





"""simple docstring"""




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_	 =logging.get_logger()




@dataclass
class 					_a		:
				UpperCamelCase								=       42
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tensor,					lowerCAmelCase__						:       Tensor,					lowerCAmelCase__						:       str				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       len(list(m.modules()				)				) == 1 or isinstance(lowerCAmelCase__,					nn.Convad				) or isinstance(lowerCAmelCase__,					nn.BatchNormad				)
										if has_not_submodules:
																self.traced.append(lowerCAmelCase__				)
																_UpperCamelCase     :   Union[str, Any]      =       m






				def __call__(					self						:       Tuple,					lowerCAmelCase__						:       Tensor				)      -> Dict:




										'''simple docstring'''



										for name, m in self.module.named_modules():
																self.handles.append(m.register_forward_hook(partial(self._forward_hook,					name=lowerCAmelCase__				)				)				)
										self.module(lowerCAmelCase__				)
										[x.remove() for x in self.handles]
										return self






				@property
				def 							snake_case      (					self						:       Union[str, Any]				)      -> Tuple:




										'''simple docstring'''



										return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}




class 					_a		( nn.Module							):






				def __init__(					self						:       str,					lowerCAmelCase__						:       nn.Module				)      -> str:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase     :   List[Tuple[str, nn.Module]]      =       []
										# - get the stem
										feature_blocks.append(('''conv1''', model.stem)				)
										# - get all the feature blocks
										for k, v in model.trunk_output.named_children():
																assert k.startswith('''block'''				), f"""Unexpected layer name {k}"""
																_UpperCamelCase     :   Tuple      =       len(lowerCAmelCase__				) + 1
																feature_blocks.append((f"""res{block_index}""", v)				)

										_UpperCamelCase     :   str      =       nn.ModuleDict(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Tensor				)      -> int:




										'''simple docstring'''



										return get_trunk_forward_outputs(
										    lowerCAmelCase__,					out_feat_keys=lowerCAmelCase__,					feature_blocks=self._feature_blocks,					)




class 					_a		( _lowerCAmelCase							):






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




										'''simple docstring'''



										return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Optional[Any]      =       RegNetConfig(depths=[2, 7, 17, 1]			,		hidden_sizes=[8, 8, 8, 8]			,		groups_width=8  )
						if "in1k" in model_name:
												_UpperCamelCase     :   List[str]      =       RegNetForImageClassification(_lowercase  )
						else:
												_UpperCamelCase     :   List[Any]      =       RegNetModel(_lowercase  )
						# create from model (with small weights)
						_UpperCamelCase     :   Dict      =       FakeRegNetVisslWrapper(
						    RegNet(FakeRegNetParams(depth=27			,		group_width=1010			,		w_a=1744			,		w_a=620.83			,		w_m=2.52  )  )  )

						with torch.no_grad():
												_UpperCamelCase     :   str      =       from_model.eval()
												_UpperCamelCase     :   Optional[int]      =       our_model.eval()

												_UpperCamelCase     :   Union[str, Any]      =       torch.randn((1, 3, 32, 32)  )
												# trace both
												_UpperCamelCase     :   List[str]      =       Tracker(_lowercase  )
												_UpperCamelCase     :   Optional[Any]      =       dest_tracker(_lowercase  ).parametrized

												pprint(dest_tracker.nameamodule  )
												_UpperCamelCase     :   Union[str, Any]      =       Tracker(_lowercase  )
												_UpperCamelCase     :   str      =       src_tracker(_lowercase  ).parametrized

						# convert the keys -> module dict to keys -> params
						def to_params_dict(_lowercase  ):
												_UpperCamelCase     :   int      =       OrderedDict()
												for name, module in dict_with_modules.items():
																		for param_name, param in module.state_dict().items():
																								_UpperCamelCase     :   Tuple      =       param
												return params_dict

