

'''simple docstring'''

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


lowercase__			    =			logging.get_logger(__name__)




class        A_ (				_snake_case       ,   _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:	List[Any]  		=  "maskformer-swin"

     UpperCAmelCase_						:	Any  		=  {
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }


     def __init__(		self :     str     ,			lowercase_ :     List[str]=224     ,			lowercase_ :     List[str]=4     ,			lowercase_ :     List[str]=3     ,			lowercase_ :     Tuple=96     ,			lowercase_ :     Optional[Any]=[2, 2, 6, 2]     ,			lowercase_ :     Optional[int]=[3, 6, 12, 24]     ,			lowercase_ :     Union[str, Any]=7     ,			lowercase_ :     str=4.0     ,			lowercase_ :     Optional[int]=True     ,			lowercase_ :     List[str]=0.0     ,			lowercase_ :     Optional[int]=0.0     ,			lowercase_ :     Any=0.1     ,			lowercase_ :     int="gelu"     ,			lowercase_ :     Union[str, Any]=False     ,			lowercase_ :     str=0.02     ,			lowercase_ :     List[Any]=1E-5     ,			lowercase_ :     Union[str, Any]=None     ,			lowercase_ :     List[str]=None     ,			**lowercase_ :     Union[str, Any]     ,			)						->	int:
      super().__init__(**lowercase_       )

      UpperCAmelCase       :	Union[str, Any]											= image_size
      UpperCAmelCase       :	Any											= patch_size
      UpperCAmelCase       :	Dict											= num_channels
      UpperCAmelCase       :	Optional[int]											= embed_dim
      UpperCAmelCase       :	Optional[Any]											= depths
      UpperCAmelCase       :	Optional[Any]											= len(lowercase_       )
      UpperCAmelCase       :	Tuple											= num_heads
      UpperCAmelCase       :	Tuple											= window_size
      UpperCAmelCase       :	List[str]											= mlp_ratio
      UpperCAmelCase       :	Dict											= qkv_bias
      UpperCAmelCase       :	int											= hidden_dropout_prob
      UpperCAmelCase       :	Optional[int]											= attention_probs_dropout_prob
      UpperCAmelCase       :	List[Any]											= drop_path_rate
      UpperCAmelCase       :	List[str]											= hidden_act
      UpperCAmelCase       :	Dict											= use_absolute_embeddings
      UpperCAmelCase       :	Tuple											= layer_norm_eps
      UpperCAmelCase       :	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
      UpperCAmelCase       :	Union[str, Any]											= int(embed_dim * 2 ** (len(lowercase_       ) - 1)       )
      UpperCAmelCase       :	List[str]											= ['stem'] + [f"""stage{idx}""" for idx in range(1     ,			len(lowercase_       ) + 1       )]
      UpperCAmelCase					, UpperCAmelCase       :	Union[str, Any]											= get_aligned_output_features_output_indices(
          out_features=lowercase_     ,			out_indices=lowercase_     ,			stage_names=self.stage_names       )






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_lowerCamelCase       :				Dict       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  *lowercase	:    Optional[int]			,  **lowercase	:    Any	):


				'''simple docstring'''


				warnings.warn(
				    'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use ChineseCLIPImageProcessor instead.'			,  lowercase			,  )
				super().__init__(*lowercase			,  **lowercase	)