


'''simple docstring'''


from diffusers.utils.testing_utils import require_onnxruntime



@require_onnxruntime
class 	__lowerCAmelCase		:


  '''simple docstring'''







  pass




'''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


__magic_name__			=      {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 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




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






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Optional[Any]	,_a :       Dict	,_a :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






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

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				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					(      ):
      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					(      ):
      return transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
