#!/usr/bin/env python3
"""
OpenAI-compatible API server for Ministral 14B with streaming support
Fixed chat template for base models
"""

import subprocess
import sys

def install_deps():
    try:
        import torch
        need_torch = not torch.cuda.is_available()
    except ImportError:
        need_torch = True

    print("=== Installing dependencies ===")

    if need_torch:
        subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "torch"])

    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q",
                          "git+https://github.com/huggingface/transformers.git"])

    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q",
                          "accelerate", "fastapi", "uvicorn", "pydantic", "sentencepiece", "protobuf"])

    print("=== Dependencies installed ===")

install_deps()

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from typing import List, Optional
import uvicorn
import time
import traceback
import json
import asyncio
from threading import Thread

app = FastAPI()

# Mistral chat template
MISTRAL_CHAT_TEMPLATE = """{{- bos_token }}
{%- for message in messages %}
    {%- if message['role'] == 'system' %}
        {{- '[INST] ' + message['content'] + '\n\n' }}
    {%- elif message['role'] == 'user' %}
        {%- if loop.index0 == 0 and messages[0]['role'] != 'system' %}
            {{- '[INST] ' + message['content'] + ' [/INST]' }}
        {%- elif messages[0]['role'] == 'system' and loop.index0 == 1 %}
            {{- message['content'] + ' [/INST]' }}
        {%- else %}
            {{- '[INST] ' + message['content'] + ' [/INST]' }}
        {%- endif %}
    {%- elif message['role'] == 'assistant' %}
        {{- message['content'] + eos_token }}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {%- if messages[-1]['role'] != 'assistant' %}
    {%- endif %}
{%- endif %}"""

def fix_bpe_tokens(text):
    """Fix BPE tokenization artifacts"""
    text = text.replace("Ġ", " ")
    text = text.replace("Ċ", "\n")
    text = text.replace("ĉ", "\t")
    text = text.replace("âĢĻ", "'")
    text = text.replace("âĢľ", '"')
    text = text.replace("âĢĿ", '"')
    text = text.replace("âĢĶ", "—")
    text = text.replace("âĢĵ", "–")
    text = text.replace("âĢ¦", "…")
    text = text.replace("âĢĺ", "'")
    return text

def format_messages_mistral(messages):
    """Format messages using Mistral format manually"""
    text = "<s>"

    for i, m in enumerate(messages):
        role = m["role"]
        content = m["content"]

        if role == "system":
            # System prompt gets wrapped in first INST
            text += f"[INST] {content}\n\n"
        elif role == "user":
            if i == 0:
                # First user message
                text += f"[INST] {content} [/INST]"
            elif i > 0 and messages[i-1]["role"] == "system":
                # User message right after system
                text += f"{content} [/INST]"
            else:
                # Subsequent user messages
                text += f"[INST] {content} [/INST]"
        elif role == "assistant":
            text += f"{content}</s>"

    return text

model = None
processor = None

class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    model: str = "ministral-14b"
    messages: List[Message]
    max_tokens: Optional[int] = 2048
    temperature: Optional[float] = 0.7
    top_p: Optional[float] = 0.9
    top_k: Optional[int] = None
    min_p: Optional[float] = None
    typical_p: Optional[float] = None
    repetition_penalty: Optional[float] = None
    no_repeat_ngram_size: Optional[int] = None
    stream: Optional[bool] = False

@app.on_event("startup")
async def load_model():
    global model, processor
    print("Loading Ministral 14B...")

    model_id = "RoleModel/ministral-14b-merged-official"

    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

    # Set chat template if missing
    if processor.tokenizer.chat_template is None:
        print("Setting Mistral chat template...")
        processor.tokenizer.chat_template = MISTRAL_CHAT_TEMPLATE

    # Explicitly use CUDA
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    print(f"Using device: {device}")
    print(f"CUDA available: {torch.cuda.is_available()}")

    model = AutoModelForImageTextToText.from_pretrained(
        model_id,
        torch_dtype=torch.bfloat16,
        device_map=device,
        trust_remote_code=True,
    )
    model.eval()
    print("Model loaded successfully!")

