




'''simple docstring'''




import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


a_      :		str							      =       logging.getLogger()


@unittest.skip('Temporarily disable the doc tests.'				)
@require_torch
@require_tf
@slow
class   __UpperCamelCase       ( unittest.TestCase				):






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = True,		):



              """simple docstring"""







              lowerCamelCase_							=[file for file in os.listdir(lowerCAmelCase  ) if os.path.isfile(os.path.join(lowerCAmelCase,		lowerCAmelCase  )  )]

              if identifier is not None:
                     lowerCamelCase_							=[file for file in files if identifier in file]

              if n_identifier is not None:
                     if isinstance(lowerCAmelCase,		lowerCAmelCase  ):
                            for n_ in n_identifier:
                                   lowerCamelCase_							=[file for file in files if n_ not in file]
                     else:
                            lowerCamelCase_							=[file for file in files if n_identifier not in file]

              lowerCamelCase_							=ignore_files or []
              ignore_files.append('''__init__.py'''  )
              lowerCamelCase_							=[file for file in files if file not in ignore_files]

              for file in files:
                     # Open all files
                     print('''Testing''',		lowerCAmelCase  )

                     if only_modules:
                            lowerCamelCase_							=file.split('''.'''  )[0]
                            try:
                                   lowerCamelCase_							=getattr(lowerCAmelCase,		lowerCAmelCase  )
                                   lowerCamelCase_							=doctest.DocTestSuite(lowerCAmelCase  )
                                   lowerCamelCase_							=unittest.TextTestRunner().run(lowerCAmelCase  )
                                   self.assertIs(len(result.failures  ),		0  )
                            except AttributeError:
                                   logger.info(f'''{module_identifier} is not a module.'''  )
                     else:
                            lowerCamelCase_							=doctest.testfile(str('''..''' / directory / file  ),		optionflags=doctest.ELLIPSIS  )
                            self.assertIs(result.failed,		0  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Path('''src/transformers'''  )
              lowerCamelCase_							='''modeling'''
              lowerCamelCase_							=[
                  '''modeling_ctrl.py''',
                  '''modeling_tf_ctrl.py''',
              ]
              self.analyze_directory(lowerCAmelCase,		identifier=lowerCAmelCase,		ignore_files=lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Path('''src/transformers'''  )
              lowerCamelCase_							='''tokenization'''
              self.analyze_directory(lowerCAmelCase,		identifier=lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Path('''src/transformers'''  )
              lowerCamelCase_							='''configuration'''
              self.analyze_directory(lowerCAmelCase,		identifier=lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Path('''src/transformers'''  )
              lowerCamelCase_							=['''configuration''', '''modeling''', '''tokenization''']
              self.analyze_directory(lowerCAmelCase,		n_identifier=lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=Path('''docs/source'''  )
              lowerCamelCase_							=['''favicon.ico''']
              self.analyze_directory(lowerCAmelCase,		ignore_files=lowerCAmelCase,		only_modules=lowerCAmelCase  )








'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




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

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  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			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

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

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

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

											__a			:       int					    =  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			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  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			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



