






"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
						import torch

						from transformers import XLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_torch
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  Dict:
							lowercase_ : List[str]							       =   XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
							lowercase_ : List[str]							       =   torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
							# The dog is cute and lives in the garden house

							lowercase_ : Dict							       =   torch.Size((1, 12, 7_68)   )  # batch_size, sequence_length, embedding_vector_dim
							lowercase_ : int							       =   torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
									lowercase_ : str							       =   model(A   )['''last_hidden_state'''].detach()
							self.assertEqual(output.shape    ,						A   )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]    ,						A    ,						atol=1e-3   )   )






					@slow
					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : List[Any]							       =   XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
							lowercase_ : Union[str, Any]							       =   torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]   )
							# The dog is cute and lives in the garden house

							lowercase_ : Optional[Any]							       =   torch.Size((1, 12, 10_24)   )  # batch_size, sequence_length, embedding_vector_dim
							lowercase_ : List[str]							       =   torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
									lowercase_ : Optional[int]							       =   model(A   )['''last_hidden_state'''].detach()
							self.assertEqual(output.shape    ,						A   )
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]    ,						A    ,						atol=1e-3   )   )











"""simple docstring"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   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))

						__A		:       int  				=		(
						    '''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),
						)



