






'''simple docstring'''




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


__lowerCamelCase   :					str   							=					logging.get_logger(__name__)

__lowerCamelCase   :					List[str]   							=					{'''vocab_file''': '''spiece.model'''}

__lowerCamelCase   :					str   							=					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class    A_	(a_							):
							"""simple docstring"""




							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]       ,							lowerCAmelCase__			:Tuple=False       ,							lowerCAmelCase__			:str=True       ,							lowerCAmelCase__			:Optional[Any]=False       ,							lowerCAmelCase__			:Union[str, Any]="<s>"       ,							lowerCAmelCase__			:List[str]="</s>"       ,							lowerCAmelCase__			:List[str]="<unk>"       ,							lowerCAmelCase__			:Union[str, Any]="<sep>"       ,							lowerCAmelCase__			:List[str]="<pad>"       ,							lowerCAmelCase__			:str="<cls>"       ,							lowerCAmelCase__			:Dict="<mask>"       ,							lowerCAmelCase__			:Optional[Any]=["<eop>", "<eod>"]       ,							lowerCAmelCase__			:Optional[Dict[str, Any]] = None       ,							**lowerCAmelCase__			:Union[str, Any]       ,							)				->  None:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       AddedToken(lowerCAmelCase__       ,							lstrip=lowerCAmelCase__       ,							rstrip=lowerCAmelCase__				) if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				) else mask_token

										snake_case_				:					int   =       {} if sp_model_kwargs is None else sp_model_kwargs

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

										snake_case_				:					Union[str, Any]   =       3

										snake_case_				:					str   =       do_lower_case
										snake_case_				:					Tuple   =       remove_space
										snake_case_				:					Tuple   =       keep_accents
										snake_case_				:					Tuple   =       vocab_file

										snake_case_				:					Optional[int]   =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(lowerCAmelCase__				)

										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."				)
										snake_case_				:					Any   =       jieba
										snake_case_				:					Dict   =       str.maketrans(" \n"       ,							"\u2582\u2583"				)

							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										return len(self.sp_model				)

							def 							_A	(		self			:str				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       {self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab

							def __getstate__(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.__dict__.copy()
										snake_case_				:					Optional[Any]   =       None
										return state

							def __setstate__(		self			:int       ,							lowerCAmelCase__			:Any				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       d

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

										snake_case_				:					Any   =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(self.vocab_file				)

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:str				)				->  Dict:


										'''simple docstring'''




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

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

										return outputs

							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:str				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					str   =       self.preprocess_text(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       self.sp_model.encode(lowerCAmelCase__       ,							out_type=lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       []
										for piece in pieces:
													if len(lowerCAmelCase__				) > 1 and piece[-1] == str(","				) and piece[-2].isdigit():
																snake_case_				:					Optional[Any]   =       self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__       ,							""				)				)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]				) == 1:
																						snake_case_				:					int   =       cur_pieces[1:]
																			else:
																						snake_case_				:					str   =       cur_pieces[0][1:]
																cur_pieces.append(piece[-1]				)
																new_pieces.extend(lowerCAmelCase__				)
													else:
																new_pieces.append(lowerCAmelCase__				)

										return new_pieces

							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:Optional[int]				)				->  List[Any]:


										'''simple docstring'''




										return self.sp_model.PieceToId(lowerCAmelCase__				)

							def 							_A	(		self			:int       ,							lowerCAmelCase__			:Union[str, Any]				)				->  List[Any]:


										'''simple docstring'''




										return self.sp_model.IdToPiece(lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:Any				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Tuple   =       "".join(lowerCAmelCase__				).replace(lowerCAmelCase__       ,							" "				).strip()
										return out_string

							def 							_A	(		self			:Optional[Any]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None				)				->  List[int]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       [self.sep_token_id]
										snake_case_				:					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			:Dict       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None       ,							lowerCAmelCase__			:bool = False				)				->  List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCAmelCase__       ,							token_ids_a=lowerCAmelCase__       ,							already_has_special_tokens=lowerCAmelCase__				)

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

							def 							_A	(		self			:List[str]       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None				)				->  List[int]:


										'''simple docstring'''




										snake_case_				:					str   =       [self.sep_token_id]
										snake_case_				:					int   =       [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			:Dict       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Optional[str] = None				)				->  Tuple[str]:


										'''simple docstring'''




										if not os.path.isdir(lowerCAmelCase__				):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
													return
										snake_case_				:					Optional[Any]   =       os.path.join(
										    lowerCAmelCase__       ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
													copyfile(self.vocab_file       ,							lowerCAmelCase__				)
										elif not os.path.isfile(self.vocab_file				):
													with open(lowerCAmelCase__       ,							"wb"				) as fi:
																snake_case_				:					List[str]   =       self.sp_model.serialized_model_proto()
																fi.write(lowerCAmelCase__				)

										return (out_vocab_file,)






							def 							_A	(		self			:List[str]       ,							*lowerCAmelCase__			:List[str]       ,							**lowerCAmelCase__			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       super()._decode(*lowerCAmelCase__       ,							**lowerCAmelCase__				)
										snake_case_				:					List[str]   =       text.replace(" "       ,							""				).replace("\u2582"       ,							" "				).replace("\u2583"       ,							"\n"				)
										return text












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return math.sqrt(sum(pow(a - b     ,2						) for a, b in zip(__magic_name__     ,__magic_name__						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

			try:
						if dataset.shape[1] != value_array.shape[1]:
									snake_case_				:					Dict   =       (
									    "Wrong input data's shape... "
									    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
									)
									raise ValueError(__magic_name__						)
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape"						)

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return np.dot(__magic_name__     ,__magic_name__						) / (norm(__magic_name__						) * norm(__magic_name__						))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




