'''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    :				List[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 __snake_case       (		nn.Module	):




   def __init__(					self    :       Any ,			_lowercase    :       List[str]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Any ,			_lowercase    :       List[str]   ):



     """simple docstring"""






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







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Any ,			_lowercase    :       str ,			_lowercase    :       Optional[int] ,			_lowercase    :       int ,			_lowercase    :       Tuple ,			_lowercase    :       Tuple   ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   transforms




   def __len__(					self    :       Union[str, Any]   ):



     """simple docstring"""






     return len(self.data   )




   def __getitem__(					self    :       str ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,			add_special_tokens=_lowercase   )   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__    							=   sentence[: self.max_seq_length]

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

     SCREAMING_SNAKE_CASE__    							=   Image.open(os.path.join(self.data_dir ,			self.data[index]["""img"""]   )   ).convert("""RGB"""   )
     SCREAMING_SNAKE_CASE__    							=   self.transforms(_lowercase   )

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




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






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





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [len(row["""sentence"""]			) for row in batch]
  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   len(__UpperCamelCase			), max(__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   torch.zeros(__UpperCamelCase				,  __UpperCamelCase				,  dtype=torch.long			)
  SCREAMING_SNAKE_CASE__    							=   torch.zeros(__UpperCamelCase				,  __UpperCamelCase				,  dtype=torch.long			)

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

  SCREAMING_SNAKE_CASE__    							=   torch.stack([row["""image"""] for row in batch]			)
  SCREAMING_SNAKE_CASE__    							=   torch.stack([row["""label"""] for row in batch]			)
  SCREAMING_SNAKE_CASE__    							=   torch.stack([row["""image_start_token"""] for row in batch]			)
  SCREAMING_SNAKE_CASE__    							=   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 			__SCREAMING_SNAKE_CASE						(      )							->      Dict:







  """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 			__SCREAMING_SNAKE_CASE						(      )							->      str:







  """simple docstring"""





  return transforms.Compose(
      [
          transforms.Resize(2_56			),
          transforms.CenterCrop(2_24			),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]				,  std=[0.1222_1994, 0.1214_5835, 0.1438_0469]				,  ),
      ]			)

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
