



"""simple docstring"""


def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = []
						UpperCAmelCase             = []
						UpperCAmelCase             = {
						    '^': 3,
						    '*': 2,
						    '/': 2,
						    '%': 2,
						    '+': 1,
						    '-': 1,
						}  # Priority of each operator
						UpperCAmelCase             = len(lowercase_						) if (len(lowercase_						) > 7) else 7

						# Print table header for output
						print(
						    'Symbol'.center(8						)		,	'Stack'.center(lowercase_						)		,	'Postfix'.center(lowercase_						)		,	sep=' | '		,	)
						print('-' * (print_width * 3 + 7)						)

						for x in infix:
												if x.isalpha() or x.isdigit():
																		post_fix.append(lowercase_						)  # if x is Alphabet / Digit, add it to Postfix
												elif x == "(":
																		stack.append(lowercase_						)  # if x is "(" push to Stack
												elif x == ")":  # if x is ")" pop stack until "(" is encountered
																		while stack[-1] != "(":
																								post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
																		stack.pop()
												else:
																		if len(lowercase_						) == 0:
																								stack.append(lowercase_						)  # If stack is empty, push x to stack
																		else:  # while priority of x is not > priority of element in the stack
																								while len(lowercase_						) > 0 and priority[x] <= priority[stack[-1]]:
																														post_fix.append(stack.pop()						)  # pop stack & add to Postfix
																								stack.append(lowercase_						)  # push x to stack

												print(
												    x.center(8						)		,	(''.join(lowercase_						)).ljust(lowercase_						)		,	(''.join(lowercase_						)).ljust(lowercase_						)		,	sep=' | '		,	)  # Output in tabular format

						while len(lowercase_						) > 0:  # while stack is not empty
												post_fix.append(stack.pop()						)  # pop stack & add to Postfix
												print(
												    ' '.center(8						)		,	(''.join(lowercase_						)).ljust(lowercase_						)		,	(''.join(lowercase_						)).ljust(lowercase_						)		,	sep=' | '		,	)  # Output in tabular format

						return "".join(lowercase_						)  # return Postfix as str

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = list(infix[::-1]						)  # reverse the infix equation

						for i in range(len(lowercase_						)						):
												if infix[i] == "(":
																		UpperCAmelCase             = ')'  # change "(" to ")"
												elif infix[i] == ")":
																		UpperCAmelCase             = '('  # change ")" to "("

						return (infix_2_postfix(''.join(lowercase_						)						))[
						    ::-1
						]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
				snake_case_				     =       input("""\nEnter an Infix Equation = """)  # Input an Infix equation
				snake_case_				     =       """""".join(Infix.split())  # Remove spaces from the input
				print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")












"""simple docstring"""



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


a_ =			logging.get_logger(__name__)

a_ =			{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """nat"""

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

 def __init__(    self					,						__lowerCamelCase=4					,						__lowerCamelCase=3					,						__lowerCamelCase=64					,						__lowerCamelCase=[3, 4, 6, 5]					,						__lowerCamelCase=[2, 4, 8, 16]					,						__lowerCamelCase=7					,						__lowerCamelCase=3.0					,						__lowerCamelCase=True					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.1					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-5					,						__lowerCamelCase=0.0					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

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




