"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union

import yaml
from pydantic import Field, root_validator, validator

import langchain
from langchain.callbacks.base import BaseCallbackManager
from langchain.callbacks.manager import (
    AsyncCallbackManager,
    AsyncCallbackManagerForChainRun,
    CallbackManager,
    CallbackManagerForChainRun,
    Callbacks,
)
from langchain.load.dump import dumpd
from langchain.load.serializable import Serializable
from langchain.schema import RUN_KEY, BaseMemory, RunInfo


def _get_verbosity() -> bool:
    return langchain.verbose


class Chain(Serializable, ABC):
    """Base interface that all chains should implement."""

    memory: Optional[BaseMemory] = None
    """Optional memory object. Defaults to None.
    Memory is a class that gets called at the start 
    and at the end of every chain. At the start, memory loads variables and passes
    them along in the chain. At the end, it saves any returned variables.
    There are many different types of memory - please see memory docs 
    for the full catalog."""
    callbacks: Callbacks = Field(default=None, exclude=True)
    """Optional list of callback handlers (or callback manager). Defaults to None.
    Callback handlers are called throughout the lifecycle of a call to a chain,
    starting with on_chain_start, ending with on_chain_end or on_chain_error.
    Each custom chain can optionally call additional callback methods, see Callback docs
    for full details."""
    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
    """Deprecated, use `callbacks` instead."""
    verbose: bool = Field(default_factory=_get_verbosity)
    """Whether or not run in verbose mode. In verbose mode, some intermediate logs
    will be printed to the console. Defaults to `langchain.verbose` value."""
    tags: Optional[List[str]] = None
    """Optional list of tags associated with the chain. Defaults to None
    These tags will be associated with each call to this chain,
    and passed as arguments to the handlers defined in `callbacks`.
    You can use these to eg identify a specific instance of a chain with its use case.
    """
    metadata: Optional[Dict[str, Any]] = None
    """Optional metadata associated with the chain. Defaults to None
    This metadata will be associated with each call to this chain,
    and passed as arguments to the handlers defined in `callbacks`.
    You can use these to eg identify a specific instance of a chain with its use case.
    """

    class Config:
        """Configuration for this pydantic object."""

        arbitrary_types_allowed = True

    @property
    def _chain_type(self) -> str:
        raise NotImplementedError("Saving not supported for this chain type.")

    @root_validator()
    def raise_deprecation(cls, values: Dict) -> Dict:
        """Raise deprecation warning if callback_manager is used."""
        if values.get("callback_manager") is not None:
            warnings.warn(
                "callback_manager is deprecated. Please use callbacks instead.",
                DeprecationWarning,
            )
            values["callbacks"] = values.pop("callback_manager", None)
        return values

    @validator("verbose", pre=True, always=True)
    def set_verbose(cls, verbose: Optional[bool]) -> bool:
        """If verbose is None, set it.

        This allows users to pass in None as verbose to access the global setting.
        """
        if verbose is None:
            return _get_verbosity()
        else:
            return verbose

    @property
    @abstractmethod
    def input_keys(self) -> List[str]:
        """Input keys this chain expects."""

    @property
    @abstractmethod
    def output_keys(self) -> List[str]:
        """Output keys this chain expects."""

    def _validate_inputs(self, inputs: Dict[str, Any]) -> None:
        """Check that all inputs are present."""
        missing_keys = set(self.input_keys).difference(inputs)
        if missing_keys:
            raise ValueError(f"Missing some input keys: {missing_keys}")

    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
        missing_keys = set(self.output_keys).difference(outputs)
        if missing_keys:
            raise ValueError(f"Missing some output keys: {missing_keys}")

    @abstractmethod
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run the logic of this chain and return the output."""

    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """Run the logic of this chain and return the output."""
        raise NotImplementedError("Async call not supported for this chain type.")

    def __call__(
        self,
        inputs: Union[Dict[str, Any], Any],
        return_only_outputs: bool = False,
        callbacks: Callbacks = None,
        *,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        include_run_info: bool = False,
    ) -> Dict[str, Any]:
        """Run the logic of this chain and add to output if desired.

        Args:
            inputs: Dictionary of inputs, or single input if chain expects
                only one param.
            return_only_outputs: boolean for whether to return only outputs in the
                response. If True, only new keys generated by this chain will be
                returned. If False, both input keys and new keys generated by this
                chain will be returned. Defaults to False.
            callbacks: Callbacks to use for this chain run. If not provided, will
                use the callbacks provided to the chain.
            tags: Optional list of tags associated with the chain. Defaults to None
            metadata: Optional metadata associated with the chain. Defaults to None
            include_run_info: Whether to include run info in the response. Defaults
                to False.
        """
        inputs = self.prep_inputs(inputs)
        callback_manager = CallbackManager.configure(
            callbacks,
            self.callbacks,
            self.verbose,
            tags,
            self.tags,
            metadata,
            self.metadata,
        )
        new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
        run_manager = callback_manager.on_chain_start(
            dumpd(self),
            inputs,
        )
        try:
            outputs = (
                self._call(inputs, run_manager=run_manager)
                if new_arg_supported
                else self._call(inputs)
            )
        except (KeyboardInterrupt, Exception) as e:
            run_manager.on_chain_error(e)
            raise e
        run_manager.on_chain_end(outputs)
        final_outputs: Dict[str, Any] = self.prep_outputs(
            inputs, outputs, return_only_outputs
        )
        if include_run_info:
            final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
        return final_outputs

    async def acall(
        self,
        inputs: Union[Dict[str, Any], Any],
        return_only_outputs: bool = False,
        callbacks: Callbacks = None,
        *,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        include_run_info: bool = False,
    ) -> Dict[str, Any]:
        """Run the logic of this chain and add to output if desired.

