





'''simple docstring'''

import os
import re
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


lowercase_         =			logging.get_logger(__name__)
lowercase_         =			{"""vocab_file""": """spiece.model"""}

lowercase_         =			{
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

lowercase_         =			{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

lowercase_         =			"""▁"""


class  a_					(    lowercase__	):

      '''simple docstring'''
      UpperCamelCase			   =			VOCAB_FILES_NAMES
      UpperCamelCase			   =			PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase			   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase			   =			['''input_ids''', '''token_type_ids''']


      def __init__(		self			,		A			,		A=False			,		A=True			,		A=True			,		A="<unk>"			,		A="[SEP]"			,		A="<pad>"			,		A="[CLS]"			,		A="[MASK]"			,		A = None			,		**A			,		)		->       None:
          _SCREAMING_SNAKE_CASE     =      (
              AddedToken(lowerCAmelCase__			,		lstrip=lowerCAmelCase__			,		rstrip=lowerCAmelCase__			,		normalized=lowerCAmelCase__	)
              if isinstance(lowerCAmelCase__			,		lowerCAmelCase__	)
              else mask_token
          )

          _SCREAMING_SNAKE_CASE     =      {} if sp_model_kwargs is None else sp_model_kwargs

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

          _SCREAMING_SNAKE_CASE     =      do_lower_case
          _SCREAMING_SNAKE_CASE     =      remove_space
          _SCREAMING_SNAKE_CASE     =      keep_accents
          _SCREAMING_SNAKE_CASE     =      vocab_file

          _SCREAMING_SNAKE_CASE     =      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(lowerCAmelCase__	)


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


      def       snake_case_(		self	)		->       List[str]:
          _SCREAMING_SNAKE_CASE     =      {self.convert_ids_to_tokens(lowerCAmelCase__	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab


      def __getstate__(		self	)		->       int:
          _SCREAMING_SNAKE_CASE     =      self.__dict__.copy()
          _SCREAMING_SNAKE_CASE     =      None
          return state


      def __setstate__(		self			,		A	)		->       Optional[Any]:
          _SCREAMING_SNAKE_CASE     =      d

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

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


      def       snake_case_(		self			,		A	)		->       Optional[Any]:
          if self.remove_space:
              _SCREAMING_SNAKE_CASE     =      """ """.join(inputs.strip().split()	)
          else:
              _SCREAMING_SNAKE_CASE     =      inputs
          _SCREAMING_SNAKE_CASE     =      outputs.replace("""``"""			,		"""\""""	).replace("""\'\'"""			,		"""\""""	)

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

          return outputs


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

          return new_pieces


      def       snake_case_(		self			,		A	)		->       Tuple:
          return self.sp_model.PieceToId(lowerCAmelCase__	)


      def       snake_case_(		self			,		A	)		->       Any:
          return self.sp_model.IdToPiece(lowerCAmelCase__	)


      def       snake_case_(		self			,		A	)		->       str:
          _SCREAMING_SNAKE_CASE     =      []
          _SCREAMING_SNAKE_CASE     =      """"""
          _SCREAMING_SNAKE_CASE     =      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(lowerCAmelCase__	) + token
                  _SCREAMING_SNAKE_CASE     =      True
                  _SCREAMING_SNAKE_CASE     =      []
              else:
                  current_sub_tokens.append(lowerCAmelCase__	)
                  _SCREAMING_SNAKE_CASE     =      False
          out_string += self.sp_model.decode(lowerCAmelCase__	)
          return out_string.strip()


      def       snake_case_(		self			,		A			,		A = False			,		A = None			,		A = True			,		**A			,		)		->       str:
          _SCREAMING_SNAKE_CASE     =      kwargs.pop("""use_source_tokenizer"""			,		lowerCAmelCase__	)

          _SCREAMING_SNAKE_CASE     =      self.convert_ids_to_tokens(lowerCAmelCase__			,		skip_special_tokens=lowerCAmelCase__	)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          _SCREAMING_SNAKE_CASE     =      []
          _SCREAMING_SNAKE_CASE     =      []
          for token in filtered_tokens:
              if skip_special_tokens and token in self.all_special_ids:
                  continue
              if token in self.added_tokens_encoder:
                  if current_sub_text:
                      sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__	)	)
                      _SCREAMING_SNAKE_CASE     =      []
                  sub_texts.append(lowerCAmelCase__	)
              else:
                  current_sub_text.append(lowerCAmelCase__	)
          if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__	)	)

          # Mimic the behavior of the Rust tokenizer:
          # No space after <unk>
          if spaces_between_special_tokens:
              _SCREAMING_SNAKE_CASE     =      re.sub(R"""(<unk>) """			,		R"""\1"""			,		""" """.join(lowerCAmelCase__	)	)
          else:
              _SCREAMING_SNAKE_CASE     =      """""".join(lowerCAmelCase__	)

          _SCREAMING_SNAKE_CASE     =      (
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
              _SCREAMING_SNAKE_CASE     =      self.clean_up_tokenization(lowerCAmelCase__	)
              return clean_text
          else:
              return text


      def       snake_case_(		self			,		A			,		A = None	)		->       List[int]:
          _SCREAMING_SNAKE_CASE     =      [self.sep_token_id]
          _SCREAMING_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       snake_case_(		self			,		A			,		A = None			,		A = False	)		->       List[int]:

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

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


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

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

          return (out_vocab_file,)










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A	)		->       Optional[int]:
        if isinstance(A			,		A	):  # Initializes a matrix identical to the one provided.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      len(A	)
        else:  # Initializes a square matrix of the given size and set values to zero.
            _SCREAMING_SNAKE_CASE     =      arg
            _SCREAMING_SNAKE_CASE     =      [[0 for _ in range(self.n	)] for _ in range(self.n	)]





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      Matrix(self.n	)
        for i in range(self.n	):
            for j in range(self.n	):
                for k in range(self.n	):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

    while n > 0:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      fn_a + fn_a, fn_a
        n -= 1

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



