



'''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: Union[str, Any]							=						logging.get_logger(__name__)

lowerCAmelCase: str							=						'▁'

lowerCAmelCase: Union[str, Any]							=						{'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

lowerCAmelCase: List[str]							=						{
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

lowerCAmelCase: Union[str, Any]							=						{
    '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: Tuple							=						{
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

lowerCAmelCase: Union[str, Any]							=						{
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}

class 							a__( lowerCamelCase__     ):
    lowercase__			=   ["input_ids"]

    lowercase__			=   VOCAB_FILES_NAMES
    lowercase__			=   PRETRAINED_INIT_CONFIGURATION
    lowercase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__			=   PRETRAINED_VOCAB_FILES_MAP
    lowercase__			=   RESOURCE_FILES_NAMES







    def __init__(				self			:  Tuple						,      __snake_case			:  Optional[Any]						,      __snake_case			:  List[str]=None						,      __snake_case			:  str=False						,      __snake_case			:  str="utf8"						,      __snake_case			:  Optional[Any]="[UNK]"						,      __snake_case			:  Tuple="[SEP]"						,      __snake_case			:  Any="[PAD]"						,      __snake_case			:  Optional[int]="[CLS]"						,      __snake_case			:  Any="[MASK]"						,      __snake_case			:  Optional[Dict[str, Any]] = None						,      **__snake_case			:  List[Any]						,      ):
      # 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						: str					       =					{} if sp_model_kwargs is None else sp_model_kwargs
      super().__init__(
          do_lower_case=__snake_case						,      unk_token=__snake_case						,      sep_token=__snake_case						,      pad_token=__snake_case						,      cls_token=__snake_case						,      mask_token=__snake_case						,      vocab_file=__snake_case						,      encoding=__snake_case						,      sp_model_kwargs=self.sp_model_kwargs						,      **__snake_case						,      )
      a						: List[str]					       =					do_lower_case
      a						: Union[str, Any]					       =					sentencepiece_model_ckpt
      a						: Tuple					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(__snake_case     )

      # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
      if vocab_file is not None:
        a						: Union[str, Any]					       =					self.load_vocab(filepath=__snake_case     )
      else:
        a						: Tuple					       =					{self.sp_model.id_to_piece(__snake_case     ): id for id in range(self.sp_model.get_piece_size()     )}
      a						: Dict					       =					{v: k for k, v in self.vocab.items()}







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Tuple     ):
      if text is None:
        return None

      a						: List[Any]					       =					self.tokenize(__snake_case     )
      a       ,     a						: Union[str, Any]					       =					'', []

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

      a       ,     a       ,     a						: Optional[Any]					       =					normalized_text, [], 0

      if self.do_lower_case:
        a						: List[Any]					       =					text.lower()

      for token in split_tokens:
        if token[:1] == "▁":
          a						: Tuple					       =					token[1:]
        a						: str					       =					text[offset:].index(__snake_case     ) + offset
        a						: str					       =					start + len(__snake_case     )

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







    @property
    def       lowercase_	(				self			:  int     ):
      return len(self.vocab     )







    def       lowercase_	(				self			:  Tuple     ):
      return dict(self.vocab						,      **self.added_tokens_encoder     )







    def __getstate__(				self			:  List[Any]     ):
      a						: Optional[Any]					       =					self.__dict__.copy()
      a						: Union[str, Any]					       =					None
      return state







    def __setstate__(				self			:  int						,      __snake_case			:  Dict     ):
      a						: Optional[int]					       =					d

      # for backward compatibility
      if not hasattr(self						,      'sp_model_kwargs'     ):
        a						: Optional[int]					       =					{}

      a						: Tuple					       =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
      self.sp_model.Load(self.sentencepiece_model_ckpt     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  Any     ):
      return "".join((self.SP_CHAR_MAPPING.get(__snake_case						,      __snake_case     ) for c in text)     )







