e) +# output +# 1.0 + +c = 8 +c = 5 / c +print(c + c) +# output +# 1.25 + +a = 9 +d = a + 1 +print(a * a) +# output +# 81 + +a = 0 +b = 4 +b = a / 2 +print(b) +# output +# 0.0 + +c = 8 +b = c / 7 +print(c - c) +# output +# 0 + +e = 5 +c = 8 +print(c) +# output +# 8 + +e = 6 +d = 9 +print(d) +# output +# 9 + +e = 2 +c = 3 +e = 1 * 5 +print(e * c) +# output +# 15 + +d = 5 +d = 3 +a = d * d +print(d - 8) +# output +# -5 + +e = 8 +c = 9 +c = 5 - 3 +print(c) +# output +# 2 + +a = 5 +a = 6 +c = 1 + 9 +print(a * 3) +# output +# 18 + +d = 0 +e = d + 4 +print(d * d) +# output +# 0 + +b = 8 +b = 2 / 5 +print(b / b) +# output +# 1.0 + +b = 4 +d = 4 +e = 3 / 9 +print(d + 6) +# output +# 10 + +a = 2 +c = 1 +b = 7 * c +print(b) +# output +# 7 + +a = 9 +print(a / a) +# output +# 1.0 + +d = 6 +b = d / 4 +print(d - 3) +# output +# 3 + +e = 3 +d = 8 +a = d / d +print(d - e) +# output +# 5 + +e = 7 +print(e * 9) +# output +# 63 + +b = 7 +d = 1 +print(d - b) +# output +# -6 + +d = 9 +c = d / d +print(c) +# output +# 1.0 + +d = 5 +c = d * d +print(d / d) +# output +# 1.0 + +d = 7 +print(d * d) +# output +# 49 + +b = 5 +d = 8 +print(b - 0) +# output +# 5 + +d = 2 +a = 6 +e = 2 - 6 +print(e) +# output +# -4 + +a = 2 +e = 0 +b = 5 - 4 +print(b) +# output +# 1 + +d = 2 +b = 2 * 8 +print(d - d) +# output +# 0 + +d = 0 +a = 1 * d +print(d + 6) +# output +# 6 + +a = 8 +c = 4 +b = c / a +print(b) +# output +# 0.5 + +b = 0 +print(b + b) +# output +# 0 + +d = 0 +e = d * 1 +print(e) +# output +# 0 + +d = 5 +b = 6 +print(d) +# output +# 5 + +b = 1 +a = 1 + b +print(a) +# output +# 2 + +d = 3 +c = 7 / 2 +print(d - d) +# output +# 0 + +e = 5 +a = 0 - e +print(a) +# output +# -5 + +d = 7 +e = 5 + d +print(e) +# output +# 12 + +a = 1 +b = 4 +e = 2 + 7 +print(a * b) +# output +# 4 + +a = 1 +print(a * a) +# output +# 1 + +b = 1 +b = 9 +print(b) +# output +# 9 + +e = 9 +print(e / 3) +# output +# 3.0 + +a = 8 +a = a / 8 +print(a * a) +# output +# 1.0 + +b = 4 +d = 9 +print(d + 6) +# output +# 15 + +e = 2 +d = e / e +print(d) +# output +# 1.0 + +b = 2 +b = 3 +print(b * 7) +# output +# 21 + +a = 5 +d = 7 +print(d) +# output +# 7 + +c = 3 +e = 4 +print(e) +# output +# 4 + +c = 0 +b = 3 +a = 0 + 4 +print(b * 6) +# output +# 18 + +d = 1 +print(d) +# output +# 1 + +c = 6 +a = 0 +d = a - a +print(c / 2) +# output +# 3.0 + +e = 7 +a = 2 +d = e / 3 +print(d) +# output +# 2.3333333333333335 + +b = 3 +d = 5 +d = b - 7 +print(b - b) +# output +# 0 + +c = 6 +print(c - c) +# output +# 0 + +a = 8 +b = 6 / a +print(b) +# output +# 0.75 + +e = 4 +c = 5 +print(c * e) +# output +# 20 + +e = 9 +c = 0 +print(e) +# output +# 9 + +a = 2 +e = a + a +print(a * 5) +# output +# 10 + +d = 3 +d = 8 - d +print(d + d) +# output +# 10 + +b = 7 +b = 7 +print(b) +# output +# 7 + +b = 7 +d = 4 +print(d) +# output +# 4 + +e = 9 +print(e / 1) +# output +# 9.0 + +a = 7 +d = 5 +b = 3 / 3 +print(b) +# output +# 1.0 + +c = 2 +b = c * c +print(c / 1) +# output +# 2.0 + +e = 3 +b = 7 +print(b) +# output +# 7 + +a = 8 +print(a + a) +# output +# 16 + +d = 2 +b = 4 +c = d * 9 +print(c) +# output +# 18 + +e = 9 +print(e) +# output +# 9 + +a = 4 +b = a / a +print(b) +# output +# 1.0 + +b = 8 +b = 1 +d = b - 6 +print(d) +# output +# -5 + +a = 5 +print(a) +# output +# 5 + +d = 4 +d = 3 +e = d * d +print(e) +# output +# 9 + +a = 3 +c = a / a +print(c) +# output +# 1.0 + +e = 2 +d = e + e +print(e * e) +# output +# 4 + +d = 8 +print(d) +# output +# 8 + +b = 9 +print(b * 1) +# output +# 9 + +c = 3 +a = 2 * 7 +print(c - c) +# output +# 0 + +e = 4 +b = 0 + e +print(e + 1) +# output +# 5 + +c = 8 +print(c - 2) +# output +# 6 + +c = 2 +e = 8 +print(e) +# output +# 8 + +d = 6 +b = 5 +print(d - d) +# output +# 0 + +c = 5 +a = c + 9 +print(a) +# output +# 14 + +c = 9 +print(c * c) +# output +# 81 + +a = 3 +c = 0 +print(c) +# output +# 0 + +e = 5 +print(e) +# output +# 5 + +d = 7 +e = 7 +a = 3 - d +print(a) +# output +# -4 + +d = 3 +a = 4 +a = d * d +print(a * 4) +# output +# 36 + +e = 1 +print(e) +# output +# 1 + +c = 2 +d = 6 +print(c / d) +# output +# 0.3333333333333333 + +b = 1 +c = 3 +print(c) +# output +# 3 + +e = 1 +a = 3 +e = e / a +print(e) +# output +# 0.3333333333333333 + +a = 8 +a = 8 +print(a) +# output +# 8 + +e = 4 +e = 5 +print(e) +# output +# 5 + +a = 3 +b = 7 +d = a + a +print(d) +# output +# 6 + +d = 9 +d = 6 +b = d - 0 +print(b) +# output +# 6 + +c = 9 +d = 5 +print(d) +# output +# 5 + +e = 5 +print(e + e) +# output +# 10 + +a = 1 +d = 3 +print(a * 2) +# output +# 2 + +d = 7 +b = 9 / d +print(d - d) +# output +# 0 + +b = 8 +d = 6 +print(b / d) +# output +# 1.3333333333333333 + +d = 7 +a = 4 * d +print(d - 3) +# output +# 4 + +d = 9 +c = 2 +print(c) +# output +# 2 + +d = 1 +b = 4 +print(b) +# output +# 4 + +d = 8 +b = 0 * d +print(d - 7) +# output +# 1 + +b = 1 +print(b - 9) +# output +# -8 + +b = 0 +d = 7 +e = b + b +print(d + b) +# output +# 7 + +c = 8 +b = 3 +print(b) +# output +# 3 + +c = 3 +print(c + 3) +# output +# 6 + +c = 0 +d = 4 +e = c + 6 +print(e) +# output +# 6 + +e = 1 +b = 8 +print(b) +# output +# 8 + +e = 7 +print(e + e) +# output +# 14 + +a = 9 +a = 2 + 7 +print(a) +# output +# 9 + +d = 2 +print(d / 6) +# output +# 0.3333333333333333 + +d = 9 +b = 4 +a = d + 3 +print(a) +# output +# 12 + +c = 5 +b = c * c +print(b) +# output +# 25 + +d = 0 +c = 7 +print(d - d) +# output +# 0 + +a = 9 +d = a * a +print(d) +# output +# 81 + +a = 0 +c = 8 +print(a) +# output +# 0 + +e = 5 +b = 6 +print(e) +# output +# 5 + +d = 8 +b = 2 + d +print(b) +# output +# 10 + +b = 7 +c = b * 6 +print(b * 9) +# output +# 63 + +e = 6 +a = 4 - e +print(a) +# output +# -2 + +b = 8 +c = 3 +print(b) +# output +# 8 + +e = 4 +a = e + 9 +print(a) +# output +# 13 + +b = 3 +e = 1 +d = e * 6 +print(d) +# output +# 6 + +a = 2 +e = 4 +a = a / 7 +print(a + a) +# output +# 0.5714285714285714 + +c = 1 +a = 4 / 3 +print(c - 5) +# output +# -4 + +a = 8 +print(a * a) +# output +# 64 + +b = 9 +c = 6 +a = c * 6 +print(c / 6) +# output +# 1.0 + +a = 1 +e = 6 + a +print(a * 4) +# output +# 4 + +a = 7 +e = 0 +d = 5 * e +print(a * a) +# output +# 49 + +a = 5 +d = 5 +print(d) +# output +# 5 + +a = 7 +e = 4 +d = a / e +print(d) +# output +# 1.75 + +a = 5 +b = 5 +e = 9 - 2 +print(a * a) +# output +# 25 + +d = 1 +d = d * 2 +print(d) +# output +# 2 + +a = 3 +d = a - a +print(d) +# output +# 0 + +d = 1 +b = 2 +print(b) +# output +# 2 + +d = 7 +e = 5 +e = e + d +print(e) +# output +# 12 + +d = 8 +b = 2 +e = 2 + b +print(e) +# output +# 4 + +a = 9 +e = 7 +print(e - e) +# output +# 0 + +b = 8 +a = 8 +print(b - 4) +# output +# 4 + +a = 2 +d = 8 * 2 +print(a * a) +# output +# 4 + +c = 5 +c = 0 +print(c) +# output +# 0 + +e = 3 +a = 9 +a = 8 - a +print(a) +# output +# -1 + +c = 0 +d = 2 +b = 0 / d +print(b) +# output +# 0.0 + +e = 2 +d = 9 +a = e - 2 +print(a) +# output +# 0 + +b = 5 +b = 8 +c = b - b +print(c) +# output +# 0 + +b = 2 +a = b / b +print(a) +# output +# 1.0 + +a = 0 +b = a + a +print(a / 9) +# output +# 0.0 + +e = 8 +print(e) +# output +# 8 + +b = 7 +c = 1 +print(c + 7) +# output +# 8 + +b = 4 +a = 1 +a = 2 / b +print(a) +# output +# 0.5 + +e = 3 +e = e + 5 +print(e) +# output +# 8 + +a = 4 +d = 7 + a +print(a - 9) +# output +# -5 + +b = 6 +c = 3 +b = 3 - 3 +print(c - c) +# output +# 0 + +d = 5 +a = 0 * d +print(a) +# output +# 0 + +a = 7 +e = 1 +print(a + 3) +# output +# 10 + +b = 9 +b = 1 / b +print(b * 0) +# output +# 0.0 + +d = 2 +e = 4 +a = d / 2 +print(e / 2) +# output +# 2.0 + +b = 8 +d = 3 +a = 4 - 9 +print(d + b) +# output +# 11 + +e = 8 +d = 6 +print(e + d) +# output +# 14 + +b = 1 +e = b * b +print(e) +# output +# 1 + +e = 8 +a = 1 +d = 1 - 9 +print(e * e) +# output +# 64 + +b = 5 +c = 4 +print(b) +# output +# 5 + +d = 6 +a = 6 +print(a) +# output +# 6 + +a = 0 +b = 7 +print(b) +# output +# 7 + +c = 8 +b = 6 +print(c) +# output +# 8 + +e = 1 +print(e - e) +# output +# 0 + +b = 7 +c = 9 +print(b) +# output +# 7 + +e = 6 +c = 1 / e +print(e + e) +# output +# 12 + +a = 4 +b = 4 +print(b * b) +# output +# 16 + +d = 9 +d = 8 * d +print(d * d) +# output +# 5184 + +a = 8 +c = 2 / 7 +print(a * a) +# output +# 64 + +b = 5 +e = 7 - 8 +print(e) +# output +# -1 + +d = 2 +a = 4 +c = d * 0 +print(c) +# output +# 0 + +d = 9 +c = 3 +e = d - 5 +print(d + c) +# output +# 12 + +a = 2 +print(a / 4) +# output +# 0.5 + +e = 2 +b = 3 +e = 1 - e +print(b + 6) +# output +# 9 + +d = 7 +a = d * d +print(a) +# output +# 49 + +d = 7 +print(d) +# output +# 7 + +d = 5 +print(d * d) +# output +# 25 + +e = 7 +b = 0 +c = b - b +print(e * 4) +# output +# 28 + +d = 0 +b = 2 +print(d * d) +# output +# 0 + +d = 2 +b = 4 * d +print(b) +# output +# 8 + +d = 0 +c = 6 +e = 7 * c +print(e) +# output +# 42 + +b = 4 +print(b) +# output +# 4 + +d = 9 +c = 7 +print(c + c) +# output +# 14 + +e = 7 +print(e) +# output +# 7 + +c = 6 +d = 1 +print(d) +# output +# 1 + +a = 2 +c = 3 * 5 +print(c) +# output +# 15 + +d = 7 +d = 8 +e = 7 * 5 +print(e) +# output +# 35 + +b = 7 +d = b - 4 +print(b + 8) +# output +# 15 + +e = 6 +print(e - 6) +# output +# 0 + +a = 6 +a = 6 +c = 9 - 7 +print(a * 4) +# output +# 24 + +d = 7 +d = 3 +e = d - d +print(d * 0) +# output +# 0 + +d = 7 +c = 9 +a = 0 + 9 +print(c * 4) +# output +# 36 + +e = 8 +d = e - 9 +print(d) +# output +# -1 + +b = 3 +a = 0 +print(a) +# output +# 0 + +c = 8 +print(c) +# output +# 8 + +c = 2 +c = c * c +print(c - 5) +# output +# -1 + +d = 6 +a = d - 2 +print(a) +# output +# 4 + +b = 4 +b = 4 +b = b / 8 +print(b + 3) +# output +# 3.5 + +e = 4 +d = 2 / e +print(d) +# output +# 0.5 + +a = 7 +print(a * a) +# output +# 49 + +d = 8 +print(d / d) +# output +# 1.0 + +c = 9 +d = c / 3 +print(d) +# output +# 3.0 + +c = 5 +print(c / c) +# output +# 1.0 + +e = 0 +b = 8 +c = b * b +print(c) +# output +# 64 + +e = 0 +e = e + e +print(e) +# output +# 0 + +b = 4 +c = 4 +print(b + 5) +# output +# 9 + +e = 9 +b = 8 +a = 8 + 3 +print(b * 4) +# output +# 32 + +d = 8 +d = 1 +e = d + d +print(d / 5) +# output +# 0.2 + +c = 3 +b = 7 +b = c + c +print(b / 1) +# output +# 6.0 + +e = 4 +e = 8 * 8 +print(e) +# output +# 64 + +e = 9 +b = 1 +print(b) +# output +# 1 + +d = 9 +b = 6 +a = 6 + 5 +print(a) +# output +# 11 + +e = 7 +b = 1 +print(b) +# output +# 1 + +d = 8 +a = 5 +print(d) +# output +# 8 + +c = 4 +print(c - c) +# output +# 0 + +c = 6 +print(c / 4) +# output +# 1.5 + +a = 7 +b = 8 +e = 9 - 5 +print(a * a) +# output +# 49 + +a = 9 +d = 9 + 5 +print(d) +# output +# 14 + +b = 8 +b = 2 +a = 3 / b +print(a) +# output +# 1.5 + +d = 6 +d = 6 +d = d + d +print(d) +# output +# 12 + +b = 5 +d = b - b +print(d) +# output +# 0 + +b = 9 +c = 6 / b +print(b * 2) +# output +# 18 + +d = 8 +print(d - d) +# output +# 0 + +d = 0 +e = 7 +c = d / 1 +print(e / e) +# output +# 1.0 + +a = 4 +e = a / 2 +print(a + 1) +# output +# 5 + +d = 2 +b = 5 +print(b) +# output +# 5 + +b = 6 +c = 4 +print(b / b) +# output +# 1.0 + +d = 8 +e = 8 * d +print(d + d) +# output +# 16 + +c = 4 +print(c * 4) +# output +# 16 + +c = 6 +e = 2 +d = 6 + 1 +print(c * 1) +# output +# 6 + +b = 0 +d = 8 +print(b * b) +# output +# 0 + +c = 8 +print(c * c) +# output +# 64 + +a = 6 +print(a) +# output +# 6 + +b = 1 +c = 5 +print(c) +# output +# 5 + +c = 6 +a = 4 +print(c) +# output +# 6 + +b = 4 +b = 8 +d = b - 0 +print(d) +# output +# 8 + +b = 4 +e = 8 - b +print(e) +# output +# 4 + +c = 4 +print(c * 8) +# output +# 32 + +d = 4 +c = 0 + 1 +print(c) +# output +# 1 + +d = 1 +c = 0 +d = d / 7 +print(d * 2) +# output +# 0.2857142857142857 + +d = 6 +d = 3 / 7 +print(d * 7) +# output +# 3.0 + +c = 4 +d = 8 +c = 7 + 6 +print(c) +# output +# 13 + +d = 8 +d = 5 +e = 9 - d +print(e) +# output +# 4 + +d = 9 +b = 1 +d = d / 9 +print(b * b) +# output +# 1 + +c = 1 +a = 2 +c = 2 * 3 +print(c) +# output +# 6 + +b = 9 +c = 4 +print(b) +# output +# 9 + +a = 4 +c = 0 +print(c - c) +# output +# 0 + +e = 1 +c = 3 +print(e - 3) +# output +# -2 + +d = 2 +b = 4 +e = 8 + 3 +print(d / 5) +# output +# 0.4 + +c = 3 +a = c - 9 +print(a) +# output +# -6 + +e = 8 +c = 7 +print(c) +# output +# 7 + +e = 4 +b = 6 + e +print(b) +# output +# 10 + +e = 9 +d = 6 +print(e) +# output +# 9 + +c = 1 +print(c - c) +# output +# 0 + +d = 6 +a = 5 +a = 2 + a +print(d - a) +# output +# -1 + +b = 8 +c = 7 +print(b) +# output +# 8 + +c = 8 +d = 4 +print(d / 9) +# output +# 0.4444444444444444 + +d = 6 +b = d / d +print(b) +# output +# 1.0 + +c = 6 +e = 9 +d = 4 * c +print(c - 8) +# output +# -2 + +e = 1 +a = e + 8 +print(a) +# output +# 9 + +c = 1 +b = 9 + 5 +print(c / 4) +# output +# 0.25 + +b = 9 +d = b - b +print(b - b) +# output +# 0 + +d = 8 +a = 7 +print(d) +# output +# 8 + +e = 5 +c = 4 +c = c - e +print(e / c) +# output +# -5.0 + +a = 3 +e = 4 * 7 +print(a - a) +# output +# 0 + +c = 6 +c = 2 +print(c + c) +# output +# 4 + +d = 2 +c = 5 +print(d * d) +# output +# 4 + +d = 2 +e = d + 1 +print(e) +# output +# 3 + +c = 4 +e = 3 +print(c * e) +# output +# 12 + +c = 7 +a = 3 +a = 2 / c +print(a) +# output +# 0.2857142857142857 + +e = 7 +print(e / 7) +# output +# 1.0 + +d = 7 +a = 6 +print(a) +# output +# 6 + +b = 8 +b = 5 * 4 +print(b) +# output +# 20 + +b = 5 +b = 0 - 7 +print(b) +# output +# -7 + +c = 2 +print(c - c) +# output +# 0 + +d = 1 +b = 6 +print(d) +# output +# 1 + +e = 0 +d = e - 4 +print(d) +# output +# -4 + +a = 5 +e = 5 * 9 +print(e) +# output +# 45 + +d = 8 +a = d + d +print(d - 3) +# output +# 5 + +b = 4 +b = 9 +b = b + 4 +print(b) +# output +# 13 + +b = 3 +e = 6 +print(b / 3) +# output +# 1.0 + +e = 5 +c = 1 +print(c - e) +# output +# -4 + +a = 2 +a = 4 + a +print(a) +# output +# 6 + +e = 4 +a = 5 - e +print(e - 7) +# output +# -3 + +d = 7 +b = 7 + d +print(b) +# output +# 14 + +e = 3 +e = e / e +print(e / e) +# output +# 1.0 + +e = 6 +print(e * 4) +# output +# 24 + +d = 8 +print(d - 5) +# output +# 3 + +d = 9 +c = 4 +e = d / c +print(e) +# output +# 2.25 + +c = 6 +a = 5 +d = c + 1 +print(d) +# output +# 7 + +e = 0 +b = 2 +print(b * b) +# output +# 4 + +c = 0 +b = 3 * 6 +print(c - 1) +# output +# -1 + +a = 9 +e = 9 +d = 8 + 1 +print(a * 0) +# output +# 0 + +e = 1 +a = 7 +print(a * 3) +# output +# 21 + +d = 5 +print(d + 6) +# output +# 11 + +a = 9 +b = 7 +print(b / 5) +# output +# 1.4 + +a = 7 +print(a / 2) +# output +# 3.5 + +a = 2 +a = 4 +print(a) +# output +# 4 + +a = 9 +d = 2 +d = a + d +print(d) +# output +# 11 + +d = 4 +a = 2 * d +print(a) +# output +# 8 + +b = 6 +a = 4 / 9 +print(b - b) +# output +# 0 + +a = 5 +b = 2 +print(b / a) +# output +# 0.4 + +e = 8 +a = 9 +print(e) +# output +# 8 + +d = 3 +b = 6 + 3 +print(b) +# output +# 9 + +e = 6 +b = e * e +print(e / 8) +# output +# 0.75 + +b = 9 +c = 4 + 6 +print(c) +# output +# 10 + +d = 4 +print(d * 6) +# output +# 24 + +a = 6 +e = 7 +b = e + a +print(a / 2) +# output +# 3.0 + +b = 3 +print(b - b) +# output +# 0 + +b = 2 +a = 0 +print(a * 2) +# output +# 0 + +a = 1 +print(a) +# output +# 1 + +e = 7 +c = 4 +print(e) +# output +# 7 + +d = 8 +e = d + d +print(e) +# output +# 16 + +a = 0 +print(a + a) +# output +# 0 + +a = 2 +c = a * 6 +print(a / a) +# output +# 1.0 + +a = 9 +c = 7 / 9 +print(c) +# output +# 0.7777777777777778 + +a = 4 +print(a * a) +# output +# 16 + +b = 0 +c = 3 +print(b) +# output +# 0 + +d = 7 +d = 9 +e = 3 * d +print(d - 9) +# output +# 0 + +a = 9 +e = 3 +print(a + 1) +# output +# 10 + +b = 9 +print(b / 6) +# output +# 1.5 + +d = 9 +c = 5 +e = c / 7 +print(e) +# output +# 0.7142857142857143 + +d = 6 +e = 0 +print(d * d) +# output +# 36 + +e = 2 +b = 7 +print(e) +# output +# 2 + +c = 7 +c = 4 +d = 6 * c +print(c / c) +# output +# 1.0 + +b = 8 +b = 5 +print(b) +# output +# 5 + +d = 3 +a = 2 +print(d - a) +# output +# 1 + +b = 8 +d = 3 +print(d / d) +# output +# 1.0 + +a = 9 +b = 7 +print(b - b) +# output +# 0 + +e = 5 +print(e - 0) +# output +# 5 + +a = 7 +c = 1 +a = a / 1 +print(a * c) +# output +# 7.0 + +e = 6 +c = e + e +print(e + e) +# output +# 12 + +a = 3 +d = 4 +print(a) +# output +# 3 + +d = 9 +print(d * 9) +# output +# 81 + +a = 5 +e = 1 +print(a) +# output +# 5 + +d = 7 +c = 0 + 7 +print(c) +# output +# 7 + +e = 9 +a = 1 +c = 9 * a +print(e + 9) +# output +# 18 + +b = 6 +print(b) +# output +# 6 + +b = 6 +e = b + b +print(b + 1) +# output +# 7 + +c = 5 +e = 8 + c +print(e) +# output +# 13 + +c = 4 +d = 3 +print(c) +# output +# 4 + +a = 6 +b = 6 +a = 3 - 3 +print(a) +# output +# 0 + +e = 6 +b = 1 +d = e / e +print(d) +# output +# 1.0 + +d = 4 +b = 6 +a = 0 / d +print(a) +# output +# 0.0 + +d = 9 +d = 7 +print(d) +# output +# 7 + +d = 0 +print(d * d) +# output +# 0 + +e = 7 +a = 7 +a = 7 - e +print(a * 0) +# output +# 0 + +b = 7 +e = b - b +print(b - 5) +# output +# 2 + +b = 6 +b = 4 +b = b / b +print(b - 4) +# output +# -3.0 + +d = 3 +d = d / d +print(d - 8) +# output +# -7.0 + +d = 5 +c = 9 + d +print(c) +# output +# 14 + +b = 9 +a = 3 +d = b / 7 +print(d) +# output +# 1.2857142857142858 + +e = 7 +b = 6 +print(b * 6) +# output +# 36 + +e = 4 +print(e + e) +# output +# 8 + +a = 7 +b = a / a +print(a * a) +# output +# 49 + +e = 8 +b = 6 / e +print(e / 2) +# output +# 4.0 + +d = 2 +a = 6 +print(d) +# output +# 2 + +b = 2 +a = 8 +d = b / 6 +print(a / 5) +# output +# 1.6 + +b = 9 +e = 1 +print(e) +# output +# 1 + +d = 4 +e = 0 +b = e * 0 +print(b) +# output +# 0 + +e = 9 +e = e - 7 +print(e) +# output +# 2 + +c = 9 +a = 4 +a = 8 - 5 +print(a + 8) +# output +# 11 + +d = 1 +b = 1 +a = 2 / b +print(d + 9) +# output +# 10 + +d = 9 +a = 2 +b = 7 - d +print(a / d) +# output +# 0.2222222222222222 + +d = 8 +b = d * d +print(d * d) +# output +# 64 + +c = 3 +a = 0 +e = c / 3 +print(a + c) +# output +# 3 + +c = 6 +a = 4 +print(c / c) +# output +# 1.0 + +b = 8 +a = 5 - b +print(a) +# output +# -3 + +e = 8 +e = 0 +print(e + e) +# output +# 0 + +b = 5 +a = 1 +print(b) +# output +# 5 + +e = 2 +c = 2 +print(c * 6) +# output +# 12 + +e = 3 +print(e / 6) +# output +# 0.5 + +a = 6 +d = 8 +print(a) +# output +# 6 + +e = 4 +c = 9 +e = 8 - e +print(e / 3) +# output +# 1.3333333333333333 + +e = 7 +d = 0 +e = 9 - 1 +print(e - 7) +# output +# 1 + +d = 6 +print(d + d) +# output +# 12 + +c = 0 +d = 5 +print(c) +# output +# 0 + +d = 2 +print(d) +# output +# 2 + +b = 8 +d = 9 +a = d + 5 +print(a) +# output +# 14 + +b = 1 +a = b + b +print(b / b) +# output +# 1.0 + +b = 5 +print(b + b) +# output +# 10 + +b = 3 +b = 5 +d = b * 2 +print(d) +# output +# 10 + +e = 8 +a = 1 +c = 5 * 9 +print(a * 9) +# output +# 9 + +c = 3 +a = 3 +print(c / 7) +# output +# 0.42857142857142855 + +c = 6 +a = 0 / 5 +print(a) +# output +# 0.0 + +c = 0 +a = 3 +print(c + a) +# output +# 3 + +d = 0 +a = 4 +print(a) +# output +# 4 + +e = 3 +print(e) +# output +# 3 + +a = 5 +print(a / 5) +# output +# 1.0 + +c = 4 +e = 1 +e = 4 / e +print(e) +# output +# 4.0 + +a = 7 +c = a / a +print(c) +# output +# 1.0 + +a = 3 +c = 6 +d = c / 7 +print(c - 1) +# output +# 5 + +b = 8 +a = 2 + b +print(a) +# output +# 10 + +b = 6 +a = 5 +print(a) +# output +# 5 + +b = 7 +b = 8 +print(b) +# output +# 8 + +c = 5 +d = 2 +print(d + 4) +# output +# 6 + +b = 1 +print(b) +# output +# 1 + +e = 1 +b = 0 +print(b) +# output +# 0 + +d = 3 +b = 5 +print(d) +# output +# 3 + +a = 1 +print(a * 0) +# output +# 0 + +a = 2 +print(a + a) +# output +# 4 + +d = 0 +b = 4 +print(b) +# output +# 4 + +d = 0 +a = 9 +print(a * 7) +# output +# 63 + +c = 9 +print(c / c) +# output +# 1.0 + +c = 5 +e = 1 +print(c) +# output +# 5 + +d = 4 +d = 3 +b = d + 7 +print(b) +# output +# 10 + +e = 9 +e = 3 +b = e / 8 +print(e + e) +# output +# 6 + +c = 6 +e = 0 +print(e + 1) +# output +# 1 + +b = 1 +e = 7 +e = b * 4 +print(e + e) +# output +# 8 + +b = 3 +e = 8 +print(e) +# output +# 8 + +d = 3 +print(d) +# output +# 3 + +d = 8 +e = 2 +print(d + 2) +# output +# 10 + +e = 5 +d = 2 - e +print(d) +# output +# -3 + +e = 6 +b = 9 +print(e) +# output +# 6 + +c = 4 +b = 2 +d = 6 + 3 +print(b + 9) +# output +# 11 + +d = 0 +print(d) +# output +# 0 + +b = 1 +c = b + 3 +print(c) +# output +# 4 + +c = 5 +print(c - c) +# output +# 0 + +e = 3 +e = 4 +c = 6 - e +print(e + 3) +# output +# 7 + +d = 4 +e = 3 +a = 9 - 9 +print(d * 4) +# output +# 16 + +d = 4 +a = 1 +print(d) +# output +# 4 + +d = 6 +print(d) +# output +# 6 + +e = 2 +a = 6 +print(a) +# output +# 6 + +b = 0 +d = 0 +print(d) +# output +# 0 + +b = 2 +print(b - b) +# output +# 0 + +e = 8 +a = 6 / e +print(e * e) +# output +# 64 + +b = 2 +c = 1 +print(b + 2) +# output +# 4 + +e = 7 +a = e + e +print(a) +# output +# 14 + +c = 5 +e = 0 +print(c) +# output +# 5 + +a = 3 +b = 2 +c = b * 7 +print(c) +# output +# 14 + +c = 6 +a = 6 +print(a) +# output +# 6 + +a = 2 +b = 2 / 8 +print(a / 8) +# output +# 0.25 + +e = 3 +a = 4 +print(e) +# output +# 3 + +c = 4 +d = 3 +print(d + 6) +# output +# 9 + +e = 6 +b = 5 +c = 4 / 9 +print(e * 6) +# output +# 36 + +d = 2 +a = 0 +e = 7 / 8 +print(d + a) +# output +# 2 + +e = 4 +b = e * e +print(e * 7) +# output +# 28 + +a = 4 +e = 9 +print(e + a) +# output +# 13 + +b = 8 +a = 4 +d = 5 - b +print(b * 3) +# output +# 24 + +b = 1 +d = 5 * b +print(d) +# output +# 5 + +a = 1 +a = a + a +print(a + a) +# output +# 4 + +a = 1 +d = a / 4 +print(d) +# output +# 0.25 + +a = 9 +a = 5 +e = a / 7 +print(e) +# output +# 0.7142857142857143 + +d = 2 +d = 2 + d +print(d) +# output +# 4 + +e = 0 +a = 2 +c = e + e +print(c) +# output +# 0 + +e = 3 +print(e + 8) +# output +# 11 + +c = 0 +d = c + 6 +print(c * c) +# output +# 0 + +c = 7 +b = 9 / c +print(b) +# output +# 1.2857142857142858 + +a = 6 +e = 6 +a = e * a +print(a) +# output +# 36 + +e = 3 +c = e + e +print(e - e) +# output +# 0 + +a = 2 +a = 5 +d = a + a +print(d) +# output +# 10 + +e = 8 +a = 0 +c = a + a +print(a * 7) +# output +# 0 + +d = 1 +b = 3 +b = b / 8 +print(b / d) +# output +# 0.375 + +b = 1 +c = 1 +d = c / 5 +print(b - 0) +# output +# 1 + +b = 8 +a = 6 +print(b) +# output +# 8 + +c = 1 +print(c + c) +# output +# 2 + +a = 4 +a = 5 +a = a * 7 +print(a / a) +# output +# 1.0 + +c = 9 +a = c * 1 +print(c / 1) +# output +# 9.0 + +e = 6 +b = 1 +print(b - 2) +# output +# -1 + +b = 6 +c = 4 +e = c + c +print(e) +# output +# 8 + +b = 9 +b = 9 +d = 7 + b +print(d) +# output +# 16 + +d = 9 +c = 7 - d +print(d - d) +# output +# 0 + +b = 3 +e = 6 +print(b) +# output +# 3 + +c = 6 +c = 1 * 4 +print(c) +# output +# 4 + +a = 9 +print(a - a) +# output +# 0 + +d = 3 +b = 2 +a = d * b +print(d / 6) +# output +# 0.5 + +e = 1 +d = 5 +c = d * d +print(d / 2) +# output +# 2.5 + +d = 9 +print(d * 6) +# output +# 54 + +b = 8 +d = 5 +e = 8 - b +print(d / 3) +# output +# 1.6666666666666667 + +e = 8 +d = 5 +a = e - d +print(e / e) +# output +# 1.0 + +d = 3 +d = 7 +print(d) +# output +# 7 + +a = 0 +print(a) +# output +# 0 + +a = 5 +a = 2 +print(a * a) +# output +# 4 + +e = 2 +print(e * e) +# output +# 4 + +b = 3 +c = 7 +print(c / 3) +# output +# 2.3333333333333335 + +c = 3 +b = 0 +d = c + b +print(d) +# output +# 3 + +a = 3 +print(a) +# output +# 3 + +d = 3 +c = 0 +a = c * 5 +print(c / 5) +# output +# 0.0 + +d = 6 +c = d - d +print(c) +# output +# 0 + +c = 3 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +c = 5 +a = 9 / c +print(a) +# output +# 1.8 + +e = 5 +print(e * 5) +# output +# 25 + +d = 2 +d = d - d +print(d) +# output +# 0 + +c = 4 +d = 9 +b = 6 + 9 +print(b) +# output +# 15 + +e = 6 +c = 4 +b = c + c +print(e / e) +# output +# 1.0 + +c = 1 +print(c) +# output +# 1 + +d = 0 +e = 3 +print(e) +# output +# 3 + +a = 4 +c = 7 +print(a) +# output +# 4 + +e = 9 +e = e / e +print(e - 0) +# output +# 1.0 + +c = 7 +print(c * 4) +# output +# 28 + +a = 9 +e = 1 - a +print(a - 1) +# output +# 8 + +e = 0 +a = 3 +print(a) +# output +# 3 + +c = 6 +d = 8 +print(d / 4) +# output +# 2.0 + +a = 7 +b = 0 +e = 7 - a +print(b / 7) +# output +# 0.0 + +d = 9 +print(d) +# output +# 9 + +c = 3 +e = 0 +d = e + e +print(d) +# output +# 0 + +b = 6 +e = 1 +e = 5 / 1 +print(e - 2) +# output +# 3.0 + +e = 6 +a = 8 - e +print(e + 3) +# output +# 9 + +b = 8 +b = 9 - 6 +print(b) +# output +# 3 + +d = 1 +d = 0 +b = 2 * d +print(b) +# output +# 0 + +c = 5 +e = 6 +e = e + 2 +print(e) +# output +# 8 + +e = 1 +c = 8 +print(c * 2) +# output +# 16 + +b = 8 +print(b) +# output +# 8 + +c = 2 +b = 0 * c +print(c + c) +# output +# 4 + +a = 0 +print(a - 9) +# output +# -9 + +e = 9 +e = e * 2 +print(e) +# output +# 18 + +b = 6 +b = 5 +print(b) +# output +# 5 + +a = 1 +b = 3 +print(a) +# output +# 1 + +a = 2 +d = 1 +print(d) +# output +# 1 + +d = 4 +print(d - d) +# output +# 0 + +a = 9 +b = a * 0 +print(a / a) +# output +# 1.0 + +e = 4 +c = 2 +e = e - 0 +print(c * c) +# output +# 4 + +b = 7 +print(b) +# output +# 7 + +c = 7 +print(c) +# output +# 7 + +d = 3 +print(d * 7) +# output +# 21 + +c = 2 +d = 9 +print(d) +# output +# 9 + +b = 0 +e = 1 +print(b) +# output +# 0 + +b = 2 +e = b - 5 +print(e) +# output +# -3 + +e = 9 +print(e + 9) +# output +# 18 + +a = 6 +e = a / 7 +print(a + 4) +# output +# 10 + +e = 0 +print(e) +# output +# 0 + +a = 5 +c = 8 +print(a) +# output +# 5 + +b = 9 +print(b) +# output +# 9 + +e = 8 +d = 2 - 0 +print(d) +# output +# 2 + +c = 6 +d = 9 - 5 +print(c / 1) +# output +# 6.0 + +c = 6 +print(c / c) +# output +# 1.0 + +b = 1 +c = 0 +print(c / 2) +# output +# 0.0 + +a = 0 +c = 5 +print(c * 0) +# output +# 0 + +b = 6 +c = 1 +print(c / 3) +# output +# 0.3333333333333333 + +e = 8 +b = e / 6 +print(b) +# output +# 1.3333333333333333 + +b = 3 +print(b - 6) +# output +# -3 + +c = 9 +e = 3 - 8 +print(c + 9) +# output +# 18 + +e = 7 +b = 2 +print(e) +# output +# 7 + +a = 2 +d = 1 +b = a / d +print(b) +# output +# 2.0 + +e = 4 +b = 8 +d = 4 + b +print(d) +# output +# 12 + +b = 9 +c = 6 +print(c) +# output +# 6 + +c = 5 +d = 3 +e = 2 + 6 +print(d / 8) +# output +# 0.375 + +b = 8 +print(b * 1) +# output +# 8 + +b = 7 +a = b - 9 +print(b - 6) +# output +# 1 + +d = 0 +b = d * d +print(b) +# output +# 0 + +c = 0 +c = 1 * c +print(c - c) +# output +# 0 + +b = 6 +a = 2 +d = 9 - b +print(b - 0) +# output +# 6 + +c = 1 +c = c * 8 +print(c) +# output +# 8 + +c = 1 +e = 6 +print(e / 7) +# output +# 0.8571428571428571 + +b = 2 +b = 6 +print(b - 6) +# output +# 0 + +a = 5 +e = 8 +b = a / a +print(e - e) +# output +# 0 + +e = 4 +b = 3 +print(b) +# output +# 3 + +a = 5 +print(a / 3) +# output +# 1.6666666666666667 + +a = 8 +b = 5 +print(b / 2) +# output +# 2.5 + +c = 6 +b = 2 +c = b - 4 +print(b + c) +# output +# 0 + +e = 1 +c = e + 5 +print(e + 7) +# output +# 8 + +e = 8 +print(e * e) +# output +# 64 + +b = 3 +a = 0 +c = 6 - 1 +print(c) +# output +# 5 + +a = 9 +a = a - 6 +print(a - a) +# output +# 0 + +e = 1 +a = 0 - 8 +print(a) +# output +# -8 + +d = 6 +d = 2 +c = d - 0 +print(c) +# output +# 2 + +e = 7 +c = e + e +print(c) +# output +# 14 + +b = 2 +print(b * 6) +# output +# 12 + +d = 5 +c = 8 +b = c + c +print(c + 6) +# output +# 14 + +a = 9 +b = 0 +d = a + a +print(d) +# output +# 18 + +d = 4 +c = 6 +d = 1 - 6 +print(d) +# output +# -5 + +c = 4 +a = 7 +a = c + a +print(a + 5) +# output +# 16 + +e = 2 +b = e / e +print(e + 8) +# output +# 10 + +c = 6 +b = 3 + 1 +print(b) +# output +# 4 + +d = 9 +b = 2 +c = 3 / d +print(b - b) +# output +# 0 + +e = 3 +print(e - e) +# output +# 0 + +a = 1 +print(a / 2) +# output +# 0.5 + +e = 5 +print(e / e) +# output +# 1.0 + +a = 6 +e = a + a +print(a + a) +# output +# 12 + +a = 5 +b = 7 +print(b / 2) +# output +# 3.5 + +d = 9 +b = 9 +a = b - d +print(a) +# output +# 0 + +a = 2 +print(a) +# output +# 2 + +a = 2 +e = 0 +a = a * a +print(a) +# output +# 4 + +c = 7 +a = 9 +print(a - 2) +# output +# 7 + +b = 6 +c = 5 +print(b / b) +# output +# 1.0 + +d = 8 +e = 3 +a = d / d +print(e / e) +# output +# 1.0 + +d = 6 +a = 6 +print(a / a) +# output +# 1.0 + +a = 9 +d = 1 +d = 5 + 5 +print(a / 9) +# output +# 1.0 + +b = 2 +a = 7 +print(a) +# output +# 7 + +e = 5 +a = 5 +a = e / 4 +print(a) +# output +# 1.25 + +b = 0 +print(b) +# output +# 0 + +e = 6 +print(e - e) +# output +# 0 + +c = 9 +d = 7 * c +print(d) +# output +# 63 + +a = 3 +c = 5 +e = 2 + a +print(e) +# output +# 5 + +e = 8 +e = 4 +print(e) +# output +# 4 + +a = 6 +c = 3 +a = 4 / 7 +print(a) +# output +# 0.5714285714285714 + +a = 9 +d = 8 +print(a) +# output +# 9 + +d = 7 +d = 5 +print(d) +# output +# 5 + +d = 9 +b = 6 +print(b - b) +# output +# 0 + +e = 9 +print(e / e) +# output +# 1.0 + +e = 3 +a = e * e +print(e * 0) +# output +# 0 + +c = 1 +e = 9 +d = 6 * 8 +print(e - e) +# output +# 0 + +b = 3 +print(b) +# output +# 3 + +b = 8 +a = 2 +c = b + 1 +print(b * a) +# output +# 16 + +b = 3 +a = 6 +a = 8 - 0 +print(a) +# output +# 8 + +d = 2 +e = 6 +print(e - 2) +# output +# 4 + +c = 0 +b = 8 +print(c) +# output +# 0 + +e = 1 +b = 7 +print(b - 0) +# output +# 7 + +d = 4 +d = d / d +print(d / 3) +# output +# 0.3333333333333333 + +d = 5 +print(d - d) +# output +# 0 + +a = 4 +a = 4 +print(a) +# output +# 4 + +c = 2 +e = 2 +print(c - 1) +# output +# 1 + +d = 3 +b = 4 +print(b) +# output +# 4 + +e = 5 +e = e - e +print(e - e) +# output +# 0 + +e = 4 +b = 8 +e = e - 6 +print(b / 6) +# output +# 1.3333333333333333 + +e = 7 +b = 7 +c = 7 - e +print(b + 2) +# output +# 9 + +e = 2 +print(e) +# output +# 2 + +c = 9 +b = 8 / c +print(b) +# output +# 0.8888888888888888 + +d = 9 +print(d / d) +# output +# 1.0 + +d = 1 +c = 7 +print(d) +# output +# 1 + +b = 3 +d = 8 +a = d - 6 +print(a) +# output +# 2 + +b = 7 +e = 4 +d = 0 - b +print(b - e) +# output +# 3 + +b = 7 +a = 1 +print(a) +# output +# 1 + +c = 1 +a = 7 +print(a) +# output +# 7 + +a = 4 +d = 2 +print(a) +# output +# 4 + +d = 2 +d = 6 * d +print(d) +# output +# 12 + +b = 5 +e = 8 +b = e / e +print(b * 0) +# output +# 0.0 + +e = 7 +e = 6 +b = e * 5 +print(e * e) +# output +# 36 + +d = 6 +a = d / d +print(a) +# output +# 1.0 + +b = 8 +c = 7 / 7 +print(b + b) +# output +# 16 + +e = 4 +d = 1 +print(e) +# output +# 4 + +d = 0 +e = 8 +print(e * e) +# output +# 64 + +d = 1 +b = 8 +print(d * 1) +# output +# 1 + +e = 8 +e = 4 +d = 5 * 3 +print(d) +# output +# 15 + +e = 3 +e = e / 1 +print(e) +# output +# 3.0 + +d = 1 +d = 5 / 7 +print(d / d) +# output +# 1.0 + +b = 0 +a = b * b +print(b + b) +# output +# 0 + +d = 3 +d = 6 +print(d + d) +# output +# 12 + +e = 9 +e = 9 +print(e + e) +# output +# 18 + +a = 7 +print(a) +# output +# 7 + +b = 5 +d = 1 +e = d - 7 +print(d + b) +# output +# 6 + +e = 0 +c = e / 4 +print(e + 6) +# output +# 6 + +c = 8 +print(c / 1) +# output +# 8.0 + +c = 5 +c = 8 +print(c / 5) +# output +# 1.6 + +b = 2 +print(b) +# output +# 2 + +e = 7 +e = 9 +print(e) +# output +# 9 + +d = 3 +d = 8 +print(d) +# output +# 8 + +a = 4 +e = 4 +print(e) +# output +# 4 + +b = 6 +b = 4 - b +print(b + 0) +# output +# -2 + +e = 4 +b = e + e +print(e / e) +# output +# 1.0 + +d = 2 +e = 2 +print(e) +# output +# 2 + +e = 0 +e = 3 +print(e / 6) +# output +# 0.5 + +d = 4 +b = 8 * d +print(d * d) +# output +# 16 + +d = 9 +e = 9 +b = e + 2 +print(e - e) +# output +# 0 + +a = 0 +b = a + a +print(b) +# output +# 0 + +d = 4 +print(d) +# output +# 4 + +d = 4 +print(d + 4) +# output +# 8 + +c = 7 +print(c / c) +# output +# 1.0 + +d = 7 +e = 8 +print(e / d) +# output +# 1.1428571428571428 + +d = 7 +d = 2 +print(d) +# output +# 2 + +c = 7 +e = 3 +e = 0 + 0 +print(e) +# output +# 0 + +b = 7 +print(b / 4) +# output +# 1.75 + +b = 5 +b = 5 +print(b + b) +# output +# 10 + +c = 4 +b = 5 +print(c + c) +# output +# 8 + +c = 4 +e = 4 +print(c * c) +# output +# 16 + +e = 3 +b = e * 6 +print(e / e) +# output +# 1.0 + +c = 8 +b = 7 +c = c + c +print(c - c) +# output +# 0 + +d = 4 +e = 3 +b = 0 + 8 +print(b) +# output +# 8 + +d = 5 +d = d - d +print(d) +# output +# 0 + +e = 6 +d = 3 + e +print(d) +# output +# 9 + +e = 2 +c = 5 +a = e / 3 +print(e / e) +# output +# 1.0 + +a = 3 +a = a * a +print(a) +# output +# 9 + +b = 2 +a = b - b +print(b - 6) +# output +# -4 + +b = 8 +b = b / 8 +print(b) +# output +# 1.0 + +d = 1 +b = 2 / d +print(b) +# output +# 2.0 + +d = 2 +print(d - 0) +# output +# 2 + +a = 7 +a = 7 +print(a + a) +# output +# 14 + +a = 4 +print(a - a) +# output +# 0 + +c = 6 +a = 4 +print(a) +# output +# 4 + +d = 5 +b = 2 +b = d * d +print(b) +# output +# 25 + +d = 6 +b = 2 +print(d) +# output +# 6 + +d = 2 +a = 3 +e = 1 + a +print(e) +# output +# 4 + +d = 6 +d = 0 +b = d * 5 +print(d * d) +# output +# 0 + +b = 1 +print(b * 6) +# output +# 6 + +e = 9 +d = 2 - e +print(e - 1) +# output +# 8 + +b = 3 +c = 2 / b +print(b * 8) +# output +# 24 + +c = 0 +a = 7 +print(a * a) +# output +# 49 + +c = 6 +b = 6 +print(b) +# output +# 6 + +e = 5 +e = 6 +print(e) +# output +# 6 + +b = 6 +c = 1 +print(b) +# output +# 6 + +c = 0 +a = 1 +print(a) +# output +# 1 + +b = 0 +a = 4 +print(a * 1) +# output +# 4 + +a = 7 +e = a + 9 +print(a * 9) +# output +# 63 + +e = 1 +e = 6 / 6 +print(e) +# output +# 1.0 + +d = 7 +a = 0 +print(a) +# output +# 0 + +a = 1 +e = 7 - a +print(e) +# output +# 6 + +b = 4 +print(b + b) +# output +# 8 + +e = 6 +b = 5 +a = 4 / b +print(a) +# output +# 0.8 + +c = 4 +d = 4 +e = c - c +print(e) +# output +# 0 + +c = 8 +e = 7 +print(e * 3) +# output +# 21 + +d = 1 +a = 3 +d = 7 - a +print(d / d) +# output +# 1.0 + +c = 8 +print(c / 3) +# output +# 2.6666666666666665 + +e = 3 +d = 5 +print(e - 1) +# output +# 2 + +d = 0 +b = 4 +print(d) +# output +# 0 + +a = 8 +d = 2 +a = d - 8 +print(a) +# output +# -6 + +c = 7 +c = 7 +print(c / 8) +# output +# 0.875 + +c = 9 +e = 9 +print(c / c) +# output +# 1.0 + +c = 4 +d = 5 +d = c / 1 +print(d + c) +# output +# 8.0 + +e = 1 +d = 0 +d = 4 - d +print(d - 7) +# output +# -3 + +e = 2 +e = 6 +a = e - e +print(e + e) +# output +# 12 + +c = 9 +e = 8 +b = 9 * e +print(b) +# output +# 72 + +a = 4 +a = 4 +a = 7 / a +print(a) +# output +# 1.75 + +b = 8 +b = 8 +print(b) +# output +# 8 + +a = 8 +print(a - a) +# output +# 0 + +e = 4 +d = 7 +d = e * 8 +print(e / 1) +# output +# 4.0 + +e = 7 +d = e + e +print(d) +# output +# 14 + +b = 6 +a = 5 +d = b + 9 +print(d) +# output +# 15 + +b = 2 +d = 5 * b +print(b * 0) +# output +# 0 + +c = 1 +e = c / c +print(c + 5) +# output +# 6 + +d = 2 +d = 7 +d = 7 + 4 +print(d / 9) +# output +# 1.2222222222222223 + +a = 0 +c = 3 +c = 2 + 5 +print(c) +# output +# 7 + +c = 4 +d = 6 +e = d / d +print(c / c) +# output +# 1.0 + +d = 6 +d = 8 +e = d - 9 +print(d / 4) +# output +# 2.0 + +c = 0 +b = 1 +c = 4 - 0 +print(b + b) +# output +# 2 + +a = 3 +c = a * a +print(c) +# output +# 9 + +a = 8 +c = 3 +e = 4 + 1 +print(a / a) +# output +# 1.0 + +a = 3 +b = 7 - a +print(b) +# output +# 4 + +b = 0 +e = 2 / 1 +print(b - 2) +# output +# -2 + +a = 8 +a = 4 - a +print(a) +# output +# -4 + +a = 9 +d = a / a +print(a + 6) +# output +# 15 + +a = 2 +print(a * 6) +# output +# 12 + +b = 7 +d = 6 +a = 0 / d +print(b - 3) +# output +# 4 + +e = 3 +d = e - 2 +print(e / e) +# output +# 1.0 + +b = 9 +a = 8 +c = b + a +print(b / 4) +# output +# 2.25 + +b = 0 +b = 1 +a = 1 + b +print(b - 7) +# output +# -6 + +c = 8 +e = 8 +print(e * c) +# output +# 64 + +b = 3 +d = b * b +print(d) +# output +# 9 + +b = 2 +c = 7 +c = c + c +print(c) +# output +# 14 + +d = 9 +e = 5 +print(d) +# output +# 9 + +d = 5 +d = d / d +print(d - 4) +# output +# -3.0 + +a = 3 +print(a + 3) +# output +# 6 + +c = 4 +b = 5 +c = 0 / c +print(c) +# output +# 0.0 + +b = 3 +a = 5 +print(a + a) +# output +# 10 + +d = 1 +a = 6 +print(d) +# output +# 1 + +c = 3 +d = 5 +a = c - c +print(c / d) +# output +# 0.6 + +b = 6 +e = 5 +print(b) +# output +# 6 + +c = 0 +a = c - c +print(a) +# output +# 0 + +c = 7 +c = c + 2 +print(c - 1) +# output +# 8 + +c = 3 +d = 9 +print(c * 3) +# output +# 9 + +a = 5 +a = 9 +print(a) +# output +# 9 + +b = 4 +e = b - 0 +print(b - 9) +# output +# -5 + +b = 1 +c = 5 +e = c * b +print(c - b) +# output +# 4 + +d = 0 +d = d + 7 +print(d) +# output +# 7 + +c = 9 +c = 9 +print(c - 4) +# output +# 5 + +e = 8 +c = 7 +e = e - c +print(e) +# output +# 1 + +b = 9 +c = 6 +a = c + c +print(c * 5) +# output +# 30 + +e = 6 +d = 1 - 3 +print(d) +# output +# -2 + +b = 9 +c = b - 8 +print(c) +# output +# 1 + +c = 0 +print(c / 2) +# output +# 0.0 + +d = 2 +b = 1 / 8 +print(d - d) +# output +# 0 + +d = 5 +print(d) +# output +# 5 + +d = 2 +print(d / d) +# output +# 1.0 + +c = 1 +b = 1 +print(c) +# output +# 1 + +d = 4 +b = 6 / 2 +print(b) +# output +# 3.0 + +c = 2 +print(c) +# output +# 2 + +d = 4 +a = 2 * 5 +print(d / 6) +# output +# 0.6666666666666666 + +e = 2 +c = 7 +d = 7 + 4 +print(d) +# output +# 11 + +d = 0 +a = 1 +print(a) +# output +# 1 + +b = 0 +a = 0 / 5 +print(a) +# output +# 0.0 + +d = 4 +d = 1 +a = 1 - d +print(d / d) +# output +# 1.0 + +b = 9 +b = 9 +print(b / 9) +# output +# 1.0 + +d = 8 +print(d + 9) +# output +# 17 + +a = 6 +c = 8 +c = 6 + a +print(c) +# output +# 12 + +a = 2 +e = 7 +print(e) +# output +# 7 + +c = 7 +print(c - c) +# output +# 0 + +d = 2 +print(d + d) +# output +# 4 + +b = 3 +c = 1 +e = 6 - c +print(e) +# output +# 5 + +b = 6 +d = 6 +c = 5 - 4 +print(c) +# output +# 1 + +e = 2 +a = 6 +print(e) +# output +# 2 + +c = 9 +print(c) +# output +# 9 + +d = 7 +d = 9 +print(d + 5) +# output +# 14 + +a = 6 +b = 0 +e = 5 / a +print(b * b) +# output +# 0 + +a = 3 +c = a / 3 +print(c) +# output +# 1.0 + +c = 4 +b = 1 +print(c + c) +# output +# 8 + +b = 2 +d = 4 +print(b / 1) +# output +# 2.0 + +a = 6 +print(a - 3) +# output +# 3 + +e = 8 +d = 0 - e +print(e / e) +# output +# 1.0 + +c = 1 +b = 1 + 4 +print(b) +# output +# 5 + +e = 5 +c = 0 +print(e + 1) +# output +# 6 + +c = 0 +d = 8 +d = d - 9 +print(d) +# output +# -1 + +c = 3 +print(c) +# output +# 3 + +e = 2 +b = 0 + e +print(e - 4) +# output +# -2 + +a = 6 +print(a - 9) +# output +# -3 + +a = 6 +e = a - 9 +print(e) +# output +# -3 + +c = 2 +e = 6 +b = c * 6 +print(b) +# output +# 12 + +d = 6 +a = 1 + d +print(a) +# output +# 7 + +c = 8 +print(c / 4) +# output +# 2.0 + +b = 4 +print(b * b) +# output +# 16 + +d = 2 +print(d + 9) +# output +# 11 + +d = 2 +e = d - d +print(d - d) +# output +# 0 + +d = 1 +a = 4 + 6 +print(a) +# output +# 10 \ No newline at end of file diff --git a/data/val.bin b/data/val.bin new file mode 100644 index 0000000..3ad9ee7 Binary files /dev/null and b/data/val.bin differ diff --git a/data/val.txt b/data/val.txt new file mode 100644 index 0000000..2231512 --- /dev/null +++ b/data/val.txt @@ -0,0 +1,617 @@ +d = 9 +e = 6 +print(e) +# output +# 6 + +d = 2 +a = d + d +print(a) +# output +# 4 + +a = 7 +c = a * a +print(a / a) +# output +# 1.0 + +e = 7 +d = e - 1 +print(e + e) +# output +# 14 + +a = 2 +a = 8 +e = a + a +print(a - a) +# output +# 0 + +c = 5 +print(c) +# output +# 5 + +c = 5 +print(c + c) +# output +# 10 + +d = 4 +c = d - d +print(c) +# output +# 0 + +a = 9 +d = 9 +b = a + 4 +print(a + 0) +# output +# 9 + +a = 2 +a = 1 +print(a / 2) +# output +# 0.5 + +d = 2 +a = 7 +a = 6 + d +print(a) +# output +# 8 + +a = 5 +c = a / a +print(a + a) +# output +# 10 + +d = 7 +b = 3 +print(b) +# output +# 3 + +d = 0 +a = 3 - d +print(a) +# output +# 3 + +d = 1 +b = 5 +print(b - 6) +# output +# -1 + +e = 5 +print(e - e) +# output +# 0 + +a = 3 +c = 8 +d = 5 + 3 +print(d) +# output +# 8 + +e = 0 +c = 6 +print(e) +# output +# 0 + +c = 3 +print(c - 0) +# output +# 3 + +e = 7 +print(e / 6) +# output +# 1.1666666666666667 + +c = 9 +a = 3 +print(a * 4) +# output +# 12 + +b = 5 +d = 5 +print(d - 6) +# output +# -1 + +c = 3 +a = 5 +a = 4 * 3 +print(a) +# output +# 12 + +e = 4 +a = 7 - 0 +print(e * e) +# output +# 16 + +e = 7 +e = 8 +c = 3 / 7 +print(c) +# output +# 0.42857142857142855 + +d = 9 +c = 7 +print(c - 2) +# output +# 5 + +a = 9 +print(a - 6) +# output +# 3 + +a = 7 +print(a + 4) +# output +# 11 + +e = 2 +b = e - e +print(e / e) +# output +# 1.0 + +a = 9 +c = 2 +a = c - 2 +print(a + a) +# output +# 0 + +c = 3 +d = 7 +b = d / 8 +print(b) +# output +# 0.875 + +a = 2 +a = 1 +print(a) +# output +# 1 + +d = 8 +b = 7 +print(b) +# output +# 7 + +b = 8 +c = 9 +d = c - b +print(d) +# output +# 1 + +a = 2 +e = 9 +c = 7 * a +print(a * e) +# output +# 18 + +a = 0 +d = 8 +b = a * 5 +print(b) +# output +# 0 + +a = 1 +print(a + a) +# output +# 2 + +c = 1 +c = 9 +d = 0 - 5 +print(d) +# output +# -5 + +a = 1 +d = 5 * 3 +print(a + a) +# output +# 2 + +b = 7 +d = 4 +print(b) +# output +# 7 + +b = 5 +e = 9 +print(b * b) +# output +# 25 + +b = 8 +print(b * b) +# output +# 64 + +a = 8 +d = a - 7 +print(d) +# output +# 1 + +a = 8 +e = 2 +print(a) +# output +# 8 + +e = 0 +d = 6 +a = 0 + 0 +print(e * d) +# output +# 0 + +e = 5 +d = 0 * e +print(e + e) +# output +# 10 + +d = 5 +b = 7 +print(d) +# output +# 5 + +b = 4 +c = 7 +c = c / c +print(c) +# output +# 1.0 + +b = 3 +d = 2 +d = 6 / 6 +print(d) +# output +# 1.0 + +d = 4 +e = 5 +a = 6 * e +print(e / e) +# output +# 1.0 + +c = 6 +b = 0 +c = 0 * 4 +print(c) +# output +# 0 + +c = 2 +c = 7 + c +print(c * c) +# output +# 81 + +a = 6 +d = 0 +d = d + 0 +print(d) +# output +# 0 + +c = 1 +b = 8 +a = 7 / 1 +print(a) +# output +# 7.0 + +e = 7 +print(e - e) +# output +# 0 + +e = 1 +print(e + 3) +# output +# 4 + +d = 4 +d = 4 +print(d) +# output +# 4 + +b = 0 +d = 1 +print(d) +# output +# 1 + +a = 2 +b = 1 +print(a / 9) +# output +# 0.2222222222222222 + +a = 0 +c = 8 +print(c) +# output +# 8 + +e = 2 +a = 1 +print(a + a) +# output +# 2 + +d = 7 +b = 2 +print(b) +# output +# 2 + +b = 1 +print(b * 9) +# output +# 9 + +e = 9 +d = 6 / 4 +print(e / 1) +# output +# 9.0 + +a = 3 +print(a + a) +# output +# 6 + +b = 1 +e = 0 +b = 4 + e +print(b / 3) +# output +# 1.3333333333333333 + +c = 8 +d = c + 1 +print(d) +# output +# 9 + +c = 3 +e = 7 +print(c) +# output +# 3 + +d = 7 +c = 7 +print(c) +# output +# 7 + +c = 8 +a = 4 +b = c / c +print(c + a) +# output +# 12 + +d = 1 +a = 7 - 4 +print(a) +# output +# 3 + +a = 7 +b = 1 +a = a / 5 +print(a) +# output +# 1.4 + +a = 5 +a = 7 +a = a + a +print(a) +# output +# 14 + +c = 6 +d = 0 +a = 9 - 0 +print(a) +# output +# 9 + +c = 9 +c = c + 7 +print(c) +# output +# 16 + +b = 7 +d = 8 * 6 +print(b - 5) +# output +# 2 + +c = 7 +a = c - c +print(a) +# output +# 0 + +a = 0 +e = 4 * a +print(a / 1) +# output +# 0.0 + +c = 0 +a = 4 +d = c / 4 +print(a / a) +# output +# 1.0 + +b = 6 +b = 3 +print(b) +# output +# 3 + +c = 6 +print(c * 2) +# output +# 12 + +a = 1 +c = a + 9 +print(a * 4) +# output +# 4 + +b = 6 +a = 5 +e = b / 8 +print(e) +# output +# 0.75 + +d = 8 +d = 4 * 5 +print(d) +# output +# 20 + +c = 8 +b = 5 +print(c - 8) +# output +# 0 + +c = 3 +c = 0 +a = c + 6 +print(c + 8) +# output +# 8 + +d = 6 +e = 2 +c = 2 - d +print(c) +# output +# -4 + +d = 9 +a = 5 +print(a) +# output +# 5 + +b = 4 +e = 3 +a = b - 7 +print(e / e) +# output +# 1.0 + +a = 6 +c = 7 +b = c + c +print(a - c) +# output +# -1 + +c = 7 +e = 5 +d = 1 + c +print(c - 7) +# output +# 0 + +b = 2 +print(b + 3) +# output +# 5 + +e = 6 +print(e) +# output +# 6 + +a = 4 +c = 0 +d = 9 / a +print(d) +# output +# 2.25 + +a = 8 +a = 9 +d = a / 8 +print(d) +# output +# 1.125 + +d = 5 +a = 6 +a = d / 7 +print(a) +# output +# 0.7142857142857143 + +e = 2 +print(e - 7) +# output +# -5 + +a = 9 +print(a) +# output +# 9 + +b = 8 +e = 9 +d = 0 - b +print(e - b) +# output +# 1 + +c = 5 +e = 3 +d = 8 * 3 +print(d) +# output +# 24 \ No newline at end of file diff --git a/demonstration.ipynb b/demonstration.ipynb new file mode 100644 index 0000000..d89bc91 --- /dev/null +++ b/demonstration.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "34fdf3ed-1a27-4183-b092-8c86729ba1c6", + "metadata": {}, + "source": [ + "# Evaluating Arithmetic Operations Using Our Tiny Language Model" + ] + }, + { + "cell_type": "markdown", + "id": "75460bdd-35d6-4a66-ae88-34457ca299c4", + "metadata": {}, + "source": [ + "This notebook demonstrates how to use our tiny language model to evaluate simple arithmetic operations in Python code snippets. The process involves loading the model, preparing the necessary metadata for character-integer mappings, and defining functions for encoding and decoding strings. We then evaluate various code examples to observe the model's generated results. This workflow highlights the capabilities of our model in executing arithmetic expressions.\n" + ] + }, + { + "cell_type": "markdown", + "id": "931d2724-c928-4ae0-a003-bfcffc587b55", + "metadata": {}, + "source": [ + "## Import Necessary Libraries" + ] + }, + { + "cell_type": "markdown", + "id": "53303b76-4aa4-4a15-8e62-5e71ae5f6b5c", + "metadata": {}, + "source": [ + "Import essential libraries for model loading, evaluation, and tokenization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fd9d1f74-2736-4219-8754-f66be4f98b2d", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "import numpy as np\n", + "import pickle\n", + "import re\n", + "from model import GPT" + ] + }, + { + "cell_type": "markdown", + "id": "f83b58fe-7d06-4edd-90a4-4aea840868a3", + "metadata": {}, + "source": [ + "## Load the Model" + ] + }, + { + "cell_type": "markdown", + "id": "5fd3f325-1bf4-4c78-a5b3-af55dee68a9c", + "metadata": {}, + "source": [ + "Set the model name and path, and load our tiny language model into the appropriate device (CPU or GPU)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "318cf23f-07a9-47ea-baa0-6213211dc7bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizedModule(\n", + " (_orig_mod): GPT(\n", + " (token_embedding_table): Embedding(33, 96)\n", + " (position_embedding_table): Embedding(256, 96)\n", + " (blocks): Sequential(\n", + " (0): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0-5): 6 x Head(\n", + " (key): Linear(in_features=96, out_features=16, bias=False)\n", + " (query): Linear(in_features=96, out_features=16, bias=False)\n", + " (value): Linear(in_features=96, out_features=16, bias=False)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=96, out_features=96, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " (ffwd): FeedForward(\n", 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out_features=96, bias=False)\n", + " (3): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (4): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0-5): 6 x Head(\n", + " (key): Linear(in_features=96, out_features=16, bias=False)\n", + " (query): Linear(in_features=96, out_features=16, bias=False)\n", + " (value): Linear(in_features=96, out_features=16, bias=False)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=96, out_features=96, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " (ffwd): FeedForward(\n", + " (net): Sequential(\n", + " (0): Linear(in_features=96, out_features=384, bias=False)\n", + " (1): GELU(approximate='none')\n", + " (2): Linear(in_features=384, out_features=96, bias=False)\n", + " (3): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (5): Block(\n", + " (sa): MultiHeadAttention(\n", + " (heads): ModuleList(\n", + " (0-5): 6 x Head(\n", + " (key): Linear(in_features=96, out_features=16, bias=False)\n", + " (query): Linear(in_features=96, out_features=16, bias=False)\n", + " (value): Linear(in_features=96, out_features=16, bias=False)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (proj): Linear(in_features=96, out_features=96, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " )\n", + " (ffwd): FeedForward(\n", + " (net): Sequential(\n", + " (0): Linear(in_features=96, out_features=384, bias=False)\n", + " (1): GELU(approximate='none')\n", + " (2): Linear(in_features=384, out_features=96, bias=False)\n", + " (3): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " (ln1): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " (ln2): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n", + " (lm_head): Linear(in_features=96, out_features=33, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_name = \"arithmetics_level1_696K\"\n", + "model_path = os.path.join('models', f\"{model_name}.pth\")\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "model = GPT()\n", + "model = torch.compile(model) \n", + "model.load_state_dict(torch.load(model_path, map_location=device))\n", + "model.to(device)\n", + "model.eval()" + ] + }, + { + "cell_type": "markdown", + "id": "be4f5be4-45ea-4893-bb97-b732cde2a906", + "metadata": {}, + "source": [ + "## Load Tokenizers" + ] + }, + { + "cell_type": "markdown", + "id": "c5e17dbe-14e3-4d12-a2f2-f816cbfa4c44", + "metadata": {}, + "source": [ + "Load the metadata file containing the mappings from characters to integers and vice versa." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "52455912-777d-4147-ba50-413fcb452a43", + "metadata": {}, + "outputs": [], + "source": [ + "meta_path = os.path.join('data', 'meta.pkl') \n", + "with open(meta_path, 'rb') as f:\n", + " meta = pickle.load(f)\n", + "# Récuperer le mapping Caractères-Entiers\n", + "stoi = meta['stoi']\n", + "itos = meta['itos']\n", + "\n", + "# Fonctions pour la tokenisation\n", + "def encode(s):\n", + " \"\"\"\n", + " Encode string `s` into token IDs.\n", + " \"\"\"\n", + " return [stoi[c] for c in s]\n", + "\n", + "def decode(l):\n", + " \"\"\"\n", + " Decode token IDs `l` into a string.\n", + "\"\"\"\n", + " return ''.join([itos[i] for i in l])\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "6520bed8-0e5f-4df3-bba4-72828dcbf2b3", + "metadata": {}, + "source": [ + "## Define Function to Evaluate Code Example" + ] + }, + { + "cell_type": "markdown", + "id": "63ef6763-3c4f-4f1f-95c7-c7d29f2e536b", + "metadata": {}, + "source": [ + "Implement a function to evaluate a given code snippet using our model, and extract the result from the model's output." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "01cb5618-f797-43de-8ff0-e9515e18dc0c", + "metadata": {}, + "outputs": [], + "source": [ + "# Fonction pour donner au modèle un extrait de code à executer \n", + "def evaluate_example(example , max_new_tokens=22):\n", + " \n", + " encoded_example = torch.tensor(encode(example), dtype=torch.long).unsqueeze(0).to(device)\n", + " \n", + " with torch.no_grad():\n", + " response = decode(model.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist())\n", + " splited_response = response.split(\"# output\")\n", + " result_response = splited_response[-1]\n", + " generated_results = [float(match.group()) for match in re.finditer(r\"(?<=# )-?\\d+(\\.\\d+)?\", result_response.split('\\n\\n')[0].replace(\"\\n\", \"\"))]\n", + "\n", + " return generated_results\n" + ] + }, + { + "cell_type": "markdown", + "id": "440dbda2-0e84-4386-914b-6ef812f3945d", + "metadata": {}, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f6f6bc4c-e4c6-47e5-9cbd-f3a2f89231b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example 1:\n", + "\n", + "a = 5\n", + "b = 7 \n", + "c = a * b\n", + "print(c)\n", + "# output\n", + "\n", + "Generated Result: 35.0\n" + ] + } + ], + "source": [ + "# Exemple 1\n", + "eval_prompt_1 = \"\"\"\n", + "a = 5\n", + "b = 7 \n", + "c = a * b\n", + "print(c)\n", + "# output\n", + "\"\"\"\n", + "\n", + "print(\"Example 1:\")\n", + "print(eval_prompt_1)\n", + "generated_results_1 = evaluate_example(eval_prompt_1)\n", + "print(\"Generated Result:\", generated_results_1[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e7732e8c-caa5-4176-a7a5-e1599d797e95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example 2:\n", + "\n", + "e = 10\n", + "c = 10\n", + "a = e / c\n", + "print(a)\n", + "# output\n", + "\n", + "Generated Result: 1.0\n" + ] + } + ], + "source": [ + "# Exemple 2\n", + "eval_prompt_2 = \"\"\"\n", + "e = 10\n", + "c = 10\n", + "a = e / c\n", + "print(a)\n", + "# output\n", + "\"\"\"\n", + "\n", + "print(\"Example 2:\")\n", + "print(eval_prompt_2)\n", + "generated_results_2 = evaluate_example(eval_prompt_2)\n", + "print(\"Generated Result:\", generated_results_2[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e2f6259-31b1-4459-bc44-de31a9fe7d7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example 3:\n", + "\n", + "a = 9\n", + "d = 3\n", + "d = 1\n", + "d = 5 + 8\n", + "print(d * 5)\n", + "# output\n", + "\n", + "Generated Result: 90.0\n" + ] + } + ], + "source": [ + "# Exemple 3\n", + "eval_prompt_3 = \"\"\"\n", + "a = 9\n", + "d = 3\n", + "d = 1\n", + "d = 5 + 8\n", + "print(d * 5)\n", + "# output\n", + "\"\"\"\n", + "\n", + "print(\"Example 3:\")\n", + "print(eval_prompt_3)\n", + "generated_results_3 = evaluate_example(eval_prompt_3)\n", + "print(\"Generated Result:\", generated_results_3[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "TinyLM", + "language": "python", + "name": "tinylm" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/generalization/.ipynb_checkpoints/demonstration-checkpoint.ipynb b/generalization/.ipynb_checkpoints/demonstration-checkpoint.ipynb new file mode 100644 index 0000000..e1dbb26 --- /dev/null +++ b/generalization/.ipynb_checkpoints/demonstration-checkpoint.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "176621a1-0778-4a21-9de9-dd2ebefef5a6", + "metadata": {}, + "source": [ + "# Evaluating Arithmetic Operations Using Our Finetuned Large Language Model" + ] + }, + { + "cell_type": "markdown", + "id": "26d10bd3-660c-43df-9719-43fa71b119ea", + "metadata": {}, + "source": [ + "This notebook demonstrates how to use our fine-tuned Code Llama model to evaluate simple arithmetic operations in Python code snippets. The process involves loading the pre-trained model, preparing the tokenizer, and defining functions to evaluate code examples. We then evaluate various code snippets to observe the model's generated results. This workflow highlights the capabilities of the Code Llama model in executing arithmetic expressions." + ] + }, + { + "cell_type": "markdown", + "id": "ac948737-336a-4d73-ba56-64601000e561", + "metadata": {}, + "source": [ + "## Import Necessary Libraries" + ] + }, + { + "cell_type": "markdown", + "id": "7654d2f3-bae2-4936-b86c-a5a6b0e2ed51", + "metadata": {}, + "source": [ + "Import essential libraries for model loading, evaluation, and tokenization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c3d4f58-731a-47a8-9121-4689acc8e2c6", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import warnings\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from peft import PeftModel\n", + "\n", + "# Ignore all warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import transformers\n", + "\n", + "transformers.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "markdown", + "id": "edd801aa-d88d-42b0-bfac-3901489f7642", + "metadata": {}, + "source": [ + "## Load the Pre-trained Model" + ] + }, + { + "cell_type": "markdown", + "id": "74874a67-6e25-4395-81fa-768c630db298", + "metadata": {}, + "source": [ + "Load the pre-trained Code Llama model in 8-bit precision." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b1d25be0-8aed-4825-976f-f066aa45eb8d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9e7fc7c9fd3b422fa5413277b16ac29e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00 1: + print("Enabling parallelism...") + print() + model.is_parallelizable = True + model.model_parallel = True + + # Training arguments + print("Setting up training arguments...") + print() + training_args = TrainingArguments( + per_device_train_batch_size=per_device_train_batch_size, + gradient_accumulation_steps=gradient_accumulation_steps, + warmup_steps=warmup_steps, + max_steps=max_steps, + learning_rate=learning_rate, + fp16=True, + logging_steps=logging_steps, + optim="adamw_torch", + evaluation_strategy="steps", + save_strategy="steps", + eval_steps=eval_steps, + save_steps=save_steps, + output_dir=output_dir, + group_by_length=True, + report_to="wandb", + run_name=f"codellama-{datetime.now().strftime('%Y-%m-%d-%H-%M')}", + ) + + # Initialize the Trainer + trainer = Trainer( + model=model, + train_dataset=tokenized_train_dataset, + eval_dataset=tokenized_val_dataset, + args=training_args, + data_collator=DataCollatorForSeq2Seq( + tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True + ), + ) + + # Disable caching for training + model.config.use_cache = False + + # Patch the model's state_dict + old_state_dict = model.state_dict + model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__( + model, type(model) + ) + + # Compile the model if applicable + if torch.__version__ >= "2" and sys.platform != "win32": + print("Compiling the model...") + print() + model = torch.compile(model) + + # Start training + print("Starting training...") + trainer.train() + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Train a model with specified parameters.") + parser.add_argument('--base_model', type=str, default="codellama/CodeLlama-7b-hf", help="Base model name or path") + parser.add_argument('--train_dataset_path', type=str, default="data/tokenized_train", help="Path to the tokenized training dataset") + parser.add_argument('--val_dataset_path', type=str, default="data/tokenized_val", help="Path to the tokenized validation dataset") + parser.add_argument('--resume_from_checkpoint', type=str, default="", help="Path to checkpoint to resume training from") + parser.add_argument('--wandb_project', type=str, default="tiny-coder", help="WandB project name") + parser.add_argument('--batch_size', type=int, default=128, help="Total batch size for training") + parser.add_argument('--per_device_train_batch_size', type=int, default=32, help="Batch size per device for training") + parser.add_argument('--gradient_accumulation_steps', type=int, default=4, help="Number of gradient accumulation steps") + parser.add_argument('--output_dir', type=str, default="models/code-llama-finetuned-level1", help="Directory to save the output") + parser.add_argument('--learning_rate', type=float, default=3e-4, help="Learning rate") + parser.add_argument('--warmup_steps', type=int, default=100, help="Number of warmup steps") + parser.add_argument('--max_steps', type=int, default=200, help="Maximum number of training steps") + parser.add_argument('--logging_steps', type=int, default=10, help="Number of steps between logging") + parser.add_argument('--eval_steps', type=int, default=20, help="Number of steps between evaluations") + parser.add_argument('--save_steps', type=int, default=20, help="Number of steps between saving checkpoints") + + args = parser.parse_args() + + train_model( + base_model=args.base_model, + train_dataset_path=args.train_dataset_path, + val_dataset_path=args.val_dataset_path, + resume_from_checkpoint=args.resume_from_checkpoint, + wandb_project=args.wandb_project, + batch_size=args.batch_size, + per_device_train_batch_size=args.per_device_train_batch_size, + gradient_accumulation_steps=args.gradient_accumulation_steps, + output_dir=args.output_dir, + learning_rate=args.learning_rate, + warmup_steps=args.warmup_steps, + max_steps=args.max_steps, + logging_steps=args.logging_steps, + eval_steps=args.eval_steps, + save_steps=args.save_steps + ) \ No newline at end of file diff --git a/generalization/.ipynb_checkpoints/tokenizing-checkpoint.py b/generalization/.ipynb_checkpoints/tokenizing-checkpoint.py new file mode 100644 index 0000000..14a446e --- /dev/null +++ b/generalization/.ipynb_checkpoints/tokenizing-checkpoint.py @@ -0,0 +1,82 @@ +import argparse +import pandas as pd +from datasets import Dataset +from transformers import AutoTokenizer + +import transformers + +transformers.logging.set_verbosity_error() + +def tokenize_data(train_file="../data/train.txt", + test_file="../data/val.txt", + tokenizer_name="codellama/CodeLlama-7b-hf", + train_output_dir="data/tokenized_train", + val_output_dir="data/tokenized_val"): + print("Tokenizing data...") + + # Read the training and test data + with open(train_file) as f: + train_data = f.read() + + with open(test_file) as f: + test_data = f.read() + + # Split the snippets into individual examples + train_snippets = train_data.split('\n\n') + test_snippets = test_data.split('\n\n') + + # Create datasets from the snippets + train_dataset = Dataset.from_pandas(pd.DataFrame({'snippets': train_snippets})) + eval_dataset = Dataset.from_pandas(pd.DataFrame({'snippets': test_snippets})) + + # Load the tokenizer + print("Loading tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + tokenizer.add_eos_token = True + tokenizer.pad_token_id = 0 + tokenizer.padding_side = "left" + + # Function to tokenize a prompt + def tokenize(prompt): + result = tokenizer( + prompt, + truncation=True, + max_length=512, + padding=False, + return_tensors=None, + ) + + # For self-supervised learning, labels are also the inputs + result["labels"] = result["input_ids"].copy() + + return result + + # Function to generate and tokenize a prompt + def generate_and_tokenize_prompt(data_point): + full_prompt = data_point["snippets"] + + return tokenize(full_prompt) + + # Tokenize the training and validation datasets + print("Tokenizing datasets...") + tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt) + tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt) + + # Save the tokenized datasets to disk + print(f"Saving tokenized datasets to {train_output_dir} and {val_output_dir}...") + tokenized_train_dataset.save_to_disk(train_output_dir) + tokenized_val_dataset.save_to_disk(val_output_dir) + + print("Tokenization complete.") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Tokenize data for language model training.") + parser.add_argument("--train_file", type=str, default="../data/train.txt", help="Path to the training file") + parser.add_argument("--test_file", type=str, default="../data/val.txt", help="Path to the test file") + parser.add_argument("--tokenizer_name", type=str, default="codellama/CodeLlama-7b-hf", help="Name or path of the tokenizer") + parser.add_argument("--train_output_dir", type=str, default="data/tokenized_train", help="Path to save the tokenized training dataset") + parser.add_argument("--val_output_dir", type=str, default="data/tokenized_val", help="Path to save the tokenized validation dataset") + + args = parser.parse_args() + + tokenize_data(args.train_file, args.test_file, args.tokenizer_name, args.train_output_dir, args.val_output_dir) diff --git a/generalization/data/tokenized_train/data-00000-of-00001.arrow b/generalization/data/tokenized_train/data-00000-of-00001.arrow new file mode 100644 index 0000000..88be929 Binary files /dev/null and b/generalization/data/tokenized_train/data-00000-of-00001.arrow differ diff --git a/generalization/data/tokenized_train/dataset_info.json b/generalization/data/tokenized_train/dataset_info.json new file mode 100644 index 0000000..c5ed9a8 --- /dev/null +++ b/generalization/data/tokenized_train/dataset_info.json @@ -0,0 +1,33 @@ +{ + "citation": "", + "description": "", + "features": { + "snippets": { + "dtype": "string", + "_type": "Value" + }, + "input_ids": { + "feature": { + "dtype": "int32", + "_type": "Value" + }, + "_type": "Sequence" + }, + "attention_mask": { + "feature": { + "dtype": "int8", + "_type": "Value" + }, + "_type": "Sequence" + }, + "labels": { + "feature": { + "dtype": "int64", + "_type": "Value" + }, + "_type": "Sequence" + } + }, + "homepage": "", + "license": "" +} \ No newline at end of file diff --git a/generalization/data/tokenized_train/state.json b/generalization/data/tokenized_train/state.json new file mode 100644 index 0000000..a9079c7 --- /dev/null +++ b/generalization/data/tokenized_train/state.json @@ -0,0 +1,13 @@ +{ + "_data_files": [ + { + "filename": "data-00000-of-00001.arrow" + } + ], + "_fingerprint": "0b9633717850bc07", + "_format_columns": null, + "_format_kwargs": {}, + "_format_type": null, + "_output_all_columns": false, + "_split": null +} \ No newline at end of file diff --git a/generalization/data/tokenized_val/data-00000-of-00001.arrow b/generalization/data/tokenized_val/data-00000-of-00001.arrow new file mode 100644 index 0000000..7305205 Binary files /dev/null and b/generalization/data/tokenized_val/data-00000-of-00001.arrow differ diff --git a/generalization/data/tokenized_val/dataset_info.json b/generalization/data/tokenized_val/dataset_info.json new file mode 100644 index 0000000..c5ed9a8 --- /dev/null +++ b/generalization/data/tokenized_val/dataset_info.json @@ -0,0 +1,33 @@ +{ + "citation": "", + "description": "", + "features": { + "snippets": { + "dtype": "string", + "_type": "Value" + }, + "input_ids": { + "feature": { + "dtype": "int32", + "_type": "Value" + }, + "_type": "Sequence" + }, + "attention_mask": { + "feature": { + "dtype": "int8", + "_type": "Value" + }, + "_type": "Sequence" + }, + "labels": { + "feature": { + "dtype": "int64", + "_type": "Value" + }, + "_type": "Sequence" + } + }, + "homepage": "", + "license": "" +} \ No newline at end of file diff --git a/generalization/data/tokenized_val/state.json b/generalization/data/tokenized_val/state.json new file mode 100644 index 0000000..0cb7a77 --- /dev/null +++ b/generalization/data/tokenized_val/state.json @@ -0,0 +1,13 @@ +{ + "_data_files": [ + { + "filename": "data-00000-of-00001.arrow" + } + ], + "_fingerprint": "cc8d31de4b82da8d", + "_format_columns": null, + "_format_kwargs": {}, + "_format_type": null, + "_output_all_columns": false, + "_split": null +} \ No newline at end of file diff --git a/generalization/demonstration.ipynb b/generalization/demonstration.ipynb new file mode 100644 index 0000000..e1dbb26 --- /dev/null +++ b/generalization/demonstration.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "176621a1-0778-4a21-9de9-dd2ebefef5a6", + "metadata": {}, + "source": [ + "# Evaluating Arithmetic Operations Using Our Finetuned Large Language Model" + ] + }, + { + "cell_type": "markdown", + "id": "26d10bd3-660c-43df-9719-43fa71b119ea", + "metadata": {}, + "source": [ + "This notebook demonstrates how to use our fine-tuned Code Llama model to evaluate simple arithmetic operations in Python code snippets. The process involves loading the pre-trained model, preparing the tokenizer, and defining functions to evaluate code examples. We then evaluate various code snippets to observe the model's generated results. This workflow highlights the capabilities of the Code Llama model in executing arithmetic expressions." + ] + }, + { + "cell_type": "markdown", + "id": "ac948737-336a-4d73-ba56-64601000e561", + "metadata": {}, + "source": [ + "## Import Necessary Libraries" + ] + }, + { + "cell_type": "markdown", + "id": "7654d2f3-bae2-4936-b86c-a5a6b0e2ed51", + "metadata": {}, + "source": [ + "Import essential libraries for model loading, evaluation, and tokenization." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "4c3d4f58-731a-47a8-9121-4689acc8e2c6", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import warnings\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from peft import PeftModel\n", + "\n", + "# Ignore all warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import transformers\n", + "\n", + "transformers.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "markdown", + "id": "edd801aa-d88d-42b0-bfac-3901489f7642", + "metadata": {}, + "source": [ + "## Load the Pre-trained Model" + ] + }, + { + "cell_type": "markdown", + "id": "74874a67-6e25-4395-81fa-768c630db298", + "metadata": {}, + "source": [ + "Load the pre-trained Code Llama model in 8-bit precision." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b1d25be0-8aed-4825-976f-f066aa45eb8d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9e7fc7c9fd3b422fa5413277b16ac29e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00 1: + print("Enabling parallelism...") + print() + model.is_parallelizable = True + model.model_parallel = True + + # Training arguments + print("Setting up training arguments...") + print() + training_args = TrainingArguments( + per_device_train_batch_size=per_device_train_batch_size, + gradient_accumulation_steps=gradient_accumulation_steps, + warmup_steps=warmup_steps, + max_steps=max_steps, + learning_rate=learning_rate, + fp16=True, + logging_steps=logging_steps, + optim="adamw_torch", + evaluation_strategy="steps", + save_strategy="steps", + eval_steps=eval_steps, + save_steps=save_steps, + output_dir=output_dir, + group_by_length=True, + report_to="wandb", + run_name=f"codellama-{datetime.now().strftime('%Y-%m-%d-%H-%M')}", + ) + + # Initialize the Trainer + trainer = Trainer( + model=model, + train_dataset=tokenized_train_dataset, + eval_dataset=tokenized_val_dataset, + args=training_args, + data_collator=DataCollatorForSeq2Seq( + tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True + ), + ) + + # Disable caching for training + model.config.use_cache = False + + # Patch the model's state_dict + old_state_dict = model.state_dict + model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__( + model, type(model) + ) + + # Compile the model if applicable + if torch.