						_UpperCamelCase     :   Optional[int]      =       {}

						_UpperCamelCase     :   str      =       to_params_dict(_lowercase  )
						_UpperCamelCase     :   Dict      =       to_params_dict(_lowercase  )

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()			,		dst_state_dict.items()  ):
												_UpperCamelCase     :   Dict      =       dest_key
												logger.info(F"""{src_key} -> {dest_key}"""  )
						# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
						if "in1k" in model_name:
												_UpperCamelCase     :   Union[str, Any]      =       '''classifier.1.weight'''
												_UpperCamelCase     :   Optional[Any]      =       '''classifier.1.bias'''

						return from_to_ours_keys





def 			a_							(				_lowercase			,		_lowercase = None			,		_lowercase = True  ):
						_UpperCamelCase     :   str      =       '''imagenet-1k-id2label.json'''
						_UpperCamelCase     :   int      =       1000

						_UpperCamelCase     :   int      =       '''huggingface/label-files'''
						_UpperCamelCase     :   str      =       num_labels
						_UpperCamelCase     :   List[str]      =       json.load(open(cached_download(hf_hub_url(_lowercase			,		_lowercase			,		repo_type='''dataset'''  )  )			,		'''r'''  )  )
						_UpperCamelCase     :   Union[str, Any]      =       {int(_lowercase  ): v for k, v in idalabel.items()}

						_UpperCamelCase     :   Dict      =       idalabel
						_UpperCamelCase     :   Any      =       {v: k for k, v in idalabel.items()}

						_UpperCamelCase     :   Union[str, Any]      =       partial(_lowercase			,		num_labels=_lowercase			,		idalabel=_lowercase			,		labelaid=_lowercase  )

						_UpperCamelCase     :   int      =       {
						    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,		groups_width=1010  ),
						    # finetuned on imagenet
						    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,		groups_width=1010  ),
						}

						# add seer weights logic
						def load_using_classy_vision(_lowercase  ) -> Tuple[Dict, Dict]:
												_UpperCamelCase     :   str      =       torch.hub.load_state_dict_from_url(_lowercase			,		model_dir=str(_lowercase  )			,		map_location='''cpu'''  )
												# check if we have a head, if yes add it
												_UpperCamelCase     :   Any      =       files['''classy_state_dict''']['''base_model''']['''model''']
												return model_state_dict["trunk"], model_state_dict["heads"]

						_UpperCamelCase     :   Tuple      =       {
						    '''regnet-y-10b-seer''': partial(
						        _lowercase			,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''			,		),
						    '''regnet-y-10b-seer-in1k''': partial(
						        _lowercase			,		'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''			,		),
						}

						_UpperCamelCase     :   Tuple      =       get_from_to_our_keys(_lowercase  )

						if not (save_directory / F"""{model_name}.pth""").exists():
												logger.info('''Loading original state_dict.'''  )
												_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       names_to_from_model[model_name]()
												_UpperCamelCase     :   List[str]      =       from_state_dict_trunk
												if "in1k" in model_name:
																		# add the head
																		_UpperCamelCase     :   List[Any]      =       {**from_state_dict_trunk, **from_state_dict_head}
												logger.info('''Done!'''  )

												_UpperCamelCase     :   Tuple      =       {}

												_UpperCamelCase     :   Tuple      =       list(from_state_dict.keys()  )
												_UpperCamelCase     :   Dict      =       R'''\.block.-part.'''
												# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
												for key in from_state_dict.keys():
																		# remove the weird "block[0,1]-part" from the key
																		_UpperCamelCase     :   Union[str, Any]      =       re.sub(_lowercase			,		''''''			,		_lowercase  )
																		# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																		_UpperCamelCase     :   int      =       from_to_ours_keys[src_key]
																		# store the parameter with our key
																		_UpperCamelCase     :   Dict      =       from_state_dict[key]
																		not_used_keys.remove(_lowercase  )
												# check that all keys have been updated
												assert len(_lowercase  ) == 0, F"""Some keys where not used {','.join(_lowercase  )}"""

												logger.info(F"""The following keys were not used: {','.join(_lowercase  )}"""  )

