
"""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__      :	str          =    logging.getLogger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						np.argmax(_lowercase       ,    axis=1		)
     return np.sum(outputs == labels		)



def 							a__ (				lowerCAmelCase	:       List[str]		):






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



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						[]
     for dataset in encoded_datasets:
          UpperCAmelCase__							: int 							=						len(_lowercase		)
          UpperCAmelCase__							: List[str] 							=						np.zeros((n_batch, 2, input_len)       ,    dtype=np.intaa		)
          UpperCAmelCase__							: Optional[int] 							=						np.zeros((n_batch, 2)       ,    dtype=np.intaa		)
          UpperCAmelCase__							: str 							=						np.full((n_batch, 2, input_len)       ,    fill_value=-100       ,    dtype=np.intaa		)
          UpperCAmelCase__							: Any 							=						np.zeros((n_batch,)       ,    dtype=np.intaa		)
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(_lowercase		):
               UpperCAmelCase__							: Optional[Any] 							=						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               UpperCAmelCase__							: Dict 							=						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
               UpperCAmelCase__							: Optional[int] 							=						with_conta
               UpperCAmelCase__							: List[Any] 							=						with_conta
               UpperCAmelCase__							: Optional[int] 							=						len(_lowercase		) - 1
               UpperCAmelCase__							: Optional[Any] 							=						len(_lowercase		) - 1
               UpperCAmelCase__							: List[Any] 							=						with_conta
               UpperCAmelCase__							: Tuple 							=						with_conta
               UpperCAmelCase__							: Any 							=						mc_label
          UpperCAmelCase__							: List[Any] 							=						(input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(_lowercase		) for t in all_inputs		)		)
     return tensor_datasets



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						argparse.ArgumentParser()
     parser.add_argument("--model_name"       ,    type=_lowercase       ,    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=_lowercase       ,    type=_lowercase       ,    required=_lowercase       ,    help="The output directory where the model predictions and checkpoints will be written."       ,    )
     parser.add_argument("--train_dataset"       ,    type=_lowercase       ,    default=""		)
     parser.add_argument("--eval_dataset"       ,    type=_lowercase       ,    default=""		)
     parser.add_argument("--seed"       ,    type=_lowercase       ,    default=42		)
     parser.add_argument("--num_train_epochs"       ,    type=_lowercase       ,    default=3		)
     parser.add_argument("--train_batch_size"       ,    type=_lowercase       ,    default=8		)
     parser.add_argument("--eval_batch_size"       ,    type=_lowercase       ,    default=16		)
     parser.add_argument("--adam_epsilon"       ,    default=1E-8       ,    type=_lowercase       ,    help="Epsilon for Adam optimizer."		)
     parser.add_argument("--max_grad_norm"       ,    type=_lowercase       ,    default=1		)
     parser.add_argument(
         "--max_steps"       ,    default=-1       ,    type=_lowercase       ,    help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         )       ,    )
     parser.add_argument(
         "--gradient_accumulation_steps"       ,    type=_lowercase       ,    default=1       ,    help="Number of updates steps to accumulate before                        performing a backward/update pass."       ,    )
     parser.add_argument("--learning_rate"       ,    type=_lowercase       ,    default=6.25E-5		)
     parser.add_argument("--warmup_steps"       ,    default=0       ,    type=_lowercase       ,    help="Linear warmup over warmup_steps."		)
     parser.add_argument("--lr_schedule"       ,    type=_lowercase       ,    default="warmup_linear"		)
     parser.add_argument("--weight_decay"       ,    type=_lowercase       ,    default=0.01		)
     parser.add_argument("--lm_coef"       ,    type=_lowercase       ,    default=0.9		)
     parser.add_argument("--n_valid"       ,    type=_lowercase       ,    default=374		)

     parser.add_argument("--server_ip"       ,    type=_lowercase       ,    default=""       ,    help="Can be used for distant debugging."		)
     parser.add_argument("--server_port"       ,    type=_lowercase       ,    default=""       ,    help="Can be used for distant debugging."		)
     UpperCAmelCase__							: Dict 							=						parser.parse_args()
     print(_lowercase		)

     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=_lowercase		)
          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		)

     UpperCAmelCase__							: int 							=						torch.device("cuda" if torch.cuda.is_available() else "cpu"		)
     UpperCAmelCase__							: Union[str, Any] 							=						torch.cuda.device_count()
     logger.info("device: {}, n_gpu {}".format(_lowercase       ,    _lowercase		)		)

     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
     UpperCAmelCase__							: Union[str, Any] 							=						['_start_', '_delimiter_', '_classify_']
     UpperCAmelCase__							: Optional[int] 							=						OpenAIGPTTokenizer.from_pretrained(args.model_name		)
     tokenizer.add_tokens(_lowercase		)
     UpperCAmelCase__							: Optional[int] 							=						tokenizer.convert_tokens_to_ids(_lowercase		)
     UpperCAmelCase__							: Tuple 							=						OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name		)
     model.resize_token_embeddings(len(_lowercase		)		)
     model.to(_lowercase		)

