

"""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 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Any     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		int=13     ,       __lowerCamelCase						:		Any=30     ,       __lowerCamelCase						:		Optional[Any]=2     ,       __lowerCamelCase						:		str=3     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		List[str]=32     ,       __lowerCamelCase						:		List[str]=5     ,       __lowerCamelCase						:		List[str]=4     ,       __lowerCamelCase						:		List[Any]=37     ,       __lowerCamelCase						:		Any="gelu"     ,       __lowerCamelCase						:		Tuple=0.1     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		Any=10     ,       __lowerCamelCase						:		Tuple=0.02     ,       __lowerCamelCase						:		Tuple=3     ,       __lowerCamelCase						:		Union[str, Any]=0.6     ,       __lowerCamelCase						:		Any=None     ,       )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   patch_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   is_training
       SCREAMING_SNAKE_CASE__   =   use_labels
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   type_sequence_label_size
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   mask_ratio
       SCREAMING_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)
       SCREAMING_SNAKE_CASE__   =   (image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__   =   int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )






  def 				lowercase_		(							self						:		Optional[int] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

       SCREAMING_SNAKE_CASE__   =   self.get_config()

       return config, pixel_values, labels






  def 				lowercase_		(							self						:		List[Any] )		->							Tuple:
       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 				lowercase_		(							self						:		int     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Dict )		->							Any:
       SCREAMING_SNAKE_CASE__   =   ViTMAEModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase__ )
       self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size) )






  def 				lowercase_		(							self						:		Any     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		List[Any]     ,       __lowerCamelCase						:		Dict )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   ViTMAEForPreTraining(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase__ )
       SCREAMING_SNAKE_CASE__   =   (self.image_size // self.patch_size) ** 2
       SCREAMING_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
       SCREAMING_SNAKE_CASE__   =   1
       SCREAMING_SNAKE_CASE__   =   ViTMAEForPreTraining(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       SCREAMING_SNAKE_CASE__   =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       SCREAMING_SNAKE_CASE__   =   model(UpperCamelCase__ )
       SCREAMING_SNAKE_CASE__   =   self.patch_size**2
       self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, num_patches, expected_num_channels) )







  def 				lowercase_		(							self						:		Any )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__   =   config_and_inputs
       SCREAMING_SNAKE_CASE__   =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class 	UpperCAmelCase__		(	__SCREAMING_SNAKE_CASE						,					__SCREAMING_SNAKE_CASE						,					unittest.TestCase	):






  """simple docstring"""


  a									= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  a									= {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

  a									= False
  a									= False
  a									= False
  a									= False






  def 				lowercase_		(							self						:		List[Any] )		->							int:
       SCREAMING_SNAKE_CASE__   =   ViTMAEModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=UpperCamelCase__     ,       has_text_modality=UpperCamelCase__     ,       hidden_size=37 )






  def 				lowercase_		(							self						:		Optional[int] )		->							List[Any]:
       self.config_tester.run_common_tests()






  @unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
  def 				lowercase_		(							self						:		Optional[Any] )		->							List[Any]:
       pass






  def 				lowercase_		(							self						:		List[str] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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






  def 				lowercase_		(							self						:		int )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__   =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]     ,       UpperCamelCase__ )






  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






  def 				lowercase_		(							self						:		Dict )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__ )






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		int )		->							str:
       np.random.seed(2 )

       SCREAMING_SNAKE_CASE__   =   int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
       SCREAMING_SNAKE_CASE__   =   np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
       SCREAMING_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
       SCREAMING_SNAKE_CASE__   =   pt_noise

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






  def 				lowercase_		(							self						:		str )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

                 # Make sure we don't have nans
                 SCREAMING_SNAKE_CASE__   =   after_outputs[0].cpu().numpy()
                 SCREAMING_SNAKE_CASE__   =   0
                 SCREAMING_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
    to get deterministic results.''' )
  def 				lowercase_		(							self						:		Optional[int] )		->							str:
       pass






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






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






  @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
  def 				lowercase_		(							self						:		str )		->							Optional[Any]:
       pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
  def 				lowercase_		(							self						:		List[str] )		->							str:
       pass







  @slow
  def 				lowercase_		(							self						:		Tuple )		->							Dict:







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



def     UpperCAmelCase_       (						):





     '''simple docstring'''




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







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






  """simple docstring"""


  @cached_property
  def 				lowercase_		(							self						:		int )		->							Optional[Any]:
       return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None







  @slow
  def 				lowercase_		(							self						:		List[Any] )		->							Optional[Any]:
       np.random.seed(2 )

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

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

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

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

       SCREAMING_SNAKE_CASE__   =   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 ) )





from __future__ import annotations
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   len(_A    ) // 2

     # choose the middle 3 elements
     SCREAMING_SNAKE_CASE__   =   lst[m - 1 : m + 2]

     # if middle element is peak
     if three[1] > three[0] and three[1] > three[2]:
          return three[1]

     # if increasing, recurse on right
     elif three[0] < three[2]:
          if len(lst[:m]    ) == 2:
               m -= 1
          return peak(lst[m:]    )

     # decreasing
     else:
          if len(lst[:m]    ) == 2:
               m += 1
          return peak(lst[:m]    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


