"""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


a							=logging.get_logger(__name__)
a							={'vocab_file': 'spiece.model'}

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

a							='▁'






class        __UpperCAmelCase						(     lowerCAmelCase__     ):
				A__				:       List[str]  							=	VOCAB_FILES_NAMES
				A__				:       int  							=	PRETRAINED_VOCAB_FILES_MAP
				A__				:       Optional[int]  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__				:       Dict  							=	['''input_ids''', '''token_type_ids''']

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

										lowerCamelCase__ ={} 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     ,			)

										lowerCamelCase__ =do_lower_case
										lowerCamelCase__ =remove_space
										lowerCamelCase__ =keep_accents
										lowerCamelCase__ =vocab_file

										lowerCamelCase__ =spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(__lowerCAmelCase				)

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

				def 		_a     (				self				):
										lowerCamelCase__ ={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				):
										lowerCamelCase__ =self.__dict__.copy()
										lowerCamelCase__ =None
										return state

				def __setstate__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =d

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

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

				def 		_a     (				self     ,			_lowerCamelCase				):


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

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

										return outputs

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =self.preprocess_text(__lowerCAmelCase				)
										lowerCamelCase__ =self.sp_model.encode(__lowerCAmelCase     ,			out_type=__lowerCAmelCase				)
										lowerCamelCase__ =[]
										for piece in pieces:
																if len(__lowerCAmelCase				) > 1 and piece[-1] == str(","				) and piece[-2].isdigit():
																						lowerCamelCase__ =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:
																																		lowerCamelCase__ =cur_pieces[1:]
																												else:
																																		lowerCamelCase__ =cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]				)
																						new_pieces.extend(__lowerCAmelCase				)
																else:
																						new_pieces.append(__lowerCAmelCase				)

										return new_pieces

				def 		_a     (				self     ,			_lowerCamelCase				):
										return self.sp_model.PieceToId(__lowerCAmelCase				)

				def 		_a     (				self     ,			_lowerCamelCase				):
										return self.sp_model.IdToPiece(__lowerCAmelCase				)

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =[]
										lowerCamelCase__ =""
										lowerCamelCase__ =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
																						lowerCamelCase__ =True
																						lowerCamelCase__ =[]
																else:
																						current_sub_tokens.append(__lowerCAmelCase				)
																						lowerCamelCase__ =False
										out_string += self.sp_model.decode(__lowerCAmelCase				)
										return out_string.strip()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = False     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			**_lowerCamelCase     ,			):
										lowerCamelCase__ =kwargs.pop("use_source_tokenizer"     ,			__lowerCAmelCase				)

										lowerCamelCase__ =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
										lowerCamelCase__ =[]
										lowerCamelCase__ =[]
										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				)				)
																												lowerCamelCase__ =[]
																						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:
																lowerCamelCase__ =re.sub(r"(<unk>) "     ,			r"\1"     ,			" ".join(__lowerCAmelCase				)				)
										else:
																lowerCamelCase__ ="".join(__lowerCAmelCase				)

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

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
										lowerCamelCase__ =[self.sep_token_id]
										lowerCamelCase__ =[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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
										lowerCamelCase__ =[self.sep_token_id]
										lowerCamelCase__ =[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 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):


										if not os.path.isdir(__lowerCAmelCase				):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
																return
										lowerCamelCase__ =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:
																						lowerCamelCase__ =self.sp_model.serialized_model_proto()
																						fi.write(__lowerCAmelCase				)

										return (out_vocab_file,)



"""simple docstring"""


from __future__ import annotations





def       lowerCamelCase_			( __lowerCAmelCase    )							-> int | float:




						'''simple docstring'''



						lowerCamelCase__ =sorted(__lowerCAmelCase    )
						lowerCamelCase__ =len(__lowerCAmelCase    )
						lowerCamelCase__ =length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


