'''simple docstring'''




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor





class 					__magic_name__		:







			def __init__(			self       :						Tuple       ,    lowercase_       :						int       ,    lowercase_       :						int = 13       ,    lowercase_       :						int = 64       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 3       ,    lowercase_       :						int = 3       ,    lowercase_       :						bool = True       ,    lowercase_       :						bool = True       ,    lowercase_       :						int = 128       ,    lowercase_       :						str=[16, 32, 64, 128]       ,    lowercase_       :						int = 7       ,    lowercase_       :						int = 4       ,    lowercase_       :						int = 37       ,    lowercase_       :						str = "gelu"       ,    lowercase_       :						float = 0.1       ,    lowercase_       :						float = 0.1       ,    lowercase_       :						int = 10       ,    lowercase_       :						float = 0.02       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 1       ,    lowercase_       :						int = 128       ,    lowercase_       :						List[int] = [2, 2, 2, 2]       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 2       ,    ):
										lowercase_      :    Union[str, Any]       		= parent
										lowercase_      :    Dict       		= batch_size
										lowercase_      :    int       		= image_size
										lowercase_      :    Optional[int]       		= patch_size
										lowercase_      :    int       		= num_channels
										lowercase_      :    int       		= is_training
										lowercase_      :    Optional[int]       		= use_labels
										lowercase_      :    Union[str, Any]       		= hidden_size
										lowercase_      :    Tuple       		= num_hidden_layers
										lowercase_      :    Optional[int]       		= num_attention_heads
										lowercase_      :    Any       		= intermediate_size
										lowercase_      :    int       		= hidden_act
										lowercase_      :    Optional[Any]       		= hidden_dropout_prob
										lowercase_      :    Optional[int]       		= attention_probs_dropout_prob
										lowercase_      :    Optional[int]       		= type_sequence_label_size
										lowercase_      :    Optional[Any]       		= initializer_range
										lowercase_      :    Dict       		= encoder_stride
										lowercase_      :    List[Any]       		= num_attention_outputs
										lowercase_      :    Optional[Any]       		= embed_dim
										lowercase_      :    Optional[Any]       		= embed_dim + 1
										lowercase_      :    List[str]       		= resolution
										lowercase_      :    str       		= depths
										lowercase_      :    str       		= hidden_sizes
										lowercase_      :    int       		= dim
										lowercase_      :    Optional[int]       		= mlp_expansion_ratio







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Optional[int]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowercase_      :    Union[str, Any]       		= None
										if self.use_labels:
																	lowercase_      :    str       		= ids_tensor([self.batch_size]       ,    self.type_sequence_label_size					)

										lowercase_      :    Optional[int]       		= self.get_config()

										return config, pixel_values, labels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										return EfficientFormerConfig(
										    image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    hidden_size=self.hidden_size       ,    num_hidden_layers=self.num_hidden_layers       ,    num_attention_heads=self.num_attention_heads       ,    intermediate_size=self.intermediate_size       ,    hidden_act=self.hidden_act       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    is_decoder=lowercase_       ,    initializer_range=self.initializer_range       ,    encoder_stride=self.encoder_stride       ,    resolution=self.resolution       ,    depths=self.depths       ,    hidden_sizes=self.hidden_sizes       ,    dim=self.dim       ,    mlp_expansion_ratio=self.mlp_expansion_ratio       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						int					):
										lowercase_      :    List[str]       		= TFEfficientFormerModel(config=lowercase_					)
										lowercase_      :    int       		= model(lowercase_       ,    training=lowercase_					)
										self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						Tuple					):
										lowercase_      :    Optional[int]       		= self.type_sequence_label_size
										lowercase_      :    Any       		= TFEfficientFormerForImageClassification(lowercase_					)
										lowercase_      :    Optional[int]       		= model(lowercase_       ,    labels=lowercase_       ,    training=lowercase_					)
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowercase_      :    Tuple       		= 1
										lowercase_      :    List[Any]       		= TFEfficientFormerForImageClassification(lowercase_					)

										lowercase_      :    int       		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowercase_      :    Any       		= model(lowercase_       ,    labels=lowercase_					)
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.type_sequence_label_size)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										lowercase_      :    str       		= self.prepare_config_and_inputs()
										lowercase_		,		lowercase_		,		lowercase_      :    Dict       		= config_and_inputs
										lowercase_      :    Dict       		= {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = (
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase__       = (
			    {
			        '''feature-extraction''': TFEfficientFormerModel,
			        '''image-classification''': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase__       = False

