from typing import Any, Callable, Dict, List, Optional

import pandas as pd

from ray.data.preprocessor import SerializablePreprocessorBase
from ray.data.preprocessors.utils import (
    _Computed,
    _PublicField,
    migrate_private_fields,
    simple_split_tokenizer,
)
from ray.data.preprocessors.version_support import SerializablePreprocessor
from ray.util.annotations import PublicAPI


@PublicAPI(stability="alpha")
@SerializablePreprocessor(version=1, identifier="io.ray.preprocessors.tokenizer")
class Tokenizer(SerializablePreprocessorBase):
    """Replace each string with a list of tokens.

    Examples:
        >>> import pandas as pd
        >>> import ray
        >>> df = pd.DataFrame({"text": ["Hello, world!", "foo bar\\nbaz"]})
        >>> ds = ray.data.from_pandas(df)  # doctest: +SKIP

        The default ``tokenization_fn`` delimits strings using the space character.

        >>> from ray.data.preprocessors import Tokenizer
        >>> tokenizer = Tokenizer(columns=["text"])
        >>> tokenizer.transform(ds).to_pandas()  # doctest: +SKIP
                       text
        0  [Hello,, world!]
        1   [foo, bar\\nbaz]

        If the default logic isn't adequate for your use case, you can specify a
        custom ``tokenization_fn``.

        >>> import string
        >>> def tokenization_fn(s):
        ...     for character in string.punctuation:
        ...         s = s.replace(character, "")
        ...     return s.split()
        >>> tokenizer = Tokenizer(columns=["text"], tokenization_fn=tokenization_fn)
        >>> tokenizer.transform(ds).to_pandas()  # doctest: +SKIP
                      text
        0   [Hello, world]
        1  [foo, bar, baz]

        :class:`Tokenizer` can also be used in append mode by providing the
        name of the output_columns that should hold the tokenized values.

        >>> tokenizer = Tokenizer(columns=["text"], output_columns=["text_tokenized"])
        >>> tokenizer.transform(ds).to_pandas()  # doctest: +SKIP
                    text    text_tokenized
        0  Hello, world!  [Hello,, world!]
        1   foo bar\\nbaz   [foo, bar\\nbaz]

    Args:
        columns: The columns to tokenize.
        tokenization_fn: The function used to generate tokens. This function
            should accept a string as input and return a list of tokens as
            output. If unspecified, the tokenizer uses a function equivalent to
            ``lambda s: s.split(" ")``.
        output_columns: The names of the transformed columns. If None, the transformed
            columns will be the same as the input columns. If not None, the length of
            ``output_columns`` must match the length of ``columns``, othwerwise an error
            will be raised.
    """

    _is_fittable = False

    def __init__(
        self,
        columns: List[str],
        tokenization_fn: Optional[Callable[[str], List[str]]] = None,
        output_columns: Optional[List[str]] = None,
    ):
        super().__init__()
        self._columns = columns
        # TODO(matt): Add a more robust default tokenizer.
        self._tokenization_fn = tokenization_fn or simple_split_tokenizer
        self._output_columns = (
            SerializablePreprocessorBase._derive_and_validate_output_columns(
                columns, output_columns
            )
        )

    @property
    def columns(self) -> List[str]:
        return self._columns

    @property
    def tokenization_fn(self) -> Callable[[str], List[str]]:
        return self._tokenization_fn

    @property
    def output_columns(self) -> List[str]:
        return self._output_columns

    def _transform_pandas(self, df: pd.DataFrame):
        def column_tokenizer(s: pd.Series):
            return s.map(self._tokenization_fn)

        df[self._output_columns] = df.loc[:, self._columns].transform(column_tokenizer)
        return df

    def __repr__(self):
        name = getattr(self._tokenization_fn, "__name__", self._tokenization_fn)
        return (
            f"{self.__class__.__name__}(columns={self._columns!r}, "
            f"tokenization_fn={name}, output_columns={self._output_columns!r})"
        )

    def _get_serializable_fields(self) -> Dict[str, Any]:
        return {
            "columns": self._columns,
            "tokenization_fn": self._tokenization_fn,
            "output_columns": self._output_columns,
        }

    def _set_serializable_fields(self, fields: Dict[str, Any], version: int):
        # required fields
        self._columns = fields["columns"]
        self._tokenization_fn = fields["tokenization_fn"]
        self._output_columns = fields["output_columns"]

    def __setstate__(self, state: Dict[str, Any]) -> None:
        super().__setstate__(state)
        migrate_private_fields(
            self,
            fields={
                "_columns": _PublicField(public_field="columns"),
                "_tokenization_fn": _PublicField(
                    public_field="tokenization_fn", default=simple_split_tokenizer
                ),
                "_output_columns": _PublicField(
                    public_field="output_columns",
                    default=_Computed(lambda obj: obj._columns),
                ),
            },
        )