
from typing import TYPE_CHECKING

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


UpperCAmelCase__		:     Optional[int]     			=      {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     List[str]     			=      [
       'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ASTForAudioClassification',
       'ASTModel',
       'ASTPreTrainedModel',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Dict     			=      ['ASTFeatureExtractor']

if TYPE_CHECKING:
   from .configuration_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ASTConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ASTForAudioClassification,
          ASTModel,
          ASTPreTrainedModel,
      )

   try:
      if not is_speech_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
   import sys

   UpperCAmelCase__		:     Dict     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



