

'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}






class      lowercase_      (lowerCamelCase__							,			lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     'bit'
  SCREAMING_SNAKE_CASE       :    str							     =     ['preactivation', 'bottleneck']
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     ['SAME', 'VALID']






  def __init__( self  :	int   ,lowercase__  :	Optional[int]=3   ,lowercase__  :	List[str]=6_4   ,lowercase__  :	Union[str, Any]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]   ,lowercase__  :	str=[3, 4, 6, 3]   ,lowercase__  :	Optional[Any]="preactivation"   ,lowercase__  :	int="relu"   ,lowercase__  :	Optional[int]=None   ,lowercase__  :	List[str]=3_2   ,lowercase__  :	Optional[int]=0.0   ,lowercase__  :	int=False   ,lowercase__  :	Union[str, Any]=3_2   ,lowercase__  :	str=1   ,lowercase__  :	Tuple=None   ,lowercase__  :	Tuple=None   ,**lowercase__  :	Tuple   ,):
   super().__init__(**lowercase__     )
   if layer_type not in self.layer_types:
    raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
   if global_padding is not None:
    if global_padding.upper() in self.supported_padding:
     __lowercase				    =					global_padding.upper()
    else:
     raise ValueError(F"Padding strategy {global_padding} not supported"     )
   __lowercase				    =					num_channels
   __lowercase				    =					embedding_size
   __lowercase				    =					hidden_sizes
   __lowercase				    =					depths
   __lowercase				    =					layer_type
   __lowercase				    =					hidden_act
   __lowercase				    =					global_padding
   __lowercase				    =					num_groups
   __lowercase				    =					drop_path_rate
   __lowercase				    =					embedding_dynamic_padding
   __lowercase				    =					output_stride
   __lowercase				    =					width_factor

   __lowercase				    =					['''stem'''] + [F"stage{idx}" for idx in range(1   ,len(lowercase__     ) + 1     )]
   __lowercase    ,      __lowercase				    =					get_aligned_output_features_output_indices(
       out_features=lowercase__   ,out_indices=lowercase__   ,stage_names=self.stage_names     )









"""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:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

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

lowerCamelCase_							      = {
    '''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''',
    },
}

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

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

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

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[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 + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				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(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

				return (out_vocab_file,)



