






"""simple docstring"""
import os
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 logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   "▁"

lowerCamelCase_       			=   {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

lowerCamelCase_       			=   {
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

lowerCamelCase_       			=   {"vinai/bartpho-syllable": 1024}







class     UpperCamelCase_ (__lowerCAmelCase  ):
	__magic_name__			   =       VOCAB_FILES_NAMES
	__magic_name__			   =       PRETRAINED_VOCAB_FILES_MAP
	__magic_name__			   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__			   =       ['''input_ids''', '''attention_mask''']





	def __init__(   self      :				str			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Any="<s>"			,			lowerCAmelCase_      :				List[str]="</s>"			,			lowerCAmelCase_      :				Any="</s>"			,			lowerCAmelCase_      :				Tuple="<s>"			,			lowerCAmelCase_      :				List[str]="<unk>"			,			lowerCAmelCase_      :				Tuple="<pad>"			,			lowerCAmelCase_      :				Any="<mask>"			,			lowerCAmelCase_      :				Optional[Dict[str, Any]] = None			,			**lowerCAmelCase_      :				Optional[int]			,			)		->    None:
				UpperCAmelCase_  :				List[Any]      =      AddedToken(_UpperCamelCase			,			lstrip=_UpperCamelCase			,			rstrip=_UpperCamelCase		) if isinstance(_UpperCamelCase			,			_UpperCamelCase		) else mask_token

				UpperCAmelCase_  :				Union[str, Any]      =      {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_UpperCamelCase			,			eos_token=_UpperCamelCase			,			unk_token=_UpperCamelCase			,			sep_token=_UpperCamelCase			,			cls_token=_UpperCamelCase			,			pad_token=_UpperCamelCase			,			mask_token=_UpperCamelCase			,			sp_model_kwargs=self.sp_model_kwargs			,			**_UpperCamelCase			,			)

				UpperCAmelCase_  :				Union[str, Any]      =      vocab_file
				UpperCAmelCase_  :				Optional[int]      =      monolingual_vocab_file
				UpperCAmelCase_  :				Optional[Any]      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(_UpperCamelCase		)		)

				# Load the reduced vocab

				# Keep order of special tokens for backward compatibility
				UpperCAmelCase_  :				str      =      {}
				UpperCAmelCase_  :				str      =      0
				for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
							if str(_UpperCamelCase		) not in self.fairseq_tokens_to_ids:
										UpperCAmelCase_  :				Union[str, Any]      =      cnt
										cnt += 1
				with open(_UpperCamelCase			,			"r"			,			encoding="utf-8"		) as f:
							for line in f.readlines():
										UpperCAmelCase_  :				Union[str, Any]      =      line.strip().split()[0]
										UpperCAmelCase_  :				Union[str, Any]      =      len(self.fairseq_tokens_to_ids		)
				if str(_UpperCamelCase		) not in self.fairseq_tokens_to_ids:
							UpperCAmelCase_  :				List[str]      =      len(self.fairseq_tokens_to_ids		)

				UpperCAmelCase_  :				List[str]      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def __getstate__(   self      :				Union[str, Any]		)		->    Tuple:
				UpperCAmelCase_  :				int      =      self.__dict__.copy()
				UpperCAmelCase_  :				Any      =      None
				UpperCAmelCase_  :				str      =      self.sp_model.serialized_model_proto()
				return state





	def __setstate__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      d

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

				UpperCAmelCase_  :				Union[str, Any]      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





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


				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_  :				Dict      =      [self.cls_token_id]
				UpperCAmelCase_  :				List[Any]      =      [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None			,			lowerCAmelCase_      :				bool = False		)		->    List[int]:


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

				if token_ids_a is None:
							return [1] + ([0] * len(_UpperCamelCase		)) + [1]
				return [1] + ([0] * len(_UpperCamelCase		)) + [1, 1] + ([0] * len(_UpperCamelCase		)) + [1]





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:
				UpperCAmelCase_  :				Optional[Any]      =      [self.sep_token_id]
				UpperCAmelCase_  :				List[Any]      =      [self.cls_token_id]

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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Tuple:
				return len(self.fairseq_ids_to_tokens		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				Optional[Any]      =      {self.convert_ids_to_tokens(_UpperCamelCase		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				str		)		->    List[str]:
				return self.sp_model.encode(_UpperCamelCase			,			out_type=_UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Tuple		)		->    List[Any]:


