




'''simple docstring'''




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a_      :		Optional[int]							      =       logging.getLogger(__name__)





def      a_							(      __snake_case							:			Union[str, Any]   ,       __snake_case							:			Union[str, Any]   )							->						str:



       """simple docstring"""
       lowerCamelCase_							=np.argmax(__snake_case   ,       axis=1   )
       return np.sum(outputs == labels   )





def      a_							(      __snake_case							:			List[Any]   )							->						Tuple:



       """simple docstring"""
       with open(__snake_case   ,       encoding='''utf_8'''   ) as f:
              lowerCamelCase_							=csv.reader(__snake_case   )
              lowerCamelCase_							=[]
              next(__snake_case   )  # skip the first line
              for line in tqdm(__snake_case   ):
                     output.append((''' '''.join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
       return output





def      a_							(      __snake_case							:			str   ,       __snake_case							:			Dict   ,       __snake_case							:			List[str]   ,       __snake_case							:			List[str]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Dict   )							->						Dict:



       """simple docstring"""
       lowerCamelCase_							=[]
       for dataset in encoded_datasets:
              lowerCamelCase_							=len(__snake_case   )
              lowerCamelCase_							=np.zeros((n_batch, 2, input_len)   ,       dtype=np.intaa   )
              lowerCamelCase_							=np.zeros((n_batch, 2)   ,       dtype=np.intaa   )
              lowerCamelCase_							=np.full((n_batch, 2, input_len)   ,       fill_value=-100   ,       dtype=np.intaa   )
              lowerCamelCase_							=np.zeros((n_batch,)   ,       dtype=np.intaa   )
              for (
                  i,
                  (story, conta, conta, mc_label),
              ) in enumerate(__snake_case   ):
                     lowerCamelCase_							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowerCamelCase_							=[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                     lowerCamelCase_							=with_conta
                     lowerCamelCase_							=with_conta
                     lowerCamelCase_							=len(__snake_case   ) - 1
                     lowerCamelCase_							=len(__snake_case   ) - 1
                     lowerCamelCase_							=with_conta
                     lowerCamelCase_							=with_conta
                     lowerCamelCase_							=mc_label
              lowerCamelCase_							=(input_ids, mc_token_ids, lm_labels, mc_labels)
              tensor_datasets.append(tuple(torch.tensor(__snake_case   ) for t in all_inputs   )   )
       return tensor_datasets





def      a_							(      )							->						Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser()
       parser.add_argument('''--model_name'''   ,       type=__snake_case   ,       default='''openai-gpt'''   ,       help='''pretrained model name'''   )
       parser.add_argument('''--do_train'''   ,       action='''store_true'''   ,       help='''Whether to run training.'''   )
       parser.add_argument('''--do_eval'''   ,       action='''store_true'''   ,       help='''Whether to run eval on the dev set.'''   )
       parser.add_argument(
           '''--output_dir'''   ,       default=__snake_case   ,       type=__snake_case   ,       required=__snake_case   ,       help='''The output directory where the model predictions and checkpoints will be written.'''   ,       )
       parser.add_argument('''--train_dataset'''   ,       type=__snake_case   ,       default=''''''   )
       parser.add_argument('''--eval_dataset'''   ,       type=__snake_case   ,       default=''''''   )
       parser.add_argument('''--seed'''   ,       type=__snake_case   ,       default=42   )
       parser.add_argument('''--num_train_epochs'''   ,       type=__snake_case   ,       default=3   )
       parser.add_argument('''--train_batch_size'''   ,       type=__snake_case   ,       default=8   )
       parser.add_argument('''--eval_batch_size'''   ,       type=__snake_case   ,       default=16   )
       parser.add_argument('''--adam_epsilon'''   ,       default=1e-8   ,       type=__snake_case   ,       help='''Epsilon for Adam optimizer.'''   )
       parser.add_argument('''--max_grad_norm'''   ,       type=__snake_case   ,       default=1   )
       parser.add_argument(
           '''--max_steps'''   ,       default=-1   ,       type=__snake_case   ,       help=(
               '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
           )   ,       )
       parser.add_argument(
           '''--gradient_accumulation_steps'''   ,       type=__snake_case   ,       default=1   ,       help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''   ,       )
       parser.add_argument('''--learning_rate'''   ,       type=__snake_case   ,       default=6.25e-5   )
       parser.add_argument('''--warmup_steps'''   ,       default=0   ,       type=__snake_case   ,       help='''Linear warmup over warmup_steps.'''   )
       parser.add_argument('''--lr_schedule'''   ,       type=__snake_case   ,       default='''warmup_linear'''   )
       parser.add_argument('''--weight_decay'''   ,       type=__snake_case   ,       default=0.0_1   )
       parser.add_argument('''--lm_coef'''   ,       type=__snake_case   ,       default=0.9   )
       parser.add_argument('''--n_valid'''   ,       type=__snake_case   ,       default=374   )

