code: 
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)
  
  UpperCamelCase__				       =			{
      'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
  }
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  '''swiftformer'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       str=3	,      UpperCamelCase       :       Optional[Any]=[3, 3, 6, 4]	,      UpperCamelCase       :       str=[48, 56, 1_12, 2_20]	,      UpperCamelCase       :       Optional[int]=4	,      UpperCamelCase       :       Any=[True, True, True, True]	,      UpperCamelCase       :       Any="gelu"	,      UpperCamelCase       :       List[str]=3	,      UpperCamelCase       :       Union[str, Any]=2	,      UpperCamelCase       :       List[str]=1	,      UpperCamelCase       :       Dict=0.0	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       Union[str, Any]=1E-5	,      UpperCamelCase       :       Optional[int]=1E-5	,      **UpperCamelCase       :       List[str]	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__(**UpperCamelCase		)
              _lowercase					:				List[str]           =			num_channels
              _lowercase					:				Optional[Any]           =			depths
              _lowercase					:				Optional[Any]           =			embed_dims
              _lowercase					:				Tuple           =			mlp_ratio
              _lowercase					:				List[str]           =			downsamples
              _lowercase					:				List[str]           =			hidden_act
              _lowercase					:				Optional[int]           =			down_patch_size
              _lowercase					:				List[str]           =			down_stride
              _lowercase					:				str           =			down_pad
              _lowercase					:				List[Any]           =			drop_path_rate
              _lowercase					:				Any           =			use_layer_scale
              _lowercase					:				Optional[Any]           =			layer_scale_init_value
              _lowercase					:				Dict           =			batch_norm_eps
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  version.parse('''1.11'''		)
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]		)
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return 1E-4
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_wav2vec2_conformer': [
          'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'Wav2Vec2ConformerConfig',
      ],
  }
  
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Wav2Vec2ConformerForAudioFrameClassification',
         'Wav2Vec2ConformerForCTC',
         'Wav2Vec2ConformerForPreTraining',
         'Wav2Vec2ConformerForSequenceClassification',
         'Wav2Vec2ConformerForXVector',
         'Wav2Vec2ConformerModel',
         'Wav2Vec2ConformerPreTrainedModel',
     ]
  
  if TYPE_CHECKING:
     from .configuration_wavaveca_conformer import (
         WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WavaVecaConformerConfig,
     )
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)