




"""simple docstring"""

from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter






def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float = 1 / sqrt(2				)				)       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float = 1 / sqrt(2				)				)       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float = 1 / sqrt(2				)				)       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float = 1 / sqrt(2				)				)       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float							,    _snake_case				:   float = 1 / sqrt(2				)							,    )       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float							,    _snake_case				:   float = 1 / sqrt(2				)							,    )       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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(_snake_case				) * 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 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int							,    _snake_case				:   float							,    _snake_case				:   float = 1 / sqrt(2				)							,    )       ->   IIRFilter:






      '''simple docstring'''


      _A     =  tau * frequency / samplerate
      _A     =  sin(_snake_case				)
      _A     =  cos(_snake_case				)
      _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(_snake_case				) * 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







"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Any						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(prediction_type='v_prediction'						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps       ,				device=_UpperCAmelCase						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.to(_UpperCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3

