





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 :   Optional[int]  			=  {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        __SCREAMING_SNAKE_CASE      (			nn.Module ):







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







       def 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     int      )       ->Optional[int]:
          # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
          lowerCamelCase__       :						Union[str, Any]        =							self.pool(self.model(A      )      )
          lowerCamelCase__       :						Optional[Any]        =							torch.flatten(A		, start_dim=2      )
          lowerCamelCase__       :						Dict        =							out.transpose(1		, 2      ).contiguous()
          return out  # BxNx2048


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







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

          lowerCamelCase__       :						Tuple        =							transforms







       def __len__(				self					:     Any      )       ->Tuple:
          return len(self.data      )







       def __getitem__(				self					:     int		, A					:     str      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']		, add_special_tokens=A      )      )
          lowerCamelCase__       :						Tuple        =							sentence[0], sentence[1:-1], sentence[-1]
          lowerCamelCase__       :						Tuple        =							sentence[: self.max_seq_length]

          lowerCamelCase__       :						List[str]        =							torch.zeros(self.n_classes      )
          lowerCamelCase__       :						Dict        =							1

          lowerCamelCase__       :						Union[str, Any]        =							Image.open(os.path.join(self.data_dir		, self.data[index]['''img''']      )      ).convert('''RGB'''      )
          lowerCamelCase__       :						Optional[int]        =							self.transforms(A      )

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







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          lowerCamelCase__       :						List[Any]        =							Counter()
          for row in self.data:
             label_freqs.update(row['''label''']      )
          return label_freqs



def 	_a						(   UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							[len(row['''sentence''']					) for row in batch]
   lowerCamelCase__       :						Union[str, Any]        =							len(UpperCAmelCase					), max(UpperCAmelCase					)

   lowerCamelCase__       :						str        =							torch.zeros(UpperCAmelCase      ,  UpperCAmelCase      ,  dtype=torch.long					)
   lowerCamelCase__       :						Optional[int]        =							torch.zeros(UpperCAmelCase      ,  UpperCAmelCase      ,  dtype=torch.long					)

   for i_batch, (input_row, length) in enumerate(zip(UpperCAmelCase      ,  UpperCAmelCase					)					):
      lowerCamelCase__       :						Tuple        =							input_row['''sentence''']
      lowerCamelCase__       :						Optional[int]        =							1

   lowerCamelCase__       :						Tuple        =							torch.stack([row['''image'''] for row in batch]					)
   lowerCamelCase__       :						Dict        =							torch.stack([row['''label'''] for row in batch]					)
   lowerCamelCase__       :						int        =							torch.stack([row['''image_start_token'''] for row in batch]					)
   lowerCamelCase__       :						str        =							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 	_a						(   )    ->    Union[str, Any]:







   """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 	_a						(   )    ->    int:







   """simple docstring"""
   return transforms.Compose(
       [
           transforms.Resize(256					),
           transforms.CenterCrop(224					),
           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]      ,  ),
       ]					)













from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A :   Any  			=  logging.get_logger(__name__)

_A :   int  			=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "timesformer"







       def __init__(				self					:     List[str]		, A					:     int=2_2_4		, A					:     Optional[Any]=1_6		, A					:     str=3		, A					:     str=8		, A					:     Any=7_6_8		, A					:     Dict=1_2		, A					:     Optional[Any]=1_2		, A					:     Any=3_0_7_2		, A					:     str="gelu"		, A					:     Optional[int]=0.0		, A					:     Union[str, Any]=0.0		, A					:     List[Any]=0.02		, A					:     int=1e-6		, A					:     Tuple=True		, A					:     Any="divided_space_time"		, A					:     Optional[Any]=0		, **A					:     Tuple		, )       ->str:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[Any]        =							image_size
          lowerCamelCase__       :						int        =							patch_size
          lowerCamelCase__       :						Any        =							num_channels
          lowerCamelCase__       :						Optional[int]        =							num_frames

          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							hidden_dropout_prob
          lowerCamelCase__       :						Dict        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							initializer_range
          lowerCamelCase__       :						str        =							layer_norm_eps
          lowerCamelCase__       :						Union[str, Any]        =							qkv_bias

          lowerCamelCase__       :						str        =							attention_type
          lowerCamelCase__       :						List[str]        =							drop_path_rate



