"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def    __lowerCAmelCase    (_UpperCamelCase		):



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

	return name
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):



	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase      :						Optional[Any]						      =				orig_state_dict.pop(A__		)

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

		else:
			__lowerCAmelCase      :						str						      =				val

	return orig_state_dict
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				ViTMAEConfig()
	if "large" in checkpoint_url:
		__lowerCAmelCase      :						Dict						      =				1024
		__lowerCAmelCase      :						Optional[Any]						      =				4096
		__lowerCAmelCase      :						Optional[int]						      =				24
		__lowerCAmelCase      :						Any						      =				16
	elif "huge" in checkpoint_url:
		__lowerCAmelCase      :						str						      =				14
		__lowerCAmelCase      :						Optional[int]						      =				1280
		__lowerCAmelCase      :						List[Any]						      =				5120
		__lowerCAmelCase      :						Tuple						      =				32
		__lowerCAmelCase      :						Any						      =				16

	__lowerCAmelCase      :						List[str]						      =				ViTMAEForPreTraining(A__		)

	__lowerCAmelCase      :						int						      =				torch.hub.load_state_dict_from_url(A__	,  map_location='cpu'		)['model']

	__lowerCAmelCase      :						Optional[Any]						      =				ViTMAEImageProcessor(size=config.image_size		)

	__lowerCAmelCase      :						List[str]						      =				convert_state_dict(A__	,  A__		)

	model.load_state_dict(A__		)
	model.eval()

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

	__lowerCAmelCase      :						str						      =				Image.open(requests.get(A__	,  stream=A__		).raw		)
	__lowerCAmelCase      :						Optional[int]						      =				ViTMAEImageProcessor(size=config.image_size		)
	__lowerCAmelCase      :						int						      =				image_processor(images=A__	,  return_tensors='pt'		)

	# forward pass
	torch.manual_seed(2		)
	__lowerCAmelCase      :						Tuple						      =				model(**A__		)
	__lowerCAmelCase      :						List[Any]						      =				outputs.logits

	if "large" in checkpoint_url:
		__lowerCAmelCase      :						Any						      =				torch.tensor(
		    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]		)
	elif "huge" in checkpoint_url:
		__lowerCAmelCase      :						str						      =				torch.tensor(
		    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]		)
	else:
		__lowerCAmelCase      :						Dict						      =				torch.tensor(
		    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]		)

	# 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__        =		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__        =		parser.parse_args()
							convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

"""simple docstring"""







class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				size
			__lowerCAmelCase      :						str						      =				[0] * size
			__lowerCAmelCase      :						Any						      =				[0] * size

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return index | (index + 1)

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return (index & (index + 1)) - 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				value
			while index < self.size:
				__lowerCAmelCase      :						Dict						      =				self.get_prev(_SCREAMING_SNAKE_CASE			) + 1
				if current_left_border == index:
					__lowerCAmelCase      :						Any						      =				value
				else:
					__lowerCAmelCase      :						Any						      =				max(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				self.get_next(_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			right -= 1  # Because of right is exclusive
			__lowerCAmelCase      :						Optional[int]						      =				0
			while left <= right:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_prev(_SCREAMING_SNAKE_CASE			)
				if left <= current_left:
					__lowerCAmelCase      :						Optional[Any]						      =				max(_SCREAMING_SNAKE_CASE	,		self.tree[right]			)
					__lowerCAmelCase      :						Optional[Any]						      =				current_left
				else:
					__lowerCAmelCase      :						List[str]						      =				max(_SCREAMING_SNAKE_CASE	,		self.arr[right]			)
					right -= 1
			return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()