__version__ >= "2" and sys.platform != "win32": + print("Compiling the model...") + print() + model = torch.compile(model) + + # Start training + print("Starting training...") + trainer.train() + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Train a model with specified parameters.") + parser.add_argument('--base_model', type=str, default="codellama/CodeLlama-7b-hf", help="Base model name or path") + parser.add_argument('--train_dataset_path', type=str, default="data/tokenized_train", help="Path to the tokenized training dataset") + parser.add_argument('--val_dataset_path', type=str, default="data/tokenized_val", help="Path to the tokenized validation dataset") + parser.add_argument('--resume_from_checkpoint', type=str, default="", help="Path to checkpoint to resume training from") + parser.add_argument('--wandb_project', type=str, default="tiny-coder", help="WandB project name") + parser.add_argument('--batch_size', type=int, default=128, help="Total batch size for training") + parser.add_argument('--per_device_train_batch_size', type=int, default=32, help="Batch size per device for training") + parser.add_argument('--gradient_accumulation_steps', type=int, default=4, help="Number of gradient accumulation steps") + parser.add_argument('--output_dir', type=str, default="models/code-llama-finetuned-level1", help="Directory to save the output") + parser.add_argument('--learning_rate', type=float, default=3e-4, help="Learning rate") + parser.add_argument('--warmup_steps', type=int, default=100, help="Number of warmup steps") + parser.add_argument('--max_steps', type=int, default=200, help="Maximum number of training steps") + parser.add_argument('--logging_steps', type=int, default=10, help="Number of steps between logging") + parser.add_argument('--eval_steps', type=int, default=20, help="Number of steps between evaluations") + parser.add_argument('--save_steps', type=int, default=20, help="Number of steps between saving checkpoints") + + args = parser.parse_args() + + train_model( + base_model=args.base_model, + train_dataset_path=args.train_dataset_path, + val_dataset_path=args.val_dataset_path, + resume_from_checkpoint=args.resume_from_checkpoint, + wandb_project=args.wandb_project, + batch_size=args.batch_size, + per_device_train_batch_size=args.per_device_train_batch_size, + gradient_accumulation_steps=args.gradient_accumulation_steps, + output_dir=args.output_dir, + learning_rate=args.learning_rate, + warmup_steps=args.warmup_steps, + max_steps=args.max_steps, + logging_steps=args.logging_steps, + eval_steps=args.eval_steps, + save_steps=args.save_steps + ) \ No newline at end of file diff --git a/generalization/results/.ipynb_checkpoints/result_llama-checkpoint.txt b/generalization/results/.ipynb_checkpoints/result_llama-checkpoint.txt new file mode 100644 index 0000000..c71fe79 --- /dev/null +++ b/generalization/results/.ipynb_checkpoints/result_llama-checkpoint.txt @@ -0,0 +1 @@ +Accuracy: 80.00% diff --git a/generalization/results/.ipynb_checkpoints/results_llama-checkpoint.csv b/generalization/results/.ipynb_checkpoints/results_llama-checkpoint.csv new file mode 100644 index 0000000..9cbdcd6 --- /dev/null +++ b/generalization/results/.ipynb_checkpoints/results_llama-checkpoint.csv @@ -0,0 +1,47 @@ +Prompt,Real_Results,Generated_Results +"b = 7 +e = 2 +e = 1 +d = 8 +print(b - b) +# output +",0.0,0.0 +"a = 7 +c = 0 +a = 1 +for a in range(8, 10) : + print(c) +# output +",0.0,0.0 +"e = 7 +d = 1 +c = 8 +b = 1 +b = (b / d)-(b * 2)/(2 * d)*(b / c)/(e + 0)-(c + 8) +if not (b == 9) or (e < 3) or (c > b) and ( d == b) : + print(c - e) +elif (not c < 0) : + print(d - b) +else : + print(b) +# output +",1.0,7.0 +"d = 2 +d = 0 +d = 6 +if d != 0 : + print(d) +elif not d != d : + print(d) +else : + print(d) +# output +",6.0,6.0 +"e = 6 +d = 4 +e = 0 +b = d / 8 +for d in range(8, 5) : + print(d + 3) +# output +",0.0,0.0 diff --git a/generalization/results/result_llama.txt b/generalization/results/result_llama.txt new file mode 100644 index 0000000..2fe40e2 --- /dev/null +++ b/generalization/results/result_llama.txt @@ -0,0 +1 @@ +Accuracy: 96.00% diff --git a/generalization/results/results_llama.csv b/generalization/results/results_llama.csv new file mode 100644 index 0000000..5d3fb53 --- /dev/null +++ b/generalization/results/results_llama.csv @@ -0,0 +1,510 @@ +Prompt,Real_Results,Generated_Results +"c = 3 +e = 9 +e = 0 * c +print(e) +# output +",0.0,0.0 +"e = 2 +a = 6 +c = a / 4 +print(c) +# output +",1.5,1.5 +"e = 6 +c = 3 / e +print(c) +# output +",0.5,0.5 +"e = 8 +a = 3 +print(e / a) +# output +",2.6666666666666665,2.6666666666666665 +"a = 8 +e = a + 1 +print(a / a) +# output +",1.0,1.0 +"e = 6 +a = 7 +d = a - 1 +print(d) +# output +",6.0,6.0 +"d = 9 +print(d * d) +# output +",81.0,81.0 +"c = 4 +print(c + 3) +# output +",7.0,7.0 +"d = 7 +print(d / d) +# output +",1.0,1.0 +"d = 5 +a = 0 +print(a + d) +# output +",5.0,5.0 +"a = 6 +b = 0 +a = 3 - 3 +print(b / 9) +# output +",0.0,0.0 +"a = 0 +a = 0 +print(a) +# output +",0.0,0.0 +"c = 3 +e = 7 +print(c * c) +# output +",9.0,9.0 +"a = 2 +e = 0 / 8 +print(a * a) +# output +",4.0,4.0 +"e = 9 +a = 9 +print(a * e) +# output +",81.0,81.0 +"d = 8 +e = 4 +print(e + d) +# output +",12.0,12.0 +"a = 2 +c = a - 6 +print(a / a) +# output +",1.0,1.0 +"a = 6 +b = 7 +d = a - 0 +print(d) +# output +",6.0,6.0 +"b = 7 +e = 2 +b = 0 * 7 +print(b) +# output +",0.0,0.0 +"c = 9 +e = 4 +print(c - 5) +# output +",4.0,4.0 +"d = 4 +d = 7 * 4 +print(d * d) +# output +",784.0,289.0 +"a = 4 +print(a / a) +# output +",1.0,1.0 +"e = 3 +b = 4 +a = e - 6 +print(a) +# output +",-3.0,-3.0 +"d = 5 +d = 9 - 7 +print(d) +# output +",2.0,2.0 +"b = 7 +b = 3 / b +print(b) +# output +",0.42857142857142855,0.42857142857142855 +"a = 9 +b = a + a +print(a / a) +# output +",1.0,1.0 +"a = 0 +d = a * 9 +print(d) +# output +",0.0,0.0 +"e = 1 +c = e + e +print(c) +# output +",2.0,2.0 +"e = 3 +c = e + 2 +print(e + e) +# output +",6.0,6.0 +"a = 4 +e = 0 * 7 +print(a + 2) +# output +",6.0,6.0 +"b = 6 +b = 5 +b = b - b +print(b / 3) +# output +",0.0,0.0 +"b = 0 +d = 9 * 1 +print(d) +# output +",9.0,9.0 +"a = 4 +a = 2 +print(a) +# output +",2.0,2.0 +"e = 9 +b = 3 +print(b) +# output +",3.0,3.0 +"d = 6 +c = 4 + 1 +print(d + 2) +# output +",8.0,8.0 +"d = 8 +c = d - 6 +print(c) +# output +",2.0,2.0 +"a = 9 +d = 1 +print(a * d) +# output +",9.0,9.0 +"c = 4 +a = 4 +print(a) +# output +",4.0,4.0 +"b = 5 +a = 1 +b = a - a +print(b) +# output +",0.0,0.0 +"d = 2 +c = 1 +print(d) +# output +",2.0,2.0 +"e = 3 +b = 5 +print(b * 9) +# output +",45.0,45.0 +"e = 9 +d = 5 +d = e * d +print(e / d) +# output +",0.2,0.09090909090909091 +"c = 5 +print(c * c) +# output +",25.0,25.0 +"e = 2 +c = 8 / e +print(e * 9) +# output +",18.0,18.0 +"c = 0 +c = 6 - 8 +print(c) +# output +",-2.0,-2.0 +"e = 7 +a = 2 +d = 5 * 7 +print(a * 7) +# output +",14.0,14.0 +"e = 1 +print(e * e) +# output +",1.0,1.0 +"d = 6 +b = d * d +print(b) +# output +",36.0,36.0 +"e = 8 +b = e / 5 +print(e / 2) +# output +",4.0,4.0 +"d = 6 +c = 3 +a = 9 + d +print(a) +# output +",15.0,15.0 +"d = 4 +a = 8 +print(a * a) +# output +",64.0,64.0 +"e = 6 +print(e + e) +# output +",12.0,12.0 +"b = 3 +a = 3 +print(a) +# output +",3.0,3.0 +"b = 9 +b = 9 +print(b - 5) +# output +",4.0,4.0 +"a = 8 +print(a) +# output +",8.0,8.0 +"b = 2 +e = 2 +print(e) +# output +",2.0,2.0 +"e = 1 +c = e / 7 +print(e - e) +# output +",0.0,0.0 +"e = 3 +c = 0 +print(c) +# output +",0.0,0.0 +"a = 1 +a = 3 +print(a / 1) +# output +",3.0,3.0 +"c = 3 +print(c / c) +# output +",1.0,1.0 +"c = 0 +c = c - c +print(c) +# output +",0.0,0.0 +"a = 7 +d = 2 +print(a) +# output +",7.0,7.0 +"c = 3 +a = 0 +print(c - 4) +# output +",-1.0,-1.0 +"e = 9 +a = 5 +print(e) +# output +",9.0,9.0 +"d = 3 +b = 4 +d = 4 / 3 +print(b * b) +# output +",16.0,16.0 +"c = 5 +d = 5 +d = c - 4 +print(d) +# output +",1.0,1.0 +"e = 6 +b = 3 +e = e - 6 +print(e) +# output +",0.0,0.0 +"d = 4 +c = 6 +print(d + c) +# output +",10.0,10.0 +"b = 8 +a = 9 +e = 5 / 2 +print(b * a) +# output +",72.0,72.0 +"e = 4 +print(e) +# output +",4.0,4.0 +"b = 2 +c = 3 +print(b / b) +# output +",1.0,1.0 +"d = 8 +d = 0 +c = 7 - 1 +print(c) +# output +",6.0,6.0 +"e = 9 +print(e * e) +# output +",81.0,81.0 +"e = 1 +a = 6 +print(a / 6) +# output +",1.0,1.0 +"d = 5 +d = 9 +e = 0 - d +print(d * d) +# output +",81.0,81.0 +"d = 5 +d = d * d +print(d) +# output +",25.0,25.0 +"b = 7 +a = 2 +print(a) +# output +",2.0,2.0 +"b = 3 +e = 1 +e = 9 + e +print(e) +# output +",10.0,10.0 +"e = 9 +e = 7 * e +print(e + 7) +# output +",70.0,103.0 +"e = 3 +c = 2 +d = 1 + 5 +print(c * c) +# output +",4.0,4.0 +"d = 9 +b = 5 +print(d) +# output +",9.0,9.0 +"d = 6 +e = 6 +print(e) +# output +",6.0,6.0 +"e = 6 +e = 7 +print(e) +# output +",7.0,7.0 +"b = 9 +a = 2 +b = a - 9 +print(a - 4) +# output +",-2.0,-2.0 +"e = 2 +c = 3 +print(c) +# output +",3.0,3.0 +"b = 4 +b = 3 - b +print(b) +# output +",-1.0,-1.0 +"d = 0 +d = 7 +print(d + 3) +# output +",10.0,10.0 +"d = 9 +print(d - 9) +# output +",0.0,0.0 +"c = 4 +print(c) +# output +",4.0,4.0 +"c = 3 +e = 2 +print(c) +# output +",3.0,3.0 +"e = 6 +c = 4 +print(e / 9) +# output +",0.6666666666666666,0.6666666666666666 +"a = 4 +print(a) +# output +",4.0,4.0 +"c = 8 +c = 0 +b = 9 * c +print(b) +# output +",0.0,0.0 +"a = 8 +a = 9 +print(a) +# output +",9.0,9.0 +"a = 0 +e = a + a +print(a + a) +# output +",0.0,0.0 +"a = 7 +e = 6 +print(a + a) +# output +",14.0,14.0 +"b = 8 +e = b + b +print(e) +# output +",16.0,16.0 +"a = 3 +e = 9 +b = e - a +print(b) +# output +",6.0,6.0 +"e = 4 +e = 4 * e +print(e / 8) +# output +",2.0,1.0 +"d = 7 +c = 2 + 8 +print(d * 1) +# output +",7.0,7.0 diff --git a/generalization/tokenizing.py b/generalization/tokenizing.py new file mode 100644 index 0000000..14a446e --- /dev/null +++ b/generalization/tokenizing.py @@ -0,0 +1,82 @@ +import argparse +import pandas as pd +from datasets import Dataset +from transformers import AutoTokenizer + +import transformers + +transformers.logging.set_verbosity_error() + +def tokenize_data(train_file="../data/train.txt", + test_file="../data/val.txt", + tokenizer_name="codellama/CodeLlama-7b-hf", + train_output_dir="data/tokenized_train", + val_output_dir="data/tokenized_val"): + print("Tokenizing data...") + + # Read the training and test data + with open(train_file) as f: + train_data = f.read() + + with open(test_file) as f: + test_data = f.read() + + # Split the snippets into individual examples + train_snippets = train_data.split('\n\n') + test_snippets = test_data.split('\n\n') + + # Create datasets from the snippets + train_dataset = Dataset.from_pandas(pd.DataFrame({'snippets': train_snippets})) + eval_dataset = Dataset.from_pandas(pd.DataFrame({'snippets': test_snippets})) + + # Load the tokenizer + print("Loading tokenizer...") + tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + tokenizer.add_eos_token = True + tokenizer.pad_token_id = 0 + tokenizer.padding_side = "left" + + # Function to tokenize a prompt + def tokenize(prompt): + result = tokenizer( + prompt, + truncation=True, + max_length=512, + padding=False, + return_tensors=None, + ) + + # For self-supervised learning, labels are also the inputs + result["labels"] = result["input_ids"].copy() + + return result + + # Function to generate and tokenize a prompt + def generate_and_tokenize_prompt(data_point): + full_prompt = data_point["snippets"] + + return tokenize(full_prompt) + + # Tokenize the training and validation datasets + print("Tokenizing datasets...") + tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt) + tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt) + + # Save the tokenized datasets to disk + print(f"Saving tokenized datasets to {train_output_dir} and {val_output_dir}...") + tokenized_train_dataset.save_to_disk(train_output_dir) + tokenized_val_dataset.save_to_disk(val_output_dir) + + print("Tokenization complete.") + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Tokenize data for language model training.") + parser.add_argument("--train_file", type=str, default="../data/train.txt", help="Path to the training file") + parser.add_argument("--test_file", type=str, default="../data/val.txt", help="Path to the test file") + parser.add_argument("--tokenizer_name", type=str, default="codellama/CodeLlama-7b-hf", help="Name or path of the tokenizer") + parser.add_argument("--train_output_dir", type=str, default="data/tokenized_train", help="Path to save the tokenized training dataset") + parser.add_argument("--val_output_dir", type=str, default="data/tokenized_val", help="Path to save the tokenized validation dataset") + + args = parser.parse_args() + + tokenize_data(args.train_file, args.test_file, args.tokenizer_name, args.train_output_dir, args.val_output_dir) diff --git a/line-level_code_completion.py b/line-level_code_completion.py new file mode 100644 index 0000000..530c982 --- /dev/null +++ b/line-level_code_completion.py @@ -0,0 +1,99 @@ +import os +import pickle +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import pandas as pd +from tqdm import tqdm +import re +from model import GPT + +# Argument parsing +parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on token-level code completion.') +parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') +parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + +# Parse the command-line arguments +args = parser.parse_args() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +torch.manual_seed(1337) + + +# Constants for dataset and file paths +MODEL_FILE = f"models/{args.model_name}.pth" +ACCURACY_FILE = f"results/{args.model_name}_acc_line-level_code_completion.txt" +RESULTS_FILE = f"results/{args.model_name}_line-level_code_completion.csv" + + +data_dir = args.dataset_dir +test_data = np.memmap(os.path.join(data_dir, 'test.bin'), dtype=np.uint16, mode='r') + + +# attempt to derive vocab_size from the dataset +meta_path = os.path.join(data_dir, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})") + +stoi = meta['stoi'] +itos = meta['itos'] +encode = lambda s: [stoi[c] for c in s] +decode = lambda l: ''.join([itos[i] for i in l]) + +model = GPT() +print("Compiling model...") +model = torch.compile(model) # pytorch 2.0 +model.load_state_dict(torch.load(MODEL_FILE)) +m = model.to(device) + +examples = decode(test_data).split("\n\n") +examples = [example for example in examples if example] + +correct_predictions = 0 +total_predictions = 0 + +results = [] + +for code_snippet in tqdm(examples): + + lines = code_snippet.split('\n') + for i in range(1, len(lines)): + + context_lines = lines[:i] + actual_next_line = lines[i] + + context_tokens = torch.tensor(encode('\n'.join(context_lines) + '\n'), dtype=torch.long).unsqueeze(0).to(device) + actual_next_line_tokens = torch.tensor(encode(actual_next_line), dtype=torch.long).unsqueeze(0).to(device) + + n = actual_next_line_tokens.shape[1] # Limit to length of actual next line + predicted_next_line_tokens = m.generate(context_tokens, max_new_tokens=n) + predicted_next_line_tokens = predicted_next_line_tokens[:, -n:] + is_correct = torch.equal(predicted_next_line_tokens, actual_next_line_tokens) + + if is_correct: + correct_predictions += 1 + results.append({ + 'context': context_tokens.cpu(), + 'actual_next_line': actual_next_line_tokens.cpu(), + 'predicted_next_line': predicted_next_line_tokens.cpu(), + 'is_correct': is_correct + }) + + total_predictions += 1 + +df = pd.DataFrame(results) +df.to_csv(RESULTS_FILE, index=False) + +accuracy = (correct_predictions / total_predictions) * 100 + +# Store accuracy in a file +with open(ACCURACY_FILE, 'w') as f: + f.write(f"Accuracy: {accuracy:.2f}%\n") + +print(accuracy) \ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 0000000..9d869d5 --- /dev/null +++ b/model.py @@ -0,0 +1,162 @@ +import random +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import pandas as pd +import os +import pickle + +# Set the random seed for reproducibility +seed = 42 +torch.manual_seed(seed) +random.seed(seed) +np.random.seed(seed) + +# Set the device to GPU if available, otherwise CPU +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# Directory where the dataset is stored +DATA_DIR = 'data' + +# Hyperparameters for the GPT model +block_size = 256 # Maximum context length +n_embd = 96 # Embedding dimension +n_head = 6 # Number of attention heads +n_layer = 6 # Number of transformer blocks +dropout = 0 # Dropout rate + +# Attempt to derive vocab_size from the dataset +meta_path = os.path.join(DATA_DIR, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + +vocab_size = meta_vocab_size + +class LayerNorm(nn.Module): + """ LayerNorm with an optional bias. PyTorch's LayerNorm doesn't support simply bias=False """ + + def __init__(self, ndim, bias): + super().__init__() + self.weight = nn.Parameter(torch.ones(ndim)) + self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None + + def forward(self, input): + return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5) + +class Head(nn.Module): + """One head of self-attention.""" + + def __init__(self, head_size): + super().__init__() + self.key = nn.Linear(n_embd, head_size, bias=False) + self.query = nn.Linear(n_embd, head_size, bias=False) + self.value = nn.Linear(n_embd, head_size, bias=False) + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + B, T, C = x.shape + k = self.key(x) # (B, T, head_size) + q = self.query(x) # (B, T, head_size) + v = self.value(x) # (B, T, head_size) + + # Apply scaled dot-product attention + out = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=None, dropout_p=dropout if self.training else 0, is_causal=True + ) + + return out + +class MultiHeadAttention(nn.Module): + """Multiple heads of self-attention in parallel.""" + + def __init__(self, num_heads, head_size): + super().__init__() + self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) + self.proj = nn.Linear(n_embd, n_embd) + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + # Concatenate the outputs from each head + out = torch.cat([h(x) for h in self.heads], dim=-1) + out = self.dropout(self.proj(out)) + return out + +class FeedForward(nn.Module): + """A simple linear layer followed by a non-linearity.""" + + def __init__(self, n_embd): + super().__init__() + self.net = nn.Sequential( + nn.Linear(n_embd, 4 * n_embd, bias=False), + nn.GELU(), + nn.Linear(4 * n_embd, n_embd, bias=False), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + +class Block(nn.Module): + """Transformer block: communication followed by feedforward.""" + + def __init__(self, n_embd, n_head): + super().__init__() + head_size = n_embd // n_head + self.sa = MultiHeadAttention(n_head, head_size) + self.ffwd = FeedForward(n_embd) + self.ln1 = nn.LayerNorm(n_embd, bias=False) + self.ln2 = nn.LayerNorm(n_embd, bias=False) + + def forward(self, x): + x = x + self.sa(self.ln1(x)) + x = x + self.ffwd(self.ln2(x)) + return x + +class GPT(nn.Module): + """GPT language model.""" + + def __init__(self): + super().__init__() + self.token_embedding_table = nn.Embedding(vocab_size, n_embd) + self.position_embedding_table = nn.Embedding(block_size, n_embd) + self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)]) + self.ln_f = nn.LayerNorm(n_embd, bias=False) + self.lm_head = nn.Linear(n_embd, vocab_size) + + def forward(self, idx, targets=None): + B, T = idx.shape + + # Token and position embeddings + tok_emb = self.token_embedding_table(idx) # (B, T, n_embd) + pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, n_embd) + x = tok_emb + pos_emb # (B, T, n_embd) + x = self.blocks(x) # (B, T, n_embd) + x = self.ln_f(x) # (B, T, n_embd) + logits = self.lm_head(x) # (B, T, vocab_size) + + # Compute loss if targets are provided + if targets is None: + loss = None + else: + B, T, C = logits.shape + logits = logits.view(B * T, C) + targets = targets.view(B * T) + loss = F.cross_entropy(logits, targets) + + return logits, loss + + def generate(self, idx, max_new_tokens): + """Generate new tokens given an initial context `idx`.""" + for _ in range(max_new_tokens): + idx_cond = idx[:, -block_size:] # Crop to the last block_size tokens + logits, _ = self(idx_cond) + logits = logits[:, -1, :] # Focus on the last time step + probs = F.softmax(logits, dim=-1) # Convert to probabilities + idx_next = torch.multinomial(probs, num_samples=1) # Sample from the distribution + idx = torch.cat((idx, idx_next), dim=1) # Append sampled index to the sequence + return idx diff --git a/models/arithmetics_level1_696K.pth b/models/arithmetics_level1_696K.pth new file mode 100644 index 0000000..170fd1e Binary files /dev/null and b/models/arithmetics_level1_696K.pth differ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..122940a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +anytree==2.12.1 +tqdm==4.66.2 +requests==2.31.0 +numpy==1.26.4 +pandas==2.2.1 +torch==2.2.2 +transformers==4.30.2 +datasets==2.18.0 +psutil==5.9.8 +argparse==1.1 +peft @ git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08 diff --git a/results/.ipynb_checkpoints/arithmetics_level1_696K_accuracy-checkpoint.txt b/results/.ipynb_checkpoints/arithmetics_level1_696K_accuracy-checkpoint.txt new file mode 100644 index 0000000..2fe40e2 --- /dev/null +++ b/results/.ipynb_checkpoints/arithmetics_level1_696K_accuracy-checkpoint.txt @@ -0,0 +1 @@ +Accuracy: 96.00% diff --git a/results/arithmetics_level1_696K_acc_line-level_code_completion.txt b/results/arithmetics_level1_696K_acc_line-level_code_completion.txt new file mode 100644 index 0000000..95d063c --- /dev/null +++ b/results/arithmetics_level1_696K_acc_line-level_code_completion.txt @@ -0,0 +1 @@ +Accuracy: 51.83% diff --git a/results/arithmetics_level1_696K_acc_token-level_code_completion.txt b/results/arithmetics_level1_696K_acc_token-level_code_completion.txt new file mode 100644 index 0000000..0a26c80 --- /dev/null +++ b/results/arithmetics_level1_696K_acc_token-level_code_completion.txt @@ -0,0 +1 @@ +Accuracy: 82.54% diff --git a/results/arithmetics_level1_696K_accuracy.txt b/results/arithmetics_level1_696K_accuracy.txt new file mode 100644 index 0000000..2fe40e2 --- /dev/null +++ b/results/arithmetics_level1_696K_accuracy.txt @@ -0,0 +1 @@ +Accuracy: 96.00% diff --git a/results/arithmetics_level1_696K_line-level_code_completion.csv 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16]])","tensor([[22, 1, 20, 1, 14]])",False +"tensor([[21, 1, 20, 1, 17, 0, 25, 1, 20, 1, 16, 0]])","tensor([[29, 30, 26, 27, 31, 3, 21, 1, 6, 1, 21, 4]])","tensor([[23, 1, 20, 1, 21, 1, 5, 1, 19, 0, 29, 30]])",False +"tensor([[21, 1, 20, 1, 17, 0, 25, 1, 20, 1, 16, 0, 29, 30, 26, 27, 31, 3, + 21, 1, 6, 1, 21, 4, 0]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])",True +"tensor([[21, 1, 20, 1, 17, 0, 25, 1, 20, 1, 16, 0, 29, 30, 26, 27, 31, 3, + 21, 1, 6, 1, 21, 4, 0, 2, 1, 28, 32, 31, 29, 32, 31, 0]])","tensor([[ 2, 1, 11, 14]])","tensor([[ 2, 1, 11, 14]])",True +"tensor([[22, 1, 20, 1, 18, 0]])","tensor([[25, 1, 20, 1, 22, 1, 6, 1, 22]])","tensor([[22, 1, 20, 1, 19, 1, 6, 1, 15]])",False +"tensor([[22, 1, 20, 1, 18, 0, 25, 1, 20, 1, 22, 1, 6, 1, 22, 0]])","tensor([[29, 30, 26, 27, 31, 3, 25, 4]])","tensor([[29, 30, 26, 27, 31, 3, 25, 4]])",True +"tensor([[22, 1, 20, 1, 18, 0, 25, 1, 20, 1, 22, 1, 6, 1, 22, 0, 29, 30, + 26, 27, 31, 3, 25, 4, 0]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])",True +"tensor([[22, 1, 20, 1, 18, 0, 25, 1, 20, 1, 22, 1, 6, 1, 22, 0, 29, 30, + 26, 27, 31, 3, 25, 4, 0, 2, 1, 28, 32, 31, 29, 32, 31, 0]])","tensor([[ 2, 1, 11, 16]])","tensor([[ 2, 1, 11, 16]])",True +"tensor([[21, 1, 20, 1, 13, 0]])","tensor([[25, 1, 20, 1, 19]])","tensor([[23, 1, 20, 1, 21]])",False +"tensor([[21, 1, 20, 1, 13, 0, 25, 1, 20, 1, 19, 0]])","tensor([[22, 1, 20, 1, 25, 1, 7, 1, 21]])","tensor([[29, 30, 26, 27, 31, 3, 21, 1, 6]])",False +"tensor([[21, 1, 20, 1, 13, 0, 25, 1, 20, 1, 19, 0, 22, 1, 20, 1, 25, 1, + 7, 1, 21, 0]])","tensor([[29, 30, 26, 27, 31, 3, 22, 4]])","tensor([[29, 30, 26, 27, 31, 3, 25, 1]])",False +"tensor([[21, 1, 20, 1, 13, 0, 25, 1, 20, 1, 19, 0, 22, 1, 20, 1, 25, 1, + 7, 1, 21, 0, 29, 30, 26, 27, 31, 3, 22, 4, 0]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])",True +"tensor([[21, 1, 20, 1, 13, 0, 25, 1, 20, 1, 19, 0, 22, 1, 20, 1, 25, 1, + 7, 1, 21, 0, 29, 30, 26, 27, 31, 3, 22, 4, 0, 2, 1, 28, 32, 31, + 29, 32, 31, 0]])","tensor([[ 2, 1, 16]])","tensor([[ 2, 1, 16]])",True +"tensor([[25, 1, 20, 1, 14, 0]])","tensor([[25, 1, 20, 1, 14, 1, 5, 1, 25]])","tensor([[23, 1, 20, 1, 10, 0, 21, 1, 20]])",False +"tensor([[25, 1, 20, 1, 14, 0, 25, 1, 20, 1, 14, 1, 5, 1, 25, 0]])","tensor([[29, 30, 26, 27, 31, 3, 25, 1, 9, 1, 18, 4]])","tensor([[29, 30, 26, 27, 31, 3, 25, 1, 6, 1, 25, 4]])",False +"tensor([[25, 1, 20, 1, 14, 0, 25, 1, 20, 1, 14, 1, 5, 1, 25, 0, 29, 30, + 26, 27, 31, 3, 25, 1, 9, 1, 18, 4, 0]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])",True +"tensor([[25, 1, 20, 1, 14, 0, 25, 1, 20, 1, 14, 1, 5, 1, 25, 0, 29, 30, + 26, 27, 31, 3, 25, 1, 9, 1, 18, 4, 0, 2, 1, 28, 32, 31, 29, 32, + 31, 0]])","tensor([[ 2, 1, 12, 8, 10]])","tensor([[ 2, 1, 11, 8, 15]])",False +"tensor([[24, 1, 20, 1, 17, 0]])","tensor([[23, 1, 20, 1, 12, 1, 6, 1, 18]])","tensor([[24, 1, 20, 1, 12, 0, 22, 1, 20]])",False +"tensor([[24, 1, 20, 1, 17, 0, 23, 1, 20, 1, 12, 1, 6, 1, 18, 0]])","tensor([[29, 30, 26, 27, 31, 3, 24, 1, 5, 1, 11, 4]])","tensor([[29, 30, 26, 27, 31, 3, 23, 4, 0, 2, 1, 28]])",False +"tensor([[24, 1, 20, 1, 17, 0, 23, 1, 20, 1, 12, 1, 6, 1, 18, 0, 29, 30, + 26, 27, 31, 3, 24, 1, 5, 1, 11, 4, 0]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])","tensor([[ 2, 1, 28, 32, 31, 29, 32, 31]])",True +"tensor([[24, 1, 20, 1, 17, 0, 23, 1, 20, 1, 12, 1, 6, 1, 18, 0, 29, 30, + 26, 27, 31, 3, 24, 1, 5, 1, 11, 4, 0, 2, 1, 28, 32, 31, 29, 32, + 31, 0]])","tensor([[ 2, 1, 17]])","tensor([[ 2, 1, 17]])",True diff --git a/results/arithmetics_level1_696K_results.csv b/results/arithmetics_level1_696K_results.csv new file mode 100644 index 0000000..f25c674 --- /dev/null +++ b/results/arithmetics_level1_696K_results.csv @@ -0,0 +1,410 @@ +Prompt,Real_Results,Generated_Results +"c = 3 +e = 9 +e = 0 * c +print(e) +# output",[0.0],[0.0] +"e = 2 +a = 6 +c = a / 4 +print(c) +# output",[1.5],[1.5] +"e = 6 +c = 3 / e +print(c) +# output",[0.5],[0.5] +"e = 8 +a = 3 +print(e / a) +# output",[2.6666666666666665],[2.6666666666666665] +"a = 8 +e = a + 1 +print(a / a) +# output",[1.0],[1.0] +"e = 6 +a = 7 +d = a - 1 +print(d) +# output",[6.0],[6.0] +"d = 9 +print(d * d) +# output",[81.0],[81.0] +"c = 4 +print(c + 3) +# output",[7.0],[7.0] +"d = 7 +print(d / d) +# output",[1.0],[1.0] +"d = 5 +a = 0 +print(a + d) +# output",[5.0],[5.0] +"a = 6 +b = 0 +a = 3 - 3 +print(b / 9) +# output",[0.0],[0.0] +"a = 0 +a = 0 +print(a) +# output",[0.0],[0.0] +"c = 3 +e = 7 +print(c * c) +# output",[9.0],[9.0] +"a = 2 +e = 0 / 8 +print(a * a) +# output",[4.0],[4.0] +"e = 9 +a = 9 +print(a * e) +# output",[81.0],[81.0] +"d = 8 +e = 4 +print(e + d) +# output",[12.0],[12.0] +"a = 2 +c = a - 6 +print(a / a) +# output",[1.0],[1.0] +"a = 6 +b = 7 +d = a - 0 +print(d) +# output",[6.0],[6.0] +"b = 7 +e = 2 +b = 0 * 7 +print(b) +# output",[0.0],[0.0] +"c = 9 +e = 4 +print(c - 5) +# output",[4.0],[4.0] +"d = 4 +d = 7 * 4 +print(d * d) +# output",[784.0],[196.0] +"a = 4 +print(a / a) +# output",[1.0],[1.0] +"e = 3 +b = 4 +a = e - 6 +print(a) +# output",[-3.0],[-3.0] +"d = 5 +d = 9 - 7 +print(d) +# output",[2.0],[2.0] +"b = 7 +b = 3 / b +print(b) +# output",[0.42857142857142855],[0.42857142857142855] +"a = 9 +b = a + a +print(a / a) +# output",[1.0],[1.0] +"a = 0 +d = a * 9 +print(d) +# output",[0.0],[0.0] +"e = 1 +c = e + e +print(c) +# output",[2.0],[2.0] +"e = 3 +c = e + 2 +print(e + e) +# output",[6.0],[6.0] +"a = 4 +e = 0 * 7 +print(a + 2) +# output",[6.0],[6.0] +"b = 6 +b = 5 +b = b - b +print(b / 3) +# output",[0.0],[0.0] +"b = 0 +d = 9 * 1 +print(d) +# output",[9.0],[9.0] +"a = 4 +a = 2 +print(a) +# output",[2.0],[2.0] +"e = 9 +b = 3 +print(b) +# output",[3.0],[3.0] +"d = 6 +c = 4 + 1 +print(d + 2) +# output",[8.0],[8.0] +"d = 8 +c = d - 6 +print(c) +# output",[2.0],[2.0] +"a = 9 +d = 1 +print(a * d) +# output",[9.0],[9.0] +"c = 4 +a = 4 +print(a) +# output",[4.0],[4.0] +"b = 5 +a = 1 +b = a - a +print(b) +# output",[0.0],[0.0] +"d = 2 +c = 1 +print(d) +# output",[2.0],[2.0] +"e = 3 +b = 5 +print(b * 9) +# output",[45.0],[45.0] +"e = 9 +d = 5 +d = e * d +print(e / d) +# output",[0.2],[0.9] +"c = 5 +print(c * c) +# output",[25.0],[25.0] +"e = 2 +c = 8 / e +print(e * 9) +# output",[18.0],[18.0] +"c = 0 +c = 6 - 8 +print(c) +# output",[-2.0],[-2.0] +"e = 7 +a = 2 +d = 5 * 7 +print(a * 7) +# output",[14.0],[14.0] +"e = 1 +print(e * e) +# output",[1.0],[1.0] +"d = 6 +b = d * d +print(b) +# output",[36.0],[36.0] +"e = 8 +b = e / 5 +print(e / 2) +# output",[4.0],[4.0] +"d = 6 +c = 3 +a = 9 + d +print(a) +# output",[15.0],[15.0] +"d = 4 +a = 8 +print(a * a) +# output",[64.0],[64.0] +"e = 6 +print(e + e) +# output",[12.0],[12.0] +"b = 3 +a = 3 +print(a) +# output",[3.0],[3.0] +"b = 9 +b = 9 +print(b - 5) +# output",[4.0],[4.0] +"a = 8 +print(a) +# output",[8.0],[8.0] +"b = 2 +e = 2 +print(e) +# output",[2.0],[2.0] +"e = 1 +c = e / 7 +print(e - e) +# output",[0.0],[0.0] +"e = 3 +c = 0 +print(c) +# output",[0.0],[0.0] +"a = 1 +a = 3 +print(a / 1) +# output",[3.0],[3.0] +"c = 3 +print(c / c) +# output",[1.0],[1.0] +"c = 0 +c = c - c +print(c) +# output",[0.0],[0.0] +"a = 7 +d = 2 +print(a) +# output",[7.0],[7.0] +"c = 3 +a = 0 +print(c - 4) +# output",[-1.0],[-1.0] +"e = 9 +a = 5 +print(e) +# output",[9.0],[9.0] +"d = 3 +b = 4 +d = 4 / 3 +print(b * b) +# output",[16.0],[16.0] +"c = 5 +d = 5 +d = c - 4 +print(d) +# output",[1.0],[1.0] +"e = 6 +b = 3 +e = e - 6 +print(e) +# output",[0.0],[0.0] +"d = 4 +c = 6 +print(d + c) +# output",[10.0],[10.0] +"b = 8 +a = 9 +e = 5 / 2 +print(b * a) +# output",[72.0],[72.0] +"e = 4 +print(e) +# output",[4.0],[4.0] +"b = 2 +c = 3 +print(b / b) +# output",[1.0],[1.0] +"d = 8 +d = 0 +c = 7 - 1 +print(c) +# output",[6.0],[6.0] +"e = 9 +print(e * e) +# output",[81.0],[81.0] +"e = 1 +a = 6 +print(a / 6) +# output",[1.0],[1.0] +"d = 5 +d = 9 +e = 0 - d +print(d * d) +# output",[81.0],[81.0] +"d = 5 +d = d * d +print(d) +# output",[25.0],[25.0] +"b = 7 +a = 2 +print(a) +# output",[2.0],[2.0] +"b = 3 +e = 1 +e = 9 + e +print(e) +# output",[10.0],[10.0] +"e = 9 +e = 7 * e +print(e + 7) +# output",[70.0],[63.0] +"e = 3 +c = 2 +d = 1 + 5 +print(c * c) +# output",[4.0],[4.0] +"d = 9 +b = 5 +print(d) +# output",[9.0],[9.0] +"d = 6 +e = 6 +print(e) +# output",[6.0],[6.0] +"e = 6 +e = 7 +print(e) +# output",[7.0],[7.0] +"b = 9 +a = 2 +b = a - 9 +print(a - 4) +# output",[-2.0],[-2.0] +"e = 2 +c = 3 +print(c) +# output",[3.0],[3.0] +"b = 4 +b = 3 - b +print(b) +# output",[-1.0],[0.0] +"d = 0 +d = 7 +print(d + 3) +# output",[10.0],[10.0] +"d = 9 +print(d - 9) +# output",[0.0],[0.0] +"c = 4 +print(c) +# output",[4.0],[4.0] +"c = 3 +e = 2 +print(c) +# output",[3.0],[3.0] +"e = 6 +c = 4 +print(e / 9) +# output",[0.6666666666666666],[0.6666666666666666] +"a = 4 +print(a) +# output",[4.0],[4.0] +"c = 8 +c = 0 +b = 9 * c +print(b) +# output",[0.0],[0.0] +"a = 8 +a = 9 +print(a) +# output",[9.0],[9.0] +"a = 0 +e = a + a +print(a + a) +# output",[0.0],[0.0] +"a = 7 +e = 6 +print(a + a) +# output",[14.0],[14.0] +"b = 8 +e = b + b +print(e) +# output",[16.0],[16.0] +"a = 3 +e = 9 +b = e - a +print(b) +# output",[6.0],[6.0] +"e = 4 +e = 4 * e +print(e / 8) +# output",[2.0],[2.0] +"d = 7 +c = 2 + 8 +print(d * 1) +# output",[7.0],[7.0] diff --git a/results/arithmetics_level1_696K_token-level_code_completion.csv b/results/arithmetics_level1_696K_token-level_code_completion.csv new file mode 100644 index 0000000..54d7074 --- /dev/null +++ b/results/arithmetics_level1_696K_token-level_code_completion.csv @@ -0,0 +1,6096 @@ +context,actual_next_token,predicted_next_token,is_correct +tensor([[23]]),1,1,True +"tensor([[23, 1]])",20,20,True +"tensor([[23, 1, 20]])",1,1,True +"tensor([[23, 1, 20, 1]])",13,11,False +"tensor([[23, 1, 20, 1, 13]])",0,0,True +"tensor([[23, 1, 20, 1, 13, 0]])",25,23,False +"tensor([[23, 1, 20, 1, 13, 0, 25]])",1,1,True +"tensor([[23, 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The simplified code is then executed 150 time and analyzed to identify code clones, which are later used to form pairs of clone and non-clone examples for further usage (FineTuning the Tiny-LM). + +## How It Works + +1. **Code Generation**: + - The project generates code snippets that contain variable initializations, logic, and print statements. + - The initial code looks like: + ```python + b = 6 + g = 4 + n = 3 + o = (g + 1) - (n * g) + if (not b < 4): + print(o) + else: + print(b * n) + ``` + +2. **Simplification**: + - Using the `simplify_code_levelx.py` script (where `x` refers to the specific level), variable initializations are removed, and the print statements are modified to include the variable's formula instead of the variable itself. + - The simplified version of the above code would be: + ```python + if not b < 4: + print(g + 1 - n * g) + else: + print(b * n) + ``` + +3. **Execution**: + - Each simplified code is executed 150 times with random variable initializations using the `code_execution.py` file. + - The output of each execution is stored in the `outputs.json` file. + +4. **Clone Identification**: + - Based on the generated outputs, the `identify_clones.py` script is used to detect clone and non-clone code snippets. The clone pairs are stored in the `clones.json` file. + +5. **Pair Formation**: + - Each code snippet is paired with other snippets to form clone and non-clone pairs. Each snippet is repeated 4 times—2 times as a clone and 2 times as a non-clone example. + +## Files + +- `simplify_code_levelx.py`: Script for simplifying code at different levels by removing variable initializations. +- `code_execution.py`: Executes simplified code snippets and stores the results. +- `identify_clones.py`: Identifies clones based on execution results and stores them in `clones.json`. +- `outputs.json`: Stores the outputs from executing the code snippets. +- `clones.json`: Stores the identified clones for further processing. + +## Usage + +1. **Generating Code**: + Run the `automate.py` script to generate code snippets. + ```bash + python ./tasks/clone_detection_task/automate.py --num_programs \ No newline at end of file diff --git a/tasks/clone_detection_task/automate.py b/tasks/clone_detection_task/automate.py new file mode 100644 index 0000000..4d9c71e --- /dev/null +++ b/tasks/clone_detection_task/automate.py @@ -0,0 +1,37 @@ +import subprocess +import sys +import os + +def run_script(script_name, args): + + subprocess.run(['python', script_name] + args, check=True) + +def automate_process(programs_num): + level_folder = f"all_levels" + for i in range(1,5): + dataset_file = f"dataset_level{i}.txt" + outputs_json = f"outputs_level{i}.json" + clones_json = f"clones_level{i}.json" + clone_pairs_txt = f"code_snippets_pairs.txt" + + if i != 4: + run_script('./tasks/clone_detection_task/generating_data_process/code_generator.py', ['--num_programs', programs_num, f'--level', f'{i}.2', '--filename', dataset_file]) + else: + run_script('./tasks/clone_detection_task/generating_data_process/code_generator.py', ['--num_programs', programs_num, f'--level', f'{i}.1', '--filename', dataset_file]) + + run_script('./tasks/clone_detection_task/generating_data_process/code_execution.py', [f'--level', f'{i}',f'--dataset-file', dataset_file, '--output-file', outputs_json]) + + run_script('./tasks/clone_detection_task/generating_data_process/identify_clones.py', ['--input-file', outputs_json, '--output-file', clones_json]) + + run_script('./tasks/clone_detection_task/generating_data_process/generating_binaries.py', ['--input-clones',clones_json,'--input-snippets', dataset_file, '--output-file', clone_pairs_txt]) + + os.remove(dataset_file) + os.remove(outputs_json) + os.remove(clones_json) + + print(f"Process for all levels completed successfully!") + +if __name__ == "__main__": + + programs_num = sys.argv[2] + automate_process(programs_num) diff --git a/tasks/clone_detection_task/automate_generation.sh b/tasks/clone_detection_task/automate_generation.sh new file mode 100644 index 0000000..8ee2498 --- /dev/null +++ b/tasks/clone_detection_task/automate_generation.