






'''simple docstring'''







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)

class _lowercase			(       unittest.TestCase	):




							def __init__(							self:       int    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Any=13    ,							UpperCamelCase__:       int=7    ,							UpperCamelCase__:       int=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Optional[int]=True    ,							UpperCamelCase__:       List[Any]=True    ,							UpperCamelCase__:       Optional[int]=99    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Dict=5    ,							UpperCamelCase__:       Optional[int]=4    ,							UpperCamelCase__:       Optional[Any]=37    ,							UpperCamelCase__:       Union[str, Any]="gelu"    ,							UpperCamelCase__:       str=0.1    ,							UpperCamelCase__:       str=0.1    ,							UpperCamelCase__:       List[str]=512    ,							UpperCamelCase__:       Optional[Any]=16    ,							UpperCamelCase__:       Union[str, Any]=2    ,							UpperCamelCase__:       Tuple=0.02    ,							UpperCamelCase__:       Union[str, Any]=4    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Union[str, Any] 						= batch_size
														lowerCamelCase__      :			Union[str, Any] 						= seq_length
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			Optional[Any] 						= use_attention_mask
														lowerCamelCase__      :			List[Any] 						= use_token_type_ids
														lowerCamelCase__      :			Dict 						= use_labels
														lowerCamelCase__      :			List[Any] 						= vocab_size
														lowerCamelCase__      :			Optional[int] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			List[str] 						= num_attention_heads
														lowerCamelCase__      :			List[str] 						= intermediate_size
														lowerCamelCase__      :			Optional[Any] 						= hidden_act
														lowerCamelCase__      :			Tuple 						= hidden_dropout_prob
														lowerCamelCase__      :			Tuple 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			List[Any] 						= type_vocab_size
														lowerCamelCase__      :			Tuple 						= type_sequence_label_size
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			List[str] 						= num_choices




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Optional[int] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			List[Any] 						= None
														if self.use_attention_mask:
																					lowerCamelCase__      :			Dict 						= random_attention_mask([self.batch_size, self.seq_length]  )

														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_token_type_ids:
																					lowerCamelCase__      :			Tuple 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size  )

														lowerCamelCase__      :			Dict 						= RobertaPreLayerNormConfig(
														    vocab_size=self.vocab_size    ,							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    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							is_decoder=UpperCamelCase__    ,							initializer_range=self.initializer_range    ,							)

														return config, input_ids, token_type_ids, attention_mask




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= config_and_inputs
														lowerCamelCase__      :			Tuple 						= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
														return config, inputs_dict




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			str 						= config_and_inputs

														lowerCamelCase__      :			int 						= True
														lowerCamelCase__      :			Optional[int] 						= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
														lowerCamelCase__      :			str 						= ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2  )

														return (
														    config,
														    input_ids,
														    token_type_ids,
														    encoder_hidden_states,
														    encoder_attention_mask,
														)



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _lowercase			(       _lowercase       ,   unittest.TestCase	):
							a          = True

							a          = (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			List[Any] 						= FlaxRobertaPreLayerNormModelTester(self  )




							@slow
							def 	lowerCamelCase_       (							self:       str  ):
														for model_class_name in self.all_model_classes:
																					lowerCamelCase__      :			int 						= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,							from_pt=UpperCamelCase__  )
																					lowerCamelCase__      :			List[Any] 						= model(np.ones((1, 1)  )  )
																					self.assertIsNotNone(UpperCamelCase__  )



@require_flax
class _lowercase			(       unittest.TestCase	):




							@slow
							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[Any] 						= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,							from_pt=UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,							dtype=jnp.intaa  )
														lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__  )[0]
														lowerCamelCase__      :			Dict 						= [1, 11, 50_265]
														self.assertEqual(list(output.shape  )    ,							UpperCamelCase__  )
														# compare the actual values for a slice.
														lowerCamelCase__      :			Union[str, Any] 						= np.array(
														    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]    ,							dtype=np.floataa  )
														self.assertTrue(np.allclose(output[:, :3, :3]    ,							UpperCamelCase__    ,							atol=1e-4  )  )




							@slow
							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			Optional[int] 						= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,							from_pt=UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    ,							dtype=jnp.intaa  )
														lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__  )[0]
														# compare the actual values for a slice.
														lowerCamelCase__      :			Optional[int] 						= np.array(
														    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]    ,							dtype=np.floataa  )
														self.assertTrue(np.allclose(output[:, :3, :3]    ,							UpperCamelCase__    ,							atol=1e-4  )  )









'''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 _lowercase			:




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

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

														lowerCamelCase__      :			Any 						= self.get_config()

														return config, pixel_values, labels




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




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Dict  ):
														lowerCamelCase__      :			int 						= ViTMAEForPreTraining(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Optional[int] 						= model(UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= (self.image_size // self.patch_size) ** 2
														lowerCamelCase__      :			str 						= 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__      :			Dict 						= 1
														lowerCamelCase__      :			Optional[int] 						= ViTMAEForPreTraining(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
														lowerCamelCase__      :			Optional[Any] 						= model(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= self.patch_size**2
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, num_patches, expected_num_channels)  )




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



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   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 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Tuple 						= ViTMAEModelTester(self  )
														lowerCamelCase__      :			Union[str, Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__    ,							hidden_size=37  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       Dict  ):
														pass




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()

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




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			List[str] 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__  )




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

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

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

														super().check_pt_tf_models(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__      :			Optional[int] 						= outputs[0].cpu().numpy()
																					lowerCamelCase__      :			List[str] 						= 0

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

																												# Make sure we don't have nans
																												lowerCamelCase__      :			Dict 						= after_outputs[0].cpu().numpy()
																												lowerCamelCase__      :			Tuple 						= 0
																												lowerCamelCase__      :			Union[str, 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 	lowerCamelCase_       (							self:       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 	lowerCamelCase_       (							self:       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 	lowerCamelCase_       (							self:       List[str]  ):
														pass




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




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




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Tuple 						= ViTMAEModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> List[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




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




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

														lowerCamelCase__      :			str 						= ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""  ).to(UpperCamelCase__  )

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

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

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 196, 768)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			str 						= 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(UpperCamelCase__  )    ,							atol=1e-4  )  )

