




'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						'▁'

lowerCamelCase_			=						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase_			=						{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowerCamelCase_			=						{
    'xlm-roberta-base': 5_12,
    'xlm-roberta-large': 5_12,
    'xlm-roberta-large-finetuned-conll02-dutch': 5_12,
    'xlm-roberta-large-finetuned-conll02-spanish': 5_12,
    'xlm-roberta-large-finetuned-conll03-english': 5_12,
    'xlm-roberta-large-finetuned-conll03-german': 5_12,
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						['''input_ids''', '''attention_mask''']



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

      """simple docstring"""
      # Mask token behave like a normal word, i.e. include the space before it
      _SCREAMING_SNAKE_CASE	=				AddedToken(__lowerCamelCase  ,				lstrip=__lowerCamelCase  ,				rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ) else mask_token

      _SCREAMING_SNAKE_CASE	=				{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__lowerCamelCase  ,				eos_token=__lowerCamelCase  ,				unk_token=__lowerCamelCase  ,				sep_token=__lowerCamelCase  ,				cls_token=__lowerCamelCase  ,				pad_token=__lowerCamelCase  ,				mask_token=__lowerCamelCase  ,				sp_model_kwargs=self.sp_model_kwargs  ,				**__lowerCamelCase  ,				)

      _SCREAMING_SNAKE_CASE	=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # Mimic fairseq token-to-id alignment for the first 4 token
      _SCREAMING_SNAKE_CASE	=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      _SCREAMING_SNAKE_CASE	=				1

      _SCREAMING_SNAKE_CASE	=				len(self.sp_model   ) + self.fairseq_offset
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



   def __getstate__(   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.__dict__.copy()
      _SCREAMING_SNAKE_CASE	=				None
      _SCREAMING_SNAKE_CASE	=				self.sp_model.serialized_model_proto()
      return state



   def __setstate__(   self :     str  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				d

      # for backward compatibility
      if not hasattr(self  ,				"sp_model_kwargs"   ):
         _SCREAMING_SNAKE_CASE	=				{}

      _SCREAMING_SNAKE_CASE	=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None   ):

      """simple docstring"""

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



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None  ,				__lowerCamelCase :     bool = False   ):

      """simple docstring"""

      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=__lowerCamelCase  ,				token_ids_a=__lowerCamelCase  ,				already_has_special_tokens=__lowerCamelCase   )

      if token_ids_a is None:
         return [1] + ([0] * len(__lowerCamelCase   )) + [1]
      return [1] + ([0] * len(__lowerCamelCase   )) + [1, 1] + ([0] * len(__lowerCamelCase   )) + [1]



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[self.sep_token_id]
      _SCREAMING_SNAKE_CASE	=				[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]



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token



   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{self.convert_ids_to_tokens(__lowerCamelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      return self.sp_model.encode(__lowerCamelCase  ,				out_type=__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
      _SCREAMING_SNAKE_CASE	=				self.sp_model.PieceToId(__lowerCamelCase   )

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"".join(__lowerCamelCase   ).replace(__lowerCamelCase  ,				" "   ).strip()
      return out_string



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[str] = None   ):

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

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(__lowerCamelCase   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file  ,				__lowerCamelCase   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(__lowerCamelCase  ,				"wb"   ) as fi:
            _SCREAMING_SNAKE_CASE	=				self.sp_model.serialized_model_proto()
            fi.write(__lowerCamelCase   )

      return (out_vocab_file,)







'''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


lowerCamelCase_			=						{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 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				torchvision.models.resnetaaa(pretrained=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(model.children()   )[:-2]
      _SCREAMING_SNAKE_CASE	=				nn.Sequential(*__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,				add_special_tokens=__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				sentence[0], sentence[1:-1], sentence[-1]
      _SCREAMING_SNAKE_CASE	=				sentence[: self.max_seq_length]

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

      _SCREAMING_SNAKE_CASE	=				Image.open(os.path.join(self.data_dir  ,				self.data[index]["img"]   )   ).convert("RGB"   )
      _SCREAMING_SNAKE_CASE	=				self.transforms(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				Counter()
      for row in self.data:
         label_freqs.update(row["label"]   )
      return label_freqs


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

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

   for i_batch, (input_row, length) in enumerate(zip(__A	,					__A  )  ):
      _SCREAMING_SNAKE_CASE	=				input_row["sentence"]
      _SCREAMING_SNAKE_CASE	=				1

   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["label"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_start_token"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(2_56  ),
           transforms.CenterCrop(2_24  ),
           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]	,					),
       ]  )

