'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowercase		:				Optional[int]	       =		random.Random()




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int=1.0   ,   UpperCAmelCase__ :      List[str]=None   ,   UpperCAmelCase__ :      List[Any]=None				)    ->   List[str]:
							if rng is None:
														lowercase_      :    str       		= global_rng

							lowercase_      :    Union[str, Any]       		= []
							for batch_idx in range(shape[0]				):
														values.append([]				)
														for _ in range(shape[1]				):
																					values[-1].append(rng.random() * scale				)

							return values





class 					__magic_name__		(					unittest.TestCase):







			def __init__(			self       :						int       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						int=7       ,    lowercase_       :						List[Any]=400       ,    lowercase_       :						List[Any]=2000       ,    lowercase_       :						Union[str, Any]=1       ,    lowercase_       :						Any=0.0       ,    lowercase_       :						Any=16000       ,    lowercase_       :						Optional[Any]=True       ,    lowercase_       :						Tuple=True       ,    ):
										lowercase_      :    List[str]       		= parent
										lowercase_      :    int       		= batch_size
										lowercase_      :    str       		= min_seq_length
										lowercase_      :    int       		= max_seq_length
										lowercase_      :    Dict       		= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										lowercase_      :    str       		= feature_size
										lowercase_      :    Optional[Any]       		= padding_value
										lowercase_      :    Dict       		= sampling_rate
										lowercase_      :    List[str]       		= return_attention_mask
										lowercase_      :    str       		= do_normalize







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										return {
										    "feature_size": self.feature_size,
										    "padding_value": self.padding_value,
										    "sampling_rate": self.sampling_rate,
										    "return_attention_mask": self.return_attention_mask,
										    "do_normalize": self.do_normalize,
										}







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						List[Any]=False       ,    lowercase_       :						str=False					):
										def _flatten(lowercase_       :						str					):
																	return list(itertools.chain(*lowercase_					)					)

										if equal_length:
																	lowercase_      :    str       		= floats_list((self.batch_size, self.max_seq_length)					)
										else:
																	# make sure that inputs increase in size
																	lowercase_      :    Optional[Any]       		= [
																	    _flatten(floats_list((x, self.feature_size)					)					)
																	    for x in range(self.min_seq_length       ,    self.max_seq_length       ,    self.seq_length_diff					)
																	]

										if numpify:
																	lowercase_      :    List[Any]       		= [np.asarray(lowercase_					) for x in speech_inputs]

										return speech_inputs





class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = WavaVecaFeatureExtractor







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Tuple       		= WavaVecaFeatureExtractionTester(self					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										self.assertTrue(np.all(np.mean(lowercase_       ,    axis=0					) < 1E-3					)					)
										self.assertTrue(np.all(np.abs(np.var(lowercase_       ,    axis=0					) - 1					) < 1E-3					)					)







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

										# Test not batched input
										lowercase_      :    str       		= feat_extract(speech_inputs[0]       ,    return_tensors="""np"""					).input_values
										lowercase_      :    List[str]       		= feat_extract(np_speech_inputs[0]       ,    return_tensors="""np"""					).input_values
										self.assertTrue(np.allclose(lowercase_       ,    lowercase_       ,    atol=1E-3					)					)

										# Test batched
										lowercase_      :    List[str]       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
										lowercase_      :    Union[str, Any]       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
										for enc_seq_a, enc_seq_a in zip(lowercase_       ,    lowercase_					):
																	self.assertTrue(np.allclose(lowercase_       ,    lowercase_       ,    atol=1E-3					)					)

										# Test 2-D numpy arrays are batched.
										lowercase_      :    Union[str, Any]       		= [floats_list((1, x)					)[0] for x in (800, 800, 800)]
										lowercase_      :    str       		= np.asarray(lowercase_					)
										lowercase_      :    Any       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
										lowercase_      :    Any       		= feat_extract(lowercase_       ,    return_tensors="""np"""					).input_values
										for enc_seq_a, enc_seq_a in zip(lowercase_       ,    lowercase_					):
																	self.assertTrue(np.allclose(lowercase_       ,    lowercase_       ,    atol=1E-3					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[Any]       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
										lowercase_      :    Any       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]

										lowercase_      :    List[str]       		= ["""longest""", """max_length""", """do_not_pad"""]
										lowercase_      :    Optional[int]       		= [None, 1600, None]
										for max_length, padding in zip(lowercase_       ,    lowercase_					):
																	lowercase_      :    Union[str, Any]       		= feat_extract(lowercase_       ,    padding=lowercase_       ,    max_length=lowercase_       ,    return_tensors="""np"""					)
																	lowercase_      :    Optional[Any]       		= processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]					)
																	self.assertTrue(input_values[0][800:].sum() < 1E-6					)
																	self._check_zero_mean_unit_variance(input_values[1][:1000]					)
																	self.assertTrue(input_values[0][1000:].sum() < 1E-6					)
																	self._check_zero_mean_unit_variance(input_values[2][:1200]					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Optional[Any]       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
										lowercase_      :    List[str]       		= range(800       ,    1400       ,    200					)
										lowercase_      :    Any       		= [floats_list((1, x)					)[0] for x in lengths]

										lowercase_      :    List[str]       		= ["""longest""", """max_length""", """do_not_pad"""]
										lowercase_      :    Union[str, Any]       		= [None, 1600, None]

