



"""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


UpperCAmelCase:  int			     =				logging.get_logger(__name__)
UpperCAmelCase:  List[str]			     =				{"""vocab_file""": """spiece.model"""}

UpperCAmelCase:  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""",
    },
}

UpperCAmelCase:  Optional[int]			     =				{
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCAmelCase:  str			     =				"""▁"""





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  ["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_				,):
			# 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.
			_lowercase				: Any     =   (
			    AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_				,normalized=UpperCAmelCase_   )
			    if isinstance(UpperCAmelCase_				,UpperCAmelCase_   )
			    else mask_token
			)

			_lowercase				: Optional[int]     =   {} 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_				,)

			_lowercase				: Tuple     =   do_lower_case
			_lowercase				: List[Any]     =   remove_space
			_lowercase				: Union[str, Any]     =   keep_accents
			_lowercase				: int     =   vocab_file

			_lowercase				: Tuple     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   {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   ):
			_lowercase				: Any     =   self.__dict__.copy()
			_lowercase				: int     =   None
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: Any     =   d

			# for backward compatibility
			if not hasattr(self				,"""sp_model_kwargs"""   ):
					_lowercase				: List[str]     =   {}

			_lowercase				: str     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(self.vocab_file   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if self.remove_space:
					_lowercase				: List[str]     =   """ """.join(inputs.strip().split()   )
			else:
					_lowercase				: Dict     =   inputs
			_lowercase				: List[Any]     =   outputs.replace("""``"""				,"""\""""   ).replace("""''"""				,"""\""""   )

			if not self.keep_accents:
					_lowercase				: Dict     =   unicodedata.normalize("""NFKD"""				,UpperCAmelCase_   )
					_lowercase				: Any     =   """""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase_   )]   )
			if self.do_lower_case:
					_lowercase				: Optional[int]     =   outputs.lower()

			return outputs






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

			return new_pieces






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.PieceToId(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.IdToPiece(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Optional[Any]     =   """"""
			_lowercase				: Any     =   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
							_lowercase				: Union[str, Any]     =   True
							_lowercase				: Optional[int]     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: str     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string.strip()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = False				,UpperCAmelCase_ = None				,UpperCAmelCase_ = True				,**UpperCAmelCase_				,):
			_lowercase				: Dict     =   kwargs.pop("""use_source_tokenizer"""				,UpperCAmelCase_   )

			_lowercase				: Optional[Any]     =   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
			_lowercase				: Union[str, Any]     =   []
			_lowercase				: Union[str, 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(UpperCAmelCase_   )   )
									_lowercase				: Union[str, Any]     =   []
							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:
					_lowercase				: str     =   re.sub(R"""(<unk>) """				,R"""\1"""				,""" """.join(UpperCAmelCase_   )   )
			else:
					_lowercase				: Union[str, Any]     =   """""".join(UpperCAmelCase_   )

			_lowercase				: Any     =   (
			    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(UpperCAmelCase_   )
					return clean_text
			else:
					return text






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: List[Any]     =   [self.sep_token_id]
			_lowercase				: List[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    lowerCamelCase__					(     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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Tuple     =   [self.sep_token_id]
			_lowercase				: int     =   [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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			_lowercase				: str     =   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:
							_lowercase				: int     =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase:  Union[str, Any]			     =				{"""tokenization_phobert""": ["""PhobertTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_phobert import PhobertTokenizer

else:
		import sys

		UpperCAmelCase:  Optional[int]			     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



