




"""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       SCREAMING_SNAKE_CASE__					:


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

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


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			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=lowerCAmelCase						,						initializer_range=self.initializer_range						,						mask_ratio=self.mask_ratio						,						)


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


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

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




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
										lowerCAmelCase  =				{"""pixel_values""": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a						,				unittest.TestCase					):
						_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				:			Tuple					):
										lowerCAmelCase  =				ViTMAEModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Optional[int]					):
										self.config_tester.run_common_tests()


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


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase  =				["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase					)


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Optional[Any]					):
										# make masks reproducible
										np.random.seed(2					)

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

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

										super().check_pt_tf_models(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																		# Make sure we don't have nans
																		lowerCAmelCase  =				after_outputs[0].cpu().numpy()
																		lowerCAmelCase  =				0
																		lowerCAmelCase  =				np.amax(np.abs(out_a - out_a					)					)
																		self.assertLessEqual(lowerCAmelCase						,						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				:			Union[str, Any]					):
										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				:			Union[str, Any]					):
										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				:			int					):
										pass


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


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




						@slow
						def   __lowercase (							self				:			Any					):
										for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				ViTMAEModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)





def 							lowercase					()     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image






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


						@cached_property
						def   __lowercase (							self				:			Union[str, Any]					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None




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

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

										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(images=lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)

										# 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)
										lowerCAmelCase  =				ViTMAEConfig()
										lowerCAmelCase  =				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
										lowerCAmelCase  =				np.random.uniform(size=(1, num_patches)					)

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

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

										lowerCAmelCase  =				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(lowerCAmelCase					)						,						atol=1e-4					)					)











"""simple docstring"""







import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class       SCREAMING_SNAKE_CASE__					:


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

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowerCAmelCase  =				(image_size // patch_size) ** 2
										lowerCAmelCase  =				num_patches + 1


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			Tuple					):
										return ViTMSNConfig(
										    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						,						initializer_range=self.initializer_range						,						)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModel(config=lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.type_sequence_label_size
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase					)
										print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""					)
										print("""Labels: {labels}"""					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowerCAmelCase  =				1
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

										lowerCAmelCase  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)




						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
										lowerCAmelCase  =				{"""pixel_values""": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a						,				unittest.TestCase					):
						_a    						=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_a    						=						(
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Dict					):
										self.config_tester.run_common_tests()


						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""					)
						def   __lowercase (							self				:			Optional[Any]					):
										pass


						def   __lowercase (							self				:			int					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase  =				["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,						lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)




						@slow
						def   __lowercase (							self				:			Optional[Any]					):
										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				ViTMSNModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
				return image






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


						@cached_property
						def   __lowercase (							self				:			Dict					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""					) if is_vision_available() else None




						@slow
						def   __lowercase (							self				:			Tuple					):
										torch.manual_seed(2					)
										lowerCAmelCase  =				ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""					).to(lowerCAmelCase					)

										lowerCAmelCase  =				self.default_image_processor
										lowerCAmelCase  =				prepare_img()
										lowerCAmelCase  =				image_processor(images=lowerCAmelCase						,						return_tensors="""pt"""					).to(lowerCAmelCase					)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

										# verify the logits
										lowerCAmelCase  =				torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape						,						lowerCAmelCase					)

										lowerCAmelCase  =				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowerCAmelCase					)

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





