





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


_A					:				Tuple				=			logging.get_logger(__name__)

_A					:				Optional[Any]				=			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A					:				List[Any]				=			{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_A					:				Dict				=			{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
_A					:				int				=			{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

_A					:				str				=			{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_A					:				int				=			{
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_A					:				Optional[int]				=			{
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


_A					:				List[Any]				=			{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A					:				Optional[int]				=			{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_A					:				List[str]				=			{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Any				=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		List[Any]				=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Optional[int]				=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[str]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Dict				=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		Dict				=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Union[str, Any]				=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





_A					:				Optional[Any]				=			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

_A					:				Any				=			collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A					:				Optional[Any]				=			r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """



@add_start_docstrings(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	:


    '''simple docstring'''







    def __call__(       self   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = False   ,				A_ = False   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        if titles is None and texts is None:
            return super().__call__(
                A_   ,				padding=A_   ,				truncation=A_   ,				max_length=A_   ,				return_tensors=A_   ,				return_attention_mask=A_   ,				**A_   ,				)
        elif titles is None or texts is None:
            SCREAMING_SNAKE_CASE__											=			titles if texts is None else texts
            return super().__call__(
                A_   ,				A_   ,				padding=A_   ,				truncation=A_   ,				max_length=A_   ,				return_tensors=A_   ,				return_attention_mask=A_   ,				**A_   ,				)
        SCREAMING_SNAKE_CASE__											=			titles if not isinstance(A_   ,				A_     ) else [titles]
        SCREAMING_SNAKE_CASE__											=			texts if not isinstance(A_   ,				A_     ) else [texts]
        SCREAMING_SNAKE_CASE__											=			len(A_     )
        SCREAMING_SNAKE_CASE__											=			questions if not isinstance(A_   ,				A_     ) else [questions] * n_passages
        if len(A_     ) != len(A_     ):
            raise ValueError(
                f'''There should be as many titles than texts but got {len(A_     )} titles and {len(A_     )} texts.'''     )
        SCREAMING_SNAKE_CASE__											=			super().__call__(A_   ,				A_   ,				padding=A_   ,				truncation=A_     )['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			super().__call__(A_   ,				add_special_tokens=A_   ,				padding=A_   ,				truncation=A_     )['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			{
            '''input_ids''': [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(A_   ,				A_     )
            ]
        }
        if return_attention_mask is not False:
            SCREAMING_SNAKE_CASE__											=			[]
            for input_ids in encoded_inputs["input_ids"]:
                attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
            SCREAMING_SNAKE_CASE__											=			attention_mask
        return self.pad(A_   ,				padding=A_   ,				max_length=A_   ,				return_tensors=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = 16   ,				A_ = 64   ,				A_ = 4   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			reader_input['''input_ids''']
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			reader_output[:3]
        SCREAMING_SNAKE_CASE__											=			len(A_     )
        SCREAMING_SNAKE_CASE__											=			sorted(range(A_     )   ,				reverse=A_   ,				key=relevance_logits.__getitem__     )
        SCREAMING_SNAKE_CASE__											=			[]
        for doc_id in sorted_docs:
            SCREAMING_SNAKE_CASE__											=			list(input_ids[doc_id]     )
            # assuming question & title information is at the beginning of the sequence
            SCREAMING_SNAKE_CASE__											=			sequence_ids.index(self.sep_token_id   ,				2     ) + 1  # second sep id
            if sequence_ids[-1] == self.pad_token_id:
                SCREAMING_SNAKE_CASE__											=			sequence_ids.index(self.pad_token_id     )
            else:
                SCREAMING_SNAKE_CASE__											=			len(A_     )

            SCREAMING_SNAKE_CASE__											=			self._get_best_spans(
                start_logits=start_logits[doc_id][passage_offset:sequence_len]   ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]   ,				max_answer_length=A_   ,				top_spans=A_   ,				)
            for start_index, end_index in best_spans:
                start_index += passage_offset
                end_index += passage_offset
                nbest_spans_predictions.append(
                    DPRSpanPrediction(
                        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]   ,				relevance_score=relevance_logits[doc_id]   ,				doc_id=A_   ,				start_index=A_   ,				end_index=A_   ,				text=self.decode(sequence_ids[start_index : end_index + 1]     )   ,				)     )
            if len(A_     ) >= num_spans:
                break
        return nbest_spans_predictions[:num_spans]




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[]
        for start_index, start_score in enumerate(A_     ):
            for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]     ):
                scores.append(((start_index, start_index + answer_length), start_score + end_score)     )
        SCREAMING_SNAKE_CASE__											=			sorted(A_   ,				key=lambda A_     : x[1]   ,				reverse=A_     )
        SCREAMING_SNAKE_CASE__											=			[]
        for (start_index, end_index), score in scores:
            if start_index > end_index:
                raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''     )
            SCREAMING_SNAKE_CASE__											=			end_index - start_index + 1
            if length > max_answer_length:
                raise ValueError(f'''Span is too long: {length} > {max_answer_length}'''     )
            if any(
                start_index <= prev_start_index <= prev_end_index <= end_index
                or prev_start_index <= start_index <= end_index <= prev_end_index
                for (prev_start_index, prev_end_index) in chosen_span_intervals     ):
                continue
            chosen_span_intervals.append((start_index, end_index)     )

            if len(A_     ) == top_spans:
                break
        return chosen_span_intervals





