




'''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


_lowerCamelCase							:						List[str]								=  logging.get_logger(__name__)
_lowerCamelCase							:						Dict								=  {'vocab_file': 'spiece.model'}

_lowerCamelCase							:						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',
    },
}

_lowerCamelCase							:						int								=  {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

_lowerCamelCase							:						Optional[int]								=  '▁'





class      lowerCamelCase_							(       __lowerCamelCase							):




					'''simple docstring'''






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






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

							_snake_case	       =       {} if sp_model_kwargs is None else sp_model_kwargs

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

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

							_snake_case	       =       spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(_UpperCamelCase )






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return len(self.sp_model )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       {self.convert_ids_to_tokens(_UpperCamelCase ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab






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






					def __setstate__(    self,  __UpperCamelCase ):
							_snake_case	       =       d

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

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






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):





							if self.remove_space:
									_snake_case	       =       " ".join(inputs.strip().split() )
							else:
									_snake_case	       =       inputs
							_snake_case	       =       outputs.replace("``",  "\"" ).replace("''",  "\"" )

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

							return outputs






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

							return new_pieces






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.sp_model.PieceToId(_UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.sp_model.IdToPiece(_UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       []
							_snake_case	       =       ""
							_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(_UpperCamelCase ) + token
											_snake_case	       =       True
											_snake_case	       =       []
									else:
											current_sub_tokens.append(_UpperCamelCase )
											_snake_case	       =       False
							out_string += self.sp_model.decode(_UpperCamelCase )
							return out_string.strip()






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = False,  __UpperCamelCase = None,  __UpperCamelCase = True,  **__UpperCamelCase,  ):
							_snake_case	       =       kwargs.pop("use_source_tokenizer",  _UpperCamelCase )

							_snake_case	       =       self.convert_ids_to_tokens(_UpperCamelCase,  skip_special_tokens=_UpperCamelCase )

							# 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	       =       []
							_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(_UpperCamelCase ) )
													_snake_case	       =       []
											sub_texts.append(_UpperCamelCase )
									else:
											current_sub_text.append(_UpperCamelCase )
							if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase ) )

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

							_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:
									_snake_case	       =       self.clean_up_tokenization(_UpperCamelCase )
									return clean_text
							else:
									return text






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None,  __UpperCamelCase = False ):





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

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






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):





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

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

							return (out_vocab_file,)











from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T", bound=bool)
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=value
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None




     @property
     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         return self._value




     @staticmethod
     def   __A				(	_UpperCamelCase			: RandomizedHeapNode[T] | None				,_UpperCamelCase			: RandomizedHeapNode[T] | None						)		->   RandomizedHeapNode[T] | None:


         '''simple docstring'''





         if not roota:
             return roota

         if not roota:
             return roota

         if roota.value > roota.value:
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota, roota

         if random.choice([True, False]						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=roota.right, roota.left

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(roota.left				,_UpperCamelCase						)

         return roota






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Any				,_UpperCamelCase			: Iterable[T] | None = ()						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None

         if data:
             for item in data:
                 self.insert(_UpperCamelCase						)




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root				,RandomizedHeapNode(_UpperCamelCase						)						)




     def   __A				(	self			: Dict						)		->   T | None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.top()

         if self._root is None:
             return None

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root.left				,self._root.right						)

         return result




     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         if not self._root:
             raise IndexError("""Can't get top element for the empty heap."""						)
         return self._root.value




     def   __A				(	self			: Optional[int]						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: List[Any]						)		->   list[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         while self:
             result.append(self.pop()						)

         return result




     def __bool__(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()






