




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


UpperCamelCase__              =						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__              =						{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
UpperCamelCase__              =						{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCamelCase__              =						sorted(arg_to_scheduler.keys())
UpperCamelCase__              =						'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 				__SCREAMING_SNAKE_CASE							(		pl.LightningModule  ):

					def __init__(				self ,       __lowercase ,       __lowercase=None ,       __lowercase="base" ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None ,       **__lowercase ,       ):







						'''simple docstring'''




						super().__init__()
						# TODO: move to self.save_hyperparameters()
						# self.save_hyperparameters()
						# can also expand arguments into trainer signature for easier reading

						self.save_hyperparameters(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							0
						lowerCamelCase_							:		Optional[Any]							   =							Path(self.hparams.output_dir					)
						lowerCamelCase_							:		Dict							   =							self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
							lowerCamelCase_							:		Tuple							   =							AutoConfig.from_pretrained(
							    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path ,       **({'num_labels': num_labels} if num_labels is not None else {}) ,       cache_dir=__lowercase ,       **__lowercase ,       )
						else:
							lowerCamelCase_							:		PretrainedConfig							   =							config

						lowerCamelCase_							:		Dict							   =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
						for p in extra_model_params:
							if getattr(self.hparams ,       __lowercase ,       __lowercase					):
								assert hasattr(self.config ,       __lowercase					), f"model config doesn't have a `{p}` attribute"
								setattr(self.config ,       __lowercase ,       getattr(self.hparams ,       __lowercase					)					)

						if tokenizer is None:
							lowerCamelCase_							:		Union[str, Any]							   =							AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		PreTrainedTokenizer							   =							tokenizer
						lowerCamelCase_							:		str							   =							MODEL_MODES[mode]
						if model is None:
							lowerCamelCase_							:		List[Any]							   =							self.model_type.from_pretrained(
							    self.hparams.model_name_or_path ,       from_tf=bool('.ckpt' in self.hparams.model_name_or_path					) ,       config=self.config ,       cache_dir=__lowercase ,       )
						else:
							lowerCamelCase_							:		List[str]							   =							model

					def 	lowercase_							(				self ,       *__lowercase ,       **__lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							self.model_type.from_pretrained(*__lowercase ,       **__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							arg_to_scheduler[self.hparams.lr_scheduler]
						lowerCamelCase_							:		List[Any]							   =							get_schedule_func(
						    self.opt ,       num_warmup_steps=self.hparams.warmup_steps ,       num_training_steps=self.total_steps()					)
						lowerCamelCase_							:		int							   =							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.model
						lowerCamelCase_							:		int							   =							['bias', 'LayerNorm.weight']
						lowerCamelCase_							:		int							   =							[
						    {
						        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay					)],
						        'weight_decay': self.hparams.weight_decay,
						    },
						    {
						        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay					)],
						        'weight_decay': 0.0,
						    },
						]
						if self.hparams.adafactor:
							lowerCamelCase_							:		int							   =							Adafactor(
							    __lowercase ,       lr=self.hparams.learning_rate ,       scale_parameter=__lowercase ,       relative_step=__lowercase					)

						else:
							lowerCamelCase_							:		int							   =							AdamW(
							    __lowercase ,       lr=self.hparams.learning_rate ,       eps=self.hparams.adam_epsilon					)
						lowerCamelCase_							:		str							   =							optimizer

						lowerCamelCase_							:		int							   =							self.get_lr_scheduler()

						return [optimizer], [scheduler]

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						return self.validation_step(__lowercase ,       __lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return self.validation_end(__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							max(1 ,       self.hparams.gpus					)  # TODO: consider num_tpu_cores
						lowerCamelCase_							:		str							   =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
						return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						if stage == "test":
							lowerCamelCase_							:		Dict							   =							len(self.test_dataloader().dataset					)
						else:
							lowerCamelCase_							:		List[str]							   =							self.get_dataloader('train' ,       self.hparams.train_batch_size ,       shuffle=__lowercase					)
							lowerCamelCase_							:		int							   =							len(self.train_dataloader().dataset					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase = False					):







