



"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


A:   List[str]								=		logging.get_logger(__name__)

A:   Any								=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

A:   List[str]								=		{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

A:   Dict								=		{
    "yjernite/retribert-base-uncased": 5_1_2,
}


A:   Dict								=		{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   str						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   Dict						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   Any						=						PRETRAINED_INIT_CONFIGURATION
   __lowerCAmelCase  :   Any						=						RetriBertTokenizer
   __lowerCAmelCase  :   Any						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE="[UNK]"      ,     _SCREAMING_SNAKE_CASE="[SEP]"      ,     _SCREAMING_SNAKE_CASE="[PAD]"      ,     _SCREAMING_SNAKE_CASE="[CLS]"      ,     _SCREAMING_SNAKE_CASE="[MASK]"      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Dict:





    '''simple docstring'''




    super().__init__(
        _SCREAMING_SNAKE_CASE      ,     tokenizer_file=_SCREAMING_SNAKE_CASE      ,     do_lower_case=_SCREAMING_SNAKE_CASE      ,     unk_token=_SCREAMING_SNAKE_CASE      ,     sep_token=_SCREAMING_SNAKE_CASE      ,     pad_token=_SCREAMING_SNAKE_CASE      ,     cls_token=_SCREAMING_SNAKE_CASE      ,     mask_token=_SCREAMING_SNAKE_CASE      ,     tokenize_chinese_chars=_SCREAMING_SNAKE_CASE      ,     strip_accents=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Optional[Any]	=	json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
    if (
        normalizer_state.get("""lowercase"""      ,     _SCREAMING_SNAKE_CASE						) != do_lower_case
        or normalizer_state.get("""strip_accents"""      ,     _SCREAMING_SNAKE_CASE						) != strip_accents
        or normalizer_state.get("""handle_chinese_chars"""      ,     _SCREAMING_SNAKE_CASE						) != tokenize_chinese_chars
    ):
     UpperCAmelCase	:					List[str]	=	getattr(_SCREAMING_SNAKE_CASE      ,     normalizer_state.pop("""type"""						)						)
     UpperCAmelCase	:					int	=	do_lower_case
     UpperCAmelCase	:					Union[str, Any]	=	strip_accents
     UpperCAmelCase	:					Union[str, Any]	=	tokenize_chinese_chars
     UpperCAmelCase	:					Dict	=	normalizer_class(**_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[int]	=	do_lower_case

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=None						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	[self.sep_token_id]
    UpperCAmelCase	:					Tuple	=	[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						) * [0] + len(token_ids_a + sep						) * [1]




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple[str]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self._tokenizer.model.save(_SCREAMING_SNAKE_CASE      ,     name=_SCREAMING_SNAKE_CASE						)
    return tuple(_SCREAMING_SNAKE_CASE						)








"""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


_SCREAMING_SNAKE_CASE					:				List[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		):






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






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






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

      lowerCamelCase_         =						transforms






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






    def __getitem__(       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		List[str]:
      lowerCamelCase_         =						torch.LongTensor(self.tokenizer.encode(self.data[index]['text']       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						sentence[0], sentence[1:-1], sentence[-1]
      lowerCamelCase_         =						sentence[: self.max_seq_length]

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

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

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






    def    UpperCamelCase       (       self     :							Dict					)     ->		Dict:
      lowerCamelCase_         =						Counter()
      for row in self.data:
        label_freqs.update(row['label']					)
      return label_freqs







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

  for i_batch, (input_row, length) in enumerate(zip(_lowerCamelCase		,				_lowerCamelCase ) ):
    lowerCamelCase_         =						input_row['sentence']
    lowerCamelCase_         =						1

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




