
'''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


UpperCAmelCase    					=   {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 						a   (				nn.Module    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				int      ):
										super().__init__()
										snake_case       :     int =    torchvision.models.resnetaaa(pretrained=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    list(model.children()      )[:-2]
										snake_case       :     Union[str, Any] =    nn.Sequential(*SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )




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




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     int =    [json.loads(SCREAMING_SNAKE_CASE_      ) for l in open(SCREAMING_SNAKE_CASE_      )]
										snake_case       :     List[Any] =    os.path.dirname(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    tokenizer
										snake_case       :     str =    labels
										snake_case       :     List[str] =    len(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    max_seq_length

										snake_case       :     Tuple =    transforms




				def __len__(							self	:				Dict      ):
										return len(self.data      )




				def __getitem__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										snake_case       :     List[Any] =    torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''],    add_special_tokens=SCREAMING_SNAKE_CASE_      )      )
										snake_case,					snake_case,					snake_case       :     Dict =    sentence[0], sentence[1:-1], sentence[-1]
										snake_case       :     Tuple =    sentence[: self.max_seq_length]

										snake_case       :     str =    torch.zeros(self.n_classes      )
										snake_case       :     Union[str, Any] =    1

										snake_case       :     List[Any] =    Image.open(os.path.join(self.data_dir,    self.data[index]['''img''']      )      ).convert('''RGB'''      )
										snake_case       :     Optional[int] =    self.transforms(SCREAMING_SNAKE_CASE_      )

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




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Union[str, Any] =    Counter()
										for row in self.data:
																label_freqs.update(row['''label''']      )
										return label_freqs




def      A (       A_							:					List[Any]       ):
						snake_case       :     str =    [len(row['''sentence''']       ) for row in batch]
						snake_case,					snake_case       :     Any =    len(A_       ), max(A_       )

						snake_case       :     Tuple =    torch.zeros(A_    ,							A_    ,							dtype=torch.long       )
						snake_case       :     Optional[Any] =    torch.zeros(A_    ,							A_    ,							dtype=torch.long       )

						for i_batch, (input_row, length) in enumerate(zip(A_    ,							A_       )       ):
												snake_case       :     str =    input_row['''sentence''']
												snake_case       :     Any =    1

						snake_case       :     str =    torch.stack([row['''image'''] for row in batch]       )
						snake_case       :     List[Any] =    torch.stack([row['''label'''] for row in batch]       )
						snake_case       :     List[str] =    torch.stack([row['''image_start_token'''] for row in batch]       )
						snake_case       :     Optional[Any] =    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      A (       ):
						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      A (       ):
						return transforms.Compose(
						    [
						        transforms.Resize(256       ),
						        transforms.CenterCrop(224       ),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]    ,							std=[0.1222_1994, 0.1214_5835, 0.1438_0469]    ,							),
						    ]       )



'''simple docstring'''





import numpy as np

class 						a   :




				def __init__(							self	:				Dict      ):
										snake_case       :     List[str] =    (0, 0)
										snake_case       :     Tuple =    None
										snake_case       :     Union[str, Any] =    0
										snake_case       :     Dict =    0
										snake_case       :     List[str] =    0




				def __eq__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.position == cell.position




				def 	__snake_case							(							self	:				Optional[int]      ):
										print(self.position      )




class 						a   :




				def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=(5, 5)      ):
										snake_case       :     int =    np.zeros(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    world_size[0]
										snake_case       :     Optional[Any] =    world_size[1]




				def 	__snake_case							(							self	:				Any      ):
										print(self.w      )




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Union[str, Any] =    [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										snake_case       :     str =    cell.position[0]
										snake_case       :     Optional[Any] =    cell.position[1]
										snake_case       :     Any =    []
										for n in neughbour_cord:
																snake_case       :     List[str] =    current_x + n[0]
																snake_case       :     Tuple =    current_y + n[1]
																if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																						snake_case       :     int =    Cell()
																						snake_case       :     int =    (x, y)
																						snake_case       :     Any =    cell
																						neighbours.append(SCREAMING_SNAKE_CASE_      )
										return neighbours




def      A (       A_							:					Tuple    ,							A_							:					Tuple    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Union[str, Any] =    []
						snake_case       :     Union[str, Any] =    []
						_open.append(A_       )

						while _open:
												snake_case       :     List[Any] =    np.argmin([n.f for n in _open]       )
												snake_case       :     List[Any] =    _open[min_f]
												_closed.append(_open.pop(A_       )       )
												if current == goal:
																		break
												for n in world.get_neigbours(A_       ):
																		for c in _closed:
																								if c == n:
																														continue
																		snake_case       :     Dict =    current.g + 1
																		snake_case,					snake_case       :     Optional[Any] =    n.position
																		snake_case,					snake_case       :     Union[str, Any] =    goal.position
																		snake_case       :     Tuple =    (ya - ya) ** 2 + (xa - xa) ** 2
																		snake_case       :     Union[str, Any] =    n.h + n.g

																		for c in _open:
																								if c == n and c.f < n.f:
																														continue
																		_open.append(A_       )
						snake_case       :     Dict =    []
						while current.parent is not None:
												path.append(current.position       )
												snake_case       :     Tuple =    current.parent
						path.append(current.position       )
						return path[::-1]


if __name__ == "__main__":
						UpperCAmelCase    					=   Gridworld()
						# Start position and goal
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (0, 0)
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (4, 4)
						print(f'''path from {start.position} to {goal.position}''')
						UpperCAmelCase    					=   astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												UpperCAmelCase    					=   1
						print(world.w)

