





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
						A_						:    Union[str, Any]							=   (UniPCMultistepScheduler,)
						A_						:    Dict							=   (('num_inference_steps', 25),)


						def        a			(self	:				Optional[int]    ,      **a__	:				Optional[Any]				):





									"""simple docstring"""



									__snake_case								=       {
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0_0_0_1,
									    '''beta_end''': 0.0_2,
									    '''beta_schedule''': '''linear''',
									    '''solver_order''': 2,
									    '''solver_type''': '''bh2''',
									}

									config.update(**a__				)
									return config


						def        a			(self	:				int    ,      a__	:				int=0    ,      **a__	:				Optional[Any]				):





									"""simple docstring"""



									__snake_case								=       dict(self.forward_default_kwargs				)
									__snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
									__snake_case								=       self.dummy_sample
									__snake_case								=       0.1 * sample
									__snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

												__snake_case	,     __snake_case								=       sample, sample
												for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
															__snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
															__snake_case								=       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        a			(self	:				Optional[int]    ,      a__	:				str=0    ,      **a__	:				Dict				):





									"""simple docstring"""



									__snake_case								=       dict(self.forward_default_kwargs				)
									__snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
									__snake_case								=       self.dummy_sample
									__snake_case								=       0.1 * sample
									__snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
												__snake_case								=       self.get_scheduler_config()
												__snake_case								=       scheduler_class(**a__				)
												scheduler.set_timesteps(a__				)

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

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(a__				)
															__snake_case								=       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)
															__snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

												__snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
												__snake_case								=       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        a			(self	:				Optional[int]    ,      a__	:				List[Any]=None    ,      **a__	:				Dict				):





									"""simple docstring"""



									if scheduler is None:
												__snake_case								=       self.scheduler_classes[0]
												__snake_case								=       self.get_scheduler_config(**a__				)
												__snake_case								=       scheduler_class(**a__				)

									__snake_case								=       self.scheduler_classes[0]
									__snake_case								=       self.get_scheduler_config(**a__				)
									__snake_case								=       scheduler_class(**a__				)

									__snake_case								=       10
									__snake_case								=       self.dummy_model()
									__snake_case								=       self.dummy_sample_deter
									scheduler.set_timesteps(a__				)

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

									return sample


						def        a			(self	:				Dict				):





									"""simple docstring"""



									__snake_case								=       dict(self.forward_default_kwargs				)

									__snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)

									for scheduler_class in self.scheduler_classes:
												__snake_case								=       self.get_scheduler_config()
												__snake_case								=       scheduler_class(**a__				)

												__snake_case								=       self.dummy_sample
												__snake_case								=       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'''				):
															__snake_case								=       num_inference_steps

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

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

												__snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
												__snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

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


						def        a			(self	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       UniPCMultistepScheduler(**self.get_scheduler_config()				)
									__snake_case								=       self.full_loop(scheduler=a__				)
									__snake_case								=       torch.mean(torch.abs(a__				)				)

									assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3

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

									__snake_case								=       self.full_loop(scheduler=a__				)
									__snake_case								=       torch.mean(torch.abs(a__				)				)

									assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3


						def        a			(self	:				Optional[Any]				):





									"""simple docstring"""



									for timesteps in [25, 50, 100, 999, 1000]:
												self.check_over_configs(num_train_timesteps=a__				)


						def        a			(self	:				Optional[int]				):





									"""simple docstring"""



									self.check_over_configs(thresholding=a__				)
									for order in [1, 2, 3]:
												for solver_type in ["bh1", "bh2"]:
															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__    ,      solver_order=a__    ,      solver_type=a__    ,      )


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=a__				)


						def        a			(self	:				Union[str, Any]				):





									"""simple docstring"""



									for solver_type in ["bh1", "bh2"]:
												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__    ,      )
																		__snake_case								=       self.full_loop(
																		    solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      )
																		assert not torch.isnan(a__				).any(), "Samples have nan numbers"


						def        a			(self	:				str				):





									"""simple docstring"""



									self.check_over_configs(lower_order_final=a__				)
									self.check_over_configs(lower_order_final=a__				)


						def        a			(self	:				Dict				):





									"""simple docstring"""



									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
												self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


						def        a			(self	:				Optional[Any]				):





									"""simple docstring"""



									__snake_case								=       self.full_loop()
									__snake_case								=       torch.mean(torch.abs(a__				)				)

									assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3


						def        a			(self	:				Any				):





									"""simple docstring"""



									__snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
									__snake_case								=       torch.mean(torch.abs(a__				)				)

									assert abs(result_mean.item() - 0.1_0_1_4				) < 1E-3


						def        a			(self	:				str				):





									"""simple docstring"""



									__snake_case								=       self.scheduler_classes[0]
									__snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
									__snake_case								=       scheduler_class(**a__				)

									__snake_case								=       10
									__snake_case								=       self.dummy_model()
									__snake_case								=       self.dummy_sample_deter.half()
									scheduler.set_timesteps(a__				)

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

									assert sample.dtype == torch.floataa


						def        a			(self	:				Any    ,      **a__	:				Optional[int]				):





									"""simple docstring"""



									for scheduler_class in self.scheduler_classes:
												__snake_case								=       self.get_scheduler_config(**a__				)
												__snake_case								=       scheduler_class(**a__				)

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








from pathlib import Path

import fire






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  str	,				snake_case_     :  int	)				->    str:
   __snake_case								=       Path(snake_case_	)
   __snake_case								=       Path(snake_case_	)
   dest_dir.mkdir(exist_ok=snake_case_	)
   for path in src_dir.iterdir():
      __snake_case								=       [x.rstrip() for x in list(path.open().readlines()	)][:n]
      __snake_case								=       dest_dir.joinpath(path.name	)
      print(snake_case_	)
      dest_path.open('''w'''	).write('''\n'''.join(snake_case_	)	)


if __name__ == "__main__":
    fire.Fire(minify)

