"""
OpenAI-compatible HTTP server for DFlash speculative decoding on MLX.

Supports:
- POST /v1/chat/completions (with streaming via SSE)
- POST /v1/completions
- GET /v1/models
- GET /health
- GET /metrics (DFlash-specific diagnostics)

Inspired by bstnxbt/dflash-mlx server architecture and Aryagm's OpenAI server.
"""

import json
import time
from typing import Any, Dict, List, Optional

from .speculative_decode import DFlashSpeculativeDecoder
from .adapters import load_target_model, LoadedTargetModel
from .convert import load_mlx_dflash


class DFlashServer:
    """OpenAI-compatible server wrapping a DFlashSpeculativeDecoder."""
    
    def __init__(
        self,
        target_model_path: str,
        draft_model_path: Optional[str] = None,
        block_size: int = 16,
        device: str = "metal",
    ):
        """Initialize server with target and optional draft model.
        
        Args:
            target_model_path: Path or HF ID of MLX target model
            draft_model_path: Path or HF ID of converted DFlash drafter
            block_size: Draft block size
            device: MLX device
        """
        print(f"[Server] Loading target model: {target_model_path}...")
        self.loaded_target = load_target_model(target_model_path)
        
        if draft_model_path:
            print(f"[Server] Loading DFlash drafter: {draft_model_path}...")
            self.draft_model, self.draft_config = load_mlx_dflash(draft_model_path)
        else:
            # Try to auto-resolve draft model
            from .convert import _infer_target_model
            inferred = _infer_target_model(target_model_path)
            if inferred and inferred != "unknown":
                print(f"[Server] Auto-resolved drafter: {inferred}")
                # Look up in registry...
                self.draft_model, self.draft_config = None, None
            else:
                print("[Server] No draft model — will use baseline generation")
                self.draft_model, self.draft_config = None, None
        
        if self.draft_model is not None:
            self.decoder = DFlashSpeculativeDecoder(
                target_model=self.loaded_target,
                draft_model=self.draft_model,
                tokenizer=self.loaded_target.tokenizer,
                block_size=block_size,
                device=device,
            )
            self.mode = "dflash"
        else:
            self.decoder = None
            self.mode = "baseline"
        
        # Metrics
        self.request_count = 0
        self.total_tokens = 0
        self.total_time = 0.0
        self.recent_requests: List[Dict] = []
    
    def health(self) -> Dict[str, Any]:
        return {"status": "ok", "mode": self.mode, "model": self.loaded_target.requested_model}
    
    def models(self) -> Dict[str, Any]:
        return {
            "object": "list",
            "data": [{
                "id": self.loaded_target.requested_model,
                "object": "model",
                "owned_by": "dflash-mlx-universal",
            }]
        }
    
    def metrics(self) -> Dict[str, Any]:
        avg_tok_s = self.total_tokens / self.total_time if self.total_time > 0 else 0
        return {
            "request_count": self.request_count,
            "total_tokens": self.total_tokens,
            "avg_tokens_per_sec": avg_tok_s,
            "recent_requests": self.recent_requests[-32:],
            "mode": self.mode,
        }
    
    def _update_metrics(self, num_tokens: int, elapsed: float):
        self.request_count += 1
        self.total_tokens += num_tokens
        self.total_time += elapsed
        self.recent_requests.append({
            "timestamp": time.time(),
            "tokens": num_tokens,
            "time_sec": elapsed,
            "tok_s": num_tokens / elapsed if elapsed > 0 else 0,
        })
        if len(self.recent_requests) > 32:
            self.recent_requests = self.recent_requests[-32:]
    
    def chat_completions(
        self,
        messages: List[Dict[str, str]],
        max_tokens: int = 1024,
        temperature: float = 0.0,
        stream: bool = False,
        stop: Optional[List[str]] = None,
    ) -> Dict[str, Any] | Any:
        """Handle chat completion request.
        
