





'''simple docstring'''

from __future__ import annotations

__lowerCamelCase : Any											=			list[tuple[int, int]]

__lowerCamelCase : str											=			[
    [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],
]

__lowerCamelCase : Any											=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       UpperCAmelCase :




		def __init__(self      :	Any							,			A__      :	Union[str, Any]							,			A__      :	List[Any]							,			A__      :	List[str]							,			A__      :	List[str]							,			A__      :	Dict							,			A__      :	Optional[Any]							,			)					-> Optional[int]:
						lowercase         = pos_x
						lowercase         = pos_y
						lowercase         = (pos_y, pos_x)
						lowercase         = goal_x
						lowercase         = goal_y
						lowercase         = g_cost
						lowercase         = parent
						lowercase         = self.calculate_heuristic()




		def 				lowerCamelCase__   (self      :	Tuple				)					-> float:
						lowercase         = abs(self.pos_x - self.goal_x				)
						lowercase         = abs(self.pos_y - self.goal_y				)
						return dx + dy





		def __lt__(self      :	Dict							,			A__      :	List[Any]				)					-> bool:
						return self.f_cost < other.f_cost




class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	int							,			A__      :	List[str]				)					-> Union[str, Any]:
						lowercase         = Node(start[1]							,			start[0]							,			goal[1]							,			goal[0]							,			0							,			lowerCAmelCase_				)
						lowercase         = Node(goal[1]							,			goal[0]							,			goal[1]							,			goal[0]							,			9_9_9_9_9							,			lowerCAmelCase_				)

						lowercase         = [self.start]
						lowercase         = []

						lowercase         = False




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

										if current_node.pos == self.target.pos:
														lowercase         = True
														return self.retrace_path(lowerCAmelCase_				)

										self.closed_nodes.append(lowerCAmelCase_				)
										lowercase         = self.get_successors(lowerCAmelCase_				)

										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(lowerCAmelCase_				)
														else:
																		# retrieve the best current path
																		lowercase         = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase_				)				)

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

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




		def 				lowerCamelCase__   (self      :	Union[str, Any]							,			A__      :	Dict				)					-> list[Node]:
						lowercase         = []
						for action in delta:
										lowercase         = parent.pos_x + action[1]
										lowercase         = parent.pos_y + action[0]

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

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

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





		def 				lowerCamelCase__   (self      :	List[str]							,			A__      :	Any				)					-> Path:
						lowercase         = node
						lowercase         = []
						while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)				)
										lowercase         = current_node.parent
						path.reverse()
						return path


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

			print("------")

			__lowerCamelCase : Any											=			GreedyBestFirst(init, goal)
			__lowerCamelCase : str											=			greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									__lowerCamelCase : int											=			2

						for elem in grid:
									print(elem)











'''simple docstring'''

import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__lowerCamelCase : Any											=			{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class       UpperCAmelCase (						nn.Module  ):




		def __init__(self      :	Tuple							,			A__      :	Any				)					-> str:
						super().__init__()
						lowercase         = torchvision.models.resnetaaa(pretrained=A__				)
						lowercase         = list(model.children()				)[:-2]
						lowercase         = nn.Sequential(*A__				)
						lowercase         = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						lowercase         = [json.loads(A__				) for l in open(A__				)]
						lowercase         = os.path.dirname(A__				)
						lowercase         = tokenizer
						lowercase         = labels
						lowercase         = len(A__				)
						lowercase         = max_seq_length

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						lowercase   ,							lowercase   ,							lowercase         = sentence[0], sentence[1:-1], sentence[-1]
						lowercase         = sentence[: self.max_seq_length]

						lowercase         = torch.zeros(self.n_classes				)
						lowercase         = 1

						lowercase         = Image.open(os.path.join(self.data_dir							,			self.data[index]["img"]				)				).convert("RGB"				)
						lowercase         = self.transforms(A__				)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								lowercase         = input_row["sentence"]
								lowercase         = 1

				lowercase         = torch.stack([row["image"] for row in batch]							)
				lowercase         = torch.stack([row["label"] for row in batch]							)
				lowercase         = torch.stack([row["image_start_token"] for row in batch]							)
				lowercase         = torch.stack([row["image_end_token"] for row in batch]							)

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