						'''simple docstring'''




						raise NotImplementedError('You must implement this for your task'					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.train_loader

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('dev' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.get_dataloader('test' ,       self.hparams.eval_batch_size ,       shuffle=__lowercase					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return os.path.join(
						    self.hparams.data_dir ,       'cached_{}_{}_{}'.format(
						        __lowercase ,       list(filter(__lowercase ,       self.hparams.model_name_or_path.split('/'					)					)					).pop() ,       str(self.hparams.max_seq_length					) ,       ) ,       )

					@pl.utilities.rank_zero_only
					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							self.output_dir.joinpath('best_tfmr'					)
						lowerCamelCase_							:		Tuple							   =							self.step_count
						self.model.save_pretrained(__lowercase					)
						self.tokenizer.save_pretrained(__lowercase					)





					@staticmethod
					def 	lowercase_							(				__lowercase ,       __lowercase					):







						'''simple docstring'''




						parser.add_argument(
						    '--model_name_or_path' ,       default=__lowercase ,       type=__lowercase ,       required=__lowercase ,       help='Path to pretrained model or model identifier from huggingface.co/models' ,       )
						parser.add_argument(
						    '--config_name' ,       default='' ,       type=__lowercase ,       help='Pretrained config name or path if not the same as model_name'					)
						parser.add_argument(
						    '--tokenizer_name' ,       default=__lowercase ,       type=__lowercase ,       help='Pretrained tokenizer name or path if not the same as model_name' ,       )
						parser.add_argument(
						    '--cache_dir' ,       default='' ,       type=__lowercase ,       help='Where do you want to store the pre-trained models downloaded from huggingface.co' ,       )
						parser.add_argument(
						    '--encoder_layerdrop' ,       type=__lowercase ,       help='Encoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--decoder_layerdrop' ,       type=__lowercase ,       help='Decoder layer dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--dropout' ,       type=__lowercase ,       help='Dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument(
						    '--attention_dropout' ,       type=__lowercase ,       help='Attention dropout probability (Optional). Goes into model.config' ,       )
						parser.add_argument('--learning_rate' ,       default=5E-5 ,       type=__lowercase ,       help='The initial learning rate for Adam.'					)
						parser.add_argument(
						    '--lr_scheduler' ,       default='linear' ,       choices=__lowercase ,       metavar=__lowercase ,       type=__lowercase ,       help='Learning rate scheduler' ,       )
						parser.add_argument('--weight_decay' ,       default=0.0 ,       type=__lowercase ,       help='Weight decay if we apply some.'					)
						parser.add_argument('--adam_epsilon' ,       default=1E-8 ,       type=__lowercase ,       help='Epsilon for Adam optimizer.'					)
						parser.add_argument('--warmup_steps' ,       default=0 ,       type=__lowercase ,       help='Linear warmup over warmup_steps.'					)
						parser.add_argument('--num_workers' ,       default=4 ,       type=__lowercase ,       help='kwarg passed to DataLoader'					)
						parser.add_argument('--num_train_epochs' ,       dest='max_epochs' ,       default=3 ,       type=__lowercase					)
						parser.add_argument('--train_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--eval_batch_size' ,       default=32 ,       type=__lowercase					)
						parser.add_argument('--adafactor' ,       action='store_true'					)


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
							pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.


class 				__SCREAMING_SNAKE_CASE							(		pl.Callback  ):

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							   =							trainer.lr_schedulers[0]['scheduler']
						lowerCamelCase_							:		Optional[Any]							   =							{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
						pl_module.logger.log_metrics(__lowercase					)

