
"""simple docstring"""






def __A						(   a_ :							str     )->							bool:




		'''simple docstring'''






		if not all(x.isalpha() for x in string     ):
				raise ValueError('''String must only contain alphabetic characters.'''     )

		SCREAMING_SNAKE_CASE       :     Any				= sorted(string.lower()     )
		return len(a_     ) == len(set(a_     )     )


if __name__ == "__main__":
					lowerCamelCase__ :       Optional[int]						     =			input("Enter a string ").strip()

					lowerCamelCase__ :       int						     =			is_isogram(input_str)
					print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')







"""simple docstring"""






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


lowerCamelCase__ :       int						     =			logging.get_logger(__name__)




class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """maskformer-swin"""

	UpperCamelCase				=  {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :List[Any]=2_24		, lowerCamelCase_    :Tuple=4		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :List[str]=96		, lowerCamelCase_    :int=[2, 2, 6, 2]		, lowerCamelCase_    :Union[str, Any]=[3, 6, 12, 24]		, lowerCamelCase_    :Optional[int]=7		, lowerCamelCase_    :Tuple=4.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=0.0		, lowerCamelCase_    :Any=0.0		, lowerCamelCase_    :List[Any]=0.1		, lowerCamelCase_    :Dict="gelu"		, lowerCamelCase_    :Optional[int]=False		, lowerCamelCase_    :List[str]=0.0_2		, lowerCamelCase_    :Any=1E-5		, lowerCamelCase_    :Union[str, Any]=None		, lowerCamelCase_    :List[str]=None		, **lowerCamelCase_    :Union[str, Any]		, )							->			Dict:







			'''simple docstring'''




			super().__init__(**lowerCamelCase_					)

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





