



"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor

UpperCamelCase     =							random.Random()

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       int=1.0,	UpperCAmelCase_ :       Any=None,	UpperCAmelCase_ :       List[Any]=None     )	-> Dict:






	"""simple docstring"""





	if rng is None:
		A__											=					global_rng

	A__											=					[]
	for batch_idx in range(shape[0]     ):
		values.append([]     )
		for _ in range(shape[1]     ):
			values[-1].append(rng.random() * scale     )

	return values






@require_torch
@require_torchaudio
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=400						,	SCREAMING_SNAKE_CASE__=2000						,	SCREAMING_SNAKE_CASE__=24						,	SCREAMING_SNAKE_CASE__=24						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=16000						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	)      ->	Optional[int]:
				A__											=					parent
				A__											=					batch_size
				A__											=					min_seq_length
				A__											=					max_seq_length
				A__											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				A__											=					feature_size
				A__											=					num_mel_bins
				A__											=					padding_value
				A__											=					sampling_rate
				A__											=					return_attention_mask
				A__											=					do_normalize


			def   snake_case__	(      self		)      ->	Optional[int]:
				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False		)      ->	Tuple:
				def _flatten(SCREAMING_SNAKE_CASE__		):
					return list(itertools.chain(*SCREAMING_SNAKE_CASE__		)		)

				if equal_length:
					A__											=					[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
				else:
					# make sure that inputs increase in size
					A__											=					[
					    floats_list((x, self.feature_size)		)
					    for x in range(self.min_seq_length						,	self.max_seq_length						,	self.seq_length_diff		)
					]
				if numpify:
					A__											=					[np.asarray(SCREAMING_SNAKE_CASE__		) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					str							  =     SpeechaTextFeatureExtractor if is_speech_available() else None


			def   snake_case__	(      self		)      ->	str:
				A__											=					SpeechaTextFeatureExtractionTester(self		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE__						,	axis=0		) < 1e-3		)		)
				self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE__						,	axis=0		) - 1		) < 1e-3		)		)


			def   snake_case__	(      self		)      ->	List[Any]:
				# Tests that all call wrap to encode_plus and batch_encode_plus
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				# create three inputs of length 800, 1000, and 1200
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]
				A__											=					[np.asarray(SCREAMING_SNAKE_CASE__		) for speech_input in speech_inputs]

				# Test feature size
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		).input_features
				self.assertTrue(input_features.ndim == 3		)
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

				# Test not batched input
				A__											=					feature_extractor(speech_inputs[0]						,	return_tensors="np"		).input_features
				A__											=					feature_extractor(np_speech_inputs[0]						,	return_tensors="np"		).input_features
				self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-3		)		)

				# Test batched
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		).input_features
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		).input_features
				for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-3		)		)

				# Test 2-D numpy arrays are batched.
				A__											=					[floats_list((1, x)		)[0] for x in (800, 800, 800)]
				A__											=					np.asarray(SCREAMING_SNAKE_CASE__		)
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		).input_features
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		).input_features
				for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-3		)		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]

				A__											=					["longest", "max_length", "do_not_pad"]
				A__											=					[None, 16, None]
				for max_length, padding in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					feature_extractor(
					    SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	max_length=SCREAMING_SNAKE_CASE__						,	return_attention_mask=SCREAMING_SNAKE_CASE__		)
					A__											=					inputs.input_features
					A__											=					inputs.attention_mask
					A__											=					[np.sum(SCREAMING_SNAKE_CASE__		) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]

				A__											=					["longest", "max_length", "do_not_pad"]
				A__											=					[None, 16, None]
				for max_length, padding in zip(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					feature_extractor(
					    SCREAMING_SNAKE_CASE__						,	max_length=SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	return_attention_mask=SCREAMING_SNAKE_CASE__		)
					A__											=					inputs.input_features
					A__											=					inputs.attention_mask
					A__											=					[np.sum(SCREAMING_SNAKE_CASE__		) for x in attention_mask]

					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
					self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6		)
					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
					self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6		)
					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]
				A__											=					feature_extractor(
				    SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=4						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	return_attention_mask=SCREAMING_SNAKE_CASE__						,	)
				A__											=					inputs.input_features
				A__											=					inputs.attention_mask
				A__											=					np.sum(attention_mask == 1						,	axis=1		)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
				self._check_zero_mean_unit_variance(input_features[1]		)
				self._check_zero_mean_unit_variance(input_features[2]		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]
				A__											=					feature_extractor(
				    SCREAMING_SNAKE_CASE__						,	padding="longest"						,	max_length=4						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	return_attention_mask=SCREAMING_SNAKE_CASE__						,	)
				A__											=					inputs.input_features
				A__											=					inputs.attention_mask
				A__											=					np.sum(attention_mask == 1						,	axis=1		)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
				self._check_zero_mean_unit_variance(input_features[2]		)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape						,	(3, 4, 24)		)

				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]
				A__											=					feature_extractor(
				    SCREAMING_SNAKE_CASE__						,	padding="longest"						,	max_length=16						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	return_attention_mask=SCREAMING_SNAKE_CASE__						,	)
				A__											=					inputs.input_features
				A__											=					inputs.attention_mask
				A__											=					np.sum(attention_mask == 1						,	axis=1		)

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
				self._check_zero_mean_unit_variance(input_features[2]		)

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape						,	(3, 6, 24)		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				import torch

				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					np.random.rand(100						,	32		).astype(np.floataa		)
				A__											=					np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
					A__											=					feature_extractor.pad([{"input_features": inputs}]						,	return_tensors="np"		)
					self.assertTrue(np_processed.input_features.dtype == np.floataa		)
					A__											=					feature_extractor.pad([{"input_features": inputs}]						,	return_tensors="pt"		)
					self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				from datasets import load_dataset

				A__											=					load_dataset("hf-internal-testing/librispeech_asr_dummy"						,	"clean"						,	split="validation"		)
				# automatic decoding with librispeech
				A__											=					ds.sort("id"		).select(range(SCREAMING_SNAKE_CASE__		)		)[:num_samples]["audio"]

				return [x["array"] for x in speech_samples]







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				# fmt: off
				A__											=					np.array([
				    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
				    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
				    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
				]		)
				# fmt: on

				A__											=					self._load_datasamples(1		)
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		).input_features
				self.assertEquals(input_features.shape						,	(1, 584, 24)		)
				self.assertTrue(np.allclose(input_features[0, 0, :30]						,	SCREAMING_SNAKE_CASE__						,	atol=1e-4		)		)










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


def 					_a							(					lowerCAmelCase )->	float:
		return np.dot(lowerCAmelCase    ,						lowerCAmelCase )



class    lowercase_		:



						def __init__(			self	:   int					,							*,
						    snake_case__	:   float = np.inf					,							snake_case__	:   str = "linear"					,							snake_case__	:   float = 0.0					,							):

								"""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(snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

								return np.dot(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   int					,							snake_case__	:   ndarray					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   int					,							snake_case__	:   list[ndarray]					,							snake_case__	:   ndarray  ):

								"""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(snake_case__  )

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

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

								SCREAMING_SNAKE_CASE_					      =							minimize(
								    snake_case__					,							np.ones(snake_case__  )					,							bounds=snake_case__					,							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(snake_case__  ):
										for j in range(snake_case__  ):
												s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
												    observations[i]					,							observations[j]  )
								SCREAMING_SNAKE_CASE_					      =							s / n






						def 	__a					(			self	:   Any					,							snake_case__	:   ndarray  ):

								"""simple docstring"""

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()