import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				torch_builtin(SCREAMING_SNAKE_CASE_	)	)	)
       self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE_	)   ,				gelu_new(SCREAMING_SNAKE_CASE_	)	)	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]	)
       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      get_activation('''gelu_10'''	)

       UpperCamelCase__										=      torch_builtin(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      geluaa(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.where(y_gelu_aa < 10.0   ,				1   ,				0	)

       self.assertTrue(torch.max(SCREAMING_SNAKE_CASE_	).item() == 10.0	)
       self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,				y_gelu_aa * clipped_mask	)	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       get_activation('''gelu'''	)
       get_activation('''gelu_10'''	)
       get_activation('''gelu_fast'''	)
       get_activation('''gelu_new'''	)
       get_activation('''gelu_python'''	)
       get_activation('''gelu_pytorch_tanh'''	)
       get_activation('''linear'''	)
       get_activation('''mish'''	)
       get_activation('''quick_gelu'''	)
       get_activation('''relu'''	)
       get_activation('''sigmoid'''	)
       get_activation('''silu'''	)
       get_activation('''swish'''	)
       get_activation('''tanh'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation('''bogus'''	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            get_activation(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      get_activation('''gelu'''	)
       UpperCamelCase__										=      1
       UpperCamelCase__										=      get_activation('''gelu'''	)
       self.assertEqual(acta.a   ,				1	)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      acta.a




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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       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
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



