






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def      a_ (      _A  )			->			float:







				"""simple docstring"""
				return np.dot(_A      ,   _A  )


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      Any       ,	*,
							    UpperCamelCase:      float = np.inf       ,	UpperCamelCase:      str = "linear"       ,	UpperCamelCase:      float = 0.0       ,	)		->		None:
											snake_case__									=     regularization
											snake_case__									=     gamma
											if kernel == "linear":
															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'						)
															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:
															snake_case__									=     F'''Unknown kernel: {kernel}'''
															raise ValueError(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      ndarray       ,	UpperCamelCase:      ndarray						)		->		float:
											return np.dot(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      ndarray       ,	UpperCamelCase:      ndarray						)		->		float:
											return np.exp(-(self.gamma * norm_squared(vectora - vectora						))						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      list[ndarray]       ,	UpperCamelCase:      ndarray						)		->		None:
											snake_case__									=     observations
											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

											((snake_case__)							,						)									=     np.shape(UpperCamelCase						)

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

											snake_case__									=     LinearConstraint(UpperCamelCase       ,	0       ,	0						)
											snake_case__									=     Bounds(0       ,	self.regularization						)

											snake_case__									=     minimize(
											    UpperCamelCase       ,	np.ones(UpperCamelCase						)       ,	bounds=UpperCamelCase       ,	constraints=[ly_contraint]						).x
											snake_case__									=     l_star

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


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


if __name__ == "__main__":
							import doctest

							doctest.testmod()












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					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




