






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a:     List[Any]   					=					logging.get_logger(__name__)

__a:     Dict   					=					{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''vit_msn'''


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			Dict=768       ,				lowerCamelCase		:			Union[str, Any]=12       ,				lowerCamelCase		:			Optional[Any]=12       ,				lowerCamelCase		:			str=3072       ,				lowerCamelCase		:			Optional[Any]="gelu"       ,				lowerCamelCase		:			Any=0.0       ,				lowerCamelCase		:			Optional[Any]=0.0       ,				lowerCamelCase		:			int=0.02       ,				lowerCamelCase		:			List[Any]=1E-06       ,				lowerCamelCase		:			List[str]=224       ,				lowerCamelCase		:			List[Any]=16       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			int=True       ,				**lowerCamelCase		:			Union[str, Any]       ,				)						->	int:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)

         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    layer_norm_eps
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    qkv_bias







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     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			List[str]							)						->	int:


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

         _UpperCAmelCase							=    torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    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							=    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  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


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

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