'''simple docstring'''





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


lowerCAmelCase_	:		Any			=     logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''maskformer-swin'''

				UpperCAmelCase__							=							{
				    '''num_attention_heads''': '''num_heads''',
				    '''num_hidden_layers''': '''num_layers''',
				}





				def __init__(						self					:						Any       ,		lowercase__					:						List[str]=224       ,		lowercase__					:						Dict=4       ,		lowercase__					:						List[Any]=3       ,		lowercase__					:						int=96       ,		lowercase__					:						int=[2, 2, 6, 2]       ,		lowercase__					:						Any=[3, 6, 12, 24]       ,		lowercase__					:						int=7       ,		lowercase__					:						Optional[int]=4.0       ,		lowercase__					:						Tuple=True       ,		lowercase__					:						Tuple=0.0       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						List[str]=0.1       ,		lowercase__					:						Tuple="gelu"       ,		lowercase__					:						Optional[Any]=False       ,		lowercase__					:						Tuple=0.0_2       ,		lowercase__					:						List[Any]=1e-5       ,		lowercase__					:						Optional[Any]=None       ,		lowercase__					:						List[Any]=None       ,		**lowercase__					:						str       ,		)							->List[str]:


										'''simple docstring'''




										super().__init__(**lowercase__					)

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



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


