"""LangChain — LangChain Runnable with ACE learning."""

from __future__ import annotations

from collections.abc import Iterable, Sequence
from pathlib import Path
from typing import Any, Callable, Optional

from pydantic_ai.settings import ModelSettings

from pipeline import Pipeline
from pipeline.protocol import SampleResult, StepProtocol

from ..core.context import ACEStepContext, SkillbookView
from ..core.skillbook import Skillbook
from ..integrations import wrap_skillbook_context
from ..integrations.langchain import LangChainExecuteStep, LangChainToTrace
from ..protocols import (
    DeduplicationConfig,
    DeduplicationManagerLike,
    ReflectorLike,
    SkillManagerLike,
)
from ..steps import learning_tail
from .base import ACERunner


class LangChain(ACERunner):
    """LangChain Runnable with ACE learning pipeline.

    INJECT skillbook -> EXECUTE runnable -> LEARN (Reflect -> Tag -> Update -> Apply).

    Handles simple chains, AgentExecutor, and LangGraph graphs automatically.

    Two construction paths:

    1. ``LangChain.from_roles(runnable, reflector, skill_manager, ...)``
       — pre-built roles.
    2. ``LangChain.from_model(runnable, ace_model="gpt-4o-mini", ...)``
       — builds ACE roles from a model string.

    Example::

        runner = LangChain.from_model(my_chain, ace_model="gpt-4o-mini")
        results = runner.run([
            {"input": "What is ACE?"},
            {"input": "Explain skillbooks"},
        ])
        runner.save("chain_expert.json")
    """

    @classmethod
    def build_steps(
        cls,
        *,
        runnable: Any,
        reflector: ReflectorLike,
        skill_manager: SkillManagerLike,
        skillbook: Skillbook | None = None,
        skillbook_path: Optional[str] = None,
        output_parser: Optional[Callable[[Any], str]] = None,
        dedup_config: Optional[DeduplicationConfig] = None,
        dedup_manager: DeduplicationManagerLike | None = None,
        dedup_interval: int = 10,
        checkpoint_dir: str | Path | None = None,
        checkpoint_interval: int = 10,
    ) -> list[StepProtocol]:
        """Return the steps that ``from_roles()`` would compose.

        Use this to inspect, modify, or extend the pipeline before
        constructing it yourself::

            steps = LangChain.build_steps(runnable=chain, reflector=r, ...)
            steps.insert(2, MyCustomStep())
            pipe = Pipeline(steps)
            runner = ACERunner(pipeline=pipe, skillbook=skillbook)

        Args:
            runnable: Any LangChain Runnable (chain, AgentExecutor, LangGraph).
            reflector: Reflector role for analysing execution traces.
            skill_manager: SkillManager role for update operations.
            skillbook: Starting skillbook.  Creates an empty one if ``None``.
            skillbook_path: Path to load skillbook from.
            output_parser: Custom function to extract a string from runnable output.
            dedup_config: Deduplication configuration.
            dedup_manager: Optional pre-built deduplication manager.
            dedup_interval: Samples between deduplication runs.
            checkpoint_dir: Directory for checkpoint files.
            checkpoint_interval: Samples between checkpoint saves.
        """
        # Resolve skillbook
        if skillbook_path:
            skillbook = Skillbook.load_from_file(skillbook_path)
        elif skillbook is None:
            skillbook = Skillbook()

        # Resolve dedup manager
        dm = dedup_manager
        if dm is None and dedup_config is not None:
            from ..deduplication import DeduplicationManager

            dm = DeduplicationManager(dedup_config)

        steps: list[StepProtocol[ACEStepContext]] = [
            LangChainExecuteStep(runnable, output_parser=output_parser),
            LangChainToTrace(),
            *learning_tail(
                reflector,
                skill_manager,
                skillbook,
                dedup_manager=dm,
                dedup_interval=dedup_interval,
                checkpoint_dir=checkpoint_dir,
                checkpoint_interval=checkpoint_interval,
            ),
        ]
        return steps

    @classmethod
    def from_roles(
        cls,
        *,
        runnable: Any,
        reflector: ReflectorLike,
        skill_manager: SkillManagerLike,
        skillbook: Skillbook | None = None,
        skillbook_path: Optional[str] = None,
        output_parser: Optional[Callable[[Any], str]] = None,
        dedup_config: Optional[DeduplicationConfig] = None,
        dedup_manager: DeduplicationManagerLike | None = None,
        dedup_interval: int = 10,
        checkpoint_dir: str | Path | None = None,
        checkpoint_interval: int = 10,
    ) -> LangChain:
        """Construct from a LangChain Runnable and pre-built role instances.

