

'''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			:				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 							A__   (	nn.Module  ):





  def __init__(		self				:     Optional[Any]							, _a				:     List[str]			)     ->   Union[str, Any]:





       '''simple docstring'''






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





  def 						A  (		self				:     str							, _a				:     Tuple			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.pool(self.model(_a			)			)
       _SCREAMING_SNAKE_CASE							   =torch.flatten(_a							, start_dim=2			)
       _SCREAMING_SNAKE_CASE							   =out.transpose(1							, 2			).contiguous()
       return out  # BxNx2048







class 							A__   (	A__  ):





  def __init__(		self				:     int							, _a				:     Any							, _a				:     int							, _a				:     List[str]							, _a				:     List[Any]							, _a				:     Any			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[json.loads(_a			) for l in open(_a			)]
       _SCREAMING_SNAKE_CASE							   =os.path.dirname(_a			)
       _SCREAMING_SNAKE_CASE							   =tokenizer
       _SCREAMING_SNAKE_CASE							   =labels
       _SCREAMING_SNAKE_CASE							   =len(_a			)
       _SCREAMING_SNAKE_CASE							   =max_seq_length

       _SCREAMING_SNAKE_CASE							   =transforms





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





       '''simple docstring'''






       return len(self.data			)





  def __getitem__(		self				:     List[Any]							, _a				:     Tuple			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =torch.LongTensor(self.tokenizer.encode(self.data[index]['text']							, add_special_tokens=_a			)			)
       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =sentence[0], sentence[1:-1], sentence[-1]
       _SCREAMING_SNAKE_CASE							   =sentence[: self.max_seq_length]

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

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

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





  def 						A  (		self				:     int			)     ->   Union[str, Any]:





       '''simple docstring'''






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


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[len(row['sentence']    ) for row in batch]
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =len(_UpperCamelCase    ), max(_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =torch.zeros(_UpperCamelCase							,			_UpperCamelCase							,			dtype=torch.long    )
     _SCREAMING_SNAKE_CASE							   =torch.zeros(_UpperCamelCase							,			_UpperCamelCase							,			dtype=torch.long    )

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

     _SCREAMING_SNAKE_CASE							   =torch.stack([row['image'] for row in batch]    )
     _SCREAMING_SNAKE_CASE							   =torch.stack([row['label'] for row in batch]    )
     _SCREAMING_SNAKE_CASE							   =torch.stack([row['image_start_token'] for row in batch]    )
     _SCREAMING_SNAKE_CASE							   =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 (						)				->      List[str]:
     """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 	_lowerCAmelCase (						)				->      Optional[int]:
     """simple docstring"""




     return transforms.Compose(
         [
             transforms.Resize(2_56    ),
             transforms.CenterCrop(2_24    ),
             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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
