


'''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


_lowercase      :      Optional[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      :			Optional[Any]				,   lowerCAmelCase      :			Any				)->					Dict:

										"""simple docstring"""




										super().__init__()
										UpperCAmelCase					    =     torchvision.models.resnetaaa(pretrained=lowerCAmelCase				)
										UpperCAmelCase					    =     list(model.children()				)[:-2]
										UpperCAmelCase					    =     nn.Sequential(*lowerCAmelCase				)
										UpperCAmelCase					    =     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Union[str, Any]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.pool(self.model(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.flatten(lowerCAmelCase				,   start_dim=2				)
										UpperCAmelCase					    =     out.transpose(1				,   2				).contiguous()
										return out  # BxNx2048







class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     [json.loads(lowerCAmelCase				) for l in open(lowerCAmelCase				)]
										UpperCAmelCase					    =     os.path.dirname(lowerCAmelCase				)
										UpperCAmelCase					    =     tokenizer
										UpperCAmelCase					    =     labels
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     max_seq_length

										UpperCAmelCase					    =     transforms




				def __len__(		self      :			str				)->					Union[str, Any]:

										"""simple docstring"""




										return len(self.data				)




				def __getitem__(		self      :			Any				,   lowerCAmelCase      :			Any				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,   add_special_tokens=lowerCAmelCase				)				)
										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     sentence[0], sentence[1:-1], sentence[-1]
										UpperCAmelCase					    =     sentence[: self.max_seq_length]

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

										UpperCAmelCase					    =     Image.open(os.path.join(self.data_dir				,   self.data[index]['''img''']				)				).convert('''RGB'''				)
										UpperCAmelCase					    =     self.transforms(lowerCAmelCase				)

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





				def 					a__(		self      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     Counter()
										for row in self.data:
																label_freqs.update(row['''label''']				)
										return label_freqs







def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     [len(row['''sentence''']					) for row in batch]
						UpperCAmelCase    ,    UpperCAmelCase					    =     len(A					), max(A					)

						UpperCAmelCase					    =     torch.zeros(A				,       A				,       dtype=torch.long					)
						UpperCAmelCase					    =     torch.zeros(A				,       A				,       dtype=torch.long					)

						for i_batch, (input_row, length) in enumerate(zip(A				,       A					)					):
												UpperCAmelCase					    =     input_row['''sentence''']
												UpperCAmelCase					    =     1

						UpperCAmelCase					    =     torch.stack([row['''image'''] for row in batch]					)
						UpperCAmelCase					    =     torch.stack([row['''label'''] for row in batch]					)
						UpperCAmelCase					    =     torch.stack([row['''image_start_token'''] for row in batch]					)
						UpperCAmelCase					    =     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 				lowerCamelCase__				(	):




						'''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 				lowerCamelCase__				(	):




						'''simple docstring'''






						return transforms.Compose(
						    [
						        transforms.Resize(2_56					),
						        transforms.CenterCrop(2_24					),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]				,       std=[0.12_221_994, 0.12_145_835, 0.14_380_469]				,       ),
						    ]					)









'''simple docstring'''



from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				int
				__magic_name__    :				TreeNode | None  		=	None
				__magic_name__    :				TreeNode | None  		=	None







_lowercase      :      Tuple            =					namedtuple("""CoinsDistribResult""", """moves excess""")


def 				lowerCamelCase__				(	A       :      TreeNode | None					):




						'''simple docstring'''






						if root is None:
												return 0

						# Validation
						def count_nodes(A       :      TreeNode | None					) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left					) + count_nodes(node.right					) + 1

						def count_coins(A       :      TreeNode | None					) -> int:
												if node is None:
																		return 0

												return count_coins(node.left					) + count_coins(node.right					) + node.data

						if count_nodes(A					) != count_coins(A					):
												raise ValueError('''The nodes number should be same as the number of coins'''					)

						# Main calculation
						def get_distrib(A       :      TreeNode | None					) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0				,       1					)

												UpperCAmelCase    ,    UpperCAmelCase					    =     get_distrib(node.left					)
												UpperCAmelCase    ,    UpperCAmelCase					    =     get_distrib(node.right					)

												UpperCAmelCase					    =     1 - left_distrib_excess
												UpperCAmelCase					    =     1 - right_distrib_excess

												UpperCAmelCase					    =     (
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(A					)
												    + abs(A					)
												)
												UpperCAmelCase					    =     node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(A				,       A					)

						return get_distrib(A					)[0]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





