




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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_		:					Optional[Any]          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Union[str, Any]          =  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       """swin"""

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

						def __init__(		self :List[Any]     ,			__snake_case :Optional[int]=2_24     ,			__snake_case :str=4     ,			__snake_case :Tuple=3     ,			__snake_case :Any=96     ,			__snake_case :Any=[2, 2, 6, 2]     ,			__snake_case :Union[str, Any]=[3, 6, 12, 24]     ,			__snake_case :Optional[int]=7     ,			__snake_case :str=4.0     ,			__snake_case :Optional[Any]=True     ,			__snake_case :Any=0.0     ,			__snake_case :int=0.0     ,			__snake_case :int=0.1     ,			__snake_case :Optional[int]="gelu"     ,			__snake_case :Any=False     ,			__snake_case :Union[str, Any]=0.02     ,			__snake_case :Dict=1E-5     ,			__snake_case :Dict=32     ,			__snake_case :Any=None     ,			__snake_case :List[str]=None     ,			**__snake_case :Union[str, Any]     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

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




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]			)


						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1E-4










from __future__ import annotations

from fractions import Fraction




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						List[Any]										=11
				__magic_name__						:						Tuple										=int("""1""" + """0""" * digit_len )
				for num in range(lowerCamelCase						,  lowerCamelCase ):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(lowerCamelCase						,  lowerCamelCase ):
																				solutions.append(F"{num}/{den}" )
												den += 1
								num += 1
								__magic_name__						:						List[str]										=10
				return solutions




def 		lowerCAmelCase_ (						lowerCamelCase = 2 ):
				__magic_name__						:						str										=1.0
				for fraction in fraction_list(lowerCamelCase ):
								__magic_name__						:						int										=Fraction(lowerCamelCase )
								result *= frac.denominator / frac.numerator
				return int(lowerCamelCase )


if __name__ == "__main__":
							print(solution())