       parser.add_argument('''--server_ip'''   ,       type=__snake_case   ,       default=''''''   ,       help='''Can be used for distant debugging.'''   )
       parser.add_argument('''--server_port'''   ,       type=__snake_case   ,       default=''''''   ,       help='''Can be used for distant debugging.'''   )
       lowerCamelCase_							=parser.parse_args()
       print(__snake_case   )

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print('''Waiting for debugger attach'''   )
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)   ,       redirect_output=__snake_case   )
              ptvsd.wait_for_attach()

       random.seed(args.seed   )
       np.random.seed(args.seed   )
       torch.manual_seed(args.seed   )
       torch.cuda.manual_seed_all(args.seed   )

       lowerCamelCase_							=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )
       lowerCamelCase_							=torch.cuda.device_count()
       logger.info('''device: {}, n_gpu {}'''.format(__snake_case   ,       __snake_case   )   )

       if not args.do_train and not args.do_eval:
              raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''   )

       if not os.path.exists(args.output_dir   ):
              os.makedirs(args.output_dir   )

       # Load tokenizer and model
       # This loading functions also add new tokens and embeddings called `special tokens`
       # These new embeddings will be fine-tuned on the RocStories dataset
       lowerCamelCase_							=['''_start_''', '''_delimiter_''', '''_classify_''']
       lowerCamelCase_							=OpenAIGPTTokenizer.from_pretrained(args.model_name   )
       tokenizer.add_tokens(__snake_case   )
       lowerCamelCase_							=tokenizer.convert_tokens_to_ids(__snake_case   )
       lowerCamelCase_							=OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
       model.resize_token_embeddings(len(__snake_case   )   )
       model.to(__snake_case   )

       # Load and encode the datasets
       def tokenize_and_encode(__snake_case							:			Union[str, Any]   ):
              if isinstance(__snake_case   ,       __snake_case   ):
                     return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case   )   )
              elif isinstance(__snake_case   ,       __snake_case   ):
                     return obj
              return [tokenize_and_encode(__snake_case   ) for o in obj]

       logger.info('''Encoding dataset...'''   )
       lowerCamelCase_							=load_rocstories_dataset(args.train_dataset   )
       lowerCamelCase_							=load_rocstories_dataset(args.eval_dataset   )
       lowerCamelCase_							=(train_dataset, eval_dataset)
       lowerCamelCase_							=tokenize_and_encode(__snake_case   )

       # Compute the max input length for the Transformer
       lowerCamelCase_							=model.config.n_positions // 2 - 2
       lowerCamelCase_							=max(
           len(story[:max_length]   ) + max(len(conta[:max_length]   )   ,       len(conta[:max_length]   )   ) + 3
           for dataset in encoded_datasets
           for story, conta, conta, _ in dataset   )
       lowerCamelCase_							=min(__snake_case   ,       model.config.n_positions   )  # Max size of input for the pre-trained model

