

from typing import TYPE_CHECKING

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


UpperCAmelCase_							:					Any		 =   {
    '''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:
			UpperCAmelCase_							:					List[str]		 =   [
			    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecAudioForAudioFrameClassification''',
			    '''Data2VecAudioForCTC''',
			    '''Data2VecAudioForSequenceClassification''',
			    '''Data2VecAudioForXVector''',
			    '''Data2VecAudioModel''',
			    '''Data2VecAudioPreTrainedModel''',
			]
			UpperCAmelCase_							:					List[Any]		 =   [
			    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecTextForCausalLM''',
			    '''Data2VecTextForMaskedLM''',
			    '''Data2VecTextForMultipleChoice''',
			    '''Data2VecTextForQuestionAnswering''',
			    '''Data2VecTextForSequenceClassification''',
			    '''Data2VecTextForTokenClassification''',
			    '''Data2VecTextModel''',
			    '''Data2VecTextPreTrainedModel''',
			]
			UpperCAmelCase_							:					List[str]		 =   [
			    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecVisionForImageClassification''',
			    '''Data2VecVisionForMaskedImageModeling''',
			    '''Data2VecVisionForSemanticSegmentation''',
			    '''Data2VecVisionModel''',
			    '''Data2VecVisionPreTrainedModel''',
			]

if is_tf_available():
			UpperCAmelCase_							:					int		 =   [
			    '''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

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








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			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__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			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__       )       ):
					__A :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





