





"""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_barthez import BarthezTokenizer
else:
       __UpperCamelCase     :						List[str]						       =  None

__UpperCamelCase     :						List[Any]						       =  logging.get_logger(__name__)

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

__UpperCamelCase     :						str						       =  {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase     :						Optional[int]						       =  {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__UpperCamelCase     :						Optional[Any]						       =  '''▁'''







class       a							(   a__  ):
 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       ['''input_ids''', '''attention_mask''']
 snake_case__             =       BarthezTokenizer



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







  """simple docstring"""






  lowerCAmelCase    				= AddedToken(_snake_case				,       lstrip=_snake_case				,       rstrip=_snake_case     ) if isinstance(_snake_case				,       _snake_case     ) else mask_token

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

  lowerCAmelCase    				= vocab_file
  lowerCAmelCase    				= False if not self.vocab_file else True



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= [self.sep_token_id]
  lowerCAmelCase    				= [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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """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(_snake_case     ):
   logger.error(F'Vocabulary path ({save_directory}) should be a directory'     )
   return
  lowerCAmelCase    				= os.path.join(
      _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

  return (out_vocab_file,)












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						Optional[Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[Any]						       =  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''megatron-bert'''



 def __init__(     self				,       _snake_case=2_90_56				,       _snake_case=10_24				,       _snake_case=24				,       _snake_case=16				,       _snake_case=40_96				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache





