"""simple docstring"""




import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MaskFormerSwinBackbone
				from transformers.models.maskformer import MaskFormerSwinModel
class     UpperCamelCase__:





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=32		,__UpperCAmelCase=2		,__UpperCAmelCase=3		,__UpperCAmelCase=16		,__UpperCAmelCase=[1, 2, 1]		,__UpperCAmelCase=[2, 2, 4]		,__UpperCAmelCase=2		,__UpperCAmelCase=2.0		,__UpperCAmelCase=True		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.1		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-5		,__UpperCAmelCase=True		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=10		,__UpperCAmelCase=8		,__UpperCAmelCase=["stage1", "stage2", "stage3"]		,__UpperCAmelCase=[1, 2, 3]		,)					->				Union[str, Any]:
													A__	     =							parent
													A__	     =							batch_size
													A__	     =							image_size
													A__	     =							patch_size
													A__	     =							num_channels
													A__	     =							embed_dim
													A__	     =							depths
													A__	     =							num_heads
													A__	     =							window_size
													A__	     =							mlp_ratio
													A__	     =							qkv_bias
													A__	     =							hidden_dropout_prob
													A__	     =							attention_probs_dropout_prob
													A__	     =							drop_path_rate
													A__	     =							hidden_act
													A__	     =							use_absolute_embeddings
													A__	     =							patch_norm
													A__	     =							layer_norm_eps
													A__	     =							initializer_range
													A__	     =							is_training
													A__	     =							scope
													A__	     =							use_labels
													A__	     =							type_sequence_label_size
													A__	     =							encoder_stride
													A__	     =							out_features
													A__	     =							out_indices





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

													A__	     =							None
													if self.use_labels:
																				A__	     =							ids_tensor([self.batch_size]		,self.type_sequence_label_size     )

													A__	     =							self.get_config()

													return config, pixel_values, labels





						def 				snake_case__	(       self     )					->				List[Any]:
													return MaskFormerSwinConfig(
													    image_size=self.image_size		,patch_size=self.patch_size		,num_channels=self.num_channels		,embed_dim=self.embed_dim		,depths=self.depths		,num_heads=self.num_heads		,window_size=self.window_size		,mlp_ratio=self.mlp_ratio		,qkv_bias=self.qkv_bias		,hidden_dropout_prob=self.hidden_dropout_prob		,attention_probs_dropout_prob=self.attention_probs_dropout_prob		,drop_path_rate=self.drop_path_rate		,hidden_act=self.hidden_act		,use_absolute_embeddings=self.use_absolute_embeddings		,path_norm=self.patch_norm		,layer_norm_eps=self.layer_norm_eps		,initializer_range=self.initializer_range		,encoder_stride=self.encoder_stride		,out_features=self.out_features		,out_indices=self.out_indices		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				int:
													A__	     =							MaskFormerSwinModel(config=__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase     )

													A__	     =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
													A__	     =							int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

													self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, expected_seq_len, expected_dim)     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[str]:
													A__	     =							MaskFormerSwinBackbone(config=__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													A__	     =							model(__UpperCAmelCase     )

													# verify feature maps
													self.parent.assertEqual(len(result.feature_maps     )		,len(config.out_features     )     )
													self.parent.assertListEqual(list(result.feature_maps[0].shape     )		,[13, 16, 16, 16]     )

													# verify channels
													self.parent.assertEqual(len(model.channels     )		,len(config.out_features     )     )
													self.parent.assertListEqual(model.channels		,[16, 32, 64]     )

													# verify ValueError
													with self.parent.assertRaises(__UpperCAmelCase     ):
																				A__	     =							['stem']
																				A__	     =							MaskFormerSwinBackbone(config=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							self.prepare_config_and_inputs()
													A__     ,  A__     ,  A__	     =							config_and_inputs
													A__	     =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class     UpperCamelCase__(			__A		,						__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Union[str, Any]													=		(
						    (
						        MaskFormerSwinModel,
						        MaskFormerSwinBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase__  :						Any													=		{'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
						lowerCAmelCase__  :						Optional[int]													=		False
						lowerCAmelCase__  :						Any													=		False
						lowerCAmelCase__  :						Any													=		False
						lowerCAmelCase__  :						Optional[Any]													=		False
						lowerCAmelCase__  :						List[Any]													=		False





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							MaskFormerSwinModelTester(self     )
													A__	     =							ConfigTester(self		,config_class=__UpperCAmelCase		,embed_dim=37     )





						@require_torch_multi_gpu
						@unittest.skip(
						    reason=(
						        '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
						        ' `nn.DataParallel`'
						    )     )
						def 				snake_case__	(       self     )					->				Any:
													pass





						def 				snake_case__	(       self     )					->				Dict:
													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()





						def 				snake_case__	(       self     )					->				List[Any]:
													return





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*__UpperCAmelCase     )





