'''simple docstring'''

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

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

   for x in infix:
      if x.isalpha() or x.isdigit():
         post_fix.append(__snake_case)  # if x is Alphabet / Digit, add it to Postfix
      elif x == "(":
         stack.append(__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(__snake_case) == 0:
            stack.append(__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(__snake_case) > 0 and priority[x] <= priority[stack[-1]]:
               post_fix.append(stack.pop())  # pop stack & add to Postfix
            stack.append(__snake_case)  # push x to stack

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

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

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



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   lowerCAmelCase_			=      list(infix[::-1])  # reverse the infix equation

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

   return (infix_2_postfix(''''''.join(__snake_case)))[
       ::-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'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['''bs''']    ,     model_result['''ss'''] ):
           lowerCAmelCase_			=      model_result['''result'''][batch_size][sequence_length]
           self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sgugger/tiny-distilbert-classification'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     only_pretrain_model=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     torchscript=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Cant do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     fpaa=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     # set architectures equal to `None`
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Can\'t do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     fpaa=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     save_to_csv=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     inference_time_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' )    ,     train_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' )    ,     inference_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' )    ,     train_time_csv_file=os.path.join(_lowerCamelCase    ,     '''train_time.csv''' )    ,     env_info_csv_file=os.path.join(_lowerCamelCase    ,     '''env.csv''' )    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''env.csv''' ) ).exists() )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase ):
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''sequential''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''cumulative''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''current''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''total''' ) )

     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     log_filename=os.path.join(_lowerCamelCase    ,     '''log.txt''' )    ,     log_print=_lowerCamelCase    ,     trace_memory_line_by_line=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        lowerCAmelCase_			=      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        _check_summary_is_not_empty(result.train_summary )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''log.txt''' ) ).exists() )





