coding_expert/data/prepare_data.py

"""
Data preparation script for the Coding Expert model
"""
import os
import json
from pathlib import Path
import jsonlines
from typing import Dict, List, Any
import sys
import psutil
from datasets import load_dataset
import ast
import numpy as np

from data_processor import CodeDataProcessor

class CodeDataPreparer:
    def __init__(self, output_dir: str = "processed_data"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(exist_ok=True)
        self.datasets = {
            "CodeSearchNet": {
                "source": "codeium/codeium",
                "split": "train",
                "fields": ["code", "docstring", "language", "function_name"]
            },
            "HumanEval": {
                "source": "openai/human_eval",
                "split": "test",
                "fields": ["task_id", "prompt", "canonical_solution", "test", "entry_point"]
            },
            "MBPP": {
                "source": "mbpp/mbpp",
                "split": "train",
                "fields": ["task_id", "text", "code", "test_list", "challenge_test_list"]
            },
            "Spider": {
                "source": "yale-lily/spider",
                "split": "train",
                "fields": ["query", "question", "db_id", "sql"]
            },
            "DeepFix": {
                "source": "deepfix/deepfix",
                "split": "train",
                "fields": ["code", "fixed_code", "error_type"]
            },
            "CodeXGLUE": {
                "source": "microsoft/CodeXGLUE",
                "split": "train",
                "fields": ["code", "docstring", "task", "language"]
            }
        }

    def process_dataset(self, dataset: List[Dict[str, Any]], dataset_name: str) -> List[Dict[str, Any]]:
        """Process a specific dataset"""
        processed = []
        error_count = 0
        
        print(f"\nProcessing {dataset_name} dataset...")
        
        for idx, example in enumerate(dataset):
            try:
                processed_example = self._process_example(dataset_name, example)
                processed.append(processed_example)
            except Exception as e:
                print(f"Error processing example {idx} in {dataset_name}: {str(e)}")
                error_count += 1
                
        print(f"Processed {len(processed)} examples from {dataset_name}")
        print(f"Encountered {error_count} errors during processing")
        return processed

    def _process_example(self, dataset_name: str, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process a single example based on its dataset type"""
        if dataset_name == "CodeSearchNet":
            return self._process_code_search_net(example)
        elif dataset_name == "HumanEval":
            return self._process_human_eval(example)
        elif dataset_name == "MBPP":
            return self._process_mbpp(example)
        elif dataset_name == "Spider":
            return self._process_spider(example)
        elif dataset_name == "DeepFix":
            return self._process_deep_fix(example)
        elif dataset_name == "CodeXGLUE":
            return self._process_codexglue(example)
        else:
            raise ValueError(f"Unknown dataset: {dataset_name}")

    def _process_code_search_net(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process CodeSearchNet example"""
        return {
            "code": example["code"].strip(),
            "docstring": example["docstring"].strip(),
            "language": example["language"],
            "function_name": example["function_name"],
            "code_analysis": self._analyze_code(example["code"])
        }

    def _process_human_eval(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process HumanEval example"""
        return {
            "task_id": example["task_id"],
            "prompt": example["prompt"].strip(),
            "solution": example["canonical_solution"].strip(),
            "test": example["test"].strip(),
            "entry_point": example["entry_point"],
            "code_analysis": self._analyze_code(example["canonical_solution"])
        }

    def _process_mbpp(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process MBPP example"""
        return {
            "task_id": example["task_id"],
            "problem": example["text"].strip(),
            "solution": example["code"].strip(),
            "test_list": example["test_list"],
            "challenge_test_list": example["challenge_test_list"],
            "code_analysis": self._analyze_code(example["code"])
        }

    def _process_spider(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process Spider example"""
        return {
            "query": example["query"].strip(),
            "question": example["question"].strip(),
            "db_id": example["db_id"],
            "sql": example["sql"].strip(),
            "code_analysis": self._analyze_code(example["sql"])
        }

    def _process_deep_fix(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process DeepFix example"""
        return {
            "original_code": example["code"].strip(),
            "fixed_code": example["fixed_code"].strip(),
            "error_type": example["error_type"],
            "code_analysis": self._analyze_code(example["fixed_code"])
        }

    def _process_codexglue(self, example: Dict[str, Any]) -> Dict[str, Any]:
        """Process CodeXGLUE example"""
        return {
            "code": example["code"].strip(),
            "docstring": example["docstring"].strip(),
            "task": example["task"],
            "language": example["language"],
            "code_analysis": self._analyze_code(example["code"])
        }

    def _analyze_code(self, code: str) -> Dict[str, Any]:
        """Analyze code structure and complexity"""
        try:
            tree = ast.parse(code)
            return {
                "num_functions": len([node for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)]),
                "num_classes": len([node for node in ast.walk(tree) if isinstance(node, ast.ClassDef)]),
                "complexity": self._calculate_complexity(tree)
            }
        except Exception as e:
            return {"error": str(e)}

    def _calculate_complexity(self, tree: ast.AST) -> int:
        """Calculate cyclomatic complexity"""
        complexity = 1  # Start with 1 for the main program
        for node in ast.walk(tree):
            if isinstance(node, (ast.If, ast.For, ast.While, ast.Try, ast.ExceptHandler)):
                complexity += 1
        return complexity

    def save_to_jsonl(self, data: List[Dict[str, Any]], filename: str):
        """Save data to JSONL file"""
        filepath = self.output_dir / filename
        with jsonlines.open(filepath, mode='w') as writer:
            writer.write_all(data)
        return filepath

    def print_sample(self, data: List[Dict[str, Any]], count: int = 3):
        """Print sample of processed data"""
        print("\nSample data:")
        for i, example in enumerate(data[:count]):
            print(f"\nSample {i+1}:")
            print(json.dumps(example, indent=2))

    def print_memory_usage(self):
        """Print current memory usage"""
        process = psutil.Process()
        memory_info = process.memory_info()
        print(f"Current memory usage: {memory_info.rss / 1024 / 1024:.2f} MB")

def main():
    preparer = CodeDataPreparer()
    
    # Process each dataset
    for dataset_name, config in preparer.datasets.items():
        try:
            print(f"\nLoading {dataset_name} dataset...")
            dataset = load_dataset(config["source"], split=config["split"])
            print(f"Loaded {len(dataset)} samples from {dataset_name}")
            
            processed_data = preparer.process_dataset(dataset, dataset_name)
            print(f"Processed {len(processed_data)} samples")
            
            preparer.print_sample(processed_data)
            
            # Save processed data
            output_path = preparer.save_to_jsonl(
                processed_data, 
                f"{dataset_name.lower()}_processed.jsonl"
            )
            print(f"\nSaved {dataset_name} data to: {output_path}")
            
        except Exception as e:
            print(f"Error processing {dataset_name} dataset: {str(e)}")
            print("Continuing with next dataset...")
    
    # Print memory usage
    preparer.print_memory_usage()

if __name__ == "__main__":
    main()