
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def    a      ( a	)     ->Dict:




			'''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      ( a		,						a	)     ->Dict:




			'''simple docstring'''


			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     						= orig_state_dict.pop(a	)

						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      ( a		,						a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= ViTMAEConfig()
			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE     						= 1024
						SCREAMING_SNAKE_CASE     						= 4096
						SCREAMING_SNAKE_CASE     						= 24
						SCREAMING_SNAKE_CASE     						= 16
			elif "huge" in checkpoint_url:
						SCREAMING_SNAKE_CASE     						= 14
						SCREAMING_SNAKE_CASE     						= 1280
						SCREAMING_SNAKE_CASE     						= 5120
						SCREAMING_SNAKE_CASE     						= 32
						SCREAMING_SNAKE_CASE     						= 16

			SCREAMING_SNAKE_CASE     						= ViTMAEForPreTraining(a	)

			SCREAMING_SNAKE_CASE     						= torch.hub.load_state_dict_from_url(a		,						map_location='''cpu'''	)['''model''']

			SCREAMING_SNAKE_CASE     						= ViTMAEImageProcessor(size=config.image_size	)

			SCREAMING_SNAKE_CASE     						= convert_state_dict(a		,						a	)

			model.load_state_dict(a	)
			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(a		,						stream=a	).raw	)
			SCREAMING_SNAKE_CASE     						= ViTMAEImageProcessor(size=config.image_size	)
			SCREAMING_SNAKE_CASE     						= image_processor(images=a		,						return_tensors='''pt'''	)

			# forward pass
			torch.manual_seed(2	)
			SCREAMING_SNAKE_CASE     						= model(**a	)
			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]		,						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)

# Function to print upper half of diamond (pyramid)


def    a      ( a	)     ->Optional[Any]:




			'''simple docstring'''


			for i in range(0		,						a	):
						for _ in range(0		,						n - i - 1	):  # printing spaces
									print(''' '''		,						end=''''''	)
						for _ in range(0		,						i + 1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()


def    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			for i in range(a		,						0		,						-1	):
						for _ in range(a		,						0		,						-1	):  # printing stars
									print('''* '''		,						end=''''''	)
						print()
						for _ in range(n - i + 1		,						0		,						-1	):  # printing spaces
									print(''' '''		,						end=''''''	)


def    a      ( a	)     ->Optional[int]:




			'''simple docstring'''


			if n <= 0:
						print('''       ...       ....        nothing printing :('''	)
						return
			floyd(a	)  # upper half
			reverse_floyd(a	)  # lower half


if __name__ == "__main__":
							print(R'| /\ | |- |  |-  |--| |\  /| |-')
							print(R'|/  \| |- |_ |_  |__| | \/ | |_')
							__lowerCAmelCase					   =       1
							while K:
														__lowerCAmelCase					   =       int(input('enter the number and , and see the magic : '))
														print()
														pretty_print(user_number)
														__lowerCAmelCase					   =       int(input('press 0 to exit... and 1 to continue...'))

							print('Good Bye...')