



"""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			:


					'''simple docstring'''





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

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



					def 	snake_case ( self							:						Optional[int]  )->      Union[str, Any]:
								lowerCamelCase__	:	Optional[Any]				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowerCamelCase__	:	Dict				=self.get_config()

								return config, pixel_values, labels



					def 	snake_case ( self							:						Union[str, 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=lowerCamelCase,						initializer_range=self.initializer_range,						mask_ratio=self.mask_ratio,						)



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						Dict  )->      List[Any]:
								lowerCamelCase__	:	Any				=ViTMAEModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)  )



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Any,						lowerCamelCase							:						Optional[int]  )->      Union[str, Any]:
								lowerCamelCase__	:	Any				=ViTMAEForPreTraining(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Any				=model(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=(self.image_size // self.patch_size) ** 2
								lowerCamelCase__	:	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
								lowerCamelCase__	:	Optional[Any]				=1
								lowerCamelCase__	:	str				=ViTMAEForPreTraining(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowerCamelCase__	:	Any				=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase  )
								lowerCamelCase__	:	str				=self.patch_size**2
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, num_patches, expected_num_channels)  )




					def 	snake_case ( self							:						Optional[int]  )->      Optional[Any]:
								lowerCamelCase__	:	List[str]				=self.prepare_config_and_inputs()
								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=config_and_inputs
								lowerCamelCase__	:	Optional[int]				={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     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 	snake_case ( self							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	int				=ViTMAEModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase,						hidden_size=37  )



					def 	snake_case ( self							:						List[Any]  )->      List[str]:
								self.config_tester.run_common_tests()



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



					def 	snake_case ( self							:						Tuple  )->      List[str]:
								lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	int				=model_class(lowerCamelCase  )
											self.assertIsInstance(model.get_input_embeddings(),						(nn.Module)  )
											lowerCamelCase__	:	Union[str, Any]				=model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase,						nn.Linear  )  )



					def 	snake_case ( self							:						List[Any]  )->      int:
								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	List[Any]				=model_class(lowerCamelCase  )
											lowerCamelCase__	:	List[Any]				=inspect.signature(model.forward  )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCamelCase__	:	Union[str, Any]				=[*signature.parameters.keys()]

											lowerCamelCase__	:	Optional[Any]				=['''pixel_values''']
											self.assertListEqual(arg_names[:1],						lowerCamelCase  )



					def 	snake_case ( self							:						List[str]  )->      Optional[int]:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )



					def 	snake_case ( self							:						Dict  )->      Optional[Any]:
								lowerCamelCase__	:	str				=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*lowerCamelCase  )



					def 	snake_case ( self							:						Union[str, Any],						lowerCamelCase							:						List[str],						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Optional[int]  )->      int:
								# make masks reproducible
								np.random.seed(2  )

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

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

								super().check_pt_tf_models(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[int]  )->      int:
								lowerCamelCase__			,     lowerCamelCase__	:	Any				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__	:	int				=model_class(lowerCamelCase  )
											model.to(lowerCamelCase  )
											model.eval()
											# make random mask reproducible
											torch.manual_seed(2  )
											with torch.no_grad():
														lowerCamelCase__	:	List[str]				=model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )  )

											lowerCamelCase__	:	Any				=outputs[0].cpu().numpy()
											lowerCamelCase__	:	Optional[Any]				=0

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

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



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



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




					@slow
					def 	snake_case ( self							:						Optional[Any]  )->      Union[str, Any]:
								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__	:	Any				=ViTMAEModel.from_pretrained(lowerCamelCase  )
											self.assertIsNotNone(lowerCamelCase  )






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
			return image

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


					'''simple docstring'''





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




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

								lowerCamelCase__	:	Dict				=ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  ).to(lowerCamelCase  )

								lowerCamelCase__	:	int				=self.default_image_processor
								lowerCamelCase__	:	Tuple				=prepare_img()
								lowerCamelCase__	:	str				=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__	:	str				=ViTMAEConfig()
								lowerCamelCase__	:	Dict				=int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
								lowerCamelCase__	:	Dict				=np.random.uniform(size=(1, num_patches)  )

								# forward pass
								with torch.no_grad():
											lowerCamelCase__	:	Union[str, Any]				=model(**lowerCamelCase,						noise=torch.from_numpy(lowerCamelCase  ).to(device=lowerCamelCase  )  )

								# verify the logits
								lowerCamelCase__	:	Optional[int]				=torch.Size((1, 196, 768)  )
								self.assertEqual(outputs.logits.shape,						lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=torch.tensor(
								    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]  )

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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list							,	__lowerCamelCase       :					list							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			if index == number_of_items:
						return 0
			lowerCamelCase__	:	Optional[int]				=0
			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	List[str]				=knapsack(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	index + 1			)
			if weights[index] <= max_weight:
						lowerCamelCase__	:	Dict				=values[index] + knapsack(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	max_weight - weights[index]							,	index + 1			)
			return max(__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




