"""Functionality for splitting text."""

from __future__ import annotations

from typing import Any, Optional

from core.model_manager import ModelInstance
from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
from core.rag.splitter.text_splitter import (
    TS,
    Collection,
    Literal,
    RecursiveCharacterTextSplitter,
    Set,
    TokenTextSplitter,
    Union,
)


class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
    """
    This class is used to implement from_gpt2_encoder, to prevent using of tiktoken
    """

    @classmethod
    def from_encoder(
        cls: type[TS],
        embedding_model_instance: Optional[ModelInstance],
        allowed_special: Union[Literal["all"], Set[str]] = set(),  # noqa: UP037
        disallowed_special: Union[Literal["all"], Collection[str]] = "all",  # noqa: UP037
        **kwargs: Any,
    ):
        def _token_encoder(texts: list[str]) -> list[int]:
            if not texts:
                return []

            if embedding_model_instance:
                return embedding_model_instance.get_text_embedding_num_tokens(texts=texts)
            else:
                return [GPT2Tokenizer.get_num_tokens(text) for text in texts]

        if issubclass(cls, TokenTextSplitter):
            extra_kwargs = {
                "model_name": embedding_model_instance.model if embedding_model_instance else "gpt2",
                "allowed_special": allowed_special,
                "disallowed_special": disallowed_special,
            }
            kwargs = {**kwargs, **extra_kwargs}

        return cls(length_function=_token_encoder, **kwargs)


class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter):
    def __init__(self, fixed_separator: str = "\n\n", separators: Optional[list[str]] = None, **kwargs: Any):
        """Create a new TextSplitter."""
        super().__init__(**kwargs)
        self._fixed_separator = fixed_separator
        self._separators = separators or ["\n\n", "\n", " ", ""]

    def split_text(self, text: str) -> list[str]:
        """Split incoming text and return chunks."""
        if self._fixed_separator:
            chunks = text.split(self._fixed_separator)
        else:
            chunks = [text]

        final_chunks = []
        chunks_lengths = self._length_function(chunks)
        for chunk, chunk_length in zip(chunks, chunks_lengths):
            if chunk_length > self._chunk_size:
                final_chunks.extend(self.recursive_split_text(chunk))
            else:
                final_chunks.append(chunk)

        return final_chunks

    def recursive_split_text(self, text: str) -> list[str]:
        """Split incoming text and return chunks."""
        final_chunks = []
        # Get appropriate separator to use
        separator = self._separators[-1]
        for _s in self._separators:
            if _s == "":
                separator = _s
                break
            if _s in text:
                separator = _s
                break
        # Now that we have the separator, split the text
        if separator:
            splits = text.split(separator)
        else:
            splits = list(text)
        # Now go merging things, recursively splitting longer texts.
        _good_splits = []
        _good_splits_lengths = []  # cache the lengths of the splits
        s_lens = self._length_function(splits)
        for s, s_len in zip(splits, s_lens):
            if s_len < self._chunk_size:
                _good_splits.append(s)
                _good_splits_lengths.append(s_len)
            else:
                if _good_splits:
                    merged_text = self._merge_splits(_good_splits, separator, _good_splits_lengths)
                    final_chunks.extend(merged_text)
                    _good_splits = []
                    _good_splits_lengths = []
                other_info = self.recursive_split_text(s)
                final_chunks.extend(other_info)
        if _good_splits:
            merged_text = self._merge_splits(_good_splits, separator, _good_splits_lengths)
            final_chunks.extend(merged_text)
        return final_chunks