@app.post("/v1/chat/completions")
async def chat_completions(request: ChatRequest):
    global model, processor

    try:
        messages = [{"role": m.role, "content": m.content} for m in request.messages]
        print(f"Processing {len(messages)} messages, stream={request.stream}")

        # Try chat template, fall back to manual formatting
        try:
            chat_text = processor.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True
            )
        except Exception as e:
            print(f"Chat template error: {e}, using manual format")
            chat_text = format_messages_mistral(messages)

        print(f"Formatted prompt:\n{chat_text[:500]}...")

        inputs = processor.tokenizer(chat_text, return_tensors="pt").to(model.device)
        input_len = inputs["input_ids"].shape[1]
        print(f"Input tokens: {input_len}")

        if request.stream:
            async def generate_stream():
                streamer = TextIteratorStreamer(
                    processor.tokenizer,
                    skip_prompt=True,
                    skip_special_tokens=True
                )

                generation_kwargs = {
                    **inputs,
                    "max_new_tokens": request.max_tokens,
                    "temperature": request.temperature if request.temperature and request.temperature > 0 else 1.0,
                    "top_p": request.top_p if request.top_p else 0.9,
                    "do_sample": request.temperature is not None and request.temperature > 0,
                    "pad_token_id": processor.tokenizer.eos_token_id,
                    "streamer": streamer,
                }
                if request.top_k is not None:
                    generation_kwargs["top_k"] = request.top_k
                if request.min_p is not None:
                    generation_kwargs["min_p"] = request.min_p
                if request.typical_p is not None:
                    generation_kwargs["typical_p"] = request.typical_p
                if request.repetition_penalty is not None:
                    generation_kwargs["repetition_penalty"] = request.repetition_penalty
                if request.no_repeat_ngram_size is not None:
                    generation_kwargs["no_repeat_ngram_size"] = request.no_repeat_ngram_size

                thread = Thread(target=model.generate, kwargs=generation_kwargs)
                thread.start()

                response_id = f"chatcmpl-{int(time.time())}"

                for text in streamer:
                    if text:
                        text = fix_bpe_tokens(text)
                        chunk = {
                            "id": response_id,
                            "object": "chat.completion.chunk",
                            "created": int(time.time()),
                            "model": request.model,
                            "choices": [{
                                "index": 0,
                                "delta": {"content": text},
                                "finish_reason": None
                            }]
                        }
                        yield f"data: {json.dumps(chunk)}\n\n"
                        await asyncio.sleep(0)

                final_chunk = {
                    "id": response_id,
                    "object": "chat.completion.chunk",
                    "created": int(time.time()),
                    "model": request.model,
                    "choices": [{
                        "index": 0,
                        "delta": {},
                        "finish_reason": "stop"
                    }]
                }
                yield f"data: {json.dumps(final_chunk)}\n\n"
                yield "data: [DONE]\n\n"

                thread.join()

            return StreamingResponse(
                generate_stream(),
                media_type="text/event-stream",
                headers={
                    "Cache-Control": "no-cache, no-store, must-revalidate",
                    "Connection": "keep-alive",
                    "X-Accel-Buffering": "no",
                    "Transfer-Encoding": "chunked",
                }
            )
        else:
            generation_kwargs = {
                **inputs,
                "max_new_tokens": request.max_tokens,
                "temperature": request.temperature if request.temperature and request.temperature > 0 else 1.0,
                "top_p": request.top_p if request.top_p else 0.9,
                "do_sample": request.temperature is not None and request.temperature > 0,
                "pad_token_id": processor.tokenizer.eos_token_id,
            }
            if request.top_k is not None:
                generation_kwargs["top_k"] = request.top_k
            if request.min_p is not None:
                generation_kwargs["min_p"] = request.min_p
            if request.typical_p is not None:
                generation_kwargs["typical_p"] = request.typical_p
            if request.repetition_penalty is not None:
                generation_kwargs["repetition_penalty"] = request.repetition_penalty
            if request.no_repeat_ngram_size is not None:
                generation_kwargs["no_repeat_ngram_size"] = request.no_repeat_ngram_size

            with torch.no_grad():
                outputs = model.generate(**generation_kwargs)

            new_tokens = outputs[0][input_len:]
            response_text = processor.tokenizer.decode(
                new_tokens,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True
            )
            response_text = fix_bpe_tokens(response_text)
            print(f"Generated {len(new_tokens)} tokens")

            return {
                "id": f"chatcmpl-{int(time.time())}",
                "object": "chat.completion",
                "created": int(time.time()),
                "model": request.model,
                "choices": [{
                    "index": 0,
                    "message": {"role": "assistant", "content": response_text},
                    "finish_reason": "stop"
                }],
                "usage": {
                    "prompt_tokens": input_len,
                    "completion_tokens": len(new_tokens),
                    "total_tokens": input_len + len(new_tokens)
                }
            }
    except Exception as e:
        print(f"Error: {e}")
        traceback.print_exc()
        raise

@app.get("/v1/models")
async def list_models():
    return {
        "object": "list",
        "data": [{"id": "ministral-14b", "object": "model", "owned_by": "rolemodel"}]
    }

@app.get("/health")
async def health():
    return {"status": "ok"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)