


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






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

			return name






def 						a__			(				A__,      A__				):
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					orig_state_dict.pop(A__				)

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

						else:
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					val

			return orig_state_dict






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					ViTMAEConfig()
			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Any					=					1_0_2_4
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					4_0_9_6
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					2_4
						SCREAMING_SNAKE_CASE_		:     List[str]					=					1_6
			elif "huge" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1_4
						SCREAMING_SNAKE_CASE_		:     int					=					1_2_8_0
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					5_1_2_0
						SCREAMING_SNAKE_CASE_		:     Any					=					3_2
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1_6

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ViTMAEForPreTraining(A__				)

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

			SCREAMING_SNAKE_CASE_		:     int					=					ViTMAEImageProcessor(size=config.image_size				)

			SCREAMING_SNAKE_CASE_		:     str					=					convert_state_dict(A__,      A__				)

			model.load_state_dict(A__				)
			model.eval()

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

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

			# forward pass
			torch.manual_seed(2				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(**A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					outputs.logits

			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Any					=					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:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					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:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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__			:      str								=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)








"""simple docstring"""
import cmath
import math




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> complex:
      UpperCAmelCase__			:							str							=    math.radians(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    math.radians(lowerCAmelCase    )

      # Convert voltage and current to rectangular form
      UpperCAmelCase__			:							Union[str, Any]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[str]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )

      # Calculate apparent power
      return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()

