
"""simple docstring"""




import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A__      :	int          =    logging.getLogger(__name__)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							'''token-classification'''



     def __init__(      self ,			__UpperCamelCase  )->					Union[str, Any]:
          if type(__UpperCamelCase  ) == dict:
               UpperCAmelCase__							: int 							=						Namespace(**__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						import_module("tasks"  )
          try:
               UpperCAmelCase__							: Optional[int] 							=						getattr(__UpperCamelCase ,			hparams.task_type  )
               UpperCAmelCase__							: TokenClassificationTask 							=						token_classification_task_clazz()
          except AttributeError:
               raise ValueError(
                   F"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                   F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"  )
          UpperCAmelCase__							: List[Any] 							=						self.token_classification_task.get_labels(hparams.labels  )
          UpperCAmelCase__							: Optional[int] 							=						CrossEntropyLoss().ignore_index
          super().__init__(__UpperCamelCase ,			len(self.labels  ) ,			self.mode  )



     def  lowerCAmelCase__       (      self ,			**__UpperCamelCase  )->					Dict:
          return self.model(**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: int 							=						{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
          if self.config.model_type != "distilbert":
               UpperCAmelCase__							: Any 							=						(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids

          UpperCAmelCase__							: Union[str, Any] 							=						self(**__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						outputs[0]
          # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
          return {"loss": loss}



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: str 							=						self.hparams
          for mode in ["train", "dev", "test"]:
               UpperCAmelCase__							: Any 							=						self._feature_file(__UpperCamelCase  )
               if os.path.exists(__UpperCamelCase  ) and not args.overwrite_cache:
                    logger.info("Loading features from cached file %s" ,			__UpperCamelCase  )
                    UpperCAmelCase__							: str 							=						torch.load(__UpperCamelCase  )
               else:
                    logger.info("Creating features from dataset file at %s" ,			args.data_dir  )
                    UpperCAmelCase__							: List[Any] 							=						self.token_classification_task.read_examples_from_file(args.data_dir ,			__UpperCamelCase  )
                    UpperCAmelCase__							: Optional[int] 							=						self.token_classification_task.convert_examples_to_features(
                        __UpperCamelCase ,			self.labels ,			args.max_seq_length ,			self.tokenizer ,			cls_token_at_end=bool(self.config.model_type in ["xlnet"]  ) ,			cls_token=self.tokenizer.cls_token ,			cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0 ,			sep_token=self.tokenizer.sep_token ,			sep_token_extra=__UpperCamelCase ,			pad_on_left=bool(self.config.model_type in ["xlnet"]  ) ,			pad_token=self.tokenizer.pad_token_id ,			pad_token_segment_id=self.tokenizer.pad_token_type_id ,			pad_token_label_id=self.pad_token_label_id ,			)
                    logger.info("Saving features into cached file %s" ,			__UpperCamelCase  )
                    torch.save(__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = False  )->					DataLoader:
          UpperCAmelCase__							: Optional[Any] 							=						self._feature_file(__UpperCamelCase  )
          logger.info("Loading features from cached file %s" ,			__UpperCamelCase  )
          UpperCAmelCase__							: List[Any] 							=						torch.load(__UpperCamelCase  )
          UpperCAmelCase__							: Tuple 							=						torch.tensor([f.input_ids for f in features] ,			dtype=torch.long  )
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([f.attention_mask for f in features] ,			dtype=torch.long  )
          if features[0].token_type_ids is not None:
               UpperCAmelCase__							: str 							=						torch.tensor([f.token_type_ids for f in features] ,			dtype=torch.long  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						torch.tensor([0 for f in features] ,			dtype=torch.long  )
               # HACK(we will not use this anymore soon)
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([f.label_ids for f in features] ,			dtype=torch.long  )
          return DataLoader(
              TensorDataset(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  ) ,			batch_size=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          """Compute validation""" ""
          UpperCAmelCase__							: Tuple 							=						{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
          if self.config.model_type != "distilbert":
               UpperCAmelCase__							: List[Any] 							=						(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids
          UpperCAmelCase__							: Dict 							=						self(**__UpperCamelCase  )
          UpperCAmelCase__							: str 							=						outputs[:2]
          UpperCAmelCase__							: str 							=						logits.detach().cpu().numpy()
          UpperCAmelCase__							: Optional[int] 							=						inputs["labels"].detach().cpu().numpy()
          return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: List[Any] 							=						torch.stack([x["val_loss"] for x in outputs]  ).mean()
          UpperCAmelCase__							: Optional[Any] 							=						np.concatenate([x["pred"] for x in outputs] ,			axis=0  )
          UpperCAmelCase__							: int 							=						np.argmax(__UpperCamelCase ,			axis=2  )
          UpperCAmelCase__							: Any 							=						np.concatenate([x["target"] for x in outputs] ,			axis=0  )

