


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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
							lowercase__	     =						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]						)
							# The dog is cute and lives in the garden house

							lowercase__	     =						torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
							lowercase__	     =						torch.tensor(
							    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]						)
							#  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__	     =						model(_UpperCAmelCase						)["""last_hidden_state"""].detach()
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1] ,   _UpperCAmelCase ,   atol=1E-3						)						)


					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
							lowercase__	     =						torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]						)
							# The dog is cute and lives in the garden house

							lowercase__	     =						torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
							lowercase__	     =						torch.tensor(
							    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]						)
							#  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__	     =						model(_UpperCAmelCase						)["""last_hidden_state"""].detach()
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1] ,   _UpperCAmelCase ,   atol=1E-3						)						)





from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

