



'''simple docstring'''


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_lowerCamelCase	:       Dict      	=	logging.get_logger(__name__)

_lowerCamelCase	:       str      	=	"▁"

_lowerCamelCase	:       Any      	=	{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

_lowerCamelCase	:       str      	=	{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

_lowerCamelCase	:       Optional[int]      	=	{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

_lowerCamelCase	:       int      	=	{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

_lowerCamelCase	:       Dict      	=	{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["input_ids"]

    _SCREAMING_SNAKE_CASE	  =  VOCAB_FILES_NAMES
    _SCREAMING_SNAKE_CASE	  =  PRETRAINED_INIT_CONFIGURATION
    _SCREAMING_SNAKE_CASE	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _SCREAMING_SNAKE_CASE	  =  PRETRAINED_VOCAB_FILES_MAP
    _SCREAMING_SNAKE_CASE	  =  RESOURCE_FILES_NAMES






    def __init__(  self      :					int  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					Any=False  ,						UpperCamelCase__      :					int="utf8"  ,						UpperCamelCase__      :					Tuple="[UNK]"  ,						UpperCamelCase__      :					List[Any]="[SEP]"  ,						UpperCamelCase__      :					str="[PAD]"  ,						UpperCamelCase__      :					Union[str, Any]="[CLS]"  ,						UpperCamelCase__      :					List[Any]="[MASK]"  ,						UpperCamelCase__      :					Optional[Dict[str, Any]] = None  ,						**UpperCamelCase__      :					int  ,						):


       """simple docstring"""







       UpperCamelCase					=  {} if sp_model_kwargs is None else sp_model_kwargs
       super().__init__(
           do_lower_case=UpperCamelCase__  ,						unk_token=UpperCamelCase__  ,						sep_token=UpperCamelCase__  ,						pad_token=UpperCamelCase__  ,						cls_token=UpperCamelCase__  ,						mask_token=UpperCamelCase__  ,						vocab_file=UpperCamelCase__  ,						encoding=UpperCamelCase__  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**UpperCamelCase__  ,						)
       UpperCamelCase					=  do_lower_case
       UpperCamelCase					=  sentencepiece_model_ckpt
       UpperCamelCase					=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(UpperCamelCase__ )

       # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
       if vocab_file is not None:
          UpperCamelCase					=  self.load_vocab(filepath=UpperCamelCase__ )
       else:
          UpperCamelCase					=  {self.sp_model.id_to_piece(UpperCamelCase__ ): id for id in range(self.sp_model.get_piece_size() )}
       UpperCamelCase					=  {v: k for k, v in self.vocab.items()}






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       if text is None:
          return None

       UpperCamelCase					=  self.tokenize(UpperCamelCase__ )
       UpperCamelCase				,      UpperCamelCase					=  '', []

       for i, ch in enumerate(UpperCamelCase__ ):
          if ch in self.SP_CHAR_MAPPING:
             UpperCamelCase					=  self.SP_CHAR_MAPPING.get(UpperCamelCase__ )
          else:
             UpperCamelCase					=  unicodedata.normalize('NFKC'  ,						UpperCamelCase__ )
          if self.is_whitespace(UpperCamelCase__ ):
             continue
          normalized_text += ch
          char_mapping.extend([i] * len(UpperCamelCase__ ) )

       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  normalized_text, [], 0

       if self.do_lower_case:
          UpperCamelCase					=  text.lower()

       for token in split_tokens:
          if token[:1] == "▁":
             UpperCamelCase					=  token[1:]
          UpperCamelCase					=  text[offset:].index(UpperCamelCase__ ) + offset
          UpperCamelCase					=  start + len(UpperCamelCase__ )

          token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
          UpperCamelCase					=  end
       return token_mapping






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


       """simple docstring"""







       return len(self.vocab )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return dict(self.vocab  ,						**self.added_tokens_encoder )






    def __getstate__(  self      :					Optional[Any] ):


       """simple docstring"""







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






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


       """simple docstring"""







       UpperCamelCase					=  d

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

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






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







       return "".join((self.SP_CHAR_MAPPING.get(UpperCamelCase__  ,						UpperCamelCase__ ) for c in text) )






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					List[str]=False  ,						UpperCamelCase__      :					str=6_4  ,						UpperCamelCase__      :					Optional[Any]=0.1 ):


