
'''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 :   List[Any]			    =	random.Random()







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase=1.0      ,UpperCamelCase=None      ,UpperCamelCase=None						)			->   Optional[Any]:
		if rng is None:
				_UpperCamelCase							:			Tuple     =   global_rng

		_UpperCamelCase							:			Optional[Any]     =   []
		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	,       _snake_case	,       _snake_case=7	,       _snake_case=400	,       _snake_case=2000	,       _snake_case=24	,       _snake_case=24	,       _snake_case=0.0	,       _snake_case=16000	,       _snake_case=True	,       _snake_case=True	,       )					->     Any:
				_UpperCamelCase							:			Union[str, Any]     =   parent
				_UpperCamelCase							:			Any     =   batch_size
				_UpperCamelCase							:			Optional[int]     =   min_seq_length
				_UpperCamelCase							:			Tuple     =   max_seq_length
				_UpperCamelCase							:			List[str]     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				_UpperCamelCase							:			Union[str, Any]     =   feature_size
				_UpperCamelCase							:			List[str]     =   num_mel_bins
				_UpperCamelCase							:			str     =   padding_value
				_UpperCamelCase							:			Optional[Any]     =   sampling_rate
				_UpperCamelCase							:			List[str]     =   return_attention_mask
				_UpperCamelCase							:			Tuple     =   do_normalize
		def _lowercase	(     self  )					->     str:
				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 _lowercase	(     self	,       _snake_case=False	,       _snake_case=False  )					->     List[str]:
				def _flatten(_snake_case  ):
						return list(itertools.chain(*_snake_case  )  )

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







@require_torch
@require_torchaudio
class       UpperCAmelCase					(       snake_case_	,		unittest.TestCase			):





		"""simple docstring"""





		A__	: Union[str, Any]    =     SpeechaTextFeatureExtractor if is_speech_available() else None
		def _lowercase	(     self  )					->     List[Any]:
				_UpperCamelCase							:			Any     =   SpeechaTextFeatureExtractionTester(self  )
		def _lowercase	(     self	,       _snake_case  )					->     Any:
				self.assertTrue(np.all(np.mean(_snake_case	,       axis=0  ) < 1E-3  )  )
				self.assertTrue(np.all(np.abs(np.var(_snake_case	,       axis=0  ) - 1  ) < 1E-3  )  )
		def _lowercase	(     self  )					->     str:
				# Tests that all call wrap to encode_plus and batch_encode_plus
				_UpperCamelCase							:			Any     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				# create three inputs of length 800, 1000, and 1200
				_UpperCamelCase							:			List[str]     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]
				_UpperCamelCase							:			List[Any]     =   [np.asarray(_snake_case  ) for speech_input in speech_inputs]

				# Test feature size
				_UpperCamelCase							:			List[str]     =   feature_extractor(_snake_case	,       padding=_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
				_UpperCamelCase							:			Tuple     =   feature_extractor(speech_inputs[0]	,       return_tensors='''np'''  ).input_features
				_UpperCamelCase							:			Tuple     =   feature_extractor(np_speech_inputs[0]	,       return_tensors='''np'''  ).input_features
				self.assertTrue(np.allclose(_snake_case	,       _snake_case	,       atol=1E-3  )  )

				# Test batched
				_UpperCamelCase							:			List[Any]     =   feature_extractor(_snake_case	,       return_tensors='''np'''  ).input_features
				_UpperCamelCase							:			Optional[int]     =   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.
				_UpperCamelCase							:			Any     =   [floats_list((1, x)  )[0] for x in (800, 800, 800)]
				_UpperCamelCase							:			int     =   np.asarray(_snake_case  )
				_UpperCamelCase							:			Any     =   feature_extractor(_snake_case	,       return_tensors='''np'''  ).input_features
				_UpperCamelCase							:			Optional[int]     =   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 _lowercase	(     self  )					->     Optional[Any]:
				_UpperCamelCase							:			Optional[Any]     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			List[str]     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]

				_UpperCamelCase							:			Optional[int]     =   ['''longest''', '''max_length''', '''do_not_pad''']
				_UpperCamelCase							:			Any     =   [None, 16, None]
				for max_length, padding in zip(_snake_case	,       _snake_case  ):
						_UpperCamelCase							:			Union[str, Any]     =   feature_extractor(
						    _snake_case	,       padding=_snake_case	,       max_length=_snake_case	,       return_attention_mask=_snake_case  )
						_UpperCamelCase							:			Union[str, Any]     =   inputs.input_features
						_UpperCamelCase							:			Optional[Any]     =   inputs.attention_mask
						_UpperCamelCase							:			str     =   [np.sum(_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 _lowercase	(     self  )					->     List[Any]:
				_UpperCamelCase							:			Optional[Any]     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			Dict     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]

