'''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


__a:			Optional[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       UpperCAmelCase  (						nn.Module ):





       '''simple docstring'''



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


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Tuple:
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        lowercase__    :   Any						= self.pool(self.model(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= torch.flatten(__lowerCAmelCase     ,       start_dim=2			)
        lowercase__    :   List[Any]						= out.transpose(1     ,       2			).contiguous()
        return out  # BxNx2048



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Union[str, Any]						= [json.loads(__lowerCAmelCase			) for l in open(__lowerCAmelCase			)]
        lowercase__    :   Dict						= os.path.dirname(__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= tokenizer
        lowercase__    :   str						= labels
        lowercase__    :   List[str]						= len(__lowerCAmelCase			)
        lowercase__    :   str						= max_seq_length

        lowercase__    :   Union[str, Any]						= transforms

       def __len__(						self			)  ->  List[str]:
        return len(self.data			)

       def __getitem__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   List[str]						= torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']     ,       add_special_tokens=__lowerCAmelCase			)			)
        lowercase__			,							lowercase__			,							lowercase__    :   List[str]						= sentence[0], sentence[1:-1], sentence[-1]
        lowercase__    :   List[str]						= sentence[: self.max_seq_length]

        lowercase__    :   List[str]						= torch.zeros(self.n_classes			)
        lowercase__    :   Optional[Any]						= 1

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

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


       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   int						= Counter()
        for row in self.data:
         label_freqs.update(row['''label''']			)
        return label_freqs


def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Optional[int]						= [len(row['''sentence''']					) for row in batch]
 lowercase__			,							lowercase__    :   int						= len(UpperCAmelCase					), max(UpperCAmelCase					)

 lowercase__    :   str						= torch.zeros(UpperCAmelCase		,					UpperCAmelCase		,					dtype=torch.long					)
 lowercase__    :   Optional[int]						= torch.zeros(UpperCAmelCase		,					UpperCAmelCase		,					dtype=torch.long					)

 for i_batch, (input_row, length) in enumerate(zip(UpperCAmelCase		,					UpperCAmelCase					)					):
  lowercase__    :   Optional[int]						= input_row['''sentence''']
  lowercase__    :   Dict						= 1

 lowercase__    :   Dict						= torch.stack([row['''image'''] for row in batch]					)
 lowercase__    :   Dict						= torch.stack([row['''label'''] for row in batch]					)
 lowercase__    :   Optional[int]						= torch.stack([row['''image_start_token'''] for row in batch]					)
 lowercase__    :   List[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       __UpperCamelCase	(       ):
 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       __UpperCamelCase	(       ):
 return transforms.Compose(
     [
         transforms.Resize(256					),
         transforms.CenterCrop(224					),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,					std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,					),
     ]					)


'''simple docstring'''

import numpy as np
def       __UpperCamelCase	(       UpperCAmelCase					):
 return 1 / (1 + np.exp(-vector					))
def       __UpperCamelCase	(       UpperCAmelCase					):
 return vector * sigmoid(UpperCAmelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

