





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__lowerCamelCase			:	Optional[int]					    =	logging.getLogger(__name__)



@dataclass
class    A__  :
      _UpperCAmelCase      :str		       =  field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=__snake_case			,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=__snake_case			,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default=__snake_case			,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,  )
      _UpperCAmelCase      :bool		       =  field(default=__snake_case			,  metadata={'help': 'Whether tp freeze the encoder.'}     )
      _UpperCAmelCase      :bool		       =  field(default=__snake_case			,  metadata={'help': 'Whether  to freeze the embeddings.'}     )



@dataclass
class    A__  :
      _UpperCAmelCase      :str		       =  field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(
          default='summarization'			,  metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}			,  )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=1_0_2_4			,  metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }			,  )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=1_2_8			,  metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }			,  )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=1_4_2			,  metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }			,  )
      _UpperCAmelCase      :Optional[int]		       =  field(
          default=1_4_2			,  metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }			,  )
      _UpperCAmelCase      :Optional[int]		       =  field(default=-1			,  metadata={'help': '# training examples. -1 means use all.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=-1			,  metadata={'help': '# validation examples. -1 means use all.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=-1			,  metadata={'help': '# test examples. -1 means use all.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(default=__snake_case			,  metadata={'help': 'Source language id for translation.'}     )
      _UpperCAmelCase      :Optional[str]		       =  field(default=__snake_case			,  metadata={'help': 'Target language id for translation.'}     )
      _UpperCAmelCase      :Optional[int]		       =  field(default=__snake_case			,  metadata={'help': '# num_beams to use for evaluation.'}     )
      _UpperCAmelCase      :bool		       =  field(
          default=__snake_case			,  metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}			,  )



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					Tuple:
 logger.info(F"""***** {split} metrics *****"""	)
 for key in sorted(metrics.keys()	):
  logger.info(F"""  {key} = {metrics[key]}"""	)
 save_json(_lowerCAmelCase				,  os.path.join(_lowerCAmelCase				,  F"""{split}_results.json"""	)	)



def 						A_							(	)		->					List[Any]:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase      :   List[str]			      =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)	)

 if len(sys.argv	) == 2 and sys.argv[1].endswith(".json"	):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
 else:
  UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  parser.parse_args_into_dataclasses()

 check_output_dir(_lowerCAmelCase	)

 # 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.parallel_mode == ParallelMode.DISTRIBUTED	)				,  training_args.fpaa				,  )
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()
 # 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()
 logger.info("Training/evaluation parameters %s"				,  _lowerCAmelCase	)

 # 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.

 UpperCamelCase      :   Any			      =  AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path				,  cache_dir=model_args.cache_dir				,  )

 UpperCamelCase      :   int			      =  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
 for p in extra_model_params:
  if getattr(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	):
   assert hasattr(_lowerCAmelCase				,  _lowerCAmelCase	), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
   setattr(_lowerCAmelCase				,  _lowerCAmelCase				,  getattr(_lowerCAmelCase				,  _lowerCAmelCase	)	)

 UpperCamelCase      :   Dict			      =  AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,  cache_dir=model_args.cache_dir				,  )
 UpperCamelCase      :   str			      =  AutoModelForSeqaSeqLM.from_pretrained(
     model_args.model_name_or_path				,  from_tf=".ckpt" in model_args.model_name_or_path				,  config=_lowerCAmelCase				,  cache_dir=model_args.cache_dir				,  )

 # use task specific params
 use_task_specific_params(_lowerCAmelCase				,  data_args.task	)

 # set num_beams for evaluation
 if data_args.eval_beams is None:
  UpperCamelCase      :   Any			      =  model.config.num_beams

 # set decoder_start_token_id for MBart
 if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase				,  (MBartTokenizer, MBartTokenizerFast)	):
  assert (
      data_args.tgt_lang is not None and data_args.src_lang is not None
  ), "mBart requires --tgt_lang and --src_lang"
  if isinstance(_lowerCAmelCase				,  _lowerCAmelCase	):
   UpperCamelCase      :   Optional[Any]			      =  tokenizer.lang_code_to_id[data_args.tgt_lang]
  else:
   UpperCamelCase      :   str			      =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

 if model_args.freeze_embeds:
  freeze_embeds(_lowerCAmelCase	)
 if model_args.freeze_encoder:
  freeze_params(model.get_encoder()	)
  assert_all_frozen(model.get_encoder()	)

