




'''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						:   List[Any]      						=							logging.get_logger(__name__)
lowercase						:   List[Any]      						=							{'''vocab_file''': '''spiece.model'''}

lowercase						:   Optional[Any]      						=							{
    '''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						:   List[Any]      						=							{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

lowercase						:   Optional[int]      						=							'''▁'''

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  str       =   VOCAB_FILES_NAMES
 __snake_case						:  List[str]       =   PRETRAINED_VOCAB_FILES_MAP
 __snake_case						:  Optional[int]       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case						:  Tuple       =   ["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 ,		):






        '''simple docstring'''




        lowercase	:		List[str]    =   (
            AddedToken(A ,		lstrip=A ,		rstrip=A ,		normalized=A			)
            if isinstance(A ,		A			)
            else mask_token
        )

        lowercase	:		Optional[int]    =   {} if sp_model_kwargs is None else sp_model_kwargs

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

        lowercase	:		Union[str, Any]    =   do_lower_case
        lowercase	:		List[str]    =   remove_space
        lowercase	:		List[str]    =   keep_accents
        lowercase	:		str    =   vocab_file

        lowercase	:		Any    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(A			)



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return len(self.sp_model			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Any    =   {self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab



 def __getstate__(	self			):






        '''simple docstring'''




        lowercase	:		Any    =   self.__dict__.copy()
        lowercase	:		str    =   None
        return state



 def __setstate__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   d

        # for backward compatibility
        if not hasattr(self ,		'''sp_model_kwargs'''			):
               lowercase	:		Union[str, Any]    =   {}

        lowercase	:		Optional[Any]    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        if self.remove_space:
               lowercase	:		Optional[int]    =   ''' '''.join(inputs.strip().split()			)
        else:
               lowercase	:		int    =   inputs
        lowercase	:		Union[str, Any]    =   outputs.replace('''``''' ,		'''"'''			).replace('''\'\'''' ,		'''"'''			)

        if not self.keep_accents:
               lowercase	:		List[str]    =   unicodedata.normalize('''NFKD''' ,		A			)
               lowercase	:		int    =   ''''''.join([c for c in outputs if not unicodedata.combining(A			)]			)
        if self.do_lower_case:
               lowercase	:		str    =   outputs.lower()

        return outputs



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.preprocess_text(A			)
        lowercase	:		int    =   self.sp_model.encode(A ,		out_type=A			)
        lowercase	:		Optional[int]    =   []
        for piece in pieces:
               if len(A			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
                      lowercase	:		Tuple    =   self.sp_model.EncodeAsPieces(piece[:-1].replace(A ,		''''''			)			)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]			) == 1:
                                    lowercase	:		Dict    =   cur_pieces[1:]
                             else:
                                    lowercase	:		List[Any]    =   cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]			)
                      new_pieces.extend(A			)
               else:
                      new_pieces.append(A			)

        return new_pieces



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        return self.sp_model.PieceToId(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        return self.sp_model.IdToPiece(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   []
        lowercase	:		Dict    =   ''''''
        lowercase	:		Dict    =   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(A			) + token
                      lowercase	:		Tuple    =   True
                      lowercase	:		Union[str, Any]    =   []
               else:
                      current_sub_tokens.append(A			)
                      lowercase	:		Optional[int]    =   False
        out_string += self.sp_model.decode(A			)
        return out_string.strip()



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A = False ,		A = None ,		A = True ,		**A ,		):






        '''simple docstring'''




        lowercase	:		str    =   kwargs.pop('''use_source_tokenizer''' ,		A			)

        lowercase	:		Tuple    =   self.convert_ids_to_tokens(A ,		skip_special_tokens=A			)

        # 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
        lowercase	:		List[Any]    =   []
        lowercase	:		Any    =   []
        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(A			)			)
                             lowercase	:		Optional[int]    =   []
                      sub_texts.append(A			)
               else:
                      current_sub_text.append(A			)
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(A			)			)

        # Mimic the behavior of the Rust tokenizer:
        # No space after <unk>
        if spaces_between_special_tokens:
               lowercase	:		Tuple    =   re.sub(r'''(<unk>) ''' ,		r'''\1''' ,		''' '''.join(A			)			)
        else:
               lowercase	:		Dict    =   ''''''.join(A			)

        lowercase	:		Tuple    =   (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               lowercase	:		str    =   self.clean_up_tokenization(A			)
               return clean_text
        else:
               return text



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A = None			):






        '''simple docstring'''




        lowercase	:		Dict    =   [self.sep_token_id]
        lowercase	:		Tuple    =   [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  SCREAMING_SNAKE_CASE(	self ,		A ,		A = None ,		A = False			):






        '''simple docstring'''




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

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



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A = None			):






        '''simple docstring'''




        lowercase	:		Any    =   [self.sep_token_id]
        lowercase	:		Tuple    =   [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  SCREAMING_SNAKE_CASE(	self ,		A ,		A = None			):






        '''simple docstring'''




        if not os.path.isdir(A			):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
               return
        lowercase	:		Optional[Any]    =   os.path.join(
            A ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(A			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file ,		A			)
        elif not os.path.isfile(self.vocab_file			):
               with open(A ,		'''wb'''			) as fi:
                      lowercase	:		List[str]    =   self.sp_model.serialized_model_proto()
                      fi.write(A			)

        return (out_vocab_file,)










'''simple docstring'''






from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase						:   Any      						=							TypeVar('''T''', bound='''Enum''')

@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Union[str, Any]       =   8_0
 __snake_case						:  Optional[Any]       =   7_0
 __snake_case						:  List[str]       =   6_0
 __snake_case						:  str       =   5_0
 __snake_case						:  Tuple       =   4_0
 __snake_case						:  Union[str, Any]       =   3_0
 __snake_case						:  Dict       =   2_0
 __snake_case						:  str       =   1_0






@unique
class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  Optional[Any]       =   2_4
 __snake_case						:  List[str]       =   2_1
 __snake_case						:  Any       =   1_8
 __snake_case						:  Union[str, Any]       =   1_5
 __snake_case						:  Dict       =   1_2
 __snake_case						:  Union[str, Any]       =   9
 __snake_case						:  List[str]       =   6
 __snake_case						:  Optional[int]       =   3
 __snake_case						:  Tuple       =   2
 __snake_case						:  Any       =   1
 __snake_case						:  Optional[int]       =   -1
 __snake_case						:  Optional[int]       =   -2
 __snake_case						:  str       =   -3
 __snake_case						:  Optional[Any]       =   -6
 __snake_case						:  Optional[int]       =   -9
 __snake_case						:  List[Any]       =   -1_2
 __snake_case						:  str       =   -1_5
 __snake_case						:  str       =   -1_8
 __snake_case						:  Dict       =   -2_1
 __snake_case						:  int       =   -2_4



 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value > 0}





 @classmethod
 def  SCREAMING_SNAKE_CASE(	cls			):






        '''simple docstring'''




        return {unit.name: unit.value for unit in cls if unit.value < 0}






def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		Tuple    =   SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
       for name_prefix, value_prefix in prefixes.items():
              lowercase	:		Dict    =   value / (10**value_prefix)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {name_prefix}"""
       return str(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->str:






       '''simple docstring'''




       for prefix in BinaryUnit:
              lowercase	:		Dict    =   value / (2**prefix.value)
              if numerical_part > 1:
                     return f"""{numerical_part!s} {prefix.name}"""
       return str(lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()




