
"""simple docstring"""


import torch

from transformers import AutoModel




class  __lowerCamelCase  ( torch.nn.Module			):




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

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




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return self.bert(**UpperCAmelCase   ).last_hidden_state




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return token_embeddings.sum(2							,						keepdim=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase=1   ):
							return self.softmax(T * self.cos(UpperCAmelCase							,						UpperCAmelCase   )   )





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  W_supports['''sizes'''].tolist()
							lowerCamelCase_					=  W_supports['''start_token_id'''].item()
							lowerCamelCase_					=  W_supports['''end_token_id'''].item()

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

							lowerCamelCase_					=  self.BERT(**UpperCAmelCase   )
							lowerCamelCase_					=  self.BERT(**UpperCAmelCase   )

							lowerCamelCase_					=  None
							lowerCamelCase_					=  None

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

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

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

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

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

							return p_starts, p_ends










'''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


__UpperCamelCase					    =	logging.getLogger()




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

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Path  ,      __magic_name__					:   Union[str, None] = None  ,      __magic_name__					:   Union[List[str], None] = None  ,      __magic_name__					:   Union[str, List[str], None] = None  ,      __magic_name__					:   bool = True  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [file for file in os.listdir(__magic_name__			) if os.path.isfile(os.path.join(__magic_name__  ,      __magic_name__			)			)]

					if identifier is not None:
								__snake_case					:      List[Any]							      =     [file for file in files if identifier in file]

					if n_identifier is not None:
								if isinstance(__magic_name__  ,      __magic_name__			):
											for n_ in n_identifier:
														__snake_case					:      Optional[int]							      =     [file for file in files if n_ not in file]
								else:
											__snake_case					:      Tuple							      =     [file for file in files if n_identifier not in file]

					__snake_case					:      Dict							      =     ignore_files or []
					ignore_files.append("""__init__.py"""			)
					__snake_case					:      List[str]							      =     [file for file in files if file not in ignore_files]

					for file in files:
								# Open all files
								print("""Testing"""  ,      __magic_name__			)

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

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[Any]							      =     """modeling"""
					__snake_case					:      Union[str, Any]							      =     [
					    """modeling_ctrl.py""",
					    """modeling_tf_ctrl.py""",
					]
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__  ,      ignore_files=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      Any							      =     """tokenization"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[str]							      =     """configuration"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Path("""src/transformers"""			)
					__snake_case					:      int							      =     ["""configuration""", """modeling""", """tokenization"""]
					self.analyze_directory(__magic_name__  ,      n_identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     Path("""docs/source"""			)
					__snake_case					:      Optional[int]							      =     ["""favicon.ico"""]
					self.analyze_directory(__magic_name__  ,      ignore_files=__magic_name__  ,      only_modules=__magic_name__			)


