


'''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


__SCREAMING_SNAKE_CASE					:				Tuple									=		{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 					__lowerCAmelCase   (   nn.Module						):

						"""simple docstring"""






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      Any	):
													super().__init__()
													A_			    =		torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
													A_			    =		list(model.children()	)[:-2]
													A_			    =		nn.Sequential(*lowerCAmelCase	)
													A_			    =		nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      Optional[Any]	):
													# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
													A_			    =		self.pool(self.model(lowerCAmelCase	)	)
													A_			    =		torch.flatten(lowerCAmelCase ,  start_dim=2	)
													A_			    =		out.transpose(1 ,  2	).contiguous()
													return out  # BxNx2048






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      Dict ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Optional[int]	):
													A_			    =		[json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
													A_			    =		os.path.dirname(lowerCAmelCase	)
													A_			    =		tokenizer
													A_			    =		labels
													A_			    =		len(lowerCAmelCase	)
													A_			    =		max_seq_length

													A_			    =		transforms






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






						def __getitem__(  self							:      Dict ,  lowerCAmelCase							:      int	):
													A_			    =		torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] ,  add_special_tokens=lowerCAmelCase	)	)
													A_    ,					A_    ,					A_			    =		sentence[0], sentence[1:-1], sentence[-1]
													A_			    =		sentence[: self.max_seq_length]

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

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

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






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		Counter()
													for row in self.data:
																				label_freqs.update(row["label"]	)
													return label_freqs







def       a_				(	UpperCamelCase_ ):
							A_			    =		[len(row["sentence"] ) for row in batch]
							A_    ,					A_			    =		len(UpperCamelCase_ ), max(UpperCamelCase_ )

							A_			    =		torch.zeros(UpperCamelCase_						,  UpperCamelCase_						,  dtype=torch.long )
							A_			    =		torch.zeros(UpperCamelCase_						,  UpperCamelCase_						,  dtype=torch.long )

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

							A_			    =		torch.stack([row["image"] for row in batch] )
							A_			    =		torch.stack([row["label"] for row in batch] )
							A_			    =		torch.stack([row["image_start_token"] for row in batch] )
							A_			    =		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(2_5_6 ),
							        transforms.CenterCrop(2_2_4 ),
							        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'''
def       a_				(	UpperCamelCase_ ):

							if length <= 0 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise ValueError("Length must be a positive integer." )
							return [n * (2 * n - 1) for n in range(UpperCamelCase_ )]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=10))





