


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def   _lowercase(					__a				:						List[Any]     ):
							if "cls_token" in name:
														a__      =name.replace('cls_token'    ,     'vit.embeddings.cls_token'     )
							if "mask_token" in name:
														a__      =name.replace('mask_token'    ,     'decoder.mask_token'     )
							if "decoder_pos_embed" in name:
														a__      =name.replace('decoder_pos_embed'    ,     'decoder.decoder_pos_embed'     )
							if "pos_embed" in name and "decoder" not in name:
														a__      =name.replace('pos_embed'    ,     'vit.embeddings.position_embeddings'     )
							if "patch_embed.proj" in name:
														a__      =name.replace('patch_embed.proj'    ,     'vit.embeddings.patch_embeddings.projection'     )
							if "patch_embed.norm" in name:
														a__      =name.replace('patch_embed.norm'    ,     'vit.embeddings.norm'     )
							if "decoder_blocks" in name:
														a__      =name.replace('decoder_blocks'    ,     'decoder.decoder_layers'     )
							if "blocks" in name:
														a__      =name.replace('blocks'    ,     'vit.encoder.layer'     )
							if "attn.proj" in name:
														a__      =name.replace('attn.proj'    ,     'attention.output.dense'     )
							if "attn" in name:
														a__      =name.replace('attn'    ,     'attention.self'     )
							if "norm1" in name:
														a__      =name.replace('norm1'    ,     'layernorm_before'     )
							if "norm2" in name:
														a__      =name.replace('norm2'    ,     'layernorm_after'     )
							if "mlp.fc1" in name:
														a__      =name.replace('mlp.fc1'    ,     'intermediate.dense'     )
							if "mlp.fc2" in name:
														a__      =name.replace('mlp.fc2'    ,     'output.dense'     )
							if "decoder_embed" in name:
														a__      =name.replace('decoder_embed'    ,     'decoder.decoder_embed'     )
							if "decoder_norm" in name:
														a__      =name.replace('decoder_norm'    ,     'decoder.decoder_norm'     )
							if "decoder_pred" in name:
														a__      =name.replace('decoder_pred'    ,     'decoder.decoder_pred'     )
							if "norm.weight" in name and "decoder" not in name:
														a__      =name.replace('norm.weight'    ,     'vit.layernorm.weight'     )
							if "norm.bias" in name and "decoder" not in name:
														a__      =name.replace('norm.bias'    ,     'vit.layernorm.bias'     )

							return name






def   _lowercase(					__a				:						Any    ,     __a				:						Any     ):
							for key in orig_state_dict.copy().keys():
														a__      =orig_state_dict.pop(__a     )

														if "qkv" in key:
																					a__      =key.split('.'     )
																					a__      =int(key_split[1]     )
																					if "decoder_blocks" in key:
																												a__      =config.decoder_hidden_size
																												a__      ='decoder.decoder_layers.'
																												if "weight" in key:
																																			a__      =val[:dim, :]
																																			a__      =val[dim : dim * 2, :]
																																			a__      =val[-dim:, :]
																												elif "bias" in key:
																																			a__      =val[:dim]
																																			a__      =val[dim : dim * 2]
																																			a__      =val[-dim:]
																					else:
																												a__      =config.hidden_size
																												a__      ='vit.encoder.layer.'
																												if "weight" in key:
																																			a__      =val[:dim, :]
																																			a__      =val[dim : dim * 2, :]
																																			a__      =val[-dim:, :]
																												elif "bias" in key:
																																			a__      =val[:dim]
																																			a__      =val[dim : dim * 2]
																																			a__      =val[-dim:]

														else:
																					a__      =val

							return orig_state_dict






def   _lowercase(					__a				:						Any    ,     __a				:						Optional[Any]     ):
							a__      =ViTMAEConfig()
							if "large" in checkpoint_url:
														a__      =1024
														a__      =4096
														a__      =24
														a__      =16
							elif "huge" in checkpoint_url:
														a__      =14
														a__      =1280
														a__      =5120
														a__      =32
														a__      =16

							a__      =ViTMAEForPreTraining(__a     )

							a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'     )['model']

							a__      =ViTMAEImageProcessor(size=config.image_size     )

							a__      =convert_state_dict(__a    ,     __a     )

							model.load_state_dict(__a     )
							model.eval()

							a__      ='https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							a__      =ViTMAEImageProcessor(size=config.image_size     )
							a__      =image_processor(images=__a    ,     return_tensors='pt'     )

							# forward pass
							torch.manual_seed(2     )
							a__      =model(**__a     )
							a__      =outputs.logits

							if "large" in checkpoint_url:
														a__      =torch.tensor(
														    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]     )
							elif "huge" in checkpoint_url:
														a__      =torch.tensor(
														    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]     )
							else:
														a__      =torch.tensor(
														    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]     )

							# verify logits
							assert torch.allclose(logits[0, :3, :3]    ,     __a    ,     atol=1e-4     )

							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__":
	_lowerCAmelCase:						Dict   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
	    type=str,
	    help='URL of the checkpoint you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	_lowerCAmelCase:						str   				=       parser.parse_args()
	convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import random
from typing import Any






def 	__lowerCamelCase						(							lowerCamelCase__		:						list      ):





			'''simple docstring'''




			for _ in range(len(lowerCamelCase__      )      ):
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase		,		lowerCamelCase    		=  data[b], data[a]
			return data


if __name__ == "__main__":
		UpperCAmelCase   :    str     =		[0, 1, 2, 3, 4, 5, 6, 7]
		UpperCAmelCase   :    Union[str, Any]     =		["python", "says", "hello", "!"]
		print("Fisher-Yates Shuffle:")
		print("List", integers, strings)
		print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

