



"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import WhisperFeatureExtractor

if is_torch_available():
			import torch

UpperCamelCase     =							random.Random()

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






	"""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__=10						,	SCREAMING_SNAKE_CASE__=160						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=4000						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=True						,	)      ->	Dict:
				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__											=					padding_value
				A__											=					sampling_rate
				A__											=					return_attention_mask
				A__											=					do_normalize
				A__											=					feature_size
				A__											=					chunk_length
				A__											=					hop_length


			def   snake_case__	(      self		)      ->	Tuple:
				return {
				    "feature_size": self.feature_size,
				    "hop_length": self.hop_length,
				    "chunk_length": self.chunk_length,
				    "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		)      ->	int:

				def _flatten(SCREAMING_SNAKE_CASE__		):
					return list(itertools.chain(*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(snake_case__		) for x in speech_inputs]
				return speech_inputs






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

			"""simple docstring"""


			A__	:					Tuple							  =     WhisperFeatureExtractor if is_speech_available() else None


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					WhisperFeatureExtractionTester(self		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					A__											=					feat_extract_first.save_pretrained(snake_case__		)[0]
					check_json_file_has_correct_format(snake_case__		)
					A__											=					self.feature_extraction_class.from_pretrained(snake_case__		)

				A__											=					feat_extract_first.to_dict()
				A__											=					feat_extract_second.to_dict()
				A__											=					feat_extract_first.mel_filters
				A__											=					feat_extract_second.mel_filters
				self.assertTrue(np.allclose(snake_case__						,	snake_case__		)		)
				self.assertEqual(snake_case__						,	snake_case__		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					A__											=					os.path.join(snake_case__						,	"feat_extract.json"		)
					feat_extract_first.to_json_file(snake_case__		)
					A__											=					self.feature_extraction_class.from_json_file(snake_case__		)

				A__											=					feat_extract_first.to_dict()
				A__											=					feat_extract_second.to_dict()
				A__											=					feat_extract_first.mel_filters
				A__											=					feat_extract_second.mel_filters
				self.assertTrue(np.allclose(snake_case__						,	snake_case__		)		)
				self.assertEqual(snake_case__						,	snake_case__		)


			def   snake_case__	(      self		)      ->	Tuple:
				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(snake_case__		) for speech_input in speech_inputs]

				# Test feature size
				A__											=					feature_extractor(snake_case__						,	padding="max_length"						,	return_tensors="np"		).input_features
				self.assertTrue(input_features.ndim == 3		)
				self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames		)
				self.assertTrue(input_features.shape[-2] == 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(snake_case__						,	snake_case__						,	atol=1e-3		)		)

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

				# Test truncation required
				A__											=					[floats_list((1, x)		)[0] for x in range(200						,	(feature_extractor.n_samples + 500)						,	200		)]
				A__											=					[np.asarray(snake_case__		) for speech_input in speech_inputs]

				A__											=					[x[: feature_extractor.n_samples] for x in speech_inputs]
				A__											=					[np.asarray(snake_case__		) for speech_input in speech_inputs_truncated]

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


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

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


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					torch.tensor(
				    [
				        0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
				        0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
				        0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
				        -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
				    ]		)
				# fmt: on

				A__											=					self._load_datasamples(1		)
				A__											=					WhisperFeatureExtractor()
				A__											=					feature_extractor(snake_case__						,	return_tensors="pt"		).input_features
				self.assertEqual(input_features.shape						,	(1, 80, 3000)		)
				self.assertTrue(torch.allclose(input_features[0, 0, :30]						,	snake_case__						,	atol=1e-4		)		)







			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				A__											=					self._load_datasamples(1		)[0]
				A__											=					((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
				A__											=					feat_extract.zero_mean_unit_var_norm([audio]						,	attention_mask=snake_case__		)[0]

				self.assertTrue(np.all(np.mean(snake_case__		) < 1e-3		)		)
				self.assertTrue(np.all(np.abs(np.var(snake_case__		) - 1		) < 1e-3		)		)










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()