




'''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        _a    (						unittest.TestCase							):






     @slow
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
          _snake_case							=				torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
          # The dog is cute and lives in the garden house

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






     @slow
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
          _snake_case							=				torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
          # The dog is cute and lives in the garden house

          _snake_case							=				torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
          _snake_case							=				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():
               _snake_case							=				model(_SCREAMING_SNAKE_CASE   )["last_hidden_state"].detach()
          self.assertEqual(output.shape ,_SCREAMING_SNAKE_CASE   )
          # compare the actual values for a slice of last dim
          self.assertTrue(torch.allclose(output[:, :, -1] ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )








'''simple docstring'''





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


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''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        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

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