       # Prepare inputs tensors and dataloaders
       lowerCamelCase_							=pre_process_datasets(__snake_case   ,       __snake_case   ,       __snake_case   ,       *__snake_case   )
       lowerCamelCase_,      lowerCamelCase_							=tensor_datasets[0], tensor_datasets[1]

       lowerCamelCase_							=TensorDataset(*__snake_case   )
       lowerCamelCase_							=RandomSampler(__snake_case   )
       lowerCamelCase_							=DataLoader(__snake_case   ,       sampler=__snake_case   ,       batch_size=args.train_batch_size   )

       lowerCamelCase_							=TensorDataset(*__snake_case   )
       lowerCamelCase_							=SequentialSampler(__snake_case   )
       lowerCamelCase_							=DataLoader(__snake_case   ,       sampler=__snake_case   ,       batch_size=args.eval_batch_size   )

       # Prepare optimizer
       if args.do_train:
              if args.max_steps > 0:
                     lowerCamelCase_							=args.max_steps
                     lowerCamelCase_							=args.max_steps // (len(__snake_case   ) // args.gradient_accumulation_steps) + 1
              else:
                     lowerCamelCase_							=len(__snake_case   ) // args.gradient_accumulation_steps * args.num_train_epochs

              lowerCamelCase_							=list(model.named_parameters()   )
              lowerCamelCase_							=['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
              lowerCamelCase_							=[
                  {
                      '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay   )],
                      '''weight_decay''': args.weight_decay,
                  },
                  {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay   )], '''weight_decay''': 0.0},
              ]
              lowerCamelCase_							=AdamW(__snake_case   ,       lr=args.learning_rate   ,       eps=args.adam_epsilon   )
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  __snake_case   ,       num_warmup_steps=args.warmup_steps   ,       num_training_steps=__snake_case   )

       if args.do_train:
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=0, 0, None
              model.train()
              for _ in trange(int(args.num_train_epochs   )   ,       desc='''Epoch'''   ):
                     lowerCamelCase_							=0
                     lowerCamelCase_							=0
                     lowerCamelCase_							=tqdm(__snake_case   ,       desc='''Training'''   )
                     for step, batch in enumerate(__snake_case   ):
                            lowerCamelCase_							=tuple(t.to(__snake_case   ) for t in batch   )
                            lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=batch
                            lowerCamelCase_							=model(__snake_case   ,       mc_token_ids=__snake_case   ,       lm_labels=__snake_case   ,       mc_labels=__snake_case   )
                            lowerCamelCase_							=args.lm_coef * losses[0] + losses[1]
                            loss.backward()
                            optimizer.step()
                            scheduler.step()
                            optimizer.zero_grad()
                            tr_loss += loss.item()
                            lowerCamelCase_							=(
                                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                            )
                            nb_tr_steps += 1
                            lowerCamelCase_							='''Training loss: {:.2e} lr: {:.2e}'''.format(__snake_case   ,       scheduler.get_lr()[0]   )

    # Save a trained model
       if args.do_train:
              # Save a trained model, configuration and tokenizer
              lowerCamelCase_							=model.module if hasattr(__snake_case   ,       '''module'''   ) else model  # Only save the model itself

              # If we save using the predefined names, we can load using `from_pretrained`
              lowerCamelCase_							=os.path.join(args.output_dir   ,       __snake_case   )
              lowerCamelCase_							=os.path.join(args.output_dir   ,       __snake_case   )

              torch.save(model_to_save.state_dict()   ,       __snake_case   )
              model_to_save.config.to_json_file(__snake_case   )
              tokenizer.save_vocabulary(args.output_dir   )

              # Load a trained model and vocabulary that you have fine-tuned
              lowerCamelCase_							=OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
              lowerCamelCase_							=OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
              model.to(__snake_case   )

       if args.do_eval:
              model.eval()
              lowerCamelCase_,      lowerCamelCase_							=0, 0
              lowerCamelCase_,      lowerCamelCase_							=0, 0
              for batch in tqdm(__snake_case   ,       desc='''Evaluating'''   ):
                     lowerCamelCase_							=tuple(t.to(__snake_case   ) for t in batch   )
                     lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=batch
                     with torch.no_grad():
                            lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=model(
                                __snake_case   ,       mc_token_ids=__snake_case   ,       lm_labels=__snake_case   ,       mc_labels=__snake_case   )

