





'''simple docstring'''


import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__			:					Union[str, Any]       						=						logging.getLogger(__name__)


a__			:					Optional[int]       						=						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
a__			:					List[Any]       						=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class     lowerCAmelCase__ :

       '''simple docstring'''
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={
               "help": (
                   "The model checkpoint for weights initialization. Leave None if you want to train a model from"
                   " scratch."
               )
           }      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase_    )}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      , )






@dataclass
class     lowerCAmelCase__ :

       '''simple docstring'''
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "The input training data file (a text file)."}    )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={
               "help": (
                   "The input training data files (multiple files in glob format). "
                   "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
               )
           }      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}      , )

       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Train with masked-language modeling loss instead of language modeling."}    )
       _lowerCamelCase								=field(default=UpperCAmelCase_      , metadata={"help": "Whether ot not to use whole word mask."}    )
       _lowerCamelCase								=field(
           default=0.15      , metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}    )
       _lowerCamelCase								=field(
           default=1 / 6      , metadata={
               "help": (
                   "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
                   " modeling."
               )
           }      , )
       _lowerCamelCase								=field(
           default=5      , metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}    )

       _lowerCamelCase								=field(
           default=-1      , metadata={
               "help": (
                   "Optional input sequence length after tokenization."
                   "The training dataset will be truncated in block of this size for training."
                   "Default to the model max input length for single sentence inputs (take into account special tokens)."
               )
           }      , )
       _lowerCamelCase								=field(
           default=UpperCAmelCase_      , metadata={"help": "Overwrite the cached training and evaluation sets"}    )


def       __snake_case		( SCREAMING_SNAKE_CASE_ :			DataTrainingArguments			,	SCREAMING_SNAKE_CASE_ :			PreTrainedTokenizer			,	SCREAMING_SNAKE_CASE_ :			bool = False			,	SCREAMING_SNAKE_CASE_ :			Optional[str] = None			,	)							->     Any:

       """simple docstring"""


       def _dataset(SCREAMING_SNAKE_CASE_ :			Tuple			,	SCREAMING_SNAKE_CASE_ :			List[Any]=None ):
              if args.line_by_line:
                     if ref_path is not None:
                            if not args.whole_word_mask or not args.mlm:
                                   raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask''' )
                            return LineByLineWithRefDataset(
                                tokenizer=SCREAMING_SNAKE_CASE_			,	file_path=SCREAMING_SNAKE_CASE_			,	block_size=args.block_size			,	ref_path=SCREAMING_SNAKE_CASE_			,	)

                     return LineByLineTextDataset(tokenizer=SCREAMING_SNAKE_CASE_			,	file_path=SCREAMING_SNAKE_CASE_			,	block_size=args.block_size )
              else:
                     return TextDataset(
                         tokenizer=SCREAMING_SNAKE_CASE_			,	file_path=SCREAMING_SNAKE_CASE_			,	block_size=args.block_size			,	overwrite_cache=args.overwrite_cache			,	cache_dir=SCREAMING_SNAKE_CASE_			,	)

       if evaluate:
              return _dataset(args.eval_data_file			,	args.eval_ref_file )
       elif args.train_data_files:
              return ConcatDataset([_dataset(SCREAMING_SNAKE_CASE_ ) for f in glob(args.train_data_files )] )
       else:
              return _dataset(args.train_data_file			,	args.train_ref_file )
def       __snake_case		( )							->     Tuple:

       """simple docstring"""


       UpperCAmelCase  =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
       UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						parser.parse_args_into_dataclasses()

       if data_args.eval_data_file is None and training_args.do_eval:
              raise ValueError(
                  '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
                  '''or remove the --do_eval argument.''' )
       if (
           os.path.exists(training_args.output_dir )
           and os.listdir(training_args.output_dir )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                  ''' --overwrite_output_dir to overcome.''' )

