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": 5_12,
    "google/fnet-large": 5_12,
}

lowercase_  							=			"▁"







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Union[str, Any]          =	VOCAB_FILES_NAMES
       A							:	int          =	PRETRAINED_VOCAB_FILES_MAP
       A							:	Tuple          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A							:	List[str]          =	["input_ids", "token_type_ids"]

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Union[str, Any]=False			,			_lowerCAmelCase					:     Optional[int]=True			,			_lowerCAmelCase					:     int=True			,			_lowerCAmelCase					:     Dict="<unk>"			,			_lowerCAmelCase					:     List[str]="[SEP]"			,			_lowerCAmelCase					:     Tuple="<pad>"			,			_lowerCAmelCase					:     Union[str, Any]="[CLS]"			,			_lowerCAmelCase					:     Any="[MASK]"			,			_lowerCAmelCase					:     Optional[Dict[str, Any]] = None			,			**_lowerCAmelCase					:     Union[str, 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.
           __snake_case       :				Tuple 			=    (
               AddedToken(_lowerCAmelCase			,			lstrip=_lowerCAmelCase			,			rstrip=_lowerCAmelCase			,			normalized=_lowerCAmelCase			)
               if isinstance(_lowerCAmelCase			,			_lowerCAmelCase			)
               else mask_token
           )

           __snake_case       :				Tuple 			=    {} 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			,			)

           __snake_case       :				Optional[Any] 			=    do_lower_case
           __snake_case       :				Tuple 			=    remove_space
           __snake_case       :				int 			=    keep_accents
           __snake_case       :				str 			=    vocab_file

           __snake_case       :				Union[str, Any] 			=    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
           self.sp_model.Load(_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     Dict			):
           return len(self.sp_model			)

       def 			snake_case__    (							self					:     str			):
           __snake_case       :				Dict 			=    {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					:     Optional[Any]			):
           __snake_case       :				Tuple 			=    self.__dict__.copy()
           __snake_case       :				Tuple 			=    None
           return state

       def __setstate__(							self					:     List[Any]			,			_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				Any 			=    d

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

           __snake_case       :				Dict 			=    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
           self.sp_model.Load(self.vocab_file			)

       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     Any			):
           if self.remove_space:
               __snake_case       :				Optional[Any] 			=    """ """.join(inputs.strip().split()			)
           else:
               __snake_case       :				Optional[Any] 			=    inputs
           __snake_case       :				Any 			=    outputs.replace("""``"""			,			"""\""""			).replace("""''"""			,			"""\""""			)

           if not self.keep_accents:
               __snake_case       :				List[Any] 			=    unicodedata.normalize("""NFKD"""			,			_lowerCAmelCase			)
               __snake_case       :				int 			=    """""".join([c for c in outputs if not unicodedata.combining(_lowerCAmelCase			)]			)
           if self.do_lower_case:
               __snake_case       :				List[str] 			=    outputs.lower()

           return outputs

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     str			):
           __snake_case       :				Dict 			=    self.preprocess_text(_lowerCAmelCase			)
           __snake_case       :				Dict 			=    self.sp_model.encode(_lowerCAmelCase			,			out_type=_lowerCAmelCase			)
           __snake_case       :				Dict 			=    []
           for piece in pieces:
               if len(_lowerCAmelCase			) > 1 and piece[-1] == str(""","""			) and piece[-2].isdigit():
                   __snake_case       :				List[Any] 			=    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:
                           __snake_case       :				str 			=    cur_pieces[1:]
                       else:
                           __snake_case       :				List[str] 			=    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					:     str			,			_lowerCAmelCase					:     List[Any]			):
           return self.sp_model.PieceToId(_lowerCAmelCase			)

       def 			snake_case__    (							self					:     List[str]			,			_lowerCAmelCase					:     int			):
           return self.sp_model.IdToPiece(_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				Union[str, Any] 			=    []
           __snake_case       :				Optional[int] 			=    """"""
           __snake_case       :				Tuple 			=    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
                   __snake_case       :				Optional[int] 			=    True
                   __snake_case       :				Optional[int] 			=    []
               else:
                   current_sub_tokens.append(_lowerCAmelCase			)
                   __snake_case       :				Optional[int] 			=    False
           out_string += self.sp_model.decode(_lowerCAmelCase			)
           return out_string.strip()

       def 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     List[int]			,			_lowerCAmelCase					:     bool = False			,			_lowerCAmelCase					:     bool = None			,			_lowerCAmelCase					:     bool = True			,			**_lowerCAmelCase					:     str			,			):
           __snake_case       :				Dict 			=    kwargs.pop("""use_source_tokenizer"""			,			_lowerCAmelCase			)

           __snake_case       :				Optional[int] 			=    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
           __snake_case       :				int 			=    []
           __snake_case       :				Optional[int] 			=    []
           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			)			)
                       __snake_case       :				List[str] 			=    []
                   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:
               __snake_case       :				Optional[Any] 			=    re.sub(r"""(<unk>) """			,			r"""\1"""			,			""" """.join(_lowerCAmelCase			)			)
           else:
               __snake_case       :				Union[str, Any] 			=    """""".join(_lowerCAmelCase			)

           __snake_case       :				Optional[Any] 			=    (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
               __snake_case       :				Dict 			=    self.clean_up_tokenization(_lowerCAmelCase			)
               return clean_text
           else:
               return text

       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     List[int]			,			_lowerCAmelCase					:     Optional[List[int]] = None			):
           __snake_case       :				Optional[int] 			=    [self.sep_token_id]
           __snake_case       :				Any 			=    [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					:     Optional[Any]			,			_lowerCAmelCase					:     List[int]			,			_lowerCAmelCase					:     Optional[List[int]] = None			,			_lowerCAmelCase					:     bool = False			):

           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					:     Union[str, Any]			,			_lowerCAmelCase					:     List[int]			,			_lowerCAmelCase					:     Optional[List[int]] = None			):
           __snake_case       :				Optional[Any] 			=    [self.sep_token_id]
           __snake_case       :				Any 			=    [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					:     Union[str, Any]			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Optional[str] = None			):
           if not os.path.isdir(_lowerCAmelCase			):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
           __snake_case       :				Optional[Any] 			=    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:
                   __snake_case       :				Optional[Any] 			=    self.sp_model.serialized_model_proto()
                   fi.write(_lowerCAmelCase			)

           return (out_vocab_file,)




import math
from timeit import timeit







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    0
    __snake_case       :				Union[str, Any] 			=    abs(__SCREAMING_SNAKE_CASE   )
    while True:
        __snake_case       :				List[str] 			=    n // 1_0
        digits += 1
        if n == 0:
            break
    return digits







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return 1 if n == 0 else math.floor(math.log(abs(__SCREAMING_SNAKE_CASE   )		,							1_0   ) + 1   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return len(str(abs(__SCREAMING_SNAKE_CASE   )   )   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    from collections.abc import Callable

    def benchmark_a_function(__SCREAMING_SNAKE_CASE       :					Callable		,							__SCREAMING_SNAKE_CASE       :					int   ) -> None:
        __snake_case       :				Tuple 			=    F'''{func.__name__}({value})'''
        __snake_case       :				Union[str, Any] 			=    timeit(F'''__main__.{call}'''		,							setup="""import __main__"""   )
        print(F'''{call}: {func(__SCREAMING_SNAKE_CASE   )} -- {timing} seconds'''   )

    for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



