
"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str     ):
 A						   =   list(__A     )
 A						   =   list(__A     )
 A						   =   0
 for i in range(len(__A     )     ):
  if lista[i] != lista[i]:
   count += 1
   A						   =   '''_'''
 if count > 1:
  return False
 else:
  return "".join(__A     )






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






def       _snake_case				(		snake_case__   :     int	,      snake_case__   :     Sequence[float]     ):
 A						   =   []
 for minterm in minterms:
  A						   =   ''''''
  for _ in range(__A     ):
   A						   =   str(minterm % 2     ) + string
   minterm //= 2
  temp.append(__A     )
 return temp






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str	,      snake_case__   :     int     ):
 A						   =   list(__A     )
 A						   =   list(__A     )
 A						   =   0
 for i in range(len(__A     )     ):
  if lista[i] != lista[i]:
   count_n += 1
 return count_n == count






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






def       _snake_case				(		snake_case__   :     list[str]	,      snake_case__   :     list[str]     ):
 A						   =   [[0 for x in range(len(__A     )     )] for x in range(len(__A     )     )]
 for i in range(len(__A     )     ):
  A						   =   prime_implicants[i].count('_'     )
  for j in range(len(__A     )     ):
   if is_for_table(prime_implicants[i]	,      binary[j]	,      __A     ):
    A						   =   1

 return chart






def       _snake_case				(		):
 A						   =   int(input('Enter the no. of variables\n'     )     )
 A						   =   [
     float(__A     )
     for x in input(
         'Enter the decimal representation of Minterms \'Spaces Separated\'\n'     ).split()
 ]
 A						   =   decimal_to_binary(__A	,      __A     )

 A						   =   check(__A     )
 print('Prime Implicants are:'     )
 print(__A     )
 A						   =   prime_implicant_chart(__A	,      __A     )

 A						   =   selection(__A	,      __A     )
 print('Essential Prime Implicants are:'     )
 print(__A     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)