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Validation results *****'					)
						lowerCamelCase_							:		Optional[Any]							   =							trainer.callback_metrics
						# Log results
						for key in sorted(__lowercase					):
							if key not in ["log", "progress_bar"]:
								rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)





					def 	lowercase_							(				self ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						rank_zero_info('***** Test results *****'					)
						lowerCamelCase_							:		Union[str, Any]							   =							trainer.callback_metrics
						# Log and save results to file
						lowerCamelCase_							:		Tuple							   =							os.path.join(pl_module.hparams.output_dir ,       'test_results.txt'					)
						with open(__lowercase ,       'w'					) as writer:
							for key in sorted(__lowercase					):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)
									writer.write('{} = {}\n'.format(__lowercase ,       str(metrics[key]					)					)					)






def 					snake_case					(							A						,   A			)							->							None:




	'''simple docstring'''


	parser.add_argument(
	    '--output_dir'						,   default=A						,   type=A						,   required=A						,   help='The output directory where the model predictions and checkpoints will be written.'						,   )
	parser.add_argument(
	    '--fp16'						,   action='store_true'						,   help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,   )

	parser.add_argument(
	    '--fp16_opt_level'						,   type=A						,   default='O2'						,   help=(
	        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
	        'See details at https://nvidia.github.io/apex/amp.html'
	    )						,   )
	parser.add_argument('--n_tpu_cores'						,   dest='tpu_cores'						,   type=A			)
	parser.add_argument('--max_grad_norm'						,   dest='gradient_clip_val'						,   default=1.0						,   type=A						,   help='Max gradient norm'			)
	parser.add_argument('--do_train'						,   action='store_true'						,   help='Whether to run training.'			)
	parser.add_argument('--do_predict'						,   action='store_true'						,   help='Whether to run predictions on the test set.'			)
	parser.add_argument(
	    '--gradient_accumulation_steps'						,   dest='accumulate_grad_batches'						,   type=A						,   default=1						,   help='Number of updates steps to accumulate before performing a backward/update pass.'						,   )
	parser.add_argument('--seed'						,   type=A						,   default=42						,   help='random seed for initialization'			)
	parser.add_argument(
	    '--data_dir'						,   default=A						,   type=A						,   required=A						,   help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,   )






def 					snake_case					(							A						,   A						,   A=None						,   A=True						,   A=None						,   A=[]						,   A=None						,   A=None						,   **A						,   )							->							Union[str, Any]:




	'''simple docstring'''


	pl.seed_everything(args.seed			)

	# init model
	lowerCamelCase_							:		Tuple							   =							Path(model.hparams.output_dir			)
	odir.mkdir(exist_ok=A			)

	# add custom checkpoints
	if checkpoint_callback is None:
		lowerCamelCase_							:		Dict							   =							pl.callbacks.ModelCheckpoint(
		    filepath=args.output_dir						,   prefix='checkpoint'						,   monitor='val_loss'						,   mode='min'						,   save_top_k=1			)
	if early_stopping_callback:
		extra_callbacks.append(A			)
	if logging_callback is None:
		lowerCamelCase_							:		Optional[Any]							   =							LoggingCallback()

	lowerCamelCase_							:		Tuple							   =							{}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		lowerCamelCase_							:		str							   =							16
		# train_params["amp_level"] = args.fp16_opt_level

	if args.gpus > 1:
		lowerCamelCase_							:		Optional[int]							   =							'auto'  # "ddp"
		lowerCamelCase_							:		Any							   =							'ddp'

	lowerCamelCase_							:		Optional[int]							   =							args.accumulate_grad_batches
	lowerCamelCase_							:		str							   =							None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
	lowerCamelCase_							:		Union[str, Any]							   =							'auto'

	lowerCamelCase_							:		Optional[Any]							   =							pl.Trainer.from_argparse_args(
	    A						,   weights_summary=A						,   callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,   logger=A						,   **A						,   )

	if args.do_train:
		trainer.fit(A			)

