





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    :str							,				*,
							    __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    :Dict							,				__A    :ndarray							,				__A    :ndarray		)   ->   float:



									"""simple docstring"""



									return np.dot(__A							,				__A		)


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



									"""simple docstring"""



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


							def      _snake_case  (							self    :Tuple							,				__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()






import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow




@is_pipeline_test
@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


							@require_torch
							def      _snake_case  (							self    :Dict		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""hf-internal-testing/tiny-clap-htsat-unfused"""		)
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[{"""score""": 0.5_0_1, """label""": """Sound of a dog"""}, {"""score""": 0.4_9_9, """label""": """Sound of vaccum cleaner"""}]							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									pass


							@slow
							@require_torch
							def      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      pipeline(
									    task="""zero-shot-audio-classification"""							,				model="""laion/clap-htsat-unfused"""							,				)
									# This is an audio of a dog
									SCREAMING_SNAKE_CASE__			=      load_dataset("""ashraq/esc50"""		)
									SCREAMING_SNAKE_CASE__			=      dataset["""train"""]["""audio"""][-1]["""array"""]
									SCREAMING_SNAKE_CASE__			=      audio_classifier(__A							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)

									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									        {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									    ]							,				)

									SCREAMING_SNAKE_CASE__			=      audio_classifier([audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)
									SCREAMING_SNAKE_CASE__			=      audio_classifier(
									    [audio] * 5							,				candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]							,				batch_size=5		)
									self.assertEqual(
									    nested_simplify(__A		)							,				[
									        [
									            {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
									            {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
									        ],
									    ]
									    * 5							,				)


							@unittest.skip("""No models are available in TF"""		)
							def      _snake_case  (							self    :str		)   ->   Optional[int]:



									"""simple docstring"""



									pass