


"""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 snake_case      :







		def __init__(					self      :       Optional[Any]    ,   a__      :       str    ,   a__      :       int=13    ,   a__      :       Dict=30    ,   a__      :       int=2    ,   a__      :       Optional[Any]=3    ,   a__      :       Dict=True    ,   a__      :       Any=True    ,   a__      :       Union[str, Any]=32    ,   a__      :       List[str]=5    ,   a__      :       Optional[int]=4    ,   a__      :       List[Any]=37    ,   a__      :       List[Any]="gelu"    ,   a__      :       str=0.1    ,   a__      :       Optional[int]=0.1    ,   a__      :       List[Any]=10    ,   a__      :       List[str]=0.0_2    ,   a__      :       str=3    ,   a__      :       Optional[Any]=0.6    ,   a__      :       Dict=None    ,   )       ->						int:





						'''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      :       Tuple		)       ->						Dict:





						'''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      :       Dict		)       ->						Dict:





						'''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      :       Any    ,   a__      :       Any    ,   a__      :       Union[str, Any]    ,   a__      :       Any		)       ->						Tuple:





						'''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      :       List[str]    ,   a__      :       List[Any]    ,   a__      :       List[Any]    ,   a__      :       Tuple		)       ->						Union[str, Any]:





						'''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      :       Optional[int]		)       ->						str:





						'''simple docstring'''







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





@require_torch
class snake_case      (  _UpperCamelCase     ,				_UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
		__UpperCamelCase           =			{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

		__UpperCamelCase           =			False
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False







		def  a_ (					self      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			ViTMAEModelTester(self		)
						_A					  =			ConfigTester(self    ,   config_class=UpperCAmelCase__    ,   has_text_modality=UpperCAmelCase__    ,   hidden_size=37		)







		def  a_ (					self      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		@unittest.skip(reason="ViTMAE does not use inputs_embeds"		)
		def  a_ (					self      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						pass







		def  a_ (					self      :       Tuple		)       ->						Union[str, 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__		)
										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[int]		)       ->						Union[str, 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      :       Dict		)       ->						int:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__		)







		def  a_ (					self      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__		)







		def  a_ (					self      :       List[str]    ,   a__      :       Optional[Any]    ,   a__      :       Dict    ,   a__      :       Tuple		)       ->						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		)       ->						Dict:





						'''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\n    to get deterministic results."		)
		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						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  a_ (					self      :       str		)       ->						Dict:





						'''simple docstring'''







						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  a_ (					self      :       Optional[int]		)       ->						List[str]:





						'''simple docstring'''







						pass







		@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"		)
		def  a_ (					self      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						pass







		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
		def  a_ (					self      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						pass







		@slow
		def  a_ (					self      :       Optional[int]		)       ->						int:





						'''simple docstring'''







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






def      a__ (				)	->	List[Any]:
				_A					  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
				return image




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







		@cached_property
		def  a_ (					self      :       List[str]		)       ->						Dict:





						'''simple docstring'''







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







		@slow
		def  a_ (					self      :       List[Any]		)       ->						int:





						'''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, 1_96, 7_68)		)
						self.assertEqual(outputs.logits.shape    ,   UpperCAmelCase__		)

						_A					  =			torch.tensor(
						    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]		)

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



"""simple docstring"""


import numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()