

from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_        =      {
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

SCREAMING_SNAKE_CASE_        =      {'allegro/herbert-base-cased': 514}
SCREAMING_SNAKE_CASE_        =      {}

class 				a  (	lowercase__				):
						_lowercase    						=			VOCAB_FILES_NAMES
						_lowercase    						=			PRETRAINED_VOCAB_FILES_MAP
						_lowercase    						=			PRETRAINED_INIT_CONFIGURATION
						_lowercase    						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase    						=			HerbertTokenizer


						def __init__(       self						,					A_=None						,					A_=None						,					A_=None						,					A_="<s>"						,					A_="<unk>"						,					A_="<pad>"						,					A_="<mask>"						,					A_="</s>"						,					**A_						,					):







										'''simple docstring'''


										super().__init__(
										    A_						,					A_						,					tokenizer_file=A_						,					cls_token=A_						,					unk_token=A_						,					pad_token=A_						,					mask_token=A_						,					sep_token=A_						,					**A_						,					)


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    [self.cls_token_id]
										_UpperCAmelCase       :		List[str] 		=    [self.sep_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep

										return cls + token_ids_a + sep + token_ids_a + sep


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None						,					A_ = False	):







										'''simple docstring'''


										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=A_						,					token_ids_a=A_						,					already_has_special_tokens=A_	)

										if token_ids_a is None:
														return [1] + ([0] * len(A_	)) + [1]
										return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    [self.sep_token_id]
										_UpperCAmelCase       :		List[Any] 		=    [self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep	) * [0]
										return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    self._tokenizer.model.save(A_						,					name=A_	)
										return tuple(A_	)





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