						@unittest.skip('Swin does not use inputs_embeds'     )
						def 				snake_case__	(       self     )					->				Tuple:
													pass





						@unittest.skip('Swin does not support feedforward chunking'     )
						def 				snake_case__	(       self     )					->				Optional[Any]:
													pass





						def 				snake_case__	(       self     )					->				Tuple:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__	     =							model_class(__UpperCAmelCase     )
																				self.assertIsInstance(model.get_input_embeddings()		,(nn.Module)     )
																				A__	     =							model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(__UpperCAmelCase		,nn.Linear     )     )





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__	     =							model_class(__UpperCAmelCase     )
																				A__	     =							inspect.signature(model.forward     )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__	     =							[*signature.parameters.keys()]

																				A__	     =							['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase     )





						@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'     )
						def 				snake_case__	(       self     )					->				Any:
													pass





						@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'     )
						def 				snake_case__	(       self     )					->				Dict:
													pass





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
													A__	     =							model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()

													with torch.no_grad():
																				A__	     =							model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )     )

													A__	     =							outputs.hidden_states

													A__	     =							getattr(
													    self.model_tester		,'expected_num_hidden_layers'		,len(self.model_tester.depths     ) + 1     )
													self.assertEqual(len(__UpperCAmelCase     )		,__UpperCAmelCase     )

													# Swin has a different seq_length
													A__	     =							(
													    config.patch_size
													    if isinstance(config.patch_size		,collections.abc.Iterable     )
													    else (config.patch_size, config.patch_size)
													)

													A__	     =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]     )		,[num_patches, self.model_tester.embed_dim]		,)





						def 				snake_case__	(       self     )					->				int:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													A__	     =							(
													    self.model_tester.image_size
													    if isinstance(self.model_tester.image_size		,collections.abc.Iterable     )
													    else (self.model_tester.image_size, self.model_tester.image_size)
													)

													for model_class in self.all_model_classes:
																				A__	     =							True
																				self.check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__	     =							True

																				self.check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()
													A__	     =							3

													A__	     =							(
													    self.model_tester.image_size
													    if isinstance(self.model_tester.image_size		,collections.abc.Iterable     )
													    else (self.model_tester.image_size, self.model_tester.image_size)
													)
													A__	     =							(
													    config.patch_size
													    if isinstance(config.patch_size		,collections.abc.Iterable     )
													    else (config.patch_size, config.patch_size)
													)

													A__	     =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
													A__	     =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

													for model_class in self.all_model_classes:
																				A__	     =							True
																				self.check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,(padded_height, padded_width)     )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__	     =							True
																				self.check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,(padded_height, padded_width)     )





						@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'     )
						def 				snake_case__	(       self     )					->				str:
													pass





						@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'     )
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													pass





						@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'     )
						def 				snake_case__	(       self     )					->				Any:
													pass





						def 				snake_case__	(       self     )					->				str:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()

													def set_nan_tensor_to_zero(__UpperCAmelCase     ):
																				A__	     =							0
																				return t

													def check_equivalence(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase={}     ):
																				with torch.no_grad():
																											A__	     =							model(**__UpperCAmelCase		,return_dict=__UpperCAmelCase		,**__UpperCAmelCase     )
																											A__	     =							model(**__UpperCAmelCase		,return_dict=__UpperCAmelCase		,**__UpperCAmelCase     ).to_tuple()

																											def recursive_check(__UpperCAmelCase		,__UpperCAmelCase     ):
																																		if isinstance(__UpperCAmelCase		,(List, Tuple)     ):
																																									for tuple_iterable_value, dict_iterable_value in zip(__UpperCAmelCase		,__UpperCAmelCase     ):
																																																recursive_check(__UpperCAmelCase		,__UpperCAmelCase     )
																																		elif isinstance(__UpperCAmelCase		,__UpperCAmelCase     ):
																																									for tuple_iterable_value, dict_iterable_value in zip(
																																									    tuple_object.values()		,dict_object.values()     ):
																																																recursive_check(__UpperCAmelCase		,__UpperCAmelCase     )
																																		elif tuple_object is None:
																																									return
																																		else:
																																									self.assertTrue(
																																									    torch.allclose(
																																									        set_nan_tensor_to_zero(__UpperCAmelCase     )		,set_nan_tensor_to_zero(__UpperCAmelCase     )		,atol=1e-5     )		,msg=(
																																									        'Tuple and dict output are not equal. Difference:'
																																									        f''' {torch.max(torch.abs(tuple_object - dict_object     )     )}. Tuple has `nan`:'''
																																									        f''' {torch.isnan(__UpperCAmelCase     ).any()} and `inf`: {torch.isinf(__UpperCAmelCase     )}. Dict has'''
																																									        f''' `nan`: {torch.isnan(__UpperCAmelCase     ).any()} and `inf`: {torch.isinf(__UpperCAmelCase     )}.'''
																																									    )		,)

