






from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar







def 			lowercase__  (				A_:	float     ,			A_:	float     ,			A_:	int		)  ->							list:







       """simple docstring"""






       seed(1		)
       return [gauss(A_     ,			A_		) for _ in range(A_		)]







def 			lowercase__  (				A_:	int     ,			A_:	list		)  ->							list:







       """simple docstring"""






       return [k for k in range(A_		) for _ in range(instance_count[k]		)]







def 			lowercase__  (				A_:	int     ,			A_:	list		)  ->							float:







       """simple docstring"""






       return sum(A_		) / instance_count







def 			lowercase__  (				A_:	int     ,			A_:	int		)  ->							float:







       """simple docstring"""






       return instance_count / total_count







def 			lowercase__  (				A_:	list     ,			A_:	list     ,			A_:	int		)  ->							float:







       """simple docstring"""






       __UpperCAmelCase    							=[]  # An empty list to store all squared differences
       # iterate over number of elements in items
       for i in range(len(A_		)		):
              # for loop iterates over number of elements in inner layer of items
              for j in range(len(items[i]		)		):
                     # appending squared differences to 'squared_diff' list
                     squared_diff.append((items[i][j] - means[i]) ** 2		)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
       __UpperCAmelCase    							=len(A_		)  # Number of classes in dataset
       return 1 / (total_count - n_classes) * sum(A_		)







def 			lowercase__  (				A_:	list     ,			A_:	list     ,			A_:	float     ,			A_:	list		)  ->							list:







       """simple docstring"""






       __UpperCAmelCase    							=[]
       # for loop iterates over number of elements in list
       for i in range(len(A_		)		):
              # for loop iterates over number of inner items of each element
              for j in range(len(x_items[i]		)		):
                     __UpperCAmelCase    							=[]  # to store all discriminant values of each item as a list
                     # for loop iterates over number of classes we have in our dataset
                     for k in range(len(A_		)		):
                            # appending values of discriminants for each class to 'temp' list
                            temp.append(
                                x_items[i][j] * (means[k] / variance)
                                - (means[k] ** 2 / (2 * variance))
                                + log(probabilities[k]		)		)
                     # appending discriminant values of each item to 'results' list
                     results.append(A_		)

       return [result.index(max(A_		)		) for result in results]







def 			lowercase__  (				A_:	list     ,			A_:	list		)  ->							float:







       """simple docstring"""






       __UpperCAmelCase    							=sum(1 for i, j in zip(A_     ,			A_		) if i == j		)
       # percentage of accuracy equals to number of correct predictions divided by number
       # of all data and multiplied by 100
       return (correct / len(A_		)) * 100


__A  							=  TypeVar("num")







def 			lowercase__  (				A_:	Callable[[object], num]     ,			A_:	str     ,			A_:	str     ,			A_:	Callable[[num], bool] = lambda A_		: True     ,			A_:	str | None = None     ,			)  ->							num:







       """simple docstring"""






       while True:
              try:
                     __UpperCAmelCase    							=input_type(input(A_		).strip() or default		)
                     if condition(A_		):
                            return user_input
                     else:
                            print(F'''{user_input}: {err_msg}'''		)
                            continue
              except ValueError:
                     print(
                         F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''		)







def 			lowercase__  (				)  ->							List[Any]:







       """simple docstring"""






       while True:
              print(""" Linear Discriminant Analysis """.center(50     ,			"""*"""		)		)
              print("""*""" * 50     ,			"""\n"""		)
              print("""First of all we should specify the number of classes that"""		)
              print("""we want to generate as training dataset"""		)
              # Trying to get number of classes
              __UpperCAmelCase    							=valid_input(
                  input_type=A_     ,			condition=lambda A_		: x > 0     ,			input_msg="""Enter the number of classes (Data Groupings): """     ,			err_msg="""Number of classes should be positive!"""     ,			)

              print("""-""" * 100		)

              # Trying to get the value of standard deviation
              __UpperCAmelCase    							=valid_input(
                  input_type=A_     ,			condition=lambda A_		: x >= 0     ,			input_msg=(
                      """Enter the value of standard deviation"""
                      """(Default value is 1.0 for all classes): """
                  )     ,			err_msg="""Standard deviation should not be negative!"""     ,			default="""1.0"""     ,			)

              print("""-""" * 100		)

              # Trying to get number of instances in classes and theirs means to generate
              # dataset
              __UpperCAmelCase    							=[]  # An empty list to store instance counts of classes in dataset
              for i in range(A_		):
                     __UpperCAmelCase    							=valid_input(
                         input_type=A_     ,			condition=lambda A_		: x > 0     ,			input_msg=(F'''Enter The number of instances for class_{i+1}: ''')     ,			err_msg="""Number of instances should be positive!"""     ,			)
                     counts.append(A_		)
              print("""-""" * 100		)

