"""simple docstring"""





from typing import List, Optional, Tuple

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


__A  =				logging.get_logger(__name__)

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

__A  =				{
    '''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'''
    },
}

__A  =				{'''allegro/herbert-base-cased''': 514}
__A  =				{}
class  _snake_case     (					a__		):
						snake_case__			  =			VOCAB_FILES_NAMES
						snake_case__			  =			PRETRAINED_VOCAB_FILES_MAP
						snake_case__			  =			PRETRAINED_INIT_CONFIGURATION
						snake_case__			  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__			  =			HerbertTokenizer






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			Any=None							,					UpperCAmelCase      :			str="<s>"							,					UpperCAmelCase      :			Union[str, Any]="<unk>"							,					UpperCAmelCase      :			int="<pad>"							,					UpperCAmelCase      :			Optional[Any]="<mask>"							,					UpperCAmelCase      :			str="</s>"							,					**UpperCAmelCase      :			Union[str, Any]							,					):
													super().__init__(
													    UpperCAmelCase							,					UpperCAmelCase							,					tokenizer_file=UpperCAmelCase							,					cls_token=UpperCAmelCase							,					unk_token=UpperCAmelCase							,					pad_token=UpperCAmelCase							,					mask_token=UpperCAmelCase							,					sep_token=UpperCAmelCase							,					**UpperCAmelCase							,					)






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Any		   =     [self.cls_token_id]
													__lowerCamelCase					:		Optional[Any]		   =     [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       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None							,					UpperCAmelCase      :			bool = False    ):
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=UpperCAmelCase							,					token_ids_a=UpperCAmelCase							,					already_has_special_tokens=UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Any		   =     [self.sep_token_id]
													__lowerCamelCase					:		int		   =     [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       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[str] = None    ):
													__lowerCamelCase					:		List[Any]		   =     self._tokenizer.model.save(UpperCAmelCase							,					name=UpperCAmelCase    )
													return tuple(UpperCAmelCase    )
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)