# This file was auto-generated by Fern from our API Definition.

import datetime as dt
import typing

from ..core.datetime_utils import serialize_datetime

try:
    import pydantic
    if pydantic.__version__.startswith("1."):
        raise ImportError
    import pydantic.v1 as pydantic  # type: ignore
except ImportError:
    import pydantic  # type: ignore


class CharacterSplitter(pydantic.BaseModel):
    """
    A splitter that splits text into characters.
    """

    include_metadata: typing.Optional[bool] = pydantic.Field(
        description="Whether or not to consider metadata when splitting."
    )
    include_prev_next_rel: typing.Optional[bool] = pydantic.Field(description="Include prev/next node relationships.")
    callback_manager: typing.Optional[typing.Any]
    id_func: typing.Optional[str]
    chunk_size: typing.Optional[int] = pydantic.Field(description="The token chunk size for each chunk.")
    chunk_overlap: typing.Optional[int] = pydantic.Field(description="The token overlap of each chunk when splitting.")
    separator: typing.Optional[str] = pydantic.Field(description="Default separator for splitting into words")
    paragraph_separator: typing.Optional[str] = pydantic.Field(description="Separator between paragraphs.")
    secondary_chunking_regex: typing.Optional[str]
    class_name: typing.Optional[str]

    def json(self, **kwargs: typing.Any) -> str:
        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
        return super().json(**kwargs_with_defaults)

    def dict(self, **kwargs: typing.Any) -> typing.Dict[str, typing.Any]:
        kwargs_with_defaults: typing.Any = {"by_alias": True, "exclude_unset": True, **kwargs}
        return super().dict(**kwargs_with_defaults)

    class Config:
        frozen = True
        smart_union = True
        json_encoders = {dt.datetime: serialize_datetime}
