

import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_	:			Union[str, Any]  	=      logging.get_logger(__name__)

A_	:			Any  	=      '▁'

A_	:			int  	=      {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

A_	:			Dict  	=      {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

A_	:			Optional[int]  	=      {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

A_	:			int  	=      {
    'ernie-m-base': 514,
    'ernie-m-large': 514,
}

A_	:			int  	=      {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[str]  							=	["input_ids"]

					UpperCAmelCase__:       str  							=	VOCAB_FILES_NAMES
					UpperCAmelCase__:       List[Any]  							=	PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:       Union[str, Any]  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:       List[Any]  							=	PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:       Optional[Any]  							=	RESOURCE_FILES_NAMES






					def __init__(			self   ,	A__   ,	A__=None   ,	A__=False   ,	A__="utf8"   ,	A__="[UNK]"   ,	A__="[SEP]"   ,	A__="[PAD]"   ,	A__="[CLS]"   ,	A__="[MASK]"   ,	A__ = None   ,	**A__   ,	):
						# 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.

						A__							:   Union[str, Any]					       =		{} if sp_model_kwargs is None else sp_model_kwargs
						super().__init__(
						    do_lower_case=A__   ,	unk_token=A__   ,	sep_token=A__   ,	pad_token=A__   ,	cls_token=A__   ,	mask_token=A__   ,	vocab_file=A__   ,	encoding=A__   ,	sp_model_kwargs=self.sp_model_kwargs   ,	**A__   ,	)
						A__							:   Optional[Any]					       =		do_lower_case
						A__							:   str					       =		sentencepiece_model_ckpt
						A__							:   int					       =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
						self.sp_model.Load(A__      )

						# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
						if vocab_file is not None:
							A__							:   Tuple					       =		self.load_vocab(filepath=A__      )
						else:
							A__							:   str					       =		{self.sp_model.id_to_piece(A__      ): id for id in range(self.sp_model.get_piece_size()      )}
						A__							:   Dict					       =		{v: k for k, v in self.vocab.items()}






					def   __A      (			self   ,	A__      ):
						if text is None:
							return None

						A__							:   List[Any]					       =		self.tokenize(A__      )
						A__       ,       A__							:   List[Any]					       =		"""""", []

						for i, ch in enumerate(A__      ):
							if ch in self.SP_CHAR_MAPPING:
								A__							:   List[Any]					       =		self.SP_CHAR_MAPPING.get(A__      )
							else:
								A__							:   List[str]					       =		unicodedata.normalize("""NFKC"""   ,	A__      )
							if self.is_whitespace(A__      ):
								continue
							normalized_text += ch
							char_mapping.extend([i] * len(A__      )      )

						A__       ,       A__       ,       A__							:   Any					       =		normalized_text, [], 0

						if self.do_lower_case:
							A__							:   Tuple					       =		text.lower()

						for token in split_tokens:
							if token[:1] == "▁":
								A__							:   List[str]					       =		token[1:]
							A__							:   int					       =		text[offset:].index(A__      ) + offset
							A__							:   Dict					       =		start + len(A__      )

							token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)      )
							A__							:   Optional[Any]					       =		end
						return token_mapping






					@property
					def   __A      (			self      ):
						return len(self.vocab      )






					def   __A      (			self      ):
						return dict(self.vocab   ,	**self.added_tokens_encoder      )






					def __getstate__(			self      ):
						A__							:   Optional[Any]					       =		self.__dict__.copy()
						A__							:   List[str]					       =		None
						return state






					def __setstate__(			self   ,	A__      ):
						A__							:   Any					       =		d

						# for backward compatibility
						if not hasattr(self   ,	"""sp_model_kwargs"""      ):
							A__							:   Optional[int]					       =		{}

						A__							:   Dict					       =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
						self.sp_model.Load(self.sentencepiece_model_ckpt      )






					def   __A      (			self   ,	A__      ):
						return "".join((self.SP_CHAR_MAPPING.get(A__   ,	A__      ) for c in text)      )






					def   __A      (			self   ,	A__   ,	A__=False   ,	A__=64   ,	A__=0.1      ):

						if self.sp_model_kwargs.get("""enable_sampling"""      ) is True:
							A__							:   str					       =		True
						if self.sp_model_kwargs.get("""alpha"""      ) is not None:
							A__							:   str					       =		self.sp_model_kwargs.get("""alpha"""      )
						if self.sp_model_kwargs.get("""nbest_size"""      ) is not None:
							A__							:   Optional[int]					       =		self.sp_model_kwargs.get("""nbest_size"""      )

						if not enable_sampling:
							A__							:   Union[str, Any]					       =		self.sp_model.EncodeAsPieces(A__      )
						else:
							A__							:   Dict					       =		self.sp_model.SampleEncodeAsPieces(A__   ,	A__   ,	A__      )
						A__							:   str					       =		[]
						for pi, piece in enumerate(A__      ):
							if piece == SPIECE_UNDERLINE:
								if not pieces[pi + 1].startswith(A__      ) and pi != 0:
									new_pieces.append(A__      )
									continue
								else:
									continue
							A__							:   List[str]					       =		0
							for i, chunk in enumerate(A__      ):
								if chunk == SPIECE_UNDERLINE:
									continue
								if self.is_ch_char(A__      ) or self.is_punct(A__      ):
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
										new_pieces.append(piece[lst_i:i]      )
									new_pieces.append(A__      )
									A__							:   List[Any]					       =		i + 1
								elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
										new_pieces.append(piece[lst_i:i]      )
									A__							:   Any					       =		i
								elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
										new_pieces.append(piece[lst_i:i]      )
									A__							:   Any					       =		i
							if len(A__      ) > lst_i:
								new_pieces.append(piece[lst_i:]      )
						return new_pieces






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






					def   __A      (			self   ,	A__      ):
						A__							:   Any					       =		self.convert_ids_to_tokens(A__      )
						A__							:   Optional[int]					       =		"""""".join(A__      ).replace(A__   ,	""" """      ).strip()
						return out_string






					def   __A      (			self   ,	A__      ):
						return self.vocab.get(A__   ,	self.vocab.get(self.unk_token      )      )






					def   __A      (			self   ,	A__      ):
						return self.reverse_vocab.get(A__   ,	self.unk_token      )






					def   __A      (			self   ,	A__   ,	A__=None      ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__							:   Dict					       =		[self.cls_token_id]
						A__							:   List[Any]					       =		[self.sep_token_id]
						return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep






					def   __A      (			self   ,	A__   ,	A__=None      ):
						if offset_mapping_a is None:
							return [(0, 0)] + offset_mapping_a + [(0, 0)]

						return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]






					def   __A      (			self   ,	A__   ,	A__=None   ,	A__=False      ):

						if already_has_special_tokens:
							if token_ids_a is not None:
								raise ValueError(
								    """You should not supply a second sequence if the provided sequence of """
								    """ids is already formatted with special tokens for the model."""      )
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
							return [1] + ([0] * len(A__      )) + [1, 1] + ([0] * len(A__      )) + [1]
						return [1] + ([0] * len(A__      )) + [1]






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
						if token_ids_a is None:
							# [CLS] X [SEP]
							return (len(A__      ) + 2) * [0]

						# [CLS] A [SEP] [SEP] B [SEP]
						return [0] * (len(A__      ) + 1) + [1] * (len(A__      ) + 3)






					def   __A      (			self   ,	A__      ):
						if "\u4e00" <= char <= "\u9fff":
							return True
						return False






					def   __A      (			self   ,	A__      ):
						if ("a" <= char <= "z") or ("A" <= char <= "Z"):
							return True
						return False






					def   __A      (			self   ,	A__      ):
						if char in ",;:.?!~，；：。？！《》【】":
							return True
						return False






					def   __A      (			self   ,	A__      ):
						if char == " " or char == "\t" or char == "\n" or char == "\r":
							return True
						if len(A__      ) == 1:
							A__							:   Optional[int]					       =		unicodedata.category(A__      )
							if cat == "Zs":
								return True
						return False






					def   __A      (			self   ,	A__      ):
						A__							:   Optional[int]					       =		{}
						with io.open(A__   ,	"""r"""   ,	encoding="""utf-8"""      ) as f:
							for index, line in enumerate(A__      ):
								A__							:   Tuple					       =		line.rstrip("""\n"""      )
								A__							:   int					       =		int(A__      )

						return token_to_idx






					def   __A      (			self   ,	A__   ,	A__ = None      ):
						A__							:   List[str]					       =		0
						if os.path.isdir(A__      ):
							A__							:   Union[str, Any]					       =		os.path.join(
							    A__   ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
						else:
							A__							:   str					       =		(filename_prefix + """-""" if filename_prefix else """""") + save_directory
						with open(A__   ,	"""w"""   ,	encoding="""utf-8"""      ) as writer:
							for token, token_index in sorted(self.vocab.items()   ,	key=lambda A__      : kv[1]      ):
								if index != token_index:
									logger.warning(
									    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
									    """ Please check that the vocabulary is not corrupted!"""      )
									A__							:   int					       =		token_index
								writer.write(token + """\n"""      )
								index += 1

						A__							:   str					       =		os.path.join(A__   ,	"""sentencepiece.bpe.model"""      )
						with open(A__   ,	"""wb"""      ) as fi:
							A__							:   Dict					       =		self.sp_model.serialized_model_proto()
							fi.write(A__      )

						return (vocab_file,)






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			float   ,				lowercase_:			float   ,				lowercase_:			float )					->				dict[str, float]:
	if (voltage, current, resistance).count(0 ) != 1:
		raise ValueError("""One and only one argument must be 0""" )
	if resistance < 0:
		raise ValueError("""Resistance cannot be negative""" )
	if voltage == 0:
		return {"voltage": float(current * resistance )}
	elif current == 0:
		return {"current": voltage / resistance}
	elif resistance == 0:
		return {"resistance": voltage / current}
	else:
		raise ValueError("""Exactly one argument must be 0""" )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



