

"""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


_lowerCAmelCase						=		{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						):

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Dict  =    torchvision.models.resnetaaa(pretrained=UpperCamelCase__      )
      _lowerCAmelCase       :   Tuple  =    list(model.children()      )[:-2]
      _lowerCAmelCase       :   List[Any]  =    nn.Sequential(*UpperCamelCase__      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.pool(self.model(UpperCamelCase__      )      )
      _lowerCAmelCase       :   Dict  =    torch.flatten(UpperCamelCase__    ,start_dim=2      )
      _lowerCAmelCase       :   str  =    out.transpose(1    ,2      ).contiguous()
      return out  # BxNx2048







class 	__UpperCamelCase    (    UpperCamelCase_						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    [json.loads(UpperCamelCase__      ) for l in open(UpperCamelCase__      )]
      _lowerCAmelCase       :   Dict  =    os.path.dirname(UpperCamelCase__      )
      _lowerCAmelCase       :   Any  =    tokenizer
      _lowerCAmelCase       :   Tuple  =    labels
      _lowerCAmelCase       :   Tuple  =    len(UpperCamelCase__      )
      _lowerCAmelCase       :   Tuple  =    max_seq_length

      _lowerCAmelCase       :   int  =    transforms

  def __len__(		self      ):
      '''simple docstring'''

      return len(self.data      )

  def __getitem__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    torch.LongTensor(self.tokenizer.encode(self.data[index]['text']    ,add_special_tokens=UpperCamelCase__      )      )
      _lowerCAmelCase       :   Optional[int]  =    sentence[0], sentence[1:-1], sentence[-1]
      _lowerCAmelCase       :   List[str]  =    sentence[: self.max_seq_length]

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

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

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







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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





def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   List[str]  =    torch.zeros(_lowercase ,   _lowercase ,   dtype=torch.long   )
    _lowerCAmelCase       :   int  =    torch.zeros(_lowercase ,   _lowercase ,   dtype=torch.long   )

    for i_batch, (input_row, length) in enumerate(zip(_lowercase ,   _lowercase   )   ):
        _lowerCAmelCase       :   Optional[Any]  =    input_row['''sentence''']
        _lowerCAmelCase       :   str  =    1

    _lowerCAmelCase       :   Dict  =    torch.stack([row['image'] for row in batch]   )
    _lowerCAmelCase       :   int  =    torch.stack([row['label'] for row in batch]   )
    _lowerCAmelCase       :   Union[str, Any]  =    torch.stack([row['image_start_token'] for row in batch]   )
    _lowerCAmelCase       :   Dict  =    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__		(					):

    '''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 		lowerCamelCase__		(					):

    '''simple docstring'''



    return transforms.Compose(
        [
            transforms.Resize(256   ),
            transforms.CenterCrop(224   ),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46777044, 0.44531429, 0.40661017] ,   std=[0.12221994, 0.12145835, 0.14380469] ,   ),
        ]   )








"""simple docstring"""






import logging
import os

from .state import PartialState







class 	__UpperCamelCase    (    logging.LoggerAdapter						):

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    PartialState()
      return not main_process_only or (main_process_only and state.is_main_process)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,*_A    ,**_A      ):
      '''simple docstring'''

      if PartialState._shared_state == {}:
          raise RuntimeError(
              'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'      )
      _lowerCAmelCase       :   Tuple  =    kwargs.pop('main_process_only'    ,_A      )
      _lowerCAmelCase       :   Any  =    kwargs.pop('in_order'    ,_A      )

      if self.isEnabledFor(_A      ):
          if self._should_log(_A      ):
              _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
              self.logger.log(_A    ,_A    ,*_A    ,**_A      )

          elif in_order:
              _lowerCAmelCase       :   str  =    PartialState()
              for i in range(state.num_processes      ):
                  if i == state.process_index:
                      _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self.process(_A    ,_A      )
                      self.logger.log(_A    ,_A    ,*_A    ,**_A      )
                  state.wait_for_everyone()





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase = None   ):

    '''simple docstring'''



    if log_level is None:
        _lowerCAmelCase       :   Union[str, Any]  =    os.environ.get('ACCELERATE_LOG_LEVEL' ,   _lowerCamelCase   )
    _lowerCAmelCase       :   int  =    logging.getLogger(_lowerCamelCase   )
    if log_level is not None:
        logger.setLevel(log_level.upper()   )
        logger.root.setLevel(log_level.upper()   )
    return MultiProcessAdapter(_lowerCamelCase ,   {}   )





