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# Copyright 2025 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.
import itertools
from ...tokenization_utils_tokenizers import TokenizersBackend
class ParakeetTokenizer(TokenizersBackend):
"""
Inherits all methods from [`PreTrainedTokenizerFast`]. Users should refer to this superclass for more information regarding those methods,
except for `_decode` which is overridden to adapt it to CTC decoding:
1. Group consecutive tokens
2. Filter out the blank token
"""
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__ = ["ParakeetTokenizer"]