

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str )   ->       str | Literal[False]:
				_UpperCAmelCase       :		Tuple 		=    list(lowerCAmelCase )
				_UpperCAmelCase       :		str 		=    list(lowerCAmelCase )
				_UpperCAmelCase       :		List[Any] 		=    0
				for i in range(len(lowerCAmelCase ) ):
								if lista[i] != lista[i]:
												count += 1
												_UpperCAmelCase       :		Any 		=    "_"
				if count > 1:
								return False
				else:
								return "".join(lowerCAmelCase )

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

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Sequence[float] )   ->       list[str]:
				_UpperCAmelCase       :		Tuple 		=    []
				for minterm in minterms:
								_UpperCAmelCase       :		Dict 		=    ""
								for _ in range(lowerCAmelCase ):
												_UpperCAmelCase       :		Optional[int] 		=    str(minterm % 2 ) + string
												minterm //= 2
								temp.append(lowerCAmelCase )
				return temp

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  str  ,      lowerCAmelCase:  int )   ->       bool:
				_UpperCAmelCase       :		Dict 		=    list(lowerCAmelCase )
				_UpperCAmelCase       :		Dict 		=    list(lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(len(lowerCAmelCase ) ):
								if lista[i] != lista[i]:
												count_n += 1
				return count_n == count

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[list[int]]  ,      lowerCAmelCase:  list[str] )   ->       list[str]:
				_UpperCAmelCase       :		Tuple 		=    []
				_UpperCAmelCase       :		Dict 		=    [0] * len(lowerCAmelCase )
				for i in range(len(chart[0] ) ):
								_UpperCAmelCase       :		Optional[int] 		=    0
								_UpperCAmelCase       :		Optional[int] 		=    -1
								for j in range(len(lowerCAmelCase ) ):
												if chart[j][i] == 1:
																count += 1
																_UpperCAmelCase       :		Dict 		=    j
								if count == 1:
												_UpperCAmelCase       :		Any 		=    1
				for i in range(len(lowerCAmelCase ) ):
								if select[i] == 1:
												for j in range(len(chart[0] ) ):
																if chart[i][j] == 1:
																				for k in range(len(lowerCAmelCase ) ):
																								_UpperCAmelCase       :		Tuple 		=    0
												temp.append(prime_implicants[i] )
				while True:
								_UpperCAmelCase       :		List[Any] 		=    0
								_UpperCAmelCase       :		Any 		=    -1
								_UpperCAmelCase       :		Any 		=    0
								for i in range(len(lowerCAmelCase ) ):
												_UpperCAmelCase       :		Optional[int] 		=    chart[i].count(1 )
												if count_n > max_n:
																_UpperCAmelCase       :		List[str] 		=    count_n
																_UpperCAmelCase       :		List[str] 		=    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(lowerCAmelCase ) ):
																				_UpperCAmelCase       :		List[Any] 		=    0

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[str]  ,      lowerCAmelCase:  list[str] )   ->       list[list[int]]:
				_UpperCAmelCase       :		Optional[int] 		=    [[0 for x in range(len(lowerCAmelCase ) )] for x in range(len(lowerCAmelCase ) )]
				for i in range(len(lowerCAmelCase ) ):
								_UpperCAmelCase       :		Union[str, Any] 		=    prime_implicants[i].count("_" )
								for j in range(len(lowerCAmelCase ) ):
												if is_for_table(prime_implicants[i]  ,      binary[j]  ,      lowerCAmelCase ):
																_UpperCAmelCase       :		Optional[Any] 		=    1

				return chart

def 							__SCREAMING_SNAKE_CASE							(				)   ->       None:
				_UpperCAmelCase       :		Optional[Any] 		=    int(input("Enter the no. of variables\n" ) )
				_UpperCAmelCase       :		Any 		=    [
				    float(lowerCAmelCase )
				    for x in input(
				        "Enter the decimal representation of Minterms 'Spaces Separated'\n" ).split()
				]
				_UpperCAmelCase       :		Union[str, Any] 		=    decimal_to_binary(lowerCAmelCase  ,      lowerCAmelCase )

				_UpperCAmelCase       :		Tuple 		=    check(lowerCAmelCase )
				print("Prime Implicants are:" )
				print(lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    prime_implicant_chart(lowerCAmelCase  ,      lowerCAmelCase )

				_UpperCAmelCase       :		int 		=    selection(lowerCAmelCase  ,      lowerCAmelCase )
				print("Essential Prime Implicants are:" )
				print(lowerCAmelCase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

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


