

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 SPIECE_UNDERLINE, logging


A_			:					List[str]    			=     logging.get_logger(__name__)

A_			:					List[Any]    			=     {'vocab_file': 'spiece.model'}

A_			:					Union[str, Any]    			=     {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__=False       ,			A__=True       ,			A__=False       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<unk>"       ,			A__="<sep>"       ,			A__="<pad>"       ,			A__="<cls>"       ,			A__="<mask>"       ,			A__=["<eop>", "<eod>"]       ,			A__ = None       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       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__       ,			additional_special_tokens=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       Any	  =						3

						A__				:       List[Any]	  =						do_lower_case
						A__				:       Optional[Any]	  =						remove_space
						A__				:       int	  =						keep_accents
						A__				:       Any	  =						vocab_file

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

						try:
							import jieba
						except ModuleNotFoundError as error:
							raise error.__class__(
							    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
							    """See https://pypi.org/project/jieba/ for installation."""     )
						A__				:       Tuple	  =						jieba
						A__				:       List[str]	  =						str.maketrans(""" \n"""       ,			"""\u2582\u2583"""     )






					@property
					# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
					def     __A     (					self     ):
						return len(self.sp_model     )






					def     __A     (					self     ):
						A__				:       Tuple	  =						{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     ):
						A__				:       List[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       List[Any]	  =						d

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

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






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

						if not self.keep_accents:
							A__				:       Optional[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       ,			A__     ):
						A__				:       Any	  =						self.preprocess_text(A__     )
						A__				:       Dict	  =						self.sp_model.encode(A__       ,			out_type=A__     )
						A__				:       Dict	  =						[]
						for piece in pieces:
							if len(A__     ) > 1 and piece[-1] == str(""","""     ) and piece[-2].isdigit():
								A__				:       Union[str, 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:
										A__				:       int	  =						cur_pieces[1:]
									else:
										A__				:       Union[str, Any]	  =						cur_pieces[0][1:]
								cur_pieces.append(piece[-1]     )
								new_pieces.extend(A__     )
							else:
								new_pieces.append(A__     )

						return new_pieces






					def     __A     (					self       ,			A__     ):
						return self.sp_model.PieceToId(A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.IdToPiece(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Union[str, Any]	  =						"""""".join(A__     ).replace(A__       ,			""" """     ).strip()
						return out_string






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Tuple	  =						[self.sep_token_id]
						A__				:       Union[str, Any]	  =						[self.cls_token_id]
						if token_ids_a is None:
							return token_ids_a + sep + cls
						return token_ids_a + sep + token_ids_a + sep + cls






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = 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 ([0] * len(A__     )) + [1] + ([0] * len(A__     )) + [1, 1]
						return ([0] * len(A__     )) + [1, 1]






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       str	  =						[self.sep_token_id]
						A__				:       Optional[Any]	  =						[2]

						if token_ids_a is None:
							return len(token_ids_a + sep     ) * [0] + cls_segment_id
						return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       Union[str, 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:
								A__				:       str	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






					def     __A     (					self       ,			*A__       ,			**A__     ):
						A__				:       Tuple	  =						super()._decode(*A__       ,			**A__     )
						A__				:       Any	  =						text.replace(""" """       ,			""""""     ).replace("""\u2582"""       ,			""" """     ).replace("""\u2583"""       ,			"""\n"""     )
						return text






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



