


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


SCREAMING_SNAKE_CASE : str          =    logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any]          =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE : List[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""",
    },
}

SCREAMING_SNAKE_CASE : Any          =    {
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

SCREAMING_SNAKE_CASE : Optional[int]          =    """▁"""





class 					snake_case__  ( _SCREAMING_SNAKE_CASE       ):
	UpperCAmelCase   :							str         = VOCAB_FILES_NAMES
	UpperCAmelCase   :							str         = PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase   :							List[str]         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase   :							List[str]         = ["""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_				,    )		->			None:
								"""simple docstring"""






								a_       :   Tuple			=	(
								    AddedToken(__A				,    lstrip=__A				,    rstrip=__A				,    normalized=__A							)
								    if isinstance(__A				,    __A							)
								    else mask_token
								)

								a_       :   Any			=	{} 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				,    )

								a_       :   Tuple			=	do_lower_case
								a_       :   str			=	remove_space
								a_       :   Tuple			=	keep_accents
								a_       :   Dict			=	vocab_file

								a_       :   int			=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(__A							)





	@property
	def     A				(  self							)		->			Optional[int]:
								"""simple docstring"""






								return len(self.sp_model							)





	def     A				(  self							)		->			int:
								"""simple docstring"""






								a_       :   str			=	{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							)		->			List[Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	self.__dict__.copy()
								a_       :   Any			=	None
								return state





	def __setstate__(  self				,    UpperCamelCase_							)		->			Any:
								"""simple docstring"""






								a_       :   int			=	d

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

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





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






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

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

								return outputs





	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
								"""simple docstring"""






								a_       :   Optional[int]			=	self.preprocess_text(__A							)
								a_       :   int			=	self.sp_model.encode(__A				,    out_type=__A							)
								a_       :   Optional[int]			=	[]
								for piece in pieces:
															if len(__A							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																						a_       :   Dict			=	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:
																																				a_       :   Any			=	cur_pieces[1:]
																													else:
																																				a_       :   List[str]			=	cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]							)
																						new_pieces.extend(__A							)
															else:
																						new_pieces.append(__A							)

								return new_pieces





	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
								"""simple docstring"""






								return self.sp_model.PieceToId(__A							)





	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
								"""simple docstring"""






								return self.sp_model.IdToPiece(__A							)





	def     A				(  self				,    UpperCamelCase_							)		->			int:
								"""simple docstring"""






								a_       :   Optional[Any]			=	[]
								a_       :   int			=	""""""
								a_       :   str			=	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
																						a_       :   Optional[Any]			=	True
																						a_       :   str			=	[]
															else:
																						current_sub_tokens.append(__A							)
																						a_       :   Optional[Any]			=	False
								out_string += self.sp_model.decode(__A							)
								return out_string.strip()





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = True				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								a_       :   str			=	kwargs.pop("""use_source_tokenizer"""				,    __A							)

								a_       :   Union[str, Any]			=	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
								a_       :   str			=	[]
								a_       :   str			=	[]
								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							)							)
																													a_       :   str			=	[]
																						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:
															a_       :   List[str]			=	re.sub(R"""(<unk>) """				,    R"""\1"""				,    """ """.join(__A							)							)
								else:
															a_       :   List[str]			=	"""""".join(__A							)

								a_       :   Union[str, Any]			=	(
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
															a_       :   Dict			=	self.clean_up_tokenization(__A							)
															return clean_text
								else:
															return text





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
								"""simple docstring"""






								a_       :   str			=	[self.sep_token_id]
								a_       :   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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False							)		->			List[int]:
								"""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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
								"""simple docstring"""






								a_       :   Dict			=	[self.sep_token_id]
								a_       :   Optional[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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			Tuple[str]:
								"""simple docstring"""






								if not os.path.isdir(__A							):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
															return
								a_       :   Dict			=	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:
																						a_       :   List[Any]			=	self.sp_model.serialized_model_proto()
																						fi.write(__A							)

								return (out_vocab_file,)






def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):






							"""simple docstring"""



							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
							if n in (0, 1):
														return 0
							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