        Args:
            inputs: Dictionary of inputs, or single input if chain expects
                only one param.
            return_only_outputs: boolean for whether to return only outputs in the
                response. If True, only new keys generated by this chain will be
                returned. If False, both input keys and new keys generated by this
                chain will be returned. Defaults to False.
            callbacks: Callbacks to use for this chain run. If not provided, will
                use the callbacks provided to the chain.
            tags: Optional list of tags associated with the chain. Defaults to None
            metadata: Optional metadata associated with the chain. Defaults to None
            include_run_info: Whether to include run info in the response. Defaults
                to False.
        """
        inputs = self.prep_inputs(inputs)
        callback_manager = AsyncCallbackManager.configure(
            callbacks,
            self.callbacks,
            self.verbose,
            tags,
            self.tags,
            metadata,
            self.metadata,
        )
        new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")
        run_manager = await callback_manager.on_chain_start(
            dumpd(self),
            inputs,
        )
        try:
            outputs = (
                await self._acall(inputs, run_manager=run_manager)
                if new_arg_supported
                else await self._acall(inputs)
            )
        except (KeyboardInterrupt, Exception) as e:
            await run_manager.on_chain_error(e)
            raise e
        await run_manager.on_chain_end(outputs)
        final_outputs: Dict[str, Any] = self.prep_outputs(
            inputs, outputs, return_only_outputs
        )
        if include_run_info:
            final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
        return final_outputs

    def prep_outputs(
        self,
        inputs: Dict[str, str],
        outputs: Dict[str, str],
        return_only_outputs: bool = False,
    ) -> Dict[str, str]:
        """Validate and prep outputs."""
        self._validate_outputs(outputs)
        if self.memory is not None:
            self.memory.save_context(inputs, outputs)
        if return_only_outputs:
            return outputs
        else:
            return {**inputs, **outputs}

    def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
        """Validate and prep inputs."""
        if not isinstance(inputs, dict):
            _input_keys = set(self.input_keys)
            if self.memory is not None:
                # If there are multiple input keys, but some get set by memory so that
                # only one is not set, we can still figure out which key it is.
                _input_keys = _input_keys.difference(self.memory.memory_variables)
            if len(_input_keys) != 1:
                raise ValueError(
                    f"A single string input was passed in, but this chain expects "
                    f"multiple inputs ({_input_keys}). When a chain expects "
                    f"multiple inputs, please call it by passing in a dictionary, "
                    "eg `chain({'foo': 1, 'bar': 2})`"
                )
            inputs = {list(_input_keys)[0]: inputs}
        if self.memory is not None:
            external_context = self.memory.load_memory_variables(inputs)
            inputs = dict(inputs, **external_context)
        self._validate_inputs(inputs)
        return inputs

    def apply(
        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
    ) -> List[Dict[str, str]]:
        """Call the chain on all inputs in the list."""
        return [self(inputs, callbacks=callbacks) for inputs in input_list]

    @property
    def _run_output_key(self) -> str:
        if len(self.output_keys) != 1:
            raise ValueError(
                f"`run` not supported when there is not exactly "
                f"one output key. Got {self.output_keys}."
            )
        return self.output_keys[0]

    def run(
        self,
        *args: Any,
        callbacks: Callbacks = None,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> str:
        """Run the chain as text in, text out or multiple variables, text out."""
        # Run at start to make sure this is possible/defined
        _output_key = self._run_output_key

        if args and not kwargs:
            if len(args) != 1:
                raise ValueError("`run` supports only one positional argument.")
            return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
                _output_key
            ]

        if kwargs and not args:
            return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
                _output_key
            ]

        if not kwargs and not args:
            raise ValueError(
                "`run` supported with either positional arguments or keyword arguments,"
                " but none were provided."
            )

        raise ValueError(
            f"`run` supported with either positional arguments or keyword arguments"
            f" but not both. Got args: {args} and kwargs: {kwargs}."
        )

    async def arun(
        self,
        *args: Any,
        callbacks: Callbacks = None,
        tags: Optional[List[str]] = None,
        metadata: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> str:
        """Run the chain as text in, text out or multiple variables, text out."""
        if len(self.output_keys) != 1:
            raise ValueError(
                f"`run` not supported when there is not exactly "
                f"one output key. Got {self.output_keys}."
            )

        if args and not kwargs:
            if len(args) != 1:
                raise ValueError("`run` supports only one positional argument.")
            return (
                await self.acall(
                    args[0], callbacks=callbacks, tags=tags, metadata=metadata
                )
            )[self.output_keys[0]]

        if kwargs and not args:
            return (
                await self.acall(
                    kwargs, callbacks=callbacks, tags=tags, metadata=metadata
                )
            )[self.output_keys[0]]

        raise ValueError(
            f"`run` supported with either positional arguments or keyword arguments"
            f" but not both. Got args: {args} and kwargs: {kwargs}."
        )

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of chain."""
        if self.memory is not None:
            raise ValueError("Saving of memory is not yet supported.")
        _dict = super().dict()
        _dict["_type"] = self._chain_type
        return _dict

    def save(self, file_path: Union[Path, str]) -> None:
        """Save the chain.

        Args:
            file_path: Path to file to save the chain to.

        Example:
        .. code-block:: python

            chain.save(file_path="path/chain.yaml")
        """
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path

        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)

        # Fetch dictionary to save
        chain_dict = self.dict()

        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(chain_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(chain_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")
