"""LangChain callback for automatic cost tracking."""

import logging
from typing import Any, Dict, List, Optional, Union

from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import LLMResult

from utils.cost_tracker import CostTracker

logger = logging.getLogger(__name__)


class CostTrackingCallback(BaseCallbackHandler):
    """
    LangChain callback handler for tracking LLM API costs.

    This callback automatically extracts token usage from LLM responses
    and tracks costs using the CostTracker.
    """

    def __init__(
        self,
        cost_tracker: CostTracker,
        agent_name: str,
        provider: Optional[str] = None,
    ):
        """
        Initialize cost tracking callback.

        Args:
            cost_tracker: CostTracker instance to use
            agent_name: Name of the agent making LLM calls
            provider: Provider name (auto-detected if None)
        """
        super().__init__()
        self.cost_tracker = cost_tracker
        self.agent_name = agent_name
        self.provider = provider

    def on_llm_end(
        self,
        response: LLMResult,
        *,
        run_id: Any,
        parent_run_id: Optional[Any] = None,
        **kwargs: Any,
    ) -> Any:
        """
        Track cost when LLM call completes.

        Args:
            response: LLM response with token usage info
            run_id: Unique identifier for this run
            parent_run_id: Parent run ID if nested
            **kwargs: Additional callback arguments
        """
        try:
            logger.info(f"CostTrackingCallback.on_llm_end called for {self.agent_name}")

            # Extract token usage from response
            llm_output = response.llm_output or {}
            logger.info(f"llm_output keys: {list(llm_output.keys())}")

            token_usage = llm_output.get("token_usage", {})

            # Get token counts
            input_tokens = token_usage.get("prompt_tokens", 0)
            output_tokens = token_usage.get("completion_tokens", 0)

            logger.info(f"Token usage: input={input_tokens}, output={output_tokens}")

            # Get model name (check both "model_name" for OpenAI/Anthropic and "model" for HuggingFace)
            model = llm_output.get("model_name") or llm_output.get("model", "unknown")
            logger.info(f"Model: {model}")

            # Skip tracking if no tokens (e.g., cached response)
            if input_tokens == 0 and output_tokens == 0:
                logger.warning(f"Skipping tracking for {self.agent_name} - no tokens")
                return

            # Track the call
            cost = self.cost_tracker.track_call(
                agent_name=self.agent_name,
                model=model,
                input_tokens=input_tokens,
                output_tokens=output_tokens,
                provider=self.provider,
            )

            logger.info(
                f"✓ Cost tracked: {self.agent_name} | {model} | "
                f"{input_tokens} + {output_tokens} tokens | ${cost:.6f}"
            )

        except Exception as e:
            logger.warning(f"Failed to track cost for {self.agent_name}: {e}")

    def on_llm_error(
        self,
        error: Union[Exception, KeyboardInterrupt],
        *,
        run_id: Any,
        parent_run_id: Optional[Any] = None,
        **kwargs: Any,
    ) -> Any:
        """
        Handle LLM errors (no cost tracking needed).

        Args:
            error: The error that occurred
            run_id: Unique identifier for this run
            parent_run_id: Parent run ID if nested
            **kwargs: Additional callback arguments
        """
        logger.debug(f"LLM error in {self.agent_name}: {error}")


class WorkflowCostTracker:
    """
    Workflow-level cost tracker that manages a CostTracker instance
    and provides callbacks for agents.
    """

    def __init__(self, budget_config=None):
        """
        Initialize workflow cost tracker.

        Args:
            budget_config: Optional BudgetConfig for cost limits and alerts
        """
        self.cost_tracker = CostTracker(budget_config=budget_config)

    def get_callback(
        self,
        agent_name: str,
        provider: Optional[str] = None,
    ) -> CostTrackingCallback:
        """
        Get a cost tracking callback for an agent.

        Args:
            agent_name: Name of the agent
            provider: Provider name (auto-detected if None)

        Returns:
            CostTrackingCallback instance
        """
        return CostTrackingCallback(
            cost_tracker=self.cost_tracker,
            agent_name=agent_name,
            provider=provider,
        )

    def get_summary(self) -> Dict[str, Any]:
        """
        Get cost summary for the workflow.

        Returns:
            Dictionary with cost breakdown
        """
        return self.cost_tracker.get_summary()

    def format_summary(self) -> str:
        """
        Format cost summary as human-readable string.

        Returns:
            Formatted cost summary
        """
        return self.cost_tracker.format_summary()

    def reset(self):
        """Reset cost tracking."""
        self.cost_tracker.reset()