data/preprocessing.py

"""Advanced Preprocessing for OpenThoughts and Custom Datasets"""

import json
import logging
import re
from typing import Any, Dict, List, Optional, Tuple

logger = logging.getLogger(__name__)

# Special token markers
THOUGHT_START = "<think>"
THOUGHT_END = "</think>"
USER_START = "<user>"
USER_END = "</user>"
ASSISTANT_START = "<assistant>"
ASSISTANT_END = "</assistant>"
SYSTEM_START = "<system>"
SYSTEM_END = "</system>"


def preprocess_conversation(

    conversations: Any,

    include_thoughts: bool = True,

    include_reasoning: bool = True,

) -> Dict[str, Any]:
    """Preprocess conversation data into training format."""
    if isinstance(conversations, str):
        try:
            conversations = json.loads(conversations)
        except json.JSONDecodeError:
            return {"text": conversations, "conversations": []}

    if not isinstance(conversations, list):
        return {"text": str(conversations), "conversations": []}

    processed_messages = []
    thoughts = []
    reasoning = ""

    for msg in conversations:
        if not isinstance(msg, dict):
            continue

        role = msg.get("role", "").lower()
        content = msg.get("content", "")

        if not content:
            continue

        # Extract thoughts if present
        if include_thoughts and THOUGHT_START in content:
            thought_parts = re.findall(r'<think>(.*?)</think>', content, re.DOTALL)
            thoughts.extend([t.strip() for t in thought_parts if t.strip()])
            # Remove thought tags from content
            content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()

        # Format with special tokens
        if role == "user":
            formatted = f"{USER_START} {content} {USER_END}"
        elif role == "assistant":
            formatted = f"{ASSISTANT_START} {content} {ASSISTANT_END}"
        elif role == "system":
            formatted = f"{SYSTEM_START} {content} {SYSTEM_END}"
        else:
            formatted = content

        processed_messages.append({
            "role": role,
            "content": content,
            "formatted": formatted,
        })

    # Combine into single text
    text = "\n".join(msg["formatted"] for msg in processed_messages)

    result = {
        "text": text,
        "conversations": processed_messages,
    }

    if include_thoughts and thoughts:
        result["thoughts"] = " ".join(thoughts)

    if include_reasoning and reasoning:
        result["reasoning"] = reasoning

    return result


def extract_thoughts(text: str) -> str:
    """Extract chain-of-thought from text."""
    pattern = re.compile(r'<think>(.*?)</think>', re.DOTALL)
    thoughts = pattern.findall(text)
    return " ".join(t.strip() for t in thoughts if t.strip())


def format_for_training(

    sample: Dict[str, Any],

    include_thoughts: bool = True,

    include_reasoning: bool = True,

) -> str:
    """Format sample for model training."""
    if "text" in sample:
        text = sample["text"]
    elif "conversations" in sample:
        text = preprocess_conversation(sample["conversations"], include_thoughts, include_reasoning)["text"]
    elif "content" in sample:
        text = sample["content"]
    else:
        text = ""

    # Add thoughts if available and requested
    if include_thoughts and "thoughts" in sample and sample["thoughts"]:
        text += f"\n{THOUGHT_START} {sample['thoughts']} {THOUGHT_END}"

    return text


def detect_domain(conversations: Any) -> str:
    """Detect domain of conversation based on content."""
    if isinstance(conversations, str):
        try:
            conversations = json.loads(conversations)
        except:
            conversations = []

    text = ""
    for msg in conversations:
        if isinstance(msg, dict):
            text += msg.get("content", "") + " "

    text = text.lower()

    # Domain keywords
    domain_keywords = {
        "code": ["def ", "class ", "import ", "function", "return", "if __name__", "```python", "```java", "```cpp"],
        "mathematics": ["equation", "theorem", "proof", "calculate", "solve", "integral", "derivative", "matrix", "vector"],
        "science": ["experiment", "hypothesis", "theory", "data", "analysis", "chemical", "physical", "biological"],
        "reasoning": ["because", "therefore", "thus", "hence", "since", "logic", "deduce", "infer"],
        "dialogue": ["how are you", "what do you think", "please help", "thank you", "could you"],
    }

    scores = {}
    for domain, keywords in domain_keywords.items():
        score = sum(1 for kw in keywords if kw in text)
        scores[domain] = score

    if not scores:
        return "unknown"

    return max(scores, key=scores.get)


def estimate_difficulty(conversations: Any, thoughts: str = "") -> float:
    """Estimate difficulty on scale 0-1."""
    if isinstance(conversations, str):
        try:
            conversations = json.loads(conversations)
        except:
            conversations = []

    text = ""
    for msg in conversations:
        if isinstance(msg, dict):
            text += msg.get("content", "") + " "

    text += thoughts

    # Features for difficulty
    features = {
        "length": len(text.split()),
        "technical_terms": len(re.findall(r'\b[A-Z][a-z]+(?:[A-Z][a-z]+)+\b', text)),  # CamelCase
        "code_blocks": len(re.findall(r'```[\s\S]*?```', text)),
        "math_symbols": len(re.findall[r'[=+\-*/<>≤≥≠∈∉⊂⊆∪∩]', text]),
        "reasoning_markers": len(re.findall(r'\b(because|therefore|thus|hence|since)\b', text, re.IGNORECASE)),
    }

