"""simple docstring"""





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase     :		Union[str, Any]											=					16
_UpperCamelCase     :		Optional[Any]											=					32





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				DatasetDict     ,				_lowerCAmelCase						:				List[int]     ,				_lowerCAmelCase						:				List[int]     ,				_lowerCAmelCase						:				int = 16  ):





       '''simple docstring'''

       lowercase__    :	int     					=			AutoTokenizer.from_pretrained('bert-base-cased'  )
       lowercase__    :	str     					=			DatasetDict(
           {
               'train': dataset['train'].select(_lowerCAmelCase  ),
               'validation': dataset['train'].select(_lowerCAmelCase  ),
               'test': dataset['validation'],
           }  )

       def tokenize_function(_lowerCAmelCase						:				Dict  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[Any]     					=			tokenizer(examples['sentence1']     ,				examples['sentence2']     ,				truncation=_lowerCAmelCase     ,				max_length=_lowerCAmelCase  )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowercase__    :	Union[str, Any]     					=			datasets.map(
                  _lowerCAmelCase     ,				batched=_lowerCAmelCase     ,				remove_columns=['idx', 'sentence1', 'sentence2']     ,				)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase__    :	Any     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

       def collate_fn(_lowerCAmelCase						:				List[str]  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__    :	Tuple     					=			128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowercase__    :	Optional[Any]     					=			16
              elif accelerator.mixed_precision != "no":
                     lowercase__    :	Dict     					=			8
              else:
                     lowercase__    :	Optional[int]     					=			None

              return tokenizer.pad(
                  _lowerCAmelCase     ,				padding='longest'     ,				max_length=_lowerCAmelCase     ,				pad_to_multiple_of=_lowerCAmelCase     ,				return_tensors='pt'     ,				)

       # Instantiate dataloaders.
       lowercase__    :	List[str]     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	str     					=			DataLoader(
           tokenized_datasets['validation']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['test']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       return train_dataloader, eval_dataloader, test_dataloader





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			[]
       # Download the dataset
       lowercase__    :	Optional[Any]     					=			load_dataset('glue'     ,				'mrpc'  )
       # Create our splits
       lowercase__    :	List[str]     					=			StratifiedKFold(n_splits=int(args.num_folds  )  )
       # Initialize accelerator
       lowercase__    :	List[str]     					=			Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision  )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__    :	int     					=			config['lr']
       lowercase__    :	Optional[int]     					=			int(config['num_epochs']  )
       lowercase__    :	Optional[Any]     					=			int(config['seed']  )
       lowercase__    :	Optional[int]     					=			int(config['batch_size']  )

       lowercase__    :	Union[str, Any]     					=			evaluate.load('glue'     ,				'mrpc'  )

       # If the batch size is too big we use gradient accumulation
       lowercase__    :	Tuple     					=			1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase__    :	Dict     					=			batch_size // MAX_GPU_BATCH_SIZE
              lowercase__    :	Dict     					=			MAX_GPU_BATCH_SIZE

       set_seed(_lowerCAmelCase  )

       # New Code #
       # Create our folds:
       lowercase__    :	Optional[Any]     					=			kfold.split(np.zeros(datasets['train'].num_rows  )     ,				datasets['train']['label']  )
       lowercase__    :	Optional[Any]     					=			[]
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(_lowerCAmelCase  ):
              lowercase__    :	Optional[int]     					=			get_fold_dataloaders(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				)
              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowercase__    :	Any     					=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,				return_dict=_lowerCAmelCase  )

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              lowercase__    :	Any     					=			model.to(accelerator.device  )

              # Instantiate optimizer
              lowercase__    :	Tuple     					=			AdamW(params=model.parameters()     ,				lr=_lowerCAmelCase  )

              # Instantiate scheduler
              lowercase__    :	Dict     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=100     ,				num_training_steps=(len(_lowerCAmelCase  ) * num_epochs) // gradient_accumulation_steps     ,				)

              # Prepare everything
              # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
              # prepare method.
              lowercase__    :	List[str]     					=			accelerator.prepare(
                  _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

