





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

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




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Optional[int]         =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

											__lowerCamelCase      :      int         =None
											if self.use_labels:
																		__lowerCamelCase      :      Optional[int]         =ids_tensor([self.batch_size]						,    self.type_sequence_label_size			)

											__lowerCamelCase      :      Tuple         =self.get_config()

											return config, pixel_values, labels




				def 					__lowercase  (     self  :Dict			):
											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 					__lowercase  (     self  :Any						,    __lowercase  :Any						,    __lowercase  :Union[str, Any]						,    __lowercase  :Any			):
											__lowerCamelCase      :      str         =ViTMAEModel(config=UpperCAmelCase__			)
											model.to(UpperCAmelCase__			)
											model.eval()
											__lowerCamelCase      :      Dict         =model(UpperCAmelCase__			)
											self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)			)




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :List[Any]						,    __lowercase  :List[Any]						,    __lowercase  :Tuple			):
											__lowerCamelCase      :      Dict         =ViTMAEForPreTraining(UpperCAmelCase__			)
											model.to(UpperCAmelCase__			)
											model.eval()
											__lowerCamelCase      :      Any         =model(UpperCAmelCase__			)
											__lowerCamelCase      :      str         =(self.image_size // self.patch_size) ** 2
											__lowerCamelCase      :      List[Any]         =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      :      str         =1
											__lowerCamelCase      :      Any         =ViTMAEForPreTraining(UpperCAmelCase__			)
											model.to(UpperCAmelCase__			)
											model.eval()
											__lowerCamelCase      :      Tuple         =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											__lowerCamelCase      :      List[str]         =model(UpperCAmelCase__			)
											__lowerCamelCase      :      int         =self.patch_size**2
											self.parent.assertEqual(result.logits.shape						,    (self.batch_size, num_patches, expected_num_channels)			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[int]         =self.prepare_config_and_inputs()
											__lowerCamelCase      :      int         =config_and_inputs
											__lowerCamelCase      :      List[Any]         ={'''pixel_values''': pixel_values}
											return config, inputs_dict

@require_torch
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       str	 =		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				__snake_case   :       int	 =		{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

				__snake_case   :       int	 =		False
				__snake_case   :       Optional[Any]	 =		False
				__snake_case   :       Union[str, Any]	 =		False
				__snake_case   :       List[str]	 =		False




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Union[str, Any]         =ViTMAEModelTester(self			)
											__lowerCamelCase      :      Tuple         =ConfigTester(self						,    config_class=UpperCAmelCase__						,    has_text_modality=UpperCAmelCase__						,    hidden_size=37			)




				def 					__lowercase  (     self  :Tuple			):
											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  :Tuple			):
											__lowerCamelCase      :      Tuple         =self.model_tester.prepare_config_and_inputs_for_common()

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




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      str         =self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowerCamelCase      :      Any         =['''pixel_values''']
																		self.assertListEqual(arg_names[:1]						,    UpperCAmelCase__			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      str         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCAmelCase__			)




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Union[str, Any]         =self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__			)




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :Optional[Any]						,    __lowercase  :Dict						,    __lowercase  :Tuple			):
											np.random.seed(2			)

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

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

											super().check_pt_tf_models(UpperCAmelCase__						,    UpperCAmelCase__						,    UpperCAmelCase__			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[str]         =self.model_tester.prepare_config_and_inputs_for_common()

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

																		__lowerCamelCase      :      Optional[int]         =outputs[0].cpu().numpy()
																		__lowerCamelCase      :      List[Any]         =0

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

																									# Make sure we don't have nans
																									__lowerCamelCase      :      Union[str, Any]         =after_outputs[0].cpu().numpy()
																									__lowerCamelCase      :      Tuple         =0
																									__lowerCamelCase      :      Optional[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
    to get deterministic results.'''			)
				def 					__lowercase  (     self  :Dict			):
											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  :str			):
											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  :Optional[int]			):
											pass




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




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




				@slow
				def 					__lowercase  (     self  :Optional[int]			):
											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		__lowerCamelCase      :      Dict         =ViTMAEModel.from_pretrained(UpperCAmelCase__			)
																		self.assertIsNotNone(UpperCAmelCase__			)



def 				lowerCAmelCase_			(      ):


							'''simple docstring'''







							__lowerCamelCase      :      List[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 					__lowercase  (     self  :List[str]			):
											return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''			) if is_vision_available() else None




				@slow
				def 					__lowercase  (     self  :List[Any]			):
											np.random.seed(2			)

											__lowerCamelCase      :      str         =ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''			).to(UpperCAmelCase__			)

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

											# forward pass
											with torch.no_grad():
																		__lowerCamelCase      :      List[str]         =model(**UpperCAmelCase__						,    noise=torch.from_numpy(UpperCAmelCase__			).to(device=UpperCAmelCase__			)			)

											# verify the logits
											__lowerCamelCase      :      Optional[Any]         =torch.Size((1, 196, 768)			)
											self.assertEqual(outputs.logits.shape						,    UpperCAmelCase__			)

											__lowerCamelCase      :      List[str]         =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(UpperCAmelCase__			)						,    atol=1e-4			)			)











"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :List[str]						,    *__lowercase  :int						,    **__lowercase  :Any			):
											warnings.warn(
											    '''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use FlavaImageProcessor instead.'''						,    __lowercase						,    )
											super().__init__(*__lowercase						,    **__lowercase			)