				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				else:
							return self.unk_token_id





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				return self.fairseq_ids_to_tokens[index]





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				Dict      =      """""".join(_UpperCamelCase		).replace(_UpperCamelCase			,			" "		).strip()
				return out_string





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[str] = None		)		->    Tuple[str]:


				if not os.path.isdir(_UpperCamelCase		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_  :				Dict      =      os.path.join(
				    _UpperCamelCase			,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_  :				str      =      os.path.join(
				    _UpperCamelCase			,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]			,			)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCamelCase		) and os.path.isfile(self.vocab_file		):
							copyfile(self.vocab_file			,			_UpperCamelCase		)
				elif not os.path.isfile(self.vocab_file		):
							with open(_UpperCamelCase			,			"wb"		) as fi:
										UpperCAmelCase_  :				int      =      self.sp_model.serialized_model_proto()
										fi.write(_UpperCamelCase		)

				if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
				    _UpperCamelCase		) and os.path.isfile(self.monolingual_vocab_file		):
							copyfile(self.monolingual_vocab_file			,			_UpperCamelCase		)
				elif not os.path.isfile(self.monolingual_vocab_file		):
							with open(_UpperCamelCase			,			"w"			,			encoding="utf-8"		) as fp:
										for token in self.fairseq_tokens_to_ids:
													if token not in self.all_special_tokens:
																fp.write(f"""{str(_UpperCamelCase		)} \n"""		)

				return out_vocab_file, out_monolingual_vocab_file











"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      self.__linear
				elif kernel == "rbf":
							if self.gamma == 0:
										raise ValueError("rbf kernel requires gamma"		)
							if not isinstance(self.gamma			,			(float, int)		):
										raise ValueError("gamma must be float or int"		)
							if not self.gamma > 0:
										raise ValueError("gamma must be > 0"		)
							UpperCAmelCase_  :				Union[str, Any]      =      self.__rbf
							# in the future, there could be a default value like in sklearn
							# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
							# previously it was 1/(n_features)
				else:
							UpperCAmelCase_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				Optional[Any]      =      classes

				# using Wolfe's Dual to calculate w.
				# Primal problem: minimize 1/2*norm_squared(w)
				#   constraint: yn(w . xn + b) >= 1
				#
				# With l a vector
				# Dual problem: maximize sum_n(ln) -
				#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
				#   constraint: self.C >= ln >= 0
				#           and sum_n(ln*yn) = 0
				# Then we get w using w = sum_n(ln*yn*xn)
				# At the end we can get b ~= mean(yn - w . xn)
				#
				# Since we use kernels, we only need l_star to calculate b
				# and to classify observations

				((UpperCAmelCase_)  ,			)  :				Optional[int]      =      np.shape(lowerCAmelCase_		)

				def to_minimize(lowerCAmelCase_      :				ndarray		) -> float:
							UpperCAmelCase_  :				int      =      0
							((UpperCAmelCase_)  ,			)  :				str      =      np.shape(lowerCAmelCase_		)
							for i in range(lowerCAmelCase_		):
										for j in range(lowerCAmelCase_		):
													s += (
													    candidate[i]
													    * candidate[j]
													    * classes[i]
													    * classes[j]
													    * self.kernel(observations[i]			,			observations[j]		)
													)
							return 1 / 2 * s - sum(lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

				UpperCAmelCase_  :				Optional[Any]      =      minimize(
				    lowerCAmelCase_			,			np.ones(lowerCAmelCase_		)			,			bounds=lowerCAmelCase_			,			constraints=[ly_contraint]		).x
				UpperCAmelCase_  :				Optional[Any]      =      l_star

				# calculating mean offset of separation plane to points
				UpperCAmelCase_  :				Optional[int]      =      0
				for i in range(lowerCAmelCase_		):
							for j in range(lowerCAmelCase_		):
										s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
										    observations[i]			,			observations[j]		)
				UpperCAmelCase_  :				str      =      s / n





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				ndarray		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      sum(
				    self.optimum[n]
				    * self.classes[n]
				    * self.kernel(self.observations[n]			,			lowerCAmelCase_		)
				    for n in range(len(self.classes		)		)		)
				return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