	return trainer




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
							from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from transformers import VivitImageProcessor

class   A__							:




						def __init__(				self    :					str ,			_a    :					List[str] ,			_a    :					List[str]=2 ,			_a    :					Dict=True ,			_a    :					Tuple=True ,			_a    :					Any=10 ,			_a    :					str=10 ,			_a    :					int=8 ,			_a    :					Tuple=[2, 4, 4] ,			_a    :					Optional[int]=3 ,			_a    :					List[str]=768 ,			_a    :					int=5 ,			_a    :					Any=4 ,			_a    :					Tuple=37 ,			_a    :					Tuple="gelu_fast" ,			_a    :					Dict=0.0 ,			_a    :					Optional[Any]=0.0 ,			_a    :					Optional[Any]=0.02 ,			_a    :					str=1E-06 ,			_a    :					Tuple=True ,			_a    :					List[str]=None ,			)							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =parent
										_SCREAMING_SNAKE_CASE	     =batch_size
										_SCREAMING_SNAKE_CASE	     =is_training
										_SCREAMING_SNAKE_CASE	     =use_labels
										_SCREAMING_SNAKE_CASE	     =num_labels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =num_frames
										_SCREAMING_SNAKE_CASE	     =tubelet_size
										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =hidden_size
										_SCREAMING_SNAKE_CASE	     =num_hidden_layers
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =intermediate_size
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias
										_SCREAMING_SNAKE_CASE	     =scope

										_SCREAMING_SNAKE_CASE	     =(
										    (self.image_size // self.tubelet_size[2])
										    * (self.image_size // self.tubelet_size[1])
										    * (self.num_frames // self.tubelet_size[0])
										) + 1  # CLS token




						def 							__UpperCamelCase (				self    :					List[str]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =floats_tensor(
										    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

										_SCREAMING_SNAKE_CASE	     =None
										if self.use_labels:
														_SCREAMING_SNAKE_CASE	     =ids_tensor([self.batch_size] ,			self.num_labels     )

										_SCREAMING_SNAKE_CASE	     =self.get_config()

										return config, pixel_values, labels




						def 							__UpperCamelCase (				self    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitConfig(
										    num_frames=self.num_frames ,			image_size=self.image_size ,			tubelet_size=self.tubelet_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 ,			initializer_range=self.initializer_range ,			layer_norm_eps=self.layer_norm_eps ,			qkv_bias=self.qkv_bias ,			)
										_SCREAMING_SNAKE_CASE	     =self.num_labels
										return config




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Optional[int] ,			_a    :					Dict ,			_a    :					Tuple     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitModel(config=_a     )
										model.to(_a     )
										model.eval()
										_SCREAMING_SNAKE_CASE	     =model(_a     )
										self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)     )




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Optional[Any] ,			_a    :					Any ,			_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitForVideoClassification(_a     )
										model.to(_a     )
										model.eval()

										_SCREAMING_SNAKE_CASE	     =model(_a     )

										# verify the logits shape
										_SCREAMING_SNAKE_CASE	     =torch.Size((self.batch_size, self.num_labels)     )
										self.parent.assertEqual(result.logits.shape ,			_a     )




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =config_and_inputs
										_SCREAMING_SNAKE_CASE	     ={'''pixel_values''': pixel_values}
										return config, inputs_dict

@require_torch
class   A__							(						UpperCamelCase__      ,   unittest.TestCase     ):
						UpperCAmelCase									=  (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False
						UpperCAmelCase									=  False




						def 							__UpperCamelCase (				self    :					Any     )							->   Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitModelTester(self     )
										_SCREAMING_SNAKE_CASE	     =ConfigTester(self ,			config_class=_a ,			has_text_modality=_a ,			hidden_size=37     )




