




'''simple docstring'''




from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase__          =						'\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

lowerCAmelCase__          =						'\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

lowerCAmelCase__          =						'\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = False     ,      )							->     Union[str, Any]:
		if label_map is not None:
				for old_id, new_id in label_map.items():
						UpperCamelCase__				:					Optional[Any]					=					new_id

    # turn into Numpy arrays
		UpperCamelCase__				:					str					=					np.array(lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					np.array(lowerCamelCase_)

		if reduce_labels:
				UpperCamelCase__				:					List[str]					=					255
				UpperCamelCase__				:					Union[str, Any]					=					label - 1
				UpperCamelCase__				:					Optional[Any]					=					255

		UpperCamelCase__				:					Dict					=					label != ignore_index
		UpperCamelCase__				:					List[Any]					=					np.not_equal(lowerCamelCase_     ,      lowerCamelCase_)
		UpperCamelCase__				:					int					=					pred_label[mask]
		UpperCamelCase__				:					Optional[Any]					=					np.array(lowerCamelCase_)[mask]

		UpperCamelCase__				:					Union[str, Any]					=					pred_label[pred_label == label]

		UpperCamelCase__				:					int					=					np.histogram(lowerCamelCase_     ,      bins=lowerCamelCase_     ,      range=(0, num_labels - 1))[0]
		UpperCamelCase__				:					Optional[int]					=					np.histogram(lowerCamelCase_     ,      bins=lowerCamelCase_     ,      range=(0, num_labels - 1))[0]
		UpperCamelCase__				:					Optional[int]					=					np.histogram(lowerCamelCase_     ,      bins=lowerCamelCase_     ,      range=(0, num_labels - 1))[0]

		UpperCamelCase__				:					int					=					area_pred_label + area_label - area_intersect

		return area_intersect, area_union, area_pred_label, area_label







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = False     ,      )							->     List[Any]:
		UpperCamelCase__				:					Optional[int]					=					np.zeros((num_labels,)     ,      dtype=np.floataa)
		UpperCamelCase__				:					Dict					=					np.zeros((num_labels,)     ,      dtype=np.floataa)
		UpperCamelCase__				:					int					=					np.zeros((num_labels,)     ,      dtype=np.floataa)
		UpperCamelCase__				:					Tuple					=					np.zeros((num_labels,)     ,      dtype=np.floataa)
		for result, gt_seg_map in zip(lowerCamelCase_     ,      lowerCamelCase_):
				UpperCamelCase__				:					Tuple					=					intersect_and_union(
				    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
				total_area_intersect += area_intersect
				total_area_union += area_union
				total_area_pred_label += area_pred_label
				total_area_label += area_label
		return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = False     ,      )							->     Optional[Any]:
		UpperCamelCase__				:					Dict					=					total_intersect_and_union(
		    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		# compute metrics
		UpperCamelCase__				:					Optional[int]					=					{}

		UpperCamelCase__				:					List[Any]					=					total_area_intersect.sum() / total_area_label.sum()
		UpperCamelCase__				:					Tuple					=					total_area_intersect / total_area_union
		UpperCamelCase__				:					str					=					total_area_intersect / total_area_label

		UpperCamelCase__				:					Union[str, Any]					=					np.nanmean(lowerCamelCase_)
		UpperCamelCase__				:					List[Any]					=					np.nanmean(lowerCamelCase_)
		UpperCamelCase__				:					Any					=					all_acc
		UpperCamelCase__				:					Optional[int]					=					iou
		UpperCamelCase__				:					Union[str, Any]					=					acc

		if nan_to_num is not None:
				UpperCamelCase__				:					Optional[int]					=					{metric: np.nan_to_num(lowerCamelCase_     ,      nan=lowerCamelCase_) for metric, metric_value in metrics.items()}

		return metrics







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,				_KWARGS_DESCRIPTION    )
class       __lowercase					(datasets.Metric    ):

	def      __UpperCamelCase							(      self   :						Optional[int]):
			return datasets.MetricInfo(
			    description=_DESCRIPTION  ,					citation=_CITATION  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
			        # 1st Seq - height dim, 2nd - width dim
			        {
			            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'))),
			            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'))),
			        })  ,					reference_urls=[
			        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
			    ]  ,					)





	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						bool  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						Optional[Dict[int, int]] = None  ,					UpperCAmelCase_   :						bool = False  ,					):
			UpperCamelCase__				:					Tuple					=					mean_iou(
			    results=UpperCAmelCase_  ,					gt_seg_maps=UpperCAmelCase_  ,					num_labels=UpperCAmelCase_  ,					ignore_index=UpperCAmelCase_  ,					nan_to_num=UpperCAmelCase_  ,					label_map=UpperCAmelCase_  ,					reduce_labels=UpperCAmelCase_  ,					)
			return iou_result












'''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       __lowercase					:

