
'''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


snake_case_ :  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 						lowercase__	(  nn.Module   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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




class 						lowercase__	(  _snake_case   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







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

      UpperCamelCase       =  transforms







   def __len__(			self   ):

      '''simple docstring'''







      return len(self.data   )







   def __getitem__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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

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

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

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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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




def  __snake_case     (    _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  [len(row['''sentence''']) for row in batch]
   UpperCamelCase       =  len(_UpperCAmelCase), max(_UpperCAmelCase)

   UpperCamelCase       =  torch.zeros(_UpperCAmelCase,      _UpperCAmelCase,      dtype=torch.long)
   UpperCamelCase       =  torch.zeros(_UpperCAmelCase,      _UpperCAmelCase,      dtype=torch.long)

   for i_batch, (input_row, length) in enumerate(zip(_UpperCAmelCase,      _UpperCAmelCase)):
      UpperCamelCase       =  input_row["sentence"]
      UpperCamelCase       =  1

   UpperCamelCase       =  torch.stack([row['''image'''] for row in batch])
   UpperCamelCase       =  torch.stack([row['''label'''] for row in batch])
   UpperCamelCase       =  torch.stack([row['''image_start_token'''] for row in batch])
   UpperCamelCase       =  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  __snake_case     (    ):
   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  __snake_case     (    ):
   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 importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def  __snake_case     (    ):
   UpperCamelCase       =  ArgumentParser(
       description=(
           '''PyTorch TPU distributed training launch '''
           '''helper utility that will spawn up '''
           '''multiple distributed processes'''
       ))

   # Optional arguments for the launch helper
   parser.add_argument('''--num_cores''',      type=_UpperCAmelCase,      default=1,      help='''Number of TPU cores to use (1 or 8).''')

   # positional
   parser.add_argument(
       '''training_script''',      type=_UpperCAmelCase,      help=(
           '''The full path to the single TPU training '''
           '''program/script to be launched in parallel, '''
           '''followed by all the arguments for the '''
           '''training script'''
       ),      )

   # rest from the training program
   parser.add_argument('''training_script_args''',      nargs=_UpperCAmelCase)

   return parser.parse_args()



def  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

   # Import training_script as a module.
   UpperCamelCase       =  Path(args.training_script)
   sys.path.append(str(script_fpath.parent.resolve()))
   UpperCamelCase       =  script_fpath.stem
   UpperCamelCase       =  importlib.import_module(_UpperCAmelCase)

   # Patch sys.argv
   UpperCamelCase       =  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores)]
   xmp.spawn(mod._mp_fn,      args=(),      nprocs=args.num_cores)


if __name__ == "__main__":
   main()





