"""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


UpperCAmelCase__  		=						{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        a				(		nn.Module   ):


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


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      self.pool(self.model(__lowerCAmelCase					)					)
      _UpperCAmelCase						 =      torch.flatten(__lowerCAmelCase   ,				start_dim=2					)
      _UpperCAmelCase						 =      out.transpose(1   ,				2					).contiguous()
      return out  # BxNx2048



class        a				(		lowerCAmelCase_   ):


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

      _UpperCAmelCase						 =      transforms


    def __len__(					self	:  Dict					):
      return len(self.data					)


    def __getitem__(					self	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]   ,				add_special_tokens=__lowerCAmelCase					)					)
      _UpperCAmelCase ,				_UpperCAmelCase ,				_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(__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      Counter()
      for row in self.data:
        label_freqs.update(row["""label"""]					)
      return label_freqs






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      [len(row["""sentence"""]		) for row in batch]
  _UpperCAmelCase ,				_UpperCAmelCase						 =      len(_SCREAMING_SNAKE_CASE		), max(_SCREAMING_SNAKE_CASE		)

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

  for i_batch, (input_row, length) in enumerate(zip(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE		)		):
    _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     __UpperCAmelCase					(			):







  """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     __UpperCAmelCase					(			):







  """simple docstring"""
  return transforms.Compose(
      [
          transforms.Resize(2_56		),
          transforms.CenterCrop(2_24		),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]   ,std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]   ,),
      ]		)

"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()
