






'''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


A:      Union[str, 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 							__magic_name__     (	nn.Module				):







	"""simple docstring"""



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







	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  str:
							lowercase_      :   Tuple        =			self.pool(self.model(lowercase__       )       )
							lowercase_      :   Union[str, Any]        =			torch.flatten(lowercase__  ,      start_dim=2       )
							lowercase_      :   List[Any]        =			out.transpose(1  ,      2       ).contiguous()
							return out  # BxNx2048






class 							__magic_name__     (	a__				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							lowercase_      :   List[Any]        =			[json.loads(lowercase__       ) for l in open(lowercase__       )]
							lowercase_      :   Optional[Any]        =			os.path.dirname(lowercase__       )
							lowercase_      :   Dict        =			tokenizer
							lowercase_      :   Any        =			labels
							lowercase_      :   Tuple        =			len(lowercase__       )
							lowercase_      :   Tuple        =			max_seq_length

							lowercase_      :   Optional[Any]        =			transforms
	def __len__(							self       )						->  Optional[int]:
							return len(self.data       )
	def __getitem__(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   Tuple        =			torch.LongTensor(self.tokenizer.encode(self.data[index]['text']  ,      add_special_tokens=lowercase__       )       )
							lowercase_      :   Dict        =			sentence[0], sentence[1:-1], sentence[-1]
							lowercase_      :   int        =			sentence[: self.max_seq_length]

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

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

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







	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   Optional[Any]        =			Counter()
							for row in self.data:
													label_freqs.update(row['label']       )
							return label_freqs






def 	_UpperCAmelCase    (  a		:	Any							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   Optional[Any]        =			[len(row['sentence']							) for row in batch]
						lowercase_      :   List[str]        =			len(lowerCAmelCase_							), max(lowerCAmelCase_							)

						lowercase_      :   Dict        =			torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long							)
						lowercase_      :   Tuple        =			torch.zeros(lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=torch.long							)

						for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_						,			lowerCAmelCase_							)							):
												lowercase_      :   Dict        =			input_row['''sentence''']
												lowercase_      :   str        =			1

						lowercase_      :   Optional[int]        =			torch.stack([row['image'] for row in batch]							)
						lowercase_      :   str        =			torch.stack([row['label'] for row in batch]							)
						lowercase_      :   Dict        =			torch.stack([row['image_start_token'] for row in batch]							)
						lowercase_      :   Union[str, 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 	_UpperCAmelCase    (  )			->			Tuple:





						"""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    (  )			->			Optional[Any]:





						"""simple docstring"""
						return transforms.Compose(
						    [
						        transforms.Resize(2_5_6							),
						        transforms.CenterCrop(2_2_4							),
						        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]						,			),
						    ]							)








'''simple docstring'''

class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Dict        =			n
							lowercase_      :   Dict        =			[None] * self.n
							lowercase_      :   Tuple        =			0  # index of the first element
							lowercase_      :   List[Any]        =			0
							lowercase_      :   List[Any]        =			0
	def __len__(							self       )						->  int:
							return self.size
	def        lowerCamelCase__		(							self       )						->  bool:
							return self.size == 0
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							return False if self.is_empty() else self.array[self.front]
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Any:
							if self.size >= self.n:
													raise Exception('QUEUE IS FULL'       )

							lowercase_      :   Tuple        =			data
							lowercase_      :   List[Any]        =			(self.rear + 1) % self.n
							self.size += 1
							return self







	def        lowerCamelCase__		(							self       )						->  Any:
							if self.size == 0:
													raise Exception('UNDERFLOW'       )

							lowercase_      :   Dict        =			self.array[self.front]
							lowercase_      :   Tuple        =			None
							lowercase_      :   int        =			(self.front + 1) % self.n
							self.size -= 1
							return temp