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +# Check if the --num_programs argument is provided +if [ -z "$1" ] || [ "$1" != "--num_programs" ] || [ -z "$2" ]; then + echo "Usage: $0 --num_programs " + exit 1 +fi + +NUM_PROGRAMS=$2 + +# Run automate.py with the given number of programs +python ./tasks/clone_detection_task/automate.py --num_programs $NUM_PROGRAMS diff --git a/tasks/clone_detection_task/code_pairs_count.py b/tasks/clone_detection_task/code_pairs_count.py new file mode 100644 index 0000000..d7a6a05 --- /dev/null +++ b/tasks/clone_detection_task/code_pairs_count.py @@ -0,0 +1,19 @@ +# Initialize counters +clone_1_count = 0 +clone_0_count = 0 + +# Open and read the text file +with open("tasks/clone_detection_task/code_snippets_pairs.txt", "r") as file: + lines = file.readlines() + + # Loop through lines and count occurrences of 'is clone' values + for line in lines: + line = line.strip() # Remove any whitespace + if line == "1": + clone_1_count += 1 + elif line == "0": + clone_0_count += 1 + +# Print the counts +print(f"Number of 'is clone' 1: {clone_1_count}") +print(f"Number of 'is clone' 0: {clone_0_count}") \ No newline at end of file diff --git a/tasks/clone_detection_task/code_snippets_pairs.txt b/tasks/clone_detection_task/code_snippets_pairs.txt new file mode 100644 index 0000000..3bfe10c --- /dev/null +++ b/tasks/clone_detection_task/code_snippets_pairs.txt @@ -0,0 +1,4072 @@ +# snippet 1 +y = 4 +y = 9 +m = y * y +print(m) +# snippet 2 +c = 10 +print(c * c) +# is clone +1 + +# snippet 1 +x = 0 +h = 18 +print(h) +# snippet 2 +i = 7 +print(i) +# is clone +1 + +# snippet 1 +x = 0 +h = 18 +print(h) +# snippet 2 +x = 7 +m = 8 +g = 5 +a = 13 +m = 0 + 10 +print(m) +# is clone +1 + +# snippet 1 +i = 7 +print(i) +# snippet 2 +x = 7 +m = 8 +g = 5 +a = 13 +m = 0 + 10 +print(m) +# is clone +1 + +# snippet 1 +t = 11 +s = 2 +b = 4 +b = 1 +k = (14 * s) +print(s * 1) +# snippet 2 +m = 17 +y = 14 +print(m) +# is clone +1 + +# snippet 1 +t = 11 +s = 2 +b = 4 +b = 1 +k = (14 * s) +print(s * 1) +# snippet 2 +s = 17 +u = 14 +x = 10 +print(u) +# is clone +1 + +# snippet 1 +m = 17 +y = 14 +print(m) +# snippet 2 +s = 17 +u = 14 +x = 10 +print(u) +# is clone +1 + +# snippet 1 +k = 6 +o = 16 +print(k) +# snippet 2 +i = 13 +v = 10 +m = 8 +w = (0 + m)*(10 * i) +print(i * 1) +# is clone +1 + +# snippet 1 +k = 6 +o = 16 +print(k) +# snippet 2 +p = 19 +t = 12 +z = 14 +f = 15 +print(f) +# is clone +1 + +# snippet 1 +i = 13 +v = 10 +m = 8 +w = (0 + m)*(10 * i) +print(i * 1) +# snippet 2 +p = 19 +t = 12 +z = 14 +f = 15 +print(f) +# is clone +1 + +# snippet 1 +n = 9 +f = 3 +x = 1 - 12 +print(x) +# snippet 2 +h = 10 +print(h) +# is clone +1 + +# snippet 1 +n = 9 +f = 3 +x = 1 - 12 +print(x) +# snippet 2 +g = 18 +print(g) +# is clone +1 + +# snippet 1 +h = 10 +print(h) +# snippet 2 +g = 18 +print(g) +# is clone +1 + +# snippet 1 +i = 2 +print(i) +# snippet 2 +n = 18 +print(n) +# is clone +1 + +# snippet 1 +i = 2 +print(i) +# snippet 2 +k = 16 +i = 2 +print(i) +# is clone +1 + +# snippet 1 +n = 18 +print(n) +# snippet 2 +k = 16 +i = 2 +print(i) +# is clone +1 + +# snippet 1 +n = 19 +i = 6 +print(i) +# snippet 2 +l = 19 +z = 12 +print(z) +# is clone +1 + +# snippet 1 +n = 19 +i = 6 +print(i) +# snippet 2 +c = 1 +h = 3 +d = 10 +f = 18 +print(c) +# is clone +1 + +# snippet 1 +l = 19 +z = 12 +print(z) +# snippet 2 +c = 1 +h = 3 +d = 10 +f = 18 +print(c) +# is clone +1 + +# snippet 1 +d = 8 +n = 12 +print(d) +# snippet 2 +r = 17 +x = 10 +t = 11 +print(t) +# is clone +1 + +# snippet 1 +d = 8 +n = 12 +print(d) +# snippet 2 +o = 2 +q = 13 +print(q) +# is clone +1 + +# snippet 1 +r = 17 +x = 10 +t = 11 +print(t) +# snippet 2 +o = 2 +q = 13 +print(q) +# is clone +1 + +# snippet 1 +x = 15 +l = 12 +a = 13 / 8 +print(a) +# snippet 2 +d = 2 +print(d) +# is clone +1 + +# snippet 1 +x = 15 +l = 12 +a = 13 / 8 +print(a) +# snippet 2 +r = 19 +print(r) +# is clone +1 + +# snippet 1 +d = 2 +print(d) +# snippet 2 +r = 19 +print(r) +# is clone +1 + +# snippet 1 +z = 10 +f = 14 +p = 19 +b = 2 * z +print(f - 3) +# snippet 2 +h = 5 +a = 1 +w = 13 +z = 15 +j = w - z +print(h - 3) +# is clone +1 + +# snippet 1 +e = 18 +b = 7 / e +print(e - e) +# snippet 2 +p = 10 +o = 13 +a = o + 17 +print(o - o) +# is clone +1 + +# snippet 1 +e = 18 +b = 7 / e +print(e - e) +# snippet 2 +w = 9 +print(w - w) +# is clone +1 + +# snippet 1 +p = 10 +o = 13 +a = o + 17 +print(o - o) +# snippet 2 +w = 9 +print(w - w) +# is clone +1 + +# snippet 1 +q = 15 +h = 6 +b = h * q +print(h - h) +# snippet 2 +l = 18 +t = (l - 12)/(3 / l)*(12 + l)+(l * l)-(l + 7) +print(l - l) +# is clone +1 + +# snippet 1 +q = 15 +h = 6 +b = h * q +print(h - h) +# snippet 2 +z = 12 +e = (z - z) +print(e) +# is clone +1 + +# snippet 1 +l = 18 +t = (l - 12)/(3 / l)*(12 + l)+(l * l)-(l + 7) +print(l - l) +# snippet 2 +z = 12 +e = (z - z) +print(e) +# is clone +1 + +# snippet 1 +d = 9 +n = (11 - d)*(17 / d)/(d - d)/(2 - d)*(d / d) +print(d - 18) +# snippet 2 +o = 19 +print(o - 18) +# is clone +1 + +# snippet 1 +a = 1 +i = a - a +print(a / 15) +# snippet 2 +a = 18 +e = (9 / a)/(7 - a) +print(a / 15) +# is clone +1 + +# snippet 1 +a = 1 +i = a - a +print(a / 15) +# snippet 2 +d = 17 +print(d / 15) +# is clone +1 + +# snippet 1 +a = 18 +e = (9 / a)/(7 - a) +print(a / 15) +# snippet 2 +d = 17 +print(d / 15) +# is clone +1 + +# snippet 1 +q = 2 +l = 12 - q +print(q * 14) +# snippet 2 +l = 4 +g = (14 * l) +print(g) +# is clone +1 + +# snippet 1 +m = 8 +print(m / 14) +# snippet 2 +a = 17 +print(a / 14) +# is clone +1 + +# snippet 1 +q = 15 +i = q / q +print(q / q) +# snippet 2 +l = 5 +h = (13 + l)/(l / 11)-(11 - 14)*(l - l) +print(l / l) +# is clone +1 + +# snippet 1 +q = 15 +i = q / q +print(q / q) +# snippet 2 +v = 11 +y = (13 + 8) +print(v / v) +# is clone +1 + +# snippet 1 +l = 5 +h = (13 + l)/(l / 11)-(11 - 14)*(l - l) +print(l / l) +# snippet 2 +v = 11 +y = (13 + 8) +print(v / v) +# is clone +1 + +# snippet 1 +x = 16 +u = 18 +g = u / u +print(x / x) +# snippet 2 +e = 0 +d = 12 +y = 12 +b = (14 / 9)/(y * 12) +print(d / d) +# is clone +1 + +# snippet 1 +x = 16 +u = 18 +g = u / u +print(x / x) +# snippet 2 +h = 4 +print(h / h) +# is clone +1 + +# snippet 1 +e = 0 +d = 12 +y = 12 +b = (14 / 9)/(y * 12) +print(d / d) +# snippet 2 +h = 4 +print(h / h) +# is clone +1 + +# snippet 1 +i = 5 +p = 2 +q = i - p +print(q) +# snippet 2 +t = 17 +p = 10 +q = 4 +b = (p - t) +print(b) +# is clone +1 + +# snippet 1 +o = 14 +f = 9 +v = 4 +v = 4 +j = (v - o) +print(v / 16) +# snippet 2 +s = 8 +d = 5 - s +print(s / 16) +# is clone +1 + +# snippet 1 +f = 13 +y = f * f +print(f + f) +# snippet 2 +k = 18 +s = 7 +j = (k / s)-(s * 3)/(3 + k)*(k + 15)*(s - 11) +print(s + s) +# is clone +1 + +# snippet 1 +f = 13 +y = f * f +print(f + f) +# snippet 2 +f = 1 +x = (f + f) +print(x) +# is clone +1 + +# snippet 1 +k = 18 +s = 7 +j = (k / s)-(s * 3)/(3 + k)*(k + 15)*(s - 11) +print(s + s) +# snippet 2 +f = 1 +x = (f + f) +print(x) +# is clone +1 + +# snippet 1 +l = 9 +h = 4 + l +print(h) +# snippet 2 +g = 4 +c = 9 +g = 13 +g = 12 +v = (17 + g)-(g - 5) +print(g + 4) +# is clone +1 + +# snippet 1 +e = 5 +n = 16 / e +print(e * 16) +# snippet 2 +a = 13 +n = 13 +p = 1 +a = p * 16 +print(a) +# is clone +1 + +# snippet 1 +y = 4 +y = 9 +m = y * y +print(m) +# snippet 2 +x = 0 +h = 18 +print(h) +# is clone +0 + +# snippet 1 +y = 4 +y = 9 +m = y * y +print(m) +# snippet 2 +i = 7 +print(i) +# is clone +0 + +# snippet 1 +c = 10 +print(c * c) +# snippet 2 +x = 0 +h = 18 +print(h) +# is clone +0 + +# snippet 1 +c = 10 +print(c * c) +# snippet 2 +i = 7 +print(i) +# is clone +0 + +# snippet 1 +x = 7 +m = 8 +g = 5 +a = 13 +m = 0 + 10 +print(m) +# snippet 2 +z = 10 +f = 14 +p = 19 +b = 2 * z +print(f - 3) +# is clone +0 + +# snippet 1 +x = 7 +m = 8 +g = 5 +a = 13 +m = 0 + 10 +print(m) +# snippet 2 +h = 5 +a = 1 +w = 13 +z = 15 +j = w - z +print(h - 3) +# is clone +0 + +# snippet 1 +t = 11 +s = 2 +b = 4 +b = 1 +k = (14 * s) +print(s * 1) +# snippet 2 +z = 10 +f = 14 +p = 19 +b = 2 * z +print(f - 3) +# is clone +0 + +# snippet 1 +t = 11 +s = 2 +b = 4 +b = 1 +k = (14 * s) +print(s * 1) +# snippet 2 +h = 5 +a = 1 +w = 13 +z = 15 +j = w - z +print(h - 3) +# is clone +0 + +# snippet 1 +m = 17 +y = 14 +print(m) +# snippet 2 +e = 18 +b = 7 / e +print(e - e) +# is clone +0 + +# snippet 1 +m = 17 +y = 14 +print(m) +# snippet 2 +p = 10 +o = 13 +a = o + 17 +print(o - o) +# is clone +0 + +# snippet 1 +s = 17 +u = 14 +x = 10 +print(u) +# snippet 2 +e = 18 +b = 7 / e +print(e - e) +# is clone +0 + +# snippet 1 +s = 17 +u = 14 +x = 10 +print(u) +# snippet 2 +p = 10 +o = 13 +a = o + 17 +print(o - o) +# is clone +0 + +# snippet 1 +k = 6 +o = 16 +print(k) +# snippet 2 +w = 9 +print(w - w) +# is clone +0 + +# snippet 1 +k = 6 +o = 16 +print(k) +# snippet 2 +q = 15 +h = 6 +b = h * q +print(h - h) +# is clone +0 + +# snippet 1 +i = 13 +v = 10 +m = 8 +w = (0 + m)*(10 * i) +print(i * 1) +# snippet 2 +w = 9 +print(w - w) +# is clone +0 + +# snippet 1 +i = 13 +v = 10 +m = 8 +w = (0 + m)*(10 * i) +print(i * 1) +# snippet 2 +q = 15 +h = 6 +b = h * q +print(h - h) +# is clone +0 + +# snippet 1 +p = 19 +t = 12 +z = 14 +f = 15 +print(f) +# snippet 2 +l = 18 +t = (l - 12)/(3 / l)*(12 + l)+(l * l)-(l + 7) +print(l - l) +# is clone +0 + +# snippet 1 +p = 19 +t = 12 +z = 14 +f = 15 +print(f) +# snippet 2 +z = 12 +e = (z - z) +print(e) +# is clone +0 + +# snippet 1 +n = 9 +f = 3 +x = 1 - 12 +print(x) +# snippet 2 +l = 18 +t = (l - 12)/(3 / l)*(12 + l)+(l * l)-(l + 7) +print(l - l) +# is clone +0 + +# snippet 1 +n = 9 +f = 3 +x = 1 - 12 +print(x) +# snippet 2 +z = 12 +e = (z - z) +print(e) +# is clone +0 + +# snippet 1 +h = 10 +print(h) +# snippet 2 +b = 10 +print(b - b) +# is clone +0 + +# snippet 1 +g = 18 +print(g) +# snippet 2 +b = 10 +print(b - b) +# is clone +0 + +# snippet 1 +h = 10 +print(h) +# snippet 2 +d = 9 +n = (11 - d)*(17 / d)/(d - d)/(2 - d)*(d / d) +print(d - 18) +# is clone +0 + +# snippet 1 +g = 18 +print(g) +# snippet 2 +d = 9 +n = (11 - d)*(17 / d)/(d - d)/(2 - d)*(d / d) +print(d - 18) +# is clone +0 + +# snippet 1 +i = 2 +print(i) +# snippet 2 +o = 19 +print(o - 18) +# is clone +0 + +# snippet 1 +n = 18 +print(n) +# snippet 2 +o = 19 +print(o - 18) +# is clone +0 + +# snippet 1 +i = 2 +print(i) +# snippet 2 +a = 1 +i = a - a +print(a / 15) +# is clone +0 + +# snippet 1 +n = 18 +print(n) +# snippet 2 +a = 1 +i = a - a +print(a / 15) +# is clone +0 + +# snippet 1 +k = 16 +i = 2 +print(i) +# snippet 2 +a = 18 +e = (9 / a)/(7 - a) +print(a / 15) +# is clone +0 + +# snippet 1 +k = 16 +i = 2 +print(i) +# snippet 2 +d = 17 +print(d / 15) +# is clone +0 + +# snippet 1 +n = 19 +i = 6 +print(i) +# snippet 2 +a = 18 +e = (9 / a)/(7 - a) +print(a / 15) +# is clone +0 + +# snippet 1 +n = 19 +i = 6 +print(i) +# snippet 2 +d = 17 +print(d / 15) +# is clone +0 + +# snippet 1 +l = 19 +z = 12 +print(z) +# snippet 2 +q = 2 +l = 12 - q +print(q * 14) +# is clone +0 + +# snippet 1 +l = 19 +z = 12 +print(z) +# snippet 2 +l = 4 +g = (14 * l) +print(g) +# is clone +0 + +# snippet 1 +c = 1 +h = 3 +d = 10 +f = 18 +print(c) +# snippet 2 +q = 2 +l = 12 - q +print(q * 14) +# is clone +0 + +# snippet 1 +c = 1 +h = 3 +d = 10 +f = 18 +print(c) +# snippet 2 +l = 4 +g = (14 * l) +print(g) +# is clone +0 + +# snippet 1 +d = 8 +n = 12 +print(d) +# snippet 2 +m = 8 +print(m / 14) +# is clone +0 + +# snippet 1 +d = 8 +n = 12 +print(d) +# snippet 2 +a = 17 +print(a / 14) +# is clone +0 + +# snippet 1 +r = 17 +x = 10 +t = 11 +print(t) +# snippet 2 +m = 8 +print(m / 14) +# is clone +0 + +# snippet 1 +r = 17 +x = 10 +t = 11 +print(t) +# snippet 2 +a = 17 +print(a / 14) +# is clone +0 + +# snippet 1 +o = 2 +q = 13 +print(q) +# snippet 2 +q = 15 +i = q / q +print(q / q) +# is clone +0 + +# snippet 1 +o = 2 +q = 13 +print(q) +# snippet 2 +l = 5 +h = (13 + l)/(l / 11)-(11 - 14)*(l - l) +print(l / l) +# is clone +0 + +# snippet 1 +x = 15 +l = 12 +a = 13 / 8 +print(a) +# snippet 2 +q = 15 +i = q / q +print(q / q) +# is clone +0 + +# snippet 1 +x = 15 +l = 12 +a = 13 / 8 +print(a) +# snippet 2 +l = 5 +h = (13 + l)/(l / 11)-(11 - 14)*(l - l) +print(l / l) +# is clone +0 + +# snippet 1 +d = 2 +print(d) +# snippet 2 +v = 11 +y = (13 + 8) +print(v / v) +# is clone +0 + +# snippet 1 +d = 2 +print(d) +# snippet 2 +x = 16 +u = 18 +g = u / u +print(x / x) +# is clone +0 + +# snippet 1 +r = 19 +print(r) +# snippet 2 +v = 11 +y = (13 + 8) +print(v / v) +# is clone +0 + +# snippet 1 +r = 19 +print(r) +# snippet 2 +x = 16 +u = 18 +g = u / u +print(x / x) +# is clone +0 + +# snippet 1 +e = 0 +d = 12 +y = 12 +b = (14 / 9)/(y * 12) +print(d / d) +# snippet 2 +i = 5 +p = 2 +q = i - p +print(q) +# is clone +0 + +# snippet 1 +e = 0 +d = 12 +y = 12 +b = (14 / 9)/(y * 12) +print(d / d) +# snippet 2 +t = 17 +p = 10 +q = 4 +b = (p - t) +print(b) +# is clone +0 + +# snippet 1 +h = 4 +print(h / h) +# snippet 2 +i = 5 +p = 2 +q = i - p +print(q) +# is clone +0 + +# snippet 1 +h = 4 +print(h / h) +# snippet 2 +t = 17 +p = 10 +q = 4 +b = (p - t) +print(b) +# is clone +0 + +# snippet 1 +o = 14 +f = 9 +v = 4 +v = 4 +j = (v - o) +print(v / 16) +# snippet 2 +f = 13 +y = f * f +print(f + f) +# is clone +0 + +# snippet 1 +o = 14 +f = 9 +v = 4 +v = 4 +j = (v - o) +print(v / 16) +# snippet 2 +k = 18 +s = 7 +j = (k / s)-(s * 3)/(3 + k)*(k + 15)*(s - 11) +print(s + s) +# is clone +0 + +# snippet 1 +s = 8 +d = 5 - s +print(s / 16) +# snippet 2 +f = 13 +y = f * f +print(f + f) +# is clone +0 + +# snippet 1 +s = 8 +d = 5 - s +print(s / 16) +# snippet 2 +k = 18 +s = 7 +j = (k / s)-(s * 3)/(3 + k)*(k + 15)*(s - 11) +print(s + s) +# is clone +0 + +# snippet 1 +f = 1 +x = (f + f) +print(x) +# snippet 2 +l = 9 +h = 4 + l +print(h) +# is clone +0 + +# snippet 1 +f = 1 +x = (f + f) +print(x) +# snippet 2 +g = 4 +c = 9 +g = 13 +g = 12 +v = (17 + g)-(g - 5) +print(g + 4) +# is clone +0 + +# snippet 1 +d = 15 +m = d + d +print(m) +# snippet 2 +l = 9 +h = 4 + l +print(h) +# is clone +0 + +# snippet 1 +d = 15 +m = d + d +print(m) +# snippet 2 +g = 4 +c = 9 +g = 13 +g = 12 +v = (17 + g)-(g - 5) +print(g + 4) +# is clone +0 + +# snippet 1 +r = 0 +j = r - r +if (not r <= r) : + print(r - 18) +elif not (not r != r) or (r <= r) : + print(j) +else : + print(j) +# snippet 2 +a = 7 +d = a / 9 +if not (a >= a) : + print(a + 14) +else : + print(a * 0) +# is clone +1 + +# snippet 1 +r = 0 +j = r - r +if (not r <= r) : + print(r - 18) +elif not (not r != r) or (r <= r) : + print(j) +else : + print(j) +# snippet 2 +x = 3 +z = x - x +if ( x >= x) : + print(z) +# is clone +1 + +# snippet 1 +a = 7 +d = a / 9 +if not (a >= a) : + print(a + 14) +else : + print(a * 0) +# snippet 2 +x = 3 +z = x - x +if ( x >= x) : + print(z) +# is clone +1 + +# snippet 1 +i = 7 +u = 1 + 7 +if not (i == i) or (i != i) or (i >= i) : + print(u) +else : + print(i / i) +# snippet 2 +z = 2 +j = 1 +if not (z != z) : + print(z) +elif not (j <= 3) : + print(j) +else : + print(j) +# is clone +1 + +# snippet 1 +i = 7 +u = 1 + 7 +if not (i == i) or (i != i) or (i >= i) : + print(u) +else : + print(i / i) +# snippet 2 +j = 18 +if (j >= j) : + print(j) +# is clone +1 + +# snippet 1 +z = 2 +j = 1 +if not (z != z) : + print(z) +elif not (j <= 3) : + print(j) +else : + print(j) +# snippet 2 +j = 18 +if (j >= j) : + print(j) +# is clone +1 + +# snippet 1 +o = 11 +if (not o == 14) and (o > 16) : + print(o) +else : + print(o) +# snippet 2 +f = 11 +n = 7 +v = 3 * 11 +if (not f == f) : + print(v) +elif (n < n) and (n < 13) : + print(n / 1) +else : + print(v) +# is clone +1 + +# snippet 1 +o = 11 +if (not o == 14) and (o > 16) : + print(o) +else : + print(o) +# snippet 2 +v = 9 +if (v <= v) : + print(v) +# is clone +1 + +# snippet 1 +f = 11 +n = 7 +v = 3 * 11 +if (not f == f) : + print(v) +elif (n < n) and (n < 13) : + print(n / 1) +else : + print(v) +# snippet 2 +v = 9 +if (v <= v) : + print(v) +# is clone +1 + +# snippet 1 +f = 9 +r = 18 +if (r > 14) or (r >= 16) and (not f >= f) : + print(r) +else : + print(f * 1) +# snippet 2 +f = 7 +m = 0 +if (m != m) and (f == f) : + print(f - 2) +else : + print(m) +# is clone +1 + +# snippet 1 +f = 9 +r = 18 +if (r > 14) or (r >= 16) and (not f >= f) : + print(r) +else : + print(f * 1) +# snippet 2 +t = 1 +o = (0 - 12) +if (not t >= t) : + print(t * 0) +else : + print(o) +# is clone +1 + +# snippet 1 +f = 7 +m = 0 +if (m != m) and (f == f) : + print(f - 2) +else : + print(m) +# snippet 2 +t = 1 +o = (0 - 12) +if (not t >= t) : + print(t * 0) +else : + print(o) +# is clone +1 + +# snippet 1 +j = 10 +y = 16 + 13 +if (j != j) or (not j < j) : + print(y) +# snippet 2 +t = 11 +f = 18 + 9 +if ( t <= t) : + print(f) +elif (not t == 7) : + print(f) +else : + print(f) +# is clone +1 + +# snippet 1 +j = 10 +y = 16 + 13 +if (j != j) or (not j < j) : + print(y) +# snippet 2 +p = 11 +d = p + 12 +if ( p > p) : + print(d) +elif not (not p != p) and (p >= p) : + print(d) +else : + print(p - 0) +# is clone +1 + +# snippet 1 +t = 11 +f = 18 + 9 +if ( t <= t) : + print(f) +elif (not t == 7) : + print(f) +else : + print(f) +# snippet 2 +p = 11 +d = p + 12 +if ( p > p) : + print(d) +elif not (not p != p) and (p >= p) : + print(d) +else : + print(p - 0) +# is clone +1 + +# snippet 1 +e = 14 +o = 10 +l = 15 +r = 14 +z = (r / r) +if (not o == o) and (o == 2) : + print(z) +# snippet 2 +r = 9 +u = 18 +t = 14 +v = 3 +e = 4 +c = 2 +w = (v + v)/(c * 8)-(u + 0)+(3 + r)-(c / c) +if (c < c) and (r <= t) : + print(w) +# is clone +1 + +# snippet 1 +e = 14 +o = 10 +l = 15 +r = 14 +z = (r / r) +if (not o == o) and (o == 2) : + print(z) +# snippet 2 +j = 5 +p = 16 +t = (13 / p)+(11 - p)+(7 + p) +if not (p >= p) : + print(p - 11) +# is clone +1 + +# snippet 1 +r = 9 +u = 18 +t = 14 +v = 3 +e = 4 +c = 2 +w = (v + v)/(c * 8)-(u + 0)+(3 + r)-(c / c) +if (c < c) and (r <= t) : + print(w) +# snippet 2 +j = 5 +p = 16 +t = (13 / p)+(11 - p)+(7 + p) +if not (p >= p) : + print(p - 11) +# is clone +1 + +# snippet 1 +t = 10 +if ( t <= t) or (not t < 1) : + print(t * t) +# snippet 2 +s = 0 +a = s * s +if not ( s >= s) and (not s <= s) : + print(s * 7) +elif not (s == 13) or (s != 16) or (s != 7) and (not s == 18) and (not s >= s) : + print(a) +else : + print(s * 4) +# is clone +1 + +# snippet 1 +t = 10 +if ( t <= t) or (not t < 1) : + print(t * t) +# snippet 2 +d = 4 +v = 7 +q = 13 + v +if not (v != 19) and (d >= v) and (not d <= v) : + print(q) +else : + print(v * v) +# is clone +1 + +# snippet 1 +s = 0 +a = s * s +if not ( s >= s) and (not s <= s) : + print(s * 7) +elif not (s == 13) or (s != 16) or (s != 7) and (not s == 18) and (not s >= s) : + print(a) +else : + print(s * 4) +# snippet 2 +d = 4 +v = 7 +q = 13 + v +if not (v != 19) and (d >= v) and (not d <= v) : + print(q) +else : + print(v * v) +# is clone +1 + +# snippet 1 +s = 4 +m = (16 * s)*(8 - s) +if not (s <= s) : + print(m) +else : + print(s + s) +# snippet 2 +j = 6 +t = j + j +if not (not j >= j) : + print(t) +else : + print(t) +# is clone +1 + +# snippet 1 +r = 0 +j = r - r +if (not r <= r) : + print(r - 18) +elif not (not r != r) or (r <= r) : + print(j) +else : + print(j) +# snippet 2 +i = 7 +u = 1 + 7 +if not (i == i) or (i != i) or (i >= i) : + print(u) +else : + print(i / i) +# is clone +0 + +# snippet 1 +r = 0 +j = r - r +if (not r <= r) : + print(r - 18) +elif not (not r != r) or (r <= r) : + print(j) +else : + print(j) +# snippet 2 +z = 2 +j = 1 +if not (z != z) : + print(z) +elif not (j <= 3) : + print(j) +else : + print(j) +# is clone +0 + +# snippet 1 +a = 7 +d = a / 9 +if not (a >= a) : + print(a + 14) +else : + print(a * 0) +# snippet 2 +i = 7 +u = 1 + 7 +if not (i == i) or (i != i) or (i >= i) : + print(u) +else : + print(i / i) +# is clone +0 + +# snippet 1 +a = 7 +d = a / 9 +if not (a >= a) : + print(a + 14) +else : + print(a * 0) +# snippet 2 +z = 2 +j = 1 +if not (z != z) : + print(z) +elif not (j <= 3) : + print(j) +else : + print(j) +# is clone +0 + +# snippet 1 +x = 3 +z = x - x +if ( x >= x) : + print(z) +# snippet 2 +j = 18 +if (j >= j) : + print(j) +# is clone +0 + +# snippet 1 +x = 3 +z = x - x +if ( x >= x) : + print(z) +# snippet 2 +o = 11 +if (not o == 14) and (o > 16) : + print(o) +else : + print(o) +# is clone +0 + +# snippet 1 +j = 18 +if (j >= j) : + print(j) +# snippet 2 +e = 14 +o = 10 +l = 15 +r = 14 +z = (r / r) +if (not o == o) and (o == 2) : + print(z) +# is clone +0 + +# snippet 1 +o = 11 +if (not o == 14) and (o > 16) : + print(o) +else : + print(o) +# snippet 2 +e = 14 +o = 10 +l = 15 +r = 14 +z = (r / r) +if (not o == o) and (o == 2) : + print(z) +# is clone +0 + +# snippet 1 +f = 11 +n = 7 +v = 3 * 11 +if (not f == f) : + print(v) +elif (n < n) and (n < 13) : + print(n / 1) +else : + print(v) +# snippet 2 +r = 9 +u = 18 +t = 14 +v = 3 +e = 4 +c = 2 +w = (v + v)/(c * 8)-(u + 0)+(3 + r)-(c / c) +if (c < c) and (r <= t) : + print(w) +# is clone +0 + +# snippet 1 +f = 11 +n = 7 +v = 3 * 11 +if (not f == f) : + print(v) +elif (n < n) and (n < 13) : + print(n / 1) +else : + print(v) +# snippet 2 +j = 5 +p = 16 +t = (13 / p)+(11 - p)+(7 + p) +if not (p >= p) : + print(p - 11) +# is clone +0 + +# snippet 1 +v = 9 +if (v <= v) : + print(v) +# snippet 2 +r = 9 +u = 18 +t = 14 +v = 3 +e = 4 +c = 2 +w = (v + v)/(c * 8)-(u + 0)+(3 + r)-(c / c) +if (c < c) and (r <= t) : + print(w) +# is clone +0 + +# snippet 1 +v = 9 +if (v <= v) : + print(v) +# snippet 2 +j = 5 +p = 16 +t = (13 / p)+(11 - p)+(7 + p) +if not (p >= p) : + print(p - 11) +# is clone +0 + +# snippet 1 +f = 9 +r = 18 +if (r > 14) or (r >= 16) and (not f >= f) : + print(r) +else : + print(f * 1) +# snippet 2 +h = 9 +z = 4 +b = 16 +a = (z + z) +if not ( h <= h) : + print(a) +# is clone +0 + +# snippet 1 +f = 7 +m = 0 +if (m != m) and (f == f) : + print(f - 2) +else : + print(m) +# snippet 2 +h = 9 +z = 4 +b = 16 +a = (z + z) +if not ( h <= h) : + print(a) +# is clone +0 + +# snippet 1 +f = 9 +r = 18 +if (r > 14) or (r >= 16) and (not f >= f) : + print(r) +else : + print(f * 1) +# snippet 2 +t = 10 +if ( t <= t) or (not t < 1) : + print(t * t) +# is clone +0 + +# snippet 1 +f = 7 +m = 0 +if (m != m) and (f == f) : + print(f - 2) +else : + print(m) +# snippet 2 +t = 10 +if ( t <= t) or (not t < 1) : + print(t * t) +# is clone +0 + +# snippet 1 +t = 1 +o = (0 - 12) +if (not t >= t) : + print(t * 0) +else : + print(o) +# snippet 2 +s = 0 +a = s * s +if not ( s >= s) and (not s <= s) : + print(s * 7) +elif not (s == 13) or (s != 16) or (s != 7) and (not s == 18) and (not s >= s) : + print(a) +else : + print(s * 4) +# is clone +0 + +# snippet 1 +t = 1 +o = (0 - 12) +if (not t >= t) : + print(t * 0) +else : + print(o) +# snippet 2 +d = 4 +v = 7 +q = 13 + v +if not (v != 19) and (d >= v) and (not d <= v) : + print(q) +else : + print(v * v) +# is clone +0 + +# snippet 1 +j = 10 +y = 16 + 13 +if (j != j) or (not j < j) : + print(y) +# snippet 2 +s = 0 +a = s * s +if not ( s >= s) and (not s <= s) : + print(s * 7) +elif not (s == 13) or (s != 16) or (s != 7) and (not s == 18) and (not s >= s) : + print(a) +else : + print(s * 4) +# is clone +0 + +# snippet 1 +j = 10 +y = 16 + 13 +if (j != j) or (not j < j) : + print(y) +# snippet 2 +d = 4 +v = 7 +q = 13 + v +if not (v != 19) and (d >= v) and (not d <= v) : + print(q) +else : + print(v * v) +# is clone +0 + +# snippet 1 +t = 11 +f = 18 + 9 +if ( t <= t) : + print(f) +elif (not t == 7) : + print(f) +else : + print(f) +# snippet 2 +s = 4 +m = (16 * s)*(8 - s) +if not (s <= s) : + print(m) +else : + print(s + s) +# is clone +0 + +# snippet 1 +t = 11 +f = 18 + 9 +if ( t <= t) : + print(f) +elif (not t == 7) : + print(f) +else : + print(f) +# snippet 2 +j = 6 +t = j + j +if not (not j >= j) : + print(t) +else : + print(t) +# is clone +0 + +# snippet 1 +p = 11 +d = p + 12 +if ( p > p) : + print(d) +elif not (not p != p) and (p >= p) : + print(d) +else : + print(p - 0) +# snippet 2 +s = 4 +m = (16 * s)*(8 - s) +if not (s <= s) : + print(m) +else : + print(s + s) +# is clone +0 + +# snippet 1 +p = 11 +d = p + 12 +if ( p > p) : + print(d) +elif not (not p != p) and (p >= p) : + print(d) +else : + print(p - 0) +# snippet 2 +j = 6 +t = j + j +if not (not j >= j) : + print(t) +else : + print(t) +# is clone +0 + +# snippet 1 +q = 19 +for q in range(2, 5, 2) : + print(q) +# snippet 2 +r = 5 +i = (r - r)/(r + r)*(14 / r)/(10 - 18) +for r in range(4, 7, 2) : + print(r - 2) +# is clone +1 + +# snippet 1 +q = 19 +for q in range(2, 5, 2) : + print(q) +# snippet 2 +i = 10 +for i in range(2, 5, 2) : + print(i) +# is clone +1 + +# snippet 1 +r = 5 +i = (r - r)/(r + r)*(14 / r)/(10 - 18) +for r in range(4, 7, 2) : + print(r - 2) +# snippet 2 +i = 10 +for i in range(2, 5, 2) : + print(i) +# is clone +1 + +# snippet 1 +x = 16 +p = 15 +r = 5 +for p in range(6, 11, 2) : + print(r) +# snippet 2 +d = 14 +j = 14 +k = 6 +for d in range(15, 20, 2) : + print(j) +# is clone +1 + +# snippet 1 +x = 16 +p = 15 +r = 5 +for p in range(6, 11, 2) : + print(r) +# snippet 2 +y = 14 +k = 9 +h = 9 +for y in range(14, 19, 2) : + print(k) +# is clone +1 + +# snippet 1 +d = 14 +j = 14 +k = 6 +for d in range(15, 20, 2) : + print(j) +# snippet 2 +y = 14 +k = 9 +h = 9 +for y in range(14, 19, 2) : + print(k) +# is clone +1 + +# snippet 1 +r = 8 +z = 1 +q = 19 * 15 +for r in range(10, 15) : + print(q) +# snippet 2 +s = 7 +z = 8 +for s in range(1, 6) : + print(z) +# is clone +1 + +# snippet 1 +r = 8 +z = 1 +q = 19 * 15 +for r in range(10, 15) : + print(q) +# snippet 2 +v = 13 +p = 12 +n = 3 +q = 10 +s = 9 +for p in range(11, 16) : + print(q) +# is clone +1 + +# snippet 1 +s = 7 +z = 8 +for s in range(1, 6) : + print(z) +# snippet 2 +v = 13 +p = 12 +n = 3 +q = 10 +s = 9 +for p in range(11, 16) : + print(q) +# is clone +1 + +# snippet 1 +h = 15 +r = 13 / 19 +for h in range(1, 6) : + print(r) +# snippet 2 +e = 4 +l = 17 +a = 8 +x = 11 * 10 +for e in range(4, 9) : + print(x) +# is clone +1 + +# snippet 1 +t = 11 +t = (6 + t)/(12 * 14) +for t in range(6, 7) : + print(t * t) +# snippet 2 +h = 19 +for h in range(6, 7, 2) : + print(h * h) +# is clone +1 + +# snippet 1 +t = 1 +x = 4 + 4 +for t in range(2, 3, 1) : + print(x) +# snippet 2 +w = 12 +x = 11 +for w in range(9, 10) : + print(x) +# is clone +1 + +# snippet 1 +t = 1 +x = 4 + 4 +for t in range(2, 3, 1) : + print(x) +# snippet 2 +g = 18 +i = 5 +i = 16 / 17 +for g in range(8, 9, 1) : + print(i) +# is clone +1 + +# snippet 1 +w = 12 +x = 11 +for w in range(9, 10) : + print(x) +# snippet 2 +g = 18 +i = 5 +i = 16 / 17 +for g in range(8, 9, 1) : + print(i) +# is clone +1 + +# snippet 1 +s = 12 +u = 1 +k = 1 +for u in range(2, 3) : + print(k) +# snippet 2 +n = 8 +t = 18 +m = 4 +for n in range(3, 4) : + print(t) +# is clone +1 + +# snippet 1 +s = 12 +u = 1 +k = 1 +for u in range(2, 3) : + print(k) +# snippet 2 +n = 6 +r = 19 +h = 5 +h = 10 +e = 0 +for e in range(12, 13) : + print(h) +# is clone +1 + +# snippet 1 +n = 8 +t = 18 +m = 4 +for n in range(3, 4) : + print(t) +# snippet 2 +n = 6 +r = 19 +h = 5 +h = 10 +e = 0 +for e in range(12, 13) : + print(h) +# is clone +1 + +# snippet 1 +d = 5 +x = 7 / 19 +for d in range(6, 7) : + print(x) +# snippet 2 +l = 1 +g = 11 +p = 9 +for g in range(12, 13) : + print(l) +# is clone +1 + +# snippet 1 +k = 7 +i = (k / 11)/(k - k) +for k in range(13, 18, 2) : + print(i) +# snippet 2 +o = 13 +o = 5 +r = (o + 5)/(1 + o)/(o - o)*(o + 5)-(o * o)+(13 * o)-(10 + o) +for o in range(13, 16, 2) : + print(r) +# is clone +1 + +# snippet 1 +w = 19 +s = (w - w) +for w in range(3, 4) : + print(s) +# snippet 2 +h = 18 +n = h - h +for h in range(4, 5, 1) : + print(n) +# is clone +1 + +# snippet 1 +w = 19 +s = (w - w) +for w in range(3, 4) : + print(s) +# snippet 2 +q = 1 +q = 9 + 3 +for q in range(10, 11, 2) : + print(q - q) +# is clone +1 + +# snippet 1 +h = 18 +n = h - h +for h in range(4, 5, 1) : + print(n) +# snippet 2 +q = 1 +q = 9 + 3 +for q in range(10, 11, 2) : + print(q - q) +# is clone +1 + +# snippet 1 +e = 3 +for e in range(4, 5) : + print(e) +# snippet 2 +d = 7 +u = 14 +v = 3 +for d in range(4, 5) : + print(d) +# is clone +1 + +# snippet 1 +r = 17 +x = 10 +c = 19 +for x in range(16, 21, 3) : + print(c) +# snippet 2 +z = 9 +h = 19 - 8 +for z in range(5, 10, 3) : + print(h) +# is clone +1 + +# snippet 1 +r = 17 +x = 10 +c = 19 +for x in range(16, 21, 3) : + print(c) +# snippet 2 +o = 3 +v = 9 +o = 14 +v = 14 +for o in range(7, 10, 2) : + print(v) +# is clone +1 + +# snippet 1 +z = 9 +h = 19 - 8 +for z in range(5, 10, 3) : + print(h) +# snippet 2 +o = 3 +v = 9 +o = 14 +v = 14 +for o in range(7, 10, 2) : + print(v) +# is clone +1 + +# snippet 1 +p = 18 +m = 17 +c = 15 +u = 14 +u = 7 + 5 +for m in range(2, 5, 2) : + print(u) +# snippet 2 +v = 18 +j = 6 +for v in range(14, 17, 2) : + print(j) +# is clone +1 + +# snippet 1 +p = 18 +m = 17 +c = 15 +u = 14 +u = 7 + 5 +for m in range(2, 5, 2) : + print(u) +# snippet 2 +y = 6 +f = 17 +h = 14 + 19 +for f in range(10, 13, 2) : + print(h) +# is clone +1 + +# snippet 1 +v = 18 +j = 6 +for v in range(14, 17, 2) : + print(j) +# snippet 2 +y = 6 +f = 17 +h = 14 + 19 +for f in range(10, 13, 2) : + print(h) +# is clone +1 + +# snippet 1 +d = 16 +l = 6 +j = 17 +l = 3 +l = 0 +f = d * d +for j in range(9, 14, 3) : + print(f) +# snippet 2 +z = 11 +w = z * z +for z in range(18, 23, 3) : + print(w) +# is clone +1 + +# snippet 1 +h = 12 +for h in range(2, 7, 3) : + print(h) +# snippet 2 +s = 12 +x = 4 +y = (3 - 12) +for x in range(14, 19, 3) : + print(x - 12) +# is clone +1 + +# snippet 1 +g = 11 +w = 17 +c = 12 +l = g / w +for w in range(14, 19) : + print(l) +# snippet 2 +y = 17 +z = 18 +e = 11 +d = 12 + 0 +for z in range(16, 21) : + print(y / e) +# is clone +1 + +# snippet 1 +k = 6 +n = 1 +c = (n / n) +for k in range(11, 16) : + print(c) +# snippet 2 +q = 6 +v = q / q +for q in range(7, 12) : + print(v) +# is clone +1 + +# snippet 1 +q = 19 +for q in range(2, 5, 2) : + print(q) +# snippet 2 +x = 16 +p = 15 +r = 5 +for p in range(6, 11, 2) : + print(r) +# is clone +0 + +# snippet 1 +q = 19 +for q in range(2, 5, 2) : + print(q) +# snippet 2 +d = 14 +j = 14 +k = 6 +for d in range(15, 20, 2) : + print(j) +# is clone +0 + +# snippet 1 +r = 5 +i = (r - r)/(r + r)*(14 / r)/(10 - 18) +for r in range(4, 7, 2) : + print(r - 2) +# snippet 2 +x = 16 +p = 15 +r = 5 +for p in range(6, 11, 2) : + print(r) +# is clone +0 + +# snippet 1 +r = 5 +i = (r - r)/(r + r)*(14 / r)/(10 - 18) +for r in range(4, 7, 2) : + print(r - 2) +# snippet 2 +d = 14 +j = 14 +k = 6 +for d in range(15, 20, 2) : + print(j) +# is clone +0 + +# snippet 1 +i = 10 +for i in range(2, 5, 2) : + print(i) +# snippet 2 +y = 14 +k = 9 +h = 9 +for y in range(14, 19, 2) : + print(k) +# is clone +0 + +# snippet 1 +i = 10 +for i in range(2, 5, 2) : + print(i) +# snippet 2 +o = 16 +s = (12 + 8) +for o in range(10, 13) : + print(s) +# is clone +0 + +# snippet 1 +y = 14 +k = 9 +h = 9 +for y in range(14, 19, 2) : + print(k) +# snippet 2 +r = 8 +z = 1 +q = 19 * 15 +for r in range(10, 15) : + print(q) +# is clone +0 + +# snippet 1 +o = 16 +s = (12 + 8) +for o in range(10, 13) : + print(s) +# snippet 2 +r = 8 +z = 1 +q = 19 * 15 +for r in range(10, 15) : + print(q) +# is clone +0 + +# snippet 1 +s = 7 +z = 8 +for s in range(1, 6) : + print(z) +# snippet 2 +t = 11 +t = (6 + t)/(12 * 14) +for t in range(6, 7) : + print(t * t) +# is clone +0 + +# snippet 1 +s = 7 +z = 8 +for s in range(1, 6) : + print(z) +# snippet 2 +h = 19 +for h in range(6, 7, 2) : + print(h * h) +# is clone +0 + +# snippet 1 +v = 13 +p = 12 +n = 3 +q = 10 +s = 9 +for p in range(11, 16) : + print(q) +# snippet 2 +t = 11 +t = (6 + t)/(12 * 14) +for t in range(6, 7) : + print(t * t) +# is clone +0 + +# snippet 1 +v = 13 +p = 12 +n = 3 +q = 10 +s = 9 +for p in range(11, 16) : + print(q) +# snippet 2 +h = 19 +for h in range(6, 7, 2) : + print(h * h) +# is clone +0 + +# snippet 1 +h = 15 +r = 13 / 19 +for h in range(1, 6) : + print(r) +# snippet 2 +t = 1 +x = 4 + 4 +for t in range(2, 3, 1) : + print(x) +# is clone +0 + +# snippet 1 +h = 15 +r = 13 / 19 +for h in range(1, 6) : + print(r) +# snippet 2 +w = 12 +x = 11 +for w in range(9, 10) : + print(x) +# is clone +0 + +# snippet 1 +e = 4 +l = 17 +a = 8 +x = 11 * 10 +for e in range(4, 9) : + print(x) +# snippet 2 +t = 1 +x = 4 + 4 +for t in range(2, 3, 1) : + print(x) +# is clone +0 + +# snippet 1 +e = 4 +l = 17 +a = 8 +x = 11 * 10 +for e in range(4, 9) : + print(x) +# snippet 2 +w = 12 +x = 11 +for w in range(9, 10) : + print(x) +# is clone +0 + +# snippet 1 +g = 18 +i = 5 +i = 16 / 17 +for g in range(8, 9, 1) : + print(i) +# snippet 2 +k = 7 +i = (k / 11)/(k - k) +for k in range(13, 18, 2) : + print(i) +# is clone +0 + +# snippet 1 +g = 18 +i = 5 +i = 16 / 17 +for g in range(8, 9, 1) : + print(i) +# snippet 2 +o = 13 +o = 5 +r = (o + 5)/(1 + o)/(o - o)*(o + 5)-(o * o)+(13 * o)-(10 + o) +for o in range(13, 16, 2) : + print(r) +# is clone +0 + +# snippet 1 +s = 12 +u = 1 +k = 1 +for u in range(2, 3) : + print(k) +# snippet 2 +k = 7 +i = (k / 11)/(k - k) +for k in range(13, 18, 2) : + print(i) +# is clone +0 + +# snippet 1 +s = 12 +u = 1 +k = 1 +for u in range(2, 3) : + print(k) +# snippet 2 +o = 13 +o = 5 +r = (o + 5)/(1 + o)/(o - o)*(o + 5)-(o * o)+(13 * o)-(10 + o) +for o in range(13, 16, 2) : + print(r) +# is clone +0 + +# snippet 1 +n = 8 +t = 18 +m = 4 +for n in range(3, 4) : + print(t) +# snippet 2 +w = 19 +s = (w - w) +for w in range(3, 4) : + print(s) +# is clone +0 + +# snippet 1 +n = 8 +t = 18 +m = 4 +for n in range(3, 4) : + print(t) +# snippet 2 +h = 18 +n = h - h +for h in range(4, 5, 1) : + print(n) +# is clone +0 + +# snippet 1 +n = 6 +r = 19 +h = 5 +h = 10 +e = 0 +for e in range(12, 13) : + print(h) +# snippet 2 +w = 19 +s = (w - w) +for w in range(3, 4) : + print(s) +# is clone +0 + +# snippet 1 +n = 6 +r = 19 +h = 5 +h = 10 +e = 0 +for e in range(12, 13) : + print(h) +# snippet 2 +h = 18 +n = h - h +for h in range(4, 5, 1) : + print(n) +# is clone +0 + +# snippet 1 +d = 5 +x = 7 / 19 +for d in range(6, 7) : + print(x) +# snippet 2 +q = 1 +q = 9 + 3 +for q in range(10, 11, 2) : + print(q - q) +# is clone +0 + +# snippet 1 +d = 5 +x = 7 / 19 +for d in range(6, 7) : + print(x) +# snippet 2 +s = 1 +v = 1 +m = 8 +i = (m / 7) +for s in range(18, 19, 1) : + print(s - s) +# is clone +0 + +# snippet 1 +l = 1 +g = 11 +p = 9 +for g in range(12, 13) : + print(l) +# snippet 2 +q = 1 +q = 9 + 3 +for q in range(10, 11, 2) : + print(q - q) +# is clone +0 + +# snippet 1 +l = 1 +g = 11 +p = 9 +for g in range(12, 13) : + print(l) +# snippet 2 +s = 1 +v = 1 +m = 8 +i = (m / 7) +for s in range(18, 19, 1) : + print(s - s) +# is clone +0 + +# snippet 1 +e = 3 +for e in range(4, 5) : + print(e) +# snippet 2 +r = 17 +x = 10 +c = 19 +for x in range(16, 21, 3) : + print(c) +# is clone +0 + +# snippet 1 +e = 3 +for e in range(4, 5) : + print(e) +# snippet 2 +z = 9 +h = 19 - 8 +for z in range(5, 10, 3) : + print(h) +# is clone +0 + +# snippet 1 +d = 7 +u = 14 +v = 3 +for d in range(4, 5) : + print(d) +# snippet 2 +r = 17 +x = 10 +c = 19 +for x in range(16, 21, 3) : + print(c) +# is clone +0 + +# snippet 1 +d = 7 +u = 14 +v = 3 +for d in range(4, 5) : + print(d) +# snippet 2 +z = 9 +h = 19 - 8 +for z in range(5, 10, 3) : + print(h) +# is clone +0 + +# snippet 1 +o = 3 +v = 9 +o = 14 +v = 14 +for o in range(7, 10, 2) : + print(v) +# snippet 2 +d = 16 +l = 6 +j = 17 +l = 3 +l = 0 +f = d * d +for j in range(9, 14, 3) : + print(f) +# is clone +0 + +# snippet 1 +o = 3 +v = 9 +o = 14 +v = 14 +for o in range(7, 10, 2) : + print(v) +# snippet 2 +z = 11 +w = z * z +for z in range(18, 23, 3) : + print(w) +# is clone +0 + +# snippet 1 +p = 18 +m = 17 +c = 15 +u = 14 +u = 7 + 5 +for m in range(2, 5, 2) : + print(u) +# snippet 2 +d = 16 +l = 6 +j = 17 +l = 3 +l = 0 +f = d * d +for j in range(9, 14, 3) : + print(f) +# is clone +0 + +# snippet 1 +p = 18 +m = 17 +c = 15 +u = 14 +u = 7 + 5 +for m in range(2, 5, 2) : + print(u) +# snippet 2 +z = 11 +w = z * z +for z in range(18, 23, 3) : + print(w) +# is clone +0 + +# snippet 1 +v = 18 +j = 6 +for v in range(14, 17, 2) : + print(j) +# snippet 2 +h = 12 +for h in range(2, 7, 3) : + print(h) +# is clone +0 + +# snippet 1 +v = 18 +j = 6 +for v in range(14, 17, 2) : + print(j) +# snippet 2 +s = 12 +x = 4 +y = (3 - 12) +for x in range(14, 19, 3) : + print(x - 12) +# is clone +0 + +# snippet 1 +y = 6 +f = 17 +h = 14 + 19 +for f in range(10, 13, 2) : + print(h) +# snippet 2 +h = 12 +for h in range(2, 7, 3) : + print(h) +# is clone +0 + +# snippet 1 +y = 6 +f = 17 +h = 14 + 19 +for f in range(10, 13, 2) : + print(h) +# snippet 2 +s = 12 +x = 4 +y = (3 - 12) +for x in range(14, 19, 3) : + print(x - 12) +# is clone +0 + +# snippet 1 +g = 11 +w = 17 +c = 12 +l = g / w +for w in range(14, 19) : + print(l) +# snippet 2 +k = 6 +n = 1 +c = (n / n) +for k in range(11, 16) : + print(c) +# is clone +0 + +# snippet 1 +g = 11 +w = 17 +c = 12 +l = g / w +for w in range(14, 19) : + print(l) +# snippet 2 +q = 6 +v = q / q +for q in range(7, 12) : + print(v) +# is clone +0 + +# snippet 1 +y = 17 +z = 18 +e = 11 +d = 12 + 0 +for z in range(16, 21) : + print(y / e) +# snippet 2 +k = 6 +n = 1 +c = (n / n) +for k in range(11, 16) : + print(c) +# is clone +0 + +# snippet 1 +y = 17 +z = 18 +e = 11 +d = 12 + 0 +for z in range(16, 21) : + print(y / e) +# snippet 2 +q = 6 +v = q / q +for q in range(7, 12) : + print(v) +# is clone +0 + +# snippet 1 +k = 17 +e = 8 +while e < 13 : + print(k) + e = e + 3 +# snippet 2 +y = 7 +a = 16 +while y <= 12 : + print(a) + y = y + 3 +# is clone +1 + +# snippet 1 +y = 3 +n = 14 +while y >= 0 : + print(y) + y = y - 2 +# snippet 2 +t = 2 +while t > -1 : + print(t) + t = t - 2 +# is clone +1 + +# snippet 1 +r = 6 +while r > 1 : + print(r) + r = r - 3 +# snippet 2 +z = 7 +while z >= 2 : + print(z) + z = z - 3 +# is clone +1 + +# snippet 1 +e = 13 +l = 14 +while e >= 12 : + print(l) + e = e - 2 +# snippet 2 +f = 1 +j = 14 +while j > 11 : + print(f) + j = j - 2 +# is clone +1 + +# snippet 1 +x = 2 +e = 16 +f = 9 +while f > 8 : + print(f) + f = f - 2 +# snippet 2 +v = 11 +while v > 8 : + print(v) + v = v - 2 +# is clone +1 + +# snippet 1 +w = 6 +q = 10 +v = 8 +while q <= 11 : + print(w) + q = q + 2 +# snippet 2 +l = 2 +s = 4 +w = 6 +while w <= 11 : + print(l) + w = w + 2 +# is clone +1 + +# snippet 1 +z = 18 +while z < 21 : + print(z) + z = z + 2 +# snippet 2 +s = 19 +while s <= 20 : + print(s) + s = s + 2 +# is clone +1 + +# snippet 1 +p = 12 +while p < 13 : + print(p) + p = p + 1 +# snippet 2 +e = 12 +while e < 13 : + print(e) + e = e + 1 +# is clone +1 + +# snippet 1 +k = 0 +r = 4 +b = 2 +while b < 7 : + print(b) + b = b + 3 +# snippet 2 +p = 1 +y = 8 +while p <= 6 : + print(p) + p = p + 3 +# is clone +1 + +# snippet 1 +h = 0 +h = 11 +z = 16 +while z > 11 : + print(z) + z = z - 2 +# snippet 2 +k = 17 +while k >= 12 : + print(k) + k = k - 2 +# is clone +1 + +# snippet 1 +d = 18 +i = 7 +s = 3 +while d >= 15 : + print(d) + d = d - 2 +# snippet 2 +y = 16 +while y >= 15 : + print(y) + y = y - 2 +# is clone +1 + +# snippet 1 +j = 15 +o = 9 +while o >= 8 : + print(j) + o = o - 2 +# snippet 2 +r = 15 +c = 12 +t = 16 +while c > 7 : + print(r) + c = c - 2 +# is clone +1 + +# snippet 1 +c = 15 +o = 1 +while c > 10 : + print(o) + c = c - 2 +# snippet 2 +p = 11 +d = 7 +while p > 10 : + print(d) + p = p - 2 +# is clone +1 + +# snippet 1 +c = 15 +o = 1 +while c > 10 : + print(o) + c = c - 2 +# snippet 2 +a = 4 +v = 16 +b = 14 +while v >= 11 : + print(b) + v = v - 2 +# is clone +1 + +# snippet 1 +p = 11 +d = 7 +while p > 10 : + print(d) + p = p - 2 +# snippet 2 +a = 4 +v = 16 +b = 14 +while v >= 11 : + print(b) + v = v - 2 +# is clone +1 + +# snippet 1 +t = 0 +t = 12 +p = 8 +while p > 5 : + print(p) + p = p - 2 +# snippet 2 +l = 2 +a = 13 +l = 8 +while l > 5 : + print(l) + l = l - 2 +# is clone +1 + +# snippet 1 +y = 4 +e = 17 +while y <= 7 : + print(e) + y = y + 2 +# snippet 2 +w = 7 +l = 10 +o = 19 +while w < 8 : + print(o) + w = w + 2 +# is clone +1 + +# snippet 1 +y = 4 +e = 17 +while y <= 7 : + print(e) + y = y + 2 +# snippet 2 +l = 0 +f = 3 +while f < 8 : + print(l) + f = f + 2 +# is clone +1 + +# snippet 1 +w = 7 +l = 10 +o = 19 +while w < 8 : + print(o) + w = w + 2 +# snippet 2 +l = 0 +f = 3 +while f < 8 : + print(l) + f = f + 2 +# is clone +1 + +# snippet 1 +y = 3 +l = 18 +p = 8 +while p < 11 : + print(p) + p = p + 2 +# snippet 2 +b = 9 +while b <= 10 : + print(b) + b = b + 2 +# is clone +1 + +# snippet 1 +o = 10 +l = 11 +r = 13 +while l > 6 : + print(r) + l = l - 3 +# snippet 2 +i = 11 +a = 14 +while i > 6 : + print(a) + i = i - 3 +# is clone +1 + +# snippet 1 +k = 17 +e = 8 +while e < 13 : + print(k) + e = e + 3 +# snippet 2 +y = 3 +n = 14 +while y >= 0 : + print(y) + y = y - 2 +# is clone +0 + +# snippet 1 +k = 17 +e = 8 +while e < 13 : + print(k) + e = e + 3 +# snippet 2 +t = 2 +while t > -1 : + print(t) + t = t - 2 +# is clone +0 + +# snippet 1 +y = 7 +a = 16 +while y <= 12 : + print(a) + y = y + 3 +# snippet 2 +y = 3 +n = 14 +while y >= 0 : + print(y) + y = y - 2 +# is clone +0 + +# snippet 1 +y = 7 +a = 16 +while y <= 12 : + print(a) + y = y + 3 +# snippet 2 +t = 2 +while t > -1 : + print(t) + t = t - 2 +# is clone +0 + +# snippet 1 +r = 6 +while r > 1 : + print(r) + r = r - 3 +# snippet 2 +e = 13 +l = 14 +while e >= 12 : + print(l) + e = e - 2 +# is clone +0 + +# snippet 1 +r = 6 +while r > 1 : + print(r) + r = r - 3 +# snippet 2 +f = 1 +j = 14 +while j > 11 : + print(f) + j = j - 2 +# is clone +0 + +# snippet 1 +z = 7 +while z >= 2 : + print(z) + z = z - 3 +# snippet 2 +e = 13 +l = 14 +while e >= 12 : + print(l) + e = e - 2 +# is clone +0 + +# snippet 1 +z = 7 +while z >= 2 : + print(z) + z = z - 3 +# snippet 2 +f = 1 +j = 14 +while j > 11 : + print(f) + j = j - 2 +# is clone +0 + +# snippet 1 +x = 2 +e = 16 +f = 9 +while f > 8 : + print(f) + f = f - 2 +# snippet 2 +w = 6 +q = 10 +v = 8 +while q <= 11 : + print(w) + q = q + 2 +# is clone +0 + +# snippet 1 +x = 2 +e = 16 +f = 9 +while f > 8 : + print(f) + f = f - 2 +# snippet 2 +l = 2 +s = 4 +w = 6 +while w <= 11 : + print(l) + w = w + 2 +# is clone +0 + +# snippet 1 +v = 11 +while v > 8 : + print(v) + v = v - 2 +# snippet 2 +w = 6 +q = 10 +v = 8 +while q <= 11 : + print(w) + q = q + 2 +# is clone +0 + +# snippet 1 +v = 11 +while v > 8 : + print(v) + v = v - 2 +# snippet 2 +l = 2 +s = 4 +w = 6 +while w <= 11 : + print(l) + w = w + 2 +# is clone +0 + +# snippet 1 +z = 18 +while z < 21 : + print(z) + z = z + 2 +# snippet 2 +p = 12 +while p < 13 : + print(p) + p = p + 1 +# is clone +0 + +# snippet 1 +z = 18 +while z < 21 : + print(z) + z = z + 2 +# snippet 2 +e = 12 +while e < 13 : + print(e) + e = e + 1 +# is clone +0 + +# snippet 1 +s = 19 +while s <= 20 : + print(s) + s = s + 2 +# snippet 2 +p = 12 +while p < 13 : + print(p) + p = p + 1 +# is clone +0 + +# snippet 1 +s = 19 +while s <= 20 : + print(s) + s = s + 2 +# snippet 2 +e = 12 +while e < 13 : + print(e) + e = e + 1 +# is clone +0 + +# snippet 1 +k = 0 +r = 4 +b = 2 +while b < 7 : + print(b) + b = b + 3 +# snippet 2 +h = 0 +h = 11 +z = 16 +while z > 11 : + print(z) + z = z - 2 +# is clone +0 + +# snippet 1 +k = 0 +r = 4 +b = 2 +while b < 7 : + print(b) + b = b + 3 +# snippet 2 +k = 17 +while k >= 12 : + print(k) + k = k - 2 +# is clone +0 + +# snippet 1 +p = 1 +y = 8 +while p <= 6 : + print(p) + p = p + 3 +# snippet 2 +h = 0 +h = 11 +z = 16 +while z > 11 : + print(z) + z = z - 2 +# is clone +0 + +# snippet 1 +p = 1 +y = 8 +while p <= 6 : + print(p) + p = p + 3 +# snippet 2 +k = 17 +while k >= 12 : + print(k) + k = k - 2 +# is clone +0 + +# snippet 1 +d = 18 +i = 7 +s = 3 +while d >= 15 : + print(d) + d = d - 2 +# snippet 2 +j = 15 +o = 9 +while o >= 8 : + print(j) + o = o - 2 +# is clone +0 + +# snippet 1 +d = 18 +i = 7 +s = 3 +while d >= 15 : + print(d) + d = d - 2 +# snippet 2 +r = 15 +c = 12 +t = 16 +while c > 7 : + print(r) + c = c - 2 +# is clone +0 + +# snippet 1 +y = 16 +while y >= 15 : + print(y) + y = y - 2 +# snippet 2 +j = 15 +o = 9 +while o >= 8 : + print(j) + o = o - 2 +# is clone +0 + +# snippet 1 +y = 16 +while y >= 15 : + print(y) + y = y - 2 +# snippet 2 +r = 15 +c = 12 +t = 16 +while c > 7 : + print(r) + c = c - 2 +# is clone +0 + +# snippet 1 +c = 15 +o = 1 +while c > 10 : + print(o) + c = c - 2 +# snippet 2 +t = 0 +t = 12 +p = 8 +while p > 5 : + print(p) + p = p - 2 +# is clone +0 + +# snippet 1 +c = 15 +o = 1 +while c > 10 : + print(o) + c = c - 2 +# snippet 2 +l = 2 +a = 13 +l = 8 +while l > 5 : + print(l) + l = l - 2 +# is clone +0 + +# snippet 1 +p = 11 +d = 7 +while p > 10 : + print(d) + p = p - 2 +# snippet 2 +t = 0 +t = 12 +p = 8 +while p > 5 : + print(p) + p = p - 2 +# is clone +0 + +# snippet 1 +p = 11 +d = 7 +while p > 10 : + print(d) + p = p - 2 +# snippet 2 +l = 2 +a = 13 +l = 8 +while l > 5 : + print(l) + l = l - 2 +# is clone +0 + +# snippet 1 +a = 4 +v = 16 +b = 14 +while v >= 11 : + print(b) + v = v - 2 +# snippet 2 +y = 4 +e = 17 +while y <= 7 : + print(e) + y = y + 2 +# is clone +0 + +# snippet 1 +a = 4 +v = 16 +b = 14 +while v >= 11 : + print(b) + v = v - 2 +# snippet 2 +w = 7 +l = 10 +o = 19 +while w < 8 : + print(o) + w = w + 2 +# is clone +0 + +# snippet 1 +w = 14 +k = 13 +while w >= 11 : + print(k) + w = w - 2 +# snippet 2 +y = 4 +e = 17 +while y <= 7 : + print(e) + y = y + 2 +# is clone +0 + +# snippet 1 +w = 14 +k = 13 +while w >= 11 : + print(k) + w = w - 2 +# snippet 2 +w = 7 +l = 10 +o = 19 +while w < 8 : + print(o) + w = w + 2 +# is clone +0 + +# snippet 1 +l = 0 +f = 3 +while f < 8 : + print(l) + f = f + 2 +# snippet 2 +y = 3 +l = 18 +p = 8 +while p < 11 : + print(p) + p = p + 2 +# is clone +0 + +# snippet 1 +l = 0 +f = 3 +while f < 8 : + print(l) + f = f + 2 +# snippet 2 +b = 9 +while b <= 10 : + print(b) + b = b + 2 +# is clone +0 + +# snippet 1 +y = 3 +l = 18 +p = 8 +while p < 11 : + print(p) + p = p + 2 +# snippet 2 +o = 10 +l = 11 +r = 13 +while l > 6 : + print(r) + l = l - 3 +# is clone +0 + +# snippet 1 +b = 9 +while b <= 10 : + print(b) + b = b + 2 +# snippet 2 +o = 10 +l = 11 +r = 13 +while l > 6 : + print(r) + l = l - 3 +# is clone +0 + diff --git a/tasks/clone_detection_task/finetuning/tinylm-fine-tuning.ipynb b/tasks/clone_detection_task/finetuning/tinylm-fine-tuning.ipynb new file mode 100644 index 0000000..5de4d64 --- /dev/null +++ b/tasks/clone_detection_task/finetuning/tinylm-fine-tuning.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-09-21T17:35:22.591942Z","iopub.status.busy":"2024-09-21T17:35:22.591390Z","iopub.status.idle":"2024-09-21T17:35:26.107377Z","shell.execute_reply":"2024-09-21T17:35:26.106460Z","shell.execute_reply.started":"2024-09-21T17:35:22.591896Z"},"trusted":true},"outputs":[],"source":["import random\n","import os\n","import pickle\n","import time\n","import datetime\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.optim.lr_scheduler import StepLR\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","import re"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-09-21T17:35:26.109879Z","iopub.status.busy":"2024-09-21T17:35:26.109335Z","iopub.status.idle":"2024-09-21T17:35:26.154157Z","shell.execute_reply":"2024-09-21T17:35:26.153051Z","shell.execute_reply.started":"2024-09-21T17:35:26.109834Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Device set to cuda.\n"]}],"source":["# Set the random seed for reproducibility\n","seed = 42\n","torch.manual_seed(seed)\n","random.seed(seed)\n","np.random.seed(seed)\n","\n","# Set the device to GPU if available, otherwise CPU\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","print(f\"Device set to {device}.\")"]},{"cell_type":"markdown","metadata":{},"source":["# Data Preparation"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-09-21T17:35:26.156265Z","iopub.status.busy":"2024-09-21T17:35:26.155584Z","iopub.status.idle":"2024-09-21T17:35:26.161705Z","shell.execute_reply":"2024-09-21T17:35:26.160755Z","shell.execute_reply.started":"2024-09-21T17:35:26.156220Z"},"trusted":true},"outputs":[],"source":["# Helper functions to load and save data\n","def save_data(data, file_path):\n"," with open(file_path, 'w') as f:\n"," f.write(data)\n","\n","def load_data(file_path):\n"," with open(file_path, 'r') as f:\n"," return f.read()"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-09-21T17:35:26.164357Z","iopub.status.busy":"2024-09-21T17:35:26.164004Z","iopub.status.idle":"2024-09-21T17:35:26.171508Z","shell.execute_reply":"2024-09-21T17:35:26.170644Z","shell.execute_reply.started":"2024-09-21T17:35:26.164314Z"},"trusted":true},"outputs":[],"source":["# Directory where the data is stored\n","DATA_DIR = \"/kaggle/input/all-level-dataset-1-46m\""]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-09-21T17:35:26.173180Z","iopub.status.busy":"2024-09-21T17:35:26.172736Z","iopub.status.idle":"2024-09-21T17:35:26.184870Z","shell.execute_reply":"2024-09-21T17:35:26.183993Z","shell.execute_reply.started":"2024-09-21T17:35:26.173138Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["found vocab_size = 52 (inside /kaggle/input/all-level-dataset-1-46m/meta.pkl)\n"]}],"source":["# Attempt to derive vocab_size from the dataset\n","\n","meta_path = os.path.join(os.path.join(DATA_DIR, 'meta.pkl'))\n","vocab_size = None\n","\n","if os.path.exists(meta_path):\n"," with open(meta_path, 'rb') as f:\n"," meta = pickle.load(f)\n"," vocab_size = meta['vocab_size']\n"," print(f\"found vocab_size = {vocab_size} (inside {meta_path})\")\n","else:\n"," print(\"Meta file not found. Please ensure the meta.pkl file is present in the data directory.\")\n","\n","# Encode and decode functions for character-level Tokenzation \n","def encode(s):\n"," return [meta['stoi'][c] for c in s]\n","\n","def decode(l):\n"," return ''.join([meta['itos'][i] for i in l])"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-09-21T17:35:26.186192Z","iopub.status.busy":"2024-09-21T17:35:26.185920Z","iopub.status.idle":"2024-09-21T17:35:26.193860Z","shell.execute_reply":"2024-09-21T17:35:26.192864Z","shell.execute_reply.started":"2024-09-21T17:35:26.186162Z"},"trusted":true},"outputs":[],"source":["# Load encoded data\n","train_data = np.memmap(os.path.join(DATA_DIR, 'train.bin'), dtype=np.uint16, mode='r')\n","val_data = np.memmap(os.path.join(DATA_DIR, 'val.bin'), dtype=np.uint16, mode='r')"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:14.261052Z","iopub.status.busy":"2024-09-15T13:31:14.260152Z","iopub.status.idle":"2024-09-15T13:31:14.267116Z","shell.execute_reply":"2024-09-15T13:31:14.266144Z","shell.execute_reply.started":"2024-09-15T13:31:14.261009Z"},"trusted":true},"outputs":[],"source":["# Hyperparameters\n","block_size = 256\n","vocab_size = 53\n","n_embd = 372\n","n_head = 6\n","n_layer = 6\n","dropout = 0\n","batch_size = 64\n","max_iters = 150000\n","learning_rate = 1e-3\n","miles = [int(max_iters * m) for m in [0.7, 0.8, 0.9]]\n","eval_interval = 20000\n","eval_iters = 1000\n","lora_r = 24\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","compile = False"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:15.914961Z","iopub.status.busy":"2024-09-15T13:31:15.914192Z","iopub.status.idle":"2024-09-15T13:31:18.889208Z","shell.execute_reply":"2024-09-15T13:31:18.888287Z","shell.execute_reply.started":"2024-09-15T13:31:15.914923Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Data in tokens: 231163106\n","Number of iters for one pseudo-epoch : 14109\n","Number of pseudo-epochs : 10.63\n"]}],"source":["batch_size = 64 \n","block_size = 256 \n","num_iters = 150000\n","with open('/kaggle/input/all-level-dataset-1-46m/train.txt', 'r') as f:\n"," data = f.read()\n"," print(f\"Data in tokens: {len(data)}\")\n"," iters4epoch = len(data)//(batch_size * block_size)\n"," print(f\"Number of iters for one pseudo-epoch : {iters4epoch}\")\n"," print(f\"Number of pseudo-epochs : {num_iters / iters4epoch:.2f}\")"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:21.883001Z","iopub.status.busy":"2024-09-15T13:31:21.882645Z","iopub.status.idle":"2024-09-15T13:31:21.887668Z","shell.execute_reply":"2024-09-15T13:31:21.886398Z","shell.execute_reply.started":"2024-09-15T13:31:21.882969Z"},"trusted":true},"outputs":[],"source":["model_path = \"/kaggle/working/1777944_2024-09-14_20-51.pth\""]},{"cell_type":"markdown","metadata":{},"source":["# MODEL"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:24.849762Z","iopub.status.busy":"2024-09-15T13:31:24.848987Z","iopub.status.idle":"2024-09-15T13:31:24.976316Z","shell.execute_reply":"2024-09-15T13:31:24.975230Z","shell.execute_reply.started":"2024-09-15T13:31:24.849721Z"},"trusted":true},"outputs":[],"source":["class LayerNorm(nn.Module):\n"," \"\"\" LayerNorm with an optional bias. PyTorch's LayerNorm doesn't support simply bias=False \"\"\"\n","\n"," def __init__(self, ndim, bias):\n"," super().__init__()\n"," self.weight = nn.Parameter(torch.ones(ndim))\n"," self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n","\n"," def forward(self, input):\n"," return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)\n","\n","class Head(nn.Module):\n"," \"\"\"One head of self-attention.\"\"\"\n","\n"," def __init__(self, head_size):\n"," super().__init__()\n"," self.key = nn.Linear(n_embd, head_size, bias=False)\n"," self.query = nn.Linear(n_embd, head_size, bias=False)\n"," self.value = nn.Linear(n_embd, head_size, bias=False)\n"," self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n"," self.dropout = nn.Dropout(dropout)\n","\n"," def forward(self, x):\n"," B, T, C = x.shape\n"," k = self.key(x) # (B, T, head_size)\n"," q = self.query(x) # (B, T, head_size)\n"," v = self.value(x) # (B, T, head_size)\n","\n"," # Apply scaled dot-product attention\n"," out = torch.nn.functional.scaled_dot_product_attention(\n"," q, k, v, attn_mask=None, dropout_p=dropout if self.training else 0, is_causal=True\n"," )\n"," \n"," return out\n"," \n","\n","class MultiHeadAttention(nn.Module):\n"," \"\"\"Multiple heads of self-attention in parallel.\"\"\"\n","\n"," def __init__(self, num_heads, head_size):\n"," super().__init__()\n"," self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n"," self.proj = nn.Linear(n_embd, n_embd)\n"," self.dropout = nn.Dropout(dropout)\n"," \n"," def forward(self, x):\n"," # Concatenate the outputs from each head\n"," out = torch.cat([h(x) for h in self.heads], dim=-1)\n"," out = self.dropout(self.proj(out))\n"," return out\n"," \n","class FeedForward(nn.Module):\n"," \"\"\"A simple linear layer followed by a non-linearity.\"\"\"\n","\n"," def __init__(self, n_embd):\n"," super().__init__()\n"," self.net = nn.Sequential(\n"," nn.Linear(n_embd, 4 * n_embd, bias=False),\n"," nn.GELU(),\n"," nn.Linear(4 * n_embd, n_embd, bias=False),\n"," nn.Dropout(dropout),\n"," )\n","\n"," def forward(self, x):\n"," return self.net(x)\n","\n","class LinearLoRA(nn.Module):\n"," def __init__(self, original_layer, rank=8):\n"," super().__init__()\n"," self.original_layer = original_layer\n"," self.original_layer.weight.requires_grad = False\n"," self.rank = rank\n"," \n"," self.lora_a = nn.Parameter(torch.randn((original_layer.in_features, rank)))\n"," self.lora_b = nn.Parameter(torch.randn((rank, original_layer.out_features)))\n"," \n"," self.reset_parameters()\n"," \n"," def reset_parameters(self):\n"," nn.init.kaiming_uniform_(self.lora_a, a=np.sqrt(5))\n"," nn.init.zeros_(self.lora_b)\n"," \n"," def forward(self, x):\n"," lora_output = x @ self.lora_a @ self.lora_b\n"," return self.original_layer(x) + lora_output\n"," \n","class Block(nn.Module):\n"," \"\"\"Transformer block: communication followed by feedforward.\"\"\"\n","\n"," def __init__(self, n_embd, n_head):\n"," super().__init__()\n"," head_size = n_embd // n_head\n"," self.sa = MultiHeadAttention(n_head, head_size)\n"," self.ffwd = FeedForward(n_embd)\n"," self.ln1 = nn.LayerNorm(n_embd, bias=False)\n"," self.ln2 = nn.LayerNorm(n_embd, bias=False)\n","\n"," def forward(self, x):\n"," x = x + self.sa(self.ln1(x))\n"," x = x + self.ffwd(self.ln2(x))\n"," return x\n","\n","class GPT(nn.Module):\n"," \"\"\"GPT language model.\"\"\"\n","\n"," def __init__(self):\n"," super().__init__()\n"," self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n"," self.position_embedding_table = nn.Embedding(block_size, n_embd)\n"," self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n"," self.ln_f = nn.LayerNorm(n_embd, bias=False) \n"," self.lm_head = nn.Linear(n_embd, vocab_size)\n","\n"," def forward(self, idx, targets=None):\n"," B, T = idx.shape\n","\n"," # Token and position embeddings\n"," tok_emb = self.token_embedding_table(idx) # (B, T, n_embd)\n"," pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, n_embd)\n"," x = tok_emb + pos_emb # (B, T, n_embd)\n"," x = self.blocks(x) # (B, T, n_embd)\n"," x = self.ln_f(x) # (B, T, n_embd)\n"," logits = self.lm_head(x) # (B, T, vocab_size)\n","\n"," # Compute loss if targets are provided\n"," if targets is None:\n"," loss = None\n"," else:\n"," B, T, C = logits.shape\n"," logits = logits.view(B * T, C)\n"," targets = targets.view(B * T)\n"," loss = F.cross_entropy(logits, targets)\n","\n"," return logits, loss\n"," \n"," def generate(self, idx, max_new_tokens):\n"," \"\"\"Generate new tokens given an initial context `idx`.\"\"\"\n"," for _ in range(max_new_tokens):\n"," idx_cond = idx[:, -block_size:] # Crop to the last block_size tokens\n"," logits, _ = self(idx_cond)\n"," logits = logits[:, -1, :] # Focus on the last time step\n"," probs = F.softmax(logits, dim=-1) # Convert to probabilities\n"," idx_next = torch.multinomial(probs, num_samples=1) # Sample from the distribution\n"," idx = torch.cat((idx, idx_next), dim=1) # Append sampled index to the sequence\n"," return idx\n"," \n"," def activate_lora(self, r=8, heads_only=False, freeze_others=True):\n"," self.lora_rank = r\n"," self.replace_multihead_attention_recursion(heads_only)\n"," if freeze_others:\n"," self.freeze_parameters_except_lora_and_bias()\n"," \n"," def replace_multihead_attention_recursion(self, heads_only=False, model=None):\n"," children = self.named_children() if model is None else model.named_children()\n"," for name, module in children:\n"," if heads_only and name in {\"query\", \"key\", \"value\"}:\n"," # Replace with Lora SelfAttention\n"," new_layer = LinearLoRA(module, rank=self.lora_rank)\n","\n"," if model == None:\n"," self.__setattr__(name, new_layer)\n"," else:\n"," setattr(model, name, new_layer)\n"," \n"," elif isinstance(module, nn.Linear) and not heads_only:\n"," new_layer = LinearLoRA(module, rank=self.lora_rank)\n"," \n"," if model == None:\n"," self.__setattr__(name, new_layer)\n"," else:\n"," setattr(model, name, new_layer)\n"," \n"," else:\n"," # Recursive call for child modules\n"," self.replace_multihead_attention_recursion(heads_only, model=module)\n"," \n"," \n"," def freeze_parameters_except_lora_and_bias(self):\n"," for name, param in self.named_parameters():\n"," is_trainable = (\n"," \"lora_\" in name\n"," #(self.train_layer_norms and \"LayerNorm\" in name)\n"," )\n","\n"," param.requires_grad = is_trainable"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:24.978256Z","iopub.status.busy":"2024-09-15T13:31:24.977865Z","iopub.status.idle":"2024-09-15T13:31:24.992945Z","shell.execute_reply":"2024-09-15T13:31:24.992052Z","shell.execute_reply.started":"2024-09-15T13:31:24.978218Z"},"trusted":true},"outputs":[],"source":["# Get random batch of data\n","def get_batch(split):\n"," data = train_data if split == 'train' else val_data\n"," ix = torch.randint(len(data) - block_size, (batch_size,))\n"," x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])\n"," y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])\n"," x, y = x.to(device), y.to(device)\n"," return x, y\n","\n","# Estimate loss on train and val splits\n","@torch.no_grad()\n","def estimate_loss():\n"," out = {}\n"," model.eval()\n"," for split in ['train', 'val']:\n"," losses = torch.zeros(eval_iters) \n"," for k in range(eval_iters):\n"," X, Y = get_batch(split)\n"," logits, loss = model(X, Y)\n"," losses[k] = loss.item()\n"," out[split] = losses.mean()\n"," model.train()\n"," return out"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:24.994762Z","iopub.status.busy":"2024-09-15T13:31:24.994455Z","iopub.status.idle":"2024-09-15T13:31:25.008917Z","shell.execute_reply":"2024-09-15T13:31:25.008019Z","shell.execute_reply.started":"2024-09-15T13:31:24.994731Z"},"trusted":true},"outputs":[],"source":["def load_model():\n"," \"\"\"\n"," Load pre-trained model based on the provided model name.\n"," \"\"\"\n"," if not os.path.exists(model_path):\n"," raise FileNotFoundError(f\"Model file '{model_path}' not found.\")\n"," \n"," model = GPT()\n"," print(\"Compiling the model...\\n\")\n"," r = -1\n"," if compile:\n"," try:\n"," model = torch.compile(model) # requires PyTorch 2.0\n"," except Exception as e:\n"," pass\n","\n"," checkpoint = torch.load(model_path, map_location=device)\n"," if 'lora_rank' in checkpoint.keys():\n"," r = checkpoint['lora_rank']\n"," state = checkpoint['state_dict']\n","\n"," if r > 0:\n"," model.activate_lora(r)\n"," model.load_state_dict(state)\n"," else:\n"," model.load_state_dict(checkpoint)\n"," else:\n"," checkpoint = torch.load(model_path, map_location=device)\n"," if 'lora_rank' in checkpoint.keys():\n"," r = checkpoint['lora_rank']\n"," state_dict = checkpoint['state_dict']\n","\n"," if r > 0:\n"," model.activate_lora(r)\n"," else:\n"," state_dict = checkpoint\n"," \n"," state_dict_keys = map(lambda x: x.replace(\"_orig_mod.\", \"\"), state_dict.keys())\n"," state_dict = dict(zip(state_dict_keys, state_dict.values()))\n"," model.load_state_dict(state_dict)\n","\n"," m = model.to(device)\n"," return m, r"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:25.351243Z","iopub.status.busy":"2024-09-15T13:31:25.350410Z","iopub.status.idle":"2024-09-15T13:31:25.871257Z","shell.execute_reply":"2024-09-15T13:31:25.870319Z","shell.execute_reply.started":"2024-09-15T13:31:25.351204Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Loading model...\n","\n","Compiling the model...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/tmp/ipykernel_36/1873016250.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n"," checkpoint = torch.load(model_path, map_location=device)\n"]},{"name":"stdout","output_type":"stream","text":["The model has 1777944 trainable parameters\n"]}],"source":["# Initialize model and move it to the device (GPU)\n","if len(model_path) > 0:\n"," print(\"Loading model...\\n\")\n"," model, r = load_model()\n"," \n"," if r > 0:\n"," lora_rank = r\n","\n","else:\n"," model = GPT()\n"," m = model.to(device)\n"," r = -1\n","\n"," # compile the model\n"," if compile:\n"," print(\"compiling the model... (takes a ~minute)\")\n"," model = torch.compile(model)\n","\n","if lora_r > 0 and r < 0:\n"," print(\"Activating LoRA...\")\n"," model.activate_lora(lora_r)\n"," model = model.to(device)\n","\n","num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n","# num_parameters_hr = human_readable(num_parameters)\n","print(f'The model has {num_parameters} trainable parameters')"]},{"cell_type":"markdown","metadata":{},"source":["# TRAINING "]},{"cell_type":"code","execution_count":20,"metadata":{"execution":{"iopub.execute_input":"2024-09-14T20:51:58.474703Z","iopub.status.busy":"2024-09-14T20:51:58.474330Z","iopub.status.idle":"2024-09-14T20:51:58.483383Z","shell.execute_reply":"2024-09-14T20:51:58.482564Z","shell.execute_reply.started":"2024-09-14T20:51:58.474666Z"},"trusted":true},"outputs":[],"source":["# Initialize optimizer\n","optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n","\n","# Initialize learning rate scheduler\n","scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=miles, gamma=0.1)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-09-14T20:51:59.169305Z","iopub.status.busy":"2024-09-14T20:51:59.168949Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["iter 0 | train loss 11.9293 | val loss 11.9305\n","iter 20000 | train loss 0.4261 | val loss 0.4256\n","iter 40000 | train loss 0.4201 | val loss 0.4198\n","iter 60000 | train loss 0.4178 | val loss 0.4176\n","iter 80000 | train loss 0.4168 | val loss 0.4169\n","iter 100000 | train loss 0.4158 | val loss 0.4168\n","iter 120000 | train loss 0.4122 | val loss 0.4128\n","iter 140000 | train loss 0.4125 | val loss 0.4126\n"]}],"source":["# Get current date and hour to get track of experiments\n","now = datetime.datetime.now()\n","date_hour = now.strftime(\"%Y-%m-%d_%H-%M\")\n","\n","# Train\n","# Start training timer\n","start_time = time.time()\n","\n","# Training loop\n","for iter in range(max_iters):\n","\n"," # evaluate the model on the train and val splits and log the losses\n"," if iter % eval_interval == 0:\n"," losses = estimate_loss()\n"," print(f'iter {iter:5d} | train loss {losses[\"train\"]:.4f} | val loss {losses[\"val\"]:.4f}')\n"," \n"," # train the model for one iteration\n"," xb, yb = get_batch('train')\n","\n"," # forward pass\n"," logits, loss = model(xb, yb)\n"," optimizer.zero_grad(set_to_none=True)\n"," #loss.requires_grad = True\n"," loss.backward()\n"," optimizer.step()\n","\n"," # Step the scheduler\n"," scheduler.step()\n","\n","# End training timer\n","end_time = time.time()\n","print(f'Training time: {(end_time - start_time) / 60} min')\n","\n","# Save the trained model\n","model_path = f\"{num_parameters}_{date_hour}.pth\"\n","checkpoint = {\n"," 'lora_rank': model.lora_rank if(hasattr(model, \"lora_rank\")) else -1,\n"," 'state_dict': model.state_dict()\n","}\n","\n","torch.save(checkpoint, f\"{num_parameters}_{date_hour}.pth\")\n","print(f\"Model saved to {num_parameters}_{date_hour}.pth\\n\")\n","\n","losses = estimate_loss()\n","print(f'iter {iter:5d} | train loss {losses[\"train\"]:.4f} | val loss {losses[\"val\"]:.4f}')"]},{"cell_type":"markdown","metadata":{},"source":["# EVALUATION"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:32.911980Z","iopub.status.busy":"2024-09-15T13:31:32.911136Z","iopub.status.idle":"2024-09-15T13:31:32.919244Z","shell.execute_reply":"2024-09-15T13:31:32.918532Z","shell.execute_reply.started":"2024-09-15T13:31:32.911939Z"},"trusted":true},"outputs":[],"source":["test_data = np.memmap(os.path.join(os.path.join(DATA_DIR, 'test.bin')), dtype=np.uint16, mode='r')"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T10:42:28.256543Z","iopub.status.busy":"2024-09-15T10:42:28.255715Z","iopub.status.idle":"2024-09-15T10:44:12.790061Z","shell.execute_reply":"2024-09-15T10:44:12.789192Z","shell.execute_reply.started":"2024-09-15T10:42:28.256506Z"},"trusted":true},"outputs":[],"source":["examples = decode(test_data).split(\"\\n\\n\")\n","examples = [example for example in examples if example]"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:35.508103Z","iopub.status.busy":"2024-09-15T13:31:35.507234Z","iopub.status.idle":"2024-09-15T13:31:35.514655Z","shell.execute_reply":"2024-09-15T13:31:35.513722Z","shell.execute_reply.started":"2024-09-15T13:31:35.508063Z"},"trusted":true},"outputs":[],"source":["def evaluate_example(example, model, max_new_tokens=4):\n","\n"," # Split example and determine maximum new tokens allowed\n"," splited_example = example.split(\"# is clone\")\n"," encoded_example = torch.tensor(encode(splited_example[0] + \"# is clone\"), dtype=torch.long).unsqueeze(0).to(device)\n"," prompt_text = splited_example[0] + \"# is clone\"\n","\n"," result_example = splited_example[-1]\n"," model.eval()\n"," # Generate response from model and extract generated results\n"," response = decode(model.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist())\n"," splited_response = response.split(\"# is clone\")\n"," result_response = splited_response[-1]\n"," \n"," return prompt_text, result_example.strip(), result_response.strip()"]},{"cell_type":"code","execution_count":16,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:35.854443Z","iopub.status.busy":"2024-09-15T13:31:35.853589Z","iopub.status.idle":"2024-09-15T13:31:35.859665Z","shell.execute_reply":"2024-09-15T13:31:35.858659Z","shell.execute_reply.started":"2024-09-15T13:31:35.854397Z"},"trusted":true},"outputs":[],"source":["# Write results to file\n","def write_results_to_file(output_file, prompt, real_results, generated_results):\n"," df = pd.DataFrame({\n"," 'Prompt': prompt,\n"," 'Real_Results': real_results,\n"," 'Generated_Results': generated_results\n"," })\n"," df.to_csv(output_file, index=False)"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:31:36.520339Z","iopub.status.busy":"2024-09-15T13:31:36.519967Z","iopub.status.idle":"2024-09-15T13:31:36.525751Z","shell.execute_reply":"2024-09-15T13:31:36.524864Z","shell.execute_reply.started":"2024-09-15T13:31:36.520301Z"},"trusted":true},"outputs":[],"source":["def evaluate_pair(real, generated_result):\n"," # Determine the length of the shorter and longer strings\n"," min_len = min(len(real), len(generated_result))\n"," max_len = max(len(real), len(generated_result))\n"," match_count = 0\n"," if real == generated_result:\n"," match_count = 1\n"," ratio = match_count\n"," return ratio"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T10:47:10.395606Z","iopub.status.busy":"2024-09-15T10:47:10.394863Z","iopub.status.idle":"2024-09-15T10:47:10.400642Z","shell.execute_reply":"2024-09-15T10:47:10.399653Z","shell.execute_reply.started":"2024-09-15T10:47:10.395568Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["# snippet 1\n","o = 0\n","d = 2\n","o = 8\n","c = (o + 7)\n","for d in range(0, 10) :\n","\tprint(c)\n","# snippet 2\n","k = 2\n","f = 6\n","l = k + f\n","for f in range(5, 17, 1) :\n","\tprint(l)\n","# is clone\n","0\n"]}],"source":["print(examples[182782])"]},{"cell_type":"code","execution_count":214,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:57:37.972425Z","iopub.status.busy":"2024-09-15T13:57:37.971656Z","iopub.status.idle":"2024-09-15T13:57:37.976509Z","shell.execute_reply":"2024-09-15T13:57:37.975608Z","shell.execute_reply.started":"2024-09-15T13:57:37.972387Z"},"trusted":true},"outputs":[],"source":["# Start evaluation process\n","prompt = []\n","real_results = []\n","generated_results = []"]},{"cell_type":"code","execution_count":215,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:57:38.192269Z","iopub.status.busy":"2024-09-15T13:57:38.191368Z","iopub.status.idle":"2024-09-15T13:57:38.303982Z","shell.execute_reply":"2024-09-15T13:57:38.303107Z","shell.execute_reply.started":"2024-09-15T13:57:38.192231Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 1/1 [00:00<00:00, 9.79it/s]"]},{"name":"stdout","output_type":"stream","text":["Accuracy: 100.00%\n"]},{"name":"stderr","output_type":"stream","text":["\n"]}],"source":["for i,example in enumerate(tqdm(examples)):\n"," try:\n"," prompt_text, real_result, result = evaluate_example(example, model)\n"," prompt.append(prompt_text)\n"," real_results.append(real_result)\n"," generated_results.append(result)\n"," except:\n"," prompt.append(prompt_text)\n"," real_results.append(real_result)\n"," generated_results.append(result)"]},{"cell_type":"code","execution_count":216,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:57:38.497830Z","iopub.status.busy":"2024-09-15T13:57:38.497135Z","iopub.status.idle":"2024-09-15T13:57:38.502452Z","shell.execute_reply":"2024-09-15T13:57:38.501543Z","shell.execute_reply.started":"2024-09-15T13:57:38.497788Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["# snippet 1\n","t = 2\n","i = 9\n","g = 7\n","p = (t / i)\n","for i in range(5, 6) :\n","\tprint(i - g)\n","# snippet 2\n","y = 0\n","f = 6\n","z = (f / f)-(y / 1)\n","for y in range(5, 6) :\n","\tprint(y - f)\n","# is clone\n"]}],"source":["print(prompt[0])"]},{"cell_type":"code","execution_count":217,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:57:38.847169Z","iopub.status.busy":"2024-09-15T13:57:38.846426Z","iopub.status.idle":"2024-09-15T13:57:38.851890Z","shell.execute_reply":"2024-09-15T13:57:38.850989Z","shell.execute_reply.started":"2024-09-15T13:57:38.847129Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1\n"]}],"source":["print(real_results[0])"]},{"cell_type":"code","execution_count":218,"metadata":{"execution":{"iopub.execute_input":"2024-09-15T13:57:39.201549Z","iopub.status.busy":"2024-09-15T13:57:39.200830Z","iopub.status.idle":"2024-09-15T13:57:39.206369Z","shell.execute_reply":"2024-09-15T13:57:39.205438Z","shell.execute_reply.started":"2024-09-15T13:57:39.201504Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["1\n"]}],"source":["print(generated_results[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"trusted":true},"outputs":[],"source":["score=0\n","\n","for real,generated in zip(real_results, generated_results):\n"," score+=evaluate_pair(real.strip(),generated.strip())\n","accuracy = score / len(generated_results)\n","print(f\"Accuracy: {accuracy * 100:.2f}%\")"]},{"cell_type":"code","execution_count":109,"metadata":{"execution":{"iopub.execute_input":"2024-09-10T03:31:53.895990Z","iopub.status.busy":"2024-09-10T03:31:53.895472Z","iopub.status.idle":"2024-09-10T03:31:54.200170Z","shell.execute_reply":"2024-09-10T03:31:54.199356Z","shell.execute_reply.started":"2024-09-10T03:31:53.895942Z"},"trusted":true},"outputs":[],"source":["# Store accuracy in a file\n","with open(\"accuracy.txt\", 'w') as f:\n"," f.write(f\"Accuracy: {accuracy * 100:.2f}%\\n\")\n","# Store predictions in a CSV file\n","write_results_to_file(f\"aboud_predictions.csv\", prompt, real_results, generated_results)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":5597979,"sourceId":9252801,"sourceType":"datasetVersion"},{"datasetId":5701278,"sourceId":9394191,"sourceType":"datasetVersion"}],"dockerImageVersionId":30762,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"}},"nbformat":4,"nbformat_minor":4} diff --git a/tasks/clone_detection_task/generating_data_process/code_execution.py b/tasks/clone_detection_task/generating_data_process/code_execution.py new file mode 100644 index 0000000..9db873e --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/code_execution.py @@ -0,0 +1,96 @@ +import random +import io +import os +import contextlib +import json +from tqdm import tqdm +from used_variables_detector import get_printed_and_condition_variables +from comparison_check import has_diff_var_comparison +import argparse + +def load_simplifications(file_path): + simplifications = [] + with open(file_path, 'r') as file: + lines = file.read() + simplification = lines.split('# Simplification')[1:] + for i in range(len(simplification)): + simplifications.append(simplification[i].split('\n\n\n#')[0]) + return simplifications + +def initialize_random_values(n): + return [random.randint(0, n) for _ in range(150)] + +def execute_code_with_random_initialization(snippet, variables, num, random_initializations): + result_true = has_diff_var_comparison(snippet) + if result_true: + # Code block when there's a comparison between different variables + + variables = snippet_initialization_code(variables, random_initializations, num, mode='different') + code = f"""{variables} + {snippet} + """ + + else: + # Code block when there's no comparison between different variables + variables = snippet_initialization_code(variables, random_initializations, num, mode='same') + code = f"""{variables} + {snippet} + """ + # Capture the output of the code + output = io.StringIO() + try: + with contextlib.redirect_stdout(output): + exec(code) + except Exception as e: + return str(e) + + return output.getvalue().strip() + +def snippet_initialization_code(variables, random_initializations, num, mode='different'): + variables_init = "" + if mode == 'different': + for i, var in enumerate(variables): + + variables_init += f"{var} = {random_initializations[i][num]}\n" + else: + for var in variables: + variables_init += f"{var} = {random_initializations[0][num]}\n" + return variables_init + +def main(): + parser = argparse.ArgumentParser(description='Execute code snippets with random initializations and capture output.') + parser.add_argument('--level', required=True, help='Specify the level (e.g: 1, 2, 3)') + parser.add_argument('--dataset-file', required=True, help='Path to the dataset file to execute.') + parser.add_argument('--output-file', required=True, help='Path to save the JSON output file.') + + args = parser.parse_args() + + simplifications = load_simplifications(args.dataset_file) + printed_vars = {} + for i, snippet in enumerate(simplifications): + printed_vars[i] = get_printed_and_condition_variables(snippet) + + random.seed(42) + + random_initializations = [initialize_random_values(22) for _ in range(10)] + + outputs = {} + # Check if the file exists + if os.path.exists(args.output_file): + with open(args.output_file, 'r') as f: + outputs = json.load(f) + + for i in tqdm(range(len(simplifications))): + output = [] + for num in range(150): # Each code will be executed 150 time + output.append(execute_code_with_random_initialization(simplifications[i], list(printed_vars[i]), num, random_initializations)) + outputs[i] = output + + + with open(args.output_file, "w") as write_file: + json.dump(outputs, write_file) + + print(f'The outputs of the simplified programs are stored in {args.output_file}') + +if __name__ == "__main__": + main() diff --git a/tasks/clone_detection_task/generating_data_process/code_generator.py b/tasks/clone_detection_task/generating_data_process/code_generator.py new file mode 100644 index 0000000..860726f --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/code_generator.py @@ -0,0 +1,393 @@ +from anytree import Node, RenderTree +import random +from io import StringIO +from contextlib import redirect_stdout +import argparse +import time +from tqdm.auto import tqdm +import hashlib +import os +import psutil +import importlib + + +class CodeGenerator: + def __init__(self): + """ + Initialize the CodeGenerator object with the given context-free grammar rules. + + """ + + self.init_count = 0 + self.max_init = 0 + + # Dictionary containing context-free grammar rules. + self.cfg_rules = { + # Variables and digits + "VARIABLE": ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z" ], + "DIGIT": [str(i) for i in range(20)], + + # Operators + "ARITHMETIC_OPERATOR": ["+", "-", "*", "/"], + "RELATIONAL_OPERATOR": ["<", ">", "<=", ">=", "!