





'''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_xlnet import XLNetTokenizer
else:
    a        =						None


a        =						logging.get_logger(__name__)

a        =						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a        =						{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a        =						{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a        =						"▁"

# Segments (not really needed)
a        =						0
a        =						1
a        =						2
a        =						3
a        =						4







class __a (   _snake_case			):
   __UpperCamelCase   : Optional[int]	      =	VOCAB_FILES_NAMES
   __UpperCamelCase   : int	      =	PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase   : List[str]	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase   : Dict	      =	'left'
   __UpperCamelCase   : Optional[int]	      =	XLNetTokenizer





   def __init__(					self				:			List[str]				,lowerCamelCase				:			Any=None				,lowerCamelCase				:			Tuple=None				,lowerCamelCase				:			Optional[Any]=False				,lowerCamelCase				:			int=True				,lowerCamelCase				:			str=False				,lowerCamelCase				:			Union[str, Any]="<s>"				,lowerCamelCase				:			Optional[int]="</s>"				,lowerCamelCase				:			List[str]="<unk>"				,lowerCamelCase				:			Optional[int]="<sep>"				,lowerCamelCase				:			Any="<pad>"				,lowerCamelCase				:			str="<cls>"				,lowerCamelCase				:			Dict="<mask>"				,lowerCamelCase				:			List[Any]=["<eop>", "<eod>"]				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       AddedToken(lowerCamelCase				,lstrip=lowerCamelCase				,rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase				,lowerCamelCase    ) else mask_token

      super().__init__(
          vocab_file=lowerCamelCase				,tokenizer_file=lowerCamelCase				,do_lower_case=lowerCamelCase				,remove_space=lowerCamelCase				,keep_accents=lowerCamelCase				,bos_token=lowerCamelCase				,eos_token=lowerCamelCase				,unk_token=lowerCamelCase				,sep_token=lowerCamelCase				,pad_token=lowerCamelCase				,cls_token=lowerCamelCase				,mask_token=lowerCamelCase				,additional_special_tokens=lowerCamelCase				,**lowerCamelCase				,)

      __SCREAMING_SNAKE_CASE      							=       3
      __SCREAMING_SNAKE_CASE      							=       do_lower_case
      __SCREAMING_SNAKE_CASE      							=       remove_space
      __SCREAMING_SNAKE_CASE      							=       keep_accents
      __SCREAMING_SNAKE_CASE      							=       vocab_file
      __SCREAMING_SNAKE_CASE      							=       False if not self.vocab_file else True





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			List[int]				,lowerCamelCase				:			Optional[List[int]] = None    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			List[int]				,lowerCamelCase				:			Optional[List[int]] = None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       [self.sep_token_id]
      __SCREAMING_SNAKE_CASE      							=       [2]

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





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			Optional[str] = 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(lowerCamelCase    ):
         logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
         return
      __SCREAMING_SNAKE_CASE      							=       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,)












'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a        =						logging.get_logger(__name__)

a        =						{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'camembert'





   def __init__(					self				:			int				,lowerCamelCase				:			List[Any]=3_0522				,lowerCamelCase				:			List[Any]=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			List[str]=12				,lowerCamelCase				:			Optional[Any]=3072				,lowerCamelCase				:			Tuple="gelu"				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=512				,lowerCamelCase				:			Dict=2				,lowerCamelCase				:			Tuple=0.02				,lowerCamelCase				:			List[Any]=1E-1_2				,lowerCamelCase				:			Union[str, Any]=1				,lowerCamelCase				:			Optional[Any]=0				,lowerCamelCase				:			List[Any]=2				,lowerCamelCase				:			List[str]="absolute"				,lowerCamelCase				:			int=True				,lowerCamelCase				:			Any=None				,**lowerCamelCase				:			Optional[Any]				,):

      '''simple docstring'''

      super().__init__(pad_token_id=lowerCamelCase				,bos_token_id=lowerCamelCase				,eos_token_id=lowerCamelCase				,**lowerCamelCase    )

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







class __a (   _snake_case			):





   @property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      if self.task == "multiple-choice":
         __SCREAMING_SNAKE_CASE      							=       {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         __SCREAMING_SNAKE_CASE      							=       {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]    )