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,	datefmt='''%m/%d/%Y %H:%M:%S'''			,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,	)
       logger.warning(
           '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''			,	training_args.local_rank			,	training_args.device			,	training_args.n_gpu			,	bool(training_args.local_rank != -1 )			,	training_args.fpaa			,	)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('''Training/evaluation parameters %s'''			,	SCREAMING_SNAKE_CASE_ )

       # Set seed
       set_seed(training_args.seed )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       if model_args.config_name:
              UpperCAmelCase  =						AutoConfig.from_pretrained(model_args.config_name			,	cache_dir=model_args.cache_dir )
       elif model_args.model_name_or_path:
              UpperCAmelCase  =						AutoConfig.from_pretrained(model_args.model_name_or_path			,	cache_dir=model_args.cache_dir )
       else:
              UpperCAmelCase  =						CONFIG_MAPPING[model_args.model_type]()
              logger.warning('''You are instantiating a new config instance from scratch.''' )

       if model_args.tokenizer_name:
              UpperCAmelCase  =						AutoTokenizer.from_pretrained(model_args.tokenizer_name			,	cache_dir=model_args.cache_dir )
       elif model_args.model_name_or_path:
              UpperCAmelCase  =						AutoTokenizer.from_pretrained(model_args.model_name_or_path			,	cache_dir=model_args.cache_dir )
       else:
              raise ValueError(
                  '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
                  ''' script, save it,and load it from here, using --tokenizer_name''' )

       if model_args.model_name_or_path:
              UpperCAmelCase  =						AutoModelWithLMHead.from_pretrained(
                  model_args.model_name_or_path			,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path )			,	config=SCREAMING_SNAKE_CASE_			,	cache_dir=model_args.cache_dir			,	)
       else:
              logger.info('''Training new model from scratch''' )
              UpperCAmelCase  =						AutoModelWithLMHead.from_config(SCREAMING_SNAKE_CASE_ )

       model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_ ) )

       if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
              raise ValueError(
                  '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
                  '''--mlm flag (masked language modeling).''' )

       if data_args.block_size <= 0:
              UpperCAmelCase  =						tokenizer.max_len
              # Our input block size will be the max possible for the model
       else:
              UpperCAmelCase  =						min(data_args.block_size			,	tokenizer.max_len )

       # Get datasets

       UpperCAmelCase  =						(
           get_dataset(SCREAMING_SNAKE_CASE_			,	tokenizer=SCREAMING_SNAKE_CASE_			,	cache_dir=model_args.cache_dir ) if training_args.do_train else None
       )
       UpperCAmelCase  =						(
           get_dataset(SCREAMING_SNAKE_CASE_			,	tokenizer=SCREAMING_SNAKE_CASE_			,	evaluate=SCREAMING_SNAKE_CASE_			,	cache_dir=model_args.cache_dir )
           if training_args.do_eval
           else None
       )
       if config.model_type == "xlnet":
              UpperCAmelCase  =						DataCollatorForPermutationLanguageModeling(
                  tokenizer=SCREAMING_SNAKE_CASE_			,	plm_probability=data_args.plm_probability			,	max_span_length=data_args.max_span_length			,	)
       else:
              if data_args.mlm and data_args.whole_word_mask:
                     UpperCAmelCase  =						DataCollatorForWholeWordMask(
                         tokenizer=SCREAMING_SNAKE_CASE_			,	mlm_probability=data_args.mlm_probability )
              else:
                     UpperCAmelCase  =						DataCollatorForLanguageModeling(
                         tokenizer=SCREAMING_SNAKE_CASE_			,	mlm=data_args.mlm			,	mlm_probability=data_args.mlm_probability )

    # Initialize our Trainer
       UpperCAmelCase  =						Trainer(
           model=SCREAMING_SNAKE_CASE_			,	args=SCREAMING_SNAKE_CASE_			,	data_collator=SCREAMING_SNAKE_CASE_			,	train_dataset=SCREAMING_SNAKE_CASE_			,	eval_dataset=SCREAMING_SNAKE_CASE_			,	prediction_loss_only=SCREAMING_SNAKE_CASE_			,	)