										for max_length, padding in zip(lowercase_       ,    lowercase_					):
																	lowercase_      :    Optional[Any]       		= feat_extract(lowercase_       ,    max_length=lowercase_       ,    padding=lowercase_					)
																	lowercase_      :    Union[str, Any]       		= processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]					)
																	self._check_zero_mean_unit_variance(input_values[1][:1000]					)
																	self._check_zero_mean_unit_variance(input_values[2][:1200]					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    Any       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
										lowercase_      :    List[str]       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]
										lowercase_      :    Any       		= feat_extract(
										    lowercase_       ,    truncation=lowercase_       ,    max_length=1000       ,    padding="""max_length"""       ,    return_tensors="""np"""					)
										lowercase_      :    List[Any]       		= processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]					)
										self._check_zero_mean_unit_variance(input_values[1]					)
										self._check_zero_mean_unit_variance(input_values[2]					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    int       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
										lowercase_      :    Any       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]
										lowercase_      :    Tuple       		= feat_extract(
										    lowercase_       ,    truncation=lowercase_       ,    max_length=1000       ,    padding="""longest"""       ,    return_tensors="""np"""					)
										lowercase_      :    Optional[Any]       		= processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]					)
										self._check_zero_mean_unit_variance(input_values[1, :1000]					)
										self._check_zero_mean_unit_variance(input_values[2]					)

										# make sure that if max_length < longest -> then pad to max_length
										self.assertTrue(input_values.shape == (3, 1000)					)

										lowercase_      :    Union[str, Any]       		= [floats_list((1, x)					)[0] for x in range(800       ,    1400       ,    200					)]
										lowercase_      :    Optional[int]       		= feat_extract(
										    lowercase_       ,    truncation=lowercase_       ,    max_length=2000       ,    padding="""longest"""       ,    return_tensors="""np"""					)
										lowercase_      :    Dict       		= processed.input_values

										self._check_zero_mean_unit_variance(input_values[0, :800]					)
										self._check_zero_mean_unit_variance(input_values[1, :1000]					)
										self._check_zero_mean_unit_variance(input_values[2]					)

										# make sure that if max_length > longest -> then pad to longest
										self.assertTrue(input_values.shape == (3, 1200)					)







			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										import torch

										lowercase_      :    Tuple       		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
										lowercase_      :    Any       		= np.random.rand(100					).astype(np.floataa					)
										lowercase_      :    List[str]       		= np_speech_inputs.tolist()

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







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										# this test makes sure that models that are using
										# group norm don't have their feature extractor return the
										# attention_mask
										for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																	lowercase_      :    Any       		= WavaVecaConfig.from_pretrained(lowercase_					)
																	lowercase_      :    Dict       		= WavaVecaFeatureExtractor.from_pretrained(lowercase_					)

																	# only "layer" feature extraction norm should make use of
																	# attention_mask
																	self.assertEqual(feat_extract.return_attention_mask       ,    config.feat_extract_norm == """layer"""					)






'''simple docstring'''




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 - _cos) / 2
							lowercase_      :    Optional[int]       		= 1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    List[Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Optional[int]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Dict       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 + _cos) / 2
							lowercase_      :    str       		= -1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    List[Any]       		= 1 - alpha

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    int       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    str       		= _sin / (2 * q_factor)

							lowercase_      :    str       		= _sin / 2
							lowercase_      :    Any       		= 0
							lowercase_      :    Optional[Any]       		= -ba

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Union[str, Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    Tuple       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    List[str]       		= tau * frequency / samplerate
							lowercase_      :    Any       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= _sin / (2 * q_factor)

							lowercase_      :    Any       		= 1 - alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    Optional[int]       		= 1 + alpha

							lowercase_      :    Dict       		= IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)

							lowercase_      :    List[str]       		= 1 + alpha * big_a
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Dict       		= 1 - alpha * big_a
							lowercase_      :    str       		= 1 + alpha / big_a
							lowercase_      :    List[str]       		= -2 * _cos
							lowercase_      :    Tuple       		= 1 - alpha / big_a

							lowercase_      :    Any       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Union[str, Any]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Any       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)
							lowercase_      :    Any       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    int       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Tuple       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    Optional[Any]       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    int       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (pmc + aaa)
							lowercase_      :    List[str]       		= 2 * big_a * mpc
							lowercase_      :    Union[str, Any]       		= big_a * (pmc - aaa)
							lowercase_      :    Optional[int]       		= ppmc + aaa
							lowercase_      :    Optional[int]       		= -2 * pmpc
							lowercase_      :    Any       		= ppmc - aaa

							lowercase_      :    Optional[int]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Dict       		= _sin / (2 * q_factor)
							lowercase_      :    Union[str, Any]       		= 10 ** (gain_db / 40)
							lowercase_      :    Union[str, Any]       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    Optional[int]       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Any       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    str       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    Optional[int]       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (ppmc + aaa)
							lowercase_      :    List[Any]       		= -2 * big_a * pmpc
							lowercase_      :    Optional[Any]       		= big_a * (ppmc - aaa)
							lowercase_      :    Optional[Any]       		= pmc + aaa
							lowercase_      :    int       		= 2 * mpc
							lowercase_      :    Tuple       		= pmc - aaa

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt





