


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case_  :	int							  =							'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

snake_case_  :	List[Any]							  =							logging.getLogger(__name__)

@dataclass
class   A__							:
						UpperCAmelCase									=  field(
						    default="roberta-large-mnli"      ,   metadata={"help": "The NLI/zero-shot teacher model to be distilled."}     )
						UpperCAmelCase									=  field(
						    default="This example is {}."      ,   metadata={
						        "help": (
						            "Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"
						            "where class name is inserted."
						        )
						    }      ,   )
						UpperCAmelCase									=  field(
						    default=32      ,   metadata={"help": "Batch size for generating teacher predictions."}     )
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={
						        "help": (
						            "Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"
						            "multi-class multi-label classification)."
						        )
						    }      ,   )
						UpperCAmelCase									=  field(
						    default=1.0      ,   metadata={"help": "Temperature applied to teacher softmax for distillation."}     )

@dataclass
class   A__							:
						UpperCAmelCase									=  field(
						    default="distilbert-base-uncased"      ,   metadata={"help": "The NLI/zero-shot teacher model to be distilled."}     )

@dataclass
class   A__							:
						UpperCAmelCase									=  field(metadata={"help": "Text file with one unlabeled instance per line."}     )
						UpperCAmelCase									=  field(metadata={"help": "Text file with one class name per line."}     )
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."}      ,   )

@dataclass
class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={"help": "The output directory where the model predictions and checkpoints will be written."}      ,   )
						UpperCAmelCase									=  field(
						    default=32      ,   metadata={"help": "Batch size per GPU/TPU core/CPU for training."}     )
						UpperCAmelCase									=  field(
						    default=128      ,   metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}     )
						UpperCAmelCase									=  field(default=1.0      ,   metadata={"help": "Total number of training epochs to perform."}     )
						UpperCAmelCase									=  field(default=UpperCamelCase__      ,   metadata={"help": "Whether to run training of student model."}     )
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={
						        "help": (
						            "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
						            "after training."
						        )
						    }      ,   )
						UpperCAmelCase									=  field(
						    default=0      ,   metadata={
						        "help": (
						            "Limit the total amount of checkpoints."
						            "Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."
						        )
						    }      ,   )

class   A__							(						UpperCamelCase__     ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =inputs['''labels''']
										_SCREAMING_SNAKE_CASE	     =model(inputs['''input_ids'''] ,			attention_mask=inputs['''attention_mask''']     )
										_SCREAMING_SNAKE_CASE	     =outputs[0]

										_SCREAMING_SNAKE_CASE	     =-torch.sum(target_p * logits.log_softmax(dim=-1     ) ,			axis=-1     ).mean()

										if return_outputs:
														return loss, outputs

										return loss







def   lowerCamelCase(      a__):
				_SCREAMING_SNAKE_CASE	     =[]
				with open(a__		,'''r''') as f:
								for line in f:
												_SCREAMING_SNAKE_CASE	     =line.strip()
												if len(a__) > 0:
																lines.append(a__)
				return lines







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =[]
				for example in examples:
								for name in class_names:
												premises.append(a__)
												hypotheses.append(hypothesis_template.format(a__))
				return premises, hypotheses







def   lowerCamelCase(      a__):
				for label, ind in config.labelaid.items():
								if label.lower().startswith('''entail'''):
												return ind
				logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.''')
				return -1







def   lowerCamelCase(      a__		,a__		,a__		,a__		,a__		,a__		,a__		,a__		,a__		,a__		,):
				_SCREAMING_SNAKE_CASE	     =AutoModelForSequenceClassification.from_pretrained(a__)
				_SCREAMING_SNAKE_CASE	     =model.config
				if not no_cuda and torch.cuda.is_available():
								_SCREAMING_SNAKE_CASE	     =nn.DataParallel(model.cuda())
								batch_size *= len(model.device_ids)
				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(a__		,use_fast=a__)

				_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =get_premise_hypothesis_pairs(a__		,a__		,a__)
				_SCREAMING_SNAKE_CASE	     =[]

				for i in tqdm(range(0		,len(a__)		,a__)):
								_SCREAMING_SNAKE_CASE	     =premises[i : i + batch_size]
								_SCREAMING_SNAKE_CASE	     =hypotheses[i : i + batch_size]

								_SCREAMING_SNAKE_CASE	     =tokenizer(
								    a__		,a__		,padding=a__		,truncation='''only_first'''		,return_tensors='''pt'''		,)

								with torch.cuda.amp.autocast(enabled=a__):
												with torch.no_grad():
																_SCREAMING_SNAKE_CASE	     =model(**a__)
								logits.append(outputs.logits.detach().cpu().float())

