


import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_  :	Dict							  =							logging.get_logger(__name__)







def   lowerCamelCase(      a__		,a__=False		,a__=False):
				_SCREAMING_SNAKE_CASE	     ='''backbone.''' if is_semantic else ''''''

				_SCREAMING_SNAKE_CASE	     =[]
				for i in range(config.num_hidden_layers):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight"))
								rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias"))
								rename_keys.append(
								    (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight"))
								rename_keys.append(
								    (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias"))
								rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight"))
								rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias"))
								rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight"))
								rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias"))
								rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight"))
								rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias"))

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
				        (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
				        (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
				    ])

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ('''mask_token''', '''beit.embeddings.mask_token'''),
								        (
								            '''rel_pos_bias.relative_position_bias_table''',
								            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
								        ),
								        (
								            '''rel_pos_bias.relative_position_index''',
								            '''beit.encoder.relative_position_bias.relative_position_index''',
								        ),
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								    ])
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
								        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
								        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
								        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
								    ])
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
								        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
								        ('''head.weight''', '''classifier.weight'''),
								        ('''head.bias''', '''classifier.bias'''),
								    ])

				return rename_keys







def   lowerCamelCase(      a__		,a__		,a__=False		,a__=False):
				for i in range(config.num_hidden_layers):
								_SCREAMING_SNAKE_CASE	     ='''backbone.''' if is_semantic else ''''''
								# queries, keys and values
								_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight")
								_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias")
								_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias")

								_SCREAMING_SNAKE_CASE	     =in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE	     =q_bias
								_SCREAMING_SNAKE_CASE	     =in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE	     =in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE	     =v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.gamma_1")
								_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.gamma_2")

								_SCREAMING_SNAKE_CASE	     =gamma_a
								_SCREAMING_SNAKE_CASE	     =gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table")
												_SCREAMING_SNAKE_CASE	     =state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index")

												_SCREAMING_SNAKE_CASE	     =table
												_SCREAMING_SNAKE_CASE	     =index







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =dct.pop(a__)
				_SCREAMING_SNAKE_CASE	     =val







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE	     =Image.open(requests.get(a__		,stream=a__).raw)
				return im







@torch.no_grad()
def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =BeitConfig()
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     ='''huggingface/label-files'''
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =2_1841
								_SCREAMING_SNAKE_CASE	     ='''imagenet-22k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								# this dataset contains 21843 labels but the model only has 21841
								# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
								del idalabel[9205]
								del idalabel[1_5027]
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =1000
								_SCREAMING_SNAKE_CASE	     ='''imagenet-1k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =384
								if "512" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =150
								_SCREAMING_SNAKE_CASE	     ='''ade20k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =640
								_SCREAMING_SNAKE_CASE	     =True
				else:
								raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'''')

				# size of the architecture
				if "base" in checkpoint_url:
								pass
				elif "large" in checkpoint_url:
								_SCREAMING_SNAKE_CASE	     =1024
								_SCREAMING_SNAKE_CASE	     =4096
								_SCREAMING_SNAKE_CASE	     =24
								_SCREAMING_SNAKE_CASE	     =16
								if "ade20k" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =640
												_SCREAMING_SNAKE_CASE	     =[7, 11, 15, 23]
				else:
								raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''')

				# load state_dict of original model, remove and rename some keys
				_SCREAMING_SNAKE_CASE	     =torch.hub.load_state_dict_from_url(a__		,map_location='''cpu'''		,check_hash=a__)
				_SCREAMING_SNAKE_CASE	     =state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

				_SCREAMING_SNAKE_CASE	     =create_rename_keys(a__		,has_lm_head=a__		,is_semantic=a__)
				for src, dest in rename_keys:
								rename_key(a__		,a__		,a__)
				read_in_q_k_v(a__		,a__		,has_lm_head=a__		,is_semantic=a__)
				if is_semantic:
								# add prefix to decoder keys
								for key, val in state_dict.copy().items():
												_SCREAMING_SNAKE_CASE	     =state_dict.pop(a__)
												if key.startswith('''backbone.fpn'''):
																_SCREAMING_SNAKE_CASE	     =key.replace('''backbone.fpn'''		,'''fpn''')
												_SCREAMING_SNAKE_CASE	     =val

    # load HuggingFace model
				if checkpoint_url[-9:-4] == "pt22k":
								_SCREAMING_SNAKE_CASE	     =BeitForMaskedImageModeling(a__)
				elif "ade20k" in checkpoint_url:
								_SCREAMING_SNAKE_CASE	     =BeitForSemanticSegmentation(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =BeitForImageClassification(a__)
				model.eval()
				model.load_state_dict(a__)

				# Check outputs on an image
				if is_semantic:
								_SCREAMING_SNAKE_CASE	     =BeitImageProcessor(size=config.image_size		,do_center_crop=a__)
								_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/fixtures_ade20k'''		,split='''test''')
								_SCREAMING_SNAKE_CASE	     =Image.open(ds[0]['''file'''])
				else:
								_SCREAMING_SNAKE_CASE	     =BeitImageProcessor(
								    size=config.image_size		,resample=PILImageResampling.BILINEAR		,do_center_crop=a__)
								_SCREAMING_SNAKE_CASE	     =prepare_img()

				_SCREAMING_SNAKE_CASE	     =image_processor(images=a__		,return_tensors='''pt''')
				_SCREAMING_SNAKE_CASE	     =encoding['''pixel_values''']

				_SCREAMING_SNAKE_CASE	     =model(a__)
				_SCREAMING_SNAKE_CASE	     =outputs.logits

				# verify logits
				_SCREAMING_SNAKE_CASE	     =torch.Size([1, 1000])
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([2.2288, 2.4671, 0.7395])
								_SCREAMING_SNAKE_CASE	     =2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([1.6881, -0.2787, 0.5901])
								_SCREAMING_SNAKE_CASE	     =2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.1241, 0.0798, -0.6569])
								_SCREAMING_SNAKE_CASE	     =285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.2385, -1.0987, -1.0108])
								_SCREAMING_SNAKE_CASE	     =281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.5303, -0.9484, -0.3147])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.4610, -0.0928, 0.2086])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.4804, 0.6257, -0.1837])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([[-0.5122, 0.5117, -0.2113]])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.3062, 0.7261, 0.4852])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =torch.tensor(
								    [
								        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
								        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
								        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
								    ])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =torch.tensor(
								    [
								        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
								        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
								        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
								    ])
				else:
								raise ValueError('''Can\'t verify logits as model is not supported''')

				assert logits.shape == expected_shape, "Shape of logits not as expected"
				if not has_lm_head:
								if is_semantic:
												assert torch.allclose(
												    logits[0, :3, :3, :3]		,a__		,atol=1e-3), "First elements of logits not as expected"
								else:
												print('''Predicted class idx:'''		,logits.argmax(-1).item())
												assert torch.allclose(
												    logits[0, :3]		,a__		,atol=1e-3), "First elements of logits not as expected"
												assert logits.argmax(-1).item() == expected_class_idx, "Predicted class index not as expected"

				Path(a__).mkdir(exist_ok=a__)
				print(f"Saving model to {pytorch_dump_folder_path}")
				model.save_pretrained(a__)
				print(f"Saving image processor to {pytorch_dump_folder_path}")
				image_processor.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_  :	Any							  =							argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
							    type=str,
							    help='''URL to the original PyTorch checkpoint (.pth file).''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							snake_case_  :	int							  =							parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



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     )     )