              # Now we train the model
              for epoch in range(_lowerCAmelCase  ):
                     model.train()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device  )
                            lowercase__    :	List[str]     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	Dict     					=			outputs.loss
                            lowercase__    :	List[Any]     					=			loss / gradient_accumulation_steps
                            accelerator.backward(_lowerCAmelCase  )
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     model.eval()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device  )
                            with torch.no_grad():
                                   lowercase__    :	str     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	str     					=			outputs.logits.argmax(dim=-1  )
                            lowercase__    :	str     					=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
                            metric.add_batch(
                                predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase     ,				)

                     lowercase__    :	Optional[int]     					=			metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f"""epoch {epoch}:"""     ,				_lowerCAmelCase  )

              # New Code #
              # We also run predictions on the test set at the very end
              lowercase__    :	Optional[int]     					=			[]
              for step, batch in enumerate(_lowerCAmelCase  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     with torch.no_grad():
                            lowercase__    :	Any     					=			model(**_lowerCAmelCase  )
                     lowercase__    :	Optional[Any]     					=			outputs.logits
                     lowercase__    :	Optional[int]     					=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
                     fold_predictions.append(predictions.cpu()  )
                     if i == 0:
                            # We need all of the test predictions
                            test_references.append(references.cpu()  )
        # Use accelerator.print to print only on the main process.
              test_predictions.append(torch.cat(_lowerCAmelCase     ,				dim=0  )  )
              # We now need to release all our memory and get rid of the current model, optimizer, etc
              accelerator.free_memory()
       # New Code #
       # Finally we check the accuracy of our folded results:
       lowercase__    :	Tuple     					=			torch.cat(_lowerCAmelCase     ,				dim=0  )
       lowercase__    :	Optional[int]     					=			torch.stack(_lowerCAmelCase     ,				dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
       lowercase__    :	Optional[int]     					=			metric.compute(predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase  )
       accelerator.print('Average test metrics from all folds:'     ,				_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			argparse.ArgumentParser(description='Simple example of training script.'  )
       parser.add_argument(
           '--mixed_precision'     ,				type=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				choices=['no', 'fp16', 'bf16', 'fp8']     ,				help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.'     ,				)
       parser.add_argument('--cpu'     ,				action='store_true'     ,				help='If passed, will train on the CPU.'  )
       # New Code #
       parser.add_argument('--num_folds'     ,				type=_lowerCAmelCase     ,				default=3     ,				help='The number of splits to perform across the dataset'  )
       lowercase__    :	List[Any]     					=			parser.parse_args()
       lowercase__    :	Optional[int]     					=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
   main()


"""simple docstring"""





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 (
           MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
           VideoMAEForPreTraining,
           VideoMAEForVideoClassification,
           VideoMAEModel,
       )
       from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=1_0	,    a=3	,    a=2	,    a=2	,    a=2	,    a=True	,    a=True	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=1_0	,    a=0.02	,    a=0.9	,    a=None	,    ) ->      Optional[Any]:
             lowercase__    :	str     					=			parent
             lowercase__    :	int     					=			batch_size
             lowercase__    :	Union[str, Any]     					=			image_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	Dict     					=			patch_size
             lowercase__    :	Tuple     					=			tubelet_size
             lowercase__    :	Optional[int]     					=			num_frames
             lowercase__    :	Optional[int]     					=			is_training
             lowercase__    :	int     					=			use_labels
             lowercase__    :	Optional[int]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	Optional[int]     					=			num_attention_heads
             lowercase__    :	Any     					=			intermediate_size
             lowercase__    :	str     					=			hidden_act
             lowercase__    :	List[Any]     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Union[str, Any]     					=			type_sequence_label_size
             lowercase__    :	List[Any]     					=			initializer_range
             lowercase__    :	str     					=			mask_ratio
             lowercase__    :	Optional[Any]     					=			scope

             # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
             lowercase__    :	Optional[Any]     					=			(image_size // patch_size) ** 2
             lowercase__    :	str     					=			(num_frames // tubelet_size) * self.num_patches_per_frame

             # use this variable to define bool_masked_pos
             lowercase__    :	str     					=			int(mask_ratio * self.seq_length							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	int     					=			floats_tensor(
                 [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

