






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


a						:			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        a       (					nn.Module  ):



      """simple docstring"""


      def __init__( self   :     str						, __lowercase   :     List[str]   )				-> Dict:
        super().__init__()
        __UpperCAmelCase				:      Tuple	 =    torchvision.models.resnetaaa(pretrained=a__   )
        __UpperCAmelCase				:      int	 =    list(model.children()   )[:-2]
        __UpperCAmelCase				:      Any	 =    nn.Sequential(*a__   )
        __UpperCAmelCase				:      Dict	 =    nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )




      def       UpperCAmelCase ( self   :     int						, __lowercase   :     Union[str, Any]   )				-> List[str]:
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __UpperCAmelCase				:      List[Any]	 =    self.pool(self.model(a__   )   )
        __UpperCAmelCase				:      str	 =    torch.flatten(a__						, start_dim=2   )
        __UpperCAmelCase				:      str	 =    out.transpose(1						, 2   ).contiguous()
        return out  # BxNx2048


class        a       (					__snake_case  ):



      """simple docstring"""


      def __init__( self   :     Optional[int]						, __lowercase   :     Optional[int]						, __lowercase   :     List[str]						, __lowercase   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Optional[int]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Optional[int]	 =    [json.loads(a__   ) for l in open(a__   )]
        __UpperCAmelCase				:      int	 =    os.path.dirname(a__   )
        __UpperCAmelCase				:      List[str]	 =    tokenizer
        __UpperCAmelCase				:      Optional[int]	 =    labels
        __UpperCAmelCase				:      Optional[Any]	 =    len(a__   )
        __UpperCAmelCase				:      List[str]	 =    max_seq_length

        __UpperCAmelCase				:      Optional[int]	 =    transforms
      def __len__( self   :     Optional[Any]   )				-> Tuple:
        return len(self.data   )
      def __getitem__( self   :     List[str]						, __lowercase   :     Any   )				-> Optional[int]:
        __UpperCAmelCase				:      Dict	 =    torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]						, add_special_tokens=a__   )   )
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    sentence[0], sentence[1:-1], sentence[-1]
        __UpperCAmelCase				:      int	 =    sentence[: self.max_seq_length]

        __UpperCAmelCase				:      int	 =    torch.zeros(self.n_classes   )
        __UpperCAmelCase				:      Optional[Any]	 =    1

        __UpperCAmelCase				:      str	 =    Image.open(os.path.join(self.data_dir						, self.data[index]["""img"""]   )   ).convert("""RGB"""   )
        __UpperCAmelCase				:      Tuple	 =    self.transforms(a__   )

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




      def       UpperCAmelCase ( self   :     List[Any]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Dict	 =    Counter()
        for row in self.data:
          label_freqs.update(row["""label"""]   )
        return label_freqs






def 							lowerCamelCase__ (       __lowerCamelCase     :	int						):
  __UpperCAmelCase				:      List[str]	 =    [len(row["""sentence"""]						) for row in batch]
  __UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    len(UpperCamelCase__						), max(UpperCamelCase__						)

  __UpperCAmelCase				:      Tuple	 =    torch.zeros(UpperCamelCase__    ,						UpperCamelCase__    ,						dtype=torch.long						)
  __UpperCAmelCase				:      str	 =    torch.zeros(UpperCamelCase__    ,						UpperCamelCase__    ,						dtype=torch.long						)

  for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__    ,						UpperCamelCase__						)						):
    __UpperCAmelCase				:      Union[str, Any]	 =    input_row["""sentence"""]
    __UpperCAmelCase				:      Union[str, Any]	 =    1

  __UpperCAmelCase				:      List[Any]	 =    torch.stack([row["""image"""] for row in batch]						)
  __UpperCAmelCase				:      Any	 =    torch.stack([row["""label"""] for row in batch]						)
  __UpperCAmelCase				:      Any	 =    torch.stack([row["""image_start_token"""] for row in batch]						)
  __UpperCAmelCase				:      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__ (       ):
  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__ (       ):
  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]    ,						),
      ]						)






_lowercase							:   Dict						=						"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
				from .utils import rich



