



'''simple docstring'''

import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[int]      							=VideoMAEConfig()

			set_architecture_configs(__UpperCAmelCase				,							__UpperCAmelCase					)

			if "finetuned" not in model_name:
						snake_case:    str      							=False

			if "finetuned" in model_name:
						snake_case:    Any      							='huggingface/label-files'
						if "kinetics" in model_name:
									snake_case:    Tuple      							=4_00
									snake_case:    Union[str, Any]      							='kinetics400-id2label.json'
						elif "ssv2" in model_name:
									snake_case:    Tuple      							=1_74
									snake_case:    Dict      							='something-something-v2-id2label.json'
						else:
									raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'					)
						snake_case:    Optional[int]      							=json.load(open(hf_hub_download(__UpperCAmelCase				,							__UpperCAmelCase				,							repo_type='dataset'					)				,							'r'					)					)
						snake_case:    Dict      							={int(__UpperCAmelCase					): v for k, v in idalabel.items()}
						snake_case:    Dict      							=idalabel
						snake_case:    List[str]      							={v: k for k, v in idalabel.items()}

			return config







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			if "small" in model_name:
						snake_case:    List[Any]      							=3_84
						snake_case:    Union[str, Any]      							=15_36
						snake_case:    List[Any]      							=12
						snake_case:    List[str]      							=16
						snake_case:    Dict      							=12
						snake_case:    Dict      							=3
						snake_case:    Dict      							=1_92
						snake_case:    Union[str, Any]      							=7_68
			elif "large" in model_name:
						snake_case:    Union[str, Any]      							=10_24
						snake_case:    str      							=40_96
						snake_case:    List[str]      							=24
						snake_case:    Tuple      							=16
						snake_case:    str      							=12
						snake_case:    Tuple      							=8
						snake_case:    Optional[Any]      							=5_12
						snake_case:    Any      							=20_48
			elif "huge" in model_name:
						snake_case:    Optional[int]      							=12_80
						snake_case:    Optional[int]      							=51_20
						snake_case:    Union[str, Any]      							=32
						snake_case:    int      							=16
						snake_case:    Tuple      							=12
						snake_case:    Optional[int]      							=8
						snake_case:    Tuple      							=6_40
						snake_case:    Optional[int]      							=25_60
			elif "base" not in model_name:
						raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "encoder." in name:
						snake_case:    List[str]      							=name.replace('encoder.'				,							''					)
			if "cls_token" in name:
						snake_case:    int      							=name.replace('cls_token'				,							'videomae.embeddings.cls_token'					)
			if "decoder_pos_embed" in name:
						snake_case:    str      							=name.replace('decoder_pos_embed'				,							'decoder.decoder_pos_embed'					)
			if "pos_embed" in name and "decoder" not in name:
						snake_case:    str      							=name.replace('pos_embed'				,							'videomae.embeddings.position_embeddings'					)
			if "patch_embed.proj" in name:
						snake_case:    Optional[int]      							=name.replace('patch_embed.proj'				,							'videomae.embeddings.patch_embeddings.projection'					)
			if "patch_embed.norm" in name:
						snake_case:    Dict      							=name.replace('patch_embed.norm'				,							'videomae.embeddings.norm'					)
			if "decoder.blocks" in name:
						snake_case:    int      							=name.replace('decoder.blocks'				,							'decoder.decoder_layers'					)
			if "blocks" in name:
						snake_case:    List[Any]      							=name.replace('blocks'				,							'videomae.encoder.layer'					)
			if "attn.proj" in name:
						snake_case:    Dict      							=name.replace('attn.proj'				,							'attention.output.dense'					)
			if "attn" in name and "bias" not in name:
						snake_case:    Optional[int]      							=name.replace('attn'				,							'attention.self'					)
			if "attn" in name:
						snake_case:    Any      							=name.replace('attn'				,							'attention.attention'					)
			if "norm1" in name:
						snake_case:    Optional[Any]      							=name.replace('norm1'				,							'layernorm_before'					)
			if "norm2" in name:
						snake_case:    List[str]      							=name.replace('norm2'				,							'layernorm_after'					)
			if "mlp.fc1" in name:
						snake_case:    int      							=name.replace('mlp.fc1'				,							'intermediate.dense'					)
			if "mlp.fc2" in name:
						snake_case:    Union[str, Any]      							=name.replace('mlp.fc2'				,							'output.dense'					)
			if "decoder_embed" in name:
						snake_case:    Union[str, Any]      							=name.replace('decoder_embed'				,							'decoder.decoder_embed'					)
			if "decoder_norm" in name:
						snake_case:    List[str]      							=name.replace('decoder_norm'				,							'decoder.decoder_norm'					)
			if "decoder_pred" in name:
						snake_case:    str      							=name.replace('decoder_pred'				,							'decoder.decoder_pred'					)
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						snake_case:    Any      							=name.replace('norm.weight'				,							'videomae.layernorm.weight'					)
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						snake_case:    Optional[int]      							=name.replace('norm.bias'				,							'videomae.layernorm.bias'					)
			if "head" in name and "decoder" not in name:
						snake_case:    int      							=name.replace('head'				,							'classifier'					)

