
from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def     __lowerCAmelCase			(			a__						,    a__  )       ->  str | Literal[False]:
				__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     __lowerCAmelCase			(			a__  )       ->  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     __lowerCAmelCase			(			a__						,    a__  )       ->  list[str]:
				__a										=    []
				for minterm in minterms:
								__a										=    ''''''
								for _ in range(a__  ):
												__a										=    str(minterm % 2  ) + string
												minterm //= 2
								temp.append(a__  )
				return temp


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  bool:
				__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     __lowerCAmelCase			(			a__						,    a__  )       ->  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     __lowerCAmelCase			(			a__						,    a__  )       ->  list[list[int]]:
				__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     __lowerCAmelCase			(			)       ->  None:
				__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()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	str    = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = ['BlenderbotTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BlenderbotForCausalLM',
					    'BlenderbotForConditionalGeneration',
					    'BlenderbotModel',
					    'BlenderbotPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'TFBlenderbotForConditionalGeneration',
					    'TFBlenderbotModel',
					    'TFBlenderbotPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'FlaxBlenderbotForConditionalGeneration',
					    'FlaxBlenderbotModel',
					    'FlaxBlenderbotPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)