                     lowerCamelCase_							=mc_logits.detach().cpu().numpy()
                     lowerCamelCase_							=mc_labels.to('''cpu'''   ).numpy()
                     lowerCamelCase_							=accuracy(__snake_case   ,       __snake_case   )

                     eval_loss += mc_loss.mean().item()
                     eval_accuracy += tmp_eval_accuracy

                     nb_eval_examples += input_ids.size(0   )
                     nb_eval_steps += 1

              lowerCamelCase_							=eval_loss / nb_eval_steps
              lowerCamelCase_							=eval_accuracy / nb_eval_examples
              lowerCamelCase_							=tr_loss / nb_tr_steps if args.do_train else None
              lowerCamelCase_							={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

              lowerCamelCase_							=os.path.join(args.output_dir   ,       '''eval_results.txt'''   )
              with open(__snake_case   ,       '''w'''   ) as writer:
                     logger.info('''***** Eval results *****'''   )
                     for key in sorted(result.keys()   ):
                            logger.info('''  %s = %s'''   ,       __snake_case   ,       str(result[key]   )   )
                            writer.write('''%s = %s\n''' % (key, str(result[key]   ))   )


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_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     Tuple					,lowercase__   :     Dict=1_3					,lowercase__   :     List[str]=3_0					,lowercase__   :     Tuple=2					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[str]=True					,lowercase__   :     Tuple=True					,lowercase__   :     int=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     Tuple=4					,lowercase__   :     Any=3_7					,lowercase__   :     Any="gelu"					,lowercase__   :     Union[str, Any]=0.1					,lowercase__   :     Optional[int]=0.1					,lowercase__   :     str=1_0					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Union[str, Any]=3					,lowercase__   :     Optional[int]=0.6					,lowercase__   :     List[Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = patch_size
   __lowercase							  = num_channels
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = mask_ratio
   __lowercase							  = scope

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






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

   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[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=lowercase__					,initializer_range=self.initializer_range					,mask_ratio=self.mask_ratio					,)






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     List[Any]					,lowercase__   :     int					,lowercase__   :     Optional[Any]			):
   __lowercase							  = ViTMAEForPreTraining(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   __lowercase							  = (self.image_size // self.patch_size) ** 2
   __lowercase							  = self.patch_size**2 * self.num_channels
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, num_patches, expected_num_channels)			)

   # test greyscale images
   __lowercase							  = 1
   __lowercase							  = ViTMAEForPreTraining(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
   __lowercase							  = model(lowercase__			)
   __lowercase							  = self.patch_size**2
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, num_patches, expected_num_channels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :  Dict							=			{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = ViTMAEModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   self.config_tester.run_common_tests()






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

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

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Optional[int]					,lowercase__   :     List[str]					,lowercase__   :     Optional[Any]			):
   # make masks reproducible
   np.random.seed(2			)

   __lowercase							  = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
   __lowercase							  = np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
   __lowercase							  = torch.from_numpy(lowercase__			)

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

   super().check_pt_tf_models(lowercase__					,lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    model.to(lowercase__			)
    model.eval()
    # make random mask reproducible
    torch.manual_seed(2			)
    with torch.no_grad():
     __lowercase							  = model(**self._prepare_for_class(lowercase__					,lowercase__			)			)

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

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

     # Make sure we don't have nans
     __lowercase							  = after_outputs[0].cpu().numpy()
     __lowercase							  = 0
     __lowercase							  = np.amax(np.abs(out_a - out_a			)			)
     self.assertLessEqual(lowercase__					,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 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   pass






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






  @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   pass






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






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = ViTMAEModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)


def 		_A     (    ):






 """simple docstring"""



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






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




  """simple docstring"""




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






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

   __lowercase							  = ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''			).to(lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_img()
   __lowercase							  = image_processor(images=lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # 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)
   __lowercase							  = ViTMAEConfig()
   __lowercase							  = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
   __lowercase							  = np.random.uniform(size=(1, num_patches)			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__					,noise=torch.from_numpy(lowercase__			).to(device=lowercase__			)			)

   # verify the logits
   __lowercase							  = torch.Size((1, 1_9_6, 7_6_8)			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)

   __lowercase							  = torch.tensor(
       [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]			)

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

