"""Combine many documents together by recursively reducing them."""

from __future__ import annotations

from typing import Any, Callable, List, Optional, Protocol, Tuple

from langchain.callbacks.manager import Callbacks
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.docstore.document import Document
from langchain.pydantic_v1 import Extra


class CombineDocsProtocol(Protocol):
    """Interface for the combine_docs method."""

    def __call__(self, docs: List[Document], **kwargs: Any) -> str:
        """Interface for the combine_docs method."""


class AsyncCombineDocsProtocol(Protocol):
    """Interface for the combine_docs method."""

    async def __call__(self, docs: List[Document], **kwargs: Any) -> str:
        """Async interface for the combine_docs method."""


def _split_list_of_docs(
    docs: List[Document], length_func: Callable, token_max: int, **kwargs: Any
) -> List[List[Document]]:
    new_result_doc_list = []
    _sub_result_docs = []
    for doc in docs:
        _sub_result_docs.append(doc)
        _num_tokens = length_func(_sub_result_docs, **kwargs)
        if _num_tokens > token_max:
            if len(_sub_result_docs) == 1:
                raise ValueError(
                    "A single document was longer than the context length,"
                    " we cannot handle this."
                )
            new_result_doc_list.append(_sub_result_docs[:-1])
            _sub_result_docs = _sub_result_docs[-1:]
    new_result_doc_list.append(_sub_result_docs)
    return new_result_doc_list


def _collapse_docs(
    docs: List[Document],
    combine_document_func: CombineDocsProtocol,
    **kwargs: Any,
) -> Document:
    result = combine_document_func(docs, **kwargs)
    combined_metadata = {k: str(v) for k, v in docs[0].metadata.items()}
    for doc in docs[1:]:
        for k, v in doc.metadata.items():
            if k in combined_metadata:
                combined_metadata[k] += f", {v}"
            else:
                combined_metadata[k] = str(v)
    return Document(page_content=result, metadata=combined_metadata)


async def _acollapse_docs(
    docs: List[Document],
    combine_document_func: AsyncCombineDocsProtocol,
    **kwargs: Any,
) -> Document:
    result = await combine_document_func(docs, **kwargs)
    combined_metadata = {k: str(v) for k, v in docs[0].metadata.items()}
    for doc in docs[1:]:
        for k, v in doc.metadata.items():
            if k in combined_metadata:
                combined_metadata[k] += f", {v}"
            else:
                combined_metadata[k] = str(v)
    return Document(page_content=result, metadata=combined_metadata)


class ReduceDocumentsChain(BaseCombineDocumentsChain):
    """Combine documents by recursively reducing them.

    This involves

    - combine_documents_chain

    - collapse_documents_chain

    `combine_documents_chain` is ALWAYS provided. This is final chain that is called.
    We pass all previous results to this chain, and the output of this chain is
    returned as a final result.

    `collapse_documents_chain` is used if the documents passed in are too many to all
    be passed to `combine_documents_chain` in one go. In this case,
    `collapse_documents_chain` is called recursively on as big of groups of documents
    as are allowed.

    Example:
        .. code-block:: python

            from langchain.chains import (
                StuffDocumentsChain, LLMChain, ReduceDocumentsChain
            )
            from langchain.prompts import PromptTemplate
            from langchain.llms import OpenAI

            # This controls how each document will be formatted. Specifically,
            # it will be passed to `format_document` - see that function for more
            # details.
            document_prompt = PromptTemplate(
                input_variables=["page_content"],
                 template="{page_content}"
            )
            document_variable_name = "context"
            llm = OpenAI()
            # The prompt here should take as an input variable the
            # `document_variable_name`
            prompt = PromptTemplate.from_template(
                "Summarize this content: {context}"
            )
            llm_chain = LLMChain(llm=llm, prompt=prompt)
            combine_documents_chain = StuffDocumentsChain(
                llm_chain=llm_chain,
                document_prompt=document_prompt,
                document_variable_name=document_variable_name
            )
            chain = ReduceDocumentsChain(
                combine_documents_chain=combine_documents_chain,
            )
            # If we wanted to, we could also pass in collapse_documents_chain
            # which is specifically aimed at collapsing documents BEFORE
            # the final call.
            prompt = PromptTemplate.from_template(
                "Collapse this content: {context}"
            )
            llm_chain = LLMChain(llm=llm, prompt=prompt)
            collapse_documents_chain = StuffDocumentsChain(
                llm_chain=llm_chain,
                document_prompt=document_prompt,
                document_variable_name=document_variable_name
            )
            chain = ReduceDocumentsChain(
                combine_documents_chain=combine_documents_chain,
                collapse_documents_chain=collapse_documents_chain,
            )
    """

