"""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      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							VideoMAEConfig()

	set_architecture_configs(__UpperCAmelCase     ,__UpperCAmelCase			)

	if "finetuned" not in model_name:
		_lowercase :    Optional[Any]							=							False

	if "finetuned" in model_name:
		_lowercase :    List[str]							=							'huggingface/label-files'
		if "kinetics" in model_name:
			_lowercase :    Optional[Any]							=							400
			_lowercase :    Optional[int]							=							'kinetics400-id2label.json'
		elif "ssv2" in model_name:
			_lowercase :    List[Any]							=							174
			_lowercase :    Dict							=							'something-something-v2-id2label.json'
		else:
			raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'			)
		_lowercase :    Dict							=							json.load(open(hf_hub_download(__UpperCAmelCase     ,__UpperCAmelCase     ,repo_type='dataset'			)     ,'r'			)			)
		_lowercase :    List[Any]							=							{int(__UpperCAmelCase			): v for k, v in idalabel.items()}
		_lowercase :    str							=							idalabel
		_lowercase :    List[str]							=							{v: k for k, v in idalabel.items()}

	return config


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if "small" in model_name:
		_lowercase :    Optional[int]							=							384
		_lowercase :    Tuple							=							1_536
		_lowercase :    List[Any]							=							12
		_lowercase :    List[Any]							=							16
		_lowercase :    str							=							12
		_lowercase :    Union[str, Any]							=							3
		_lowercase :    Optional[Any]							=							192
		_lowercase :    int							=							768
	elif "large" in model_name:
		_lowercase :    List[Any]							=							1_024
		_lowercase :    Optional[int]							=							4_096
		_lowercase :    Union[str, Any]							=							24
		_lowercase :    int							=							16
		_lowercase :    List[Any]							=							12
		_lowercase :    Tuple							=							8
		_lowercase :    List[Any]							=							512
		_lowercase :    List[str]							=							2_048
	elif "huge" in model_name:
		_lowercase :    Tuple							=							1_280
		_lowercase :    Any							=							5_120
		_lowercase :    Tuple							=							32
		_lowercase :    Tuple							=							16
		_lowercase :    Any							=							12
		_lowercase :    Any							=							8
		_lowercase :    Tuple							=							640
		_lowercase :    List[Any]							=							2_560
	elif "base" not in model_name:
		raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





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

	return name


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		_lowercase :    Dict							=							orig_state_dict.pop(__UpperCAmelCase			)

		if key.startswith('encoder.'			):
			_lowercase :    int							=							key.replace('encoder.'     ,''			)

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

	return orig_state_dict


def      __lowerCAmelCase(		):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							get_videomae_config(__UpperCAmelCase			)

	if "finetuned" in model_name:
		_lowercase :    str							=							VideoMAEForVideoClassification(__UpperCAmelCase			)
	else:
		_lowercase :    Tuple							=							VideoMAEForPreTraining(__UpperCAmelCase			)

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

	model.load_state_dict(__UpperCAmelCase			)
	model.eval()

	# verify model on basic input
	_lowercase :    Optional[Any]							=							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,image_std=[0.5, 0.5, 0.5]			)
	_lowercase :    Any							=							prepare_video()
	_lowercase :    List[Any]							=							image_processor(__UpperCAmelCase     ,return_tensors='pt'			)

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

	_lowercase :    Any							=							model(**__UpperCAmelCase			)
	_lowercase :    int							=							outputs.logits

	_lowercase :    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":
		_lowercase :    Optional[Any]							=							torch.Size([1, 400]			)
		_lowercase :    Any							=							torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]			)
	elif model_name == "videomae-small-finetuned-ssv2":
		_lowercase :    Optional[Any]							=							torch.Size([1, 174]			)
		_lowercase :    Union[str, Any]							=							torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]			)
	elif model_name == "videomae-base":
		_lowercase :    Any							=							torch.Size([1, 1_408, 1_536]			)
		_lowercase :    Tuple							=							torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]			)
	elif model_name == "videomae-base-short":
		_lowercase :    Any							=							torch.Size([1, 1_408, 1_536]			)
		_lowercase :    Union[str, Any]							=							torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]			)
		# we verified the loss both for normalized and unnormalized targets for this one
		_lowercase :    str							=							torch.tensor([0.5_1_4_2]			) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]			)
	elif model_name == "videomae-large":
		_lowercase :    Any							=							torch.Size([1, 1_408, 1_536]			)
		_lowercase :    Optional[int]							=							torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]			)
	elif model_name == "videomae-large-finetuned-kinetics":
		_lowercase :    Dict							=							torch.Size([1, 400]			)
		_lowercase :    int							=							torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]			)
	elif model_name == "videomae-huge-finetuned-kinetics":
		_lowercase :    Any							=							torch.Size([1, 400]			)
		_lowercase :    Optional[int]							=							torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]			)
	elif model_name == "videomae-base-short-finetuned-kinetics":
		_lowercase :    Any							=							torch.Size([1, 400]			)
		_lowercase :    Dict							=							torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]			)
	elif model_name == "videomae-base-finetuned-kinetics":
		_lowercase :    str							=							torch.Size([1, 400]			)
		_lowercase :    int							=							torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]			)
	elif model_name == "videomae-base-short-ssv2":
		_lowercase :    List[str]							=							torch.Size([1, 1_408, 1_536]			)
		_lowercase :    List[str]							=							torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]			)
	elif model_name == "videomae-base-short-finetuned-ssv2":
		_lowercase :    str							=							torch.Size([1, 174]			)
		_lowercase :    int							=							torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]			)
	elif model_name == "videomae-base-ssv2":
		_lowercase :    Dict							=							torch.Size([1, 1_408, 1_536]			)
		_lowercase :    Any							=							torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]			)
	elif model_name == "videomae-base-finetuned-ssv2":
		_lowercase :    Dict							=							torch.Size([1, 174]			)
		_lowercase :    Any							=							torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]			)
	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":
		_lowercase :    Tuple							=							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__":
						SCREAMING_SNAKE_CASE									=						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.'
						)

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







"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	stooge(__UpperCAmelCase     ,0     ,len(__UpperCAmelCase			) - 1			)
	return arr


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		_lowercase				,      _lowercase :    List[str]							=							arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		_lowercase :    Optional[int]							=							(int)((h - i + 1) / 3			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)

		# Recursively sort last 2/3 elements
		stooge(__UpperCAmelCase     ,i + t     ,(__UpperCAmelCase)			)

		# Recursively sort first 2/3 elements
		stooge(__UpperCAmelCase     ,__UpperCAmelCase     ,(h - t)			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						input('Enter numbers separated by a comma:\n').strip()
						SCREAMING_SNAKE_CASE									=						[int(item) for item in user_input.split(',')]
						print(stooge_sort(unsorted))



