






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class __lowerCamelCase (      __a):





	"""simple docstring"""






	UpperCamelCase__								=      (DEISMultistepScheduler,)
	UpperCamelCase__								=      (("""num_inference_steps""", 25),)

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							}

							config.update(**UpperCAmelCase    )
							return config

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=0       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			dict(self.forward_default_kwargs    )
							_UpperCAmelCase									=			kwargs.pop('num_inference_steps'       ,    UpperCAmelCase    )
							_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    )
													_UpperCAmelCase									=			scheduler_class(**UpperCAmelCase    )
													scheduler.set_timesteps(UpperCAmelCase    )
													# copy over dummy past residuals
													_UpperCAmelCase									=			dummy_past_residuals[: scheduler.config.solver_order]

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

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

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=0       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			dict(self.forward_default_kwargs    )
							_UpperCAmelCase									=			kwargs.pop('num_inference_steps'       ,    UpperCAmelCase    )
							_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(**UpperCAmelCase    )
													scheduler.set_timesteps(UpperCAmelCase    )

													# 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(UpperCAmelCase    )
																			_UpperCAmelCase									=			scheduler_class.from_pretrained(UpperCAmelCase    )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(UpperCAmelCase    )

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

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

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			10
							_UpperCAmelCase									=			self.dummy_model()
							_UpperCAmelCase									=			self.dummy_sample_deter
							scheduler.set_timesteps(UpperCAmelCase    )

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

							return sample

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			dict(self.forward_default_kwargs    )

							_UpperCAmelCase									=			kwargs.pop('num_inference_steps'       ,    UpperCAmelCase    )

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

													_UpperCAmelCase									=			self.dummy_sample
													_UpperCAmelCase									=			0.1 * sample

													if num_inference_steps is not None and hasattr(UpperCAmelCase       ,    'set_timesteps'    ):
																			scheduler.set_timesteps(UpperCAmelCase    )
													elif num_inference_steps is not None and not hasattr(UpperCAmelCase       ,    '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(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ).prev_sample
													_UpperCAmelCase									=			scheduler.step(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ).prev_sample

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			DEISMultistepScheduler(**self.get_scheduler_config()    )
							_UpperCAmelCase									=			self.full_loop(scheduler=UpperCAmelCase    )
							_UpperCAmelCase									=			torch.mean(torch.abs(UpperCAmelCase    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 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=UpperCAmelCase    )
							_UpperCAmelCase									=			torch.mean(torch.abs(UpperCAmelCase    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.check_over_configs(thresholding=UpperCAmelCase    )
							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=UpperCAmelCase       ,    prediction_type=UpperCAmelCase       ,    sample_max_value=UpperCAmelCase       ,    algorithm_type='deis'       ,    solver_order=UpperCAmelCase       ,    solver_type=UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							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=UpperCAmelCase       ,    solver_type=UpperCAmelCase       ,    prediction_type=UpperCAmelCase       ,    algorithm_type=UpperCAmelCase       ,    )
																															_UpperCAmelCase									=			self.full_loop(
																															    solver_order=UpperCAmelCase       ,    solver_type=UpperCAmelCase       ,    prediction_type=UpperCAmelCase       ,    algorithm_type=UpperCAmelCase       ,    )
																															assert not torch.isnan(UpperCAmelCase    ).any(), "Samples have nan numbers"

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.check_over_configs(lower_order_final=UpperCAmelCase    )
							self.check_over_configs(lower_order_final=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.full_loop()
							_UpperCAmelCase									=			torch.mean(torch.abs(UpperCAmelCase    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.full_loop(prediction_type='v_prediction'    )
							_UpperCAmelCase									=			torch.mean(torch.abs(UpperCAmelCase    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.scheduler_classes[0]
							_UpperCAmelCase									=			self.get_scheduler_config(thresholding=UpperCAmelCase       ,    dynamic_thresholding_ratio=0    )
							_UpperCAmelCase									=			scheduler_class(**UpperCAmelCase    )

							_UpperCAmelCase									=			10
							_UpperCAmelCase									=			self.dummy_model()
							_UpperCAmelCase									=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(UpperCAmelCase    )

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

							assert sample.dtype == torch.floataa






'''simple docstring'''







import os
from distutils.util import strtobool





def 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							List[Any]     ):
    for e in env_keys:
        UpperCAmelCase			       =		int(os.environ.get(_a    ,   -1     )     )
        if val >= 0:
            return val
    return default





def 						snake_case_							(_a	:							Dict    ,   _a	:							Any=False     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return strtobool(_a     ) == 1  # As its name indicates `strtobool` actually returns an int...







def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]="no"     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return value