												# save our state dict to disk
												torch.save(_lowercase			,		save_directory / F"""{model_name}.pth"""  )

												del converted_state_dict
						else:
												logger.info('''The state_dict was already stored on disk.'''  )
						if push_to_hub:
												logger.info(F"""Token is {os.environ['HF_TOKEN']}"""  )
												logger.info('''Loading our model.'''  )
												# create our model
												_UpperCamelCase     :   Any      =       names_to_config[model_name]
												_UpperCamelCase     :   Tuple      =       RegNetModel
												if "in1k" in model_name:
																		_UpperCamelCase     :   List[str]      =       RegNetForImageClassification
												_UpperCamelCase     :   List[Any]      =       our_model_func(_lowercase  )
												# place our model to the meta device (so remove all the weights)
												our_model.to(torch.device('''meta'''  )  )
												logger.info('''Loading state_dict in our model.'''  )
												# load state dict
												_UpperCamelCase     :   Any      =       our_model.state_dict().keys()
												PreTrainedModel._load_pretrained_model_low_mem(
												    _lowercase			,		_lowercase			,		[save_directory / F"""{model_name}.pth"""]  )
												logger.info('''Finally, pushing!'''  )
												# push it to hub
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / model_name			,		commit_message='''Add model'''			,		output_dir=save_directory / model_name			,		)
												_UpperCamelCase     :   Union[str, Any]      =       384
												# we can use the convnext one
												_UpperCamelCase     :   int      =       AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''			,		size=_lowercase  )
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / model_name			,		commit_message='''Add image processor'''			,		output_dir=save_directory / model_name			,		)


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default=None,
						    type=str,
						    help=(
						        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
						        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=Path,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    default=True,
						    type=bool,
						    required=False,
						    help="""If True, push model and image processor to the hub.""",
						)

						UpperCamelCase_	 =parser.parse_args()

						UpperCamelCase_	 =args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''







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 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=2							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=8							,				__SCREAMING_SNAKE_CASE=[2, 4, 4]							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=7_68							,				__SCREAMING_SNAKE_CASE=5							,				__SCREAMING_SNAKE_CASE=4							,				__SCREAMING_SNAKE_CASE=37							,				__SCREAMING_SNAKE_CASE="gelu_fast"							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.0							,				__SCREAMING_SNAKE_CASE=0.02							,				__SCREAMING_SNAKE_CASE=1E-0_6							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				):


											"""simple docstring"""





											lowercase_      :	Any						=			parent
											lowercase_      :	Union[str, Any]						=			batch_size
											lowercase_      :	Union[str, Any]						=			is_training
											lowercase_      :	Tuple						=			use_labels
											lowercase_      :	Any						=			num_labels
											lowercase_      :	str						=			image_size
											lowercase_      :	Union[str, Any]						=			num_frames
											lowercase_      :	Any						=			tubelet_size
											lowercase_      :	Tuple						=			num_channels
											lowercase_      :	Union[str, Any]						=			hidden_size
											lowercase_      :	Dict						=			num_hidden_layers
											lowercase_      :	Optional[int]						=			num_attention_heads
											lowercase_      :	str						=			intermediate_size
											lowercase_      :	Dict						=			hidden_act
											lowercase_      :	Dict						=			hidden_dropout_prob
											lowercase_      :	Optional[Any]						=			attention_probs_dropout_prob
											lowercase_      :	Dict						=			initializer_range
											lowercase_      :	Optional[int]						=			layer_norm_eps
											lowercase_      :	str						=			qkv_bias
											lowercase_      :	Dict						=			scope

											lowercase_      :	Dict						=			(
											    (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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			floats_tensor(
											    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

											lowercase_      :	Optional[int]						=			None
											if self.use_labels:
																	lowercase_      :	Tuple						=			ids_tensor([self.batch_size]							,				self.num_labels						)

											lowercase_      :	Union[str, Any]						=			self.get_config()

