import os
from abc import ABC, abstractmethod
from functools import cached_property
from typing import ClassVar, Literal, Optional, Union

import httpx
from httpx import Limits, Timeout
from openai import AsyncOpenAI
from openai.types.chat.chat_completion import (
    ChatCompletion,
)
from pydantic import BaseModel

from proxy_lite.history import MessageHistory
from proxy_lite.logger import logger
from proxy_lite.serializer import (
    BaseSerializer,
    OpenAICompatibleSerializer,
)
from proxy_lite.tools import Tool


class BaseClientConfig(BaseModel):
    http_timeout: float = 50
    http_concurrent_connections: int = 50


class BaseClient(BaseModel, ABC):
    config: BaseClientConfig
    serializer: ClassVar[BaseSerializer]

    @abstractmethod
    async def create_completion(
        self,
        messages: MessageHistory,
        temperature: float = 0.7,
        seed: Optional[int] = None,
        tools: Optional[list[Tool]] = None,
        response_format: Optional[type[BaseModel]] = None,
    ) -> ChatCompletion: ...
    """
    Create completion from model.
    Expect subclasses to adapt from various endpoints that will handle
    requests differently, make sure to raise appropriate warnings.

    Returns:
        ChatCompletion: OpenAI ChatCompletion format for consistency
    """

    @classmethod
    def create(cls, config: BaseClientConfig) -> "BaseClient":
        supported_clients = {
            "openai": OpenAIClient,
            "openai-azure": OpenAIClient,
            "convergence": ConvergenceClient,
            "gemini": GeminiClient,
        }
        # Type assertion - we know the config will have a name attribute from subclasses
        config_name = getattr(config, 'name', None)
        if config_name not in supported_clients:
            error_message = f"Unsupported model: {config_name}."
            raise ValueError(error_message)
        return supported_clients[config_name](config=config)

    @property
    def http_client(self) -> httpx.AsyncClient:
        return httpx.AsyncClient(
            timeout=Timeout(self.config.http_timeout),
            limits=Limits(
                max_connections=self.config.http_concurrent_connections,
                max_keepalive_connections=self.config.http_concurrent_connections,
            ),
        )


class OpenAIClientConfig(BaseClientConfig):
    name: Literal["openai"] = "openai"
    model_id: str = "gpt-4o"
    api_key: str = os.environ.get("OPENAI_API_KEY", "")
    api_base: Optional[str] = None


class OpenAIClient(BaseClient):
    config: OpenAIClientConfig
    serializer: ClassVar[OpenAICompatibleSerializer] = OpenAICompatibleSerializer()

    @cached_property
    def external_client(self) -> AsyncOpenAI:
        client_params = {
            "api_key": self.config.api_key,
            "http_client": self.http_client,
        }
        if self.config.api_base:
            client_params["base_url"] = self.config.api_base
        return AsyncOpenAI(**client_params)

    async def create_completion(
        self,
        messages: MessageHistory,
        temperature: float = 0.7,
        seed: Optional[int] = None,
        tools: Optional[list[Tool]] = None,
        response_format: Optional[type[BaseModel]] = None,
    ) -> ChatCompletion:
        base_params = {
            "model": self.config.model_id,
            "messages": self.serializer.serialize_messages(messages),
            "temperature": temperature,
        }
        optional_params = {
            "seed": seed,
            "tools": self.serializer.serialize_tools(tools) if tools else None,
            "tool_choice": "required" if tools else None,
            "response_format": {"type": "json_object"} if response_format else {"type": "text"},
        }
        base_params.update(
            {k: v for k, v in optional_params.items() if v is not None})
        return await self.external_client.chat.completions.create(**base_params)


class ConvergenceClientConfig(BaseClientConfig):
    name: Literal["convergence"] = "convergence"
    model_id: str = "convergence-ai/proxy-lite-7b"
    api_base: str = "http://localhost:8000/v1"
    api_key: str = "none"


class ConvergenceClient(OpenAIClient):
    config: ConvergenceClientConfig
    serializer: ClassVar[OpenAICompatibleSerializer] = OpenAICompatibleSerializer()
    _model_validated: bool = False

    async def _validate_model(self) -> None:
        try:
            response = await self.external_client.models.list()
            assert self.config.model_id in [model.id for model in response.data], (
                f"Model {self.config.model_id} not found in {response.data}"
            )
            self._model_validated = True
            logger.debug(f"Model {self.config.model_id} validated and connected to cluster")
        except Exception as e:
            logger.error(f"Error retrieving model: {e}")
            raise e