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

     logger.info("Encoding dataset..."		)
     UpperCAmelCase__							: Any 							=						load_rocstories_dataset(args.train_dataset		)
     UpperCAmelCase__							: List[str] 							=						load_rocstories_dataset(args.eval_dataset		)
     UpperCAmelCase__							: Dict 							=						(train_dataset, eval_dataset)
     UpperCAmelCase__							: Optional[int] 							=						tokenize_and_encode(_lowercase		)

     # Compute the max input length for the Transformer
     UpperCAmelCase__							: Optional[Any] 							=						model.config.n_positions // 2 - 2
     UpperCAmelCase__							: List[Any] 							=						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		)
     UpperCAmelCase__							: List[str] 							=						min(_lowercase       ,    model.config.n_positions		)  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     UpperCAmelCase__							: Optional[int] 							=						pre_process_datasets(_lowercase       ,    _lowercase       ,    _lowercase       ,    *_lowercase		)
     UpperCAmelCase__							: Union[str, Any] 							=						tensor_datasets[0], tensor_datasets[1]

     UpperCAmelCase__							: Any 							=						TensorDataset(*_lowercase		)
     UpperCAmelCase__							: Optional[Any] 							=						RandomSampler(_lowercase		)
     UpperCAmelCase__							: Union[str, Any] 							=						DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.train_batch_size		)

     UpperCAmelCase__							: Optional[int] 							=						TensorDataset(*_lowercase		)
     UpperCAmelCase__							: List[Any] 							=						SequentialSampler(_lowercase		)
     UpperCAmelCase__							: Optional[Any] 							=						DataLoader(_lowercase       ,    sampler=_lowercase       ,    batch_size=args.eval_batch_size		)

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               UpperCAmelCase__							: Tuple 							=						args.max_steps
               UpperCAmelCase__							: List[str] 							=						args.max_steps // (len(_lowercase		) // args.gradient_accumulation_steps) + 1
          else:
               UpperCAmelCase__							: Dict 							=						len(_lowercase		) // args.gradient_accumulation_steps * args.num_train_epochs

          UpperCAmelCase__							: Optional[int] 							=						list(model.named_parameters()		)
          UpperCAmelCase__							: Any 							=						['bias', 'LayerNorm.bias', 'LayerNorm.weight']
          UpperCAmelCase__							: Tuple 							=						[
              {
                  '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},
          ]
          UpperCAmelCase__							: Tuple 							=						AdamW(_lowercase       ,    lr=args.learning_rate       ,    eps=args.adam_epsilon		)
          UpperCAmelCase__							: Optional[int] 							=						get_linear_schedule_with_warmup(
              _lowercase       ,    num_warmup_steps=args.warmup_steps       ,    num_training_steps=_lowercase		)

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

    # Save a trained model
     if args.do_train:
          # Save a trained model, configuration and tokenizer
          UpperCAmelCase__							: List[str] 							=						model.module if hasattr(_lowercase       ,    "module"		) else model  # Only save the model itself

          # If we save using the predefined names, we can load using `from_pretrained`
          UpperCAmelCase__							: Optional[int] 							=						os.path.join(args.output_dir       ,    _lowercase		)
          UpperCAmelCase__							: List[Any] 							=						os.path.join(args.output_dir       ,    _lowercase		)

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

          # Load a trained model and vocabulary that you have fine-tuned
          UpperCAmelCase__							: List[str] 							=						OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir		)
          UpperCAmelCase__							: Dict 							=						OpenAIGPTTokenizer.from_pretrained(args.output_dir		)
          model.to(_lowercase		)

     if args.do_eval:
          model.eval()
          UpperCAmelCase__							: List[Any] 							=						0, 0
          UpperCAmelCase__							: List[str] 							=						0, 0
          for batch in tqdm(_lowercase       ,    desc="Evaluating"		):
               UpperCAmelCase__							: str 							=						tuple(t.to(_lowercase		) for t in batch		)
               UpperCAmelCase__							: Any 							=						batch
               with torch.no_grad():
                    UpperCAmelCase__							: Tuple 							=						model(
                        _lowercase       ,    mc_token_ids=_lowercase       ,    lm_labels=_lowercase       ,    mc_labels=_lowercase		)

               UpperCAmelCase__							: List[str] 							=						mc_logits.detach().cpu().numpy()
               UpperCAmelCase__							: Any 							=						mc_labels.to("cpu"		).numpy()
               UpperCAmelCase__							: int 							=						accuracy(_lowercase       ,    _lowercase		)

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

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

          UpperCAmelCase__							: Tuple 							=						eval_loss / nb_eval_steps
          UpperCAmelCase__							: Optional[int] 							=						eval_accuracy / nb_eval_examples
          UpperCAmelCase__							: Tuple 							=						tr_loss / nb_tr_steps if args.do_train else None
          UpperCAmelCase__							: List[Any] 							=						{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

