# Copyright Google AI and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for CANINE."""

from ...tokenization_python import AddedToken, PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)


# Unicode defines 1,114,112 total “codepoints”
UNICODE_VOCAB_SIZE = 1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
PAD = 0
CLS = 0xE000
SEP = 0xE001
BOS = 0xE002
MASK = 0xE003
RESERVED = 0xE004

# Maps special codepoints to human-readable names.
SPECIAL_CODEPOINTS: dict[int, str] = {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SPECIAL_CODEPOINTS_BY_NAME: dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class CanineTokenizer(PreTrainedTokenizer):
    r"""
    Construct a CANINE tokenizer (i.e. a character splitter). It turns text into a sequence of characters, and then
    converts each character into its Unicode code point.

    [`CanineTokenizer`] inherits from [`PreTrainedTokenizer`].

    Refer to superclass [`PreTrainedTokenizer`] for usage examples and documentation concerning parameters.

    Args:
        model_max_length (`int`, *optional*, defaults to 2048):
                The maximum sentence length the model accepts.
    """

    model_input_names = ["input_ids", "attention_mask", "token_type_ids"]

    def __init__(
        self,
        bos_token=chr(CLS),
        eos_token=chr(SEP),
        sep_token=chr(SEP),
        cls_token=chr(CLS),
        pad_token=chr(PAD),
        mask_token=chr(MASK),
        add_prefix_space=False,
        model_max_length=2048,
        **kwargs,
    ):
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        # Creates a mapping for looking up the IDs of special symbols.
        self._special_codepoints: dict[str, int] = {}
        for codepoint, name in SPECIAL_CODEPOINTS.items():
            self._special_codepoints[name] = codepoint

        # Creates a mapping for looking up the string forms of special symbol IDs.
        self._special_codepoint_strings: dict[int, str] = {
            codepoint: name for name, codepoint in self._special_codepoints.items()
        }

        self._unicode_vocab_size = UNICODE_VOCAB_SIZE
        self._num_special_tokens = len(self._special_codepoints)

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            add_prefix_space=add_prefix_space,
            model_max_length=model_max_length,
            token_type_ids_pattern="all_zeros",
            token_type_ids_include_special_tokens=True,
            special_tokens_pattern="cls_sep",
            **kwargs,
        )

    @property
    def vocab_size(self) -> int:
        return self._unicode_vocab_size

    def get_vocab(self):
        vocab = {chr(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str) -> list[str]:
        """Tokenize a string (i.e. perform character splitting)."""
        return list(text)

    def _convert_token_to_id(self, token: str) -> int:
        """Converts a token (i.e. a Unicode character) in an id (i.e. its integer Unicode code point value)."""
        try:
            return ord(token)
        except TypeError:
            raise ValueError(f"invalid token: '{token}'")

    def _convert_id_to_token(self, index: int) -> str:
        """
        Converts a Unicode code point (integer) in a token (str). In case it's a special code point, convert to
        human-readable format.
        """
        try:
            if index in SPECIAL_CODEPOINTS:
                return SPECIAL_CODEPOINTS[index]
            return chr(index)
        except TypeError:
            raise ValueError(f"invalid id: {index}")

    def convert_tokens_to_string(self, tokens):
        return "".join(tokens)


__all__ = ["CanineTokenizer"]