

from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	list(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	list(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Any  		=	0
						for i in range(len(_UpperCAmelCase   )   ):
												if lista[i] != lista[i]:
																		count += 1
																		SCREAMING_SNAKE_CASE_:			List[Any]  		=	"_"
						if count > 1:
												return False
						else:
												return "".join(_UpperCAmelCase   )







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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	[]
						for minterm in minterms:
												SCREAMING_SNAKE_CASE_:			Any  		=	""
												for _ in range(_UpperCAmelCase   ):
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	str(minterm % 2   ) + string
																		minterm //= 2
												temp.append(_UpperCAmelCase   )
						return temp







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	list(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	list(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						for i in range(len(_UpperCAmelCase   )   ):
												if lista[i] != lista[i]:
																		count_n += 1
						return count_n == count







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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	[[0 for x in range(len(_UpperCAmelCase   )   )] for x in range(len(_UpperCAmelCase   )   )]
						for i in range(len(_UpperCAmelCase   )   ):
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	prime_implicants[i].count("_"   )
												for j in range(len(_UpperCAmelCase   )   ):
																		if is_for_table(prime_implicants[i]    ,     binary[j]    ,     _UpperCAmelCase   ):
																								SCREAMING_SNAKE_CASE_:			Tuple  		=	1

						return chart







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	int(input("Enter the no. of variables\n"   )   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[
						    float(_UpperCAmelCase   )
						    for x in input(
						        "Enter the decimal representation of Minterms 'Spaces Separated'\n"   ).split()
						]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	decimal_to_binary(_UpperCAmelCase    ,     _UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Any  		=	check(_UpperCAmelCase   )
						print("Prime Implicants are:"   )
						print(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	prime_implicant_chart(_UpperCAmelCase    ,     _UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	selection(_UpperCAmelCase    ,     _UpperCAmelCase   )
						print("Essential Prime Implicants are:"   )
						print(_UpperCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''fnet'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]=3_2000       ,						lowerCAmelCase__  :					Union[str, Any]=768       ,						lowerCAmelCase__  :					Optional[Any]=12       ,						lowerCAmelCase__  :					Tuple=3072       ,						lowerCAmelCase__  :					Any="gelu_new"       ,						lowerCAmelCase__  :					Union[str, Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=1E-12       ,						lowerCAmelCase__  :					Optional[int]=False       ,						lowerCAmelCase__  :					Any=512       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					List[Any]=2       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_tpu_fourier_optimizations
									SCREAMING_SNAKE_CASE_:			int  		=	tpu_short_seq_length






