




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													=			{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       _A				(		nn.Module     ):






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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								_UpperCAmelCase      =			self.pool(self.model(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			torch.flatten(_SCREAMING_SNAKE_CASE						,		start_dim=2			)
								_UpperCAmelCase      =			out.transpose(1						,		2			).contiguous()
								return out  # BxNx2048




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			[json.loads(_SCREAMING_SNAKE_CASE			) for l in open(_SCREAMING_SNAKE_CASE			)]
								_UpperCAmelCase      =			os.path.dirname(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			tokenizer
								_UpperCAmelCase      =			labels
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			max_seq_length

								_UpperCAmelCase      =			transforms






		def __len__(    self			):
								return len(self.data			)






		def __getitem__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						,		add_special_tokens=_SCREAMING_SNAKE_CASE			)			)
								_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(_SCREAMING_SNAKE_CASE			)

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






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




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Dict:
						_UpperCAmelCase      =			[len(row["""sentence"""]       ) for row in batch]
						_UpperCAmelCase    ,					_UpperCAmelCase      =			len(snake_case       ), max(snake_case       )

						_UpperCAmelCase      =			torch.zeros(snake_case			,     snake_case			,     dtype=torch.long       )
						_UpperCAmelCase      =			torch.zeros(snake_case			,     snake_case			,     dtype=torch.long       )

						for i_batch, (input_row, length) in enumerate(zip(snake_case			,     snake_case       )       ):
												_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        _SCREAMING_SNAKE_CASE      (		)						->				str:
						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        _SCREAMING_SNAKE_CASE      (		)						->				Optional[int]:
						return transforms.Compose(
						    [
						        transforms.Resize(2_5_6       ),
						        transforms.CenterCrop(2_2_4       ),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46777044, 0.44531429, 0.40661017]			,     std=[0.12221994, 0.12145835, 0.14380469]			,     ),
						    ]       )





# flake8: noqa
# Lint as: python3
a													=			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental