





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


UpperCAmelCase						:   str			  =     logging.get_logger(__name__)

UpperCAmelCase						:   Any			  =     '''▁'''

UpperCAmelCase						:   Union[str, Any]			  =     {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

UpperCAmelCase						:   Tuple			  =     {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

UpperCAmelCase						:   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''',
    },
}

UpperCAmelCase						:   List[Any]			  =     {
    '''ernie-m-base''': 5_14,
    '''ernie-m-large''': 5_14,
}

UpperCAmelCase						:   List[str]			  =     {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


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

				UpperCamelCase    :  str					    =	VOCAB_FILES_NAMES
				UpperCamelCase    :  Optional[int]					    =	PRETRAINED_INIT_CONFIGURATION
				UpperCamelCase    :  str					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase    :  Optional[Any]					    =	PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase    :  int					    =	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	:						List[str]       =				{} 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	:						int       =				do_lower_case
									__A	:						Any       =				sentencepiece_model_ckpt
									__A	:						Union[str, Any]       =				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	:						Any       =				self.load_vocab(filepath=_A     )
									else:
														__A	:						Optional[Any]       =				{self.sp_model.id_to_piece(_A     ): id for id in range(self.sp_model.get_piece_size()     )}
									__A	:						Tuple       =				{v: k for k, v in self.vocab.items()}



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if text is None:
														return None

									__A	:						List[str]       =				self.tokenize(_A     )
									__A    ,				__A	:						Tuple       =				'', []

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

									__A    ,				__A    ,				__A	:						Dict       =				normalized_text, [], 0

									if self.do_lower_case:
														__A	:						Union[str, Any]       =				text.lower()

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

														token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)     )
														__A	:						str       =				end
									return token_mapping



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



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



				def __getstate__(			self     ):
									__A	:						List[Any]       =				self.__dict__.copy()
									__A	:						Dict       =				None
									return state



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

									# for backward compatibility
									if not hasattr(self     ,					'sp_model_kwargs'     ):
														__A	:						int       =				{}

									__A	:						str       =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
									self.sp_model.Load(self.sentencepiece_model_ckpt     )



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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=False     ,					_A=64     ,					_A=0.1     ):

									if self.sp_model_kwargs.get('enable_sampling'     ) is True:
														__A	:						Union[str, Any]       =				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[Any]       =				self.sp_model_kwargs.get('nbest_size'     )

									if not enable_sampling:
														__A	:						List[Any]       =				self.sp_model.EncodeAsPieces(_A     )
									else:
														__A	:						Optional[int]       =				self.sp_model.SampleEncodeAsPieces(_A     ,					_A     ,					_A     )
									__A	:						int       =				[]
									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	:						Optional[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	:						Optional[Any]       =				i
														if len(_A     ) > lst_i:
																			new_pieces.append(piece[lst_i:]     )
									return new_pieces



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						List[Any]       =				''.join(_A     ).replace(_A     ,					' '     ).strip()
									return out_string



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Optional[Any]       =				self.convert_ids_to_tokens(_A     )
									__A	:						Tuple       =				''.join(_A     ).replace(_A     ,					' '     ).strip()
									return out_string



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return self.vocab.get(_A     ,					self.vocab.get(self.unk_token     )     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									return self.reverse_vocab.get(_A     ,					self.unk_token     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=None     ):
									if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									__A	:						str       =				[self.cls_token_id]
									__A	:						Union[str, Any]       =				[self.sep_token_id]
									return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



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



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



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



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if char == " " or char == "\t" or char == "\n" or char == "\r":
														return True
									if len(_A     ) == 1:
														__A	:						Union[str, Any]       =				unicodedata.category(_A     )
														if cat == "Zs":
																			return True
									return False



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

									return token_to_idx





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ):
									__A	:						int       =				0
									if os.path.isdir(_A     ):
														__A	:						List[str]       =				os.path.join(
														    _A     ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
									else:
														__A	:						Dict       =				(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	:						Tuple       =				token_index
																			writer.write(token + '\n'     )
																			index += 1

									__A	:						Dict       =				os.path.join(_A     ,					'sentencepiece.bpe.model'     )
									with open(_A     ,					'wb'     ) as fi:
														__A	:						Any       =				self.sp_model.serialized_model_proto()
														fi.write(_A     )

									return (vocab_file,)











def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




