def A__  (			lowercase:     Optional[int]					)				->		Dict:
       A						:     Tuple         =[]
       A						:     Any         =[]
       A						:     Optional[int]         ={
           '^': 3,
           '*': 2,
           '/': 2,
           '%': 2,
           '+': 1,
           '-': 1,
       }  # Priority of each operator
       A						:     Any         =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 A__  (			lowercase:     Any					)				->		Any:
       A						:     int         =list(infix[::-1]					)  # reverse the infix equation

       for i in range(len(lowercase					)					):
              if infix[i] == "(":
                     A						:     Tuple         =')'  # change "(" to ")"
              elif infix[i] == ")":
                     A						:     Dict         ='('  # change ")" to "("

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


if __name__ == "__main__":
    _lowercase							:      Tuple      							=input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
    _lowercase							:      List[Any]      							=''''''.join(Infix.split())  # Remove spaces from the input
    print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[str]      							={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     int            =				"deberta-v2"







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					Dict=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =hidden_size
         A						:     Optional[Any]         =num_hidden_layers
         A						:     Optional[int]         =num_attention_heads
         A						:     Optional[int]         =intermediate_size
         A						:     Any         =hidden_act
         A						:     Any         =hidden_dropout_prob
         A						:     Union[str, Any]         =attention_probs_dropout_prob
         A						:     Optional[Any]         =max_position_embeddings
         A						:     Tuple         =type_vocab_size
         A						:     Tuple         =initializer_range
         A						:     int         =relative_attention
         A						:     int         =max_relative_positions
         A						:     Optional[Any]         =pad_token_id
         A						:     Union[str, Any]         =position_biased_input

         # Backwards compatibility
         if type(SCREAMING_SNAKE_CASE__     ) == str:
                A						:     Any         =[x.strip() for x in pos_att_type.lower().split('|'     )]

         A						:     Any         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Any         =layer_norm_eps

         A						:     Optional[Any]         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     str         =pooler_dropout
         A						:     Any         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
                A						:     List[Any]         ={0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                A						:     int         ={0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
                return OrderedDict(
                    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
         else:
                return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional["TensorType"] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					"PreTrainedTokenizerBase" = None      ,	)    ->				Mapping[str, Any]:
         A						:     str         =super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__      ,	framework=SCREAMING_SNAKE_CASE__     )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                del dummy_inputs["token_type_ids"]
         return dummy_inputs






