'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)




class 	__snake_case	( a__		):





      '''simple docstring'''






      def __init__(					self     :				List[str]    ,  *_UpperCamelCase     :				List[Any]    ,  **_UpperCamelCase     :				Optional[Any])				->None:


          """simple docstring"""


          warnings.warn(
              """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
              """ Please use MobileViTImageProcessor instead."""    ,  lowerCAmelCase__    ,  )
          super().__init__(*lowerCAmelCase__    ,  **lowerCAmelCase__)





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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						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

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						torch.tensor(
              [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
          #  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     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						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

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor(
              [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
          #  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     :		int 						=						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))