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

             lowercase__    :	Dict     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             return VideoMAEConfig(
                 image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    num_frames=self.num_frames	,    tubelet_size=self.tubelet_size	,    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	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Dict     					=			VideoMAEModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Tuple     					=			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							) ->      Union[str, Any]:
             lowercase__    :	str     					=			VideoMAEForPreTraining(a							)
             model.to(a							)
             model.eval()
             # important: each video needs to have the same number of masked patches
             # hence we define a single mask, which we then repeat for each example in the batch
             lowercase__    :	Any     					=			torch.ones((self.num_masks,)							)
             lowercase__    :	str     					=			torch.cat([mask, torch.zeros(self.seq_length - mask.size(0							)							)]							)
             lowercase__    :	Optional[int]     					=			mask.expand(self.batch_size	,    -1							).bool()

             lowercase__    :	str     					=			model(a	,    a							)
             # model only returns predictions for masked patches
             lowercase__    :	str     					=			mask.sum().item()
             lowercase__    :	int     					=			3 * self.tubelet_size * self.patch_size**2
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, num_masked_patches, decoder_num_labels)							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Dict     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Union[str, Any]     					=			config_and_inputs
             lowercase__    :	List[str]     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
      )
      lowerCamelCase__	:					Optional[int]										=						(
          {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
          if is_torch_available()
          else {}
      )

      lowerCamelCase__	:					Any										=						False
      lowerCamelCase__	:					Any										=						False
      lowerCamelCase__	:					Union[str, Any]										=						False
      lowerCamelCase__	:					str										=						False


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Optional[Any]     					=			VideoMAEModelTester(self							)
             lowercase__    :	Optional[Any]     					=			ConfigTester(self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a=False							) ->      Optional[int]:
             lowercase__    :	Union[str, Any]     					=			copy.deepcopy(a							)

             if model_class == VideoMAEForPreTraining:
                    # important: each video needs to have the same number of masked patches
                    # hence we define a single mask, which we then repeat for each example in the batch
                    lowercase__    :	Optional[Any]     					=			torch.ones((self.model_tester.num_masks,)							)
                    lowercase__    :	Any     					=			torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0							)							)]							)
                    lowercase__    :	Any     					=			mask.expand(self.model_tester.batch_size	,    -1							).bool()
                    lowercase__    :	Union[str, Any]     					=			bool_masked_pos.to(a							)

             if return_labels:
                    if model_class in [
                        *get_values(a							),
                    ]:
                           lowercase__    :	Dict     					=			torch.zeros(
                               self.model_tester.batch_size	,    dtype=torch.long	,    device=a							)

             return inputs_dict


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             self.config_tester.run_common_tests()


      @unittest.skip(reason='VideoMAE does not use inputs_embeds'							)
      def 	_UpperCAmelCase (     self							) ->      Dict:
             pass


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

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


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

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

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


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	List[Any]     					=			VideoMAEModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             if not self.has_attentions:
                    pass

             else:
                    lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()
                    lowercase__    :	str     					=			True

                    for model_class in self.all_model_classes:
                           lowercase__    :	Union[str, Any]     					=			self.model_tester.seq_length - self.model_tester.num_masks
                           lowercase__    :	Any     					=			(
                               num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                           )

                           lowercase__    :	Optional[Any]     					=			True
                           lowercase__    :	int     					=			False
                           lowercase__    :	Any     					=			True
                           lowercase__    :	List[str]     					=			model_class(a							)
                           model.to(a							)
                           model.eval()
                           with torch.no_grad():
                                  lowercase__    :	Optional[int]     					=			model(**self._prepare_for_class(a	,    a							)							)
                           lowercase__    :	Dict     					=			outputs.attentions
                           self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                           # check that output_attentions also work using config
                           del inputs_dict["output_attentions"]
                           lowercase__    :	str     					=			True
                           lowercase__    :	List[str]     					=			model_class(a							)
                           model.to(a							)
                           model.eval()
                           with torch.no_grad():
                                  lowercase__    :	List[Any]     					=			model(**self._prepare_for_class(a	,    a							)							)
                           lowercase__    :	Optional[Any]     					=			outputs.attentions
                           self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)

                           self.assertListEqual(
                               list(attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )
                           lowercase__    :	List[str]     					=			len(a							)

