


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowerCAmelCase	: Optional[Any]  				='▁'

__lowerCAmelCase	: Union[str, Any]  				={'vocab_file': 'spiece.model'}

__lowerCAmelCase	: Optional[int]  				={
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

__lowerCAmelCase	: Optional[int]  				={
    'google/pegasus-xsum': 5_1_2,
}


__lowerCAmelCase	: Union[str, Any]  				=logging.get_logger(__name__)
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		VOCAB_FILES_NAMES

				SCREAMING_SNAKE_CASE__				:			List[str]						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Tuple						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:str			,			lowerCAmelCase__			:Tuple			,			lowerCAmelCase__			:Dict="<pad>"			,			lowerCAmelCase__			:List[Any]="</s>"			,			lowerCAmelCase__			:Tuple="<unk>"			,			lowerCAmelCase__			:str="<mask_2>"			,			lowerCAmelCase__			:Dict="<mask_1>"			,			lowerCAmelCase__			:Union[str, Any]=None			,			lowerCAmelCase__			:Union[str, Any]=103			,			lowerCAmelCase__			:Optional[Dict[str, Any]] = None			,			**lowerCAmelCase__			:str			,			)		->			None:
										__SCREAMING_SNAKE_CASE						:     int					=		offset
										if additional_special_tokens is not None:
																if not isinstance(lowerCAmelCase__			,			lowerCAmelCase__	):
																						raise TypeError(
																						    f'''additional_special_tokens should be of type {type(lowerCAmelCase__	)}, but is'''
																						    f''' {type(lowerCAmelCase__	)}'''	)

																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		(
																    ([mask_token_sent] + additional_special_tokens)
																    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																    else additional_special_tokens
																)
																# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																additional_special_tokens_extended += [
																    f'''<unk_{i}>''' for i in range(len(lowerCAmelCase__	)			,			self.offset - 1	)
																]

																if len(set(lowerCAmelCase__	)	) != len(lowerCAmelCase__	):
																						raise ValueError(
																						    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																						    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''	)
																__SCREAMING_SNAKE_CASE						:     List[str]					=		additional_special_tokens_extended
										else:
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		[mask_token_sent] if mask_token_sent is not None else []
																additional_special_tokens += [f'''<unk_{i}>''' for i in range(2			,			self.offset	)]

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			mask_token=lowerCAmelCase__			,			pad_token=lowerCAmelCase__			,			mask_token_sent=lowerCAmelCase__			,			offset=lowerCAmelCase__			,			additional_special_tokens=lowerCAmelCase__			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowerCAmelCase__			,			)
										__SCREAMING_SNAKE_CASE						:     Dict					=		mask_token_sent
										__SCREAMING_SNAKE_CASE						:     Any					=		vocab_file
										__SCREAMING_SNAKE_CASE						:     int					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(lowerCAmelCase__	)

										# add special tokens to encoder dict
										__SCREAMING_SNAKE_CASE						:     Dict[int, str]					=		{
										    0: self.pad_token,
										    1: self.eos_token,
										}

										if self.mask_token_sent is not None:
																self.encoder.update(
																    {
																        2: self.mask_token_sent,
																        3: self.mask_token,
																    }	)

										if self.offset > 0:
																# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																# mask_token_sent is already added to list -> so start at 1
																self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,			self.offset - 1	)}	)

										__SCREAMING_SNAKE_CASE						:     Dict[str, int]					=		{v: k for k, v in self.encoder.items()}


				@property
				def 		__magic_name__(       self			:Dict	)		->			int:
										return len(self.sp_model	) + self.offset


				def 		__magic_name__(       self			:Dict	)		->			Dict[str, int]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{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			:List[str]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     str					=		self.__dict__.copy()
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										return state


				def __setstate__(       self			:Any			,			lowerCAmelCase__			:Tuple	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		d

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

										__SCREAMING_SNAKE_CASE						:     Tuple					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(self.vocab_file	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str	)		->			List[str]:
										return self.sp_model.encode(lowerCAmelCase__			,			out_type=lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:str	)		->			int:
										if token in self.decoder:
																return self.decoder[token]
										elif token in self.added_tokens_decoder:
																return self.added_tokens_decoder[token]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.sp_model.piece_to_id(lowerCAmelCase__	)
										return sp_id + self.offset


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:int	)		->			str:
										if index in self.encoder:
																return self.encoder[index]
										elif index in self.added_tokens_encoder:
																return self.added_tokens_encoder[index]
										else:
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.sp_model.IdToPiece(index - self.offset	)
										return token


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Optional[int]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										__SCREAMING_SNAKE_CASE						:     Tuple					=		''''''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(lowerCAmelCase__	) + token
																						__SCREAMING_SNAKE_CASE						:     int					=		[]
																else:
																						current_sub_tokens.append(lowerCAmelCase__	)
										out_string += self.sp_model.decode(lowerCAmelCase__	)
										return out_string.strip()


				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:Optional[Any]=False	)		->			Dict:
										return 1


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		set(self.all_special_ids	)  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

										return [1 if x in all_special_ids else 0 for x in seq]


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:List			,			lowerCAmelCase__			:Optional[List] = None			,			lowerCAmelCase__			:bool = False	)		->			List[int]:
										if already_has_special_tokens:
																return self._special_token_mask(lowerCAmelCase__	)
										elif token_ids_a is None:
																return self._special_token_mask(lowerCAmelCase__	) + [1]
										else:
																return self._special_token_mask(token_ids_a + token_ids_a	) + [1]


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[Any]=None	)		->			List[int]:
										if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										if not os.path.isdir(lowerCAmelCase__	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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:
																						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase__	)

										return (out_vocab_file,)




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
