





'''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_   :   Optional[int]       =					{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			:   str , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				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 						__a   (				self			:   Dict , lowerCamelCase			:   str		):




				'''simple docstring'''
				# 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       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   int , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Tuple , lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				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			:   Optional[Any]		):




				'''simple docstring'''
				return len(self.data		)







	def __getitem__(				self			:   Tuple , lowerCamelCase			:   Any		):




				'''simple docstring'''
				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 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = Counter()
				for row in self.data:
							label_freqs.update(row["label"]		)
				return label_freqs







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[Any]:
			a__     = [len(row["sentence"]     ) for row in batch]
			a__   ,				a__     = len(__lowerCamelCase     ), max(__lowerCamelCase     )

			a__     = torch.zeros(__lowerCamelCase					,  __lowerCamelCase					,  dtype=torch.long     )
			a__     = torch.zeros(__lowerCamelCase					,  __lowerCamelCase					,  dtype=torch.long     )

			for i_batch, (input_row, length) in enumerate(zip(__lowerCamelCase					,  __lowerCamelCase     )     ):
						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    _lowerCamelCase  ()   ->    Optional[Any]:
			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  ()   ->    List[Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(256     ),
			        transforms.CenterCrop(224     ),
			        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'''


def    _lowerCamelCase  (__lowerCamelCase  :     int = 400_0000     )   ->    int:
			a__     = [0, 1]
			a__     = 0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]     )
						if fib[i + 2] > n:
									break
						i += 1
			a__     = 0
			for j in range(len(__lowerCamelCase     ) - 1     ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


if __name__ == "__main__":
		print(f"""{solution() = }""")



