





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     str | Literal[False]:
  lowercase__			    =						list(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						list(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0
  for i in range(len(_SCREAMING_SNAKE_CASE	)	):
    if lista[i] != lista[i]:
      count += 1
      lowercase__			    =						'_'
  if count > 1:
    return False
  else:
    return "".join(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     list[str]:
  lowercase__			    =						[]
  while True:
    lowercase__			    =						['$'] * len(_SCREAMING_SNAKE_CASE	)
    lowercase__			    =						[]
    for i in range(len(_SCREAMING_SNAKE_CASE	)	):
      for j in range(i + 1	,		len(_SCREAMING_SNAKE_CASE	)	):
        lowercase__			    =						compare_string(binary[i]	,		binary[j]	)
        if k is False:
          lowercase__			    =						'*'
          lowercase__			    =						'*'
          temp.append('X'	)
    for i in range(len(_SCREAMING_SNAKE_CASE	)	):
      if checka[i] == "$":
        pi.append(binary[i]	)
    if len(_SCREAMING_SNAKE_CASE	) == 0:
      return pi
    lowercase__			    =						list(set(_SCREAMING_SNAKE_CASE	)	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     list[str]:
  lowercase__			    =						[]
  for minterm in minterms:
    lowercase__			    =						''
    for _ in range(_SCREAMING_SNAKE_CASE	):
      lowercase__			    =						str(minterm % 2	) + string
      minterm //= 2
    temp.append(_SCREAMING_SNAKE_CASE	)
  return temp




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     bool:
  lowercase__			    =						list(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						list(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0
  for i in range(len(_SCREAMING_SNAKE_CASE	)	):
    if lista[i] != lista[i]:
      count_n += 1
  return count_n == count




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     list[str]:
  lowercase__			    =						[]
  lowercase__			    =						[0] * len(_SCREAMING_SNAKE_CASE	)
  for i in range(len(chart[0]	)	):
    lowercase__			    =						0
    lowercase__			    =						-1
    for j in range(len(_SCREAMING_SNAKE_CASE	)	):
      if chart[j][i] == 1:
        count += 1
        lowercase__			    =						j
    if count == 1:
      lowercase__			    =						1
  for i in range(len(_SCREAMING_SNAKE_CASE	)	):
    if select[i] == 1:
      for j in range(len(chart[0]	)	):
        if chart[i][j] == 1:
          for k in range(len(_SCREAMING_SNAKE_CASE	)	):
            lowercase__			    =						0
      temp.append(prime_implicants[i]	)
  while True:
    lowercase__			    =						0
    lowercase__			    =						-1
    lowercase__			    =						0
    for i in range(len(_SCREAMING_SNAKE_CASE	)	):
      lowercase__			    =						chart[i].count(1	)
      if count_n > max_n:
        lowercase__			    =						count_n
        lowercase__			    =						i

    if max_n == 0:
      return temp

    temp.append(prime_implicants[rem]	)

    for i in range(len(chart[0]	)	):
      if chart[rem][i] == 1:
        for j in range(len(_SCREAMING_SNAKE_CASE	)	):
          lowercase__			    =						0




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     list[list[int]]:
  lowercase__			    =						[[0 for x in range(len(_SCREAMING_SNAKE_CASE	)	)] for x in range(len(_SCREAMING_SNAKE_CASE	)	)]
  for i in range(len(_SCREAMING_SNAKE_CASE	)	):
    lowercase__			    =						prime_implicants[i].count('_'	)
    for j in range(len(_SCREAMING_SNAKE_CASE	)	):
      if is_for_table(prime_implicants[i]	,		binary[j]	,		_SCREAMING_SNAKE_CASE	):
        lowercase__			    =						1

  return chart




def     __UpperCamelCase						()       ->     None:
  lowercase__			    =						int(input('Enter the no. of variables\n'	)	)
  lowercase__			    =						[
      float(_SCREAMING_SNAKE_CASE	)
      for x in input(
          'Enter the decimal representation of Minterms \'Spaces Separated\'\n'	).split()
  ]
  lowercase__			    =						decimal_to_binary(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						check(_SCREAMING_SNAKE_CASE	)
  print('Prime Implicants are:'	)
  print(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						prime_implicant_chart(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						selection(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
  print('Essential Prime Implicants are:'	)
  print(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


