




"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


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

			return name

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			str:


			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						__UpperCAmelCase	: Optional[Any]		=					orig_state_dict.pop(UpperCamelCase							)

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

						else:
									__UpperCAmelCase	: Optional[int]		=					val

			return orig_state_dict

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					ViTMAEConfig()
			if "large" in checkpoint_url:
						__UpperCAmelCase	: str		=					1024
						__UpperCAmelCase	: Any		=					4096
						__UpperCAmelCase	: str		=					24
						__UpperCAmelCase	: int		=					16
			elif "huge" in checkpoint_url:
						__UpperCAmelCase	: Dict		=					14
						__UpperCAmelCase	: Optional[Any]		=					1280
						__UpperCAmelCase	: Optional[int]		=					5120
						__UpperCAmelCase	: Any		=					32
						__UpperCAmelCase	: Any		=					16

			__UpperCAmelCase	: Tuple		=					ViTMAEForPreTraining(UpperCamelCase							)

			__UpperCAmelCase	: int		=					torch.hub.load_state_dict_from_url(UpperCamelCase				,       map_location="cpu"							)["model"]

			__UpperCAmelCase	: str		=					ViTMAEImageProcessor(size=config.image_size							)

			__UpperCAmelCase	: str		=					convert_state_dict(UpperCamelCase				,       UpperCamelCase							)

			model.load_state_dict(UpperCamelCase							)
			model.eval()

			__UpperCAmelCase	: List[str]		=					"https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

			__UpperCAmelCase	: List[str]		=					Image.open(requests.get(UpperCamelCase				,       stream=UpperCamelCase							).raw							)
			__UpperCAmelCase	: Dict		=					ViTMAEImageProcessor(size=config.image_size							)
			__UpperCAmelCase	: Dict		=					image_processor(images=UpperCamelCase				,       return_tensors="pt"							)

			# forward pass
			torch.manual_seed(2							)
			__UpperCAmelCase	: Dict		=					model(**UpperCamelCase							)
			__UpperCAmelCase	: Union[str, Any]		=					outputs.logits

			if "large" in checkpoint_url:
						__UpperCAmelCase	: Optional[int]		=					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:
						__UpperCAmelCase	: List[Any]		=					torch.tensor(
						    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]							)
			else:
						__UpperCAmelCase	: List[Any]		=					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]				,       UpperCamelCase				,       atol=1e-4							)

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

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


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)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


