

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       (DPMSolverSinglestepScheduler,)
							UpperCAmelCase__			:		Any             =       (("num_inference_steps", 25),)


							def 							__lowercase							(     self			,				**_a			)       ->       List[str]:
												_a      :       List[str]   =						{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''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''',
												    '''lambda_min_clipped''': -float('''inf'''			),
												    '''variance_type''': None,
												}

												config.update(**_a			)
												return config


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

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

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

																	_a ,   _a      :       Any   =						sample, sample
																	for t in range(_a			,				time_step + scheduler.config.solver_order + 1			):
																						_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																						_a      :       List[str]   =						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			)       ->       Dict:
												pass


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

												for scheduler_class in self.scheduler_classes:
																	_a      :       int   =						self.get_scheduler_config()
																	_a      :       Optional[int]   =						scheduler_class(**_a			)
																	scheduler.set_timesteps(_a			)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_a			)
																						_a      :       str   =						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)
																						_a      :       Optional[Any]   =						dummy_past_residuals[: new_scheduler.config.solver_order]

																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			,				**_a			).prev_sample
																	_a      :       int   =						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			,				_a=None			,				**_a			)       ->       Dict:
												if scheduler is None:
																	_a      :       Optional[Any]   =						self.scheduler_classes[0]
																	_a      :       List[str]   =						self.get_scheduler_config(**_a			)
																	_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       int   =						self.scheduler_classes[0]
												_a      :       List[Any]   =						self.get_scheduler_config(**_a			)
												_a      :       Union[str, Any]   =						scheduler_class(**_a			)

												_a      :       Optional[Any]   =						1_0
												_a      :       List[Any]   =						self.dummy_model()
												_a      :       Union[str, Any]   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       Dict   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												return sample


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[int]   =						DPMSolverSinglestepScheduler(**self.get_scheduler_config()			)
												_a      :       Union[str, Any]   =						5_0
												_a      :       Any   =						self.dummy_model()
												_a      :       Tuple   =						self.dummy_sample_deter
												scheduler.set_timesteps(_a			)

												# make sure that the first t is uneven
												for i, t in enumerate(scheduler.timesteps[3:]			):
																	_a      :       List[Any]   =						model(_a			,				_a			)
																	_a      :       Optional[int]   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												_a      :       Union[str, Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2574			) < 1e-3


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

												assert abs(result_mean.item() - 0.2791			) < 1e-3

												_a      :       int   =						DEISMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverMultistepScheduler.from_config(scheduler.config			)
												_a      :       List[Any]   =						UniPCMultistepScheduler.from_config(scheduler.config			)
												_a      :       Union[str, Any]   =						DPMSolverSinglestepScheduler.from_config(scheduler.config			)

												_a      :       Optional[int]   =						self.full_loop(scheduler=_a			)
												_a      :       List[str]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												self.check_over_configs(thresholding=_a			)
												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=_a			,				prediction_type=_a			,				sample_max_value=_a			,				algorithm_type='''dpmsolver++'''			,				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 ["dpmsolver", "dpmsolver++"]:
																	for solver_type in ["midpoint", "heun"]:
																						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			,				)
																																_a      :       List[Any]   =						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			)       ->       List[Any]:
												self.check_over_configs(lower_order_final=_a			)
												self.check_over_configs(lower_order_final=_a			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												self.check_over_configs(lambda_min_clipped=-float('''inf'''			)			)
												self.check_over_configs(lambda_min_clipped=-5.1			)


							def 							__lowercase							(     self			)       ->       int:
												self.check_over_configs(variance_type=_a			)
												self.check_over_configs(variance_type='''learned_range'''			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												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			)       ->       Optional[Any]:
												_a      :       Any   =						self.full_loop()
												_a      :       Optional[Any]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2791			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Optional[Any]   =						self.full_loop(use_karras_sigmas=_a			)
												_a      :       int   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.2248			) < 1e-3


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						self.full_loop(prediction_type='''v_prediction'''			)
												_a      :       Optional[int]   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.1453			) < 1e-3


							def 							__lowercase							(     self			)       ->       str:
												_a      :       List[str]   =						self.full_loop(prediction_type='''v_prediction'''			,				use_karras_sigmas=_a			)
												_a      :       Tuple   =						torch.mean(torch.abs(_a			)			)

												assert abs(result_mean.item() - 0.0649			) < 1e-3




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

												_a      :       Dict   =						1_0
												_a      :       Optional[Any]   =						self.dummy_model()
												_a      :       List[str]   =						self.dummy_sample_deter.half()
												scheduler.set_timesteps(_a			)

												for i, t in enumerate(scheduler.timesteps			):
																	_a      :       Any   =						model(_a			,				_a			)
																	_a      :       int   =						scheduler.step(_a			,				_a			,				_a			).prev_sample

												assert sample.dtype == torch.floataa






from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					_a      :       Dict   =						0
					_a      :       Tuple   =						randint(__a						,__a      )
					_a      :       List[Any]   =						a[end]
					_a      :       str   =						a[pivot]
					_a      :       str   =						temp
					_a      :       Dict   =						start - 1
					for index in range(__a						,__a      ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      :       int   =						new_pivot_index + 1
															_a      :       Any   =						a[new_pivot_index]
															_a      :       Optional[int]   =						a[index]
															_a      :       str   =						temp

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)



