




'''simple docstring'''




import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowerCAmelCase__          =						logging.getLogger(__name__)







class       __lowercase					(__a    ):
	_lowerCamelCase              =  '''sequence-classification'''

	def __init__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Any):



			if type(a_) == dict:
					UpperCamelCase__				:					Dict					=					Namespace(**a_)
			UpperCamelCase__				:					List[Any]					=					glue_output_modes[hparams.task]
			UpperCamelCase__				:					Optional[Any]					=					glue_tasks_num_labels[hparams.task]

			super().__init__(a_  ,					a_  ,					self.mode)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					**UpperCAmelCase_   :						Optional[Any]):
			return self.model(**a_)

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						List[Any]  ,					UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					Optional[Any]					=					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

			if self.config.model_type not in ["distilbert", "bart"]:
					UpperCamelCase__				:					Optional[int]					=					batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

			UpperCamelCase__				:					Union[str, Any]					=					self(**a_)
			UpperCamelCase__				:					List[str]					=					outputs[0]

			UpperCamelCase__				:					Tuple					=					self.trainer.lr_schedulers[0]["""scheduler"""]
			UpperCamelCase__				:					List[Any]					=					{"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
			return {"loss": loss, "log": tensorboard_logs}

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					self.hparams
			UpperCamelCase__				:					List[str]					=					processors[args.task]()
			UpperCamelCase__				:					Optional[Any]					=					processor.get_labels()

			for mode in ["train", "dev"]:
					UpperCamelCase__				:					Union[str, Any]					=					self._feature_file(a_)
					if os.path.exists(a_) and not args.overwrite_cache:
							logger.info('Loading features from cached file %s'  ,					a_)
					else:
							logger.info('Creating features from dataset file at %s'  ,					args.data_dir)
							UpperCamelCase__				:					Any					=					(
							    processor.get_dev_examples(args.data_dir)
							    if mode == """dev"""
							    else processor.get_train_examples(args.data_dir)
							)
							UpperCamelCase__				:					str					=					convert_examples_to_features(
							    a_  ,					self.tokenizer  ,					max_length=args.max_seq_length  ,					label_list=self.labels  ,					output_mode=args.glue_output_mode  ,					)
							logger.info('Saving features into cached file %s'  ,					a_)
							torch.save(a_  ,					a_)

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						bool = False):
			UpperCamelCase__				:					Optional[int]					=					"""dev""" if mode == """test""" else mode

			UpperCamelCase__				:					str					=					self._feature_file(a_)
			logger.info('Loading features from cached file %s'  ,					a_)
			UpperCamelCase__				:					int					=					torch.load(a_)
			UpperCamelCase__				:					Optional[int]					=					torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long)
			UpperCamelCase__				:					List[str]					=					torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long)
			UpperCamelCase__				:					str					=					torch.tensor([f.token_type_ids for f in features]  ,					dtype=torch.long)
			if self.hparams.glue_output_mode == "classification":
					UpperCamelCase__				:					Optional[int]					=					torch.tensor([f.label for f in features]  ,					dtype=torch.long)
			elif self.hparams.glue_output_mode == "regression":
					UpperCamelCase__				:					List[Any]					=					torch.tensor([f.label for f in features]  ,					dtype=torch.float)

			return DataLoader(
			    TensorDataset(a_  ,					a_  ,					a_  ,					a_)  ,					batch_size=a_  ,					shuffle=a_  ,					)

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						List[str]):
			UpperCamelCase__				:					List[Any]					=					{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

			if self.config.model_type not in ["distilbert", "bart"]:
					UpperCamelCase__				:					Union[str, Any]					=					batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

			UpperCamelCase__				:					Optional[int]					=					self(**a_)
			UpperCamelCase__				:					Union[str, Any]					=					outputs[:2]
			UpperCamelCase__				:					List[str]					=					logits.detach().cpu().numpy()
			UpperCamelCase__				:					Union[str, Any]					=					inputs["""labels"""].detach().cpu().numpy()

			return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

	def      __UpperCamelCase							(      self   :						Tuple  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					Optional[Any]					=					torch.stack([x['val_loss'] for x in outputs]).mean().detach().cpu().item()
			UpperCamelCase__				:					Tuple					=					np.concatenate([x['pred'] for x in outputs]  ,					axis=0)

			if self.hparams.glue_output_mode == "classification":
					UpperCamelCase__				:					Tuple					=					np.argmax(a_  ,					axis=1)
			elif self.hparams.glue_output_mode == "regression":
					UpperCamelCase__				:					Optional[int]					=					np.squeeze(a_)