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Optional[int]=13  ,					UpperCAmelCase_   :						Tuple=30  ,					UpperCAmelCase_   :						Dict=2  ,					UpperCAmelCase_   :						Dict=3  ,					UpperCAmelCase_   :						Tuple=True  ,					UpperCAmelCase_   :						str=True  ,					UpperCAmelCase_   :						Tuple=32  ,					UpperCAmelCase_   :						List[str]=5  ,					UpperCAmelCase_   :						str=4  ,					UpperCAmelCase_   :						Optional[int]=37  ,					UpperCAmelCase_   :						str="gelu"  ,					UpperCAmelCase_   :						List[str]=0.1  ,					UpperCAmelCase_   :						Dict=0.1  ,					UpperCAmelCase_   :						Dict=10  ,					UpperCAmelCase_   :						Optional[int]=0.02  ,					UpperCAmelCase_   :						Union[str, Any]=3  ,					UpperCAmelCase_   :						Any=0.6  ,					UpperCAmelCase_   :						Dict=None  ,					):
			UpperCamelCase__				:					Tuple					=					parent
			UpperCamelCase__				:					List[str]					=					batch_size
			UpperCamelCase__				:					Optional[Any]					=					image_size
			UpperCamelCase__				:					Optional[Any]					=					patch_size
			UpperCamelCase__				:					List[str]					=					num_channels
			UpperCamelCase__				:					Union[str, Any]					=					is_training
			UpperCamelCase__				:					int					=					use_labels
			UpperCamelCase__				:					Optional[int]					=					hidden_size
			UpperCamelCase__				:					Any					=					num_hidden_layers
			UpperCamelCase__				:					str					=					num_attention_heads
			UpperCamelCase__				:					str					=					intermediate_size
			UpperCamelCase__				:					Union[str, Any]					=					hidden_act
			UpperCamelCase__				:					Optional[int]					=					hidden_dropout_prob
			UpperCamelCase__				:					Tuple					=					attention_probs_dropout_prob
			UpperCamelCase__				:					Any					=					type_sequence_label_size
			UpperCamelCase__				:					int					=					initializer_range
			UpperCamelCase__				:					Optional[int]					=					mask_ratio
			UpperCamelCase__				:					int					=					scope

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			UpperCamelCase__				:					List[str]					=					None
			if self.use_labels:
					UpperCamelCase__				:					Dict					=					ids_tensor([self.batch_size]  ,					self.type_sequence_label_size)

			UpperCamelCase__				:					Any					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[Any]):
			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   :						Tuple  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					Dict					=					ViTMAEModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Optional[int]					=					model(UpperCAmelCase_)
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size))

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Dict					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					(self.image_size // self.patch_size) ** 2
			UpperCamelCase__				:					Optional[int]					=					self.patch_size**2 * self.num_channels
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))

			# test greyscale images
			UpperCamelCase__				:					List[Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					List[str]					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			UpperCamelCase__				:					Union[str, Any]					=					model(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					self.patch_size**2
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, num_patches, expected_num_channels))





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[str]					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					config_and_inputs
			UpperCamelCase__				:					Any					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCamelCase              =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[str]					=					ViTMAEModelTester(self)
			UpperCamelCase__				:					Any					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37)

	def      __UpperCamelCase							(      self   :						Any):
			self.config_tester.run_common_tests()

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					List[str]					=					model_class(UpperCAmelCase_)
					self.assertIsInstance(model.get_input_embeddings()  ,					(nn.Module))
					UpperCamelCase__				:					Optional[Any]					=					model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_  ,					nn.Linear))

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Optional[int]					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			# make masks reproducible
			np.random.seed(2)

			UpperCamelCase__				:					str					=					int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
			UpperCamelCase__				:					Tuple					=					np.random.uniform(size=(self.model_tester.batch_size, num_patches))
			UpperCamelCase__				:					Optional[Any]					=					torch.from_numpy(UpperCAmelCase_)

			# Add `noise` argument.
			# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
			UpperCamelCase__				:					List[str]					=					pt_noise

			super().check_pt_tf_models(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Optional[Any]					=					model_class(UpperCAmelCase_)
					model.to(UpperCAmelCase_)
					model.eval()
					# make random mask reproducible
					torch.manual_seed(2)
					with torch.no_grad():
							UpperCamelCase__				:					Tuple					=					model(**self._prepare_for_class(UpperCAmelCase_  ,					UpperCAmelCase_))

					UpperCamelCase__				:					Dict					=					outputs[0].cpu().numpy()
					UpperCamelCase__				:					Optional[int]					=					0

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

							# Make sure we don't have nans
							UpperCamelCase__				:					Tuple					=					after_outputs[0].cpu().numpy()
							UpperCamelCase__				:					Any					=					0
							UpperCamelCase__				:					Union[str, Any]					=					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   :						Tuple):
			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   :						Optional[int]):
			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   :						Tuple):
			pass

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

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





	@slow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Tuple					=					ViTMAEModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					int					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
		return image







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

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





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

			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					self.default_image_processor
			UpperCamelCase__				:					Dict					=					prepare_img()
			UpperCamelCase__				:					Optional[int]					=					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)
			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEConfig()
			UpperCamelCase__				:					int					=					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
			UpperCamelCase__				:					Any					=					np.random.uniform(size=(1, num_patches))

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

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

			UpperCamelCase__				:					Any					=					torch.tensor(
			    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

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