    @cached_property
    def external_client(self) -> AsyncOpenAI:
        return AsyncOpenAI(
            api_key=self.config.api_key,
            base_url=self.config.api_base,
            http_client=self.http_client,
        )

    async def create_completion(
        self,
        messages: MessageHistory,
        temperature: float = 0.7,
        seed: Optional[int] = None,
        tools: Optional[list[Tool]] = None,
        response_format: Optional[type[BaseModel]] = None,
    ) -> ChatCompletion:
        if not self._model_validated:
            await self._validate_model()
        base_params = {
            "model": self.config.model_id,
            "messages": self.serializer.serialize_messages(messages),
            "temperature": temperature,
        }
        optional_params = {
            "seed": seed,
            "tools": self.serializer.serialize_tools(tools) if tools else None,
            "tool_choice": "auto" if tools else None,  # vLLM does not support "required"
            "response_format": response_format if response_format else {"type": "text"},
        }
        base_params.update({k: v for k, v in optional_params.items() if v is not None})
        return await self.external_client.chat.completions.create(**base_params)


class GeminiClientConfig(BaseClientConfig):
    name: Literal["gemini"] = "gemini"
    model_id: str = "gemini-2.0-flash-001"
    api_key: str = ""


class GeminiClient(BaseClient):
    config: GeminiClientConfig
    serializer: ClassVar[OpenAICompatibleSerializer] = OpenAICompatibleSerializer()

    def _convert_messages_to_gemini_format(self, messages):
        """Convert OpenAI format messages to Gemini format"""
        gemini_parts = []
        for msg in messages:
            if msg["role"] == "user":
                gemini_parts.append({"text": msg["content"]})
            elif msg["role"] == "assistant":
                gemini_parts.append({"text": msg["content"]})
            # Skip system messages or add them to the first user message
        return gemini_parts
    
    def _clean_schema_for_gemini(self, schema):
        """Clean up JSON schema for Gemini function calling - remove $defs and $ref"""
        if not isinstance(schema, dict):
            return schema
        
        cleaned = {}
        for key, value in schema.items():
            if key == "$defs":
                # Skip $defs - we'll inline the definitions
                continue
            elif key == "$ref":
                # Skip $ref - we'll inline the referenced schema
                continue
            elif isinstance(value, dict):
                cleaned[key] = self._clean_schema_for_gemini(value)
            elif isinstance(value, list):
                cleaned[key] = [self._clean_schema_for_gemini(item) for item in value]
            else:
                cleaned[key] = value
        
        # If we have $defs, we need to inline them
        if "$defs" in schema:
            cleaned = self._inline_definitions(cleaned, schema["$defs"])
        
        return cleaned
    
    def _inline_definitions(self, schema, definitions):
        """Inline $ref definitions into the schema"""
        if not isinstance(schema, dict):
            return schema
        
        if "$ref" in schema:
            # Extract the reference name (e.g., "#/$defs/TypeEntry" -> "TypeEntry")
            ref_name = schema["$ref"].split("/")[-1]
            if ref_name in definitions:
                # Replace the $ref with the actual definition
                return self._inline_definitions(definitions[ref_name], definitions)
            else:
                # If we can't find the definition, remove the $ref
                return {k: v for k, v in schema.items() if k != "$ref"}
        
        # Recursively process nested objects
        inlined = {}
        for key, value in schema.items():
            if isinstance(value, dict):
                inlined[key] = self._inline_definitions(value, definitions)
            elif isinstance(value, list):
                inlined[key] = [self._inline_definitions(item, definitions) for item in value]
            else:
                inlined[key] = value
        
        return inlined

    async def create_completion(
        self,
        messages: MessageHistory,
        temperature: float = 0.7,
        seed: Optional[int] = None,
        tools: Optional[list[Tool]] = None,
        response_format: Optional[type[BaseModel]] = None,
    ) -> ChatCompletion:
        import json
        from openai.types.chat.chat_completion import ChatCompletion, Choice
        from openai.types.chat.chat_completion_message import ChatCompletionMessage
        from openai.types.completion_usage import CompletionUsage
        from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall, Function
        
        # Convert messages to format expected by Gemini
        serialized_messages = self.serializer.serialize_messages(messages)
        