        Args:
            runnable: Any LangChain Runnable (chain, AgentExecutor, LangGraph).
            reflector: Reflector role for analysing execution traces.
            skill_manager: SkillManager role for update operations.
            skillbook: Starting skillbook.  Creates an empty one if ``None``.
            skillbook_path: Path to load skillbook from.
            output_parser: Custom function to extract a string from runnable output.
            dedup_config: Deduplication configuration.
            dedup_manager: Optional pre-built deduplication manager.
            dedup_interval: Samples between deduplication runs.
            checkpoint_dir: Directory for checkpoint files.
            checkpoint_interval: Samples between checkpoint saves.
        """
        # Resolve skillbook (must match build_steps resolution)
        if skillbook_path:
            skillbook = Skillbook.load_from_file(skillbook_path)
        elif skillbook is None:
            skillbook = Skillbook()

        steps = cls.build_steps(
            runnable=runnable,
            reflector=reflector,
            skill_manager=skill_manager,
            skillbook=skillbook,
            output_parser=output_parser,
            dedup_config=dedup_config,
            dedup_manager=dedup_manager,
            dedup_interval=dedup_interval,
            checkpoint_dir=checkpoint_dir,
            checkpoint_interval=checkpoint_interval,
        )
        return cls(pipeline=Pipeline(steps), skillbook=skillbook)

    @classmethod
    def from_model(
        cls,
        runnable: Any,
        *,
        ace_model: str = "gpt-4o-mini",
        ace_max_tokens: int = 2048,
        ace_temperature: float = 0.0,
        **kwargs: Any,
    ) -> LangChain:
        """Build ACE roles from a model string.

        Args:
            runnable: Any LangChain Runnable (chain, AgentExecutor, LangGraph).
            ace_model: Model identifier for ACE roles.
            ace_max_tokens: Max tokens for ACE LLM responses.
            ace_temperature: Sampling temperature for ACE roles.
            **kwargs: Forwarded to :meth:`from_roles`.
        """
        from ..implementations import Reflector, SkillManager

        model_settings = ModelSettings(
            temperature=ace_temperature,
            max_tokens=ace_max_tokens,
        )

        return cls.from_roles(
            runnable=runnable,
            reflector=Reflector(ace_model, model_settings=model_settings),
            skill_manager=SkillManager(ace_model, model_settings=model_settings),
            **kwargs,
        )

    def run(
        self,
        inputs: Sequence[Any] | Iterable[Any],
        epochs: int = 1,
        *,
        wait: bool = True,
    ) -> list[SampleResult]:
        """Run inputs through the chain with learning.

        Args:
            inputs: Raw inputs (strings, dicts, message lists).
                Must be a ``Sequence`` for ``epochs > 1``.
            epochs: Number of passes over all inputs.
            wait: If ``True``, block until background learning completes.
        """
        return self._run(inputs, epochs=epochs, wait=wait)

    def invoke(self, input: Any, **kwargs: Any) -> list[SampleResult]:
        """Single-input convenience — wraps in a list and delegates to :meth:`run`.

        Args:
            input: A single chain input.
            **kwargs: Forwarded to :meth:`run`.
        """
        return self.run([input], **kwargs)

    def _build_context(  # type: ignore[override]
        self,
        raw_input: Any,
        *,
        epoch: int,
        total_epochs: int,
        index: int,
        total: int | None,
        global_sample_index: int,
        **_: Any,
    ) -> ACEStepContext:
        """Place a raw input on ``ctx.sample``."""
        return ACEStepContext(
            sample=raw_input,
            skillbook=SkillbookView(self.skillbook),
            epoch=epoch,
            total_epochs=total_epochs,
            step_index=index,
            total_steps=total,
            global_sample_index=global_sample_index,
        )

    # ------------------------------------------------------------------
    # Convenience lifecycle methods
    # ------------------------------------------------------------------

    def get_strategies(self) -> str:
        """Return formatted skillbook strategies for display."""
        if not self.skillbook.skills():
            return ""
        return wrap_skillbook_context(self.skillbook)

    # Backward-compat aliases
    save_skillbook = ACERunner.save
    load_skillbook = ACERunner.load
    wait_for_learning = ACERunner.wait_for_background