 UpperCamelCase      :   List[Any]			      =  SeqaSeqDataset

 # Get datasets
 UpperCamelCase      :   Union[str, Any]			      =  (
     dataset_class(
         _lowerCAmelCase				,  type_path="train"				,  data_dir=data_args.data_dir				,  n_obs=data_args.n_train				,  max_target_length=data_args.max_target_length				,  max_source_length=data_args.max_source_length				,  prefix=model.config.prefix or ""				,  )
     if training_args.do_train
     else None
 )
 UpperCamelCase      :   Optional[Any]			      =  (
     dataset_class(
         _lowerCAmelCase				,  type_path="val"				,  data_dir=data_args.data_dir				,  n_obs=data_args.n_val				,  max_target_length=data_args.val_max_target_length				,  max_source_length=data_args.max_source_length				,  prefix=model.config.prefix or ""				,  )
     if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
     else None
 )
 UpperCamelCase      :   Any			      =  (
     dataset_class(
         _lowerCAmelCase				,  type_path="test"				,  data_dir=data_args.data_dir				,  n_obs=data_args.n_test				,  max_target_length=data_args.test_max_target_length				,  max_source_length=data_args.max_source_length				,  prefix=model.config.prefix or ""				,  )
     if training_args.do_predict
     else None
 )

 # Initialize our Trainer
 UpperCamelCase      :   str			      =  (
     build_compute_metrics_fn(data_args.task				,  _lowerCAmelCase	) if training_args.predict_with_generate else None
 )
 UpperCamelCase      :   List[Any]			      =  SeqaSeqTrainer(
     model=_lowerCAmelCase				,  args=_lowerCAmelCase				,  data_args=_lowerCAmelCase				,  train_dataset=_lowerCAmelCase				,  eval_dataset=_lowerCAmelCase				,  data_collator=SeqaSeqDataCollator(
         _lowerCAmelCase				,  _lowerCAmelCase				,  model.config.decoder_start_token_id				,  training_args.tpu_num_cores	)				,  compute_metrics=_lowerCAmelCase				,  tokenizer=_lowerCAmelCase				,  )

 UpperCamelCase      :   Optional[int]			      =  {}
 # Training
 if training_args.do_train:
  logger.info("*** Train ***"	)

  UpperCamelCase      :   int			      =  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
  UpperCamelCase      :   Tuple			      =  train_result.metrics
  UpperCamelCase      :   int			      =  data_args.n_train

  trainer.save_model()  # this also saves the tokenizer

  if trainer.is_world_process_zero():
   handle_metrics("train"				,  _lowerCAmelCase				,  training_args.output_dir	)
   all_metrics.update(_lowerCAmelCase	)

   # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir				,  "trainer_state.json"	)	)

   # For convenience, we also re-save the tokenizer to the same directory,
   # so that you can share your model easily on huggingface.co/models =)
   tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"	)

  UpperCamelCase      :   Tuple			      =  trainer.evaluate(metric_key_prefix="val"	)
  UpperCamelCase      :   Any			      =  data_args.n_val
  UpperCamelCase      :   Union[str, Any]			      =  round(metrics["val_loss"]				,  4	)

  if trainer.is_world_process_zero():
   handle_metrics("val"				,  _lowerCAmelCase				,  training_args.output_dir	)
   all_metrics.update(_lowerCAmelCase	)

 if training_args.do_predict:
  logger.info("*** Predict ***"	)

  UpperCamelCase      :   Union[str, Any]			      =  trainer.predict(test_dataset=_lowerCAmelCase				,  metric_key_prefix="test"	)
  UpperCamelCase      :   List[Any]			      =  test_output.metrics
  UpperCamelCase      :   List[str]			      =  data_args.n_test

  if trainer.is_world_process_zero():
   UpperCamelCase      :   Dict			      =  round(metrics["test_loss"]				,  4	)
   handle_metrics("test"				,  _lowerCAmelCase				,  training_args.output_dir	)
   all_metrics.update(_lowerCAmelCase	)

   if training_args.predict_with_generate:
    UpperCamelCase      :   str			      =  tokenizer.batch_decode(
        test_output.predictions				,  skip_special_tokens=_lowerCAmelCase				,  clean_up_tokenization_spaces=_lowerCAmelCase	)
    UpperCamelCase      :   Any			      =  lmap(str.strip				,  _lowerCAmelCase	)
    write_txt_file(_lowerCAmelCase				,  os.path.join(training_args.output_dir				,  "test_generations.txt"	)	)

 if trainer.is_world_process_zero():
  save_json(_lowerCAmelCase				,  os.path.join(training_args.output_dir				,  "all_results.json"	)	)

 return all_metrics



def 						A_							(	_lowerCAmelCase	)		->					Union[str, Any]:
 # For xla_spawn (TPUs)
 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    A__  :





