



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


_lowerCAmelCase    :Dict     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[Any]     =					{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''ctrl'''
     a__									=['''past_key_values''']
     a__									={
         '''max_position_embeddings''': '''n_positions''',
         '''hidden_size''': '''n_embd''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }





     def __init__(					self       ,	A=2_4_6_5_3_4       ,	A=2_5_6       ,	A=1_2_8_0       ,	A=8_1_9_2       ,	A=4_8       ,	A=1_6       ,	A=0.1       ,	A=0.1       ,	A=1E-6       ,	A=0.02       ,	A=True       ,	**A       ,	)							->					str:
         _UpperCAmelCase							:  List[str]											=    vocab_size
         _UpperCAmelCase							:  Dict											=    n_positions
         _UpperCAmelCase							:  Dict											=    n_embd
         _UpperCAmelCase							:  List[Any]											=    n_layer
         _UpperCAmelCase							:  Dict											=    n_head
         _UpperCAmelCase							:  List[str]											=    dff
         _UpperCAmelCase							:  List[str]											=    resid_pdrop
         _UpperCAmelCase							:  int											=    embd_pdrop
         _UpperCAmelCase							:  Dict											=    layer_norm_epsilon
         _UpperCAmelCase							:  Union[str, Any]											=    initializer_range

         _UpperCAmelCase							:  Optional[Any]											=    use_cache

         super().__init__(**A		)







"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :str     =					{
    '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    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      '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    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