          UpperCAmelCase__							: Dict 							=						dict(enumerate(self.labels  )  )
          UpperCAmelCase__							: List[Any] 							=						[[] for _ in range(out_label_ids.shape[0]  )]
          UpperCAmelCase__							: List[Any] 							=						[[] for _ in range(out_label_ids.shape[0]  )]

          for i in range(out_label_ids.shape[0]  ):
               for j in range(out_label_ids.shape[1]  ):
                    if out_label_ids[i, j] != self.pad_token_label_id:
                         out_label_list[i].append(label_map[out_label_ids[i][j]]  )
                         preds_list[i].append(label_map[preds[i][j]]  )

          UpperCAmelCase__							: List[Any] 							=						{
              "val_loss": val_loss_mean,
              "accuracy_score": accuracy_score(__UpperCamelCase ,			__UpperCamelCase  ),
              "precision": precision_score(__UpperCamelCase ,			__UpperCamelCase  ),
              "recall": recall_score(__UpperCamelCase ,			__UpperCamelCase  ),
              "f1": fa_score(__UpperCamelCase ,			__UpperCamelCase  ),
          }

          UpperCAmelCase__							: Union[str, Any] 							=						dict(results.items()  )
          UpperCAmelCase__							: Dict 							=						results
          return ret, preds_list, out_label_list



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
          # when stable
          UpperCAmelCase__							: Optional[Any] 							=						self._eval_end(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						ret["log"]
          return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          # updating to test_epoch_end instead of deprecated test_end
          UpperCAmelCase__							: List[str] 							=						self._eval_end(__UpperCamelCase  )

          # Converting to the dict required by pl
          # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
          # pytorch_lightning/trainer/logging.py#L139
          UpperCAmelCase__							: List[str] 							=						ret["log"]
          # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
          return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



     @staticmethod
     def  lowerCAmelCase__       (      __UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          # Add NER specific options
          BaseTransformer.add_model_specific_args(__UpperCamelCase ,			__UpperCamelCase  )
          parser.add_argument(
              "--task_type" ,			default="NER" ,			type=__UpperCamelCase ,			help="Task type to fine tune in training (e.g. NER, POS, etc)"  )
          parser.add_argument(
              "--max_seq_length" ,			default=1_28 ,			type=__UpperCamelCase ,			help=(
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              ) ,			)

          parser.add_argument(
              "--labels" ,			default="" ,			type=__UpperCamelCase ,			help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used." ,			)
          parser.add_argument(
              "--gpus" ,			default=0 ,			type=__UpperCamelCase ,			help="The number of GPUs allocated for this, it is by default 0 meaning none" ,			)

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

          return parser


if __name__ == "__main__":
    A__      :	Optional[Any]          =    argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    A__      :	str          =    NERTransformer.add_model_specific_args(parser, os.getcwd())
    A__      :	Dict          =    parser.parse_args()
    A__      :	Union[str, Any]          =    NERTransformer(args)
    A__      :	str          =    generic_train(model, args)

    if args.do_predict:
        # See https://github.com/huggingface/transformers/issues/3159
        # pl use this default format to create a checkpoint:
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
        # /pytorch_lightning/callbacks/model_checkpoint.py#L322
        A__      :	Optional[int]          =    sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
        A__      :	str          =    model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)





"""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  )  )



