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 ):

     '''simple docstring'''

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






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

           _lowerCamelCase			    =				torch.Size((1, 12, 7_68)						)  # batch_size, sequence_length, embedding_vector_dim
           _lowerCamelCase			    =				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():
                 _lowerCamelCase			    =				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						) ->				List[str]:






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

           _lowerCamelCase			    =				torch.Size((1, 12, 10_24)						)  # batch_size, sequence_length, embedding_vector_dim
           _lowerCamelCase			    =				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():
                 _lowerCamelCase			    =				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						)						)


def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    bool:



      '''simple docstring'''

      _lowerCamelCase			    =				len(__UpperCAmelCase		)
      _lowerCamelCase			    =				[[False] * (required_sum + 1) for _ in range(arr_len + 1		)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1		):
            _lowerCamelCase			    =				True

      # sum is not zero and set is empty then false
      for i in range(1 ,						required_sum + 1		):
            _lowerCamelCase			    =				False

      for i in range(1 ,						arr_len + 1		):
            for j in range(1 ,						required_sum + 1		):
                  if arr[i - 1] > j:
                        _lowerCamelCase			    =				subset[i - 1][j]
                  if arr[i - 1] <= j:
                        _lowerCamelCase			    =				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
     import doctest

     doctest.testmod()