


"""simple docstring"""
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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				10



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				[1, 2, 3, 4]
			__snake_case	:						Dict						=				[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
			self.assertEqual(truncate_or_pad(_a				,       self.block_size				,       0					)				,       _a					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
			__snake_case	:						Optional[int]						=				[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
			self.assertEqual(truncate_or_pad(_a				,       self.block_size				,       0					)				,       _a					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				''
			__snake_case	:						Union[str, Any]						=				process_story(_a					)
			self.assertEqual(_a				,       []					)
			self.assertEqual(_a				,       []					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[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'
			)
			__snake_case	:						List[str]						=				process_story(_a					)

			__snake_case	:						List[Any]						=				[
			    '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(_a				,       _a					)

			__snake_case	:						List[Any]						=				['It was the best of times.']
			self.assertEqual(_a				,       _a					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				torch.tensor([1, 2, 3, 4]					)
			__snake_case	:						int						=				torch.tensor([1, 1, 1, 1]					)
			np.testing.assert_array_equal(build_mask(_a				,       0					).numpy()				,       expected.numpy()					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				torch.tensor([1, 2, 3, 4, 23, 23, 23]					)
			__snake_case	:						int						=				torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
			np.testing.assert_array_equal(build_mask(_a				,       23					).numpy()				,       expected.numpy()					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				torch.tensor([8, 2, 3, 4, 1, 1, 1]					)
			__snake_case	:						Union[str, Any]						=				torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
			np.testing.assert_array_equal(build_mask(_a				,       1					).numpy()				,       expected.numpy()					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				1_01
			__snake_case	:						List[Any]						=				torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]]					)
			__snake_case	:						Any						=				torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]					)

			__snake_case	:						Optional[int]						=				compute_token_type_ids(_a				,       _a					)
			np.testing.assert_array_equal(_a				,       _a					)







'''simple docstring'''







from __future__ import annotations

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



