





'''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


UpperCAmelCase					:		Union[str, Any]		=							{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					):







  def __init__(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						str:






        """simple docstring"""





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





class lowerCamelCase						(a__					):







  def __init__(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





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

        _snake_case		:	Optional[Any]					     =     transforms







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






        """simple docstring"""





        return len(self.data		)







  def __getitem__(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']     ,     add_special_tokens=lowercase__		)		)
        _snake_case     ,							_snake_case     ,							_snake_case		:	Dict					     =     sentence[0], sentence[1:-1], sentence[-1]
        _snake_case		:	int					     =     sentence[: self.max_seq_length]

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

        _snake_case		:	List[str]					     =     Image.open(os.path.join(self.data_dir     ,     self.data[index]['''img''']		)		).convert('''RGB'''		)
        _snake_case		:	Optional[Any]					     =     self.transforms(lowercase__		)

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







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     [len(row['''sentence''']	) for row in batch]
      _snake_case     ,							_snake_case		:	List[str]					     =     len(lowerCAmelCase_	), max(lowerCAmelCase_	)

      _snake_case		:	Dict					     =     torch.zeros(lowerCAmelCase_			,      lowerCAmelCase_			,      dtype=torch.long	)
      _snake_case		:	Tuple					     =     torch.zeros(lowerCAmelCase_			,      lowerCAmelCase_			,      dtype=torch.long	)

      for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_			,      lowerCAmelCase_	)	):
            _snake_case		:	Dict					     =     input_row['''sentence''']
            _snake_case		:	str					     =     1

      _snake_case		:	Optional[int]					     =     torch.stack([row['''image'''] for row in batch]	)
      _snake_case		:	str					     =     torch.stack([row['''label'''] for row in batch]	)
      _snake_case		:	Dict					     =     torch.stack([row['''image_start_token'''] for row in batch]	)
      _snake_case		:	Union[str, Any]					     =     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_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 math

from numpy import inf
from scipy.integrate import quad




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      if num <= 0:
            raise ValueError('''math domain error'''	)

      return quad(lowerCAmelCase_			,      0			,      lowerCAmelCase_			,      args=(lowerCAmelCase_)	)[0]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return math.pow(lowerCAmelCase_			,      z - 1	) * math.exp(-x	)


if __name__ == "__main__":
   from doctest import testmod

   testmod()


