




'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

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


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

UpperCamelCase_						:      List[Any]       =						{'''vocab_file''': '''sentencepiece.model'''}

UpperCamelCase_						:      Optional[Any]       =						{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

UpperCamelCase_						:      Any       =						{
    '''google/rembert''': 256,
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   str       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE="[CLS]" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[UNK]" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[PAD]" ,_SCREAMING_SNAKE_CASE="[CLS]" ,_SCREAMING_SNAKE_CASE="[MASK]" ,**_SCREAMING_SNAKE_CASE ,)							->      Union[str, Any]:
          super().__init__(
              do_lower_case=_SCREAMING_SNAKE_CASE ,remove_space=_SCREAMING_SNAKE_CASE ,keep_accents=_SCREAMING_SNAKE_CASE ,bos_token=_SCREAMING_SNAKE_CASE ,eos_token=_SCREAMING_SNAKE_CASE ,unk_token=_SCREAMING_SNAKE_CASE ,sep_token=_SCREAMING_SNAKE_CASE ,pad_token=_SCREAMING_SNAKE_CASE ,cls_token=_SCREAMING_SNAKE_CASE ,mask_token=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)

          _snake_case							=				do_lower_case
          _snake_case							=				remove_space
          _snake_case							=				keep_accents
          _snake_case							=				vocab_file

          _snake_case							=				spm.SentencePieceProcessor()
          self.sp_model.Load(_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      List[Any]:
          return len(self.sp_model   )






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def __getstate__(			self   )							->      Dict:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				d
          _snake_case							=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=False   )							->      List[Any]:
          _snake_case							=				self.sp_model.EncodeAsPieces(_SCREAMING_SNAKE_CASE   )
          return pieces






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				self.sp_model.decode_pieces(_SCREAMING_SNAKE_CASE   )
          return out_string






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:

          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(_SCREAMING_SNAKE_CASE   )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE   )) + [1]
          return [1] + ([0] * len(_SCREAMING_SNAKE_CASE   )) + [1]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error("Vocabulary path ({}) should be a directory".format(_SCREAMING_SNAKE_CASE   )   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

          if os.path.abspath(self.vocab_file   ) != os.path.abspath(_SCREAMING_SNAKE_CASE   ):
               copyfile(self.vocab_file ,_SCREAMING_SNAKE_CASE   )

          return (out_vocab_file,)








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """simple docstring"""




        return x**3 + x**2

   print('''f(x) = x^3 + x^2''')
   print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
   UpperCamelCase_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


