





"""simple docstring"""






import torch

from transformers import AutoModel




class 					__lowercase      (      torch.nn.Module				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase="sayef/fsner-bert-base-uncased"  ):
									super(_UpperCAmelCase						,      self  ).__init__()

									__a			:    List[Any]      							=	AutoModel.from_pretrained(_UpperCAmelCase						,      return_dict=_UpperCAmelCase  )
									__a			:    Union[str, Any]      							=	torch.nn.CosineSimilarity(3						,      1e-0_8  )
									__a			:    Dict      							=	torch.nn.Softmax(dim=1  )







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									return self.bert(**_UpperCAmelCase  ).last_hidden_state







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									return token_embeddings.sum(2						,      keepdim=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase=1  ):
									return self.softmax(T * self.cos(_UpperCAmelCase						,      _UpperCAmelCase  )  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a			:    Optional[int]      							=	W_supports['''sizes'''].tolist()
									__a			:    str      							=	W_supports['''start_token_id'''].item()
									__a			:    Optional[int]      							=	W_supports['''end_token_id'''].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									__a			:    Union[str, Any]      							=	self.BERT(**_UpperCAmelCase  )
									__a			:    int      							=	self.BERT(**_UpperCAmelCase  )

									__a			:    Tuple      							=	None
									__a			:    Union[str, Any]      							=	None

									__a			:    Optional[Any]      							=	W_supports['''input_ids'''] == start_token_id
									__a			:    Dict      							=	W_supports['''input_ids'''] == end_token_id

									for i, size in enumerate(_UpperCAmelCase  ):
													if i == 0:
																	__a			:    Optional[int]      							=	0
													else:
																	__a			:    Union[str, Any]      							=	support_sizes[i - 1]

													__a			:    Tuple      							=	S[s : s + size][start_token_masks[s : s + size]]
													__a			:    Union[str, Any]      							=	S[s : s + size][end_token_masks[s : s + size]]

													__a			:    str      							=	torch.matmul(q[i]						,      s_start.T  ).sum(1  ).softmax(0  )
													__a			:    Union[str, Any]      							=	torch.matmul(q[i]						,      s_end.T  ).sum(1  ).softmax(0  )

													if p_starts is not None:
																	__a			:    str      							=	torch.vstack((p_starts, p_start)  )
																	__a			:    Optional[int]      							=	torch.vstack((p_ends, p_end)  )
													else:
																	__a			:    List[Any]      							=	p_start
																	__a			:    int      							=	p_end

									return p_starts, p_ends


"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def   _A					(	_a	:						str     ,   _a	:						str				):



      """simple docstring"""






      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   _A					(	_a	:						list[str]				):



      """simple docstring"""






      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   _A					(	_a	:						int     ,   _a	:						Sequence[float]				):



      """simple docstring"""






      A				  =		[]
      for minterm in minterms:
            A				  =		""""""
            for _ in range(_a				):
                  A				  =		str(minterm % 2				) + string
                  minterm //= 2
            temp.append(_a				)
      return temp



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						int				):



      """simple docstring"""






      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   _A					(	_a	:						list[list[int]]     ,   _a	:						list[str]				):



      """simple docstring"""






      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   _A					(	_a	:						list[str]     ,   _a	:						list[str]				):



      """simple docstring"""






      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   _A					(	):



      """simple docstring"""






      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()