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[Any]						,      __snake_case			:  int=False						,      __snake_case			:  Optional[int]=64						,      __snake_case			:  List[Any]=0.1     ):

      if self.sp_model_kwargs.get('enable_sampling'     ) is True:
        a						: Dict					       =					True
      if self.sp_model_kwargs.get('alpha'     ) is not None:
        a						: Union[str, Any]					       =					self.sp_model_kwargs.get('alpha'     )
      if self.sp_model_kwargs.get('nbest_size'     ) is not None:
        a						: str					       =					self.sp_model_kwargs.get('nbest_size'     )

      if not enable_sampling:
        a						: Any					       =					self.sp_model.EncodeAsPieces(__snake_case     )
      else:
        a						: List[Any]					       =					self.sp_model.SampleEncodeAsPieces(__snake_case						,      __snake_case						,      __snake_case     )
      a						: str					       =					[]
      for pi, piece in enumerate(__snake_case     ):
        if piece == SPIECE_UNDERLINE:
          if not pieces[pi + 1].startswith(__snake_case     ) and pi != 0:
            new_pieces.append(__snake_case     )
            continue
          else:
            continue
        a						: Optional[Any]					       =					0
        for i, chunk in enumerate(__snake_case     ):
          if chunk == SPIECE_UNDERLINE:
            continue
          if self.is_ch_char(__snake_case     ) or self.is_punct(__snake_case     ):
            if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
              new_pieces.append(piece[lst_i:i]     )
            new_pieces.append(__snake_case     )
            a						: int					       =					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]     )
            a						: List[Any]					       =					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]     )
            a						: int					       =					i
        if len(__snake_case     ) > lst_i:
          new_pieces.append(piece[lst_i:]     )
      return new_pieces







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Dict     ):
      a						: List[Any]					       =					''.join(__snake_case     ).replace(__snake_case						,      ' '     ).strip()
      return out_string







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[str]     ):
      a						: str					       =					self.convert_ids_to_tokens(__snake_case     )
      a						: Dict					       =					''.join(__snake_case     ).replace(__snake_case						,      ' '     ).strip()
      return out_string







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Tuple     ):
      return self.vocab.get(__snake_case						,      self.vocab.get(self.unk_token     )     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  int     ):
      return self.reverse_vocab.get(__snake_case						,      self.unk_token     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  List[str]						,      __snake_case			:  Union[str, Any]=None     ):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a						: Tuple					       =					[self.cls_token_id]
      a						: Optional[Any]					       =					[self.sep_token_id]
      return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Dict=None     ):
      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       lowercase_	(				self			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  int=None						,      __snake_case			:  Any=False     ):

      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(__snake_case     )) + [1, 1] + ([0] * len(__snake_case     )) + [1]
      return [1] + ([0] * len(__snake_case     )) + [1]







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[int]						,      __snake_case			:  Optional[List[int]] = None     ):
      # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
      if token_ids_a is None:
        # [CLS] X [SEP]
        return (len(__snake_case     ) + 2) * [0]

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







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[str]     ):
      if "\u4e00" <= char <= "\u9fff":
        return True
      return False







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Union[str, Any]     ):
      if ("a" <= char <= "z") or ("A" <= char <= "Z"):
        return True
      return False







    def       lowercase_	(				self			:  Any						,      __snake_case			:  List[Any]     ):
      if char in ",;:.?!~，；：。？！《》【】":
        return True
      return False







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  str     ):
      if char == " " or char == "\t" or char == "\n" or char == "\r":
        return True
      if len(__snake_case     ) == 1:
        a						: Optional[Any]					       =					unicodedata.category(__snake_case     )
        if cat == "Zs":
          return True
      return False







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Optional[Any]     ):
      a						: str					       =					{}
      with io.open(__snake_case						,      'r'						,      encoding='utf-8'     ) as f:
        for index, line in enumerate(__snake_case     ):
          a						: int					       =					line.rstrip('\n'     )
          a						: Optional[int]					       =					int(__snake_case     )

      return token_to_idx







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  str						,      __snake_case			:  Optional[str] = None     ):
      a						: Optional[int]					       =					0
      if os.path.isdir(__snake_case     ):
        a						: Optional[int]					       =					os.path.join(
            __snake_case						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
      else:
        a						: Any					       =					(filename_prefix + '-' if filename_prefix else '') + save_directory
      with open(__snake_case						,      'w'						,      encoding='utf-8'     ) as writer:
        for token, token_index in sorted(self.vocab.items()						,      key=lambda __snake_case     : 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!'     )
            a						: Optional[int]					       =					token_index
          writer.write(token + '\n'     )
          index += 1

      a						: Any					       =					os.path.join(__snake_case						,      'sentencepiece.bpe.model'     )
      with open(__snake_case						,      'wb'     ) as fi:
        a						: List[Any]					       =					self.sp_model.serialized_model_proto()
        fi.write(__snake_case     )

      return (vocab_file,)




'''simple docstring'''






from math import factorial, pi
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'    )

  a						: Dict					       =					float(_A    )
  a						: List[Any]					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'    )

  a						: int					       =					float(_A    )
  a						: str					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))