
'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__snake_case:	Union[str, Any]										=      logging.get_logger(__name__)

__snake_case:	List[str]										=      {
    '''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 			_UpperCAmelCase			(			lowerCAmelCase__     ):

     """simple docstring"""

     a_     						=       "camembert"


     def __init__(   self				,		lowerCAmelCase_=3_05_22				,		lowerCAmelCase_=7_68				,		lowerCAmelCase_=12				,		lowerCAmelCase_=12				,		lowerCAmelCase_=30_72				,		lowerCAmelCase_="gelu"				,		lowerCAmelCase_=0.1				,		lowerCAmelCase_=0.1				,		lowerCAmelCase_=5_12				,		lowerCAmelCase_=2				,		lowerCAmelCase_=0.02				,		lowerCAmelCase_=1E-1_2				,		lowerCAmelCase_=1				,		lowerCAmelCase_=0				,		lowerCAmelCase_=2				,		lowerCAmelCase_="absolute"				,		lowerCAmelCase_=True				,		lowerCAmelCase_=None				,		**lowerCAmelCase_				,		):

       '''simple docstring'''

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

       a_   :						Any   =				vocab_size
       a_   :						Optional[int]   =				hidden_size
       a_   :						int   =				num_hidden_layers
       a_   :						Dict   =				num_attention_heads
       a_   :						List[str]   =				hidden_act
       a_   :						Any   =				intermediate_size
       a_   :						Optional[int]   =				hidden_dropout_prob
       a_   :						Dict   =				attention_probs_dropout_prob
       a_   :						str   =				max_position_embeddings
       a_   :						str   =				type_vocab_size
       a_   :						List[Any]   =				initializer_range
       a_   :						int   =				layer_norm_eps
       a_   :						Optional[int]   =				position_embedding_type
       a_   :						Any   =				use_cache
       a_   :						Optional[Any]   =				classifier_dropout







class 			_UpperCAmelCase			(			lowerCAmelCase__     ):

     """simple docstring"""

     @property
     def 				_lowerCAmelCase	(   self			):

       '''simple docstring'''

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


