"""
Voice Model Module

Load and run the fine-tuned Qwen3 voice model for CEO-style response generation.
Optimized for Hugging Face Spaces GPU instances.

Example usage:
    model = VoiceModel.from_hub("username/ceo-voice-model")
    response = model.generate("What is your vision for AI?")
"""

import os
from pathlib import Path
from typing import Iterator, Optional

from loguru import logger

try:
    import torch
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        BitsAndBytesConfig,
        TextIteratorStreamer,
    )
    from peft import PeftModel

    INFERENCE_AVAILABLE = True
except ImportError:
    INFERENCE_AVAILABLE = False
    logger.warning("Inference dependencies not available")

from .prompt_templates import VOICE_MODEL_SYSTEM_PROMPT, get_voice_prompt


class VoiceModel:
    """
    CEO Voice Model for generating authentic responses.

    Loads a fine-tuned Qwen3 model with LoRA adapter and generates
    responses in the CEO's communication style.

    Example:
        >>> model = VoiceModel.from_hub("username/ceo-voice-model")
        >>> response = model.generate("What's your take on AI regulation?")
        >>> print(response)
    """

    def __init__(
        self,
        model,
        tokenizer,
        system_prompt: Optional[str] = None,
        device: str = "auto",
    ):
        """
        Initialize with loaded model and tokenizer.

        Args:
            model: Loaded HuggingFace model
            tokenizer: Loaded tokenizer
            system_prompt: Custom system prompt (uses default if None)
            device: Device for inference
        """
        self.model = model
        self.tokenizer = tokenizer
        self.system_prompt = system_prompt or VOICE_MODEL_SYSTEM_PROMPT
        self.device = device

        # Ensure padding token
        if self.tokenizer.pad_token is None:
            self.tokenizer.pad_token = self.tokenizer.eos_token

    @classmethod
    def from_hub(
        cls,
        model_id: str,
        adapter_id: Optional[str] = None,
        load_in_4bit: bool = True,
        load_in_8bit: bool = False,
        torch_dtype: str = "bfloat16",
        device_map: str = "auto",
        system_prompt: Optional[str] = None,
        token: Optional[str] = None,
    ) -> "VoiceModel":
        """
        Load voice model from Hugging Face Hub.

        Args:
            model_id: Base model or merged model ID
            adapter_id: Optional adapter ID (if separate from base)
            load_in_4bit: Use 4-bit quantization
            load_in_8bit: Use 8-bit quantization
            torch_dtype: Torch dtype
            device_map: Device mapping
            system_prompt: Custom system prompt
            token: HF token

        Returns:
            VoiceModel instance
        """
        if not INFERENCE_AVAILABLE:
            raise ImportError(
                "Inference dependencies not available. Install with:\n"
                "pip install torch transformers peft bitsandbytes"
            )

        token = token or os.environ.get("HF_TOKEN")

        # Get torch dtype
        dtype_map = {
            "float16": torch.float16,
            "bfloat16": torch.bfloat16,
            "float32": torch.float32,
        }
        dtype = dtype_map.get(torch_dtype, torch.bfloat16)

        # Quantization config
        quantization_config = None
        if load_in_4bit:
            quantization_config = BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_quant_type="nf4",
                bnb_4bit_compute_dtype=dtype,
                bnb_4bit_use_double_quant=True,
            )
        elif load_in_8bit:
            quantization_config = BitsAndBytesConfig(load_in_8bit=True)

        logger.info(f"Loading model: {model_id}")

        # Load base model
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            quantization_config=quantization_config,
            device_map=device_map,
            torch_dtype=dtype,
            trust_remote_code=True,
            token=token,
        )

        # Load adapter if specified
        if adapter_id:
            logger.info(f"Loading adapter: {adapter_id}")
            model = PeftModel.from_pretrained(model, adapter_id, token=token)

        # Load tokenizer
        tokenizer_id = adapter_id or model_id
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_id,
            trust_remote_code=True,
            token=token,
        )

        logger.info("Model loaded successfully")
        return cls(model, tokenizer, system_prompt, device_map)

    @classmethod
    def from_local(
        cls,
        model_path: str | Path,
        adapter_path: Optional[str | Path] = None,
        load_in_4bit: bool = True,
        torch_dtype: str = "bfloat16",
        system_prompt: Optional[str] = None,
    ) -> "VoiceModel":
        """
        Load voice model from local path.

        Args:
            model_path: Path to model
            adapter_path: Optional path to adapter
            load_in_4bit: Use 4-bit quantization
            torch_dtype: Torch dtype
            system_prompt: Custom system prompt

        Returns:
            VoiceModel instance
        """
        return cls.from_hub(
            model_id=str(model_path),
            adapter_id=str(adapter_path) if adapter_path else None,
            load_in_4bit=load_in_4bit,
            torch_dtype=torch_dtype,
            system_prompt=system_prompt,
        )

    def generate(
        self,
        user_message: str,
        conversation_history: Optional[list[dict]] = None,
        max_new_tokens: int = 1024,
        temperature: float = 0.7,
        top_p: float = 0.9,
        top_k: int = 50,
        do_sample: bool = True,
        repetition_penalty: float = 1.1,
    ) -> str:
        """
        Generate a response to the user message.

