





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     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   @slow
   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      XLMRobertaModel.from_pretrained('''xlm-roberta-base'''							)
       UpperCamelCase__							:				Union[str, Any]          =      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__							:				str          =      torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
       UpperCamelCase__							:				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():
           UpperCamelCase__							:				str          =      model(__magic_name__							)['''last_hidden_state'''].detach()
       self.assertEqual(output.shape,		__magic_name__							)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1],		__magic_name__,		atol=1E-3							)							)




   @slow
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      XLMRobertaModel.from_pretrained('''xlm-roberta-large'''							)
       UpperCamelCase__							:				str          =      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__							:				Optional[Any]          =      torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
       UpperCamelCase__							:				Optional[int]          =      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():
           UpperCamelCase__							:				Optional[int]          =      model(__magic_name__							)['''last_hidden_state'''].detach()
       self.assertEqual(output.shape,		__magic_name__							)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1],		__magic_name__,		atol=1E-3							)							)








import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