@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Dict				=	READER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		Any				=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Optional[Any]				=	READER_PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase__    :		str				=	["""input_ids""", """attention_mask"""]




'''simple docstring'''

import os
import unicodedata
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 SPIECE_UNDERLINE, logging


lowerCamelCase				:  str    		=      logging.get_logger(__name__)

lowerCamelCase				:  str    		=      {'vocab_file': 'spiece.model'}

lowerCamelCase				:  int    		=      {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Any=False  ,		UpperCamelCase       :       Dict=True  ,		UpperCamelCase       :       int=False  ,		UpperCamelCase       :       Optional[Any]="<s>"  ,		UpperCamelCase       :       Union[str, Any]="</s>"  ,		UpperCamelCase       :       Optional[int]="<unk>"  ,		UpperCamelCase       :       List[Any]="<sep>"  ,		UpperCamelCase       :       Dict="<pad>"  ,		UpperCamelCase       :       int="<cls>"  ,		UpperCamelCase       :       int="<mask>"  ,		UpperCamelCase       :       str=["<eop>", "<eod>"]  ,		UpperCamelCase       :       Optional[Dict[str, Any]] = None  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







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

             lowercase__      =					{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=UpperCamelCase  ,		remove_space=UpperCamelCase  ,		keep_accents=UpperCamelCase  ,		bos_token=UpperCamelCase  ,		eos_token=UpperCamelCase  ,		unk_token=UpperCamelCase  ,		sep_token=UpperCamelCase  ,		pad_token=UpperCamelCase  ,		cls_token=UpperCamelCase  ,		mask_token=UpperCamelCase  ,		additional_special_tokens=UpperCamelCase  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**UpperCamelCase  ,		)

             lowercase__      =					3

             lowercase__      =					do_lower_case
             lowercase__      =					remove_space
             lowercase__      =					keep_accents
             lowercase__      =					vocab_file

             lowercase__      =					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(UpperCamelCase					)

             try:
                    import jieba
             except ModuleNotFoundError as error:
                    raise error.__class__(
                        '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                        '''See https://pypi.org/project/jieba/ for installation.'''					)
             lowercase__      =					jieba
             lowercase__      =					str.maketrans(''' \n'''  ,		'''\u2582\u2583'''					)



      @property
      # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             return len(self.sp_model					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					{self.convert_ids_to_tokens(UpperCamelCase					): i for i in range(self.vocab_size					)}
             vocab.update(self.added_tokens_encoder					)
             return vocab



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

             '''simple docstring'''







             lowercase__      =					self.__dict__.copy()
             lowercase__      =					None
             return state



      def __setstate__(self       :       Dict  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					d

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

             lowercase__      =					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(self.vocab_file					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             if self.remove_space:
                    lowercase__      =					''' '''.join(inputs.strip().split()					)
             else:
                    lowercase__      =					inputs
             lowercase__      =					outputs.replace('''``'''  ,		'''"'''					).replace('''\'\''''  ,		'''"'''					)

             if not self.keep_accents:
                    lowercase__      =					unicodedata.normalize('''NFKD'''  ,		UpperCamelCase					)
                    lowercase__      =					''''''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase					)]					)
             if self.do_lower_case:
                    lowercase__      =					outputs.lower()

             return outputs



      def  UpperCamelCase__						(self       :       List[Any]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					self.preprocess_text(UpperCamelCase					)
             lowercase__      =					self.sp_model.encode(UpperCamelCase  ,		out_type=UpperCamelCase					)
             lowercase__      =					[]
             for piece in pieces:
                    if len(UpperCamelCase					) > 1 and piece[-1] == str(''','''					) and piece[-2].isdigit():
                           lowercase__      =					self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase  ,		''''''					)					)
                           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                  if len(cur_pieces[0]					) == 1:
                                         lowercase__      =					cur_pieces[1:]
                                  else:
                                         lowercase__      =					cur_pieces[0][1:]
                           cur_pieces.append(piece[-1]					)
                           new_pieces.extend(UpperCamelCase					)
                    else:
                           new_pieces.append(UpperCamelCase					)

             return new_pieces



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             return self.sp_model.PieceToId(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             return self.sp_model.IdToPiece(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					''''''.join(UpperCamelCase					).replace(UpperCamelCase  ,		''' '''					).strip()
             return out_string



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       List[int]  ,		UpperCamelCase       :       Optional[List[int]] = None					):

             '''simple docstring'''







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



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

             '''simple docstring'''







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

             if token_ids_a is not None:
                    return ([0] * len(UpperCamelCase					)) + [1] + ([0] * len(UpperCamelCase					)) + [1, 1]
             return ([0] * len(UpperCamelCase					)) + [1, 1]



      def  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       List[int]  ,		UpperCamelCase       :       Optional[List[int]] = None					):

             '''simple docstring'''







             lowercase__      =					[self.sep_token_id]
             lowercase__      =					[2]

             if token_ids_a is None:
                    return len(token_ids_a + sep					) * [0] + cls_segment_id
             return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Optional[str] = None					):

             '''simple docstring'''







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

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

             return (out_vocab_file,)






      def  UpperCamelCase__						(self       :       List[str]  ,		*UpperCamelCase       :       List[Any]  ,		**UpperCamelCase       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					super()._decode(*UpperCamelCase  ,		**UpperCamelCase					)
             lowercase__      =					text.replace(''' '''  ,		''''''					).replace('''\u2582'''  ,		''' '''					).replace('''\u2583'''  ,		'''\n'''					)
             return text




