





'''simple docstring'''




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



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return np.dot(lowerCamelCase_			,			lowerCamelCase_	)




class      UpperCamelCase__					:




					"""simple docstring"""


					def __init__(						self				:			List[str]      ,						*,
					    lowerCamelCase_				:			float = np.inf      ,						lowerCamelCase_				:			str = "linear"      ,						lowerCamelCase_				:			float = 0.0      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			regularization
							SCREAMING_SNAKE_CASE :				str		=			gamma
							if kernel == "linear":
									SCREAMING_SNAKE_CASE :				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"""						)
									SCREAMING_SNAKE_CASE :				Tuple		=			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 :				Union[str, Any]		=			f'''Unknown kernel: {kernel}'''
									raise ValueError(lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			ndarray      ,						lowerCamelCase_				:			ndarray						):

							'''simple docstring'''



							return np.dot(lowerCamelCase_      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			int      ,						lowerCamelCase_				:			ndarray      ,						lowerCamelCase_				:			ndarray						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			list[ndarray]      ,						lowerCamelCase_				:			ndarray						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			observations
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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),							) :				Optional[int]		=			np.shape(lowerCamelCase_						)

							def to_minimize(lowerCamelCase_				:			ndarray						) -> float:
									SCREAMING_SNAKE_CASE :				str		=			0
									((SCREAMING_SNAKE_CASE),							) :				Union[str, Any]		=			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_						)

							SCREAMING_SNAKE_CASE :				Any		=			LinearConstraint(lowerCamelCase_      ,						0      ,						0						)
							SCREAMING_SNAKE_CASE :				List[str]		=			Bounds(0      ,						self.regularization						)

							SCREAMING_SNAKE_CASE :				List[str]		=			minimize(
							    lowerCamelCase_      ,						np.ones(lowerCamelCase_						)      ,						bounds=lowerCamelCase_      ,						constraints=[ly_contraint]						).x
							SCREAMING_SNAKE_CASE :				Dict		=			l_star

							# calculating mean offset of separation plane to points
							SCREAMING_SNAKE_CASE :				str		=			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]						)
							SCREAMING_SNAKE_CASE :				List[str]		=			s / n



					def lowerCamelCase_			(						self				:			Optional[int]      ,						lowerCamelCase_				:			ndarray						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			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
import sys
import unittest


__UpperCAmelCase            =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase            =       os.path.join(git_repo_path, """src""", """transformers""")

__UpperCAmelCase            =       """
{0} = None
"""

__UpperCAmelCase            =       """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


__UpperCAmelCase            =       """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""						)
							self.assertIsNone(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			find_backend("""    if not is_tokenizers_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend("""    if not is_tensorflow_text_available():"""						)
							self.assertEqual(lowerCamelCase_      ,						"""tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tensorflow_text"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			find_backend(
							    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""						)
							self.assertEqual(lowerCamelCase_      ,						"""sentencepiece_and_tokenizers_and_vision"""						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn("""torch"""      ,						lowerCamelCase_						)
							self.assertIn("""tensorflow_text"""      ,						lowerCamelCase_						)
							self.assertIn("""sentencepiece_and_tokenizers"""      ,						lowerCamelCase_						)

							# Likewise, we can't assert on the exact content of a key
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertModel"""      ,						objects["""tf"""]						)
							self.assertIn("""FlaxBertModel"""      ,						objects["""flax"""]						)
							self.assertIn("""BertModel"""      ,						objects["""torch"""]						)
							self.assertIn("""TFBertTokenizer"""      ,						objects["""tensorflow_text"""]						)
							self.assertIn("""convert_slow_tokenizer"""      ,						objects["""sentencepiece_and_tokenizers"""]						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			create_dummy_object("""CONSTANT"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						"""\nCONSTANT = None\n"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_object("""function"""      ,						"""'torch'"""						)
							self.assertEqual(
							    lowerCamelCase_      ,						"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""						)

							SCREAMING_SNAKE_CASE :				str		=			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			create_dummy_object("""FakeClass"""      ,						"""'torch'"""						)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
							SCREAMING_SNAKE_CASE :				List[Any]		=			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}						)
							self.assertEqual(dummy_files["""torch"""]      ,						lowerCamelCase_						)



