
def 							a		(				snake_case__:   int = 200   ):


				'''simple docstring'''
				lowercase_	    =   [1, 2, 5, 10, 20, 50, 100, 200]
				lowercase_	    =   [0] * (pence + 1)
				lowercase_	    =   1  # base case: 1 way to make 0 pence

				for coin in coins:
								for i in range(snake_case__		,  pence + 1		,  1   ):
												number_of_ways[i] += number_of_ways[i - coin]
				return number_of_ways[pence]


if __name__ == "__main__":
						assert solution(2_0_0) == 7_3_6_8_2






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


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

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

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

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

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

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

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

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

				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   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         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




