def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	[]
 __a							=	[]
 __a							=	{
     "^": 3,
     "*": 2,
     "/": 2,
     "%": 2,
     "+": 1,
     "-": 1,
 }  # Priority of each operator
 __a							=	len(_A       ) if (len(_A       ) > 7) else 7

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

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

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

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

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



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	list(infix[::-1]       )  # reverse the infix equation

 for i in range(len(_A       )       ):
  if infix[i] == "(":
   __a							=	")"  # change "(" to ")"
  elif infix[i] == ")":
   __a							=	"("  # change ")" to "("

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


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



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


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

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """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  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

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

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	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(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


