#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/lasr/modular_lasr.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_lasr.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 The HuggingFace Inc. team and Google LLC. 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.

import itertools
import re

from tokenizers import Tokenizer, decoders, pre_tokenizers, processors
from tokenizers.models import Unigram

from ...tokenization_utils_tokenizers import TokenizersBackend


VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}


class LasrTokenizer(TokenizersBackend):
    """
    Construct a LASR tokenizer (backed by HuggingFace's *tokenizers* library). Based on
    [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).

    This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should
    refer to this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`, *optional*):
            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
            contains the vocabulary necessary to instantiate a tokenizer.
        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
            The token used is the `sep_token`.

            </Tip>

        unk_token (`str`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        extra_ids (`int`, *optional*, defaults to 100):
            Add a number of extra ids added to the vocabulary for use as sentinels. These tokens are accessible as
            "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. These tokens can be retrieved by
            calling get_sentinel_tokens method and token ids can be by calling get_sentinel_token_ids method
        additional_special_tokens (`list[str]`, *optional*):
            Additional special tokens used by the tokenizer.
        vocab (`str`, `dict` or `list`, *optional*):
            Custom vocabulary dict. If not provided, a minimal vocabulary is created using the special tokens.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]
    model = Unigram

    def __init__(
        self,
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        extra_ids=100,
        additional_special_tokens=None,
        vocab=None,
        vocab_file=None,
        **kwargs,
    ):
        self._extra_ids = extra_ids

        # Handle extra_ids and additional_special_tokens
        if additional_special_tokens is not None:
            extra_tokens = [x for x in additional_special_tokens if "<extra_id_" in str(x)]
            if len(extra_tokens) < 1:
                additional_special_tokens += [f"<extra_id_{i}>" for i in range(extra_ids)]
            elif extra_ids > 0 and extra_ids != len(extra_tokens):
                raise ValueError(
                    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
                    " provided to LasrTokenizer. In this case the additional_special_tokens must include the extra_ids"
                    " tokens"
                )
        else:
            extra_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
            additional_special_tokens = extra_tokens

        # LASR vocab structure: <pad>=0, </s>=1, <unk>=2, then regular vocab, then extra_ids in reverse
        if vocab is not None:
            self._vocab_scores = vocab
        else:
            self._vocab_scores = [
                (str(pad_token), 0.0),
                (str(eos_token), 0.0),
                (str(unk_token), 0.0),
                ("▁", -2.0),  # Space token
            ]
            for i in range(extra_ids - 1, -1, -1):
                self._vocab_scores.append((f"<extra_id_{i}>", 0.0))
        self._tokenizer = Tokenizer(
            Unigram(
                self._vocab_scores,
                unk_id=3,
                byte_fallback=False,
            )
        )

        self._tokenizer.normalizer = None

        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
            [
                pre_tokenizers.WhitespaceSplit(),
                pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True),
            ]
        )

        self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True)

        super().__init__(
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            extra_ids=extra_ids,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=["$A", "</s>"],
            pair=["$A", "</s>", "$B", "</s>"],
            special_tokens=[
                ("</s>", self.eos_token_id),
            ],
        )

    def get_sentinel_tokens(self):
        """Get the list of sentinel tokens (extra_id tokens) from additional_special_tokens."""
        return list(
            set(filter(lambda x: bool(re.search(r"<extra_id_\d+>", x)) is not None, self.additional_special_tokens))
        )

    def get_sentinel_token_ids(self):
        """Get the token IDs for sentinel tokens."""
        return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()]

    def _decode(
        self,
        token_ids: int | list[int],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool | None = None,
        group_tokens: bool = True,
        **kwargs,
    ) -> str:
        if isinstance(token_ids, int):
            token_ids = [token_ids]
        if group_tokens:
            token_ids = [token_group[0] for token_group in itertools.groupby(token_ids)]

        # for CTC we filter out the blank token, which is the pad token
        token_ids = [token for token in token_ids if token != self.pad_token_id]

        return super()._decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )


__all__ = ["LasrTokenizer"]