    combine_documents_chain: BaseCombineDocumentsChain
    """Final chain to call to combine documents.
    This is typically a StuffDocumentsChain."""
    collapse_documents_chain: Optional[BaseCombineDocumentsChain] = None
    """Chain to use to collapse documents if needed until they can all fit.
    If None, will use the combine_documents_chain.
    This is typically a StuffDocumentsChain."""
    token_max: int = 3000
    """The maximum number of tokens to group documents into. For example, if
    set to 3000 then documents will be grouped into chunks of no greater than
    3000 tokens before trying to combine them into a smaller chunk."""

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

        extra = Extra.forbid
        arbitrary_types_allowed = True

    @property
    def _collapse_chain(self) -> BaseCombineDocumentsChain:
        if self.collapse_documents_chain is not None:
            return self.collapse_documents_chain
        else:
            return self.combine_documents_chain

    def combine_docs(
        self,
        docs: List[Document],
        token_max: Optional[int] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Tuple[str, dict]:
        """Combine multiple documents recursively.

        Args:
            docs: List of documents to combine, assumed that each one is less than
                `token_max`.
            token_max: Recursively creates groups of documents less than this number
                of tokens.
            callbacks: Callbacks to be passed through
            **kwargs: additional parameters to be passed to LLM calls (like other
                input variables besides the documents)

        Returns:
            The first element returned is the single string output. The second
            element returned is a dictionary of other keys to return.
        """
        result_docs, extra_return_dict = self._collapse(
            docs, token_max=token_max, callbacks=callbacks, **kwargs
        )
        return self.combine_documents_chain.combine_docs(
            docs=result_docs, callbacks=callbacks, **kwargs
        )

    async def acombine_docs(
        self,
        docs: List[Document],
        token_max: Optional[int] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Tuple[str, dict]:
        """Async combine multiple documents recursively.

        Args:
            docs: List of documents to combine, assumed that each one is less than
                `token_max`.
            token_max: Recursively creates groups of documents less than this number
                of tokens.
            callbacks: Callbacks to be passed through
            **kwargs: additional parameters to be passed to LLM calls (like other
                input variables besides the documents)

        Returns:
            The first element returned is the single string output. The second
            element returned is a dictionary of other keys to return.
        """
        result_docs, extra_return_dict = await self._acollapse(
            docs, token_max=token_max, callbacks=callbacks, **kwargs
        )
        return await self.combine_documents_chain.acombine_docs(
            docs=result_docs, callbacks=callbacks, **kwargs
        )

    def _collapse(
        self,
        docs: List[Document],
        token_max: Optional[int] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Tuple[List[Document], dict]:
        result_docs = docs
        length_func = self.combine_documents_chain.prompt_length
        num_tokens = length_func(result_docs, **kwargs)

        def _collapse_docs_func(docs: List[Document], **kwargs: Any) -> str:
            return self._collapse_chain.run(
                input_documents=docs, callbacks=callbacks, **kwargs
            )

        _token_max = token_max or self.token_max
        while num_tokens is not None and num_tokens > _token_max:
            new_result_doc_list = _split_list_of_docs(
                result_docs, length_func, _token_max, **kwargs
            )
            result_docs = []
            for docs in new_result_doc_list:
                new_doc = _collapse_docs(docs, _collapse_docs_func, **kwargs)
                result_docs.append(new_doc)
            num_tokens = length_func(result_docs, **kwargs)
        return result_docs, {}

    async def _acollapse(
        self,
        docs: List[Document],
        token_max: Optional[int] = None,
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Tuple[List[Document], dict]:
        result_docs = docs
        length_func = self.combine_documents_chain.prompt_length
        num_tokens = length_func(result_docs, **kwargs)

        async def _collapse_docs_func(docs: List[Document], **kwargs: Any) -> str:
            return await self._collapse_chain.arun(
                input_documents=docs, callbacks=callbacks, **kwargs
            )

        _token_max = token_max or self.token_max
        while num_tokens is not None and num_tokens > _token_max:
            new_result_doc_list = _split_list_of_docs(
                result_docs, length_func, _token_max, **kwargs
            )
            result_docs = []
            for docs in new_result_doc_list:
                new_doc = await _acollapse_docs(docs, _collapse_docs_func, **kwargs)
                result_docs.append(new_doc)
            num_tokens = length_func(result_docs, **kwargs)
        return result_docs, {}

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