





'''simple docstring'''




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class   A_	(  A_			):
 _lowerCamelCase  : List[str]           =							(DEISMultistepScheduler,)
 _lowerCamelCase  : List[Any]           =							(("num_inference_steps", 25),)




 def      lowercase	(					self       :    Any	,			**snake_case_       :    Optional[Any]    ):
      _UpperCAmelCase     							=   {
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "solver_order": 2,
      }

      config.update(**A_    )
      return config




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[str]=0	,			**snake_case_       :    int    ):
      _UpperCAmelCase     							=   dict(self.forward_default_kwargs    )
      _UpperCAmelCase     							=   kwargs.pop("num_inference_steps"	,			A_    )
      _UpperCAmelCase     							=   self.dummy_sample
      _UpperCAmelCase     							=   0.1 * sample
      _UpperCAmelCase     							=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase     							=   self.get_scheduler_config(**A_    )
           _UpperCAmelCase     							=   scheduler_class(**A_    )
           scheduler.set_timesteps(A_    )
           # copy over dummy past residuals
           _UpperCAmelCase     							=   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A_    )
                _UpperCAmelCase     							=   scheduler_class.from_pretrained(A_    )
                new_scheduler.set_timesteps(A_    )
                # copy over dummy past residuals
                _UpperCAmelCase     							=   dummy_past_residuals[: new_scheduler.config.solver_order]

           _UpperCAmelCase							,  _UpperCAmelCase     							=   sample, sample
           for t in range(A_	,			time_step + scheduler.config.solver_order + 1    ):
                _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample
                _UpperCAmelCase     							=   new_scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample

                assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"




 def      lowercase	(					self       :    Any    ):
      pass




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Any=0	,			**snake_case_       :    int    ):
      _UpperCAmelCase     							=   dict(self.forward_default_kwargs    )
      _UpperCAmelCase     							=   kwargs.pop("num_inference_steps"	,			A_    )
      _UpperCAmelCase     							=   self.dummy_sample
      _UpperCAmelCase     							=   0.1 * sample
      _UpperCAmelCase     							=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase     							=   self.get_scheduler_config()
           _UpperCAmelCase     							=   scheduler_class(**A_    )
           scheduler.set_timesteps(A_    )

           # copy over dummy past residuals (must be after setting timesteps)
           _UpperCAmelCase     							=   dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A_    )
                _UpperCAmelCase     							=   scheduler_class.from_pretrained(A_    )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(A_    )

                # copy over dummy past residual (must be after setting timesteps)
                _UpperCAmelCase     							=   dummy_past_residuals[: new_scheduler.config.solver_order]

           _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample
           _UpperCAmelCase     							=   new_scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample

           assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"




 def      lowercase	(					self       :    str	,			snake_case_       :    str=None	,			**snake_case_       :    Union[str, Any]    ):
      if scheduler is None:
           _UpperCAmelCase     							=   self.scheduler_classes[0]
           _UpperCAmelCase     							=   self.get_scheduler_config(**A_    )
           _UpperCAmelCase     							=   scheduler_class(**A_    )

      _UpperCAmelCase     							=   self.scheduler_classes[0]
      _UpperCAmelCase     							=   self.get_scheduler_config(**A_    )
      _UpperCAmelCase     							=   scheduler_class(**A_    )

      _UpperCAmelCase     							=   1_0
      _UpperCAmelCase     							=   self.dummy_model()
      _UpperCAmelCase     							=   self.dummy_sample_deter
      scheduler.set_timesteps(A_    )

      for i, t in enumerate(scheduler.timesteps    ):
           _UpperCAmelCase     							=   model(A_	,			A_    )
           _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_    ).prev_sample

      return sample




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   dict(self.forward_default_kwargs    )

      _UpperCAmelCase     							=   kwargs.pop("num_inference_steps"	,			A_    )

      for scheduler_class in self.scheduler_classes:
           _UpperCAmelCase     							=   self.get_scheduler_config()
           _UpperCAmelCase     							=   scheduler_class(**A_    )

           _UpperCAmelCase     							=   self.dummy_sample
           _UpperCAmelCase     							=   0.1 * sample

           if num_inference_steps is not None and hasattr(A_	,			"set_timesteps"    ):
                scheduler.set_timesteps(A_    )
           elif num_inference_steps is not None and not hasattr(A_	,			"set_timesteps"    ):
                _UpperCAmelCase     							=   num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _UpperCAmelCase     							=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
           _UpperCAmelCase     							=   dummy_past_residuals[: scheduler.config.solver_order]

