




'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_camembert import CamembertTokenizer
else:
      SCREAMING_SNAKE_CASE						=	None


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE						=	{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE						=	{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE						=	'▁'
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			VOCAB_FILES_NAMES
      UpperCamelCase_								=			PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_								=			['''input_ids''', '''attention_mask''']
      UpperCamelCase_								=			CamembertTokenizer






      def __init__( self			:		Union[str, Any]     ,      UpperCAmelCase			:		Optional[Any]=None     ,      UpperCAmelCase			:		str=None     ,      UpperCAmelCase			:		Union[str, Any]="<s>"     ,      UpperCAmelCase			:		Tuple="</s>"     ,      UpperCAmelCase			:		Dict="</s>"     ,      UpperCAmelCase			:		Tuple="<s>"     ,      UpperCAmelCase			:		Union[str, Any]="<unk>"     ,      UpperCAmelCase			:		Union[str, Any]="<pad>"     ,      UpperCAmelCase			:		List[Any]="<mask>"     ,      UpperCAmelCase			:		Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]     ,      **UpperCAmelCase			:		Any     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =AddedToken(UpperCAmelCase     ,      lstrip=UpperCAmelCase     ,      rstrip=UpperCAmelCase		) if isinstance(UpperCAmelCase     ,      UpperCAmelCase		) else mask_token

       super().__init__(
           UpperCAmelCase     ,      tokenizer_file=UpperCAmelCase     ,      bos_token=UpperCAmelCase     ,      eos_token=UpperCAmelCase     ,      sep_token=UpperCAmelCase     ,      cls_token=UpperCAmelCase     ,      unk_token=UpperCAmelCase     ,      pad_token=UpperCAmelCase     ,      mask_token=UpperCAmelCase     ,      additional_special_tokens=UpperCAmelCase     ,      **UpperCAmelCase     ,      )

       lowercase					:   Optional[int]				  =vocab_file
       lowercase					:   Any				  =False if not self.vocab_file else True






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase					:   Optional[int]				  =[self.cls_token_id]
       lowercase					:   Dict				  =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[int]     ,      UpperCAmelCase			:		Optional[List[int]] = None		)  ->  List[int]:


       '''simple docstring'''

       lowercase					:   Tuple				  =[self.sep_token_id]
       lowercase					:   Optional[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 + sep + token_ids_a + sep		) * [0]






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  Tuple[str]:


       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
        raise ValueError(
            '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
            '''tokenizer.'''		)

       if not os.path.isdir(UpperCAmelCase		):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
        return
       lowercase					:   Optional[Any]				  =os.path.join(
           UpperCAmelCase     ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCAmelCase		):
        copyfile(self.vocab_file     ,      UpperCAmelCase		)

       return (out_vocab_file,)











'''simple docstring'''



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE						=	{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	['RemBertTokenizer']

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RemBertForCausalLM',
          'RemBertForMaskedLM',
          'RemBertForMultipleChoice',
          'RemBertForQuestionAnswering',
          'RemBertForSequenceClassification',
          'RemBertForTokenClassification',
          'RemBertLayer',
          'RemBertModel',
          'RemBertPreTrainedModel',
          'load_tf_weights_in_rembert',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE						=	[
          'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRemBertForCausalLM',
          'TFRemBertForMaskedLM',
          'TFRemBertForMultipleChoice',
          'TFRemBertForQuestionAnswering',
          'TFRemBertForSequenceClassification',
          'TFRemBertForTokenClassification',
          'TFRemBertLayer',
          'TFRemBertModel',
          'TFRemBertPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

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



