

"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

def 		A				(   snake_case__			):




			'''simple docstring'''


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

			return name

def 		A				(   snake_case__						, snake_case__			):




			'''simple docstring'''


			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE__									=					orig_state_dict.pop(_lowercase			)

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

						else:
									SCREAMING_SNAKE_CASE__									=					val

			return orig_state_dict

def 		A				(   snake_case__						, snake_case__			):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									=					ViTMAEConfig()
			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE__									=					10_24
						SCREAMING_SNAKE_CASE__									=					40_96
						SCREAMING_SNAKE_CASE__									=					24
						SCREAMING_SNAKE_CASE__									=					16
			elif "huge" in checkpoint_url:
						SCREAMING_SNAKE_CASE__									=					14
						SCREAMING_SNAKE_CASE__									=					12_80
						SCREAMING_SNAKE_CASE__									=					51_20
						SCREAMING_SNAKE_CASE__									=					32
						SCREAMING_SNAKE_CASE__									=					16

			SCREAMING_SNAKE_CASE__									=					ViTMAEForPreTraining(_lowercase			)

			SCREAMING_SNAKE_CASE__									=					torch.hub.load_state_dict_from_url(_lowercase						, map_location="""cpu"""			)["""model"""]

			SCREAMING_SNAKE_CASE__									=					ViTMAEImageProcessor(size=config.image_size			)

			SCREAMING_SNAKE_CASE__									=					convert_state_dict(_lowercase						, _lowercase			)

			model.load_state_dict(_lowercase			)
			model.eval()

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

			SCREAMING_SNAKE_CASE__									=					Image.open(requests.get(_lowercase						, stream=_lowercase			).raw			)
			SCREAMING_SNAKE_CASE__									=					ViTMAEImageProcessor(size=config.image_size			)
			SCREAMING_SNAKE_CASE__									=					image_processor(images=_lowercase						, return_tensors="""pt"""			)

			# forward pass
			torch.manual_seed(2			)
			SCREAMING_SNAKE_CASE__									=					model(**_lowercase			)
			SCREAMING_SNAKE_CASE__									=					outputs.logits

			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE__									=					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__									=					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__									=					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]						, _lowercase						, atol=1e-4			)

			print(f"""Saving model to {pytorch_dump_folder_path}"""			)
			model.save_pretrained(_lowercase			)

			print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
			image_processor.save_pretrained(_lowercase			)


if __name__ == "__main__":
						A_							:	List[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."
						)

						A_							:	List[str]      =       parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''




from PIL import Image



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Image:

							def brightness(_lowercase		) -> float:
														return 1_2_8 + level + (c - 1_2_8)

							if not -255.0 <= level <= 255.0:
														raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
							return img.point(_lowercase		)


if __name__ == "__main__":
							# Load image
							with Image.open("""image_data/lena.jpg""") as img:
														# Change brightness to 100
														a   :			Optional[Any]					  =    change_brightness(img, 1_0_0)
														brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