=", "=="], + "LOGICAL_OPERATOR_INFIX": ["and", "or"], + "LOGICAL_OPERATOR_PREFIX": ["not"], + "LOGICAL_OPERATOR": ["LOGICAL_OPERATOR_INFIX", "LOGICAL_OPERATOR_PREFIX"], + "OPERATOR": ["ARITHMETIC_OPERATOR"], + + # Formatting + "NEW_LINE": ["\n"], + "TAB_INDENT": ["\t"], + "BRACKET_OPEN": ['('], + "BRACKET_CLOSE": [')'], + "EQUALS": ["="], + "COLON": [":"], + "COMMA": [","], + + + # Keywords + "IF": ["if"], + "ELIF": ["elif"], + "ELSE": ["else"], + "FOR": ["for"], + "IN": ["in"], + "RANGE": ["range"], + "WHILE": ["while"], + "PRINT": ["print"], + + # Terms and expressions + "TERM": ["EXPRESSION_IDENTIFIER", "DIGIT"], + "EXPRESSION": ["TERM SPACE OPERATOR SPACE TERM"], + "ENCLOSED_EXPRESSION": ["BRACKET_OPEN EXPRESSION BRACKET_CLOSE"], + "DISPLAY_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE EXPRESSION_IDENTIFIER" , + "EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE DIGIT"], + + # Initializations and assignments + "IDENTIFIER_INITIALIZATION": ["IDENTIFIER_INITIALIZATION INITIALIZATION", + "INITIALIZATION"], + + "INITIALIZATION": ["VARIABLE SPACE EQUALS SPACE DIGIT NEW_LINE"], + + "SIMPLE_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , ""], + "ADVANCED_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE SIMPLE_ARITHMETIC_EVALUATION NEW_LINE", + "VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , + ""], + + "SIMPLE_ARITHMETIC_EVALUATION": ["SIMPLE_ARITHMETIC_EVALUATION ARITHMETIC_OPERATOR ENCLOSED_EXPRESSION", + "ENCLOSED_EXPRESSION", + ], + + # Conditions + "SIMPLE_IF_STATEMENT": ["IF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_IF_STATEMENT": ["IF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "SIMPLE_ELIF_STATEMENT": ["ELIF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_ELIF_STATEMENT": ["ELIF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "ELSE_STATEMENT": ["ELSE SPACE COLON NEW_LINE"], + + "CHAIN_CONDITION": ["CHAIN_CONDITION SPACE LOGICAL_OPERATOR_INFIX SPACE ENCLOSED_CONDITION", + "LOGICAL_OPERATOR_PREFIX SPACE ENCLOSED_CONDITION", + "ENCLOSED_CONDITION"], + "ENCLOSED_CONDITION": ["BRACKET_OPEN CONDITION BRACKET_CLOSE"], + "CONDITION": ["OPTIONAL_NOT CONDITION_EXPRESSION", "CONDITION_EXPRESSION"], + "CONDITION_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE EXPRESSION_IDENTIFIER", + "EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE DIGIT"], + "OPTIONAL_NOT": ["LOGICAL_OPERATOR_PREFIX SPACE", "SPACE"], + + # Loops + "FOR_HEADER": ["FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL COMMA SPACE STEP BRACKET_CLOSE SPACE COLON", + "FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL BRACKET_CLOSE SPACE COLON"], + "INITIAL": ["DIGIT"], + + "FOR_LOOP": ["FOR_HEADER NEW_LINE TAB_INDENT DISPLAY"], + "ADVANCED_FOR_LOOP": ["FOR_LOOP", + "FOR_HEADER NEW_LINE TAB_INDENT ADVANCED_DISPLAY"], + + + # While Loops + + # Definitions for relational operators + "RELATIONAL_OPERATOR_LESS": [ "<", "<="], + "RELATIONAL_OPERATOR_GREATER": [">", ">="], + + # Less than or equal conditions + "CONDITION_EXPRESSION_LESS": [ + "EXPRESSION_IDENTIFIER_WHILE SPACE RELATIONAL_OPERATOR_LESS SPACE FINAL_LESS" + ], + + # Greater than or equal conditions + "CONDITION_EXPRESSION_GREATER": [ + "EXPRESSION_IDENTIFIER_WHILE SPACE RELATIONAL_OPERATOR_GREATER SPACE FINAL_GREATER" + ], + + # While + "WHILE_HEADER_LESS": ["WHILE SPACE CONDITION_EXPRESSION_LESS SPACE COLON NEW_LINE"], + "WHILE_LOOP_LESS": ["WHILE_HEADER_LESS TAB_INDENT DISPLAY NEW_LINE TAB_INDENT UPDATE_LESS"], + "UPDATE_LESS": ["WHILE_IDENTIFIER SPACE EQUALS SPACE WHILE_IDENTIFIER SPACE + SPACE STEP"], + + "WHILE_HEADER_GREATER": ["WHILE SPACE CONDITION_EXPRESSION_GREATER SPACE COLON NEW_LINE"], + "WHILE_LOOP_GREATER": ["WHILE_HEADER_GREATER TAB_INDENT DISPLAY NEW_LINE TAB_INDENT UPDATE_GREATER"], + "UPDATE_GREATER": ["WHILE_IDENTIFIER SPACE EQUALS SPACE WHILE_IDENTIFIER SPACE - SPACE STEP"], + + # Displaying + "DISPLAY" : ["PRINT BRACKET_OPEN DISPLAY_IDENTIFIER BRACKET_CLOSE"], + "ADVANCED_DISPLAY" : ["DISPLAY", + "PRINT BRACKET_OPEN DISPLAY_EXPRESSION BRACKET_CLOSE"], + + + "LEVEL1.1": ["IDENTIFIER_INITIALIZATION SIMPLE_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL1.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL2.1": ["IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE SIMPLE_ELIF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY"], + "LEVEL2.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ADVANCED_ELIF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY"], + "LEVEL3.1": ["IDENTIFIER_INITIALIZATION FOR_LOOP"], + "LEVEL3.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_FOR_LOOP"], + + "LEVEL4.1": ["IDENTIFIER_INITIALIZATION WHILE_LOOP_LESS", "IDENTIFIER_INITIALIZATION WHILE_LOOP_GREATER"], + + "ALL": ["LEVEL1.1", "LEVEL1.2", "LEVEL2.1", "LEVEL2.2", "LEVEL3.1", "LEVEL3.2", "LEVEL4.1"], + + + } + + + + + def generate_code(self, symbol, assigned_identifiers, last_variable, for_init_step, parent=None ): + """ + Generate code recursively based on the context-free grammar rules. + + Parameters: + - symbol (str): The symbol to generate code for. + - assigned_identifiers (dict): Dictionary of assigned identifiers and their values. + - last_variable (set): Set of the last used variables. + - parent (Node): Parent node in the syntax tree. + + Returns: + - str: The generated code. + """ + node = Node(symbol, parent=parent) + + if symbol in self.cfg_rules: + if symbol == "IDENTIFIER_INITIALIZATION": + if self.init_count < self.max_init: + self.init_count += 1 + else: + symbol = "INITIALIZATION" + + rule = random.choice(self.cfg_rules[symbol]) + symbols = rule.split(" ") + + generated_symbols = [self.generate_code(s, assigned_identifiers, last_variable, for_init_step, node) for s in symbols] + + if symbol == "INITIAL": + init = generated_symbols[0] + for_init_step["initial_value"] = init + + + if symbol == "INITIALIZATION": + variable_name = generated_symbols[0] + variable_value = generated_symbols[4] + assigned_identifiers[variable_name] = variable_value + # assigned_identifiers.add(generated_symbols[0]) + + if (symbol == "SIMPLE_ASSIGNMENTS") or (symbol == "ADVANCED_ASSIGNMENTS"): + if generated_symbols[0]: + last_variable.add(generated_symbols[0]) + + return ''.join(generated_symbols) + + + if symbol == "WHILE_IDENTIFIER": + + return for_init_step.get("initial_var", "*") + + elif (symbol == "FINAL") or (symbol == "FINAL_LESS"): + + initial_value = for_init_step.get("initial_value", "0") + # Generate valid step_value and execution_count + valid_values = [(1, 2), (2, 1), (2, 2), (2, 3), (3, 2)] + step_value, execution_count = random.choice(valid_values) + for_init_step["step"] = str(step_value) + + final_value = step_value * execution_count + int(initial_value) - 1 + return str(final_value) + + elif symbol == "FINAL_GREATER": + + initial_value = for_init_step.get("initial_value", "0") + # Generate valid step_value and execution_count + valid_values = [(1, 2), (2, 1), (2, 2), (2, 3), (3, 2)] + step_value, execution_count = random.choice(valid_values) + for_init_step["step"] = str(step_value) + + final_value = int(initial_value) - step_value * execution_count + 1 + return str(final_value) + + + + elif symbol == "STEP": + + return for_init_step.get("step", "0") + + elif symbol == "EXPRESSION_IDENTIFIER": + identifier = random.choice(tuple(assigned_identifiers.keys())) if assigned_identifiers else random.choice(self.cfg_rules["DIGIT"]) + return identifier + + if symbol == "EXPRESSION_IDENTIFIER_WHILE": + + initial_var = random.choice(tuple(assigned_identifiers.keys())) if assigned_identifiers else random.choice(self.cfg_rules["DIGIT"]) + for_init_step["initial_var"] = initial_var + for_init_step["initial_value"] = assigned_identifiers[initial_var] + return initial_var + elif symbol == "DISPLAY_IDENTIFIER": + try: + return f"{tuple(last_variable)[0]}" + except: + return f"{random.choice(tuple(assigned_identifiers.keys()))}" + else: + return symbol + + + + + def print_tree(self, root): + """ + Print the syntax tree using the RenderTree utility from the anytree module. + + Parameters: + - root (Node): The root node of the syntax tree. + """ + for pre, _, node in RenderTree(root): + print(f"{pre}{node.name}") + + def generate_program(self, level): + """ + Generate a program based on the specified level. + + Parameters: + - level (str): The level of the program. + + Returns: + - Tuple[Node, str]: The syntax tree root node and the generated program. + """ + # assigned = set() + assigned = {} + last_variable = set() + for_init_step = {} + root = Node("ROOT") + + self.init_count = 0 + if level == "1.1": + self.max_init = 2 + elif level == "1.2": + self.max_init = 3 + elif level == "2.1": + self.max_init = 2 + elif level == "3.1": + self.max_init = 2 + elif level == "3.2": + self.max_init = 4 + elif level == "4.1": + self.max_init = 2 + else: + self.max_init = 5 + + if level == "ALL" : + level_passed = level + else : + level_passed = "LEVEL" + level + + program = self.generate_code(level_passed, assigned, last_variable, for_init_step, root) + + return root, program.replace("SPACE", " ") + + def memory_usage(self): + process = psutil.Process(os.getpid()) + mem_info = process.memory_info() + return mem_info.rss + + def generate_and_write_programs(self, num_programs, level, filename='data.txt', deduplicate=True): + """ + Generate and write a specified number of programs to a file. + + Parameters: + - num_programs (int): Number of programs to generate and write. + - level (str): The level of the programs. + - filename (str): Name of the file to write the programs (default is 'data.txt'). + - deduplicate (bool, optional): Whether to perform deduplication of generated programs (default is True). + """ + start_time = time.time() + start_mem = self.memory_usage() + max_tries = 1000 + num_tries = 0 + + level_module = importlib.import_module(f'simplify_code') + simplify_function = getattr(level_module, f'simplify_code_funct') + + with open(filename, 'a') as file: + + generated_programs = 0 + hashes = set() + pbar = tqdm(desc="Generation", total=num_programs) + + while generated_programs < num_programs: + try: + root, program = self.generate_program(level) + + code =f"# {generated_programs}\n"+ program + "\n# Simplification" + + output = simplify_function(code) + + output = '\n'.join([f'{line}' for line in output.split('\n')]) + result = f"""{code}\n{output}""" + + program_hash = hashlib.sha256(result.encode('utf-8')).hexdigest() + + if deduplicate: + if program_hash not in hashes: + hashes.add(program_hash) + file.write(result + '\n\n') + generated_programs += 1 + pbar.update(1) + num_tries = 0 + else: + num_tries += 1 + if num_tries >= max_tries: + print("Hit max tries in deduplication, stopping generation.") + break + else: + file.write(result + '\n\n') + + generated_programs += 1 + pbar.update(1) + + except Exception as e: + continue + + pbar.close() + end_time = time.time() + end_mem = self.memory_usage() + deduplication_info = "with deduplication" if deduplicate else "without deduplication" + print(f"Code generation completed in {end_time - start_time:.2f} seconds.") + print(f"Memory used during code generation: {end_mem - start_mem} bytes") + print(f"Generated {generated_programs} {'unique ' if deduplicate else ''}programs {deduplication_info}.") + print(f"Programs are saved to {filename}.") + + +def main(): + parser = argparse.ArgumentParser(description='Generate and write programs based on a specified level. ') + parser.add_argument('--num_programs', type=int, default=1000, help='Number of programs to generate and write (default is 1000)') + parser.add_argument('--level', default="ALL", help='The level of the programs (1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 4.1, ALL)') + parser.add_argument('--filename', default='data.txt', help='Name of the file to write the programs (default is data.txt)') + parser.add_argument('--deduplicate', action='store_true', default=True, help='Perform deduplication of generated programs (default is True)') + + args = parser.parse_args() + + code_generator = CodeGenerator() + code_generator.generate_and_write_programs(num_programs=args.num_programs, level=args.level, filename=args.filename, deduplicate=args.deduplicate) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tasks/clone_detection_task/generating_data_process/comparison_check.py b/tasks/clone_detection_task/generating_data_process/comparison_check.py new file mode 100644 index 0000000..2962939 --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/comparison_check.py @@ -0,0 +1,49 @@ +import ast + +class ComparisonDetector(ast.NodeVisitor): + """This class check if our code snippet contain a comparison between two diffrent variables + or if we have a variable depanding on 2 other variables + example: + a = b - c""" + def __init__(self): + self.has_comparison_between_diff_vars = False + self.has_binary_operation_between_diff_vars = False + + def visit_Compare(self, node): + # Check if the left side of the comparison is a variable + if isinstance(node.left, ast.Name): + left_var = node.left.id + + # Check all comparators (right-hand side of the comparison) + for comparator in node.comparators: + if isinstance(comparator, ast.Name) and comparator.id != left_var: + # If there is a comparison between two different variables + self.has_comparison_between_diff_vars = True + return # No need to continue if we already found a match + + self.generic_visit(node) + + def visit_BinOp(self, node): + # check if the binary operation is within a print statement + if isinstance(node.op, (ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Mod, ast.Pow, ast.LShift, ast.RShift, ast.BitOr, ast.BitXor, ast.BitAnd, ast.FloorDiv)): + if isinstance(node.left, ast.Name) and isinstance(node.right, ast.Name): + if node.left.id != node.right.id: + self.has_binary_operation_between_diff_vars = True + return + + self.generic_visit(node) + + def visit_Call(self, node): + + if isinstance(node.func, ast.Name) and node.func.id == 'print': + # visit the arguments of the print statement to check for binary operations + for arg in node.args: + self.visit(arg) + + self.generic_visit(node) + +def has_diff_var_comparison(code): + tree = ast.parse(code) + detector = ComparisonDetector() + detector.visit(tree) + return detector.has_comparison_between_diff_vars or detector.has_binary_operation_between_diff_vars diff --git a/tasks/clone_detection_task/generating_data_process/generating_binaries.py b/tasks/clone_detection_task/generating_data_process/generating_binaries.py new file mode 100644 index 0000000..ee02b1c --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/generating_binaries.py @@ -0,0 +1,89 @@ +import itertools +import json +import os +import argparse +from collections import defaultdict +from tqdm import tqdm +def get_code_snippets(filename): + """ + Reads the code snippets from the given file and returns a dictionary where keys are the snippet IDs + and values are the code snippets without the simplification part. + """ + with open(filename, 'r') as file: + lines = file.read() + + snippets_list = lines.split("# ")[1::2] + snippets = {} + + for snippet in snippets_list: + snippet_id, snippet_code = snippet.split('\n', 1) + snippets[snippet_id] = snippet_code + + return snippets + +def generate_binary_pairs(snippets, clones, max_usage=1): + """ + Generates all binary pairs of code snippets and labels them as either clones (1) or not clones (0), + ensuring that each snippet is used at most `max_usage` times. + """ + binary_pairs = [] + snippet_usage_0 = defaultdict(int) + snippet_usage_1 = defaultdict(int) + + # Progress tracking for clone pairs generation + for key, indices in tqdm(clones.items(), desc="Processing clone pairs"): + for pair in itertools.combinations(indices, 2): + if snippet_usage_1[pair[0]] <= max_usage and snippet_usage_1[pair[1]] <= max_usage: + snippet1, snippet2 = snippets[f'{pair[0]}'], snippets[f'{pair[1]}'] + binary_pairs.append((snippet1, snippet2, 1)) + snippet_usage_1[pair[0]] += 1 + snippet_usage_1[pair[1]] += 1 + + # Progress tracking for non-clone pairs generation + all_keys = list(clones.keys()) + for key1, key2 in tqdm(itertools.combinations(all_keys, 2), desc="Processing non-clone pairs"): + for idx1 in clones[key1]: + for idx2 in clones[key2]: + if snippet_usage_0[idx1] <= max_usage and snippet_usage_0[idx2] <= max_usage: + snippet1, snippet2 = snippets[f'{idx1}'], snippets[f'{idx2}'] + binary_pairs.append((snippet1, snippet2, 0)) + snippet_usage_0[idx1] += 1 + snippet_usage_0[idx2] += 1 + + return binary_pairs + +def write_binary_pairs_to_file(binary_pairs, output_filename): + """ + Writes the binary pairs and their labels to the specified output file. + """ + with open(output_filename, 'a') as file: + for i, (snippet1, snippet2, label) in tqdm(enumerate(binary_pairs), total=len(binary_pairs)): + file.write(f"# snippet 1\n{snippet1}") + file.write(f"# snippet 2\n{snippet2}") + file.write(f"# is clone\n{label}\n\n") + +def main(): + # Argument parsing + parser = argparse.ArgumentParser(description="Generate binary pairs of code snippets and their clone labels") + parser.add_argument('--input-clones', required=True, help='Path to the input JSON file with clone data') + parser.add_argument('--input-snippets', required=True, help='Path to the input file with code snippets') + parser.add_argument('--output-file', required=True, help='Path to the output file to store binary pairs') + args = parser.parse_args() + + # Load clones from the specified file + with open(args.input_clones) as f: + clones = json.load(f) + + # Load code snippets from the specified file + snippets = get_code_snippets(args.input_snippets) + + # Generate binary pairs + binary_pairs = generate_binary_pairs(snippets, clones) + + # Write binary pairs to the output file + write_binary_pairs_to_file(binary_pairs, args.output_file) + + print(f'Clones and non-clones pairs are generated in {args.output_file}') + +if __name__ == "__main__": + main() diff --git a/tasks/clone_detection_task/generating_data_process/identify_clones.py b/tasks/clone_detection_task/generating_data_process/identify_clones.py new file mode 100644 index 0000000..90a4ae4 --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/identify_clones.py @@ -0,0 +1,48 @@ +import json +import argparse +from collections import defaultdict +from tqdm import tqdm + +def main(): + # Argument parsing + parser = argparse.ArgumentParser(description="Identify clones in the output JSON files") + parser.add_argument('--input-file', required=True, help='Path to the input JSON file with outputs') + parser.add_argument('--output-file', required=True, help='Path to the output JSON file to store clones') + args = parser.parse_args() + + # Load the outputs from the specified file + with open(args.input_file) as f: + outputs = json.load(f) + + value_to_keys = defaultdict(list) + for key, value in outputs.items(): + value_to_keys[tuple(value)].append(int(key)) + + clones = {} + function = 0 + for keys in value_to_keys.values(): + if len(keys) > 1: + clones[function] = keys + function += 1 + + def trim_map_values(input_map, max_values): + trimmed_map = {} + + for key, values in input_map.items(): + if len(values) > max_values: + trimmed_map[key] = values[:max_values] + else: + trimmed_map[key] = values # Keep the original list if it's already <= 200 values + + return trimmed_map + + clones = trim_map_values(clones, 200) # Keep only the first 200 values + + # Save the clones to the specified output file + with open(args.output_file, "w") as write_file: + json.dump(clones, write_file) + + print(f'The clones are stored in {args.output_file}') + +if __name__ == "__main__": + main() diff --git a/tasks/clone_detection_task/generating_data_process/simplify_code.py b/tasks/clone_detection_task/generating_data_process/simplify_code.py new file mode 100644 index 0000000..0a7d0ae --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/simplify_code.py @@ -0,0 +1,77 @@ +import ast +import astor + +class CodeSimplification(ast.NodeTransformer): + def __init__(self): + super().__init__() + self.assignments = {} + self.in_loop = False # Track if we are inside a loop + self.for_loop_variable = None + self.is_variable_used_in_print = False + + def visit_Assign(self, node): + # If inside a loop, keep the assignment; otherwise, remove it + if self.in_loop: + return node # Keep the assignment in loops + else: + # Remove assignments outside loops unless they are constants + if isinstance(node.targets[0], ast.Name): + if isinstance(node.value, ast.BinOp): + if not (isinstance(node.value.left, ast.Constant) and isinstance(node.value.right, ast.Constant)): + self.assignments[node.targets[0].id] = node.value + elif isinstance(node.value, ast.Name): + self.assignments[node.targets[0].id] = node.value + return None # Remove the assignment outside loops + + def visit_For(self, node): + if isinstance(node.target, ast.Name): + self.for_loop_variable = node.target.id + + for stmt in node.body: + self.generic_visit(stmt) + + if not self.is_variable_used_in_print: + node.target.id = '_' + + return node + + def visit_While(self, node): + # Enter the loop + self.in_loop = True + self.generic_visit(node) + self.in_loop = False # Exit the loop after visiting it + return node + + def visit_Print(self, node): + # Visit all child nodes (which will include Name nodes) + self.generic_visit(node) + return node + + def visit_Name(self, node): + # Check if the loop variable is used in a print statement + if node.id == self.for_loop_variable: + self.is_variable_used_in_print = True + + # Replace variables with their initialization values recursively + if isinstance(node.ctx, ast.Load) and node.id in self.assignments and not self.is_variable_used_in_print: + return ast.copy_location(self._replace_with_value(self.assignments[node.id]), node) + return node + + def _replace_with_value(self, value): + if isinstance(value, ast.Name) and value.id in self.assignments: + return self._replace_with_value(self.assignments[value.id]) + elif isinstance(value, ast.BinOp): + value.left = self._replace_with_value(value.left) + value.right = self._replace_with_value(value.right) + return value + +def simplify_code_funct(code): + # Parse the code into an AST + tree = ast.parse(code) + + # Transform the AST + transformer = CodeSimplification() + transformed_tree = transformer.visit(tree) + + # Convert the AST back to code + return astor.to_source(transformed_tree) diff --git a/tasks/clone_detection_task/generating_data_process/used_variables_detector.py b/tasks/clone_detection_task/generating_data_process/used_variables_detector.py new file mode 100644 index 0000000..336a5dc --- /dev/null +++ b/tasks/clone_detection_task/generating_data_process/used_variables_detector.py @@ -0,0 +1,59 @@ +import ast + +class UsedVariablesDetector(ast.NodeVisitor): + """This class detect the used variables in our simplified code version (variables within the print statments + or inside the for loop or the if conditions)""" + def __init__(self): + self.printed_vars = set() + self.if_condition_vars = set() + + def visit_Call(self, node): + # Check if the function being called is 'print' + if isinstance(node.func, ast.Name) and node.func.id == 'print': + for arg in node.args: + self._extract_variables(arg, self.printed_vars) + self.generic_visit(node) + + def visit_If(self, node): + # Extract variables from the if condition + self._extract_variables(node.test, self.if_condition_vars) + self.generic_visit(node) + + def visit_While(self, node): + # Extract variables from the if condition + self._extract_variables(node.test, self.if_condition_vars) + self.generic_visit(node) + + def _extract_variables(self, node, target_set): + # Recursively extract variable names from the expression + if isinstance(node, ast.Name): + target_set.add(node.id) + elif isinstance(node, ast.BinOp): + self._extract_variables(node.left, target_set) + self._extract_variables(node.right, target_set) + elif isinstance(node, ast.UnaryOp): + self._extract_variables(node.operand, target_set) + elif isinstance(node, ast.Call): + for arg in node.args: + self._extract_variables(arg, target_set) + elif isinstance(node, ast.Attribute): + target_set.add(node.attr) + elif isinstance(node, ast.Subscript): + self._extract_variables(node.value, target_set) + elif isinstance(node, ast.Compare): + for comparator in node.comparators: + self._extract_variables(comparator, target_set) + self._extract_variables(node.left, target_set) + elif isinstance(node, ast.BoolOp): + for value in node.values: + self._extract_variables(value, target_set) + +def get_printed_and_condition_variables(code): + detector = UsedVariablesDetector() + tree = ast.parse(code) + + detector.visit(tree) + + used_vars = detector.printed_vars | detector.if_condition_vars + + return used_vars \ No newline at end of file diff --git a/tasks/line _execution_counting/README.md b/tasks/line _execution_counting/README.md new file mode 100644 index 0000000..d7145c0 --- /dev/null +++ b/tasks/line _execution_counting/README.md @@ -0,0 +1,30 @@ +# Line execution counting guide + +the task consists of the model predicting the count of how many lines of code will be executed given a random python code snippet + + +this folder contains .py and .ipynb files achieving the following goals : + +## Data generation + +[tinypy_generator.py](./tinypy_generator.py) is the main file that generates the dataset in the form of labeled python code snippets, this file is a modified version of the original [tinypy_generator](https://github.com/MarwaNair/TinyPy-Generator) which generates python code snippets and labels it with the output of that code. + +for our case, we will keep the code snippet, and just change the label, instead of labeling with the code output, we will label with the line count, hence the modification. + +the modification that you would notice after exploring that [file](./tinypy_generator.py) is that a new independent method has been added - given a python code snippet, the method returns its count - + +for the sake of experiments, that same method has been written in a separate demonstrative .py file : [lineCounter.py](./lineCounter.py), the method accepts any functionning python code as an input. + +a detailed explanation of how that method works is provided in the following [Docs](https://docs.google.com/document/d/1Fz0KGN1wb-6rVqU0BdrTBSaodM-pksPXhfoAGQbU7Dk/edit?usp=sharing) file. + +## Data split + +before moving on to finetuning, [prepare.py](./prepare.py) makes it possible to format the data generated previously, from a .txt file it splits the code examples into training, evaluation and test examples, saved in 3 separate files, in addition to generating a meta.pkl file that will help in the next stage "retrieve the needed information about the generated data"... + +## Finetuning the model + +[finetuning.ipynb](./finetuning.ipynb) contains the entire process "explained in the comments" that follows data generation : +- Data preparation "tokenization.. etc" +- Model structure definition "and lora implementation" +- Training "or finetuning if lora is activated" +- Evaluation \ No newline at end of file diff --git a/tasks/line _execution_counting/finetuning.ipynb b/tasks/line _execution_counting/finetuning.ipynb new file mode 100644 index 0000000..4d6b600 --- /dev/null +++ b/tasks/line _execution_counting/finetuning.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"aa556b17-8ea0-4788-aea8-8d6259526157","_uuid":"a14f6813-426a-4666-9280-7ed88ebdb85e","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:30.282920Z","iopub.status.busy":"2024-09-23T12:47:30.282569Z","iopub.status.idle":"2024-09-23T12:47:30.288207Z","shell.execute_reply":"2024-09-23T12:47:30.287329Z","shell.execute_reply.started":"2024-09-23T12:47:30.282894Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["import random\n","import os\n","import pickle\n","import time\n","import datetime\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.optim.lr_scheduler import StepLR\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm\n","import re"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"063623b6-121d-4c18-a660-93d2f1be3305","_uuid":"f10e66ef-f466-4cfc-8ddb-594df92adb45","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:31.741666Z","iopub.status.busy":"2024-09-23T12:47:31.741274Z","iopub.status.idle":"2024-09-23T12:47:31.747701Z","shell.execute_reply":"2024-09-23T12:47:31.746726Z","shell.execute_reply.started":"2024-09-23T12:47:31.741638Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Set the random seed for reproducibility\n","seed = 42\n","torch.manual_seed(seed) \n","random.seed(seed)\n","np.random.seed(seed)\n","\n","# Set the device to GPU if available, otherwise CPU\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","print(f\"Device set to {device}.\")"]},{"cell_type":"markdown","metadata":{"_cell_guid":"4776333c-08cd-4127-bea7-d7ec8898df7b","_uuid":"f61836e4-3f71-432d-8c50-9de1ff2e05e0","trusted":true},"source":["# Data Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fcc4b173-f5e5-4110-b14f-46a8fa6da9ae","_uuid":"0aa1c1b8-a945-4baa-8d46-3a08056a9004","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:33.450870Z","iopub.status.busy":"2024-09-23T12:47:33.450042Z","iopub.status.idle":"2024-09-23T12:47:33.455749Z","shell.execute_reply":"2024-09-23T12:47:33.454855Z","shell.execute_reply.started":"2024-09-23T12:47:33.450837Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Helper functions to load and save data\n","def save_data(data, file_path):\n"," with open(file_path, 'w') as f:\n"," f.write(data)\n","\n","def load_data(file_path):\n"," with open(file_path, 'r') as f:\n"," return f.read()"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9731ee3f-b4d1-4b6e-afb2-859c56bef6c6","_uuid":"3da5ca68-e0d7-4aed-b89f-5f2a4ab910d9","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:44.813132Z","iopub.status.busy":"2024-09-23T12:47:44.812785Z","iopub.status.idle":"2024-09-23T12:47:44.817401Z","shell.execute_reply":"2024-09-23T12:47:44.816456Z","shell.execute_reply.started":"2024-09-23T12:47:44.813103Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Directory where the data is stored \"must contain 4 files : train.txt, val.txt, test.txt and a meta.pkl file\"\n","DATA_DIR = \"/yourDataDirectoryHere\"\n","# Directory where the model is stored\n","MODEL_DIR = \"/yourModelDirectoryHere\""]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ddae0037-0f42-425d-a2e9-4238f4c608f2","_uuid":"6d064118-585d-46a9-8f40-f9472fe879b4","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:46.392475Z","iopub.status.busy":"2024-09-23T12:47:46.391663Z","iopub.status.idle":"2024-09-23T12:47:46.403456Z","shell.execute_reply":"2024-09-23T12:47:46.402524Z","shell.execute_reply.started":"2024-09-23T12:47:46.392441Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Attempt to derive vocab_size from the dataset\n","\n","meta_path = os.path.join(DATA_DIR, 'meta.pkl')\n","vocab_size = None\n","\n","if os.path.exists(meta_path):\n"," with open(meta_path, 'rb') as f:\n"," meta = pickle.load(f)\n"," vocab_size = meta['vocab_size']\n"," print(f\"found vocab_size = {vocab_size} (inside {meta_path})\")\n","else:\n"," print(\"Meta file not found. Please ensure the meta.pkl file is present in the data directory.\")\n","\n","# Encode and decode functions for character-level Tokenzation \n","def encode(s):\n"," return [meta['stoi'][c] for c in s]\n","\n","def decode(l):\n"," return ''.join([meta['itos'][i] for i in l])"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ff53a3e0-09ab-4396-90d9-cef86df0605b","_uuid":"1b2892b5-a904-4550-a8d6-ae8f51f1841f","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:48.476650Z","iopub.status.busy":"2024-09-23T12:47:48.476261Z","iopub.status.idle":"2024-09-23T12:47:49.391422Z","shell.execute_reply":"2024-09-23T12:47:49.390496Z","shell.execute_reply.started":"2024-09-23T12:47:48.476620Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Load data\n","train_data = load_data(os.path.join(DATA_DIR, 'train.txt'))\n","val_data = load_data(os.path.join(DATA_DIR, 'val.txt'))\n","test_data = load_data(os.path.join(DATA_DIR, 'test.txt'))\n","\n","# Encode data\n","train_ids = encode(train_data)\n","val_ids = encode(val_data)\n","test_ids = encode(test_data)\n","\n","# Save encoded data to bin files, make sure to choose \"Files only\" on the persistence option of the session so that you don't encode data each time\n","train_ids = np.array(train_ids, dtype=np.uint16)\n","val_ids = np.array(val_ids, dtype=np.uint16)\n","test_ids = np.array(test_ids, dtype=np.uint16)\n","\n","train_ids.tofile( 'train.bin')\n","val_ids.tofile( 'val.bin')\n","test_ids.tofile('test.bin')\n","\n","print(\"Encoded data saved as binary files.