    # Normalize and combine
    difficulty = (
        min(features["length"] / 500, 1.0) * 0.3 +
        min(features["technical_terms"] / 20, 1.0) * 0.25 +
        min(features["code_blocks"] / 3, 1.0) * 0.25 +
        min(features["math_symbols"] / 10, 1.0) * 0.1 +
        min(features["reasoning_markers"] / 5, 1.0) * 0.1
    )

    return min(difficulty, 1.0)


def clean_text(text: str) -> str:
    """Clean and normalize text."""
    # Remove excessive whitespace
    text = re.sub(r'\s+', ' ', text)

    # Remove control characters
    text = re.sub(r'[\x00-\x1F\x7F]', '', text)

    # Normalize quotes
    text = text.replace('"', '"').replace('"', '"')
    text = text.replace(''', "'").replace(''', "'")

    # Strip
    text = text.strip()

    return text


def truncate_with_overlap(

    text: str,

    max_length: int,

    stride: int,

    tokenizer: Any,

) -> List[Dict[str, Any]]:
    """Truncate long text with overlapping windows."""
    tokens = tokenizer.encode(text, add_special_tokens=False)

    if len(tokens) <= max_length:
        return [{"input_ids": tokens, "attention_mask": [1] * len(tokens)}]

    chunks = []
    start = 0

    while start < len(tokens):
        end = min(start + max_length, len(tokens))
        chunk_tokens = tokens[start:end]

        chunks.append({
            "input_ids": chunk_tokens,
            "attention_mask": [1] * len(chunk_tokens),
        })

        if end >= len(tokens):
            break

        start += stride

    return chunks


def compute_length_statistics(lengths: List[int]) -> Dict[str, float]:
    """Compute statistics for length distribution."""
    import numpy as np

    if not lengths:
        return {}

    arr = np.array(lengths)
    return {
        "mean": float(np.mean(arr)),
        "std": float(np.std(arr)),
        "min": float(np.min(arr)),
        "max": float(np.max(arr)),
        "p50": float(np.percentile(arr, 50)),
        "p90": float(np.percentile(arr, 90)),
        "p95": float(np.percentile(arr, 95)),
        "p99": float(np.percentile(arr, 99)),
    }


def analyze_dataset_quality(dataset: Any, sample_size: int = 1000) -> Dict[str, Any]:
    """Analyze dataset quality metrics."""
    logger.info("Analyzing dataset quality...")

    # Sample dataset
    if hasattr(dataset, "__len__"):
        sample_size = min(sample_size, len(dataset))
        indices = list(range(sample_size))
    else:
        # Streaming dataset
        samples = []
        for i, sample in enumerate(dataset):
            if i >= sample_size:
                break
            samples.append(sample)
        dataset = samples
        sample_size = len(samples)

    analysis = {
        "total_samples": sample_size,
        "domains": {},
        "difficulty_distribution": {},
        "length_stats": {},
        "thoughts_coverage": 0.0,
        "conversation_turns": [],
    }

    domains = []
    difficulties = []
    lengths = []
    thoughts_counts = []
    turns = []

    for sample in dataset:
        # Domain
        domain = sample.get("domain", detect_domain(sample.get("conversations", [])))
        domains.append(domain)

        # Difficulty
        difficulty = sample.get("difficulty", estimate_difficulty(sample.get("conversations", []), sample.get("thoughts", "")))
        difficulties.append(difficulty)

        # Length
        text = sample.get("text", "")
        if not text and "conversations" in sample:
            text = preprocess_conversation(sample["conversations"])["text"]
        lengths.append(len(text.split()))

        # Thoughts
        if "thoughts" in sample and sample["thoughts"]:
            thoughts_counts.append(1)
        else:
            thoughts_counts.append(0)

        # Turns
        if "conversations" in sample and isinstance(sample["conversations"], list):
            turns.append(len(sample["conversations"]))

    # Compute statistics
    from collections import Counter

    analysis["domains"] = dict(Counter(domains))
    analysis["difficulty_distribution"] = {
        "mean": float(np.mean(difficulties)) if difficulties else 0.0,
        "std": float(np.std(difficulties)) if difficulties else 0.0,
        "histogram": np.histogram(difficulties, bins=10, range=(0, 1))[0].tolist(),
    }
    analysis["length_stats"] = compute_length_statistics(lengths)
    analysis["thoughts_coverage"] = sum(thoughts_counts) / len(thoughts_counts) if thoughts_counts else 0.0
    analysis["conversation_turns"] = {
        "mean": float(np.mean(turns)) if turns else 0.0,
        "max": int(max(turns)) if turns else 0,
    }

    logger.info(f"Dataset analysis complete: {analysis}")
    return analysis