"""
Chat Formatter for TouchGrass.
Formats data into chat format compatible with Qwen3.5 fine-tuning.
"""

from typing import List, Dict, Any, Optional
import json
from pathlib import Path


class ChatFormatter:
    """
    Formats music QA data into chat format for instruction tuning.
    
    Handles:
    - System prompt injection
    - Context tags (instrument, skill level, emotion)
    - Tokenization-ready format
    - Multi-turn conversations
    """

    def __init__(
        self,
        tokenizer=None,
        max_seq_length: int = 4096,
        system_prompt: Optional[str] = None,
    ):
        """
        Initialize chat formatter.

        Args:
            tokenizer: Optional tokenizer for length validation
            max_seq_length: Maximum sequence length
            system_prompt: Optional custom system prompt
        """
        self.tokenizer = tokenizer
        self.max_seq_length = max_seq_length

        self.default_system_prompt = system_prompt or self._get_default_system_prompt()

    def _get_default_system_prompt(self) -> str:
        """Get default system prompt."""
        return """You are Touch Grass 🌿, a warm, encouraging, and knowledgeable music assistant.

You help people with:
- Learning instruments (guitar, bass, piano, keys, drums, vocals)
- Understanding music theory at any level
- Writing songs (lyrics, chord progressions, structure)
- Ear training and developing musicality
- DJ skills and music production
- Genre knowledge and music history

Your personality:
- Patient and encouraging — learning music is hard and takes time
- Adapt to the learner's level automatically — simpler for beginners, deeper for advanced
- When someone is frustrated, acknowledge it warmly before helping
- Use tabs, chord diagrams, and notation when helpful
- Make learning fun, not intimidating
- Celebrate small wins

When generating tabs use this format:
[TAB]
e|---------|
B|---------|
G|---------|
D|---------|
A|---------|
E|---------|
[/TAB]

When showing chord progressions use: [PROGRESSION]I - IV - V - I[/PROGRESSION]"""

    def format_qa_pair(
        self,
        question: str,
        answer: str,
        context: Optional[str] = None,
        system_prompt: Optional[str] = None,
    ) -> Dict[str, Any]:
        """
        Format a single QA pair into chat format.

        Args:
            question: User question
            answer: Assistant answer
            context: Optional context tags (e.g., "[GUITAR][BEGINNER]")
            system_prompt: Optional system prompt override

        Returns:
            Formatted chat dictionary
        """
        system = system_prompt or self.default_system_prompt

        # Build user message with context
        user_message = question
        if context:
            user_message = f"{context} {question}".strip()

        messages = [
            {"role": "system", "content": system},
            {"role": "user", "content": user_message},
            {"role": "assistant", "content": answer},
        ]

        # Validate length if tokenizer provided
        if self.tokenizer:
            total_length = self._estimate_length(messages)
            if total_length > self.max_seq_length:
                print(f"Warning: Sample exceeds max length ({total_length} > {self.max_seq_length})")
                # Truncate answer if needed
                messages = self._truncate_answers(messages)

        return {"messages": messages}

    def format_multi_turn(
        self,
        conversations: List[Dict[str, str]],
        system_prompt: Optional[str] = None,
    ) -> Dict[str, Any]:
        """
        Format multi-turn conversation.

        Args:
            conversations: List of {"role": "...", "content": "..."} dicts
            system_prompt: Optional system prompt

        Returns:
            Formatted chat dictionary
        """
        system = system_prompt or self.default_system_prompt

        # Ensure system is first
        if conversations[0]["role"] != "system":
            messages = [{"role": "system", "content": system}] + conversations
        else:
            messages = conversations

        # Validate length
        if self.tokenizer:
            total_length = self._estimate_length(messages)
            if total_length > self.max_seq_length:
                print(f"Warning: Multi-turn sample exceeds max length ({total_length} > {self.max_seq_length})")
                messages = self._truncate_multi_turn(messages)

        return {"messages": messages}

    def _estimate_length(self, messages: List[Dict[str, str]]) -> int:
        """Estimate token length of messages."""
        if not self.tokenizer:
            return 0

        total = 0
        for msg in messages:
            tokens = self.tokenizer.encode(msg["content"])
            total += len(tokens["input_ids"])
        return total

    def _truncate_answers(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
        """Truncate answer to fit max length."""
        if not self.tokenizer:
            return messages

        system_len = self._estimate_length([messages[0]])
        user_len = self._estimate_length([messages[1]])
        available = self.max_seq_length - system_len - user_len - 10  # buffer

        # Truncate answer
        answer_msg = messages[2].copy()
        answer_tokens = self.tokenizer.encode(answer_msg["content"])
        if len(answer_tokens["input_ids"]) > available:
            # Truncate and add ellipsis
            truncated = self.tokenizer.decode(answer_tokens["input_ids"][:available-3])
            answer_msg["content"] = truncated + "..."
            messages[2] = answer_msg

        return messages

    def _truncate_multi_turn(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
        """Truncate multi-turn conversation from the end."""
        if not self.tokenizer:
            return messages

