
import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A__       :   List[str]   						=					0.25 + 0.0j
A__       :   Union[str, Any]   						=					-0.4 + 0.6j
A__       :   Optional[int]   						=					-0.1 + 0.6_51j
A__       :   Dict   						=					-2.0
A__       :   int   						=					5_6
A__       :   Optional[Any]   						=					2.0
A__       :   Dict   						=					6_6_6



def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  List[str]    ):
			return numpy.exp(UpperCamelCase_    ) + c_parameter



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  List[str]    ):
			return z_values * z_values + c_parameter



def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  str    ):
			lowerCAmelCase__ :    Optional[Any]				      =			numpy.linspace(-window_size	,UpperCamelCase_	,UpperCamelCase_    )
			lowerCAmelCase__ :    Any				      =			x.reshape((nb_pixels, 1)    )
			lowerCAmelCase__ :    Optional[Any]				      =			numpy.linspace(-window_size	,UpperCamelCase_	,UpperCamelCase_    )
			lowerCAmelCase__ :    List[Any]				      =			y.reshape((1, nb_pixels)    )
			return x + 1.0j * y



def      _a     (   __UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  str	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  Union[str, Any] = None	,):
			lowerCAmelCase__ :    Any				      =			z_a.astype('''complex64'''    )
			for _ in range(UpperCamelCase_    ):
						lowerCAmelCase__ :    Optional[int]				      =			eval_function(UpperCamelCase_	,UpperCamelCase_    )
						if infinity is not None:
									numpy.nan_to_num(UpperCamelCase_	,copy=UpperCamelCase_	,nan=UpperCamelCase_    )
									lowerCAmelCase__ :    Any				      =			infinity
			return z_n



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  int	,__UpperCamelCase       :  str	,__UpperCamelCase       :  Dict	,):
			lowerCAmelCase__ :    Union[str, Any]				      =			(abs(UpperCamelCase_    )).transpose()
			lowerCAmelCase__ :    Union[str, Any]				      =			abs_z_final[::-1, :]
			pyplot.matshow(abs_z_final < escape_radius    )
			pyplot.title(f'''Julia set of ${function_label}$, $c={function_params}$'''    )
			pyplot.show()



def      _a     (   ):
			warnings.filterwarnings(
			    '''ignore'''	,category=UpperCamelCase_	,message='''overflow encountered in multiply'''    )
			warnings.filterwarnings(
			    '''ignore'''	,category=UpperCamelCase_	,message='''invalid value encountered in multiply'''	,)
			warnings.filterwarnings(
			    '''ignore'''	,category=UpperCamelCase_	,message='''overflow encountered in absolute'''    )
			warnings.filterwarnings(
			    '''ignore'''	,category=UpperCamelCase_	,message='''overflow encountered in exp'''    )


if __name__ == "__main__":
							A__       :   str   						=					prepare_grid(window_size, nb_pixels)

							ignore_overflow_warnings()  # See file header for explanations

							A__       :   Any   						=					2_4
							A__       :   Dict   						=					2 * abs(c_cauliflower) + 1
							A__       :   Optional[Any]   						=					iterate_function(
							    eval_quadratic_polynomial,
							    c_cauliflower,
							    nb_iterations,
							    z_a,
							    infinity=1.1 * escape_radius,
							)
							show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

							A__       :   List[str]   						=					6_4
							A__       :   Any   						=					2 * abs(c_polynomial_a) + 1
							A__       :   List[str]   						=					iterate_function(
							    eval_quadratic_polynomial,
							    c_polynomial_a,
							    nb_iterations,
							    z_a,
							    infinity=1.1 * escape_radius,
							)
							show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

							A__       :   List[Any]   						=					1_6_1
							A__       :   List[Any]   						=					2 * abs(c_polynomial_a) + 1
							A__       :   Dict   						=					iterate_function(
							    eval_quadratic_polynomial,
							    c_polynomial_a,
							    nb_iterations,
							    z_a,
							    infinity=1.1 * escape_radius,
							)
							show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

							A__       :   Optional[Any]   						=					1_2
							A__       :   Dict   						=					1_0000.0
							A__       :   Tuple   						=					iterate_function(
							    eval_exponential,
							    c_exponential,
							    nb_iterations,
							    z_a + 2,
							    infinity=1.0E10,
							)
							show_results("""e^z+c""", c_exponential, escape_radius, z_final)





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

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

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Any				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    Union[str, Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



