




'''simple docstring'''



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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.nn.Linear(1_0  ,				1_0   )
      _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()







'''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 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				torchvision.models.resnetaaa(pretrained=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(model.children()   )[:-2]
      _SCREAMING_SNAKE_CASE	=				nn.Sequential(*__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,				add_special_tokens=__lowerCamelCase   )   )
      _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(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

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


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )
   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )

   for i_batch, (input_row, length) in enumerate(zip(__A	,					__A  )  ):
      _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_			(							)			->			Optional[int]:
   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_			(							)			->			Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(2_56  ),
           transforms.CenterCrop(2_24  ),
           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]	,					),
       ]  )