              # An empty list to store values of user-entered means of classes
              __UpperCAmelCase    							=[]
              for a in range(A_		):
                     __UpperCAmelCase    							=valid_input(
                         input_type=A_     ,			input_msg=(F'''Enter the value of mean for class_{a+1}: ''')     ,			err_msg="""This is an invalid value."""     ,			)
                     user_means.append(A_		)
              print("""-""" * 100		)

              print("""Standard deviation: """     ,			A_		)
              # print out the number of instances in classes in separated line
              for i, count in enumerate(A_     ,			1		):
                     print(F'''Number of instances in class_{i} is: {count}'''		)
              print("""-""" * 100		)

              # print out mean values of classes separated line
              for i, user_mean in enumerate(A_     ,			1		):
                     print(F'''Mean of class_{i} is: {user_mean}'''		)
              print("""-""" * 100		)

              # Generating training dataset drawn from gaussian distribution
              __UpperCAmelCase    							=[
                  gaussian_distribution(user_means[j]     ,			A_     ,			counts[j]		)
                  for j in range(A_		)
              ]
              print("""Generated Normal Distribution: \n"""     ,			A_		)
              print("""-""" * 100		)

              # Generating Ys to detecting corresponding classes
              __UpperCAmelCase    							=y_generator(A_     ,			A_		)
              print("""Generated Corresponding Ys: \n"""     ,			A_		)
              print("""-""" * 100		)

              # Calculating the value of actual mean for each class
              __UpperCAmelCase    							=[calculate_mean(counts[k]     ,			x[k]		) for k in range(A_		)]
              # for loop iterates over number of elements in 'actual_means' list and print
              # out them in separated line
              for i, actual_mean in enumerate(A_     ,			1		):
                     print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''		)
              print("""-""" * 100		)

              # Calculating the value of probabilities for each class
              __UpperCAmelCase    							=[
                  calculate_probabilities(counts[i]     ,			sum(A_		)		) for i in range(A_		)
              ]

              # for loop iterates over number of elements in 'probabilities' list and print
              # out them in separated line
              for i, probability in enumerate(A_     ,			1		):
                     print(F'''Probability of class_{i} is: {probability}'''		)
              print("""-""" * 100		)

              # Calculating the values of variance for each class
              __UpperCAmelCase    							=calculate_variance(A_     ,			A_     ,			sum(A_		)		)
              print("""Variance: """     ,			A_		)
              print("""-""" * 100		)

              # Predicting Y values
              # storing predicted Y values in 'pre_indexes' variable
              __UpperCAmelCase    							=predict_y_values(A_     ,			A_     ,			A_     ,			A_		)
              print("""-""" * 100		)

              # Calculating Accuracy of the model
              print(F'''Accuracy: {accuracy(A_     ,			A_		)}'''		)
              print("""-""" * 100		)
              print(""" DONE """.center(100     ,			"""+"""		)		)

              if input("""Press any key to restart or 'q' for quit: """		).strip().lower() == "q":
                     print("""\n""" + """GoodBye!""".center(100     ,			"""-"""		) + """\n"""		)
                     break
              system("""cls""" if name == """nt""" else """clear"""		)  # noqa: S605


if __name__ == "__main__":
      main()












import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 							_A (    UpperCamelCase							):
    """simple docstring"""

    lowerCamelCase  :       str   =   (DPMSolverMultistepScheduler,)
    lowerCamelCase  :       List[str]   =   (('num_inference_steps', 25),)



    def _a       (							self			:    Dict   ,    **__SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Dict:
           __UpperCAmelCase    							={
               """num_train_timesteps""": 1000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """solver_order""": 2,
               """prediction_type""": """epsilon""",
               """thresholding""": False,
               """sample_max_value""": 1.0,
               """algorithm_type""": """dpmsolver++""",
               """solver_type""": """midpoint""",
               """lower_order_final""": False,
               """lambda_min_clipped""": -float("""inf"""			),
               """variance_type""": None,
           }

           config.update(**__SCREAMING_SNAKE_CASE			)
           return config



    def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=0   ,    **__SCREAMING_SNAKE_CASE			:    int			)			->       Dict:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.dummy_sample
           __UpperCAmelCase    							=0.1 * sample
           __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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



    def _a       (							self			:    Any			)			->       Union[str, Any]:
           pass



    def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0   ,    **__SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[Any]:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)
           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=self.dummy_sample
           __UpperCAmelCase    							=0.1 * sample
           __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)
                  scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