			UpperCamelCase__				:					Optional[Any]					=					np.concatenate([x['target'] for x in outputs]  ,					axis=0)
			UpperCamelCase__				:					Optional[Any]					=					[[] for _ in range(out_label_ids.shape[0])]
			UpperCamelCase__				:					Tuple					=					[[] for _ in range(out_label_ids.shape[0])]

			UpperCamelCase__				:					Tuple					=					{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task  ,					a_  ,					a_)}

			UpperCamelCase__				:					Optional[int]					=					dict(results.items())
			UpperCamelCase__				:					str					=					results
			return ret, preds_list, out_label_list

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						list):
			UpperCamelCase__				:					Tuple					=					self._eval_end(a_)
			UpperCamelCase__				:					int					=					ret["""log"""]
			return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					str					=					self._eval_end(a_)
			UpperCamelCase__				:					List[str]					=					ret["""log"""]
			# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
			return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						Tuple):
			BaseTransformer.add_model_specific_args(a_  ,					a_)
			parser.add_argument(
			    '--max_seq_length'  ,					default=128  ,					type=a_  ,					help=(
			        'The maximum total input sequence length after tokenization. Sequences longer '
			        'than this will be truncated, sequences shorter will be padded.'
			    )  ,					)

			parser.add_argument(
			    '--task'  ,					default=''  ,					type=a_  ,					required=a_  ,					help='The GLUE task to run'  ,					)
			parser.add_argument(
			    '--gpus'  ,					default=0  ,					type=a_  ,					help='The number of GPUs allocated for this, it is by default 0 meaning none'  ,					)

			parser.add_argument(
			    '--overwrite_cache'  ,					action='store_true'  ,					help='Overwrite the cached training and evaluation sets')

			return parser







def       __UpperCAmelCase						(  )							->     str:
		UpperCamelCase__				:					Dict					=					argparse.ArgumentParser()
		add_generic_args(snake_case__     ,      os.getcwd())
		UpperCamelCase__				:					Optional[Any]					=					GLUETransformer.add_model_specific_args(snake_case__     ,      os.getcwd())
		UpperCamelCase__				:					Tuple					=					parser.parse_args()

		# If output_dir not provided, a folder will be generated in pwd
		if args.output_dir is None:
				UpperCamelCase__				:					int					=					os.path.join(
				    './results'     ,      f'{args.task}_{time.strftime("%Y%m%d_%H%M%S")}'     ,      )
				os.makedirs(args.output_dir)

		UpperCamelCase__				:					Optional[Any]					=					GLUETransformer(snake_case__)
		UpperCamelCase__				:					Union[str, Any]					=					generic_train(snake_case__     ,      snake_case__)

		# Optionally, predict on dev set and write to output_dir
		if args.do_predict:
				UpperCamelCase__				:					List[Any]					=					sorted(glob.glob(os.path.join(args.output_dir     ,      'checkpoint-epoch=*.ckpt')     ,      recursive=snake_case__))
				UpperCamelCase__				:					Any					=					model.load_from_checkpoint(checkpoints[-1])
				return trainer.test(snake_case__)


if __name__ == "__main__":
		main()












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

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

	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

			UpperCamelCase__				:					Any					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[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=UpperCAmelCase_  ,					initializer_range=self.initializer_range  ,					mask_ratio=self.mask_ratio  ,					)

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

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





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					List[str]					=					self.prepare_config_and_inputs()
			UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					config_and_inputs
			UpperCamelCase__				:					Any					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
	_lowerCamelCase              =  {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False
	_lowerCamelCase              =  False

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					List[str]					=					ViTMAEModelTester(self)
			UpperCamelCase__				:					Any					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37)

	def      __UpperCamelCase							(      self   :						Any):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='ViTMAE does not use inputs_embeds')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__,	UpperCamelCase__				:					Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Optional[int]					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			# make masks reproducible
			np.random.seed(2)

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

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

			super().check_pt_tf_models(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Dict					=					outputs[0].cpu().numpy()
					UpperCamelCase__				:					Optional[int]					=					0

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

							# Make sure we don't have nans
							UpperCamelCase__				:					Tuple					=					after_outputs[0].cpu().numpy()
							UpperCamelCase__				:					Any					=					0
							UpperCamelCase__				:					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\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

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

	@unittest.skip(
	    reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.')
	def      __UpperCamelCase							(      self   :						Tuple):
			pass

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

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





	@slow
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Tuple					=					ViTMAEModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					int					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
		return image







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

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





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

			UpperCamelCase__				:					Union[str, Any]					=					ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(UpperCAmelCase_)

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

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

			# verify the logits
			UpperCamelCase__				:					Tuple					=					torch.Size((1, 196, 768))
			self.assertEqual(outputs.logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					torch.tensor(
			    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]])

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




