






"""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()











"""simple docstring"""
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_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   '''▁'''

lowerCamelCase_       			=   {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase_       			=   {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

lowerCamelCase_       			=   {
    '''facebook/xglm-564M''': 2048,
}







class     UpperCamelCase_ (__A  ):
	__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      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Union[str, Any]="<s>"			,			lowerCAmelCase_      :				Any="</s>"			,			lowerCAmelCase_      :				Tuple="</s>"			,			lowerCAmelCase_      :				List[str]="<s>"			,			lowerCAmelCase_      :				Union[str, Any]="<unk>"			,			lowerCAmelCase_      :				Any="<pad>"			,			lowerCAmelCase_      :				Optional[Dict[str, Any]] = None			,			**lowerCAmelCase_      :				List[str]			,			)		->    None:
				UpperCAmelCase_  :				Any      =      {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				UpperCAmelCase_  :				Any      =      7
				UpperCAmelCase_  :				Dict      =      [f"""<madeupword{i}>""" for i in range(self.num_madeup_words		)]

				UpperCAmelCase_  :				Optional[int]      =      kwargs.get("additional_special_tokens"			,			[]		)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

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

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

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				UpperCAmelCase_  :				int      =      1

				# Mimic fairseq token-to-id alignment for the first 4 token
				UpperCAmelCase_  :				Tuple      =      {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				UpperCAmelCase_  :				List[str]      =      len(self.sp_model		)
				UpperCAmelCase_  :				List[Any]      =      {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
				self.fairseq_tokens_to_ids.update(lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}





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





	def __setstate__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				Any		)		->    Any:
				UpperCAmelCase_  :				Tuple      =      d

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

				UpperCAmelCase_  :				int      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





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

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				UpperCAmelCase_  :				Optional[Any]      =      [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[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=lowerCAmelCase_			,			token_ids_a=lowerCAmelCase_			,			already_has_special_tokens=lowerCAmelCase_		)

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





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

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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      {self.convert_ids_to_tokens(lowerCAmelCase_		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Optional[Any]		)		->    Optional[int]:
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				UpperCAmelCase_  :				Optional[int]      =      self.sp_model.PieceToId(lowerCAmelCase_		)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, Any]		)		->    Any:
				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Tuple		)		->    Optional[int]:
				UpperCAmelCase_  :				Any      =      "".join(lowerCAmelCase_		).replace(lowerCAmelCase_			,			" "		).strip()
				return out_string





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			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
				UpperCAmelCase_  :				List[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:
										UpperCAmelCase_  :				str      =      self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase_		)

				return (out_vocab_file,)