                  # 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(__SCREAMING_SNAKE_CASE			)
                         __UpperCAmelCase    							=scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE			)
                         # copy over dummy past residuals
                         new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=new_scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=None   ,    **__SCREAMING_SNAKE_CASE			:    Optional[int]			)			->       Optional[Any]:
           if scheduler is None:
                  __UpperCAmelCase    							=self.scheduler_classes[0]
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           return sample



    def _a       (							self			:    Dict			)			->       Optional[int]:
           __UpperCAmelCase    							=dict(self.forward_default_kwargs			)

           __UpperCAmelCase    							=kwargs.pop("""num_inference_steps"""   ,    __SCREAMING_SNAKE_CASE			)

           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config()
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

                  __UpperCAmelCase    							=self.dummy_sample
                  __UpperCAmelCase    							=0.1 * sample

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

                  # copy over dummy past residuals (must be done after set_timesteps)
                  __UpperCAmelCase    							=[residual + 0.2, residual + 0.15, residual + 0.10]
                  __UpperCAmelCase    							=dummy_past_residuals[: scheduler.config.solver_order]

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

                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
                  __UpperCAmelCase    							=scheduler.step(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample

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



    def _a       (							self			:    List[Any]			)			->       Tuple:
           for timesteps in [25, 50, 100, 999, 1000]:
                  self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       int:
           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE			)
           for order in [1, 2, 3]:
                  for solver_type in ["midpoint", "heun"]:
                         for threshold in [0.5, 1.0, 2.0]:
                                for prediction_type in ["epsilon", "sample"]:
                                       self.check_over_configs(
                                           thresholding=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    sample_max_value=__SCREAMING_SNAKE_CASE   ,    algorithm_type="""dpmsolver++"""   ,    solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    )



    def _a       (							self			:    Union[str, Any]			)			->       List[str]:
           for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Tuple:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                  for solver_type in ["midpoint", "heun"]:
                         for order in [1, 2, 3]:
                                for prediction_type in ["epsilon", "sample"]:
                                       if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                              if order == 3:
                                                     continue
                                       else:
                                              self.check_over_configs(
                                                  solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                       __UpperCAmelCase    							=self.full_loop(
                                           solver_order=__SCREAMING_SNAKE_CASE   ,    solver_type=__SCREAMING_SNAKE_CASE   ,    prediction_type=__SCREAMING_SNAKE_CASE   ,    algorithm_type=__SCREAMING_SNAKE_CASE   ,    )
                                       assert not torch.isnan(__SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"



    def _a       (							self			:    Dict			)			->       str:
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE			)



    def _a       (							self			:    Union[str, Any]			)			->       Optional[Any]:
           self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
           self.check_over_configs(lambda_min_clipped=-5.1			)



    def _a       (							self			:    Any			)			->       List[str]:
           self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE			)
           self.check_over_configs(variance_type="""learned_range"""			)



    def _a       (							self			:    List[Any]			)			->       int:
           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                  self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE   ,    time_step=0			)



    def _a       (							self			:    Optional[int]			)			->       str:
           __UpperCAmelCase    							=self.full_loop()
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3



    def _a       (							self			:    Union[str, Any]			)			->       str:
           __UpperCAmelCase    							=self.full_loop(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0.87   ,    sample_max_value=0.5			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 1.1_364			) < 1e-3



    def _a       (							self			:    List[Any]			)			->       List[Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.2_251			) < 1e-3



    def _a       (							self			:    int			)			->       Union[str, Any]:
           __UpperCAmelCase    							=self.full_loop(prediction_type="""v_prediction"""   ,    use_karras_sigmas=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.2_096			) < 1e-3



    def _a       (							self			:    int			)			->       Optional[Any]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           __UpperCAmelCase    							=DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
           __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3

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

           __UpperCAmelCase    							=self.full_loop(scheduler=__SCREAMING_SNAKE_CASE			)
           __UpperCAmelCase    							=torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

           assert abs(result_mean.item() - 0.3_301			) < 1e-3



    def _a       (							self			:    List[Any]			)			->       str:
           __UpperCAmelCase    							=self.scheduler_classes[0]
           __UpperCAmelCase    							=self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE   ,    dynamic_thresholding_ratio=0			)
           __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							=10
           __UpperCAmelCase    							=self.dummy_model()
           __UpperCAmelCase    							=self.dummy_sample_deter.half()
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			)

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

           assert sample.dtype == torch.floataa



    def _a       (							self			:    Optional[int]   ,    **__SCREAMING_SNAKE_CASE			:    Tuple			)			->       Optional[int]:
           for scheduler_class in self.scheduler_classes:
                  __UpperCAmelCase    							=self.get_scheduler_config(**__SCREAMING_SNAKE_CASE			)
                  __UpperCAmelCase    							=scheduler_class(**__SCREAMING_SNAKE_CASE			)

                  scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
                  assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps




