





'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




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

	return name




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[Any]:
	for key in orig_state_dict.copy().keys():
		snake_case__	       =   orig_state_dict.pop(UpperCAmelCase_							)

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

		else:
			snake_case__	       =   val

	return orig_state_dict




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:
	snake_case__	       =   ViTMAEConfig()
	if "large" in checkpoint_url:
		snake_case__	       =   10_24
		snake_case__	       =   40_96
		snake_case__	       =   24
		snake_case__	       =   16
	elif "huge" in checkpoint_url:
		snake_case__	       =   14
		snake_case__	       =   12_80
		snake_case__	       =   51_20
		snake_case__	       =   32
		snake_case__	       =   16

	snake_case__	       =   ViTMAEForPreTraining(UpperCAmelCase_							)

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

	snake_case__	       =   ViTMAEImageProcessor(size=config.image_size							)

	snake_case__	       =   convert_state_dict(UpperCAmelCase_					,				UpperCAmelCase_							)

	model.load_state_dict(UpperCAmelCase_							)
	model.eval()

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

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

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

	if "large" in checkpoint_url:
		snake_case__	       =   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:
		snake_case__	       =   torch.tensor(
		    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]							)
	else:
		snake_case__	       =   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__					:			Tuple          =     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__					:			Union[str, Any]          =     parser.parse_args()
			convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

			if current_node.pos == self.target.pos:
				snake_case__	       =   True
				return self.retrace_path(UpperCamelCase_		)

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			for child_node in successors:
				if child_node in self.closed_nodes:
					continue

				if child_node not in self.open_nodes:
					self.open_nodes.append(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

					if child_node.g_cost < better_node.g_cost:
						self.open_nodes.append(UpperCamelCase_		)
					else:
						self.open_nodes.append(UpperCamelCase_		)

		if not self.reached:
			return [self.start.pos]
		return None





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

			if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCamelCase_		) - 1):
				continue

			if grid[pos_y][pos_x] != 0:
				continue

			successors.append(
			    Node(
			        UpperCamelCase_      ,       UpperCamelCase_      ,       self.target.pos_y      ,       self.target.pos_x      ,       parent.g_cost + 1      ,       UpperCamelCase_      ,       )		)
		return successors





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


if __name__ == "__main__":
			a__					:			List[str]          =     (0, 0)
			a__					:			Dict          =     (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



