


import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		10






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		[1, 2, 3, 4]
              UpperCamelCase    :	int   	=		[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
              self.assertEqual(truncate_or_pad(__SCREAMING_SNAKE_CASE ,					self.block_size ,					0						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
              UpperCamelCase    :	int   	=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
              self.assertEqual(truncate_or_pad(__SCREAMING_SNAKE_CASE ,					self.block_size ,					0						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
              UpperCamelCase    :	int   	=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
              self.assertEqual(truncate_or_pad(__SCREAMING_SNAKE_CASE ,					self.block_size ,					0						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		'''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
              UpperCamelCase	, UpperCamelCase    :	List[str]   	=		process_story(__SCREAMING_SNAKE_CASE						)
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					[]						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		''''''
              UpperCamelCase	, UpperCamelCase    :	List[str]   	=		process_story(__SCREAMING_SNAKE_CASE						)
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					[]						)
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					[]						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		(
                  '''It was the year of Our Lord one thousand seven hundred and '''
                  '''seventy-five\n\nSpiritual revelations were conceded to England '''
                  '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
              )
              UpperCamelCase	, UpperCamelCase    :	str   	=		process_story(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	str   	=		[
                  '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
                  '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
              ]
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		['''It was the best of times.''']
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		torch.tensor([1, 2, 3, 4]						)
              UpperCamelCase    :	Tuple   	=		torch.tensor([1, 1, 1, 1]						)
              np.testing.assert_array_equal(build_mask(__SCREAMING_SNAKE_CASE ,					0						).numpy() ,					expected.numpy()						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		torch.tensor([1, 2, 3, 4, 23, 23, 23]						)
              UpperCamelCase    :	int   	=		torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
              np.testing.assert_array_equal(build_mask(__SCREAMING_SNAKE_CASE ,					23						).numpy() ,					expected.numpy()						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		torch.tensor([8, 2, 3, 4, 1, 1, 1]						)
              UpperCamelCase    :	int   	=		torch.tensor([1, 1, 1, 1, 0, 0, 0]						)
              np.testing.assert_array_equal(build_mask(__SCREAMING_SNAKE_CASE ,					1						).numpy() ,					expected.numpy()						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		101
              UpperCamelCase    :	Dict   	=		torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]						)
              UpperCamelCase    :	List[Any]   	=		torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]						)

              UpperCamelCase    :	Optional[int]   	=		compute_token_type_ids(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              np.testing.assert_array_equal(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)





import torch

from transformers import AutoModel



class    UpperCAmelCase_	(							torch.nn.Module):
       '''simple docstring'''






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




              """simple docstring"""




              super(__SCREAMING_SNAKE_CASE ,					self						).__init__()

              UpperCamelCase    :	List[str]   	=		AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		torch.nn.CosineSimilarity(3 ,					1e-08						)
              UpperCamelCase    :	List[Any]   	=		torch.nn.Softmax(dim=1						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.bert(**__SCREAMING_SNAKE_CASE						).last_hidden_state






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return token_embeddings.sum(2 ,					keepdim=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1						):




              """simple docstring"""




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






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		W_supports['''sizes'''].tolist()
              UpperCamelCase    :	Optional[int]   	=		W_supports['''start_token_id'''].item()
              UpperCamelCase    :	Any   	=		W_supports['''end_token_id'''].item()

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

              UpperCamelCase    :	Union[str, Any]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		None
              UpperCamelCase    :	Any   	=		None

              UpperCamelCase    :	Optional[Any]   	=		W_supports['''input_ids'''] == start_token_id
              UpperCamelCase    :	Any   	=		W_supports['''input_ids'''] == end_token_id

              for i, size in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == 0:
                            UpperCamelCase    :	Optional[int]   	=		0
                     else:
                            UpperCamelCase    :	Tuple   	=		support_sizes[i - 1]

                     UpperCamelCase    :	Tuple   	=		S[s : s + size][start_token_masks[s : s + size]]
                     UpperCamelCase    :	List[str]   	=		S[s : s + size][end_token_masks[s : s + size]]

                     UpperCamelCase    :	Dict   	=		torch.matmul(q[i] ,					s_start.T						).sum(1						).softmax(0						)
                     UpperCamelCase    :	Tuple   	=		torch.matmul(q[i] ,					s_end.T						).sum(1						).softmax(0						)

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

              return p_starts, p_ends