        # For Gemini API, we need to format contents correctly with proper roles
        contents = []
        current_user_text = ""
        
        for msg in serialized_messages:
            # Extract the actual text content from the serialized message
            content_text = ""
            if isinstance(msg["content"], list):
                # Handle complex content format
                for item in msg["content"]:
                    if isinstance(item, dict) and "text" in item:
                        content_text += item["text"]
                    elif isinstance(item, str):
                        content_text += item
            elif isinstance(msg["content"], str):
                content_text = msg["content"]
            
            if msg["role"] == "user":
                # Accumulate user messages
                current_user_text += content_text + "\n"
            elif msg["role"] == "assistant":
                # If we have accumulated user text, add it first
                if current_user_text.strip():
                    contents.append({
                        "role": "user",
                        "parts": [{"text": current_user_text.strip()}]
                    })
                    current_user_text = ""
                
                # Add assistant message with role "model"
                contents.append({
                    "role": "model", 
                    "parts": [{"text": content_text}]
                })
            elif msg["role"] == "tool":
                # Add tool messages as user messages so they're included in context
                # Format tool message more clearly for the agent to understand
                current_user_text += f"[ACTION COMPLETED] {content_text}\n"
        
        # Add any remaining user text
        if current_user_text.strip():
            contents.append({
                "role": "user",
                "parts": [{"text": current_user_text.strip()}]
            })
        
        payload = {
            "contents": contents,
            "generationConfig": {
                "temperature": temperature,
            }
        }
        
        # Add function calling support if tools are provided
        if tools:
            # Convert tools to Gemini function declaration format
            function_declarations = []
            for tool in tools:
                for tool_schema in tool.schema:
                    # Clean up the schema for Gemini - remove $defs and $ref
                    cleaned_parameters = self._clean_schema_for_gemini(tool_schema["parameters"])
                    function_declarations.append({
                        "name": tool_schema["name"],
                        "description": tool_schema["description"],
                        "parameters": cleaned_parameters
                    })
            
            payload["tools"] = [{
                "function_declarations": function_declarations
            }]
        
        # Make direct HTTP request to native Gemini API
        url = f"https://generativelanguage.googleapis.com/v1beta/models/{self.config.model_id}:generateContent?key={self.config.api_key}"
        
        response = await self.http_client.post(
            url,
            json=payload,
            headers={"Content-Type": "application/json"}
        )
        
        response.raise_for_status()
        response_data = response.json()
        
        # Convert Gemini response to OpenAI ChatCompletion format
        if "candidates" in response_data and len(response_data["candidates"]) > 0:
            candidate = response_data["candidates"][0]
            
            # Extract text from response
            content = ""
            tool_calls = []
            
            if "content" in candidate and "parts" in candidate["content"]:
                for part in candidate["content"]["parts"]:
                    if "text" in part:
                        content += part["text"]
                    elif "functionCall" in part:
                        # Handle function call
                        func_call = part["functionCall"]
                        tool_call = ChatCompletionMessageToolCall(
                            id=f"call_{hash(str(func_call))}"[:16],
                            type="function",
                            function=Function(
                                name=func_call["name"],
                                arguments=json.dumps(func_call.get("args", {}))
                            )
                        )
                        tool_calls.append(tool_call)
            
            choice = Choice(
                index=0,
                message=ChatCompletionMessage(
                    role="assistant",
                    content=content if content else None,
                    tool_calls=tool_calls if tool_calls else None
                ),
                finish_reason="stop"
            )
            
            # Create a mock ChatCompletion response
            completion = ChatCompletion(
                id="gemini-" + str(hash(content))[:8],
                choices=[choice],
                created=int(__import__('time').time()),
                model=self.config.model_id,
                object="chat.completion",
                usage=CompletionUsage(
                    completion_tokens=len(content.split()),
                    prompt_tokens=sum(len(str(msg.get("content", "")).split()) for msg in serialized_messages),
                    total_tokens=len(content.split()) + sum(len(str(msg.get("content", "")).split()) for msg in serialized_messages)
                )
            )
            
            return completion
        else:
            raise Exception(f"No valid response from Gemini API: {response_data}")


ClientConfigTypes = Union[OpenAIClientConfig, ConvergenceClientConfig, GeminiClientConfig]
ClientTypes = Union[OpenAIClient, ConvergenceClient, GeminiClient]