"""Base interface for chains combining documents."""

from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Tuple

from pydantic import Field

from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.docstore.document import Document
from langchain.schema import BasePromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter


def format_document(doc: Document, prompt: BasePromptTemplate) -> str:
    """Format a document into a string based on a prompt template."""
    base_info = {"page_content": doc.page_content}
    base_info.update(doc.metadata)
    missing_metadata = set(prompt.input_variables).difference(base_info)
    if len(missing_metadata) > 0:
        required_metadata = [
            iv for iv in prompt.input_variables if iv != "page_content"
        ]
        raise ValueError(
            f"Document prompt requires documents to have metadata variables: "
            f"{required_metadata}. Received document with missing metadata: "
            f"{list(missing_metadata)}."
        )
    document_info = {k: base_info[k] for k in prompt.input_variables}
    return prompt.format(**document_info)


class BaseCombineDocumentsChain(Chain, ABC):
    """Base interface for chains combining documents."""

    input_key: str = "input_documents"  #: :meta private:
    output_key: str = "output_text"  #: :meta private:

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

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

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

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

    def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
        """Return the prompt length given the documents passed in.

        Returns None if the method does not depend on the prompt length.
        """
        return None

    @abstractmethod
    def combine_docs(self, docs: List[Document], **kwargs: Any) -> Tuple[str, dict]:
        """Combine documents into a single string."""

    @abstractmethod
    async def acombine_docs(
        self, docs: List[Document], **kwargs: Any
    ) -> Tuple[str, dict]:
        """Combine documents into a single string asynchronously."""

    def _call(
        self,
        inputs: Dict[str, List[Document]],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        docs = inputs[self.input_key]
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        output, extra_return_dict = self.combine_docs(
            docs, callbacks=_run_manager.get_child(), **other_keys
        )
        extra_return_dict[self.output_key] = output
        return extra_return_dict

    async def _acall(
        self,
        inputs: Dict[str, List[Document]],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
        docs = inputs[self.input_key]
        # Other keys are assumed to be needed for LLM prediction
        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
        output, extra_return_dict = await self.acombine_docs(
            docs, callbacks=_run_manager.get_child(), **other_keys
        )
        extra_return_dict[self.output_key] = output
        return extra_return_dict


class AnalyzeDocumentChain(Chain):
    """Chain that splits documents, then analyzes it in pieces."""

    input_key: str = "input_document"  #: :meta private:
    text_splitter: TextSplitter = Field(default_factory=RecursiveCharacterTextSplitter)
    combine_docs_chain: BaseCombineDocumentsChain

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

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

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

        :meta private:
        """
        return self.combine_docs_chain.output_keys

    def _call(
        self,
        inputs: Dict[str, str],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        document = inputs[self.input_key]
        docs = self.text_splitter.create_documents([document])
        # Other keys are assumed to be needed for LLM prediction
        other_keys: Dict = {k: v for k, v in inputs.items() if k != self.input_key}
        other_keys[self.combine_docs_chain.input_key] = docs
        return self.combine_docs_chain(
            other_keys, return_only_outputs=True, callbacks=_run_manager.get_child()
        )