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"125ce42e-8df9-4094-b0c5-242fcd99a597","_uuid":"6a2d1ac2-5ef7-441c-9837-050c59120ab9","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:51.225679Z","iopub.status.busy":"2024-09-23T12:47:51.225322Z","iopub.status.idle":"2024-09-23T12:47:51.230098Z","shell.execute_reply":"2024-09-23T12:47:51.229117Z","shell.execute_reply.started":"2024-09-23T12:47:51.225651Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["del(train_ids)\n","del(val_ids)\n","del(test_ids)"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"c53f3930-8d16-443d-a5ec-a6926f3f6cf4","_uuid":"9cd8ff5a-2170-4c53-be17-02ac7d0cffd9","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:52.915803Z","iopub.status.busy":"2024-09-23T12:47:52.915072Z","iopub.status.idle":"2024-09-23T12:47:52.920735Z","shell.execute_reply":"2024-09-23T12:47:52.919741Z","shell.execute_reply.started":"2024-09-23T12:47:52.915770Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Load encoded data\n","train_data = np.memmap(\"/kaggle/working/train.bin\", dtype=np.uint16, mode='r')\n","val_data = np.memmap(\"/kaggle/working/val.bin\", dtype=np.uint16, mode='r')"]},{"cell_type":"markdown","metadata":{"_cell_guid":"8574d987-cef6-47d1-b889-e8242a0bcd23","_uuid":"f4fc1523-1d72-49db-a3bc-8d521f236993","trusted":true},"source":["# Model"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2d4305c5-c1c6-48b0-a048-953a98954854","_uuid":"1fd63d8c-f842-444c-9dc8-cab3263ae6e4","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:54.647417Z","iopub.status.busy":"2024-09-23T12:47:54.647052Z","iopub.status.idle":"2024-09-23T12:47:54.653828Z","shell.execute_reply":"2024-09-23T12:47:54.652930Z","shell.execute_reply.started":"2024-09-23T12:47:54.647386Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Hyperparameters for the GPT model\n","block_size = 256 # Maximum context length\n","n_embd = 372 # Embedding dimension\n","n_head = 6 # Number of attention heads\n","n_layer = 6 # Number of transformer blocks\n","dropout = 0 # Dropout rate\n","batch_size = 64 # Batch size for training\n","max_iters = 100_000 # Maximum number of iterations\n","learning_rate = 1e-3 # Initial Learning rate value\n","miles = [int(max_iters * m) for m in [0.7, 0.8, 0.9]] # Milestones for learning rate decay as fractions of max_iters\n","eval_interval = 10_000 # Evaluation interval\n","eval_iters = 1000 # Number of iterations for evaluation\n","vocab_size = 53 # Vocabulary size\n","\n","# Model to be fine-tuned \"set the model name without .pth\" (Keep it empty for training from scratch)\n","model_name = 'yourModelNameWithoutExtensionHere'\n","\n","# LoRA Rank - Set it to 0 if you want to train from scratch or perform full fine-tuning\n","lora_r = 12\n","\n","compile = False"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-09-23T12:47:57.166947Z","iopub.status.busy":"2024-09-23T12:47:57.166102Z","iopub.status.idle":"2024-09-23T12:47:57.171883Z","shell.execute_reply":"2024-09-23T12:47:57.170912Z","shell.execute_reply.started":"2024-09-23T12:47:57.166910Z"},"trusted":true},"outputs":[],"source":["print(f\"Data in tokens: {len(train_data)}\")\n","iters4epoch = len(train_data)//(batch_size * block_size)\n","print(f\"Number of iters for one pseudo-epoch : {iters4epoch}\")\n","print(f\"Number of pseudo-epochs : {max_iters / iters4epoch:.2f}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"17ff6e02-86d2-4f49-a384-be8c035377a7","_uuid":"9c3a2af2-99a7-4657-bb8d-168a3e8dfcfb","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:47:59.282904Z","iopub.status.busy":"2024-09-23T12:47:59.282430Z","iopub.status.idle":"2024-09-23T12:47:59.430364Z","shell.execute_reply":"2024-09-23T12:47:59.429483Z","shell.execute_reply.started":"2024-09-23T12:47:59.282864Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# defining the entire structure of the model, and in parallel implementing lora\n","class LayerNorm(nn.Module):\n"," \"\"\" LayerNorm with an optional bias. PyTorch's LayerNorm doesn't support simply bias=False \"\"\"\n","\n"," def __init__(self, ndim, bias):\n"," super().__init__()\n"," self.weight = nn.Parameter(torch.ones(ndim))\n"," self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n","\n"," def forward(self, input):\n"," return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)\n","\n","class Head(nn.Module):\n"," \"\"\"One head of self-attention.\"\"\"\n","\n"," def __init__(self, head_size):\n"," super().__init__()\n"," self.key = nn.Linear(n_embd, head_size, bias=False)\n"," self.query = nn.Linear(n_embd, head_size, bias=False)\n"," self.value = nn.Linear(n_embd, head_size, bias=False)\n"," self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n"," self.dropout = nn.Dropout(dropout)\n","\n"," def forward(self, x):\n"," B, T, C = x.shape\n"," k = self.key(x) # (B, T, head_size)\n"," q = self.query(x) # (B, T, head_size)\n"," v = self.value(x) # (B, T, head_size)\n","\n"," # Apply scaled dot-product attention\n"," out = torch.nn.functional.scaled_dot_product_attention(\n"," q, k, v, attn_mask=None, dropout_p=dropout if self.training else 0, is_causal=True\n"," )\n"," \n"," return out\n"," \n","\n","class MultiHeadAttention(nn.Module):\n"," \"\"\"Multiple heads of self-attention in parallel.\"\"\"\n","\n"," def __init__(self, num_heads, head_size):\n"," super().__init__()\n"," self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n"," self.proj = nn.Linear(n_embd, n_embd)\n"," self.dropout = nn.Dropout(dropout)\n"," \n"," def forward(self, x):\n"," # Concatenate the outputs from each head\n"," out = torch.cat([h(x) for h in self.heads], dim=-1)\n"," out = self.dropout(self.proj(out))\n"," return out\n"," \n","class FeedForward(nn.Module):\n"," \"\"\"A simple linear layer followed by a non-linearity.\"\"\"\n","\n"," def __init__(self, n_embd):\n"," super().__init__()\n"," self.net = nn.Sequential(\n"," nn.Linear(n_embd, 4 * n_embd, bias=False),\n"," nn.GELU(),\n"," nn.Linear(4 * n_embd, n_embd, bias=False),\n"," nn.Dropout(dropout),\n"," )\n","\n"," def forward(self, x):\n"," return self.net(x)\n","\n","class LinearLoRA(nn.Module):\n"," def __init__(self, original_layer, rank=8):\n"," super().__init__()\n"," self.original_layer = original_layer\n"," self.original_layer.weight.requires_grad = False\n"," self.rank = rank\n"," \n"," self.lora_a = nn.Parameter(torch.randn((original_layer.in_features, rank)))\n"," self.lora_b = nn.Parameter(torch.randn((rank, original_layer.out_features)))\n"," \n"," self.reset_parameters()\n"," \n"," def reset_parameters(self):\n"," nn.init.kaiming_uniform_(self.lora_a, a=np.sqrt(5))\n"," nn.init.zeros_(self.lora_b)\n"," \n"," def forward(self, x):\n"," lora_output = x @ self.lora_a @ self.lora_b\n"," return self.original_layer(x) + lora_output\n"," \n","class Block(nn.Module):\n"," \"\"\"Transformer block: communication followed by feedforward.\"\"\"\n","\n"," def __init__(self, n_embd, n_head):\n"," super().__init__()\n"," head_size = n_embd // n_head\n"," self.sa = MultiHeadAttention(n_head, head_size)\n"," self.ffwd = FeedForward(n_embd)\n"," self.ln1 = nn.LayerNorm(n_embd, bias=False)\n"," self.ln2 = nn.LayerNorm(n_embd, bias=False)\n","\n"," def forward(self, x):\n"," x = x + self.sa(self.ln1(x))\n"," x = x + self.ffwd(self.ln2(x))\n"," return x\n","\n","class GPT(nn.Module):\n"," \"\"\"GPT language model.\"\"\"\n","\n"," def __init__(self):\n"," super().__init__()\n"," self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n"," self.position_embedding_table = nn.Embedding(block_size, n_embd)\n"," self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n"," self.ln_f = nn.LayerNorm(n_embd, bias=False) \n"," self.lm_head = nn.Linear(n_embd, vocab_size)\n","\n"," def forward(self, idx, targets=None):\n"," B, T = idx.shape\n","\n"," # Token and position embeddings\n"," tok_emb = self.token_embedding_table(idx) # (B, T, n_embd)\n"," pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T, n_embd)\n"," x = tok_emb + pos_emb # (B, T, n_embd)\n"," x = self.blocks(x) # (B, T, n_embd)\n"," x = self.ln_f(x) # (B, T, n_embd)\n"," logits = self.lm_head(x) # (B, T, vocab_size)\n","\n"," # Compute loss if targets are provided\n"," if targets is None:\n"," loss = None\n"," else:\n"," B, T, C = logits.shape\n"," logits = logits.view(B * T, C)\n"," targets = targets.view(B * T)\n"," loss = F.cross_entropy(logits, targets)\n","\n"," return logits, loss\n"," \n"," def generate(self, idx, max_new_tokens):\n"," \"\"\"Generate new tokens given an initial context `idx`.\"\"\"\n"," for _ in range(max_new_tokens):\n"," idx_cond = idx[:, -block_size:] # Crop to the last block_size tokens\n"," logits, _ = self(idx_cond)\n"," logits = logits[:, -1, :] # Focus on the last time step\n"," probs = F.softmax(logits, dim=-1) # Convert to probabilities\n"," idx_next = torch.multinomial(probs, num_samples=1) # Sample from the distribution\n"," idx = torch.cat((idx, idx_next), dim=1) # Append sampled index to the sequence\n"," return idx\n"," \n"," def activate_lora(self, r=8, heads_only=False, freeze_others=True):\n"," self.lora_rank = r\n"," self.replace_multihead_attention_recursion(heads_only)\n"," if freeze_others:\n"," self.freeze_parameters_except_lora_and_bias()\n"," \n"," def replace_multihead_attention_recursion(self, heads_only=False, model=None):\n"," children = self.named_children() if model is None else model.named_children()\n"," for name, module in children:\n"," if heads_only and name in {\"query\", \"key\", \"value\"}:\n"," # Replace with Lora SelfAttention\n"," new_layer = LinearLoRA(module, rank=self.lora_rank)\n","\n"," if model == None:\n"," self.__setattr__(name, new_layer)\n"," else:\n"," setattr(model, name, new_layer)\n"," \n"," elif isinstance(module, nn.Linear) and not heads_only:\n"," new_layer = LinearLoRA(module, rank=self.lora_rank)\n"," \n"," if model == None:\n"," self.__setattr__(name, new_layer)\n"," else:\n"," setattr(model, name, new_layer)\n"," \n"," else:\n"," # Recursive call for child modules\n"," self.replace_multihead_attention_recursion(heads_only, model=module)\n"," \n"," \n"," def freeze_parameters_except_lora_and_bias(self):\n"," for name, param in self.named_parameters():\n"," is_trainable = (\n"," \"lora_\" in name\n"," #(self.train_layer_norms and \"LayerNorm\" in name)\n"," )\n","\n"," param.requires_grad = is_trainable"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"a716f789-f605-42d0-9494-d8927ed09a6f","_uuid":"be441d8d-c18b-4694-b2ff-607aac4b11e6","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:48:03.987746Z","iopub.status.busy":"2024-09-23T12:48:03.987386Z","iopub.status.idle":"2024-09-23T12:48:03.998567Z","shell.execute_reply":"2024-09-23T12:48:03.997639Z","shell.execute_reply.started":"2024-09-23T12:48:03.987716Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Get random batch of data\n","def get_batch(split):\n"," data = train_data if split == 'train' else val_data\n"," ix = torch.randint(len(data) - block_size, (batch_size,))\n"," x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])\n"," y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])\n"," x, y = x.to(device), y.to(device)\n"," return x, y\n","\n","# Estimate loss on train and val splits\n","@torch.no_grad()\n","def estimate_loss():\n"," out = {}\n"," model.eval()\n"," for split in ['train', 'val']:\n"," losses = torch.zeros(eval_iters) \n"," for k in range(eval_iters):\n"," X, Y = get_batch(split)\n"," logits, loss = model(X, Y)\n"," losses[k] = loss.item()\n"," out[split] = losses.mean()\n"," model.train()\n"," return out\n","\n","\n","# Helper function to make large numbers of parameters human-readable\n","def human_readable(num):\n"," magnitude = 0\n"," while abs(num) >= 1000:\n"," magnitude += 1\n"," num /= 1000.0\n"," return '%.0f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2024-09-23T12:48:08.446054Z","iopub.status.busy":"2024-09-23T12:48:08.445693Z","iopub.status.idle":"2024-09-23T12:48:08.456231Z","shell.execute_reply":"2024-09-23T12:48:08.455320Z","shell.execute_reply.started":"2024-09-23T12:48:08.446025Z"},"trusted":true},"outputs":[],"source":["# load the language model\n","def load_model():\n"," \"\"\"\n"," Load pre-trained model based on the provided model name.\n"," \"\"\"\n"," model_path = os.path.join(MODEL_DIR, f\"{model_name}.pth\")\n"," if not os.path.exists(model_path):\n"," raise FileNotFoundError(f\"Model file '{model_path}' not found.\")\n"," \n"," model = GPT()\n"," print(\"Compiling the model...\\n\")\n"," r = -1\n"," if compile:\n"," try:\n"," model = torch.compile(model) # requires PyTorch 2.0\n"," except Exception as e:\n"," pass\n","\n"," checkpoint = torch.load(model_path, map_location=device)\n"," if 'lora_rank' in checkpoint.keys():\n"," r = checkpoint['lora_rank']\n"," state = checkpoint['state_dict']\n","\n"," if r > 0:\n"," model.activate_lora(r)\n"," model.load_state_dict(state)\n"," else:\n"," model.load_state_dict(checkpoint)\n"," else:\n"," checkpoint = torch.load(model_path, map_location=device)\n"," if 'lora_rank' in checkpoint.keys():\n"," r = checkpoint['lora_rank']\n"," state_dict = checkpoint['state_dict']\n","\n"," if r > 0:\n"," model.activate_lora(r)\n"," else:\n"," state_dict = checkpoint\n"," \n"," state_dict_keys = map(lambda x: x.replace(\"_orig_mod.\", \"\"), state_dict.keys())\n"," state_dict = dict(zip(state_dict_keys, state_dict.values()))\n"," model.load_state_dict(state_dict)\n","\n"," m = model.to(device)\n"," return m, (r > 0)"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"21de39d0-d298-45ce-a590-c6be400f31e8","_uuid":"db1edcb0-7dae-40b8-99f0-3a524bd1311e","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:48:10.715656Z","iopub.status.busy":"2024-09-23T12:48:10.714845Z","iopub.status.idle":"2024-09-23T12:48:11.061542Z","shell.execute_reply":"2024-09-23T12:48:11.060652Z","shell.execute_reply.started":"2024-09-23T12:48:10.715624Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Initialize model and move it to the device (GPU)\n","if len(model_name) > 0:\n"," print(\"Loading model...\\n\")\n"," model, r_exists = load_model()\n","\n","else:\n"," model = GPT()\n"," m = model.to(device)\n"," r_exists = False\n","\n"," # compile the model\n"," if compile:\n"," print(\"compiling the model... (takes a ~minute)\")\n"," model = torch.compile(model)\n","\n","if lora_r > 0 and not r_exists:\n"," print(\"Activating LoRA...\")\n"," model.activate_lora(lora_r)\n"," model = model.to(device)\n","\n","num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n","num_parameters_hr = human_readable(num_parameters)\n","print(f'The model has {num_parameters_hr} trainable parameters')"]},{"cell_type":"markdown","metadata":{"_cell_guid":"ac1fe251-e0c8-4079-9da4-68aff59262f4","_uuid":"8cdf45cc-0d3a-43a9-b10d-5381799a21f2","trusted":true},"source":["# Training"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e725706a-19a1-4e82-91b1-514dd0488f33","_uuid":"45093d41-9498-45e4-b93b-95b0b239c0af","collapsed":false,"execution":{"iopub.execute_input":"2024-09-11T16:44:05.970233Z","iopub.status.busy":"2024-09-11T16:44:05.969481Z","iopub.status.idle":"2024-09-11T16:44:07.752808Z","shell.execute_reply":"2024-09-11T16:44:07.751536Z","shell.execute_reply.started":"2024-09-11T16:44:05.970172Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Initialize optimizer\n","optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n","\n","# Initialize learning rate scheduler\n","scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=miles, gamma=0.1)"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"76b8e469-893d-4151-a175-99b54dbabe60","_uuid":"534a6c6a-e6b8-4632-8078-86aab93500de","collapsed":false,"execution":{"iopub.execute_input":"2024-09-11T10:57:07.371504Z","iopub.status.busy":"2024-09-11T10:57:07.371046Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Get current date and hour to get track of experiments\n","now = datetime.datetime.now()\n","date_hour = now.strftime(\"%Y-%m-%d_%H-%M\")\n","\n","# Train\n","# Start training timer\n","start_time = time.time()\n","\n","# Training loop\n","for iter in range(max_iters):\n","\n"," # evaluate the model on the train and val splits and log the losses\n"," if iter % eval_interval == 0:\n"," losses = estimate_loss()\n"," print(f'iter {iter:5d} | train loss {losses[\"train\"]:.4f} | val loss {losses[\"val\"]:.4f}')\n"," \n"," # train the model for one iteration\n"," xb, yb = get_batch('train')\n","\n"," # forward passd\n"," logits, loss = model(xb, yb)\n"," optimizer.zero_grad(set_to_none=True)\n"," #loss.requires_grad = True\n"," loss.backward()\n"," optimizer.step()\n","\n"," # Step the scheduler\n"," scheduler.step()\n","\n","# End training timer\n","end_time = time.time()\n","print(f'Training time: {(end_time - start_time) / 60} min')\n","\n","# Save the trained model\n","model_path = f\"{num_parameters_hr}_{date_hour}.pth\"\n","checkpoint = {\n"," 'lora_rank': model.lora_rank if(hasattr(model, \"lora_rank\")) else -1,\n"," 'state_dict': model.state_dict()\n","}\n","\n","torch.save(checkpoint, model_path)\n","print(f\"Model saved to {model_path}\\n\")"]},{"cell_type":"markdown","metadata":{"_cell_guid":"e831564c-6b76-489b-98b0-69cad098fdd6","_uuid":"facd8250-1fd4-4486-a9a6-f099df266caf","trusted":true},"source":["# Evaluation"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d8071f1a-961b-4410-ae36-ba54b5b525d0","_uuid":"f4e10d4c-a4c8-4e6b-891e-f3d14947adfb","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:48:15.483123Z","iopub.status.busy":"2024-09-23T12:48:15.482531Z","iopub.status.idle":"2024-09-23T12:48:15.490084Z","shell.execute_reply":"2024-09-23T12:48:15.489192Z","shell.execute_reply.started":"2024-09-23T12:48:15.483092Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["test_data = np.memmap('test.bin', dtype=np.uint16, mode='r')"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f3d6ae4b-e069-43bd-be3f-9e46f19146d3","_uuid":"2e9f95ba-ca83-48bc-bb18-8910efc37422","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:48:20.950761Z","iopub.status.busy":"2024-09-23T12:48:20.950432Z","iopub.status.idle":"2024-09-23T12:48:20.961347Z","shell.execute_reply":"2024-09-23T12:48:20.960565Z","shell.execute_reply.started":"2024-09-23T12:48:20.950737Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Evaluate example \"line execution counting\"\n","def evaluate_example(model, example, max_new_tokens=30):\n"," \n"," # Split example and determine maximum new tokens allowed\n"," splited_example = example.split(\"# count\")\n"," if not (\"for\" in splited_example[0]):\n"," max_new_tokens = 22\n"," # Encode prompt and prepare for evaluation \n"," encoded_example = torch.tensor(encode(splited_example[0] + \"# count\"), dtype=torch.long).unsqueeze(0).to(device)\n"," prompt_text = splited_example[0] + \"# count\"\n"," result_example = splited_example[-1]\n"," \n"," # Extract real results from example\n"," real_results = [float(match.group()) for match in re.finditer(r\"(?<=# )-?\\d+(\\.\\d+)?\", result_example.split('\\n\\n')[0].replace(\"\\n\", \"\"))]\n"," \n"," # Generate response from model and extract generated results\n"," try:\n"," response = decode(model.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist())\n"," splited_response = response.split(\"# count\")\n"," result_response = splited_response[-1]\n"," generated_results = [float(match.group()) for match in re.finditer(r\"(?<=# )-?\\d+(\\.\\d+)?\", result_response.split('\\n\\n')[0].replace(\"\\n\", \"\"))]\n"," except:\n"," generated_results = \"error\"\n"," return prompt_text, real_results, generated_results\n","\n","\n","\n","# Write results to file\n","def write_results_to_file(output_file, prompt, real_results, generated_results):\n"," df = pd.DataFrame({\n"," 'Prompt': prompt,\n"," 'Real_Results': real_results,\n"," 'Generated_Results': generated_results\n"," })\n"," df.to_csv(output_file, index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2536ece9-1d3c-4373-b308-fd1049f3297f","_uuid":"7b21f8fd-2e4c-443b-8120-e0af732bf558","collapsed":false,"execution":{"iopub.execute_input":"2024-09-23T12:48:31.214124Z","iopub.status.busy":"2024-09-23T12:48:31.213222Z","iopub.status.idle":"2024-09-23T13:32:13.381039Z","shell.execute_reply":"2024-09-23T13:32:13.380177Z","shell.execute_reply.started":"2024-09-23T12:48:31.214089Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"outputs":[],"source":["# Evaluation Loop\n","\n","# Split examples and initialize lists for results\n","examples = decode(test_data).split(\"\\n\\n\")\n","examples = [example for example in examples if example]\n","# Taking a subset of the examples for short \"aimed for verification purposes\" evaluations\n","example_subset = examples[:5000]\n","# Start evaluation process\n","prompt = []\n","real_results = []\n","generated_results = []\n","\n","# Iterate through examples and evaluate the model on each one\n","for example in tqdm(example_subset):\n"," prompt_text, real_result, result = evaluate_example(model, example)\n"," prompt.append(prompt_text)\n"," real_results.append(real_result)\n"," generated_results.append(result)\n","\n","# Calculate and print accuracy\n","correct_count = sum(1 for real, generated in zip(real_results, generated_results) if real == generated)\n","accuracy = correct_count / len(generated_results)\n","print(f\"Accuracy: {accuracy * 100:.2f}%\")\n","\n","# Store accuracy in a file\n","with open(\"accuracy.txt\", 'w') as f:\n"," f.write(f\"Accuracy: {accuracy * 100:.2f}%\\n\")\n","\n","# Store predictions in a CSV file\n"," write_results_to_file(\"predictions.csv\", prompt, real_results, generated_results)"]}],"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":5419152,"sourceId":9104825,"sourceType":"datasetVersion"},{"datasetId":5544565,"sourceId":9174472,"sourceType":"datasetVersion"},{"datasetId":5546822,"sourceId":9177797,"sourceType":"datasetVersion"},{"datasetId":5527817,"sourceId":9194009,"sourceType":"datasetVersion"},{"datasetId":5559645,"sourceId":9196288,"sourceType":"datasetVersion"},{"datasetId":5560892,"sourceId":9198028,"sourceType":"datasetVersion"},{"datasetId":5560896,"sourceId":9198035,"sourceType":"datasetVersion"},{"datasetId":5560904,"sourceId":9198045,"sourceType":"datasetVersion"},{"datasetId":5566438,"sourceId":9206254,"sourceType":"datasetVersion"},{"datasetId":5592996,"sourceId":9245526,"sourceType":"datasetVersion"},{"datasetId":5596284,"sourceId":9250376,"sourceType":"datasetVersion"},{"datasetId":5603809,"sourceId":9261202,"sourceType":"datasetVersion"},{"datasetId":5603815,"sourceId":9261210,"sourceType":"datasetVersion"},{"datasetId":5628994,"sourceId":9297219,"sourceType":"datasetVersion"},{"datasetId":5628996,"sourceId":9297222,"sourceType":"datasetVersion"},{"datasetId":5628998,"sourceId":9297227,"sourceType":"datasetVersion"},{"datasetId":5628999,"sourceId":9297228,"sourceType":"datasetVersion"},{"datasetId":5629001,"sourceId":9297232,"sourceType":"datasetVersion"},{"datasetId":5629005,"sourceId":9297237,"sourceType":"datasetVersion"},{"datasetId":5670920,"sourceId":9354642,"sourceType":"datasetVersion"},{"datasetId":5673838,"sourceId":9358533,"sourceType":"datasetVersion"},{"datasetId":5673878,"sourceId":9358581,"sourceType":"datasetVersion"},{"datasetId":5676378,"sourceId":9361789,"sourceType":"datasetVersion"},{"datasetId":5676476,"sourceId":9361942,"sourceType":"datasetVersion"},{"datasetId":5680088,"sourceId":9366638,"sourceType":"datasetVersion"},{"datasetId":5681041,"sourceId":9367903,"sourceType":"datasetVersion"},{"datasetId":5707886,"sourceId":9402486,"sourceType":"datasetVersion"},{"datasetId":5708526,"sourceId":9403279,"sourceType":"datasetVersion"},{"datasetId":5708753,"sourceId":9403553,"sourceType":"datasetVersion"},{"datasetId":5720522,"sourceId":9418762,"sourceType":"datasetVersion"},{"datasetId":5749118,"sourceId":9457179,"sourceType":"datasetVersion"},{"datasetId":5749126,"sourceId":9457191,"sourceType":"datasetVersion"},{"datasetId":5752981,"sourceId":9462317,"sourceType":"datasetVersion"},{"datasetId":5753388,"sourceId":9462832,"sourceType":"datasetVersion"},{"modelId":103985,"modelInstanceId":79512,"sourceId":94818,"sourceType":"modelInstanceVersion"},{"modelId":104098,"modelInstanceId":79617,"sourceId":94938,"sourceType":"modelInstanceVersion"},{"modelId":106026,"modelInstanceId":81700,"sourceId":97385,"sourceType":"modelInstanceVersion"},{"modelId":106655,"modelInstanceId":82335,"sourceId":98147,"sourceType":"modelInstanceVersion"},{"modelId":107006,"modelInstanceId":82700,"sourceId":98573,"sourceType":"modelInstanceVersion"},{"modelId":107017,"modelInstanceId":82711,"sourceId":98585,"sourceType":"modelInstanceVersion"},{"modelId":108993,"modelInstanceId":84758,"sourceId":101069,"sourceType":"modelInstanceVersion"},{"isSourceIdPinned":true,"modelId":109445,"modelInstanceId":85225,"sourceId":101650,"sourceType":"modelInstanceVersion"},{"isSourceIdPinned":true,"modelId":117231,"modelInstanceId":93025,"sourceId":111042,"sourceType":"modelInstanceVersion"},{"modelId":121705,"modelInstanceId":97518,"sourceId":116074,"sourceType":"modelInstanceVersion"},{"isSourceIdPinned":true,"modelId":124007,"modelInstanceId":99834,"sourceId":118695,"sourceType":"modelInstanceVersion"},{"isSourceIdPinned":true,"modelId":124376,"modelInstanceId":100207,"sourceId":119159,"sourceType":"modelInstanceVersion"}],"dockerImageVersionId":30747,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"}},"nbformat":4,"nbformat_minor":4} diff --git a/tasks/line _execution_counting/lineCounter.py b/tasks/line _execution_counting/lineCounter.py new file mode 100644 index 0000000..7964dbd --- /dev/null +++ b/tasks/line _execution_counting/lineCounter.py @@ -0,0 +1,47 @@ +import sys +from io import StringIO +from contextlib import redirect_stdout + +def line_counter(code_snippet): + """ + this function counts how many lines of code in total have been executed + the function follows the following rules : + - a line is not counted if : + - it falls in a condition bloc where the condition is not verified + - it falls in a loop where the number of iterations is equal to zero + - a line is counted as much as it has been iterated through "if it sits in a for loop bloc for example " + """ + counter = 0 + + def trace_lines(frame, event, arg): + nonlocal counter # declaring the outer variable + if event == 'line': # every time the tracer detects the execution of a line of code + filename = frame.f_code.co_filename + if filename == '' : # counting only the lines that are in the code snippet we provided "and not in some other internal libraries" + counter += 1 # increment the global counter + return trace_lines + + + # Set the trace function + sys.settrace(trace_lines) + + # Capture the output of the program. + SIO = StringIO() + with redirect_stdout(SIO): + # executing the code, the execution is being traced by the trace_lines() function that has been set previously + exec(code_snippet,{'__file__': ''}) # Execute the code and setting the "fake file" name to so that we can recognise this code snippet later in trace_lines() + + # Disable the trace function + sys.settrace(None) + + return counter + +code_snippet = """e = 6 +e = 0 +e = 7 +if not (e != e) or ( e <= e) : + print(e) +else : + print(e)""" +number = line_counter(code_snippet) +print(f"\n{number} lines executed successfully\n") \ No newline at end of file diff --git a/tasks/line _execution_counting/prepare.py b/tasks/line _execution_counting/prepare.py new file mode 100644 index 0000000..52612cd --- /dev/null +++ b/tasks/line _execution_counting/prepare.py @@ -0,0 +1,84 @@ +import os +import pickle +import requests +import numpy as np + +# change the file name which contains the entire dataset, so that it can be fragmented later on to : train, eval and test data +input_file_path = os.path.join(os.path.dirname(__file__), 'fileNameHere.txt' ) + +with open(input_file_path, 'r') as f: + data = f.read() +print(f"length of dataset in characters: {len(data):,}\n") + + +# get all the unique characters that occur in this text +chars = sorted(list(set(data))) +vocab_size = len(chars) +print("all the unique characters:", ''.join(chars)) +print(f"vocab size: {vocab_size:,}") + +# create a mapping from characters to integers +stoi = { ch:i for i,ch in enumerate(chars) } +itos = { i:ch for i,ch in enumerate(chars) } +def encode(s): + return [stoi[c] for c in s] # encoder: take a string, output a list of integers +def decode(l): + ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string + + +# save the meta information as well, to help us encode/decode later +meta = { + 'vocab_size': vocab_size, + 'itos': itos, + 'stoi': stoi, +} +with open(f'meta.pkl', 'wb') as f: + pickle.dump(meta, f) + + +# split by examples using "\n\n" +examples = data.split("\n\n")[:-1] +n = len(examples) +print(f"total number of examples: {n:,}\n") +# shuffle the examples +np.random.shuffle(examples) + +# split into train, val, and test sets +train_examples = examples[:int(n*0.8)] +val_examples = examples[int(n*0.8):int(n*0.9)] +test_examples = examples[int(n*0.9):] + +# join the examples back into strings +train_data = "\n\n".join(train_examples) +val_data = "\n\n".join(val_examples) +test_data = "\n\n".join(test_examples) + + + +# Save train, val, and test sets to separate files +with open(os.path.join(os.path.dirname(__file__), 'train.txt'), 'w') as f: + f.write(train_data) +with open(os.path.join(os.path.dirname(__file__), 'val.txt'), 'w') as f: + f.write(val_data) +with open(os.path.join(os.path.dirname(__file__), 'test.txt'), 'w') as f: + f.write(test_data) + + + + +# encode both to integers +train_ids = encode(train_data) +val_ids = encode(val_data) +test_ids = encode(test_data) +print(f"train has {len(train_ids):,} tokens for {len(train_examples):,} examples") +print(f"val has {len(val_ids):,} tokens for {len(val_examples):,} examples") +print(f"test has {len(test_ids):,} tokens for {len(test_examples):,} examples\n") + +# export to bin files +train_ids = np.array(train_ids, dtype=np.uint16) +val_ids = np.array(val_ids, dtype=np.uint16) +test_ids = np.array(test_ids, dtype=np.uint16) +train_ids.tofile(os.path.join(os.path.dirname(__file__), 'train.bin')) +val_ids.tofile(os.path.join(os.path.dirname(__file__), 'val.bin')) +test_ids.tofile(os.path.join(os.path.dirname(__file__), 'test.bin')) + diff --git a/tasks/line _execution_counting/tinypy_generator.py b/tasks/line _execution_counting/tinypy_generator.py new file mode 100644 index 0000000..087c76d --- /dev/null +++ b/tasks/line _execution_counting/tinypy_generator.py @@ -0,0 +1,358 @@ +from anytree import Node, RenderTree +import random +from io import StringIO +from contextlib import redirect_stdout +import argparse +import time +from tqdm.auto import tqdm +import hashlib +import os +import psutil +import sys + + +class CodeGenerator: + def __init__(self): + """ + Initialize the CodeGenerator object with the given context-free grammar rules. + + """ + + self.init_count = 0 + self.max_init = 0 + # Dictionary containing context-free grammar rules. + self.cfg_rules = { + # Variables and digits + "VARIABLE": ["a", "b", "c", "d", "e"], + "DIGIT": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + + # Operators + "ARITHMETIC_OPERATOR": ["+", "-", "*", "/"], + "RELATIONAL_OPERATOR": ["<", ">", "<=", ">=", "!=", "=="], + "LOGICAL_OPERATOR_INFIX": ["and", "or"], + "LOGICAL_OPERATOR_PREFIX": ["not"], + "LOGICAL_OPERATOR": ["LOGICAL_OPERATOR_INFIX", "LOGICAL_OPERATOR_PREFIX"], + "OPERATOR": ["ARITHMETIC_OPERATOR"], + + # Formatting + "NEW_LINE": ["\n"], + "TAB_INDENT": ["\t"], + "BRACKET_OPEN": ['('], + "BRACKET_CLOSE": [')'], + "EQUALS": ["="], + "COLON": [":"], + "COMMA": [","], + + + # Keywords + "IF": ["if"], + "ELIF": ["elif"], + "ELSE": ["else"], + "FOR": ["for"], + "IN": ["in"], + "RANGE": ["range"], + "WHILE": ["while"], + "PRINT": ["print"], + + # Terms and expressions + "TERM": ["EXPRESSION_IDENTIFIER", "DIGIT"], + "EXPRESSION": ["TERM SPACE OPERATOR SPACE TERM"], + "ENCLOSED_EXPRESSION": ["BRACKET_OPEN EXPRESSION BRACKET_CLOSE"], + "DISPLAY_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE EXPRESSION_IDENTIFIER" , + "EXPRESSION_IDENTIFIER SPACE OPERATOR SPACE DIGIT"], + + # Initializations and assignments + "IDENTIFIER_INITIALIZATION": ["IDENTIFIER_INITIALIZATION INITIALIZATION", + "INITIALIZATION"], + + "INITIALIZATION": ["VARIABLE SPACE EQUALS SPACE DIGIT NEW_LINE"], + + "SIMPLE_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , ""], + "ADVANCED_ASSIGNMENTS": ["VARIABLE SPACE EQUALS SPACE SIMPLE_ARITHMETIC_EVALUATION NEW_LINE", + "VARIABLE SPACE EQUALS SPACE EXPRESSION NEW_LINE" , + ""], + + "SIMPLE_ARITHMETIC_EVALUATION": ["SIMPLE_ARITHMETIC_EVALUATION ARITHMETIC_OPERATOR ENCLOSED_EXPRESSION", + "ENCLOSED_EXPRESSION", + ], + + # Conditions + "SIMPLE_IF_STATEMENT": ["IF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_IF_STATEMENT": ["IF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "SIMPLE_ELIF_STATEMENT": ["ELIF SPACE CONDITION SPACE COLON NEW_LINE"], + "ADVANCED_ELIF_STATEMENT": ["ELIF SPACE CHAIN_CONDITION SPACE COLON NEW_LINE"], + "ELSE_STATEMENT": ["ELSE SPACE COLON NEW_LINE"], + + "CHAIN_CONDITION": ["CHAIN_CONDITION SPACE LOGICAL_OPERATOR_INFIX SPACE ENCLOSED_CONDITION", + "LOGICAL_OPERATOR_PREFIX SPACE ENCLOSED_CONDITION", + "ENCLOSED_CONDITION"], + "ENCLOSED_CONDITION": ["BRACKET_OPEN CONDITION BRACKET_CLOSE"], + "CONDITION": ["OPTIONAL_NOT CONDITION_EXPRESSION", "CONDITION_EXPRESSION"], + "CONDITION_EXPRESSION": ["EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE EXPRESSION_IDENTIFIER", + "EXPRESSION_IDENTIFIER SPACE RELATIONAL_OPERATOR SPACE DIGIT"], + "OPTIONAL_NOT": ["LOGICAL_OPERATOR_PREFIX SPACE", "SPACE"], + + # Loops + "FOR_HEADER": ["FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL COMMA SPACE STEP BRACKET_CLOSE SPACE COLON", + "FOR SPACE EXPRESSION_IDENTIFIER SPACE IN SPACE RANGE BRACKET_OPEN INITIAL COMMA SPACE FINAL BRACKET_CLOSE SPACE COLON"], + "INITIAL": ["DIGIT"], + "FINAL": ["STEP * EXECUTION_COUNT + INITIAL - 1"], + "STEP": ["1", "2", "3"], + "EXECUTION_COUNT": [ "2", "3"], + "FOR_LOOP": ["FOR_HEADER NEW_LINE TAB_INDENT DISPLAY"], + "ADVANCED_FOR_LOOP": ["FOR_LOOP", + "FOR_HEADER NEW_LINE TAB_INDENT ADVANCED_DISPLAY"], + + + # Displaying + "DISPLAY" : ["PRINT BRACKET_OPEN DISPLAY_IDENTIFIER BRACKET_CLOSE"], + "ADVANCED_DISPLAY" : ["DISPLAY", + "PRINT BRACKET_OPEN DISPLAY_EXPRESSION BRACKET_CLOSE"], + + + "LEVEL1.1": ["IDENTIFIER_INITIALIZATION SIMPLE_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL1.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_DISPLAY"], + "LEVEL2.1": ["IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE SIMPLE_ELIF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY", + "IDENTIFIER_INITIALIZATION SIMPLE_IF_STATEMENT TAB_INDENT DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT DISPLAY"], + "LEVEL2.