


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


SCREAMING_SNAKE_CASE__					:     int       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__					:     str       =     {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

SCREAMING_SNAKE_CASE__					:     Dict       =     {'bert_for_seq_generation': 512}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    []
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


     def __init__(  self							,					A_							,					A_="<s>"							,					A_="</s>"							,					A_="<unk>"							,					A_="<pad>"							,					A_="<::::>"							,					A_ = None							,					**A_							,					):
       _UpperCAmelCase		:							Any							=     {} if sp_model_kwargs is None else sp_model_kwargs

       # Add extra_ids to the special token list
       super().__init__(
           bos_token=A_							,					eos_token=A_							,					unk_token=A_							,					pad_token=A_							,					sep_token=A_							,					sp_model_kwargs=self.sp_model_kwargs							,					**A_							,					)

       _UpperCAmelCase		:							Tuple							=     vocab_file

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


     @property
     def        __snake_case(  self    ):
       return self.sp_model.get_piece_size()


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, 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    ):
       _UpperCAmelCase		:							Optional[Any]							=     self.__dict__.copy()
       _UpperCAmelCase		:							Any							=     None
       return state


     def __setstate__(  self							,					A_    ):
       _UpperCAmelCase		:							int							=     d

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

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


     def        __snake_case(  self							,					A_    ):
       return self.sp_model.encode(A_							,					out_type=A_    )


     def        __snake_case(  self							,					A_    ):
       return self.sp_model.piece_to_id(A_    )


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Tuple							=     self.sp_model.IdToPiece(A_    )
       return token


     def        __snake_case(  self							,					A_    ):
       _UpperCAmelCase		:							Dict							=     []
       _UpperCAmelCase		:							Dict							=     """"""
       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
           _UpperCAmelCase		:							Any							=     []
         else:
           current_sub_tokens.append(A_    )
       out_string += self.sp_model.decode(A_    )
       return out_string.strip()




     def        __snake_case(  self							,					A_							,					A_ = None    ):
       if not os.path.isdir(A_    ):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
         return
       _UpperCAmelCase		:							Optional[Any]							=     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:
           _UpperCAmelCase		:							Tuple							=     self.sp_model.serialized_model_proto()
           fi.write(A_    )

       return (out_vocab_file,)









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=A     )
class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    field(default='''image-classification'''							,	metadata={'''include_in_asdict_even_if_is_default''': True}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''image''': Image()}     )
     __SCREAMING_SNAKE_CASE     					=    Features({'''labels''': ClassLabel}     )
     __SCREAMING_SNAKE_CASE     					=    "image"
     __SCREAMING_SNAKE_CASE     					=    "labels"


     def        __snake_case(  self							,					A_    ):
       if self.label_column not in features:
         raise ValueError(F'''Column {self.label_column} is not present in features.'''    )
       if not isinstance(features[self.label_column]							,					A_    ):
         raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''    )
       _UpperCAmelCase		:							Tuple							=     copy.deepcopy(self    )
       _UpperCAmelCase		:							str							=     self.label_schema.copy()
       _UpperCAmelCase		:							Optional[Any]							=     features[self.label_column]
       _UpperCAmelCase		:							int							=     label_schema
       return task_template




     @property
     def        __snake_case(  self    ):
       return {
           self.image_column: "image",
           self.label_column: "labels",
       }