				_UpperCamelCase							:			Any     =   ['''longest''', '''max_length''', '''do_not_pad''']
				_UpperCamelCase							:			List[Any]     =   [None, 16, None]
				for max_length, padding in zip(_snake_case	,       _snake_case  ):
						_UpperCamelCase							:			List[Any]     =   feature_extractor(
						    _snake_case	,       max_length=_snake_case	,       padding=_snake_case	,       return_tensors='''np'''	,       return_attention_mask=_snake_case  )
						_UpperCamelCase							:			List[Any]     =   inputs.input_features
						_UpperCamelCase							:			str     =   inputs.attention_mask
						_UpperCamelCase							:			str     =   [np.sum(_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 _lowercase	(     self  )					->     Optional[int]:
				_UpperCamelCase							:			int     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			Union[str, Any]     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]
				_UpperCamelCase							:			Tuple     =   feature_extractor(
				    _snake_case	,       padding='''max_length'''	,       max_length=4	,       truncation=_snake_case	,       return_tensors='''np'''	,       return_attention_mask=_snake_case	,       )
				_UpperCamelCase							:			str     =   inputs.input_features
				_UpperCamelCase							:			Tuple     =   inputs.attention_mask
				_UpperCamelCase							:			str     =   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 _lowercase	(     self  )					->     Any:
				_UpperCamelCase							:			Any     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			str     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]
				_UpperCamelCase							:			str     =   feature_extractor(
				    _snake_case	,       padding='''longest'''	,       max_length=4	,       truncation=_snake_case	,       return_tensors='''np'''	,       return_attention_mask=_snake_case	,       )
				_UpperCamelCase							:			Optional[Any]     =   inputs.input_features
				_UpperCamelCase							:			Optional[int]     =   inputs.attention_mask
				_UpperCamelCase							:			Dict     =   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)  )

				_UpperCamelCase							:			Tuple     =   [floats_list((1, x)  )[0] for x in range(800	,       1400	,       200  )]
				_UpperCamelCase							:			Optional[int]     =   feature_extractor(
				    _snake_case	,       padding='''longest'''	,       max_length=16	,       truncation=_snake_case	,       return_tensors='''np'''	,       return_attention_mask=_snake_case	,       )
				_UpperCamelCase							:			Tuple     =   inputs.input_features
				_UpperCamelCase							:			List[Any]     =   inputs.attention_mask
				_UpperCamelCase							:			Union[str, Any]     =   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 _lowercase	(     self  )					->     Tuple:
				import torch

				_UpperCamelCase							:			str     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			int     =   np.random.rand(100	,       32  ).astype(np.floataa  )
				_UpperCamelCase							:			str     =   np_speech_inputs.tolist()

				for inputs in [py_speech_inputs, np_speech_inputs]:
						_UpperCamelCase							:			Optional[Any]     =   feature_extractor.pad([{'''input_features''': inputs}]	,       return_tensors='''np'''  )
						self.assertTrue(np_processed.input_features.dtype == np.floataa  )
						_UpperCamelCase							:			Tuple     =   feature_extractor.pad([{'''input_features''': inputs}]	,       return_tensors='''pt'''  )
						self.assertTrue(pt_processed.input_features.dtype == torch.floataa  )
		def _lowercase	(     self	,       _snake_case  )					->     List[Any]:
				from datasets import load_dataset

				_UpperCamelCase							:			Union[str, Any]     =   load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,       '''clean'''	,       split='''validation'''  )
				# automatic decoding with librispeech
				_UpperCamelCase							:			int     =   ds.sort('''id'''  ).select(range(_snake_case  )  )[:num_samples]['''audio''']

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




		def _lowercase	(     self  )					->     Tuple:
				# fmt: off
				_UpperCamelCase							:			str     =   np.array([
				    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
				    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
				    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
				]  )
				# fmt: on

				_UpperCamelCase							:			List[Any]     =   self._load_datasamples(1  )
				_UpperCamelCase							:			Any     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
				_UpperCamelCase							:			str     =   feature_extractor(_snake_case	,       return_tensors='''pt'''  ).input_features
				self.assertEquals(input_features.shape	,       (1, 584, 24)  )
				self.assertTrue(np.allclose(input_features[0, 0, :30]	,       _snake_case	,       atol=1E-4  )  )













import unittest

import numpy as np





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_ = None				,	):
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)
							snake_case_           =				np.shape(a_)

							if shape_a[0] != shape_b[0]:
														snake_case_           =				(
														    'Expected the same number of rows for A and B. '
														    f'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(a_)

							if shape_b[1] != shape_c[1]:
														snake_case_           =				(
														    'Expected the same number of columns for B and C. '
														    f'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(a_)

							snake_case_           =				pseudo_inv
							if a_inv is None:
														try:
																					snake_case_           =				np.linalg.inv(a_)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    'Input matrix A is not invertible. Cannot compute Schur complement.')

							return mat_c - mat_b.T @ a_inv @ mat_b




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								snake_case_           =				schur_complement(a			,     a			,     a   )

								snake_case_           =				np.block([[a, b], [b.T, c]]   )

								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )
								snake_case_           =				np.linalg.det(a   )

								self.assertAlmostEqual(a			,     det_a * det_s   )




	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1], [6, 3]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )






	def 				_UpperCamelCase     (			self   )			->       None:
								snake_case_           =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
								snake_case_           =				np.array([[0, 3], [3, 0], [2, 3]]   )
								snake_case_           =				np.array([[2, 1, 3], [6, 3, 5]]   )

								with self.assertRaises(a   ):
															schur_complement(a			,     a			,     a   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()