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			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							,				)
											lowercase_      :	Optional[Any]						=			self.num_labels
											return config






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	str						=			VivitModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.last_hidden_state.shape							,				(self.batch_size, self.seq_length, self.hidden_size)						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			VivitForVideoClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()

											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE						)

											# verify the logits shape
											lowercase_      :	Union[str, Any]						=			torch.Size((self.batch_size, self.num_labels)						)
											self.parent.assertEqual(result.logits.shape							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			self.prepare_config_and_inputs()
											lowercase_		,					lowercase_		,					lowercase_      :	Dict						=			config_and_inputs
											lowercase_      :	List[str]						=			{'''pixel_values''': pixel_values}
											return config, inputs_dict







@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			VivitModelTester(self						)
											lowercase_      :	Optional[int]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				has_text_modality=__SCREAMING_SNAKE_CASE							,				hidden_size=37						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=False						):


											"""simple docstring"""





											lowercase_      :	str						=			copy.deepcopy(__SCREAMING_SNAKE_CASE						)

											if return_labels:
																	if model_class in get_values(__SCREAMING_SNAKE_CASE						):
																							lowercase_      :	List[str]						=			torch.zeros(
																							    self.model_tester.batch_size							,				dtype=torch.long							,				device=__SCREAMING_SNAKE_CASE						)

											return inputs_dict






					def       _snake_case       (  self						):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					@unittest.skip(reason='''Vivit does not use inputs_embeds'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	str						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_      :	Optional[Any]						=			model_class(__SCREAMING_SNAKE_CASE						)
																	self.assertIsInstance(model.get_input_embeddings()							,				(nn.Module)						)
																	lowercase_      :	int						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE							,				nn.Linear						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	str						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_      :	str						=			['''pixel_values''', '''head_mask''']
																	self.assertListEqual(arg_names[:2]							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	int						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_video_classification(*__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	Union[str, Any]						=			VivitModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											for model_class in self.all_model_classes:
																	lowercase_      :	int						=			self.model_tester.seq_length

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

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

																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]						)							,				[self.model_tester.num_attention_heads, seq_len, seq_len]							,				)
																	lowercase_      :	Dict						=			len(__SCREAMING_SNAKE_CASE						)

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

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

																	lowercase_      :	List[str]						=			outputs.attentions

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				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       _snake_case       (  self						):


											"""simple docstring"""





											def check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																	lowercase_      :	List[str]						=			model_class(__SCREAMING_SNAKE_CASE						)
																	model.to(__SCREAMING_SNAKE_CASE						)
																	model.eval()

																	with torch.no_grad():
																							lowercase_      :	List[Any]						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

																	lowercase_      :	List[str]						=			outputs.hidden_states
																	lowercase_      :	Any						=			self.model_tester.num_hidden_layers + 1
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				__SCREAMING_SNAKE_CASE						)

																	lowercase_      :	List[Any]						=			self.model_tester.seq_length

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

											lowercase_		,					lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_      :	int						=			True
																	check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowercase_      :	List[Any]						=			True

																	check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass







def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	List[str]						=			hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti_32_frames.npy''' , repo_type='''dataset'''				)
						lowercase_      :	List[str]						=			np.load(__SCREAMING_SNAKE_CASE				)
						return list(__SCREAMING_SNAKE_CASE				)






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






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return VivitImageProcessor() if is_vision_available() else None






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''						).to(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Optional[int]						=			self.default_image_processor
											lowercase_      :	Union[str, Any]						=			prepare_video()
											lowercase_      :	str						=			image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	Any						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	Any						=			torch.Size((1, 4_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)

											# taken from original model
											lowercase_      :	Any						=			torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]						).to(__SCREAMING_SNAKE_CASE						)

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



