



'''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     (      lowerCAmelCase_  )			->      str:
		_a     :      Dict        =							VideoMAEConfig()

		set_architecture_configs(lowerCAmelCase_						,			lowerCAmelCase_  )

		if "finetuned" not in model_name:
				_a     :      List[str]        =							False

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

		return config



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[Any]:
		if "small" in model_name:
				_a     :      Tuple        =							384
				_a     :      Optional[Any]        =							1536
				_a     :      Optional[Any]        =							12
				_a     :      Tuple        =							16
				_a     :      List[str]        =							12
				_a     :      List[str]        =							3
				_a     :      Optional[Any]        =							192
				_a     :      List[str]        =							768
		elif "large" in model_name:
				_a     :      Dict        =							1024
				_a     :      Dict        =							4096
				_a     :      Tuple        =							24
				_a     :      Any        =							16
				_a     :      Any        =							12
				_a     :      Optional[Any]        =							8
				_a     :      List[str]        =							512
				_a     :      Union[str, Any]        =							2048
		elif "huge" in model_name:
				_a     :      List[str]        =							1280
				_a     :      Dict        =							5120
				_a     :      List[Any]        =							32
				_a     :      List[Any]        =							16
				_a     :      Dict        =							12
				_a     :      Any        =							8
				_a     :      Dict        =							640
				_a     :      Dict        =							2560
		elif "base" not in model_name:
				raise ValueError('Model name should include either "small", "base", "large", or "huge"'  )



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

		return name



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Union[str, Any]:
		for key in orig_state_dict.copy().keys():
				_a     :      Tuple        =							orig_state_dict.pop(lowerCAmelCase_  )

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

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

		return orig_state_dict



def      __lowerCamelCase     (      )			->      Optional[Any]:
		_a     :      Tuple        =							hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video'						,			filename='eating_spaghetti.npy'						,			repo_type='dataset'  )
		_a     :      str        =							np.load(lowerCAmelCase_  )
		return list(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      Tuple        =							get_videomae_config(lowerCAmelCase_  )

		if "finetuned" in model_name:
				_a     :      Optional[int]        =							VideoMAEForVideoClassification(lowerCAmelCase_  )
		else:
				_a     :      str        =							VideoMAEForPreTraining(lowerCAmelCase_  )

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

		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

		# verify model on basic input
		_a     :      str        =							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,			image_std=[0.5, 0.5, 0.5]  )
		_a     :      List[Any]        =							prepare_video()
		_a     :      Dict        =							image_processor(lowerCAmelCase_						,			return_tensors='pt'  )

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

		_a     :      int        =							model(**lowerCAmelCase_  )
		_a     :      int        =							outputs.logits

		_a     :      Any        =							[
		    '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":
				_a     :      Optional[int]        =							torch.Size([1, 400]  )
				_a     :      str        =							torch.tensor([-0.9_291, -0.4_061, -0.9_307]  )
		elif model_name == "videomae-small-finetuned-ssv2":
				_a     :      Union[str, Any]        =							torch.Size([1, 174]  )
				_a     :      List[Any]        =							torch.tensor([0.2_671, -0.4_689, -0.8_235]  )
		elif model_name == "videomae-base":
				_a     :      int        =							torch.Size([1, 1408, 1536]  )
				_a     :      Any        =							torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]  )
		elif model_name == "videomae-base-short":
				_a     :      Optional[int]        =							torch.Size([1, 1408, 1536]  )
				_a     :      Tuple        =							torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]  )
				# we verified the loss both for normalized and unnormalized targets for this one
				_a     :      Dict        =							torch.tensor([0.5_142]  ) if config.norm_pix_loss else torch.tensor([0.6_469]  )
		elif model_name == "videomae-large":
				_a     :      str        =							torch.Size([1, 1408, 1536]  )
				_a     :      int        =							torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]  )
		elif model_name == "videomae-large-finetuned-kinetics":
				_a     :      Optional[int]        =							torch.Size([1, 400]  )
				_a     :      Dict        =							torch.tensor([0.0_771, 0.0_011, -0.3_625]  )
		elif model_name == "videomae-huge-finetuned-kinetics":
				_a     :      Any        =							torch.Size([1, 400]  )
				_a     :      List[Any]        =							torch.tensor([0.2_433, 0.1_632, -0.4_894]  )
		elif model_name == "videomae-base-short-finetuned-kinetics":
				_a     :      Optional[Any]        =							torch.Size([1, 400]  )
				_a     :      Any        =							torch.tensor([0.6_588, 0.0_990, -0.2_493]  )
		elif model_name == "videomae-base-finetuned-kinetics":
				_a     :      List[str]        =							torch.Size([1, 400]  )
				_a     :      Union[str, Any]        =							torch.tensor([0.3_669, -0.0_688, -0.2_421]  )
		elif model_name == "videomae-base-short-ssv2":
				_a     :      str        =							torch.Size([1, 1408, 1536]  )
				_a     :      Optional[int]        =							torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]  )
		elif model_name == "videomae-base-short-finetuned-ssv2":
				_a     :      Optional[int]        =							torch.Size([1, 174]  )
				_a     :      Union[str, Any]        =							torch.tensor([-0.0_537, -0.1_539, -0.3_266]  )
		elif model_name == "videomae-base-ssv2":
				_a     :      List[Any]        =							torch.Size([1, 1408, 1536]  )
				_a     :      Optional[Any]        =							torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]  )
		elif model_name == "videomae-base-finetuned-ssv2":
				_a     :      List[Any]        =							torch.Size([1, 174]  )
				_a     :      str        =							torch.tensor([0.1_961, -0.8_337, -0.6_389]  )
		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]						,			lowerCAmelCase_						,			atol=1E-4  )
		else:
				print('Logits:'						,			logits[0, :3, :3]  )
				assert torch.allclose(logits[0, :3, :3]						,			lowerCAmelCase_						,			atol=1E-4  )
		print('Logits ok!'  )

		# verify loss, if applicable
		if model_name == "videomae-base-short":
				_a     :      List[str]        =							outputs.loss
				assert torch.allclose(lowerCAmelCase_						,			lowerCAmelCase_						,			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(lowerCAmelCase_  )
				model.save_pretrained(lowerCAmelCase_  )

		if push_to_hub:
				print('Pushing to the hub...'  )
				model.push_to_hub(lowerCAmelCase_						,			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 datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
 lowerCamelCase				:		int     =   input("Enter image url: ").strip()
 print(F"""Downloading image from {url} ...""")
 lowerCamelCase				:		Tuple     =   BeautifulSoup(requests.get(url).content, "html.parser")
 # The image URL is in the content field of the first meta tag with property og:image
 lowerCamelCase				:		int     =   soup.find("meta", {"property": "og:image"})["content"]
 lowerCamelCase				:		Dict     =   requests.get(image_url).content
 lowerCamelCase				:		Optional[int]     =   F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
 with open(file_name, "wb") as fp:
  fp.write(image_data)
 print(F"""Done. Image saved to disk as {file_name}.""")