       # Training
       if training_args.do_train:
              UpperCAmelCase  =						(
                  model_args.model_name_or_path
                  if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
                  else None
              )
              trainer.train(model_path=SCREAMING_SNAKE_CASE_ )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
       UpperCAmelCase  =						{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***''' )

              UpperCAmelCase  =						trainer.evaluate()

              UpperCAmelCase  =						math.exp(eval_output['''eval_loss'''] )
              UpperCAmelCase  =						{'''perplexity''': perplexity}

              UpperCAmelCase  =						os.path.join(training_args.output_dir			,	'''eval_results_lm.txt''' )
              if trainer.is_world_master():
                     with open(SCREAMING_SNAKE_CASE_			,	'''w''' ) as writer:
                            logger.info('''***** Eval results *****''' )
                            for key in sorted(result.keys() ):
                                   logger.info('''  %s = %s'''			,	SCREAMING_SNAKE_CASE_			,	str(result[key] ) )
                                   writer.write('''%s = %s\n''' % (key, str(result[key] )) )

              results.update(SCREAMING_SNAKE_CASE_ )

       return results
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any] )							->     Optional[Any]:

       """simple docstring"""


       main()


if __name__ == "__main__":
    main()





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 ,  snake_case ,  snake_case=13 ,  snake_case=30 ,  snake_case=2 ,  snake_case=3 ,  snake_case=True ,  snake_case=True ,  snake_case=32 ,  snake_case=5 ,  snake_case=4 ,  snake_case=37 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=10 ,  snake_case=0.02 ,  snake_case=3 ,  snake_case=0.6 ,  snake_case=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			):
											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			):
											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=snake_case ,  initializer_range=self.initializer_range ,  mask_ratio=self.mask_ratio ,  )




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       ViTMAEModel(config=snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case			)
											self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       ViTMAEForPreTraining(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       model(snake_case			)
											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(snake_case			)
											model.to(snake_case			)
											model.eval()
											snake_case_     							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											snake_case_     							=       model(snake_case			)
											snake_case_     							=       self.patch_size**2
											self.parent.assertEqual(result.logits.shape ,  (self.batch_size, num_patches, expected_num_channels)			)




				def        a				(   self			):
											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 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Dict			     =     (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     False
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     False




				def        a				(   self			):
											snake_case_     							=       ViTMAEModelTester(self			)
											snake_case_     							=       ConfigTester(self ,  config_class=snake_case ,  has_text_modality=snake_case ,  hidden_size=37			)




				def        a				(   self			):
											self.config_tester.run_common_tests()




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




				def        a				(   self			):
											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(snake_case			)
																		self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)			)
																		snake_case_     							=       model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(snake_case ,  nn.Linear			)			)




				def        a				(   self			):
											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(snake_case			)
																		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] ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case			)




				def        a				(   self			):
											snake_case_     							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*snake_case			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											# 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(snake_case			)

											# 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(snake_case ,  snake_case ,  snake_case			)




				def        a				(   self			):
											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(snake_case			)
																		model.to(snake_case			)
																		model.eval()
																		# make random mask reproducible
																		torch.manual_seed(2			)
																		with torch.no_grad():
																									snake_case_     							=       model(**self._prepare_for_class(snake_case ,  snake_case			)			)

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

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

																									# 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(snake_case ,  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        a				(   self			):
											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        a				(   self			):
											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        a				(   self			):
											pass




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




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




				@slow
				def        a				(   self			):
											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_     							=       ViTMAEModel.from_pretrained(snake_case			)
																		self.assertIsNotNone(snake_case			)





def    __lowerCamelCase      (					):







							'''simple docstring'''






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







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




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




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

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

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

											# 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(**snake_case ,  noise=torch.from_numpy(snake_case			).to(device=snake_case			)			)

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

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




