






'''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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      @slow
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    XLMRobertaModel.from_pretrained('xlm-roberta-base'      )
         __a				     =    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

         __a				     =    torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
         __a				     =    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():
            __a				     =    model(lowerCamelCase      )['last_hidden_state'].detach()
         self.assertEqual(output.shape ,   lowerCamelCase      )
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1] ,   lowerCamelCase ,   atol=1e-3      )      )






      @slow
      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    XLMRobertaModel.from_pretrained('xlm-roberta-large'      )
         __a				     =    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

         __a				     =    torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
         __a				     =    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():
            __a				     =    model(lowerCamelCase      )['last_hidden_state'].detach()
         self.assertEqual(output.shape ,   lowerCamelCase      )
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1] ,   lowerCamelCase ,   atol=1e-3      )      )







'''simple docstring'''



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			bool:

   """simple docstring"""
   return number & 1 == 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()