




'''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 		a							:


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

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


				def     A_					(						self       :   int   ):
									snake_case_		     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

									snake_case_		     =							self.get_config()

									return config, pixel_values, labels


				def     A_					(						self       :   Union[str, 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=lowercase_			,							initializer_range=self.initializer_range			,							mask_ratio=self.mask_ratio			,							)


				def     A_					(						self       :   int			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									snake_case_		     =							ViTMAEModel(config=lowercase_   )
									model.to(lowercase_   )
									model.eval()
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)   )


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

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


				def     A_					(						self       :   Any   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									snake_case_     ,snake_case_     ,snake_case_		     =							config_and_inputs
									snake_case_		     =							{'''pixel_values''': pixel_values}
									return config, inputs_dict







@require_torch
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				snake_case_           =						{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   Optional[int]   ):
									snake_case_		     =							ViTMAEModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							has_text_modality=lowercase_			,							hidden_size=37   )


				def     A_					(						self       :   Tuple   ):
									self.config_tester.run_common_tests()


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


				def     A_					(						self       :   Any   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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


				def     A_					(						self       :   Optional[int]   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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

														snake_case_		     =							['''pixel_values''']
														self.assertListEqual(arg_names[:1]			,							lowercase_   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase_   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowercase_   )


				def     A_					(						self       :   Dict			,							lowercase_       :   List[str]			,							lowercase_       :   int			,							lowercase_       :   int   ):
									# make masks reproducible
									np.random.seed(2   )

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

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

									super().check_pt_tf_models(lowercase_			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   int   ):
									snake_case_     ,snake_case_		     =							self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

																			# Make sure we don't have nans
																			snake_case_		     =							after_outputs[0].cpu().numpy()
																			snake_case_		     =							0
																			snake_case_		     =							np.amax(np.abs(out_a - out_a   )   )
																			self.assertLessEqual(lowercase_			,							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     A_					(						self       :   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     A_					(						self       :   Optional[int]   ):
									pass


				@unittest.skip(
				    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
				def     A_					(						self       :   Optional[Any]   ):
									pass


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


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


				@slow
				def     A_					(						self       :   List[Any]   ):
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							ViTMAEModel.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






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


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


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

									snake_case_		     =							ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''   ).to(lowercase_   )

									snake_case_		     =							self.default_image_processor
									snake_case_		     =							prepare_img()
									snake_case_		     =							image_processor(images=lowercase_			,							return_tensors='''pt'''   ).to(lowercase_   )

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

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

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

									snake_case_		     =							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(lowercase_   )			,							atol=1e-4   )   )







'''simple docstring'''

from ....utils import logging


a    :							Optional[int]										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Any=None			,							lowercase_       :   List[Any]=2048   ):
									snake_case_		     =							config.__dict__
									snake_case_		     =							modal_hidden_size
									if num_labels:
														snake_case_		     =							num_labels