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ADVANCED_ELIF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY", + "IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_IF_STATEMENT TAB_INDENT ADVANCED_DISPLAY NEW_LINE ELSE_STATEMENT TAB_INDENT ADVANCED_DISPLAY"], + "LEVEL3.1": ["IDENTIFIER_INITIALIZATION FOR_LOOP"], + "LEVEL3.2": ["IDENTIFIER_INITIALIZATION ADVANCED_ASSIGNMENTS ADVANCED_FOR_LOOP"], + + "ALL": ["LEVEL1.1", "LEVEL1.2","LEVEL2.1", "LEVEL2.2","LEVEL3.1", "LEVEL3.2"], + + } + + def line_counter(self,code_snippet): + """ + this function counts how many lines of code in total have been executed + the function follows the following rules : + - a line is not counted if : + - it falls in a condition bloc where the condition is not verified + - it falls in a loop where the number of iterations is equal to zero + - a line is counted as much as it has been iterated through "if it sits in a for loop bloc for example " + """ + counter = 0 + + def trace_lines(frame, event, arg): + nonlocal counter # declaring the outer variable + if event == 'line': # every time the tracer detects the execution of a line of code + filename = frame.f_code.co_filename + if filename == '' : # counting only the lines that are in the code snippet we provided "and not in some other internal libraries" + counter += 1 # increment the global counter + return trace_lines + + + # Set the trace function + sys.settrace(trace_lines) + + # Capture the output of the program. + SIO = StringIO() + with redirect_stdout(SIO): + # executing the code, the execution is being traced by the trace_lines() function that has been set previously + exec(code_snippet,{'__file__': ''}) # Execute the code and setting the "fake file" name to so that we can recognise this code snippet later in trace_lines() + + # Disable the trace function + sys.settrace(None) + + return counter + + def generate_code(self, symbol, assigned_identifiers, last_variable, parent=None): + """ + Generate code recursively based on the context-free grammar rules. + + Parameters: + - symbol (str): The symbol to generate code for. + - assigned_identifiers (set): Set of assigned identifiers. + - last_variable (set): Set of the last used variables. + - parent (Node): Parent node in the syntax tree. + + Returns: + - str: The generated code. + """ + node = Node(symbol, parent=parent) + + # If the symbol is a non-terminal, expand it using the CFG rules. + if symbol in self.cfg_rules: + # Initialization count. + if symbol == "IDENTIFIER_INITIALIZATION": + if self.init_count < self.max_init: + self.init_count += 1 + else: + symbol = "INITIALIZATION" + # Choose a random rule for the symbol and split it into individual symbols. + rule = random.choice(self.cfg_rules[symbol]) + symbols = rule.split(" ") + + # Recursively generate code for each symbol in the rule. + generated_symbols = [self.generate_code(s, assigned_identifiers, last_variable, node) for s in symbols] + + # Handle special case for "FINAL" symbol where we need to evaluate an expression. + if symbol == "FINAL": + return str(eval(''.join(generated_symbols))) + + # Add initialized variables to the assigned identifiers set. + if symbol == "INITIALIZATION": + assigned_identifiers.add(generated_symbols[0]) + + # Keep track of the last used variables for assignments. + if (symbol == "SIMPLE_ASSIGNMENTS") or (symbol == "ADVANCED_ASSIGNMENTS"): + if generated_symbols[0]: + last_variable.add(generated_symbols[0]) + + return ''.join(generated_symbols) + + # Handle the terminal symbols. + elif symbol == "EXPRESSION_IDENTIFIER": + identifier = random.choice(tuple(assigned_identifiers)) if assigned_identifiers else random.choice(self.cfg_rules["DIGIT"]) + return identifier + + elif symbol == "DISPLAY_IDENTIFIER": + try: + return f"{tuple(last_variable)[0]}" + except: + return f"{random.choice(tuple(assigned_identifiers))}" + else: + return symbol + + def print_tree(self, root): + """ + Print the syntax tree using the RenderTree utility from the anytree module. + + Parameters: + - root (Node): The root node of the syntax tree. + """ + for pre, _, node in RenderTree(root): + print(f"{pre}{node.name}") + + def generate_program(self, level): + """ + Generate a program based on the specified level. + + Parameters: + - level (str): The level of the program. + + Returns: + - Tuple[Node, str]: The syntax tree root node and the generated program. + """ + assigned = set() + last_variable = set() + root = Node("ROOT") + + # Set the maximum number of initializations based on the level. + self.init_count = 0 + if level == "1.1": + self.max_init = 1 + elif level == "1.2": + self.max_init = 3 + elif level == "3.1": + self.max_init = 2 + elif level == "3.2": + self.max_init = 4 + else: + self.max_init = 5 + + # Choose a rule for the specified level and generate code. + if level == "ALL" : + level_passed = level + else : + level_passed = "LEVEL" + level + + program = self.generate_code(level_passed, assigned, last_variable, root) + + return root, program.replace("SPACE", " ") + + def memory_usage(self): + """ + Get the current memory usage of the process. + + Returns: + - int: The memory usage in bytes. + """ + process = psutil.Process(os.getpid()) + mem_info = process.memory_info() + return mem_info.rss + + def generate_and_write_programs(self, num_programs, level, filename='data.txt', deduplicate=True): + """ + Generate and write a specified number of programs to a file. + + Parameters: + - num_programs (int): Number of programs to generate and write. + - level (str): The level of the programs. + - filename (str): Name of the file to write the programs (default is 'data.txt'). + - deduplicate (bool, optional): Whether to perform deduplication of generated programs (default is True). + """ + start_time = time.time() # Track the start time for performance measurement. + start_mem = self.memory_usage() # Track the initial memory usage. + max_tries = 1000 # Set the maximum number of tries for deduplication. + num_tries = 0 # Initialize the number of tries counter. + + with open(filename, 'w') as file: + + generated_programs = 0 # Initialize the counter for generated programs. + hashes = set() # Set to keep track of unique program hashes for deduplication. + pbar = tqdm(desc="Generation", total=num_programs) + + while generated_programs < num_programs: + try: + root, program = self.generate_program(level) # Generate a program. + + count = self.line_counter(program)# count how many executed lines + + result = f"""# Snippet\n{program}\n# count\n# {count}""" # fuse the code snippet with its label "count" + + program_hash = hashlib.sha256(result.encode('utf-8')).hexdigest() + + if deduplicate: + if program_hash not in hashes: + hashes.add(program_hash) # Add the hash to the set if it's unique. + file.write(result + '\n\n') # Write the program to the file. + generated_programs += 1 # Increment the counter for generated programs. + pbar.update(1) + num_tries = 0 # Reset the tries counter. + else: + num_tries += 1 # Increment the tries counter. + if num_tries >= max_tries: + print("Hit max tries in deduplication, stopping generation.") + break # Stop generation if max tries are reached. + else: + + file.write(result + '\n\n') # Write the program to the file without deduplication. + generated_programs += 1 # Increment the counter for generated programs. + pbar.update(1) + + except Exception as e: + continue # Ignore code snippets containing division by zero error. + + + pbar.close() + end_time = time.time() # Track the end time for performance measurement. + end_mem = self.memory_usage() # Track the final memory usage. + deduplication_info = "with deduplication" if deduplicate else "without deduplication" + print(f"Code generation completed in {end_time - start_time:.2f} seconds.") + print(f"Memory used during code generation: {end_mem - start_mem} bytes") + print(f"Generated {generated_programs} {'unique ' if deduplicate else ''}programs {deduplication_info}.") + print(f"Programs are saved to {filename}.") + + +def main(): + parser = argparse.ArgumentParser(description='Generate and write programs based on a specified level. ') + parser.add_argument('--num_programs', type=int, default=1000, help='Number of programs to generate and write (default is 1000)') + parser.add_argument('--level', default="ALL", help='The level of the programs (1.1, 1.2, 2.1, 2.2, 3.1, 3.2, ALL)') + parser.add_argument('--filename', default='data/data.txt', help='Name of the file to write the programs (default is data/data.txt)') + parser.add_argument('--deduplicate', action='store_true', default=True, help='Perform deduplication of generated programs (default is True)') + + args = parser.parse_args() + + valid_levels = ["1.1", "1.2", "2.1", "2.2", "3.1", "3.2", "ALL"] + if args.level not in valid_levels: + print(f"Error: Invalid level '{args.level}'. Please choose from {', '.join(valid_levels)}.") + return + code_generator = CodeGenerator() + code_generator.generate_and_write_programs(num_programs=args.num_programs, level=args.level, filename=args.filename, deduplicate=args.deduplicate) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tasks/test.txt b/tasks/test.txt new file mode 100644 index 0000000..e69de29 diff --git a/tinylm-starter-notebook.ipynb b/tinylm-starter-notebook.ipynb new file mode 100644 index 0000000..a779101 --- /dev/null +++ b/tinylm-starter-notebook.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":8996886,"sourceType":"datasetVersion","datasetId":5419152}],"dockerImageVersionId":30747,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import random\nimport os\nimport pickle\nimport time\nimport datetime\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim.lr_scheduler import StepLR\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nimport re","metadata":{"_uuid":"a14f6813-426a-4666-9280-7ed88ebdb85e","_cell_guid":"aa556b17-8ea0-4788-aea8-8d6259526157","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2024-07-20T17:22:00.966528Z","iopub.execute_input":"2024-07-20T17:22:00.966880Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set the random seed for reproducibility\nseed = 42\ntorch.manual_seed(seed)\nrandom.seed(seed)\nnp.random.seed(seed)\n\n# Set the device to GPU if available, otherwise CPU\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f\"Device set to {device}.\")","metadata":{"_uuid":"f10e66ef-f466-4cfc-8ddb-594df92adb45","_cell_guid":"063623b6-121d-4c18-a660-93d2f1be3305","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:56:35.876424Z","iopub.execute_input":"2024-07-20T14:56:35.876901Z","iopub.status.idle":"2024-07-20T14:56:35.918570Z","shell.execute_reply.started":"2024-07-20T14:56:35.876871Z","shell.execute_reply":"2024-07-20T14:56:35.917691Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Preparation","metadata":{"_uuid":"f61836e4-3f71-432d-8c50-9de1ff2e05e0","_cell_guid":"4776333c-08cd-4127-bea7-d7ec8898df7b","trusted":true}},{"cell_type":"code","source":"# Helper functions to load and save data\ndef save_data(data, file_path):\n with open(file_path, 'w') as f:\n f.write(data)\n\ndef load_data(file_path):\n with open(file_path, 'r') as f:\n return f.read()","metadata":{"_uuid":"0aa1c1b8-a945-4baa-8d46-3a08056a9004","_cell_guid":"fcc4b173-f5e5-4110-b14f-46a8fa6da9ae","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:56:35.919649Z","iopub.execute_input":"2024-07-20T14:56:35.919920Z","iopub.status.idle":"2024-07-20T14:56:35.925793Z","shell.execute_reply.started":"2024-07-20T14:56:35.919896Z","shell.execute_reply":"2024-07-20T14:56:35.924714Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Directory where the data is stored\nDATA_DIR = \"/kaggle/input/tinylm-data\"","metadata":{"_uuid":"3da5ca68-e0d7-4aed-b89f-5f2a4ab910d9","_cell_guid":"9731ee3f-b4d1-4b6e-afb2-859c56bef6c6","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:56:35.927835Z","iopub.execute_input":"2024-07-20T14:56:35.928144Z","iopub.status.idle":"2024-07-20T14:56:35.934561Z","shell.execute_reply.started":"2024-07-20T14:56:35.928121Z","shell.execute_reply":"2024-07-20T14:56:35.933680Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Attempt to derive vocab_size from the dataset\n\nmeta_path = os.path.join(DATA_DIR, 'meta.pkl')\nvocab_size = None\n\nif os.path.exists(meta_path):\n with open(meta_path, 'rb') as f:\n meta = pickle.load(f)\n vocab_size = meta['vocab_size']\n print(f\"found vocab_size = {vocab_size} (inside {meta_path})\")\nelse:\n print(\"Meta file not found. Please ensure the meta.pkl file is present in the data directory.\")\n\n# Encode and decode functions for character-level Tokenzation \ndef encode(s):\n return [meta['stoi'][c] for c in s]\n\ndef decode(l):\n return ''.join([meta['itos'][i] for i in l])","metadata":{"_uuid":"6d064118-585d-46a9-8f40-f9472fe879b4","_cell_guid":"ddae0037-0f42-425d-a2e9-4238f4c608f2","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:56:35.935881Z","iopub.execute_input":"2024-07-20T14:56:35.936315Z","iopub.status.idle":"2024-07-20T14:56:35.954533Z","shell.execute_reply.started":"2024-07-20T14:56:35.936285Z","shell.execute_reply":"2024-07-20T14:56:35.953576Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load data\ntrain_data = load_data(os.path.join(DATA_DIR, 'train.txt'))\nval_data = load_data(os.path.join(DATA_DIR, 'val.txt'))\ntest_data = load_data(os.path.join(DATA_DIR, 'test.txt'))\n\n# Encode data\ntrain_ids = encode(train_data)\nval_ids = encode(val_data)\ntest_ids = encode(test_data)\n\n# Save encoded data to bin files, make sure to choose \"Files only\" on the persistence option of the session so that you don't encode data each time\ntrain_ids = np.array(train_ids, dtype=np.uint16)\nval_ids = np.array(val_ids, dtype=np.uint16)\ntest_ids = np.array(test_ids, dtype=np.uint16)\n\ntrain_ids.tofile( 'train.bin')\nval_ids.tofile( 'val.bin')\ntest_ids.tofile('test.bin')\n\nprint(\"Encoded data saved as binary files.\")","metadata":{"_uuid":"1b2892b5-a904-4550-a8d6-ae8f51f1841f","_cell_guid":"ff53a3e0-09ab-4396-90d9-cef86df0605b","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:56:35.955777Z","iopub.execute_input":"2024-07-20T14:56:35.956201Z","iopub.status.idle":"2024-07-20T14:58:30.560356Z","shell.execute_reply.started":"2024-07-20T14:56:35.956153Z","shell.execute_reply":"2024-07-20T14:58:30.559345Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del(train_ids)\ndel(val_ids)\ndel(test_ids)","metadata":{"_uuid":"6a2d1ac2-5ef7-441c-9837-050c59120ab9","_cell_guid":"125ce42e-8df9-4094-b0c5-242fcd99a597","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.561546Z","iopub.execute_input":"2024-07-20T14:58:30.561837Z","iopub.status.idle":"2024-07-20T14:58:30.569102Z","shell.execute_reply.started":"2024-07-20T14:58:30.561811Z","shell.execute_reply":"2024-07-20T14:58:30.568232Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load encoded data\ntrain_data = np.memmap(\"/kaggle/working/train.bin\", dtype=np.uint16, mode='r')\nval_data = np.memmap(\"/kaggle/working/val.bin\", dtype=np.uint16, mode='r')","metadata":{"_uuid":"9cd8ff5a-2170-4c53-be17-02ac7d0cffd9","_cell_guid":"c53f3930-8d16-443d-a5ec-a6926f3f6cf4","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.570357Z","iopub.execute_input":"2024-07-20T14:58:30.570777Z","iopub.status.idle":"2024-07-20T14:58:30.611964Z","shell.execute_reply.started":"2024-07-20T14:58:30.570746Z","shell.execute_reply":"2024-07-20T14:58:30.611224Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model","metadata":{"_uuid":"f4fc1523-1d72-49db-a3bc-8d521f236993","_cell_guid":"8574d987-cef6-47d1-b889-e8242a0bcd23","trusted":true}},{"cell_type":"code","source":"# Hyperparameters for the GPT model\nblock_size = 256 # Maximum context length\nn_embd = 120 # Embedding dimension\nn_head = 6 # Number of attention heads\nn_layer = 6 # Number of transformer blocks\ndropout = 0 # Dropout rate\nbatch_size = 64 # Batch size for training\nmax_iters = 60000 # Maximum number of iterations\nlearning_rate = 1e-3 # Initial Learning rate value\nmiles = [int(max_iters * m) for m in [0.7, 0.8, 0.9]] # Milestones for learning rate decay as fractions of max_iters\neval_interval = 10000 # Evaluation interval\neval_iters = 500 # Number of iterations for evaluation\n\ncompile = False # requires PyTorch 2.0","metadata":{"_uuid":"1fd63d8c-f842-444c-9dc8-cab3263ae6e4","_cell_guid":"2d4305c5-c1c6-48b0-a048-953a98954854","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.613008Z","iopub.execute_input":"2024-07-20T14:58:30.613362Z","iopub.status.idle":"2024-07-20T14:58:30.626685Z","shell.execute_reply.started":"2024-07-20T14:58:30.613331Z","shell.execute_reply":"2024-07-20T14:58:30.625843Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class LayerNorm(nn.Module):\n \"\"\" LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False \"\"\"\n\n def __init__(self, ndim, bias):\n super().__init__()\n self.weight = nn.Parameter(torch.ones(ndim))\n self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None\n\n def forward(self, input):\n return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)\n\nclass Head(nn.Module):\n \"\"\"One head of self-attention.\"\"\"\n\n def __init__(self, head_size):\n super().__init__()\n self.key = nn.Linear(n_embd, head_size, bias=False)\n self.query = nn.Linear(n_embd, head_size, bias=False)\n self.value = nn.Linear(n_embd, head_size, bias=False)\n self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')\n\n self.dropout = nn.Dropout(dropout)\n\n def forward(self, x):\n B,T,C = x.shape\n k = self.key(x) # (B, T, 16)\n q = self.query(x) # (B, T, 16)\n v = self.value(x)\n \n out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=dropout if self.training else 0, is_causal=True)\n \n return out\n\nclass MultiHeadAttention(nn.Module):\n \"\"\"multiple heads of self-attention in parallel.\"\"\"\n\n def __init__(self, num_heads, head_size):\n super().__init__()\n self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n self.proj = nn.Linear(n_embd, n_embd)\n self.dropout = nn.Dropout(dropout)\n \n def forward(self, x):\n out = torch.cat([h(x) for h in self.heads], dim=-1)\n out = self.dropout(self.proj(out))\n return out\n \nclass FeedForward(nn.Module):\n \"\"\" a simple linear layer followed by a non-linearity.\"\"\"\n\n def __init__(self, n_embd):\n super().__init__()\n self.net = nn.Sequential(\n nn.Linear(n_embd, 4 * n_embd, bias=False),\n nn.GELU(),\n nn.Linear( 4 * n_embd, n_embd, bias=False),\n nn.Dropout(dropout),\n )\n\n def forward(self, x):\n return self.net(x)\n \nclass Block(nn.Module):\n \"\"\" Transformer block: communication followed by feedforward.\"\"\"\n\n def __init__(self, n_embd, n_head):\n super().__init__()\n head_size = n_embd // n_head\n self.sa = MultiHeadAttention(n_head, head_size)\n self.ffwd = FeedForward(n_embd)\n self.ln1 = nn.LayerNorm(n_embd, bias=False)\n self.ln2 = nn.LayerNorm(n_embd, bias=False)\n\n def forward(self, x):\n x = x + self.sa(self.ln1(x))\n x = x + self.ffwd(self.ln2(x))\n return x\n\nclass GPT(nn.Module):\n\n def __init__(self):\n super().__init__()\n # each token directly reads off the logits for the next token from a lookup table\n self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n self.position_embedding_table = nn.Embedding(block_size, n_embd)\n self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n self.ln_f = nn.LayerNorm(n_embd, bias=False) \n self.lm_head = nn.Linear(n_embd, vocab_size)\n\n def forward(self, idx, targets=None):\n B, T = idx.shape\n\n # idx and targets are both (B,T) tensor of integers\n tok_emb = self.token_embedding_table(idx) # (B,T,C)\n pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n x = tok_emb + pos_emb # (B,T,C)\n x = self.blocks(x) # (B,T,C)\n x = self.ln_f(x) # (B,T,C)\n logits = self.lm_head(x) # (B,T,vocab_size)\n\n if targets is None:\n loss = None\n else:\n B, T, C = logits.shape\n logits = logits.view(B*T, C)\n targets = targets.view(B*T)\n loss = F.cross_entropy(logits, targets)\n\n return logits, loss\n \n def generate(self, idx, max_new_tokens):\n # idx is (B, T) array of indices in the current context\n for _ in range(max_new_tokens):\n # crop idx to the last block_size tokens\n idx_cond = idx[:, -block_size:] # (B, T)\n # get the predictions\n logits, loss = self(idx_cond)\n # focus only on the last time step\n logits = logits[:, -1, :] # becomes (B, C)\n # apply softmax to get probabilities\n probs = F.softmax(logits, dim=-1) # (B, C)\n # sample from the distribution\n idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n # append sampled index to the running sequence\n idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n return idx","metadata":{"_uuid":"9c3a2af2-99a7-4657-bb8d-168a3e8dfcfb","_cell_guid":"17ff6e02-86d2-4f49-a384-be8c035377a7","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.630074Z","iopub.execute_input":"2024-07-20T14:58:30.630394Z","iopub.status.idle":"2024-07-20T14:58:30.656067Z","shell.execute_reply.started":"2024-07-20T14:58:30.630370Z","shell.execute_reply":"2024-07-20T14:58:30.655234Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get random batch of data\ndef get_batch(split):\n data = train_data if split == 'train' else val_data\n ix = torch.randint(len(data) - block_size, (batch_size,))\n x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])\n y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])\n x, y = x.to(device), y.to(device)\n return x, y\n\n# Estimate loss on train and val splits\n@torch.no_grad()\ndef estimate_loss():\n out = {}\n model.eval()\n for split in ['train', 'val']:\n losses = torch.zeros(eval_iters) \n for k in range(eval_iters):\n X, Y = get_batch(split)\n logits, loss = model(X, Y)\n losses[k] = loss.item()\n out[split] = losses.mean()\n model.train()\n return out\n\n\n# Helper function to make large numbers of parameters human-readable\ndef human_readable(num):\n magnitude = 0\n while abs(num) >= 1000:\n magnitude += 1\n num /= 1000.0\n return '%.0f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])","metadata":{"_uuid":"be441d8d-c18b-4694-b2ff-607aac4b11e6","_cell_guid":"a716f789-f605-42d0-9494-d8927ed09a6f","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.657121Z","iopub.execute_input":"2024-07-20T14:58:30.657474Z","iopub.status.idle":"2024-07-20T14:58:30.670624Z","shell.execute_reply.started":"2024-07-20T14:58:30.657443Z","shell.execute_reply":"2024-07-20T14:58:30.669729Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Initialize model and move it to the device (GPU)\nmodel = GPT()\nm = model.to(device)\nnum_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)\n\n# compile the model\nif compile:\n print(\"compiling the model... (takes a ~minute)\")\n model = torch.compile(model) \n \nnum_parameters_hr = human_readable(num_parameters)\nprint(f'The model has {num_parameters_hr} trainable parameters')","metadata":{"_uuid":"db1edcb0-7dae-40b8-99f0-3a524bd1311e","_cell_guid":"21de39d0-d298-45ce-a590-c6be400f31e8","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.671622Z","iopub.execute_input":"2024-07-20T14:58:30.671881Z","iopub.status.idle":"2024-07-20T14:58:30.893571Z","shell.execute_reply.started":"2024-07-20T14:58:30.671859Z","shell.execute_reply":"2024-07-20T14:58:30.892594Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training","metadata":{"_uuid":"8cdf45cc-0d3a-43a9-b10d-5381799a21f2","_cell_guid":"ac1fe251-e0c8-4079-9da4-68aff59262f4","trusted":true}},{"cell_type":"code","source":"# Initialize optimizer\noptimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n\n# Initialize learning rate scheduler\nscheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=miles, gamma=0.1)","metadata":{"_uuid":"45093d41-9498-45e4-b93b-95b0b239c0af","_cell_guid":"e725706a-19a1-4e82-91b1-514dd0488f33","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:30.894863Z","iopub.execute_input":"2024-07-20T14:58:30.895301Z","iopub.status.idle":"2024-07-20T14:58:32.215917Z","shell.execute_reply.started":"2024-07-20T14:58:30.895264Z","shell.execute_reply":"2024-07-20T14:58:32.215087Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get current date and hour to get track of experiments\nnow = datetime.datetime.now()\ndate_hour = now.strftime(\"%Y-%m-%d_%H-%M\")\n\n# Train\n# Start training timer\nstart_time = time.time()\n\n# Training loop\nfor iter in range(max_iters):\n\n # evaluate the model on the train and val splits and log the losses\n if iter % eval_interval == 0:\n losses = estimate_loss()\n print(f'iter {iter:5d} | train loss {losses[\"train\"]:.4f} | val loss {losses[\"val\"]:.4f}')\n \n # train the model for one iteration\n xb, yb = get_batch('train')\n\n # forward pass\n logits, loss = model(xb, yb)\n optimizer.zero_grad(set_to_none=True)\n loss.backward()\n optimizer.step()\n\n # Step the scheduler\n \n \n \n \n scheduler.step()\n\n# End training timer\nend_time = time.time()\nprint(f'Training time: {(end_time - start_time) / 60} min')\n\n# Save the trained model\ntorch.save(model.state_dict(), f\"{num_parameters_hr}_{date_hour}.pth\")","metadata":{"_uuid":"534a6c6a-e6b8-4632-8078-86aab93500de","_cell_guid":"76b8e469-893d-4151-a175-99b54dbabe60","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T14:58:32.216993Z","iopub.execute_input":"2024-07-20T14:58:32.217407Z","iopub.status.idle":"2024-07-20T15:28:44.728335Z","shell.execute_reply.started":"2024-07-20T14:58:32.217380Z","shell.execute_reply":"2024-07-20T15:28:44.726717Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Evaluation","metadata":{"_uuid":"facd8250-1fd4-4486-a9a6-f099df266caf","_cell_guid":"e831564c-6b76-489b-98b0-69cad098fdd6","trusted":true}},{"cell_type":"code","source":"test_data = np.memmap('test.bin', dtype=np.uint16, mode='r')","metadata":{"_uuid":"f4e10d4c-a4c8-4e6b-891e-f3d14947adfb","_cell_guid":"d8071f1a-961b-4410-ae36-ba54b5b525d0","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T15:28:53.687568Z","iopub.execute_input":"2024-07-20T15:28:53.688312Z","iopub.status.idle":"2024-07-20T15:28:53.719102Z","shell.execute_reply.started":"2024-07-20T15:28:53.688275Z","shell.execute_reply":"2024-07-20T15:28:53.718217Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluate example\ndef evaluate_example(example, model, max_new_tokens=30):\n \n # Split example and determine maximum new tokens allowed\n splited_example = example.split(\"# output\")\n if not (\"for\" in splited_example[0]):\n max_new_tokens = 22\n # Encode prompt and prepare for evaluation \n encoded_example = torch.tensor(encode(splited_example[0] + \"# output\"), dtype=torch.long).unsqueeze(0).to(device)\n prompt_text = splited_example[0] + \"# output\"\n result_example = splited_example[-1]\n \n # Extract real results from example\n real_results = [float(match.group()) for match in re.finditer(r\"(?<=# )-?\\d+(\\.\\d+)?\", result_example.split('\\n\\n')[0].replace(\"\\n\", \"\"))]\n \n # Generate response from model and extract generated results\n response = decode(model.generate(encoded_example, max_new_tokens=max_new_tokens)[0].tolist())\n splited_response = response.split(\"# output\")\n result_response = splited_response[-1]\n generated_results = [float(match.group()) for match in re.finditer(r\"(?<=# )-?\\d+(\\.\\d+)?\", result_response.split('\\n\\n')[0].replace(\"\\n\", \"\"))]\n\n return prompt_text, real_results, generated_results\n\n\n\n# Write results to file\ndef write_results_to_file(output_file, prompt, real_results, generated_results):\n df = pd.DataFrame({\n 'Prompt': prompt,\n 'Real_Results': real_results,\n 'Generated_Results': generated_results\n })\n df.to_csv(output_file, index=False)","metadata":{"_uuid":"2e9f95ba-ca83-48bc-bb18-8910efc37422","_cell_guid":"f3d6ae4b-e069-43bd-be3f-9e46f19146d3","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T15:31:17.995588Z","iopub.execute_input":"2024-07-20T15:31:17.996005Z","iopub.status.idle":"2024-07-20T15:31:18.006157Z","shell.execute_reply.started":"2024-07-20T15:31:17.995974Z","shell.execute_reply":"2024-07-20T15:31:18.005244Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluation Loop\n\n# Split examples and initialize lists for results\nexamples = decode(test_data).split(\"\\n\\n\")\nexamples = [example for example in examples if example]\n\n# Start evaluation process\nprompt = []\nreal_results = []\ngenerated_results = []\n\n# Iterate through examples and evaluate the model on each one\nfor example in tqdm(examples):\n prompt_text, real_result, result = evaluate_example(example, model)\n prompt.append(prompt_text)\n real_results.append(real_result)\n generated_results.append(result)\n\n# Calculate and print accuracy\ncorrect_count = sum(1 for real, generated in zip(real_results, generated_results) if real == generated)\naccuracy = correct_count / len(generated_results)\nprint(f\"Accuracy: {accuracy * 100:.2f}%\")\n\n# Store accuracy in a file\nwith open(\"accuracy.txt\", 'w') as f:\n f.write(f\"Accuracy: {accuracy * 100:.2f}%\\n\")\n\n# Store predictions in a CSV file\n write_results_to_file(\"predictions.csv\", prompt, real_results, generated_results)","metadata":{"_uuid":"7b21f8fd-2e4c-443b-8120-e0af732bf558","_cell_guid":"2536ece9-1d3c-4373-b308-fd1049f3297f","collapsed":false,"execution":{"iopub.status.busy":"2024-07-20T15:32:25.564254Z","iopub.execute_input":"2024-07-20T15:32:25.564645Z","iopub.status.idle":"2024-07-20T15:32:30.853268Z","shell.execute_reply.started":"2024-07-20T15:32:25.564616Z","shell.execute_reply":"2024-07-20T15:32:30.852339Z"},"jupyter":{"outputs_hidden":false},"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/token-level_code_completion.py b/token-level_code_completion.py new file mode 100644 index 0000000..4bb6211 --- /dev/null +++ b/token-level_code_completion.py @@ -0,0 +1,96 @@ +import os +import pickle +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import pandas as pd +from tqdm import tqdm +import re +from model import GPT + + +# Argument parsing +parser = argparse.ArgumentParser(description='Evaluate NanoGPT model on token-level code completion.') +parser.add_argument('--dataset_dir', type=str, default='data', help='Directory where the dataset is stored') +parser.add_argument('--model_name', type=str, required=True, help='Name of the pre-trained model (without .pth extension)') + +# Parse the command-line arguments +args = parser.parse_args() + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +torch.manual_seed(1337) + + +# Constants for dataset and file paths +MODEL_FILE = f"models/{args.model_name}.pth" +ACCURACY_FILE = f"results/{args.model_name}_acc_token-level_code_completion.txt" +RESULTS_FILE = f"results/{args.model_name}_token-level_code_completion.csv" + + +data_dir = args.dataset_dir +test_data = np.memmap(os.path.join(data_dir, 'test.bin'), dtype=np.uint16, mode='r') + +# attempt to derive vocab_size from the dataset +meta_path = os.path.join(data_dir, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"found vocab_size = {meta_vocab_size} (inside {meta_path})") + +stoi = meta['stoi'] +itos = meta['itos'] +encode = lambda s: [stoi[c] for c in s] +decode = lambda l: ''.join([itos[i] for i in l]) + +model = GPT() +print("Compiling model...") +model = torch.compile(model) # pytorch 2.0 +model.load_state_dict(torch.load(MODEL_FILE)) +m = model.to(device) + +examples = decode(test_data).split("\n\n") +examples = [example for example in examples if example] + +correct_predictions = 0 +total_predictions = 0 + +results = [] + +for code_snippet in tqdm(examples): + + tokens = torch.tensor(encode(code_snippet), dtype=torch.long).unsqueeze(0).to(device) + + for i in range(1, tokens.shape[1]): + + context = tokens[:, :i] + actual_next_token = tokens[:, i].item() + predicted_next_token = m.generate(context, max_new_tokens=1) + predicted_next_token = predicted_next_token[:, -1].item() + is_correct = (predicted_next_token == actual_next_token) + + if is_correct: + correct_predictions += 1 + results.append({ + 'context': context.cpu(), + 'actual_next_token': actual_next_token, + 'predicted_next_token': predicted_next_token, + 'is_correct': is_correct + }) + + total_predictions += 1 + +df = pd.DataFrame(results) +df.to_csv(RESULTS_FILE, index=False) + + +accuracy = (correct_predictions / total_predictions) * 100 + +# Store accuracy in a file +with open(ACCURACY_FILE, 'w') as f: + f.write(f"Accuracy: {accuracy:.2f}%\n") + +print(accuracy) \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000..37bae77 --- /dev/null +++ b/train.py @@ -0,0 +1,183 @@ +import os +import time +import datetime +import pickle +import argparse + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.optim.lr_scheduler import StepLR + +import numpy as np +import pandas as pd +from tqdm import tqdm + +from model import GPT + +# Argument parsing +parser = argparse.ArgumentParser(description='Training script for NanoGPT model.') +# Define command-line arguments +parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training') +parser.add_argument('--max_iters', type=int, default=35000, help='Maximum number of iterations') +parser.add_argument('--learning_rate', type=float, default=1e-2, help='Learning rate') +parser.add_argument('--miles', type=float, nargs='+', default=[0.7, 0.8, 0.9], help='Milestones for learning rate decay as fractions of max_iters') +parser.add_argument('--eval_interval', type=int, default=10000, help='Evaluation interval') +parser.add_argument('--eval_iters', type=int, default=500, help='Number of iterations for evaluation') +parser.add_argument('--data_dir', type=str, default='data', help='Directory where the data is stored') + +# Parse the command-line arguments +args = parser.parse_args() + +# Use the parsed arguments +batch_size = args.batch_size +max_iters = args.max_iters +learning_rate = args.learning_rate +# Calculate milestone iterations based on fractions +miles = [int(max_iters * m) for m in args.miles] +eval_interval = args.eval_interval +eval_iters = args.eval_iters +data_dir = args.data_dir + +# Set directories for data and models +DATA_DIR = data_dir +MODELS_DIR = os.path.join(os.path.dirname(__file__), 'models') + +# Other settings +block_size = 256 +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +dropout = 0 +compile = True + +# wandb settings +wandb_log = True +wandb_project = 'TinyLanguageModel' + +# For logging purposes +config_keys = [k for k,v in globals().items() if not k.startswith('_') and isinstance(v, (int, float, bool, str))] +config = {k: globals()[k] for k in config_keys} + +# Set random seed for reproducibility +torch.manual_seed(1337) + +print(f"Loading training data from {DATA_DIR}...\n") +train_data = np.memmap(os.path.join(DATA_DIR, 'train.bin'), dtype=np.uint16, mode='r') +val_data = np.memmap(os.path.join(DATA_DIR, 'val.bin'), dtype=np.uint16, mode='r') + +# Attempt to derive vocab_size from the dataset +meta_path = os.path.join(DATA_DIR, 'meta.pkl') +meta_vocab_size = None +if os.path.exists(meta_path): + with open(meta_path, 'rb') as f: + meta = pickle.load(f) + meta_vocab_size = meta['vocab_size'] + print(f"Found vocab_size = {meta_vocab_size} (inside {meta_path})\n") + +# Function to get random batch of data +def get_batch(split): + data = train_data if split == 'train' else val_data + ix = torch.randint(len(data) - block_size, (batch_size,)) + x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix]) + y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix]) + x, y = x.to(device), y.to(device) + return x, y + +# Function to calculate loss on train and validation splits +@torch.no_grad() +def estimate_loss(): + out = {} + model.eval() + for split in ['train', 'val']: + losses = torch.zeros(eval_iters) + for k in range(eval_iters): + X, Y = get_batch(split) + logits, loss = model(X, Y) + losses[k] = loss.item() + out[split] = losses.mean() + model.train() + return out + +# Initialize model and move it to the device (GPU/CPU) +print("Initializing the model...\n") +model = GPT() +m = model.to(device) +num_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + +# Compile the model if enabled +if compile: + print("Compiling the model... (takes a ~minute)\n") + try: + model = torch.compile(model) # requires PyTorch 2.0 + except Exception as e: + pass + +# Helper function to make large numbers of parameters human-readable +def human_readable(num): + magnitude = 0 + while abs(num) >= 1000: + magnitude += 1 + num /= 1000.0 + return '%.0f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude]) + +num_parameters_hr = human_readable(num_parameters) +print(f'The model has {num_parameters_hr} trainable parameters\n') + +# Get current date and time +now = datetime.datetime.now() +date_hour = now.strftime("%Y-%m-%d_%H-%M") + +# Construct wandb run name +wandb_run_name = f'TLM_RUN_{num_parameters_hr}_{date_hour}' + +# Initialize optimizer +optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + +# Initialize learning rate scheduler +scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=miles, gamma=0.1) + +# Initialize wandb if logging is enabled +if wandb_log: + import wandb + wandb.init(project=wandb_project, name=wandb_run_name, config=config) + print(f"wandb logging is enabled. Project: {wandb_project}, Run name: {wandb_run_name}\n") + +# Start training timer +start_time = time.time() +print("Starting training...\n") + +# Training loop +for iter in range(max_iters): + # Evaluate the model on the train and val splits and log the losses + if iter % eval_interval == 0: + losses = estimate_loss() + print(f'iter {iter:5d} | train loss {losses["train"]:.4f} | val loss {losses["val"]:.4f}') + if wandb_log: + wandb.log({ + "iter": iter, + "train/loss": losses['train'], + "val/loss": losses['val'], + "lr": scheduler.get_last_lr()[0], + }) + + # Train the model for one iteration + xb, yb = get_batch('train') + + # Forward pass + logits, loss = model(xb, yb) + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + + # Step the scheduler + scheduler.step() + + + +# End training timer +end_time = time.time() +print(f'Training complete! Total time: {(end_time - start_time) / 60:.2f} minutes\n') + +# Save the trained model +model_path = f"{MODELS_DIR}/{num_parameters_hr}_{date_hour}.pth" +torch.save(model.state_dict(), model_path) +print(f"Model saved to {model_path}\n")