          UpperCAmelCase__							: Optional[Any] 							=						os.path.join(args.output_dir       ,    "eval_results.txt"		)
          with open(_lowercase       ,    "w"		) as writer:
               logger.info("***** Eval results *****"		)
               for key in sorted(result.keys()		):
                    logger.info("  %s = %s"       ,    _lowercase       ,    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 ,			__UpperCamelCase ,			__UpperCamelCase=13 ,			__UpperCamelCase=30 ,			__UpperCamelCase=2 ,			__UpperCamelCase=3 ,			__UpperCamelCase=True ,			__UpperCamelCase=True ,			__UpperCamelCase=32 ,			__UpperCamelCase=5 ,			__UpperCamelCase=4 ,			__UpperCamelCase=37 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=10 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=3 ,			__UpperCamelCase=0.6 ,			__UpperCamelCase=None ,			)->					List[Any]:
          UpperCAmelCase__							: str 							=						parent
          UpperCAmelCase__							: Optional[Any] 							=						batch_size
          UpperCAmelCase__							: Any 							=						image_size
          UpperCAmelCase__							: Dict 							=						patch_size
          UpperCAmelCase__							: Optional[Any] 							=						num_channels
          UpperCAmelCase__							: Union[str, Any] 							=						is_training
          UpperCAmelCase__							: Any 							=						use_labels
          UpperCAmelCase__							: List[Any] 							=						hidden_size
          UpperCAmelCase__							: Dict 							=						num_hidden_layers
          UpperCAmelCase__							: List[str] 							=						num_attention_heads
          UpperCAmelCase__							: List[Any] 							=						intermediate_size
          UpperCAmelCase__							: Dict 							=						hidden_act
          UpperCAmelCase__							: List[Any] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Optional[int] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						type_sequence_label_size
          UpperCAmelCase__							: Union[str, Any] 							=						initializer_range
          UpperCAmelCase__							: int 							=						mask_ratio
          UpperCAmelCase__							: Tuple 							=						scope

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



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[Any] 							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

          UpperCAmelCase__							: Optional[int] 							=						self.get_config()

          return config, pixel_values, labels



     def  lowerCAmelCase__       (      self  )->					int:
          return ViTMAEConfig(
              image_size=self.image_size ,			patch_size=self.patch_size ,			num_channels=self.num_channels ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			is_decoder=__UpperCamelCase ,			initializer_range=self.initializer_range ,			mask_ratio=self.mask_ratio ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						ViTMAEModel(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: List[Any] 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: Optional[int] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						(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__							: Dict 							=						1
          UpperCAmelCase__							: str 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: int 							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						self.patch_size**2
          self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						self.prepare_config_and_inputs()
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						config_and_inputs
          UpperCAmelCase__							: int 							=						{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





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

     _A								=							False
     _A								=							False
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						ViTMAEModelTester(self  )
          UpperCAmelCase__							: List[str] 							=						ConfigTester(self ,			config_class=__UpperCamelCase ,			has_text_modality=__UpperCamelCase ,			hidden_size=37  )



     def  lowerCAmelCase__       (      self  )->					int:
          self.config_tester.run_common_tests()



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



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						self.model_tester.prepare_config_and_inputs_for_common()

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



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase__							: Tuple 							=						["pixel_values"]
               self.assertListEqual(arg_names[:1] ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          # make masks reproducible
          np.random.seed(2  )

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

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

          super().check_pt_tf_models(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase__							: Optional[Any] 							=						outputs[0].cpu().numpy()
               UpperCAmelCase__							: Union[str, Any] 							=						0

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

                    # Make sure we don't have nans
                    UpperCAmelCase__							: Tuple 							=						after_outputs[0].cpu().numpy()
                    UpperCAmelCase__							: int 							=						0
                    UpperCAmelCase__							: str 							=						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  lowerCAmelCase__       (      self  )->					List[str]:
          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  lowerCAmelCase__       (      self  )->					Any:
          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  lowerCAmelCase__       (      self  )->					Optional[Any]:
          pass



     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          pass



     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          pass



     @slow
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__							: Tuple 							=						ViTMAEModel.from_pretrained(__UpperCamelCase  )
               self.assertIsNotNone(__UpperCamelCase  )





def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     return image







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


     '''simple docstring'''





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



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2  )

          UpperCAmelCase__							: Any 							=						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(__UpperCamelCase  )

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

          # forward pass
          with torch.no_grad():
               UpperCAmelCase__							: List[str] 							=						model(**__UpperCamelCase ,			noise=torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )  )

          # verify the logits
          UpperCAmelCase__							: str 							=						torch.Size((1, 1_96, 7_68)  )
          self.assertEqual(outputs.logits.shape ,			__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

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



