


'''simple docstring'''






def 	_A				(lowerCAmelCase__  :Union[str, Any]		)   ->  str:




     '''simple docstring'''



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

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

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

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

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

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






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Union[str, Any]:




     '''simple docstring'''



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

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

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


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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					:    str          =    {
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[str]          =    [
     "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecAudioForAudioFrameClassification",
     "Data2VecAudioForCTC",
     "Data2VecAudioForSequenceClassification",
     "Data2VecAudioForXVector",
     "Data2VecAudioModel",
     "Data2VecAudioPreTrainedModel",
 ]
 a_					:    str          =    [
     "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecTextForCausalLM",
     "Data2VecTextForMaskedLM",
     "Data2VecTextForMultipleChoice",
     "Data2VecTextForQuestionAnswering",
     "Data2VecTextForSequenceClassification",
     "Data2VecTextForTokenClassification",
     "Data2VecTextModel",
     "Data2VecTextPreTrainedModel",
 ]
 a_					:    Tuple          =    [
     "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Data2VecVisionForImageClassification",
     "Data2VecVisionForMaskedImageModeling",
     "Data2VecVisionForSemanticSegmentation",
     "Data2VecVisionModel",
     "Data2VecVisionPreTrainedModel",
 ]

if is_tf_available():
 a_					:    Dict          =    [
     "TFData2VecVisionForImageClassification",
     "TFData2VecVisionForSemanticSegmentation",
     "TFData2VecVisionModel",
     "TFData2VecVisionPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 a_					:    List[Any]          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

