


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 logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							    =							{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							    =							{
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE							    =							{
    'albert-base-v1': 5_1_2,
    'albert-large-v1': 5_1_2,
    'albert-xlarge-v1': 5_1_2,
    'albert-xxlarge-v1': 5_1_2,
    'albert-base-v2': 5_1_2,
    'albert-large-v2': 5_1_2,
    'albert-xlarge-v2': 5_1_2,
    'albert-xxlarge-v2': 5_1_2,
}

SCREAMING_SNAKE_CASE							    =							'▁'


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      VOCAB_FILES_NAMES
     _lowerCamelCase   =      PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(	self  ,       __A  ,       __A=True  ,       __A=True  ,       __A=False  ,       __A="[CLS]"  ,       __A="[SEP]"  ,       __A="<unk>"  ,       __A="[SEP]"  ,       __A="<pad>"  ,       __A="[CLS]"  ,       __A="[MASK]"  ,       __A = None  ,       **__A  ,       ):
        # Mask token behave like a normal word, i.e. include the space before it and
        # is included in the raw text, there should be a match in a non-normalized sentence.
        __a					=  (
            AddedToken(__A  ,       lstrip=__A  ,       rstrip=__A  ,       normalized=__A    )
            if isinstance(__A  ,       __A    )
            else mask_token
        )

        __a					=  {} if sp_model_kwargs is None else sp_model_kwargs

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

        __a					=  do_lower_case
        __a					=  remove_space
        __a					=  keep_accents
        __a					=  vocab_file

        __a					=  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(__A    )


     @property
     def 	snake_case_  (	self    ):
        return len(self.sp_model    )


     def 	snake_case_  (	self    ):
        __a					=  {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    ):
        __a					=  self.__dict__.copy()
        __a					=  None
        return state


     def __setstate__(	self  ,       __A    ):
        __a					=  d

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

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


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

        if not self.keep_accents:
           __a					=  unicodedata.normalize("""NFKD"""  ,       __A    )
           __a					=  """""".join([c for c in outputs if not unicodedata.combining(__A    )]    )
        if self.do_lower_case:
           __a					=  outputs.lower()

        return outputs


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

        return new_pieces


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.PieceToId(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        return self.sp_model.IdToPiece(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  []
        __a					=  """"""
        __a					=  False
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              if not prev_is_special:
                 out_string += " "
              out_string += self.sp_model.decode(__A    ) + token
              __a					=  True
              __a					=  []
           else:
              current_sub_tokens.append(__A    )
              __a					=  False
        out_string += self.sp_model.decode(__A    )
        return out_string.strip()


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep


     def 	snake_case_  (	self  ,       __A  ,       __A = None  ,       __A = False    ):

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        __a					=  [self.sep_token_id]
        __a					=  [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 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        if not os.path.isdir(__A    ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
           return
        __a					=  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					=  self.sp_model.serialized_model_proto()
              fi.write(__A    )

        return (out_vocab_file,)







"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


A	:  Optional[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  **__a      ):
										warnings.warn(
										    "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
										    " use YolosImageProcessor instead."       ,  __a       ,  )
										super().__init__(*__a       ,  **__a      )




