


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


a_       :Any				  =				logging.get_logger(__name__)
a_       :Optional[Any]				  =				{"vocab_file": "spiece.model"}

a_       :Optional[int]				  =				{
    "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",
    },
}

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

a_       :Optional[Any]				  =				"▁"




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE            =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE            =     ["""input_ids""", """token_type_ids"""]






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

            snake_case__							:     Any            =     {} if sp_model_kwargs is None else sp_model_kwargs

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

            snake_case__							:     Dict            =     do_lower_case
            snake_case__							:     Optional[Any]            =     remove_space
            snake_case__							:     str            =     keep_accents
            snake_case__							:     Dict            =     vocab_file

            snake_case__							:     List[Any]            =     spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(_snake_case )






     @property
     def        lowercase_       (	self  :  Dict )       ->Tuple:
            return len(self.sp_model )






     def        lowercase_       (	self  :  str )       ->List[str]:
            snake_case__							:     Tuple            =     {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab






     def __getstate__(	self  :  Optional[Any] )       ->List[str]:
            snake_case__							:     Optional[Any]            =     self.__dict__.copy()
            snake_case__							:     int            =     None
            return state






     def __setstate__(	self  :  List[Any],			_snake_case  :  Any )       ->int:
            snake_case__							:     Dict            =     d

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

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






     def        lowercase_       (	self  :  int,			_snake_case  :  Optional[Any] )       ->Tuple:
            if self.remove_space:
                   snake_case__							:     Dict            =     ' '.join(inputs.strip().split() )
            else:
                   snake_case__							:     Any            =     inputs
            snake_case__							:     List[str]            =     outputs.replace('``',			'"' ).replace('\'\'',			'"' )

            if not self.keep_accents:
                   snake_case__							:     Union[str, Any]            =     unicodedata.normalize('NFKD',			_snake_case )
                   snake_case__							:     Optional[Any]            =     ''.join([c for c in outputs if not unicodedata.combining(_snake_case )] )
            if self.do_lower_case:
                   snake_case__							:     Optional[Any]            =     outputs.lower()

            return outputs






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

            return new_pieces






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  Tuple )       ->int:
            return self.sp_model.PieceToId(_snake_case )






     def        lowercase_       (	self  :  int,			_snake_case  :  Dict )       ->Tuple:
            return self.sp_model.IdToPiece(_snake_case )






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  List[Any] )       ->str:
            snake_case__							:     int            =     []
            snake_case__							:     int            =     ''
            snake_case__							:     int            =     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(_snake_case ) + token
                          snake_case__							:     str            =     True
                          snake_case__							:     int            =     []
                   else:
                          current_sub_tokens.append(_snake_case )
                          snake_case__							:     List[str]            =     False
            out_string += self.sp_model.decode(_snake_case )
            return out_string.strip()






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  List[int],			_snake_case  :  bool = False,			_snake_case  :  bool = None,			_snake_case  :  bool = True,			**_snake_case  :  Any,			)       ->str:
            snake_case__							:     str            =     kwargs.pop('use_source_tokenizer',			_snake_case )

            snake_case__							:     Optional[Any]            =     self.convert_ids_to_tokens(_snake_case,			skip_special_tokens=_snake_case )

            # 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__							:     List[str]            =     []
            snake_case__							:     Dict            =     []
            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(_snake_case ) )
                                 snake_case__							:     int            =     []
                          sub_texts.append(_snake_case )
                   else:
                          current_sub_text.append(_snake_case )
            if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(_snake_case ) )

            # Mimic the behavior of the Rust tokenizer:
            # No space after <unk>
            if spaces_between_special_tokens:
                   snake_case__							:     Union[str, Any]            =     re.sub(R'(<unk>) ',			R'\1',			' '.join(_snake_case ) )
            else:
                   snake_case__							:     Optional[Any]            =     ''.join(_snake_case )

            snake_case__							:     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__							:     Any            =     self.clean_up_tokenization(_snake_case )
                   return clean_text
            else:
                   return text






     def        lowercase_       (	self  :  int,			_snake_case  :  List[int],			_snake_case  :  Optional[List[int]] = None )       ->List[int]:
            snake_case__							:     Dict            =     [self.sep_token_id]
            snake_case__							:     List[str]            =     [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  :  int,			_snake_case  :  List[int],			_snake_case  :  Optional[List[int]] = None,			_snake_case  :  bool = False )       ->List[int]:

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

            if token_ids_a is not None:
                   return [1] + ([0] * len(_snake_case )) + [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 )       ->List[int]:
            snake_case__							:     str            =     [self.sep_token_id]
            snake_case__							:     List[str]            =     [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  :  Optional[int],			_snake_case  :  str,			_snake_case  :  Optional[str] = None )       ->Tuple[str]:
            if not os.path.isdir(_snake_case ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                   return
            snake_case__							:     Dict            =     os.path.join(
                _snake_case,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

            if os.path.abspath(self.vocab_file ) != os.path.abspath(_snake_case ) and os.path.isfile(self.vocab_file ):
                   copyfile(self.vocab_file,			_snake_case )
            elif not os.path.isfile(self.vocab_file ):
                   with open(_snake_case,			'wb' ) as fi:
                          snake_case__							:     Optional[Any]            =     self.sp_model.serialized_model_proto()
                          fi.write(_snake_case )

            return (out_vocab_file,)











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






