"""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


lowercase__  	=     {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        __snake_case	(					nn.Module    ):



      def __init__(						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        super().__init__()
        a__:					Union[str, Any]      							=     torchvision.models.resnetaaa(pretrained=lowercase)
        a__:					Any      							=     list(model.children())[:-2]
        a__:					int      							=     nn.Sequential(*lowercase)
        a__:					str      							=     nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.pool(self.model(lowercase))
        a__:					Optional[Any]      							=     torch.flatten(lowercase   ,      start_dim=2)
        a__:					List[str]      							=     out.transpose(1   ,      2).contiguous()
        return out  # BxNx2048







class        __snake_case	(					__UpperCamelCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





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

        a__:					List[str]      							=     transforms



      def __len__(						self)     ->      Optional[Any]:





        '''simple docstring'''





        return len(self.data)



      def __getitem__(						self   ,      lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     torch.LongTensor(self.tokenizer.encode(self.data[index]['text']   ,      add_special_tokens=lowercase))
        a__:					Union[str, Any]      							=     sentence[0], sentence[1:-1], sentence[-1]
        a__:					Optional[Any]      							=     sentence[: self.max_seq_length]

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

        a__:					List[Any]      							=     Image.open(os.path.join(self.data_dir   ,      self.data[index]['img'])).convert('RGB')
        a__:					Dict      							=     self.transforms(lowercase)

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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  a__:					str      							=     [len(row['sentence']       ) for row in batch]
  a__:					Union[str, Any]      							=     len(_UpperCAmelCase       ), max(_UpperCAmelCase       )

  a__:					Union[str, Any]      							=     torch.zeros(_UpperCAmelCase		,					_UpperCAmelCase		,					dtype=torch.long       )
  a__:					Dict      							=     torch.zeros(_UpperCAmelCase		,					_UpperCAmelCase		,					dtype=torch.long       )

  for i_batch, (input_row, length) in enumerate(zip(_UpperCAmelCase		,					_UpperCAmelCase       )       ):
    a__:					int      							=     input_row['sentence']
    a__:					Optional[Any]      							=     1

  a__:					List[str]      							=     torch.stack([row['image'] for row in batch]       )
  a__:					Dict      							=     torch.stack([row['label'] for row in batch]       )
  a__:					Optional[int]      							=     torch.stack([row['image_start_token'] for row in batch]       )
  a__:					str      							=     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       (     )     ->Optional[int]:
  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       (     )     ->Tuple:
  return transforms.Compose(
      [
          transforms.Resize(256       ),
          transforms.CenterCrop(224       ),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]		,					std=[0.12_221_994, 0.12_145_835, 0.14_380_469]		,					),
      ]       )



"""simple docstring"""
import unittest

from knapsack import knapsack as k




class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					List[Any]      							=     0
        a__:					Dict      							=     [0]
        a__:					int      							=     [0]
        a__:					Optional[Any]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)

        a__:					str      							=     [60]
        a__:					Dict      							=     [10]
        a__:					List[str]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      0)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     3
        a__:					str      							=     [1, 2, 3]
        a__:					Dict      							=     [3, 2, 1]
        a__:					Optional[int]      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      5)



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





        '''simple docstring'''





        a__:					Any      							=     50
        a__:					Optional[int]      							=     [60, 1_00, 1_20]
        a__:					str      							=     [10, 20, 30]
        a__:					int      							=     len(lowercase)
        self.assertEqual(k.knapsack(lowercase   ,      lowercase   ,      lowercase   ,      lowercase)   ,      2_20)


if __name__ == "__main__":
 unittest.main()


