


def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

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

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

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

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

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

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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

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

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


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



from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }



 def __init__(			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "student_t"	,   _UpperCAmelCase = "nll"	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The cardinality should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

       # Transformer architecture configuration
       snake_case_ =   input_size * len(self.lags_sequence      ) + self._number_of_features
       snake_case_ =   d_model
       snake_case_ =   encoder_attention_heads
       snake_case_ =   decoder_attention_heads
       snake_case_ =   encoder_ffn_dim
       snake_case_ =   decoder_ffn_dim
       snake_case_ =   encoder_layers
       snake_case_ =   decoder_layers

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

       super().__init__(is_encoder_decoder=_UpperCAmelCase	,   **_UpperCAmelCase      )





 @property
 def 		UpperCamelCase__  (			self      ):
       return (
           sum(self.embedding_dimension      )
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )