


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(	lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			VideoMAEConfig()

		set_architecture_configs(lowercase__   , lowercase__	)

		if "finetuned" not in model_name:
				__lowercase=			False

		if "finetuned" in model_name:
				__lowercase=			'huggingface/label-files'
				if "kinetics" in model_name:
						__lowercase=			4_0_0
						__lowercase=			'kinetics400-id2label.json'
				elif "ssv2" in model_name:
						__lowercase=			1_7_4
						__lowercase=			'something-something-v2-id2label.json'
				else:
						raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'	)
				__lowercase=			json.load(open(hf_hub_download(lowercase__   , lowercase__   , repo_type='dataset'	)   , 'r'	)	)
				__lowercase=			{int(lowercase__	): v for k, v in idalabel.items()}
				__lowercase=			idalabel
				__lowercase=			{v: k for k, v in idalabel.items()}

		return config







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		if "small" in model_name:
				__lowercase=			3_8_4
				__lowercase=			1_5_3_6
				__lowercase=			1_2
				__lowercase=			1_6
				__lowercase=			1_2
				__lowercase=			3
				__lowercase=			1_9_2
				__lowercase=			7_6_8
		elif "large" in model_name:
				__lowercase=			1_0_2_4
				__lowercase=			4_0_9_6
				__lowercase=			2_4
				__lowercase=			1_6
				__lowercase=			1_2
				__lowercase=			8
				__lowercase=			5_1_2
				__lowercase=			2_0_4_8
		elif "huge" in model_name:
				__lowercase=			1_2_8_0
				__lowercase=			5_1_2_0
				__lowercase=			3_2
				__lowercase=			1_6
				__lowercase=			1_2
				__lowercase=			8
				__lowercase=			6_4_0
				__lowercase=			2_5_6_0
		elif "base" not in model_name:
				raise ValueError('Model name should include either "small", "base", "large", or "huge"'	)







def        _lowerCamelCase(	lowercase__	)						->	Optional[int]:

		'''simple docstring'''







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

		return name







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Tuple:

		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				__lowercase=			orig_state_dict.pop(lowercase__	)

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

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

		return orig_state_dict







def        _lowerCamelCase(	)						->	Optional[int]:

		'''simple docstring'''







		__lowercase=			hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'   , filename='eating_spaghetti.npy'   , repo_type='dataset'	)
		__lowercase=			np.load(lowercase__	)
		return list(lowercase__	)







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__   , lowercase__	)						->	int:

		'''simple docstring'''







		__lowercase=			get_videomae_config(lowercase__	)

		if "finetuned" in model_name:
				__lowercase=			VideoMAEForVideoClassification(lowercase__	)
		else:
				__lowercase=			VideoMAEForPreTraining(lowercase__	)

		# download original checkpoint, hosted on Google Drive
		__lowercase=			'pytorch_model.bin'
		gdown.cached_download(lowercase__   , lowercase__   , quiet=lowercase__	)
		__lowercase=			torch.load(lowercase__   , map_location='cpu'	)
		if "model" in files:
				__lowercase=			files['model']
		else:
				__lowercase=			files['module']
		__lowercase=			convert_state_dict(lowercase__   , lowercase__	)

		model.load_state_dict(lowercase__	)
		model.eval()

		# verify model on basic input
		__lowercase=			VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   , image_std=[0.5, 0.5, 0.5]	)
		__lowercase=			prepare_video()
		__lowercase=			image_processor(lowercase__   , return_tensors='pt'	)

		if "finetuned" not in model_name:
				__lowercase=			hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'   , filename='bool_masked_pos.pt'	)
				__lowercase=			torch.load(lowercase__	)

		__lowercase=			model(**lowercase__	)
		__lowercase=			outputs.logits

		__lowercase=			[
		    '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=			torch.Size([1, 4_0_0]	)
				__lowercase=			torch.tensor([-0.9291, -0.4061, -0.9307]	)
		elif model_name == "videomae-small-finetuned-ssv2":
				__lowercase=			torch.Size([1, 1_7_4]	)
				__lowercase=			torch.tensor([0.2671, -0.4689, -0.8235]	)
		elif model_name == "videomae-base":
				__lowercase=			torch.Size([1, 1_4_0_8, 1_5_3_6]	)
				__lowercase=			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":
				__lowercase=			torch.Size([1, 1_4_0_8, 1_5_3_6]	)
				__lowercase=			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
				__lowercase=			torch.tensor([0.5142]	) if config.norm_pix_loss else torch.tensor([0.6469]	)
		elif model_name == "videomae-large":
				__lowercase=			torch.Size([1, 1_4_0_8, 1_5_3_6]	)
				__lowercase=			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":
				__lowercase=			torch.Size([1, 4_0_0]	)
				__lowercase=			torch.tensor([0.0771, 0.0011, -0.3625]	)
		elif model_name == "videomae-huge-finetuned-kinetics":
				__lowercase=			torch.Size([1, 4_0_0]	)
				__lowercase=			torch.tensor([0.2433, 0.1632, -0.4894]	)
		elif model_name == "videomae-base-short-finetuned-kinetics":
				__lowercase=			torch.Size([1, 4_0_0]	)
				__lowercase=			torch.tensor([0.6588, 0.0990, -0.2493]	)
		elif model_name == "videomae-base-finetuned-kinetics":
				__lowercase=			torch.Size([1, 4_0_0]	)
				__lowercase=			torch.tensor([0.3669, -0.0688, -0.2421]	)
		elif model_name == "videomae-base-short-ssv2":
				__lowercase=			torch.Size([1, 1_4_0_8, 1_5_3_6]	)
				__lowercase=			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":
				__lowercase=			torch.Size([1, 1_7_4]	)
				__lowercase=			torch.tensor([-0.0537, -0.1539, -0.3266]	)
		elif model_name == "videomae-base-ssv2":
				__lowercase=			torch.Size([1, 1_4_0_8, 1_5_3_6]	)
				__lowercase=			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":
				__lowercase=			torch.Size([1, 1_7_4]	)
				__lowercase=			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]   , lowercase__   , atol=1E-4	)
		else:
				print('Logits:'   , logits[0, :3, :3]	)
				assert torch.allclose(logits[0, :3, :3]   , lowercase__   , atol=1E-4	)
		print('Logits ok!'	)

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				__lowercase=			outputs.loss
				assert torch.allclose(lowercase__   , lowercase__   , 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(lowercase__	)
				model.save_pretrained(lowercase__	)

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


if __name__ == "__main__":
	lowerCAmelCase           =		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.'''
	)

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










from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		if len(lowercase__	) <= 1 or n <= 1:
				return

		insert_next(lowercase__   , n - 1	)
		rec_insertion_sort(lowercase__   , n - 1	)







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		if index >= len(lowercase__	) or collection[index - 1] <= collection[index]:
				return

		# Swaps adjacent elements since they are not in ascending order
		__lowercase,							__lowercase=			(
		    collection[index],
		    collection[index - 1],
		)

		insert_next(lowercase__   , index + 1	)


if __name__ == "__main__":
	lowerCAmelCase           =		input('''Enter integers separated by spaces: ''')
	lowerCAmelCase           =		[int(num) for num in numbers.split()]
	rec_insertion_sort(number_list, len(number_list))
	print(number_list)