        Args:
            user_message: User's input message
            conversation_history: Optional list of prior messages
            max_new_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            top_p: Top-p sampling
            top_k: Top-k sampling
            do_sample: Whether to sample
            repetition_penalty: Repetition penalty

        Returns:
            Generated response text
        """
        # Build messages
        messages = [{"role": "system", "content": self.system_prompt}]

        # Add conversation history
        if conversation_history:
            for msg in conversation_history:
                messages.append(msg)

        # Add current message
        messages.append({"role": "user", "content": user_message})

        # Format with chat template
        prompt = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
        )

        # Tokenize
        inputs = self.tokenizer(
            prompt,
            return_tensors="pt",
            truncation=True,
            max_length=2048 - max_new_tokens,
        ).to(self.model.device)

        # Generate
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                do_sample=do_sample,
                repetition_penalty=repetition_penalty,
                pad_token_id=self.tokenizer.pad_token_id,
                eos_token_id=self.tokenizer.eos_token_id,
            )

        # Decode response only (skip input)
        response = self.tokenizer.decode(
            outputs[0][inputs["input_ids"].shape[1]:],
            skip_special_tokens=True,
        )

        return response.strip()

    def generate_stream(
        self,
        user_message: str,
        conversation_history: Optional[list[dict]] = None,
        max_new_tokens: int = 1024,
        temperature: float = 0.7,
        top_p: float = 0.9,
        **kwargs,
    ) -> Iterator[str]:
        """
        Generate a streaming response.

        Args:
            user_message: User's input message
            conversation_history: Optional prior messages
            max_new_tokens: Maximum tokens
            temperature: Sampling temperature
            top_p: Top-p sampling
            **kwargs: Additional generation kwargs

        Yields:
            Token strings as they're generated
        """
        from threading import Thread

        # Build messages
        messages = [{"role": "system", "content": self.system_prompt}]
        if conversation_history:
            messages.extend(conversation_history)
        messages.append({"role": "user", "content": user_message})

        # Format prompt
        prompt = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
        )

        # Tokenize
        inputs = self.tokenizer(
            prompt,
            return_tensors="pt",
            truncation=True,
            max_length=2048 - max_new_tokens,
        ).to(self.model.device)

        # Create streamer
        streamer = TextIteratorStreamer(
            self.tokenizer,
            skip_prompt=True,
            skip_special_tokens=True,
        )

        # Generation kwargs
        generation_kwargs = dict(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            do_sample=True,
            streamer=streamer,
            pad_token_id=self.tokenizer.pad_token_id,
            eos_token_id=self.tokenizer.eos_token_id,
            **kwargs,
        )

        # Run generation in thread
        thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
        thread.start()

        # Yield tokens
        for token in streamer:
            yield token

        thread.join()

    def update_system_prompt(self, new_prompt: str) -> None:
        """Update the system prompt."""
        self.system_prompt = new_prompt
        logger.info("System prompt updated")

    def get_system_prompt(self) -> str:
        """Get current system prompt."""
        return self.system_prompt


def main():
    """CLI entry point for testing the voice model."""
    import argparse

    parser = argparse.ArgumentParser(
        description="Test the CEO voice model",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
    python voice_model.py --model username/ceo-voice-model --prompt "What is AI?"
    python voice_model.py --model ./local_model --prompt "Your vision?"
        """,
    )

    parser.add_argument("--model", required=True, help="Model ID or path")
    parser.add_argument("--adapter", help="Adapter ID or path")
    parser.add_argument("--prompt", required=True, help="User prompt")
    parser.add_argument("--no-4bit", action="store_true", help="Disable 4-bit")
    parser.add_argument("--temperature", type=float, default=0.7)
    parser.add_argument("--max-tokens", type=int, default=512)
    parser.add_argument("--stream", action="store_true", help="Stream output")

    args = parser.parse_args()

    # Load model
    print(f"Loading model: {args.model}")
    model = VoiceModel.from_hub(
        model_id=args.model,
        adapter_id=args.adapter,
        load_in_4bit=not args.no_4bit,
    )

    # Generate
    print(f"\nPrompt: {args.prompt}\n")
    print("-" * 50)

    if args.stream:
        for token in model.generate_stream(
            args.prompt,
            max_new_tokens=args.max_tokens,
            temperature=args.temperature,
        ):
            print(token, end="", flush=True)
        print()
    else:
        response = model.generate(
            args.prompt,
            max_new_tokens=args.max_tokens,
            temperature=args.temperature,
        )
        print(response)


if __name__ == "__main__":
    main()