



'''simple docstring'''





import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A=13 ,   A=30 ,   A=2 ,   A=3 ,   A=True ,   A=True ,   A=32 ,   A=5 ,   A=4 ,   A=37 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=10 ,   A=0.02 ,   A=3 ,   A=0.6 ,   A=None ,   )    ->     Dict:


      """simple docstring"""



      _a      =  parent
      _a      =  batch_size
      _a      =  image_size
      _a      =  patch_size
      _a      =  num_channels
      _a      =  is_training
      _a      =  use_labels
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  intermediate_size
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  type_sequence_label_size
      _a      =  initializer_range
      _a      =  mask_ratio
      _a      =  scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      _a      =  (image_size // patch_size) ** 2
      _a      =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

      _a      =  None
      if self.use_labels:
           _a      =  ids_tensor([self.batch_size] ,   self.type_sequence_label_size       )

      _a      =  self.get_config()

      return config, pixel_values, labels



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return ViTMAEConfig(
          image_size=self.image_size ,   patch_size=self.patch_size ,   num_channels=self.num_channels ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   is_decoder=UpperCamelCase__ ,   initializer_range=self.initializer_range ,   mask_ratio=self.mask_ratio ,   )



 def 					a__  (self ,   A ,   A ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  ViTMAEModel(config=UpperCamelCase__       )
      model.to(UpperCamelCase__       )
      model.eval()
      _a      =  model(UpperCamelCase__       )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )



 def 					a__  (self ,   A ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  ViTMAEForPreTraining(UpperCamelCase__       )
      model.to(UpperCamelCase__       )
      model.eval()
      _a      =  model(UpperCamelCase__       )
      _a      =  (self.image_size // self.patch_size) ** 2
      _a      =  self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, num_patches, expected_num_channels)       )

      # test greyscale images
      _a      =  1
      _a      =  ViTMAEForPreTraining(UpperCamelCase__       )
      model.to(UpperCamelCase__       )
      model.eval()
      _a      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
      _a      =  model(UpperCamelCase__       )
      _a      =  self.patch_size**2
      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, num_patches, expected_num_channels)       )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  self.prepare_config_and_inputs()
      _a      =  config_and_inputs
      _a      =  {"""pixel_values""": pixel_values}
      return config, inputs_dict






@require_torch
class __A			(  _lowercase   ,   _lowercase   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 __lowerCamelCase    :       Any          =       {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

 __lowerCamelCase    :       Optional[Any]          =       False
 __lowerCamelCase    :       Dict          =       False
 __lowerCamelCase    :       List[str]          =       False
 __lowerCamelCase    :       Any          =       False



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  ViTMAEModelTester(self       )
      _a      =  ConfigTester(self ,   config_class=UpperCamelCase__ ,   has_text_modality=UpperCamelCase__ ,   hidden_size=37       )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''       )
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =  model_class(UpperCamelCase__       )
           self.assertIsInstance(model.get_input_embeddings() ,   (nn.Module)       )
           _a      =  model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCamelCase__ ,   nn.Linear       )       )



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


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =  model_class(UpperCamelCase__       )
           _a      =  inspect.signature(model.forward       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a      =  [*signature.parameters.keys()]

           _a      =  ["""pixel_values"""]
           self.assertListEqual(arg_names[:1] ,   UpperCamelCase__       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase__       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__       )



 def 					a__  (self ,   A ,   A ,   A       )    ->     Optional[int]:


      """simple docstring"""



      np.random.seed(2       )

      _a      =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2       )
      _a      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
      _a      =  torch.from_numpy(UpperCamelCase__       )

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      _a      =  pt_noise

      super().check_pt_tf_models(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a      =  model_class(UpperCamelCase__       )
           model.to(UpperCamelCase__       )
           model.eval()
           # make random mask reproducible
           torch.manual_seed(2       )
           with torch.no_grad():
                _a      =  model(**self._prepare_for_class(UpperCamelCase__ ,   UpperCamelCase__       )       )

           _a      =  outputs[0].cpu().numpy()
           _a      =  0

           with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(UpperCamelCase__       )
                _a      =  model_class.from_pretrained(UpperCamelCase__       )
                model.to(UpperCamelCase__       )
                # make random mask reproducible
                torch.manual_seed(2       )
                with torch.no_grad():
                     _a      =  model(**self._prepare_for_class(UpperCamelCase__ ,   UpperCamelCase__       )       )

                # Make sure we don't have nans
                _a      =  after_outputs[0].cpu().numpy()
                _a      =  0
                _a      =  np.amax(np.abs(out_a - out_a       )       )
                self.assertLessEqual(UpperCamelCase__ ,   1E-5       )



 @unittest.skip(
     reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''       )
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      pass



 @unittest.skip(
     reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''       )
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      pass



 @unittest.skip(
     reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''       )
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      pass



 @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''       )
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      pass



 @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      pass



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


      """simple docstring"""



      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =  ViTMAEModel.from_pretrained(UpperCamelCase__       )
           self.assertIsNotNone(UpperCamelCase__       )





def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
     return image






@require_torch
@require_vision
class __A			(  unittest.TestCase						):



 '''simple docstring'''







 @cached_property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''       ) if is_vision_available() else None



 @slow
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      np.random.seed(2       )

      _a      =  ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''       ).to(UpperCamelCase__       )

      _a      =  self.default_image_processor
      _a      =  prepare_img()
      _a      =  image_processor(images=UpperCamelCase__ ,   return_tensors='''pt'''       ).to(UpperCamelCase__       )

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      _a      =  ViTMAEConfig()
      _a      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
      _a      =  np.random.uniform(size=(1, num_patches)       )

      # forward pass
      with torch.no_grad():
           _a      =  model(**UpperCamelCase__ ,   noise=torch.from_numpy(UpperCamelCase__       ).to(device=UpperCamelCase__       )       )

      # verify the logits
      _a      =  torch.Size((1, 196, 768)       )
      self.assertEqual(outputs.logits.shape ,   UpperCamelCase__       )

      _a      =  torch.tensor(
          [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]       )

      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,   expected_slice.to(UpperCamelCase__       ) ,   atol=1E-4       )       )











'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = 1000      )      ->       int:
							lowerCamelCase__					:	str         =       -1
							lowerCamelCase__					:	Dict         =       0
							for a in range(1				,			n // 3      ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														lowerCamelCase__					:	Dict         =       (n * n - 2 * a * n) // (2 * n - 2 * a)
														lowerCamelCase__					:	Any         =       n - a - b
														if c * c == (a * a + b * b):
																					lowerCamelCase__					:	Dict         =       a * b * c
																					if candidate >= product:
																												lowerCamelCase__					:	Union[str, Any]         =       candidate
							return product


if __name__ == "__main__":
			print(F'{solution() = }')