        Returns dict for non-streaming, generator for streaming.
        """
        # Build prompt from messages
        prompt = self._messages_to_prompt(messages)
        
        if stream:
            return self._stream_chat(prompt, max_tokens, temperature, stop)
        
        # Non-streaming
        start = time.time()
        
        if self.mode == "dflash" and self.decoder is not None:
            output = self.decoder.generate(
                prompt=prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                stop_strings=stop,
            )
        else:
            # Baseline mlx_lm generation
            from mlx_lm.utils import generate as mlx_generate
            output = mlx_generate(
                model=self.loaded_target.model,
                tokenizer=self.loaded_target.tokenizer,
                prompt=prompt,
                max_tokens=max_tokens,
                temp=temperature,
            )
        
        elapsed = time.time() - start
        num_tokens = len(self.loaded_target.tokenizer.encode(output))
        self._update_metrics(num_tokens, elapsed)
        
        return {
            "id": f"chatcmpl-{int(time.time()*1000)}",
            "object": "chat.completion",
            "created": int(time.time()),
            "model": self.loaded_target.requested_model,
            "choices": [{
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": output,
                },
                "finish_reason": "stop",
            }],
            "usage": {
                "prompt_tokens": len(self.loaded_target.tokenizer.encode(prompt)),
                "completion_tokens": num_tokens,
                "total_tokens": len(self.loaded_target.tokenizer.encode(prompt)) + num_tokens,
            }
        }
    
    def _stream_chat(self, prompt: str, max_tokens: int, temperature: float, stop):
        """Generator for streaming SSE chunks."""
        
        def event(data: Dict) -> str:
            return f"data: {json.dumps(data)}\n\n"
        
        # Yield initial role
        yield event({
            "id": f"chatcmpl-{int(time.time()*1000)}",
            "object": "chat.completion.chunk",
            "created": int(time.time()),
            "model": self.loaded_target.requested_model,
            "choices": [{"index": 0, "delta": {"role": "assistant"}, "finish_reason": None}],
        })
        
        accumulated = ""
        
        if self.mode == "dflash" and self.decoder is not None:
            # Use streaming generate
            for chunk in self.decoder.generate(
                prompt=prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                stop_strings=stop,
                stream=True,
            ):
                accumulated += chunk
                yield event({
                    "id": f"chatcmpl-{int(time.time()*1000)}",
                    "object": "chat.completion.chunk",
                    "created": int(time.time()),
                    "model": self.loaded_target.requested_model,
                    "choices": [{"index": 0, "delta": {"content": chunk}, "finish_reason": None}],
                })
        else:
            # Baseline: generate then stream word-by-word (not true streaming)
            from mlx_lm.utils import generate as mlx_generate
            output = mlx_generate(
                model=self.loaded_target.model,
                tokenizer=self.loaded_target.tokenizer,
                prompt=prompt,
                max_tokens=max_tokens,
                temp=temperature,
            )
            for word in output.split(" "):
                chunk = word + " "
                accumulated += chunk
                yield event({
                    "id": f"chatcmpl-{int(time.time()*1000)}",
                    "object": "chat.completion.chunk",
                    "created": int(time.time()),
                    "model": self.loaded_target.requested_model,
                    "choices": [{"index": 0, "delta": {"content": chunk}, "finish_reason": None}],
                })
        
        # Final chunk
        yield event({
            "id": f"chatcmpl-{int(time.time()*1000)}",
            "object": "chat.completion.chunk",
            "created": int(time.time()),
            "model": self.loaded_target.requested_model,
            "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
        })
        yield "data: [DONE]\n\n"
    
    def _messages_to_prompt(self, messages: List[Dict[str, str]]) -> str:
        """Convert OpenAI messages format to prompt string."""
        # Try chat template
        tokenizer = self.loaded_target.tokenizer
        if hasattr(tokenizer, "apply_chat_template"):
            try:
                return tokenizer.apply_chat_template(
                    messages,
                    tokenize=False,
                    add_generation_prompt=True,
                )
            except Exception:
                pass
        
        # Fallback: simple concatenation
        prompt = ""
        for msg in messages:
            role = msg.get("role", "user")
            content = msg.get("content", "")
            if role == "system":
                prompt += f"System: {content}\n"
            elif role == "user":
                prompt += f"User: {content}\n"
            elif role == "assistant":
                prompt += f"Assistant: {content}\n"
        prompt += "Assistant: "
        return prompt


def create_app(target_model: str, draft_model: Optional[str] = None, block_size: int = 16):
    """Create a Flask/FastAPI-style app for serving."""
    try:
        from fastapi import FastAPI, Request
        from fastapi.responses import StreamingResponse
        
        app = FastAPI(title="DFlash MLX Server")
        server = DFlashServer(target_model, draft_model, block_size)
        