           _UpperCAmelCase     							=   scheduler.timesteps[5]
           _UpperCAmelCase     							=   scheduler.timesteps[6]

           _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample
           _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_	,			**A_    ).prev_sample

           self.assertEqual(output_a.shape	,			sample.shape    )
           self.assertEqual(output_a.shape	,			output_a.shape    )




 def      lowercase	(					self       :    Dict    ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _UpperCAmelCase     							=   DEISMultistepScheduler(**self.get_scheduler_config()    )
      _UpperCAmelCase     							=   self.full_loop(scheduler=A_    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(A_    )    )

      assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1e-3

      _UpperCAmelCase     							=   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
      _UpperCAmelCase     							=   DPMSolverMultistepScheduler.from_config(scheduler.config    )
      _UpperCAmelCase     							=   UniPCMultistepScheduler.from_config(scheduler.config    )
      _UpperCAmelCase     							=   DEISMultistepScheduler.from_config(scheduler.config    )

      _UpperCAmelCase     							=   self.full_loop(scheduler=A_    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(A_    )    )

      assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1e-3




 def      lowercase	(					self       :    Any    ):
      for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=A_    )




 def      lowercase	(					self       :    List[str]    ):
      self.check_over_configs(thresholding=A_    )
      for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
                for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              thresholding=A_	,			prediction_type=A_	,			sample_max_value=A_	,			algorithm_type="deis"	,			solver_order=A_	,			solver_type=A_	,			)




 def      lowercase	(					self       :    Tuple    ):
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_    )




 def      lowercase	(					self       :    Dict    ):
      for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
                for order in [1, 2, 3]:
                     for prediction_type in ["epsilon", "sample"]:
                          self.check_over_configs(
                              solver_order=A_	,			solver_type=A_	,			prediction_type=A_	,			algorithm_type=A_	,			)
                          _UpperCAmelCase     							=   self.full_loop(
                              solver_order=A_	,			solver_type=A_	,			prediction_type=A_	,			algorithm_type=A_	,			)
                          assert not torch.isnan(A_    ).any(), "Samples have nan numbers"




 def      lowercase	(					self       :    Union[str, Any]    ):
      self.check_over_configs(lower_order_final=A_    )
      self.check_over_configs(lower_order_final=A_    )




 def      lowercase	(					self       :    Optional[int]    ):
      for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_forward(num_inference_steps=A_	,			time_step=0    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.full_loop()
      _UpperCAmelCase     							=   torch.mean(torch.abs(A_    )    )

      assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1e-3




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.full_loop(prediction_type="v_prediction"    )
      _UpperCAmelCase     							=   torch.mean(torch.abs(A_    )    )

      assert abs(result_mean.item() - 0.0_9_1    ) < 1e-3




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   self.scheduler_classes[0]
      _UpperCAmelCase     							=   self.get_scheduler_config(thresholding=A_	,			dynamic_thresholding_ratio=0    )
      _UpperCAmelCase     							=   scheduler_class(**A_    )

      _UpperCAmelCase     							=   1_0
      _UpperCAmelCase     							=   self.dummy_model()
      _UpperCAmelCase     							=   self.dummy_sample_deter.half()
      scheduler.set_timesteps(A_    )

      for i, t in enumerate(scheduler.timesteps    ):
           _UpperCAmelCase     							=   model(A_	,			A_    )
           _UpperCAmelCase     							=   scheduler.step(A_	,			A_	,			A_    ).prev_sample

      assert sample.dtype == torch.floataa








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     __UpperCamelCase    							=max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)
     __UpperCamelCase    							=20 * np.logaa(SCREAMING_SNAKE_CASE__				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     # Display within reasonable bounds
     __UpperCamelCase    							=get_bounds(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     plt.ylim(max([-80, bounds[0]]				)			,     min([80, bounds[1]]				)				)
     plt.ylabel('Gain (dB)'				)

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     plt.ylim(-2 * pi			,     2 * pi				)
     plt.ylabel('Phase shift (Radians)'				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__			,     -2 * pi				)				)
     plt.show()