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					List[str] ,			_a    :					Union[str, Any] ,			_a    :					Dict=False     )							->   List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =copy.deepcopy(_a     )

										if return_labels:
														if model_class in get_values(_a     ):
																		_SCREAMING_SNAKE_CASE	     =torch.zeros(
																		    self.model_tester.batch_size ,			dtype=torch.long ,			device=_a     )

										return inputs_dict




						def 							__UpperCamelCase (				self    :					str     )							->   Optional[int]:


										"""simple docstring"""





										self.config_tester.run_common_tests()




						@unittest.skip(reason='''Vivit does not use inputs_embeds'''     )
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   Any:


										"""simple docstring"""





										pass




						def 							__UpperCamelCase (				self    :					str     )							->   Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

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




						def 							__UpperCamelCase (				self    :					int     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

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

														_SCREAMING_SNAKE_CASE	     =['''pixel_values''', '''head_mask''']
														self.assertListEqual(arg_names[:2] ,			_a     )




						def 							__UpperCamelCase (				self    :					Tuple     )							->   List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a     )




						def 							__UpperCamelCase (				self    :					str     )							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_video_classification(*_a     )




						@slow
						def 							__UpperCamelCase (				self    :					str     )							->   Optional[Any]:


										"""simple docstring"""





										for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE	     =VivitModel.from_pretrained(_a     )
														self.assertIsNotNone(_a     )




						def 							__UpperCamelCase (				self    :					List[Any]     )							->   Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE	     =True

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =self.model_tester.seq_length

														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =False
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =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"]
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )
														_SCREAMING_SNAKE_CASE	     =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] ,			)
														_SCREAMING_SNAKE_CASE	     =len(_a     )

														# Check attention is always last and order is fine
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =True
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()
														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )

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

														_SCREAMING_SNAKE_CASE	     =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    :					Any     )							->   Optional[int]:


										"""simple docstring"""





										def check_hidden_states_output(_a    :					Dict ,			_a    :					Optional[Any] ,			_a    :					List[Any]     ):
														_SCREAMING_SNAKE_CASE	     =model_class(_a     )
														model.to(_a     )
														model.eval()

														with torch.no_grad():
																		_SCREAMING_SNAKE_CASE	     =model(**self._prepare_for_class(_a ,			_a     )     )

														_SCREAMING_SNAKE_CASE	     =outputs.hidden_states
														_SCREAMING_SNAKE_CASE	     =self.model_tester.num_hidden_layers + 1
														self.assertEqual(len(_a     ) ,			_a     )

														_SCREAMING_SNAKE_CASE	     =self.model_tester.seq_length

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

										_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE	     =True
														check_hidden_states_output(_a ,			_a ,			_a     )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_SCREAMING_SNAKE_CASE	     =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    :					Optional[int]     )							->   List[Any]:


										"""simple docstring"""





										pass







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video'''		,filename='''eating_spaghetti_32_frames.npy'''		,repo_type='''dataset''')
				_SCREAMING_SNAKE_CASE	     =np.load(a__)
				return list(a__)


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




						@cached_property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   List[Any]:


										"""simple docstring"""





										return VivitImageProcessor() if is_vision_available() else None




						@slow
						def 							__UpperCamelCase (				self    :					Optional[Any]     )							->   str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''     ).to(_a     )

										_SCREAMING_SNAKE_CASE	     =self.default_image_processor
										_SCREAMING_SNAKE_CASE	     =prepare_video()
										_SCREAMING_SNAKE_CASE	     =image_processor(_a ,			return_tensors='''pt'''     ).to(_a     )

										# forward pass
										with torch.no_grad():
														_SCREAMING_SNAKE_CASE	     =model(**_a     )

										# verify the logits
										_SCREAMING_SNAKE_CASE	     =torch.Size((1, 400)     )
										self.assertEqual(outputs.logits.shape ,			_a     )

										# taken from original model
										_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]     ).to(_a     )

										self.assertTrue(torch.allclose(outputs.logits[0, :5] ,			_a ,			atol=1E-4     )     )