






"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__				):
    try:
        A__							 =       float(__snake_case				)
    except ValueError:
        raise ValueError('Please enter a valid number'				)
    A__							 =       decimal - int(__snake_case				)
    if fractional_part == 0:
        return int(__snake_case				), 1
    else:
        A__							 =       len(str(__snake_case				).split('.'				)[1]				)
        A__							 =       int(decimal * (10**number_of_frac_digits)				)
        A__							 =       10**number_of_frac_digits
        A__ ,			A__							 =       denominator, numerator
        while True:
            A__							 =       dividend % divisor
            if remainder == 0:
                break
            A__ ,			A__							 =       divisor, remainder
        A__ ,			A__							 =       numerator / divisor, denominator / divisor
        return int(__snake_case				), int(__snake_case				)


if __name__ == "__main__":
     print(f'''{decimal_to_fraction(2) = }''')
     print(f'''{decimal_to_fraction(8_9.0) = }''')
     print(f'''{decimal_to_fraction("67") = }''')
     print(f'''{decimal_to_fraction("45.0") = }''')
     print(f'''{decimal_to_fraction(1.5) = }''')
     print(f'''{decimal_to_fraction("6.25") = }''')
     print(f'''{decimal_to_fraction("78td") = }''')










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

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


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

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

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