			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False
			UpperCamelCase__       = False







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    List[Any]       		= TFEfficientFormerModelTester(self					)
										lowercase_      :    List[Any]       		= ConfigTester(
										    self       ,    config_class=lowercase_       ,    has_text_modality=lowercase_       ,    hidden_size=37					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										self.config_tester.run_common_tests()







			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										pass







			@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_		,		lowercase_      :    Any       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    str       		= model_class(lowercase_					)
																	lowercase_      :    Dict       		= inspect.signature(model.call					)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase_      :    Tuple       		= [*signature.parameters.keys()]

																	lowercase_      :    Dict       		= ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										def check_hidden_states_output(lowercase_       :						List[str]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						List[str]					):
																	lowercase_      :    int       		= model_class(lowercase_					)

																	lowercase_      :    Optional[Any]       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)       ,    training=lowercase_					)
																	lowercase_      :    Optional[int]       		= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	lowercase_      :    str       		= getattr(
																	    self.model_tester       ,    """expected_num_hidden_layers"""       ,    self.model_tester.num_hidden_layers + 1					)
																	self.assertEqual(len(lowercase_					)       ,    lowercase_					)

																	if hasattr(self.model_tester       ,    """encoder_seq_length"""					):
																								lowercase_      :    str       		= self.model_tester.encoder_seq_length
																								if hasattr(self.model_tester       ,    """chunk_length"""					) and self.model_tester.chunk_length > 1:
																															lowercase_      :    Optional[Any]       		= seq_length * self.model_tester.chunk_length
																	else:
																								lowercase_      :    Union[str, Any]       		= self.model_tester.seq_length

																	self.assertListEqual(
																	    list(hidden_states[-1].shape[-2:]					)       ,    [seq_length, self.model_tester.hidden_size]       ,    )

																	if config.is_encoder_decoder:
																								lowercase_      :    Dict       		= outputs.decoder_hidden_states

																								self.asseretIsInstance(lowercase_       ,    (list, tuple)					)
																								self.assertEqual(len(lowercase_					)       ,    lowercase_					)
																								lowercase_      :    Union[str, Any]       		= getattr(self.model_tester       ,    """seq_length"""       ,    lowercase_					)
																								lowercase_      :    Optional[Any]       		= getattr(self.model_tester       ,    """decoder_seq_length"""       ,    lowercase_					)

																								self.assertListEqual(
																								    list(hidden_states[-1].shape[-2:]					)       ,    [decoder_seq_length, self.model_tester.hidden_size]       ,    )

										lowercase_		,		lowercase_      :    List[str]       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowercase_      :    List[str]       		= True
																	check_hidden_states_output(lowercase_       ,    lowercase_       ,    lowercase_					)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowercase_      :    List[str]       		= True

																	check_hidden_states_output(lowercase_       ,    lowercase_       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						str       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Optional[Any]=False					):
										lowercase_      :    int       		= super()._prepare_for_class(lowercase_       ,    lowercase_       ,    return_labels=lowercase_					)

										if return_labels:
																	if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																								del inputs_dict["labels"]

										return inputs_dict







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										lowercase_      :    Union[str, Any]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_					)







			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""					)
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Optional[int]       		= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase_					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :    Any       		= TFEfficientFormerModel.from_pretrained(lowercase_					)
																	self.assertIsNotNone(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_		,		lowercase_      :    Any       		= self.model_tester.prepare_config_and_inputs_for_common()
										lowercase_      :    Union[str, Any]       		= True

										lowercase_      :    Optional[Any]       		= getattr(self.model_tester       ,    """seq_length"""       ,    lowercase_					)
										lowercase_      :    Tuple       		= getattr(self.model_tester       ,    """encoder_seq_length"""       ,    lowercase_					)
										lowercase_      :    Dict       		= getattr(self.model_tester       ,    """key_length"""       ,    lowercase_					)
										lowercase_      :    Optional[int]       		= getattr(self.model_tester       ,    """chunk_length"""       ,    lowercase_					)

