





'''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     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''

       @slow
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-base'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						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():
                     UpperCAmelCase  =						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 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						XLMRobertaModel.from_pretrained('''xlm-roberta-large'''						)
              UpperCAmelCase  =						torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]						)
              # The dog is cute and lives in the garden house

              UpperCAmelCase  =						torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
              UpperCAmelCase  =						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():
                     UpperCAmelCase  =						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						)						)









def   __lowerCamelCase   (				__a  :str       )						->							bool:






		"""simple docstring"""

		A__     =  0
		for ch in input_str:
				A__     =  ord(__a       )
				A__     =  pow(2       ,	__a       )

				# If we already turned on bit for current character's unicode
				if bitmap >> ch_unicode & 1 == 1:
						return False
				bitmap |= ch_bit_index_on
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