      def __init__(					self			,				A_			,				A_=13			,				A_=30			,				A_=2			,				A_=3			,				A_=True			,				A_=True			,				A_=32			,				A_=5			,				A_=4			,				A_=37			,				A_="gelu"			,				A_=0.1			,				A_=0.1			,				A_=10			,				A_=0.02			,				A_=3			,				A_=0.6			,				A_=None			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  parent
       UpperCamelCase      :   Union[str, Any]			      =  batch_size
       UpperCamelCase      :   Any			      =  image_size
       UpperCamelCase      :   Tuple			      =  patch_size
       UpperCamelCase      :   Union[str, Any]			      =  num_channels
       UpperCamelCase      :   Tuple			      =  is_training
       UpperCamelCase      :   int			      =  use_labels
       UpperCamelCase      :   Tuple			      =  hidden_size
       UpperCamelCase      :   Optional[Any]			      =  num_hidden_layers
       UpperCamelCase      :   str			      =  num_attention_heads
       UpperCamelCase      :   Optional[int]			      =  intermediate_size
       UpperCamelCase      :   Dict			      =  hidden_act
       UpperCamelCase      :   List[str]			      =  hidden_dropout_prob
       UpperCamelCase      :   str			      =  attention_probs_dropout_prob
       UpperCamelCase      :   Optional[Any]			      =  type_sequence_label_size
       UpperCamelCase      :   Tuple			      =  initializer_range
       UpperCamelCase      :   Any			      =  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      :   Union[str, Any]			      =  (image_size // patch_size) ** 2
       UpperCamelCase      :   int			      =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       UpperCamelCase      :   str			      =  self.get_config()

       return config, pixel_values, labels





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       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=A_			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Any			      =  ViTMAEModel(config=A_  )
       model.to(A_  )
       model.eval()
       UpperCamelCase      :   Optional[Any]			      =  model(A_  )
       self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  self.prepare_config_and_inputs()
       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  config_and_inputs
       UpperCamelCase      :   Optional[int]			      =  {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class    A__  (			__snake_case			,  __snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :Any		       =  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
      _UpperCAmelCase      :int		       =  {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

      _UpperCAmelCase      :Optional[Any]		       =  False
      _UpperCAmelCase      :Any		       =  False
      _UpperCAmelCase      :Optional[int]		       =  False
      _UpperCAmelCase      :Dict		       =  False





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEModelTester(self  )
       UpperCamelCase      :   Any			      =  ConfigTester(self			,				config_class=A_			,				has_text_modality=A_			,				hidden_size=37  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   List[Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase      :   Optional[int]			      =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       np.random.seed(2  )

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

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

       super().check_pt_tf_models(A_			,				A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       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(A_  )
        model.to(A_  )
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2  )
        with torch.no_grad():
         UpperCamelCase      :   str			      =  model(**self._prepare_for_class(A_			,				A_  )  )

        UpperCamelCase      :   Any			      =  outputs[0].cpu().numpy()
        UpperCamelCase      :   str			      =  0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(A_  )
         UpperCamelCase      :   Optional[int]			      =  model_class.from_pretrained(A_  )
         model.to(A_  )
         # make random mask reproducible
         torch.manual_seed(2  )
         with torch.no_grad():
          UpperCamelCase      :   Tuple			      =  model(**self._prepare_for_class(A_			,				A_  )  )

         # Make sure we don't have nans
         UpperCamelCase      :   Dict			      =  after_outputs[0].cpu().numpy()
         UpperCamelCase      :   int			      =  0
         UpperCamelCase      :   str			      =  np.amax(np.abs(out_a - out_a  )  )
         self.assertLessEqual(A_			,				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  ):
       '''simple docstring'''




       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  ):
       '''simple docstring'''




       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  ):
       '''simple docstring'''




       pass





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




       pass





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




       pass





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase      :   List[str]			      =  ViTMAEModel.from_pretrained(A_  )
        self.assertIsNotNone(A_  )



def 						A_							(	)		->					Optional[int]:
 UpperCamelCase      :   List[Any]			      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image



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





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   Union[str, Any]			      =  ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(A_  )

       UpperCamelCase      :   Any			      =  self.default_image_processor
       UpperCamelCase      :   Optional[Any]			      =  prepare_img()
       UpperCamelCase      :   int			      =  image_processor(images=A_			,				return_tensors="pt"  ).to(A_  )

       # 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      :   Dict			      =  ViTMAEConfig()
       UpperCamelCase      :   Any			      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(1, num_patches)  )

       # forward pass
       with torch.no_grad():
        UpperCamelCase      :   Optional[Any]			      =  model(**A_			,				noise=torch.from_numpy(A_  ).to(device=A_  )  )

       # verify the logits
       UpperCamelCase      :   str			      =  torch.Size((1, 196, 768)  )
       self.assertEqual(outputs.logits.shape			,				A_  )

       UpperCamelCase      :   str			      =  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(A_  )			,				atol=1e-4  )  )
