



"""simple docstring"""

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     int				=	list(_a    )
    snake_case_ :     Union[str, Any]				=	list(_a    )
    snake_case_ :     Dict				=	0
    for i in range(len(_a    )    ):
        if lista[i] != lista[i]:
            count += 1
            snake_case_ :     Any				=	'''_'''
    if count > 1:
        return False
    else:
        return "".join(_a    )




def __lowercase			(							_a    ):
    snake_case_ :     Optional[int]				=	[]
    while True:
        snake_case_ :     Optional[int]				=	['''$'''] * len(_a    )
        snake_case_ :     Any				=	[]
        for i in range(len(_a    )    ):
            for j in range(i + 1		,			len(_a    )    ):
                snake_case_ :     Optional[Any]				=	compare_string(binary[i]		,			binary[j]    )
                if k is False:
                    snake_case_ :     Union[str, Any]				=	'''*'''
                    snake_case_ :     Dict				=	'''*'''
                    temp.append('''X'''    )
        for i in range(len(_a    )    ):
            if checka[i] == "$":
                pi.append(binary[i]    )
        if len(_a    ) == 0:
            return pi
        snake_case_ :     str				=	list(set(_a    )    )




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     Dict				=	[]
    for minterm in minterms:
        snake_case_ :     Optional[int]				=	''''''
        for _ in range(_a    ):
            snake_case_ :     str				=	str(minterm % 2    ) + string
            minterm //= 2
        temp.append(_a    )
    return temp




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     List[str]				=	list(_a    )
    snake_case_ :     Any				=	list(_a    )
    snake_case_ :     int				=	0
    for i in range(len(_a    )    ):
        if lista[i] != lista[i]:
            count_n += 1
    return count_n == count




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     str				=	[]
    snake_case_ :     int				=	[0] * len(_a    )
    for i in range(len(chart[0]    )    ):
        snake_case_ :     int				=	0
        snake_case_ :     Optional[Any]				=	-1
        for j in range(len(_a    )    ):
            if chart[j][i] == 1:
                count += 1
                snake_case_ :     Dict				=	j
        if count == 1:
            snake_case_ :     int				=	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    )    ):
                        snake_case_ :     List[str]				=	0
            temp.append(prime_implicants[i]    )
    while True:
        snake_case_ :     str				=	0
        snake_case_ :     Tuple				=	-1
        snake_case_ :     Optional[int]				=	0
        for i in range(len(_a    )    ):
            snake_case_ :     Optional[Any]				=	chart[i].count(1    )
            if count_n > max_n:
                snake_case_ :     Union[str, Any]				=	count_n
                snake_case_ :     Tuple				=	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    )    ):
                    snake_case_ :     Tuple				=	0




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	[[0 for x in range(len(_a    )    )] for x in range(len(_a    )    )]
    for i in range(len(_a    )    ):
        snake_case_ :     Tuple				=	prime_implicants[i].count('''_'''    )
        for j in range(len(_a    )    ):
            if is_for_table(prime_implicants[i]		,			binary[j]		,			_a    ):
                snake_case_ :     Optional[Any]				=	1

    return chart




def __lowercase			(							):
    snake_case_ :     int				=	int(input('''Enter the no. of variables\n'''    )    )
    snake_case_ :     Dict				=	[
        float(_a    )
        for x in input(
            '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''    ).split()
    ]
    snake_case_ :     List[Any]				=	decimal_to_binary(_a		,			_a    )

    snake_case_ :     int				=	check(_a    )
    print('''Prime Implicants are:'''    )
    print(_a    )
    snake_case_ :     Optional[int]				=	prime_implicant_chart(_a		,			_a    )

    snake_case_ :     List[str]				=	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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__    :  List[Any]							    =	{
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Any							    =	['''DistilBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  int							    =	[
         '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DistilBertForMaskedLM''',
         '''DistilBertForMultipleChoice''',
         '''DistilBertForQuestionAnswering''',
         '''DistilBertForSequenceClassification''',
         '''DistilBertForTokenClassification''',
         '''DistilBertModel''',
         '''DistilBertPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Dict							    =	[
         '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDistilBertForMaskedLM''',
         '''TFDistilBertForMultipleChoice''',
         '''TFDistilBertForQuestionAnswering''',
         '''TFDistilBertForSequenceClassification''',
         '''TFDistilBertForTokenClassification''',
         '''TFDistilBertMainLayer''',
         '''TFDistilBertModel''',
         '''TFDistilBertPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__    :  Tuple							    =	[
         '''FlaxDistilBertForMaskedLM''',
         '''FlaxDistilBertForMultipleChoice''',
         '''FlaxDistilBertForQuestionAnswering''',
         '''FlaxDistilBertForSequenceClassification''',
         '''FlaxDistilBertForTokenClassification''',
         '''FlaxDistilBertModel''',
         '''FlaxDistilBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

     lowercase__    :  Union[str, Any]							    =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