																											recursive_check(__UpperCAmelCase		,__UpperCAmelCase     )

													for model_class in self.all_model_classes:
																				A__	     =							model_class(__UpperCAmelCase     )
																				model.to(__UpperCAmelCase     )
																				model.eval()

																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )
																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )
																				check_equivalence(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )

																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase     )
																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase     )
																				check_equivalence(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )

																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )
																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase     )
																				check_equivalence(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,{'output_hidden_states': True}     )

																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase     )
																				A__	     =							self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase     )
																				check_equivalence(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,{'output_hidden_states': True}     )






@require_torch
class     UpperCamelCase__(			unittest.TestCase		,						__A ):
						lowerCAmelCase__  :						Optional[Any]													=		(MaskFormerSwinBackbone,) if is_torch_available() else ()
						lowerCAmelCase__  :						int													=		MaskFormerSwinConfig





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							MaskFormerSwinModelTester(self     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__     ,  A__	     =							self.model_tester.prepare_config_and_inputs_for_common()
													A__	     =							inputs_dict['pixel_values'].shape[0]

													for backbone_class in self.all_model_classes:
																				A__	     =							backbone_class(__UpperCAmelCase     )
																				backbone.to(__UpperCAmelCase     )
																				backbone.eval()

																				A__	     =							backbone(**__UpperCAmelCase     )

																				# Test default outputs and verify feature maps
																				self.assertIsInstance(outputs.feature_maps		,__UpperCAmelCase     )
																				self.assertTrue(len(outputs.feature_maps     ) == len(backbone.channels     )     )
																				for feature_map, n_channels in zip(outputs.feature_maps		,backbone.channels     ):
																											self.assertTrue(feature_map.shape[:2]		,(batch_size, n_channels)     )
																				self.assertIsNone(outputs.hidden_states     )
																				self.assertIsNone(outputs.attentions     )

																				# Test output_hidden_states=True
																				A__	     =							backbone(**__UpperCAmelCase		,output_hidden_states=__UpperCAmelCase     )
																				self.assertIsNotNone(outputs.hidden_states     )
																				self.assertTrue(len(outputs.hidden_states     )		,len(backbone.stage_names     )     )
																				# We skip the stem layer
																				for hidden_states, n_channels in zip(outputs.hidden_states[1:]		,backbone.channels     ):
																											for hidden_state in hidden_states:
																																		# Hidden states are in the format (batch_size, (height * width), n_channels)
																																		A__     ,  A__     ,  A__	     =							hidden_state.shape
																																		self.assertTrue((h_batch_size, h_n_channels)		,(batch_size, n_channels)     )

            # Test output_attentions=True
																				if self.has_attentions:
																											A__	     =							backbone(**__UpperCAmelCase		,output_attentions=__UpperCAmelCase     )
																											self.assertIsNotNone(outputs.attentions     )






"""simple docstring"""




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 - _cos) / 2
							A__	     =							1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							(1 + _cos) / 2
							A__	     =							-1 - _cos

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							_sin / 2
							A__	     =							0
							A__	     =							-ba

							A__	     =							1 + alpha
							A__	     =							-2 * _cos
							A__	     =							1 - alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)				):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)

							A__	     =							1 - alpha
							A__	     =							-2 * _cos
							A__	     =							1 + alpha

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)

							A__	     =							1 + alpha * big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha * big_a
							A__	     =							1 + alpha / big_a
							A__	     =							-2 * _cos
							A__	     =							1 - alpha / big_a

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (pmc + aaa)
							A__	     =							2 * big_a * mpc
							A__	     =							big_a * (pmc - aaa)
							A__	     =							ppmc + aaa
							A__	     =							-2 * pmpc
							A__	     =							ppmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = 1 / sqrt(2				)       ,    ):






							"""simple docstring"""





							A__	     =							tau * frequency / samplerate
							A__	     =							sin(UpperCamelCase__				)
							A__	     =							cos(UpperCamelCase__				)
							A__	     =							_sin / (2 * q_factor)
							A__	     =							10 ** (gain_db / 40)
							A__	     =							(big_a + 1) - (big_a - 1) * _cos
							A__	     =							(big_a + 1) + (big_a - 1) * _cos
							A__	     =							(big_a - 1) - (big_a + 1) * _cos
							A__	     =							(big_a - 1) + (big_a + 1) * _cos
							A__	     =							2 * sqrt(UpperCamelCase__				) * alpha

							A__	     =							big_a * (ppmc + aaa)
							A__	     =							-2 * big_a * pmpc
							A__	     =							big_a * (ppmc - aaa)
							A__	     =							pmc + aaa
							A__	     =							2 * mpc
							A__	     =							pmc - aaa

							A__	     =							IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]       ,    [ba, ba, ba]				)
							return filt