			return name







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			for key in orig_state_dict.copy().keys():
						snake_case:    List[Any]      							=orig_state_dict.pop(__UpperCAmelCase					)

						if key.startswith('encoder.'					):
									snake_case:    Tuple      							=key.replace('encoder.'				,							''					)

						if "qkv" in key:
									snake_case:    List[str]      							=key.split('.'					)
									if key.startswith('decoder.blocks'					):
												snake_case:    Union[str, Any]      							=config.decoder_hidden_size
												snake_case:    Union[str, Any]      							=int(key_split[2]					)
												snake_case:    Dict      							='decoder.decoder_layers.'
												if "weight" in key:
															snake_case:    int      							=val[:dim, :]
															snake_case:    Any      							=val[dim : dim * 2, :]
															snake_case:    Dict      							=val[-dim:, :]
									else:
												snake_case:    Union[str, Any]      							=config.hidden_size
												snake_case:    List[str]      							=int(key_split[1]					)
												snake_case:    Union[str, Any]      							='videomae.encoder.layer.'
												if "weight" in key:
															snake_case:    Tuple      							=val[:dim, :]
															snake_case:    Union[str, Any]      							=val[dim : dim * 2, :]
															snake_case:    Tuple      							=val[-dim:, :]
						else:
									snake_case:    Dict      							=val

			return orig_state_dict







def a_     (    )				->					int:





			"""simple docstring"""







			snake_case:    List[Any]      							=hf_hub_download(
			    repo_id='hf-internal-testing/spaghetti-video'				,							filename='eating_spaghetti.npy'				,							repo_type='dataset'					)
			snake_case:    Optional[int]      							=np.load(__UpperCAmelCase					)
			return list(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    int      							=get_videomae_config(__UpperCAmelCase					)

			if "finetuned" in model_name:
						snake_case:    List[str]      							=VideoMAEForVideoClassification(__UpperCAmelCase					)
			else:
						snake_case:    Dict      							=VideoMAEForPreTraining(__UpperCAmelCase					)

			# download original checkpoint, hosted on Google Drive
			snake_case:    int      							='pytorch_model.bin'
			gdown.cached_download(__UpperCAmelCase				,							__UpperCAmelCase				,							quiet=__UpperCAmelCase					)
			snake_case:    str      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			if "model" in files:
						snake_case:    Optional[Any]      							=files['model']
			else:
						snake_case:    Tuple      							=files['module']
			snake_case:    List[str]      							=convert_state_dict(__UpperCAmelCase				,							__UpperCAmelCase					)

			model.load_state_dict(__UpperCAmelCase					)
			model.eval()

			# verify model on basic input
			snake_case:    Tuple      							=VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,							image_std=[0.5, 0.5, 0.5]					)
			snake_case:    Dict      							=prepare_video()
			snake_case:    Optional[int]      							=image_processor(__UpperCAmelCase				,							return_tensors='pt'					)

			if "finetuned" not in model_name:
						snake_case:    List[str]      							=hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'				,							filename='bool_masked_pos.pt'					)
						snake_case:    Optional[int]      							=torch.load(__UpperCAmelCase					)

			snake_case:    List[Any]      							=model(**__UpperCAmelCase					)
			snake_case:    Optional[int]      							=outputs.logits

			snake_case:    Optional[int]      							=[
			    'videomae-small-finetuned-kinetics',
			    'videomae-small-finetuned-ssv2',
			    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
			    'videomae-base-short',
			    'videomae-base-short-finetuned-kinetics',
			    'videomae-base',
			    'videomae-base-finetuned-kinetics',
			    'videomae-large',
			    'videomae-large-finetuned-kinetics',
			    'videomae-huge-finetuned-kinetics',
			    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
			    'videomae-base-short-ssv2',
			    'videomae-base-short-finetuned-ssv2',
			    'videomae-base-ssv2',
			    'videomae-base-finetuned-ssv2',
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "videomae-small-finetuned-kinetics":
						snake_case:    Tuple      							=torch.Size([1, 4_00]					)
						snake_case:    Optional[Any]      							=torch.tensor([-0.9291, -0.4061, -0.9307]					)
			elif model_name == "videomae-small-finetuned-ssv2":
						snake_case:    Union[str, Any]      							=torch.Size([1, 1_74]					)
						snake_case:    Optional[Any]      							=torch.tensor([0.2671, -0.4689, -0.8235]					)
			elif model_name == "videomae-base":
						snake_case:    Any      							=torch.Size([1, 14_08, 15_36]					)
						snake_case:    Any      							=torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]					)
			elif model_name == "videomae-base-short":
						snake_case:    int      							=torch.Size([1, 14_08, 15_36]					)
						snake_case:    Dict      							=torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]					)
						# we verified the loss both for normalized and unnormalized targets for this one
						snake_case:    int      							=torch.tensor([0.5142]					) if config.norm_pix_loss else torch.tensor([0.6469]					)
			elif model_name == "videomae-large":
						snake_case:    Optional[Any]      							=torch.Size([1, 14_08, 15_36]					)
						snake_case:    int      							=torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]					)
			elif model_name == "videomae-large-finetuned-kinetics":
						snake_case:    List[Any]      							=torch.Size([1, 4_00]					)
						snake_case:    Any      							=torch.tensor([0.0771, 0.0011, -0.3625]					)
			elif model_name == "videomae-huge-finetuned-kinetics":
						snake_case:    str      							=torch.Size([1, 4_00]					)
						snake_case:    Dict      							=torch.tensor([0.2433, 0.1632, -0.4894]					)
			elif model_name == "videomae-base-short-finetuned-kinetics":
						snake_case:    Tuple      							=torch.Size([1, 4_00]					)
						snake_case:    Union[str, Any]      							=torch.tensor([0.6588, 0.0990, -0.2493]					)
			elif model_name == "videomae-base-finetuned-kinetics":
						snake_case:    str      							=torch.Size([1, 4_00]					)
						snake_case:    List[str]      							=torch.tensor([0.3669, -0.0688, -0.2421]					)
			elif model_name == "videomae-base-short-ssv2":
						snake_case:    List[str]      							=torch.Size([1, 14_08, 15_36]					)
						snake_case:    Dict      							=torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]					)
			elif model_name == "videomae-base-short-finetuned-ssv2":
						snake_case:    Optional[int]      							=torch.Size([1, 1_74]					)
						snake_case:    List[str]      							=torch.tensor([-0.0537, -0.1539, -0.3266]					)
			elif model_name == "videomae-base-ssv2":
						snake_case:    Dict      							=torch.Size([1, 14_08, 15_36]					)
						snake_case:    Dict      							=torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]					)
			elif model_name == "videomae-base-finetuned-ssv2":
						snake_case:    List[str]      							=torch.Size([1, 1_74]					)
						snake_case:    Optional[Any]      							=torch.tensor([0.1961, -0.8337, -0.6389]					)
			else:
						raise ValueError(f'''Model name not supported. Should be one of {model_names}'''					)

			# verify logits
			assert logits.shape == expected_shape
			if "finetuned" in model_name:
						assert torch.allclose(logits[0, :3]				,							__UpperCAmelCase				,							atol=1e-4					)
			else:
						print('Logits:'				,							logits[0, :3, :3]					)
						assert torch.allclose(logits[0, :3, :3]				,							__UpperCAmelCase				,							atol=1e-4					)
			print('Logits ok!'					)

			# verify loss, if applicable
			if model_name == "videomae-base-short":
						snake_case:    Optional[Any]      							=outputs.loss
						assert torch.allclose(__UpperCAmelCase				,							__UpperCAmelCase				,							atol=1e-4					)
						print('Loss ok!'					)

			if pytorch_dump_folder_path is not None:
						print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''					)
						image_processor.save_pretrained(__UpperCAmelCase					)
						model.save_pretrained(__UpperCAmelCase					)

			if push_to_hub:
						print('Pushing to the hub...'					)
						model.push_to_hub(__UpperCAmelCase				,							organization='nielsr'					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
	    type=str,
	    help=(
	        'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
	        ' download link.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='/Users/nielsrogge/Documents/VideoMAE/Test',
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	a							   =				parser.parse_args()
	convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)











'''simple docstring'''






from __future__ import annotations

import numpy as np
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				np.shape(lowerCAmelCase    )
      if rows != columns:
            _lowerCAmelCase					=				(
                """'table' has to be of square shaped array but got a """
                f"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(lowerCAmelCase    )

      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      for i in range(lowerCAmelCase    ):
            for j in range(lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""    )
                  _lowerCAmelCase					=				(table[i][j] - total) / upper[j][j]
            _lowerCAmelCase					=				1
            for j in range(lowerCAmelCase				,      lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  _lowerCAmelCase					=				table[i][j] - total
      return lower, upper


if __name__ == "__main__":
      import doctest

      doctest.testmod()