        # Keep system and first few messages, truncate later ones
        system_msg = messages[0]
        other_msgs = messages[1:]

        current_length = self._estimate_length([system_msg])
        kept_msgs = []

        for msg in other_msgs:
            msg_len = self._estimate_length([msg])
            if current_length + msg_len <= self.max_seq_length - 10:
                kept_msgs.append(msg)
                current_length += msg_len
            else:
                break

        return [system_msg] + kept_msgs

    def save_as_jsonl(
        self,
        samples: List[Dict[str, Any]],
        output_path: str,
    ):
        """
        Save formatted samples as JSONL.

        Args:
            samples: List of formatted samples
            output_path: Output file path
        """
        output_path = Path(output_path)
        output_path.parent.mkdir(parents=True, exist_ok=True)

        with open(output_path, "w", encoding="utf-8") as f:
            for sample in samples:
                f.write(json.dumps(sample, ensure_ascii=False) + "\n")

        print(f"Saved {len(samples)} samples to {output_path}")

    def load_from_jsonl(
        self,
        input_path: str,
    ) -> List[Dict[str, Any]]:
        """
        Load formatted samples from JSONL.

        Args:
            input_path: Input file path

        Returns:
            List of samples
        """
        samples = []
        with open(input_path, "r", encoding="utf-8") as f:
            for line in f:
                samples.append(json.loads(line))

        print(f"Loaded {len(samples)} samples from {input_path}")
        return samples

    def validate_sample(
        self,
        sample: Dict[str, Any],
    ) -> bool:
        """
        Validate a formatted sample.

        Args:
            sample: Sample to validate

        Returns:
            True if valid
        """
        if "messages" not in sample:
            print("Error: Missing 'messages' field")
            return False

        messages = sample["messages"]
        if len(messages) < 2:
            print("Error: At least 2 messages required (system + user)")
            return False

        if messages[0]["role"] != "system":
            print("Error: First message must be system")
            return False

        # Check alternating user/assistant
        for i in range(1, len(messages), 2):
            if messages[i]["role"] != "user":
                print(f"Error: Expected user at position {i}, got {messages[i]['role']}")
                return False
            if i + 1 < len(messages) and messages[i + 1]["role"] != "assistant":
                print(f"Error: Expected assistant at position {i+1}, got {messages[i+1]['role']}")
                return False

        return True

    def create_pretraining_dataset(
        self,
        qa_samples: List[Dict[str, Any]],
        output_dir: str,
        train_split: float = 0.9,
    ) -> Dict[str, str]:
        """
        Create train/val splits for fine-tuning.

        Args:
            qa_samples: List of QA samples
            output_dir: Output directory
            train_split: Train split ratio (0-1)

        Returns:
            Dictionary with train/val file paths
        """
        import random
        random.shuffle(qa_samples)

        split_idx = int(len(qa_samples) * train_split)
        train_samples = qa_samples[:split_idx]
        val_samples = qa_samples[split_idx:]

        output_dir = Path(output_dir)
        output_dir.mkdir(parents=True, exist_ok=True)

        train_path = output_dir / "train.jsonl"
        val_path = output_dir / "val.jsonl"

        self.save_as_jsonl(train_samples, str(train_path))
        self.save_as_jsonl(val_samples, str(val_path))

        print(f"Created splits: train={len(train_samples)}, val={len(val_samples)}")

        return {
            "train": str(train_path),
            "val": str(val_path),
        }


def test_chat_formatter():
    """Test the ChatFormatter."""
    # Create formatter
    formatter = ChatFormatter()

    print("Testing ChatFormatter...\n")

    # Test QA pair formatting
    qa = formatter.format_qa_pair(
        question="How do I play a G chord?",
        answer="[TAB]...[/TAB] Here's how...",
        context="[GUITAR][BEGINNER]",
    )

    print("Formatted QA pair:")
    for msg in qa["messages"]:
        print(f"  {msg['role']}: {msg['content'][:80]}...")

    # Test validation
    is_valid = formatter.validate_sample(qa)
    print(f"\nSample valid: {is_valid}")

    # Test multi-turn
    multi_turn = formatter.format_multi_turn([
        {"role": "user", "content": "What is a chord?"},
        {"role": "assistant", "content": "A chord is..."},
        {"role": "user", "content": "Can you give an example?"},
        {"role": "assistant", "content": "C major is C-E-G"},
    ])

    print("\nMulti-turn format:")
    for msg in multi_turn["messages"]:
        print(f"  {msg['role']}: {msg['content'][:60]}...")

    print("\nChatFormatter test complete!")


if __name__ == "__main__":
    test_chat_formatter()