
import torch

from transformers import AutoModel
class 	A__   (						torch.nn.Module				):





							def __init__( self  :   Optional[int]     , _UpperCAmelCase  :   int="sayef/fsner-bert-base-uncased"				)						->		List[Any]:



											"""simple docstring"""


											super(lowercase_     , self				).__init__()

											__lowercase								=					AutoModel.from_pretrained(lowercase_     , return_dict=lowercase_				)
											__lowercase								=					torch.nn.CosineSimilarity(3     , 1e-0_8				)
											__lowercase								=					torch.nn.Softmax(dim=1				)





							def 			a__ ( self  :   int     , **_UpperCAmelCase  :   int				)						->		List[Any]:



											"""simple docstring"""


											return self.bert(**lowercase_				).last_hidden_state





							def 			a__ ( self  :   List[Any]     , _UpperCAmelCase  :   Any				)						->		str:



											"""simple docstring"""


											return token_embeddings.sum(2     , keepdim=lowercase_				)





							def 			a__ ( self  :   int     , _UpperCAmelCase  :   Optional[int]     , _UpperCAmelCase  :   Tuple     , _UpperCAmelCase  :   List[str]=1				)						->		Optional[Any]:



											"""simple docstring"""


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





							def 			a__ ( self  :   Any     , _UpperCAmelCase  :   Union[str, Any]     , _UpperCAmelCase  :   List[Any]				)						->		int:



											"""simple docstring"""


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

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

											__lowercase								=					self.BERT(**lowercase_				)
											__lowercase								=					self.BERT(**lowercase_				)

											__lowercase								=					None
											__lowercase								=					None

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

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

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

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

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

											return p_starts, p_ends








from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif electron_conc < 0:
    raise ValueError('''Electron concentration cannot be negative in a semiconductor'''							)
  elif hole_conc < 0:
    raise ValueError('''Hole concentration cannot be negative in a semiconductor'''							)
  elif intrinsic_conc < 0:
    raise ValueError(
        '''Intrinsic concentration cannot be negative in a semiconductor'''							)
  elif electron_conc == 0:
    return (
        "electron_conc",
        intrinsic_conc**2 / hole_conc,
    )
  elif hole_conc == 0:
    return (
        "hole_conc",
        intrinsic_conc**2 / electron_conc,
    )
  elif intrinsic_conc == 0:
    return (
        "intrinsic_conc",
        (electron_conc * hole_conc) ** 0.5,
    )
  else:
    return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
