"""Chain that interprets a prompt and executes bash operations."""
from __future__ import annotations

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

from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.schema import BasePromptTemplate, OutputParserException
from langchain.schema.language_model import BaseLanguageModel

from langchain_experimental.llm_bash.bash import BashProcess
from langchain_experimental.llm_bash.prompt import PROMPT
from langchain_experimental.pydantic_v1 import Extra, Field, root_validator

logger = logging.getLogger(__name__)


class LLMBashChain(Chain):
    """Chain that interprets a prompt and executes bash operations.

    Example:
        .. code-block:: python

            from langchain.chains import LLMBashChain
            from langchain.llms import OpenAI
            llm_bash = LLMBashChain.from_llm(OpenAI())
    """

    llm_chain: LLMChain
    llm: Optional[BaseLanguageModel] = None
    """[Deprecated] LLM wrapper to use."""
    input_key: str = "question"  #: :meta private:
    output_key: str = "answer"  #: :meta private:
    prompt: BasePromptTemplate = PROMPT
    """[Deprecated]"""
    bash_process: BashProcess = Field(default_factory=BashProcess)  #: :meta private:

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

        extra = Extra.forbid
        arbitrary_types_allowed = True

    @root_validator(pre=True)
    def raise_deprecation(cls, values: Dict) -> Dict:
        if "llm" in values:
            warnings.warn(
                "Directly instantiating an LLMBashChain with an llm is deprecated. "
                "Please instantiate with llm_chain or using the from_llm class method."
            )
            if "llm_chain" not in values and values["llm"] is not None:
                prompt = values.get("prompt", PROMPT)
                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
        return values

    @root_validator
    def validate_prompt(cls, values: Dict) -> Dict:
        if values["llm_chain"].prompt.output_parser is None:
            raise ValueError(
                "The prompt used by llm_chain is expected to have an output_parser."
            )
        return values

    @property
    def input_keys(self) -> List[str]:
        """Expect input key.

        :meta private:
        """
        return [self.input_key]

    @property
    def output_keys(self) -> List[str]:
        """Expect output key.

        :meta private:
        """
        return [self.output_key]

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        _run_manager.on_text(inputs[self.input_key], verbose=self.verbose)

        t = self.llm_chain.predict(
            question=inputs[self.input_key], callbacks=_run_manager.get_child()
        )
        _run_manager.on_text(t, color="green", verbose=self.verbose)
        t = t.strip()
        try:
            parser = self.llm_chain.prompt.output_parser
            command_list = parser.parse(t)  # type: ignore[union-attr]
        except OutputParserException as e:
            _run_manager.on_chain_error(e, verbose=self.verbose)
            raise e

        if self.verbose:
            _run_manager.on_text("\nCode: ", verbose=self.verbose)
            _run_manager.on_text(
                str(command_list), color="yellow", verbose=self.verbose
            )
        output = self.bash_process.run(command_list)
        _run_manager.on_text("\nAnswer: ", verbose=self.verbose)
        _run_manager.on_text(output, color="yellow", verbose=self.verbose)
        return {self.output_key: output}

    @property
    def _chain_type(self) -> str:
        return "llm_bash_chain"

    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        prompt: BasePromptTemplate = PROMPT,
        **kwargs: Any,
    ) -> LLMBashChain:
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, **kwargs)