


'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {'vocab_file': 'spm_char.model'}

__magic_name__			=      {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

__magic_name__			=      {
    'microsoft/speecht5_asr': 1_024,
    'microsoft/speecht5_tts': 1_024,
    'microsoft/speecht5_vc': 1_024,
}



class 	__lowerCAmelCase		(							_UpperCamelCase    ):


  '''simple docstring'''







  a_  					=							VOCAB_FILES_NAMES
  a_  					=							PRETRAINED_VOCAB_FILES_MAP
  a_  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_  					=							["""input_ids""", """attention_mask"""]






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

        '''simple docstring'''




        A_						:  str          =				{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_a	,eos_token=_a	,unk_token=_a	,pad_token=_a	,sp_model_kwargs=self.sp_model_kwargs	,**_a	,)

        A_						:  Dict          =				vocab_file

        A_						:  List[str]          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(_a )






  @property
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        return self.sp_model.get_piece_size()






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				{self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab






  def __getstate__(							self :       Any ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.__dict__.copy()
        A_						:  Dict          =				None
        return state






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

        '''simple docstring'''




        A_						:  List[str]          =				d

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

        A_						:  Tuple          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )






  def        _a							(							self :       Union[str, Any]	,_a :       str ):

        '''simple docstring'''




        return self.sp_model.encode(_a	,out_type=_a )






  def        _a							(							self :       List[Any]	,_a :       Dict ):

        '''simple docstring'''




        return self.sp_model.piece_to_id(_a )






  def        _a							(							self :       Dict	,_a :       Any ):

        '''simple docstring'''




        A_						:  int          =				self.sp_model.IdToPiece(_a )
        return token






  def        _a							(							self :       List[str]	,_a :       Any ):

        '''simple docstring'''




        A_						:  List[Any]          =				[]
        A_						:  str          =				""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_a ) + token
                    A_						:  List[Any]          =				[]
              else:
                    current_sub_tokens.append(_a )
        out_string += self.sp_model.decode(_a )
        return out_string.strip()






  def        _a							(							self :       str	,_a :       List[Any]	,_a :       List[str]=None ):

        '''simple docstring'''




        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]






  def        _a							(							self :       Tuple	,_a :       List[int]	,_a :       Optional[List[int]] = None	,_a :       bool = False ):

        '''simple docstring'''




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

        A_						:  Optional[Any]          =				[1]
        if token_ids_a is None:
              return ([0] * len(_a )) + suffix_ones
        return ([0] * len(_a )) + ([0] * len(_a )) + suffix_ones






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

        '''simple docstring'''




        if not os.path.isdir(_a ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
              return
        A_						:  Tuple          =				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 ) and os.path.isfile(self.vocab_file ):
              copyfile(self.vocab_file	,_a )
        elif not os.path.isfile(self.vocab_file ):
              with open(_a	,"""wb""" ) as fi:
                    A_						:  Dict          =				self.sp_model.serialized_model_proto()
                    fi.write(_a )

        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


__magic_name__			=      {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 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




        super().__init__()
        A_						:  List[str]          =				torchvision.models.resnetaaa(pretrained=_a )
        A_						:  int          =				list(model.children() )[:-2]
        A_						:  int          =				nn.Sequential(*_a )
        A_						:  Optional[int]          =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






  def __getitem__(							self :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

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






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				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=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
