






import os
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      :    Optional[int]   		=   logging.get_logger(__name__)
__lowerCamelCase      :    Optional[Any]   		=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase      :    Union[str, Any]   		=   {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

__lowerCamelCase      :    Any   		=   {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

__lowerCamelCase      :    Tuple   		=   """▁"""



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



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

						snake_case__				:   Optional[int]     =						{} if sp_model_kwargs is None else sp_model_kwargs

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

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

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



					@property
					def  _lowercase  (      self  :						str ):
						return len(self.sp_model )



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



					def __setstate__(      self  :						Optional[int]							,	__A  :						int ):
						snake_case__				:   Optional[int]     =						d

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

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



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

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

						return outputs



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

						return new_pieces



					def  _lowercase  (      self  :						Tuple							,	__A  :						str ):
						return self.sp_model.PieceToId(__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						Optional[int] ):
						return self.sp_model.IdToPiece(__A )



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



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   Tuple     =						[self.sep_token_id]
						snake_case__				:   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  _lowercase  (      self  :						Tuple							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):

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

						return (out_vocab_file,)











def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	snake_case__				:   Any     =						[0] * len(snake_case_     )

	for i in range(1  ,						len(snake_case_     )     ):
		# use last results for better performance - dynamic programming
		snake_case__				:   Union[str, Any]     =						prefix_result[i - 1]
		while j > 0 and input_string[i] != input_string[j]:
			snake_case__				:   str     =						prefix_result[j - 1]

		if input_string[i] == input_string[j]:
			j += 1
		snake_case__				:   int     =						j

	return prefix_result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	return max(prefix_function(snake_case_     )     )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



