



'''simple docstring'''



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def 					UpperCAmelCase   (						A  :				ndarray				):
							return np.dot(A					,		A				)
class 	lowerCamelCase_							:







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

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			regularization
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			gamma
													if kernel == "linear":
																				SCREAMING_SNAKE_CASE					:      int     					=			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					:      Optional[int]     					=			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					:      List[Any]     					=			F"""Unknown kernel: {kernel}"""
																				raise ValueError(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      ndarray				,    lowerCAmelCase__   :      ndarray				):

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







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      ndarray				,    lowerCAmelCase__   :      ndarray				):

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







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[ndarray]				,    lowerCAmelCase__   :      ndarray				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			observations
													SCREAMING_SNAKE_CASE					:      List[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

													((SCREAMING_SNAKE_CASE)					,)					:      str     					=			np.shape(lowerCAmelCase__				)

													def to_minimize(lowerCAmelCase__   :      ndarray				) -> float:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			0
																				((SCREAMING_SNAKE_CASE)					,)					:      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					:      int     					=			LinearConstraint(lowerCAmelCase__				,    0				,    0				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			Bounds(0				,    self.regularization				)

													SCREAMING_SNAKE_CASE					:      int     					=			minimize(
													    lowerCAmelCase__				,    np.ones(lowerCAmelCase__				)				,    bounds=lowerCAmelCase__				,    constraints=[ly_contraint]				).x
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			l_star

													# calculating mean offset of separation plane to points
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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					:      Dict     					=			s / n







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      ndarray				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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 shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class 	lowerCamelCase_							(					unittest.TestCase			):







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			'''laion/clap-htsat-unfused'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()







						def 					__lowercase    (  self   :      Tuple				,    **lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				,    **lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_feature_extractor()

													SCREAMING_SNAKE_CASE					:      List[str]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													processor.save_pretrained(self.tmpdirname				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor.from_pretrained(self.tmpdirname				)

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,    lowerCAmelCase__				)

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()				)
													self.assertIsInstance(processor.feature_extractor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()				)
													processor.save_pretrained(self.tmpdirname				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_tokenizer(bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor(do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													SCREAMING_SNAKE_CASE					:      str     					=			ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token='''(BOS)'''				,    eos_token='''(EOS)'''				,    do_normalize=lowerCAmelCase__				,    padding_value=1.0				)

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,    lowerCAmelCase__				)

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.feature_extractor				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Tuple     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			floats_list((3, 10_00)				)

													SCREAMING_SNAKE_CASE					:      Any     					=			feature_extractor(lowerCAmelCase__				,    return_tensors='''np'''				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			processor(audios=lowerCAmelCase__				,    return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			'''This is a test string'''

													SCREAMING_SNAKE_CASE					:      int     					=			processor(text=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer(lowerCAmelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Any     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			processor.batch_decode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer.batch_decode(lowerCAmelCase__				)

													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.get_feature_extractor()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_tokenizer()

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ClapProcessor(tokenizer=lowerCAmelCase__				,    feature_extractor=lowerCAmelCase__				)

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg='''`processor` and `feature_extractor` model input names do not match'''				,    )




