"""OpenAI model adapter with full streaming support."""

from __future__ import annotations

import json
import logging
from typing import Any, AsyncIterator

from openai import AsyncOpenAI

from ..config import settings
from .base import ModelAdapter, ModelConfig, ModelResponse, StreamChunk

logger = logging.getLogger(__name__)


class OpenAIAdapter(ModelAdapter):
    """Adapter for the OpenAI API (GPT-4o, o1, etc.)."""

    def __init__(self, api_key: str | None = None, base_url: str | None = None) -> None:
        kwargs: dict[str, Any] = {
            "api_key": api_key or settings.openai_api_key,
        }
        url = base_url or settings.openai_base_url
        if url:
            kwargs["base_url"] = url
        self._client = AsyncOpenAI(**kwargs)

    # ------------------------------------------------------------------
    # Streaming
    # ------------------------------------------------------------------

    async def stream(
        self,
        messages: list[dict[str, Any]],
        tools: list[dict[str, Any]] | None = None,
        config: ModelConfig | None = None,
    ) -> AsyncIterator[StreamChunk]:
        config = config or ModelConfig(
            model_id=settings.default_model_id,
            max_tokens=settings.max_tokens,
            temperature=settings.temperature,
        )

        kwargs: dict[str, Any] = {
            "model": config.model_id or "gpt-4o",
            "max_tokens": config.max_tokens,
            "temperature": config.temperature,
            "messages": messages,
            "stream": True,
            "stream_options": {"include_usage": True},
        }
        if tools:
            kwargs["tools"] = self._format_tools(tools)

        stream = await self._client.chat.completions.create(**kwargs)

        tool_calls_acc: dict[int, dict[str, str]] = {}

        final_usage: dict[str, int] = {}

        async for chunk in stream:
            # With include_usage, the last chunk has usage but no choices
            if chunk.usage:
                final_usage = {
                    "input_tokens": chunk.usage.prompt_tokens or 0,
                    "output_tokens": chunk.usage.completion_tokens or 0,
                }

            choice = chunk.choices[0] if chunk.choices else None
            if not choice:
                # Usage-only chunk (last one with include_usage) — emit it
                if final_usage:
                    yield StreamChunk(usage=final_usage)
                    final_usage = {}  # Only emit once
                continue

            delta = choice.delta

            # Text content
            if delta and delta.content:
                yield StreamChunk(delta=delta.content)

            # Tool calls
            if delta and delta.tool_calls:
                for tc in delta.tool_calls:
                    idx = tc.index
                    if idx not in tool_calls_acc:
                        tool_calls_acc[idx] = {
                            "id": tc.id or "",
                            "name": "",
                            "arguments": "",
                        }
                    if tc.id:
                        tool_calls_acc[idx]["id"] = tc.id
                    if tc.function and tc.function.name:
                        tool_calls_acc[idx]["name"] = tc.function.name
                        yield StreamChunk(
                            tool_call_id=tc.id or tool_calls_acc[idx]["id"],
                            tool_name=tc.function.name,
                        )
                    if tc.function and tc.function.arguments:
                        tool_calls_acc[idx]["arguments"] += tc.function.arguments
                        yield StreamChunk(
                            tool_call_id=tool_calls_acc[idx]["id"],
                            tool_name=tool_calls_acc[idx]["name"],
                            tool_arguments=tc.function.arguments,
                        )

            # Finish
            if choice.finish_reason:
                if choice.finish_reason == "tool_calls":
                    for acc in tool_calls_acc.values():
                        yield StreamChunk(
                            tool_call_id=acc["id"],
                            tool_name=acc["name"],
                            tool_arguments=acc["arguments"],
                            finish_reason="tool_use",
                        )
                    # Emit usage after tool_use chunks
                    if final_usage:
                        yield StreamChunk(usage=final_usage)
                else:
                    yield StreamChunk(
                        finish_reason="end_turn"
                        if choice.finish_reason == "stop"
                        else choice.finish_reason,
                        usage=final_usage,
                    )

    # ------------------------------------------------------------------
    # Non-streaming
    # ------------------------------------------------------------------

    async def complete(
        self,
        messages: list[dict[str, Any]],
        tools: list[dict[str, Any]] | None = None,
        config: ModelConfig | None = None,
    ) -> ModelResponse:
        config = config or ModelConfig(
            model_id=settings.default_model_id,
            max_tokens=settings.max_tokens,
            temperature=settings.temperature,
        )

        kwargs: dict[str, Any] = {
            "model": config.model_id or "gpt-4o",
            "max_tokens": config.max_tokens,
            "temperature": config.temperature,
            "messages": messages,
        }
        if tools:
            kwargs["tools"] = self._format_tools(tools)

        response = await self._client.chat.completions.create(**kwargs)
        choice = response.choices[0]

        content = choice.message.content or ""
        tool_calls: list[dict[str, Any]] = []

        if choice.message.tool_calls:
            for tc in choice.message.tool_calls:
                tool_calls.append(
                    {
                        "id": tc.id,
                        "name": tc.function.name,
                        "arguments": json.loads(tc.function.arguments)
                        if tc.function.arguments
                        else {},
                    }
                )

        return ModelResponse(
            content=content,
            tool_calls=tool_calls,
            finish_reason=choice.finish_reason or "",
            usage={
                "input_tokens": response.usage.prompt_tokens if response.usage else 0,
                "output_tokens": response.usage.completion_tokens if response.usage else 0,
            },
            raw=response,
        )

    # ------------------------------------------------------------------
    # Token counting
    # ------------------------------------------------------------------

    async def count_tokens(self, text: str) -> int:
        from ..context.compactor import estimate_tokens
        return estimate_tokens(text)

    # ------------------------------------------------------------------
    # Helpers
    # ------------------------------------------------------------------

    @staticmethod
    def _format_tools(tools: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Convert internal tool definitions to OpenAI function calling format."""
        formatted: list[dict[str, Any]] = []
        for tool in tools:
            formatted.append(
                {
                    "type": "function",
                    "function": {
                        "name": tool["name"],
                        "description": tool.get("description", ""),
                        "parameters": tool.get(
                            "input_schema", tool.get("parameters", {"type": "object"})
                        ),
                    },
                }
            )
        return formatted