import os
from typing import Any, Optional

from langchain_google_genai import ChatGoogleGenerativeAI

from .base_client import BaseLLMClient
from .validators import validate_model


class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
    """ChatGoogleGenerativeAI with normalized content output.

    Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}]
    This normalizes to string for consistent downstream handling.
    """

    def _normalize_content(self, response):
        content = response.content
        if isinstance(content, list):
            texts = [
                item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
                else item if isinstance(item, str) else ""
                for item in content
            ]
            response.content = "\n".join(t for t in texts if t)
        return response

    def invoke(self, input, config=None, **kwargs):
        return self._normalize_content(super().invoke(input, config, **kwargs))


class GoogleClient(BaseLLMClient):
    """Client for Google Gemini models."""

    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
        super().__init__(model, base_url, **kwargs)

    def get_llm(self) -> Any:
        """Return configured ChatGoogleGenerativeAI instance."""
        import certifi
        
        # Fix SSL certificate path issue on Windows with conda
        # Conda sets SSL_CERT_FILE to a non-existent path, so we clear it
        # and let certifi handle it properly
        ssl_cert_file = os.environ.get("SSL_CERT_FILE", "")
        if ssl_cert_file and not os.path.exists(ssl_cert_file):
            # Remove invalid SSL_CERT_FILE and use certifi instead
            os.environ.pop("SSL_CERT_FILE", None)
            os.environ["SSL_CERT_FILE"] = certifi.where()
        
        llm_kwargs = {"model": self.model}

        # Get Google API key from kwargs or environment
        if "google_api_key" in self.kwargs:
            llm_kwargs["google_api_key"] = self.kwargs["google_api_key"]
        else:
            api_key = os.environ.get("GOOGLE_API_KEY")
            if api_key:
                llm_kwargs["google_api_key"] = api_key

        for key in ("timeout", "max_retries", "callbacks"):
            if key in self.kwargs:
                llm_kwargs[key] = self.kwargs[key]

        # Map thinking_level to appropriate API param based on model
        # Gemini 3 Pro: low, high
        # Gemini 3 Flash: minimal, low, medium, high
        # Gemini 2.5: thinking_budget (0=disable, -1=dynamic)
        thinking_level = self.kwargs.get("thinking_level")
        if thinking_level:
            model_lower = self.model.lower()
            if "gemini-3" in model_lower:
                # Gemini 3 Pro doesn't support "minimal", use "low" instead
                if "pro" in model_lower and thinking_level == "minimal":
                    thinking_level = "low"
                llm_kwargs["thinking_level"] = thinking_level
            else:
                # Gemini 2.5: map to thinking_budget
                llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0

        return NormalizedChatGoogleGenerativeAI(**llm_kwargs)

    def validate_model(self) -> bool:
        """Validate model for Google."""
        return validate_model("google", self.model)