





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


lowercase_					     =	{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    :       List[str]    ,  a    :       Optional[int] )->     Optional[Any]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Union[str, Any] )->     List[str]:





     """simple docstring"""







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






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       str    ,  a    :       Optional[Any]    ,  a    :       List[Any]    ,  a    :       Dict )->     Union[str, Any]:





     """simple docstring"""







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

     lowercase__			    =						transforms


   def __len__(					self    :       Dict )->     Any:





     """simple docstring"""







     return len(self.data )


   def __getitem__(					self    :       Optional[Any]    ,  a    :       Optional[Any] )->     Any:





     """simple docstring"""







     lowercase__			    =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']    ,  add_special_tokens=a ) )
     lowercase__    ,					lowercase__    ,					lowercase__			    =						sentence[0], sentence[1:-1], sentence[-1]
     lowercase__			    =						sentence[: self.max_seq_length]

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

     lowercase__			    =						Image.open(os.path.join(self.data_dir    ,  self.data[index]['img'] ) ).convert('RGB' )
     lowercase__			    =						self.transforms(a )

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[str]:





     """simple docstring"""







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






def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  lowercase__			    =						[len(row['sentence']	) for row in batch]
  lowercase__    ,					lowercase__			    =						len(_SCREAMING_SNAKE_CASE	), max(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						torch.zeros(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		dtype=torch.long	)
  lowercase__			    =						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	)	):
    lowercase__			    =						input_row['sentence']
    lowercase__			    =						1

  lowercase__			    =						torch.stack([row['image'] for row in batch]	)
  lowercase__			    =						torch.stack([row['label'] for row in batch]	)
  lowercase__			    =						torch.stack([row['image_start_token'] for row in batch]	)
  lowercase__			    =						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						()       ->     List[str]:
  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						()       ->     Dict:
  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]	,		),
      ]	)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      lowercase_					     =	None

lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_					     =	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase_					     =	{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

lowercase_					     =	"""▁"""
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Dict				      = VOCAB_FILES_NAMES
   _UpperCamelCase					:	str				      = PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase					:	Optional[int]				      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase					:	Optional[Any]				      = ['input_ids', 'attention_mask']
   _UpperCamelCase					:	int				      = BarthezTokenizer


   def __init__(					self    :       List[Any]    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[Any]=None    ,  a    :       Dict="<s>"    ,  a    :       Union[str, Any]="</s>"    ,  a    :       List[str]="</s>"    ,  a    :       Optional[Any]="<s>"    ,  a    :       int="<unk>"    ,  a    :       str="<pad>"    ,  a    :       Optional[int]="<mask>"    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else mask_token

     super().__init__(
         a    ,  tokenizer_file=a    ,  bos_token=a    ,  eos_token=a    ,  unk_token=a    ,  sep_token=a    ,  cls_token=a    ,  pad_token=a    ,  mask_token=a    ,  **a    ,  )

     lowercase__			    =						vocab_file
     lowercase__			    =						False if not self.vocab_file else True


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__			    =						[self.cls_token_id]
     lowercase__			    =						[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[self.sep_token_id]
     lowercase__			    =						[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       str    ,  a    :       Optional[str] = None )->     Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.' )

     if not os.path.isdir(a ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowercase__			    =						os.path.join(
         a    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(a ):
       copyfile(self.vocab_file    ,  a )

     return (out_vocab_file,)