       """simple docstring"""







       if self.sp_model_kwargs.get('enable_sampling' ) is True:
          UpperCamelCase					=  True
       if self.sp_model_kwargs.get('alpha' ) is not None:
          UpperCamelCase					=  self.sp_model_kwargs.get('alpha' )
       if self.sp_model_kwargs.get('nbest_size' ) is not None:
          UpperCamelCase					=  self.sp_model_kwargs.get('nbest_size' )

       if not enable_sampling:
          UpperCamelCase					=  self.sp_model.EncodeAsPieces(UpperCamelCase__ )
       else:
          UpperCamelCase					=  self.sp_model.SampleEncodeAsPieces(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  []
       for pi, piece in enumerate(UpperCamelCase__ ):
          if piece == SPIECE_UNDERLINE:
             if not pieces[pi + 1].startswith(UpperCamelCase__ ) and pi != 0:
                new_pieces.append(UpperCamelCase__ )
                continue
             else:
                continue
          UpperCamelCase					=  0
          for i, chunk in enumerate(UpperCamelCase__ ):
             if chunk == SPIECE_UNDERLINE:
                continue
             if self.is_ch_char(UpperCamelCase__ ) or self.is_punct(UpperCamelCase__ ):
                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                   new_pieces.append(piece[lst_i:i] )
                new_pieces.append(UpperCamelCase__ )
                UpperCamelCase					=  i + 1
             elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                   new_pieces.append(piece[lst_i:i] )
                UpperCamelCase					=  i
             elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                   new_pieces.append(piece[lst_i:i] )
                UpperCamelCase					=  i
          if len(UpperCamelCase__ ) > lst_i:
             new_pieces.append(piece[lst_i:] )
       return new_pieces






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Any ):


       """simple docstring"""







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






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


       """simple docstring"""







       UpperCamelCase					=  self.convert_ids_to_tokens(UpperCamelCase__ )
       UpperCamelCase					=  ''.join(UpperCamelCase__ ).replace(UpperCamelCase__  ,						' ' ).strip()
       return out_string






    def  A       (  self      :					int  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.vocab.get(UpperCamelCase__  ,						self.vocab.get(self.unk_token ) )






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.reverse_vocab.get(UpperCamelCase__  ,						self.unk_token )






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


       """simple docstring"""







       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase					=  [self.cls_token_id]
       UpperCamelCase					=  [self.sep_token_id]
       return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep






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


       """simple docstring"""







       if offset_mapping_a is None:
          return [(0, 0)] + offset_mapping_a + [(0, 0)]

       return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Union[str, Any]=False ):


       """simple docstring"""







       if already_has_special_tokens:
          if token_ids_a is not None:
             raise ValueError(
                 'You should not supply a second sequence if the provided sequence of '
                 'ids is already formatted with special tokens for the model.' )
          return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






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


       """simple docstring"""







       if token_ids_a is None:
          # [CLS] X [SEP]
          return (len(UpperCamelCase__ ) + 2) * [0]

       # [CLS] A [SEP] [SEP] B [SEP]
       return [0] * (len(UpperCamelCase__ ) + 1) + [1] * (len(UpperCamelCase__ ) + 3)






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


       """simple docstring"""







       if "\u4e00" <= char <= "\u9fff":
          return True
       return False






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       if ("a" <= char <= "z") or ("A" <= char <= "Z"):
          return True
       return False






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       if char in ",;:.?!~，；：。？！《》【】":
          return True
       return False






    def  A       (  self      :					str  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       if char == " " or char == "\t" or char == "\n" or char == "\r":
          return True
       if len(UpperCamelCase__ ) == 1:
          UpperCamelCase					=  unicodedata.category(UpperCamelCase__ )
          if cat == "Zs":
             return True
       return False






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


       """simple docstring"""







       UpperCamelCase					=  {}
       with io.open(UpperCamelCase__  ,						'r'  ,						encoding='utf-8' ) as f:
          for index, line in enumerate(UpperCamelCase__ ):
             UpperCamelCase					=  line.rstrip('\n' )
             UpperCamelCase					=  int(UpperCamelCase__ )

       return token_to_idx






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


       """simple docstring"""







       UpperCamelCase					=  0
       if os.path.isdir(UpperCamelCase__ ):
          UpperCamelCase					=  os.path.join(
              UpperCamelCase__  ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
       else:
          UpperCamelCase					=  (filename_prefix + '-' if filename_prefix else '') + save_directory
       with open(UpperCamelCase__  ,						'w'  ,						encoding='utf-8' ) as writer:
          for token, token_index in sorted(self.vocab.items()  ,						key=lambda UpperCamelCase__ : kv[1] ):
             if index != token_index:
                logger.warning(
                    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                    ' Please check that the vocabulary is not corrupted!' )
                UpperCamelCase					=  token_index
             writer.write(token + '\n' )
             index += 1

       UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'sentencepiece.bpe.model' )
       with open(UpperCamelCase__  ,						'wb' ) as fi:
          UpperCamelCase					=  self.sp_model.serialized_model_proto()
          fi.write(UpperCamelCase__ )

       return (vocab_file,)






'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

   if not -255.0 <= level <= 255.0:
      raise ValueError('level must be between -255.0 (black) and 255.0 (white)'    )
   return img.point(A__    )


if __name__ == "__main__":
   # Load image
   with Image.open("image_data/lena.jpg") as img:
      # Change brightness to 100
      _lowerCamelCase	:       List[str]      	=	change_brightness(img, 100)
      brigt_img.save("image_data/lena_brightness.png", format="png")

