
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			: List[Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Union[str, Any]=13			,							UpperCamelCase__			: int=30			,							UpperCamelCase__			: Union[str, Any]=2			,							UpperCamelCase__			: List[str]=3			,							UpperCamelCase__			: Any=True			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=32			,							UpperCamelCase__			: Optional[Any]=5			,							UpperCamelCase__			: Union[str, Any]=4			,							UpperCamelCase__			: List[Any]=37			,							UpperCamelCase__			: str="gelu"			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: int=0.1			,							UpperCamelCase__			: List[str]=10			,							UpperCamelCase__			: Dict=0.02			,							UpperCamelCase__			: str=3			,							UpperCamelCase__			: int=0.6			,							UpperCamelCase__			: str=None			,							)			->	Dict:







    """simple docstring"""






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

    # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
    # (we add 1 for the [CLS] token)
    __magic_name__				     =       (image_size // patch_size) ** 2
    __magic_name__				     =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )
  def 							_lowercase							( self			: Optional[int]    )			->	Optional[Any]:







    """simple docstring"""






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

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

    __magic_name__				     =       self.get_config()

    return config, pixel_values, labels
  def 							_lowercase							( self			: List[str]    )			->	Optional[Any]:







    """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 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Dict    )			->	str:







    """simple docstring"""






    __magic_name__				     =       ViTMAEModel(config=UpperCamelCase__    )
    model.to(UpperCamelCase__    )
    model.eval()
    __magic_name__				     =       model(UpperCamelCase__    )
    self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Union[str, Any]			,							UpperCamelCase__			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






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

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





  def 							_lowercase							( self			: Optional[Any]    )			->	List[str]:







    """simple docstring"""






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







@require_torch
class        UpperCAmelCase_			( _A  ,       _A  ,       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			: Dict    )			->	str:







    """simple docstring"""






    __magic_name__				     =       ViTMAEModelTester(self    )
    __magic_name__				     =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Optional[int]:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""    )
  def 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    pass
  def 							_lowercase							( self			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__				     =       model_class(UpperCamelCase__    )
      self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)    )
      __magic_name__				     =       model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__			,							nn.Linear    )    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	int:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__				     =       ["""pixel_values"""]
      self.assertListEqual(arg_names[:1]			,							UpperCamelCase__    )
  def 							_lowercase							( self			: int    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Any    )			->	Any:







    """simple docstring"""






    np.random.seed(2    )

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

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

    super().check_pt_tf_models(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: str    )			->	str:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

        # Make sure we don't have nans
        __magic_name__				     =       after_outputs[0].cpu().numpy()
        __magic_name__				     =       0
        __magic_name__				     =       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			: List[Any]    )			->	List[Any]:







    """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 							_lowercase							( self			: str    )			->	int:







    """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 							_lowercase							( self			: Optional[int]    )			->	Optional[int]:







    """simple docstring"""






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







    """simple docstring"""






    pass
  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    pass





  @slow
  def 							_lowercase							( self			: Tuple    )			->	Optional[Any]:







    """simple docstring"""






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







def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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			: Tuple    )			->	Optional[int]:







    """simple docstring"""






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





  @slow
  def 							_lowercase							( self			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    np.random.seed(2    )

    __magic_name__				     =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    ).to(UpperCamelCase__    )

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

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

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

    __magic_name__				     =       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

from collections.abc import Iterator




class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Dict			,							UpperCamelCase__			: int    )			->	None:







    """simple docstring"""






    __magic_name__				     =       value
    __magic_name__				     =       None
    __magic_name__				     =       None







class        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: Union[str, Any]			,							UpperCamelCase__			: Node    )			->	None:







    """simple docstring"""






    __magic_name__				     =       tree
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: Node | None    )			->	int:







    """simple docstring"""






    if node is None:
      return 0
    return node.value + (
        self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
    )





  def __iter__( self			: int    )			->	Iterator[int]:







    """simple docstring"""






    yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