										if chunk_length is not None and hasattr(self.model_tester       ,    """num_hashes"""					):
																	lowercase_      :    Optional[Any]       		= encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
																	lowercase_      :    Union[str, Any]       		= True
																	lowercase_      :    Dict       		= False
																	lowercase_      :    List[str]       		= True
																	lowercase_      :    List[str]       		= model_class(lowercase_					)

																	lowercase_      :    List[Any]       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)       ,    training=lowercase_					)
																	lowercase_      :    Optional[int]       		= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																	self.assertEqual(len(lowercase_					)       ,    self.model_tester.num_attention_outputs					)

																	# check that output_attentions also work using config
																	del inputs_dict["output_attentions"]
																	lowercase_      :    str       		= True
																	lowercase_      :    Dict       		= model_class(lowercase_					)
																	lowercase_      :    List[Any]       		= model(**self._prepare_for_class(lowercase_       ,    lowercase_					)       ,    training=lowercase_					)

																	lowercase_      :    str       		= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																	self.assertEqual(len(lowercase_					)       ,    self.model_tester.num_attention_outputs					)

																	if chunk_length is not None:
																								self.assertListEqual(
																								    list(attentions[0].shape[-4:]					)       ,    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,    )
																	else:
																								self.assertListEqual(
																								    list(attentions[0].shape[-3:]					)       ,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										# We use a simplified version of this test for EfficientFormer because it requires training=False
										# and Keras refuses to let us force that during functional construction
										lowercase_		,		lowercase_      :    int       		= self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	# Prepare our model
																	lowercase_      :    Optional[int]       		= model_class(lowercase_					)
																	# These are maximally general inputs for the model, with multiple None dimensions
																	# Hopefully this will catch any conditionals that fail for flexible shapes
																	lowercase_      :    Union[str, Any]       		= {
																	    key: tf.keras.Input(shape=val.shape[1:]       ,    dtype=val.dtype       ,    name=lowercase_					)
																	    for key, val in model.input_signature.items()
																	    if key in model.dummy_inputs
																	}
																	lowercase_      :    int       		= model(lowercase_					)
																	self.assertTrue(outputs_dict is not None					)




def 							lowerCamelCase			(   )    ->   Dict:
							lowercase_      :    Any       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





@require_tf
@require_vision
class 					__magic_name__		(					unittest.TestCase):







			@cached_property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										return (
										    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""					)
										    if is_vision_available()
										    else None
										)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    int       		= TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""					)
										lowercase_      :    int       		= self.default_image_processor
										lowercase_      :    Optional[Any]       		= prepare_img()
										lowercase_      :    str       		= image_processor(images=lowercase_       ,    return_tensors="""tf"""					)
										# forward pass
										lowercase_      :    int       		= model(**lowercase_       ,    training=lowercase_					)
										# verify the logits
										lowercase_      :    Tuple       		= tf.TensorShape((1, 1000)					)
										self.assertEqual(outputs.logits.shape       ,    lowercase_					)
										lowercase_      :    List[Any]       		= tf.constant([-0.05_55, 0.48_25, -0.08_52]					)
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,    lowercase_       ,    atol=1E-4					)					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    """snap-research/efficientformer-l1-300"""					)
										lowercase_      :    int       		= self.default_image_processor
										lowercase_      :    Union[str, Any]       		= prepare_img()
										lowercase_      :    Any       		= image_processor(images=lowercase_       ,    return_tensors="""tf"""					)
										# forward pass
										lowercase_      :    Any       		= model(**lowercase_       ,    training=lowercase_					)
										# verify the logits
										lowercase_      :    Dict       		= tf.TensorShape((1, 1000)					)
										self.assertEqual(outputs.logits.shape       ,    lowercase_					)
										lowercase_      :    List[str]       		= tf.constant([-0.13_12, 0.43_53, -1.04_99]					)
										self.assertTrue(np.allclose(outputs.logits[0, :3]       ,    lowercase_       ,    atol=1E-4					)					)






'''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





