





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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				ndarray     ):
		return np.dot(UpperCamelCase__       , UpperCamelCase__     )




class 	UpperCamelCase_							:


							def __init__(				self     :int				,					*,
							    __A     :float = np.inf				,					__A     :str = "linear"				,					__A     :float = 0.0				,					) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   regularization
									SCREAMING_SNAKE_CASE__								=   gamma
									if kernel == "linear":
											SCREAMING_SNAKE_CASE__								=   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"""		)
											SCREAMING_SNAKE_CASE__								=   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:
											SCREAMING_SNAKE_CASE__								=   f'''Unknown kernel: {kernel}'''
											raise ValueError(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



									return np.dot(__A				,					__A		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :ndarray				,					__A     :ndarray		) -> float:



									"""simple docstring"""



									return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :list[ndarray]				,					__A     :ndarray		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   observations
									SCREAMING_SNAKE_CASE__								=   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

									((SCREAMING_SNAKE_CASE__)						,    )								=   np.shape(__A		)

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

									SCREAMING_SNAKE_CASE__								=   LinearConstraint(__A				,					0				,					0		)
									SCREAMING_SNAKE_CASE__								=   Bounds(0				,					self.regularization		)

									SCREAMING_SNAKE_CASE__								=   minimize(
									    __A				,					np.ones(__A		)				,					bounds=__A				,					constraints=[ly_contraint]		).x
									SCREAMING_SNAKE_CASE__								=   l_star

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


							def   _snake_case	(				self     :Optional[int]				,					__A     :ndarray		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]				,					__A		)
									    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 doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					    =	logging.getLogger()




@unittest.skip('''Temporarily disable the doc tests.'''		)
@require_torch
@require_tf
@slow
class  _A (					unittest.TestCase		):

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Path  ,      __magic_name__					:   Union[str, None] = None  ,      __magic_name__					:   Union[List[str], None] = None  ,      __magic_name__					:   Union[str, List[str], None] = None  ,      __magic_name__					:   bool = True  ,      )					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [file for file in os.listdir(__magic_name__			) if os.path.isfile(os.path.join(__magic_name__  ,      __magic_name__			)			)]

					if identifier is not None:
								__snake_case					:      List[Any]							      =     [file for file in files if identifier in file]

					if n_identifier is not None:
								if isinstance(__magic_name__  ,      __magic_name__			):
											for n_ in n_identifier:
														__snake_case					:      Optional[int]							      =     [file for file in files if n_ not in file]
								else:
											__snake_case					:      Tuple							      =     [file for file in files if n_identifier not in file]

					__snake_case					:      Dict							      =     ignore_files or []
					ignore_files.append("""__init__.py"""			)
					__snake_case					:      List[str]							      =     [file for file in files if file not in ignore_files]

					for file in files:
								# Open all files
								print("""Testing"""  ,      __magic_name__			)

								if only_modules:
											__snake_case					:      List[Any]							      =     file.split("""."""			)[0]
											try:
														__snake_case					:      List[Any]							      =     getattr(__magic_name__  ,      __magic_name__			)
														__snake_case					:      Union[str, Any]							      =     doctest.DocTestSuite(__magic_name__			)
														__snake_case					:      Dict							      =     unittest.TextTestRunner().run(__magic_name__			)
														self.assertIs(len(result.failures			)  ,      0			)
											except AttributeError:
														logger.info(f'''{module_identifier} is not a module.'''			)
								else:
											__snake_case					:      Tuple							      =     doctest.testfile(str("""..""" / directory / file			)  ,      optionflags=doctest.ELLIPSIS			)
											self.assertIs(result.failed  ,      0			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[Any]							      =     """modeling"""
					__snake_case					:      Union[str, Any]							      =     [
					    """modeling_ctrl.py""",
					    """modeling_tf_ctrl.py""",
					]
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__  ,      ignore_files=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      Any							      =     """tokenization"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     Path("""src/transformers"""			)
					__snake_case					:      List[str]							      =     """configuration"""
					self.analyze_directory(__magic_name__  ,      identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Path("""src/transformers"""			)
					__snake_case					:      int							      =     ["""configuration""", """modeling""", """tokenization"""]
					self.analyze_directory(__magic_name__  ,      n_identifier=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     Path("""docs/source"""			)
					__snake_case					:      Optional[int]							      =     ["""favicon.ico"""]
					self.analyze_directory(__magic_name__  ,      ignore_files=__magic_name__  ,      only_modules=__magic_name__			)


