
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()






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Dict						=    {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Union[str, Any]      =       num_hidden_layers
          __A						: List[str]      =       num_attention_heads
          __A						: Optional[int]      =       hidden_act
          __A						: str      =       intermediate_size
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Dict      =       attention_probs_dropout_prob
          __A						: int      =       max_position_embeddings
          __A						: str      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: int      =       layer_norm_eps
          __A						: Optional[int]      =       position_embedding_type
          __A						: int      =       use_cache
          __A						: Union[str, Any]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: str      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: List[str]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])