        @app.get("/health")
        async def health():
            return server.health()
        
        @app.get("/v1/models")
        async def models():
            return server.models()
        
        @app.get("/metrics")
        async def metrics():
            return server.metrics()
        
        @app.post("/v1/chat/completions")
        async def chat_completions(request: Request):
            body = await request.json()
            messages = body.get("messages", [])
            max_tokens = body.get("max_tokens", 1024)
            temperature = body.get("temperature", 0.0)
            stream = body.get("stream", False)
            stop = body.get("stop", None)
            
            result = server.chat_completions(
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature,
                stream=stream,
                stop=stop,
            )
            
            if stream:
                return StreamingResponse(result, media_type="text/event-stream")
            return result
        
        return app
    
    except ImportError:
        print("[Server] FastAPI not installed. Install with: pip install fastapi uvicorn")
        
        # Fallback: simple HTTP server
        from http.server import BaseHTTPRequestHandler, HTTPServer
        import threading
        
        class Handler(BaseHTTPRequestHandler):
            server_instance = None
            
            def do_GET(self):
                if self.path == "/health":
                    self._json_response(200, self.server_instance.health())
                elif self.path == "/v1/models":
                    self._json_response(200, self.server_instance.models())
                elif self.path == "/metrics":
                    self._json_response(200, self.server_instance.metrics())
                else:
                    self._json_response(404, {"error": "Not found"})
            
            def do_POST(self):
                if self.path == "/v1/chat/completions":
                    content_len = int(self.headers.get("Content-Length", 0))
                    body = json.loads(self.rfile.read(content_len))
                    result = self.server_instance.chat_completions(
                        messages=body.get("messages", []),
                        max_tokens=body.get("max_tokens", 1024),
                        temperature=body.get("temperature", 0.0),
                        stream=False,
                        stop=body.get("stop", None),
                    )
                    self._json_response(200, result)
                else:
                    self._json_response(404, {"error": "Not found"})
            
            def _json_response(self, status: int, data: Dict):
                self.send_response(status)
                self.send_header("Content-Type", "application/json")
                self.end_headers()
                self.wfile.write(json.dumps(data).encode())
        
        Handler.server_instance = DFlashServer(target_model, draft_model, block_size)
        return Handler


def main():
    import argparse
    parser = argparse.ArgumentParser(description="DFlash MLX OpenAI-compatible server")
    parser.add_argument("--target", required=True, help="Target model path or HF ID")
    parser.add_argument("--draft", default=None, help="Draft model path or HF ID")
    parser.add_argument("--block-size", type=int, default=16)
    parser.add_argument("--host", default="127.0.0.1")
    parser.add_argument("--port", type=int, default=8000)
    parser.add_argument("--device", default="metal")
    args = parser.parse_args()
    
    server = DFlashServer(args.target, args.draft, args.block_size, args.device)
    
    try:
        import uvicorn
        from fastapi import FastAPI, Request
        from fastapi.responses import StreamingResponse
        
        app = FastAPI()
        
        @app.get("/health")
        async def health():
            return server.health()
        
        @app.get("/v1/models")
        async def models():
            return server.models()
        
        @app.get("/metrics")
        async def metrics():
            return server.metrics()
        
        @app.post("/v1/chat/completions")
        async def chat_completions(request: Request):
            body = await request.json()
            result = server.chat_completions(
                messages=body.get("messages", []),
                max_tokens=body.get("max_tokens", 1024),
                temperature=body.get("temperature", 0.0),
                stream=body.get("stream", False),
                stop=body.get("stop", None),
            )
            if body.get("stream", False):
                return StreamingResponse(result, media_type="text/event-stream")
            return result
        
        print(f"[Server] Starting FastAPI on http://{args.host}:{args.port}")
        uvicorn.run(app, host=args.host, port=args.port)
    
    except ImportError:
        print("[Server] FastAPI/uvicorn not available, using simple HTTP server")
        from http.server import HTTPServer
        handler = create_app(args.target, args.draft, args.block_size)
        httpd = HTTPServer((args.host, args.port), handler)
        print(f"[Server] Starting simple HTTP on http://{args.host}:{args.port}")
        httpd.serve_forever()


if __name__ == "__main__":
    main()