"""Base LLM provider interface."""

from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any


@dataclass
class ToolCallRequest:
    """A tool call request from the LLM."""
    id: str
    name: str
    arguments: dict[str, Any]


@dataclass
class LLMResponse:
    """Response from an LLM provider."""
    content: str | None
    tool_calls: list[ToolCallRequest] = field(default_factory=list)
    finish_reason: str = "stop"
    usage: dict[str, int] = field(default_factory=dict)
    reasoning_content: str | None = None  # Kimi, DeepSeek-R1 etc.
    thinking_blocks: list[dict] | None = None  # Anthropic extended thinking
    
    @property
    def has_tool_calls(self) -> bool:
        """Check if response contains tool calls."""
        return len(self.tool_calls) > 0


class LLMProvider(ABC):
    """
    Abstract base class for LLM providers.
    
    Implementations should handle the specifics of each provider's API
    while maintaining a consistent interface.
    """

    def __init__(self, api_key: str | None = None, api_base: str | None = None):
        self.api_key = api_key
        self.api_base = api_base

    @staticmethod
    def _sanitize_empty_content(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Replace empty text content that causes provider 400 errors.

        Empty content can appear when MCP tools return nothing. Most providers
        reject empty-string content or empty text blocks in list content.
        """
        result: list[dict[str, Any]] = []
        for msg in messages:
            content = msg.get("content")

            if isinstance(content, str) and not content:
                clean = dict(msg)
                clean["content"] = None if (msg.get("role") == "assistant" and msg.get("tool_calls")) else "(empty)"
                result.append(clean)
                continue

            if isinstance(content, list):
                filtered = [
                    item for item in content
                    if not (
                        isinstance(item, dict)
                        and item.get("type") in ("text", "input_text", "output_text")
                        and not item.get("text")
                    )
                ]
                if len(filtered) != len(content):
                    clean = dict(msg)
                    if filtered:
                        clean["content"] = filtered
                    elif msg.get("role") == "assistant" and msg.get("tool_calls"):
                        clean["content"] = None
                    else:
                        clean["content"] = "(empty)"
                    result.append(clean)
                    continue

            if isinstance(content, dict):
                clean = dict(msg)
                clean["content"] = [content]
                result.append(clean)
                continue

            result.append(msg)
        return result

    @staticmethod
    def _sanitize_request_messages(
        messages: list[dict[str, Any]],
        allowed_keys: frozenset[str],
    ) -> list[dict[str, Any]]:
        """Keep only provider-safe message keys and normalize assistant content."""
        sanitized = []
        for msg in messages:
            clean = {k: v for k, v in msg.items() if k in allowed_keys}
            if clean.get("role") == "assistant" and "content" not in clean:
                clean["content"] = None
            sanitized.append(clean)
        return sanitized

    @abstractmethod
    async def chat(
        self,
        messages: list[dict[str, Any]],
        tools: list[dict[str, Any]] | None = None,
        model: str | None = None,
        max_tokens: int = 4096,
        temperature: float = 0.7,
        reasoning_effort: str | None = None,
    ) -> LLMResponse:
        """
        Send a chat completion request.
        
        Args:
            messages: List of message dicts with 'role' and 'content'.
            tools: Optional list of tool definitions.
            model: Model identifier (provider-specific).
            max_tokens: Maximum tokens in response.
            temperature: Sampling temperature.
        
        Returns:
            LLMResponse with content and/or tool calls.
        """
        pass

    @abstractmethod
    def get_default_model(self) -> str:
        """Get the default model for this provider."""
        pass
