






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__			:    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    _UpperCAmelCase      ( nn.Module			):

				"""simple docstring"""




				def __init__(  self		:					List[str],						lowerCamelCase		:					Any						):

								'''simple docstring'''
								super().__init__()
								lowercase__     				=    torchvision.models.resnetaaa(pretrained=lowerCamelCase						)
								lowercase__     				=    list(model.children()						)[:-2]
								lowercase__     				=    nn.Sequential(*lowerCamelCase						)
								lowercase__     				=    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								lowercase__     				=    self.pool(self.model(lowerCamelCase						)						)
								lowercase__     				=    torch.flatten(lowerCamelCase,						start_dim=2						)
								lowercase__     				=    out.transpose(1,						2						).contiguous()
								return out  # BxNx2048



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    [json.loads(lowerCamelCase						) for l in open(lowerCamelCase						)]
								lowercase__     				=    os.path.dirname(lowerCamelCase						)
								lowercase__     				=    tokenizer
								lowercase__     				=    labels
								lowercase__     				=    len(lowerCamelCase						)
								lowercase__     				=    max_seq_length

								lowercase__     				=    transforms




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

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




				def __getitem__(  self		:					int,						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''],						add_special_tokens=lowerCamelCase						)						)
								lowercase__				,		lowercase__				,		lowercase__     				=    sentence[0], sentence[1:-1], sentence[-1]
								lowercase__     				=    sentence[: self.max_seq_length]

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

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

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




				def        lowercase__     (  self		:					Any						):

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






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    [len(row['''sentence''']       ) for row in batch]
				lowercase__				,		lowercase__     				=    len(lowerCamelCase_       ), max(lowerCamelCase_       )

				lowercase__     				=    torch.zeros(lowerCamelCase_	,     lowerCamelCase_	,     dtype=torch.long       )
				lowercase__     				=    torch.zeros(lowerCamelCase_	,     lowerCamelCase_	,     dtype=torch.long       )

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

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






				'''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 						a    (		):






				'''simple docstring'''
				return transforms.Compose(
				    [
				        transforms.Resize(256       ),
				        transforms.CenterCrop(224       ),
				        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]	,     ),
				    ]       )












from __future__ import annotations






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								if resistor <= 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative or zero value!"""
												raise ValueError(lowerCamelCase_       )
								first_sum += 1 / float(lowerCamelCase_       )
								index += 1
				return 1 / first_sum






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								sum_r += resistor
								if resistor < 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative value!"""
												raise ValueError(lowerCamelCase_       )
								index += 1
				return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()