				_SCREAMING_SNAKE_CASE	     =get_entailment_id(a__)
				_SCREAMING_SNAKE_CASE	     =-1 if entail_id == 0 else 0
				_SCREAMING_SNAKE_CASE	     =torch.cat(a__		,dim=0)  # N*K x 3
				_SCREAMING_SNAKE_CASE	     =logits.reshape(len(a__)		,len(a__)		,-1)[..., [contr_id, entail_id]]  # N x K x 2

				if multi_label:
								# softmax over (contr, entail) logits for each class independently
								_SCREAMING_SNAKE_CASE	     =(nli_logits / temperature).softmax(-1)
				else:
								# softmax over entail logits across classes s.t. class probabilities sum to 1.
								_SCREAMING_SNAKE_CASE	     =(nli_logits / temperature).softmax(1)

				return nli_prob[..., 1]  # N x K







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =HfArgumentParser(
				    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)		,description=a__		,)

				if len(sys.argv) == 2 and sys.argv[1].endswith('''.json'''):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =parser.parse_json_file(
								    json_file=os.path.abspath(sys.argv[1]))
				else:
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				_SCREAMING_SNAKE_CASE	     =None
				if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
								_SCREAMING_SNAKE_CASE	     =get_last_checkpoint(training_args.output_dir)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
												raise ValueError(
												    f"Output directory ({training_args.output_dir}) already exists and is not empty. "
												    '''Use --overwrite_output_dir to overcome.''')
								elif last_checkpoint is not None:
												logger.info(
												    f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''')

    # Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,datefmt='''%m/%d/%Y %H:%M:%S'''		,handlers=[logging.StreamHandler(sys.stdout)]		,)
				logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

				# Log on each process the small summary:
				logger.warning(
				    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
				    + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}")
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank):
								utils.logging.set_verbosity_info()
								utils.logging.enable_default_handler()
								utils.logging.enable_explicit_format()

				if training_args.local_rank != -1:
								raise ValueError('''Distributed training is not currently supported.''')
				if training_args.tpu_num_cores is not None:
								raise ValueError('''TPU acceleration is not currently supported.''')

				logger.info(f"Training/evaluation parameters {training_args}")

				# Set seed before initializing model.
				set_seed(training_args.seed)

				# 1. read in data
				_SCREAMING_SNAKE_CASE	     =read_lines(data_args.data_file)
				_SCREAMING_SNAKE_CASE	     =read_lines(data_args.class_names_file)

				# 2. get teacher predictions and load into dataset
				logger.info('''Generating predictions from zero-shot teacher model''')
				_SCREAMING_SNAKE_CASE	     =get_teacher_predictions(
				    teacher_args.teacher_name_or_path		,a__		,a__		,teacher_args.hypothesis_template		,teacher_args.teacher_batch_size		,teacher_args.temperature		,teacher_args.multi_label		,data_args.use_fast_tokenizer		,training_args.no_cuda		,training_args.fpaa		,)
				_SCREAMING_SNAKE_CASE	     =Dataset.from_dict(
				    {
				        '''text''': examples,
				        '''labels''': teacher_soft_preds,
				    })

				# 3. create student
				logger.info('''Initializing student model''')
				_SCREAMING_SNAKE_CASE	     =AutoModelForSequenceClassification.from_pretrained(
				    student_args.student_name_or_path		,num_labels=len(a__))
				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(student_args.student_name_or_path		,use_fast=data_args.use_fast_tokenizer)
				_SCREAMING_SNAKE_CASE	     =dict(enumerate(a__))
				_SCREAMING_SNAKE_CASE	     ={label: i for i, label in enumerate(a__)}

				# 4. train student on teacher predictions
				_SCREAMING_SNAKE_CASE	     =dataset.map(a__		,input_columns='''text''')
				dataset.set_format('''torch''')

				def compute_metrics(a__		,a__=False):
								_SCREAMING_SNAKE_CASE	     =p.predictions.argmax(-1)
								_SCREAMING_SNAKE_CASE	     =p.label_ids.argmax(-1)  # "label_ids" are actually distributions
								return {"agreement": (preds == proxy_labels).mean().item()}

				_SCREAMING_SNAKE_CASE	     =DistillationTrainer(
				    model=a__		,tokenizer=a__		,args=a__		,train_dataset=a__		,compute_metrics=a__		,)

				if training_args.do_train:
								logger.info('''Training student model on teacher predictions''')
								trainer.train()

				if training_args.do_eval:
								_SCREAMING_SNAKE_CASE	     =trainer.evaluate(eval_dataset=a__)['''eval_agreement''']
								logger.info(f"Agreement of student and teacher predictions: {agreement * 100:0.2f}%")

				trainer.save_model()


if __name__ == "__main__":
							main()



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     )     )