                           # Check attention is always last and order is fine
                           lowercase__    :	Optional[int]     					=			True
                           lowercase__    :	List[str]     					=			True
                           lowercase__    :	int     					=			model_class(a							)
                           model.to(a							)
                           model.eval()
                           with torch.no_grad():
                                  lowercase__    :	List[str]     					=			model(**self._prepare_for_class(a	,    a							)							)

                           self.assertEqual(out_len + 1	,    len(a							)							)

                           lowercase__    :	int     					=			outputs.attentions

                           self.assertEqual(len(a							)	,    self.model_tester.num_hidden_layers							)
                           self.assertListEqual(
                               list(self_attentions[0].shape[-3:]							)	,    [self.model_tester.num_attention_heads, seq_len, seq_len]	,    )


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             def check_hidden_states_output(a	,    a	,    a							):
                    lowercase__    :	Optional[int]     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

                    with torch.no_grad():
                           lowercase__    :	Optional[Any]     					=			model(**self._prepare_for_class(a	,    a							)							)

                    lowercase__    :	Optional[int]     					=			outputs.hidden_states
                    lowercase__    :	List[Any]     					=			self.model_tester.num_hidden_layers + 1
                    self.assertEqual(len(a							)	,    a							)

                    lowercase__    :	Optional[Any]     					=			self.model_tester.seq_length - self.model_tester.num_masks
                    lowercase__    :	Union[str, Any]     					=			num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

                    self.assertListEqual(
                        list(hidden_states[0].shape[-2:]							)	,    [seq_length, self.model_tester.hidden_size]	,    )

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

             for model_class in self.all_model_classes:
                    lowercase__    :	Tuple     					=			True
                    check_hidden_states_output(a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	Union[str, Any]     					=			True

                    check_hidden_states_output(a	,    a	,    a							)


      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			hf_hub_download(
           repo_id='hf-internal-testing/spaghetti-video'     ,				filename='eating_spaghetti.npy'     ,				repo_type='dataset'  )
       lowercase__    :	str     					=			np.load(_lowerCAmelCase  )
       return list(_lowerCAmelCase  )






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


      @cached_property
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             # logits were tested with a different mean and std, so we use the same here
             return (
                 VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,    image_std=[0.5, 0.5, 0.5]							)
                 if is_vision_available()
                 else None
             )


      @slow
      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'							).to(
                 a							)

             lowercase__    :	str     					=			self.default_image_processor
             lowercase__    :	List[str]     					=			prepare_video()
             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	Union[str, Any]     					=			model(**a							)

             # verify the logits
             lowercase__    :	str     					=			torch.Size((1, 4_0_0)							)
             self.assertEqual(outputs.logits.shape	,    a							)

             lowercase__    :	List[Any]     					=			torch.tensor([0.3_669, -0.0_688, -0.2_421]							).to(a							)

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


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'							).to(a							)

             lowercase__    :	Optional[Any]     					=			self.default_image_processor
             lowercase__    :	List[str]     					=			prepare_video()
             lowercase__    :	str     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # add boolean mask, indicating which patches to mask
             lowercase__    :	Union[str, Any]     					=			hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'	,    filename='bool_masked_pos.pt'							)
             lowercase__    :	str     					=			torch.load(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[Any]     					=			model(**a							)

             # verify the logits
             lowercase__    :	Dict     					=			torch.Size([1, 1_4_0_8, 1_5_3_6]							)
             lowercase__    :	List[str]     					=			torch.tensor(
                 [[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]	,    device=a							)
             self.assertEqual(outputs.logits.shape	,    a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]	,    a	,    atol=1e-4							)							)

             # verify the loss (`config.norm_pix_loss` = `True`)
             lowercase__    :	List[Any]     					=			torch.tensor([0.5_142]	,    device=a							)
             self.assertTrue(torch.allclose(outputs.loss	,    a	,    atol=1e-4							)							)

             # verify the loss (`config.norm_pix_loss` = `False`)
             lowercase__    :	Tuple     					=			VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'	,    norm_pix_loss=a							).to(
                 a							)

             with torch.no_grad():
                    lowercase__    :	Any     					=			model(**a							)

             lowercase__    :	List[Any]     					=			torch.tensor(torch.tensor([0.6_469]							)	,    device=a							)
             self.assertTrue(torch.allclose(outputs.loss	,    a	,    atol=1e-4							)							)

