


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=30		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=32		,				_UpperCAmelCase=5		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=10		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=3		,				_UpperCAmelCase=0.6		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				patch_size
       snake_case_	=				num_channels
       snake_case_	=				is_training
       snake_case_	=				use_labels
       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_	=				type_sequence_label_size
       snake_case_	=				initializer_range
       snake_case_	=				mask_ratio
       snake_case_	=				scope

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



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

       snake_case_	=				self.get_config()

       return config, pixel_values, labels



 def 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				ViTMAEModel(config=UpperCAmelCase__ )
       model.to(UpperCAmelCase__ )
       model.eval()
       snake_case_	=				model(UpperCAmelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size) )



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

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





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs
       snake_case_	=				{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			__lowercase ,						__lowercase ,						unittest.TestCase	):

 '''simple docstring'''

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

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ViTMAEModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=UpperCAmelCase__		,				has_text_modality=UpperCAmelCase__		,				hidden_size=37 )



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				UpperCAmelCase__ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase__ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__ )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # make masks reproducible
       np.random.seed(2 )

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

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

       super().check_pt_tf_models(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__ )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                   # Make sure we don't have nans
                   snake_case_	=				after_outputs[0].cpu().numpy()
                   snake_case_	=				0
                   snake_case_	=				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\n    to get deterministic results.''' )
 def 						UpperCamelCase__					(     self ):
       pass



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



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



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



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





 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				ViTMAEModel.from_pretrained(UpperCAmelCase__ )
             self.assertIsNotNone(UpperCAmelCase__ )






def 	__lowerCAmelCase   ()->  Any:




      """simple docstring"""

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







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

 '''simple docstring'''

 @cached_property
 def 						UpperCamelCase__					(     self ):
       return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None





 @slow
 def 						UpperCamelCase__					(     self ):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2 )

       snake_case_	=				ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(UpperCAmelCase__ )

       snake_case_	=				self.default_image_processor
       snake_case_	=				prepare_img()
       snake_case_	=				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)
       snake_case_	=				ViTMAEConfig()
       snake_case_	=				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
       snake_case_	=				np.random.uniform(size=(1, num_patches) )

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

       # verify the logits
       snake_case_	=				torch.Size((1, 1_96, 7_68) )
       self.assertEqual(outputs.logits.shape		,				UpperCAmelCase__ )

       snake_case_	=				torch.tensor(
           [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )

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



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
            snake_case_	=				f'''Input value of [number={number}] must be an integer'''
            raise TypeError(SCREAMING_SNAKE_CASE )

      if number < 1:
            snake_case_	=				f'''Input value of [number={number}] must be > 0'''
            raise ValueError(SCREAMING_SNAKE_CASE )

      snake_case_	=				1

      for i in range(1 , SCREAMING_SNAKE_CASE ):
            current_number *= 4 * i - 2
            current_number //= i + 1

      return current_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()