






"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 	snake_case__ (							__SCREAMING_SNAKE_CASE				):
	lowercase__						:					Optional[Any]											=   (DEISMultistepScheduler,)
	lowercase__						:					str											=   (('''num_inference_steps''', 25),)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Tuple       						= {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''solver_order''': 2,
							}

							config.update(**UpperCamelCase__			)
							return config
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				List[Any]:
							__magic_name__      :       Optional[int]       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       str       						= kwargs.pop("""num_inference_steps"""      ,       UpperCamelCase__			)
							__magic_name__      :       int       						= self.dummy_sample
							__magic_name__      :       Optional[Any]       						= 0.1 * sample
							__magic_name__      :       List[str]       						= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       Tuple       						= self.get_scheduler_config(**UpperCamelCase__			)
													__magic_name__      :       int       						= scheduler_class(**UpperCamelCase__			)
													scheduler.set_timesteps(UpperCamelCase__			)
													# copy over dummy past residuals
													__magic_name__      :       Any       						= dummy_past_residuals[: scheduler.config.solver_order]

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

													__magic_name__      :       Optional[int]       						= sample, sample
													for t in range(UpperCamelCase__      ,       time_step + scheduler.config.solver_order + 1			):
																			__magic_name__      :       Dict       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       **UpperCamelCase__			).prev_sample
																			__magic_name__      :       Optional[int]       						= 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       __magic_name__    (    self			)							->				Union[str, Any]:
							pass
	def       __magic_name__    (    self      ,       lowerCAmelCase__=0      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							__magic_name__      :       Optional[int]       						= dict(self.forward_default_kwargs			)
							__magic_name__      :       Optional[Any]       						= kwargs.pop("""num_inference_steps"""      ,       UpperCamelCase__			)
							__magic_name__      :       Optional[int]       						= self.dummy_sample
							__magic_name__      :       str       						= 0.1 * sample
							__magic_name__      :       Optional[Any]       						= [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

													__magic_name__      :       Optional[Any]       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       **UpperCamelCase__			).prev_sample
													__magic_name__      :       str       						= 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       __magic_name__    (    self      ,       lowerCAmelCase__=None      ,       **lowerCAmelCase__			)							->				int:







							if scheduler is None:
													__magic_name__      :       Union[str, Any]       						= self.scheduler_classes[0]
													__magic_name__      :       Any       						= self.get_scheduler_config(**UpperCamelCase__			)
													__magic_name__      :       str       						= scheduler_class(**UpperCamelCase__			)

							__magic_name__      :       Optional[int]       						= self.scheduler_classes[0]
							__magic_name__      :       List[Any]       						= self.get_scheduler_config(**UpperCamelCase__			)
							__magic_name__      :       Tuple       						= scheduler_class(**UpperCamelCase__			)

							__magic_name__      :       str       						= 10
							__magic_name__      :       List[Any]       						= self.dummy_model()
							__magic_name__      :       List[Any]       						= self.dummy_sample_deter
							scheduler.set_timesteps(UpperCamelCase__			)

							for i, t in enumerate(scheduler.timesteps			):
													__magic_name__      :       Any       						= model(UpperCamelCase__      ,       UpperCamelCase__			)
													__magic_name__      :       List[Any]       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__			).prev_sample

							return sample
	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       Union[str, Any]       						= dict(self.forward_default_kwargs			)

							__magic_name__      :       Optional[Any]       						= kwargs.pop("""num_inference_steps"""      ,       UpperCamelCase__			)

							for scheduler_class in self.scheduler_classes:
													__magic_name__      :       Dict       						= self.get_scheduler_config()
													__magic_name__      :       int       						= scheduler_class(**UpperCamelCase__			)

													__magic_name__      :       Optional[int]       						= self.dummy_sample
													__magic_name__      :       Any       						= 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"""			):
																			__magic_name__      :       Any       						= num_inference_steps

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

													__magic_name__      :       Any       						= scheduler.timesteps[5]
													__magic_name__      :       List[Any]       						= scheduler.timesteps[6]

													__magic_name__      :       List[str]       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       **UpperCamelCase__			).prev_sample
													__magic_name__      :       Dict       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       **UpperCamelCase__			).prev_sample

													self.assertEqual(output_a.shape      ,       sample.shape			)
													self.assertEqual(output_a.shape      ,       output_a.shape			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[int]       						= DEISMultistepScheduler(**self.get_scheduler_config()			)
							__magic_name__      :       List[str]       						= self.full_loop(scheduler=UpperCamelCase__			)
							__magic_name__      :       Optional[Any]       						= torch.mean(torch.abs(UpperCamelCase__			)			)

							assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

							__magic_name__      :       int       						= DPMSolverSinglestepScheduler.from_config(scheduler.config			)
							__magic_name__      :       int       						= DPMSolverMultistepScheduler.from_config(scheduler.config			)
							__magic_name__      :       Optional[Any]       						= UniPCMultistepScheduler.from_config(scheduler.config			)
							__magic_name__      :       List[str]       						= DEISMultistepScheduler.from_config(scheduler.config			)

							__magic_name__      :       Any       						= self.full_loop(scheduler=UpperCamelCase__			)
							__magic_name__      :       Union[str, Any]       						= torch.mean(torch.abs(UpperCamelCase__			)			)

							assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3
	def       __magic_name__    (    self			)							->				int:







							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=UpperCamelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:
							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       __magic_name__    (    self			)							->				Tuple:







							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCamelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:







							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__      ,       )
																															__magic_name__      :       List[Any]       						= 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       __magic_name__    (    self			)							->				int:
							self.check_over_configs(lower_order_final=UpperCamelCase__			)
							self.check_over_configs(lower_order_final=UpperCamelCase__			)
	def       __magic_name__    (    self			)							->				Optional[Any]:







							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=UpperCamelCase__      ,       time_step=0			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Any       						= self.full_loop()
							__magic_name__      :       str       						= torch.mean(torch.abs(UpperCamelCase__			)			)

							assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Tuple       						= self.full_loop(prediction_type="""v_prediction"""			)
							__magic_name__      :       int       						= torch.mean(torch.abs(UpperCamelCase__			)			)

							assert abs(result_mean.item() - 0.0_9_1			) < 1e-3







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Tuple       						= self.scheduler_classes[0]
							__magic_name__      :       Optional[Any]       						= self.get_scheduler_config(thresholding=UpperCamelCase__      ,       dynamic_thresholding_ratio=0			)
							__magic_name__      :       Optional[int]       						= scheduler_class(**UpperCamelCase__			)

							__magic_name__      :       Tuple       						= 10
							__magic_name__      :       Optional[Any]       						= self.dummy_model()
							__magic_name__      :       Any       						= self.dummy_sample_deter.half()
							scheduler.set_timesteps(UpperCamelCase__			)

							for i, t in enumerate(scheduler.timesteps			):
													__magic_name__      :       Dict       						= model(UpperCamelCase__      ,       UpperCamelCase__			)
													__magic_name__      :       int       						= scheduler.step(UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__			).prev_sample

							assert sample.dtype == torch.floataa








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__magic_name__:					int							=     "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
		pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
		pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
		if pkg in deps:
				if pkg == "tokenizers":
						# must be loaded here, or else tqdm check may fail
						from .utils import is_tokenizers_available

						if not is_tokenizers_available():
								continue  # not required, check version only if installed

				require_version_core(deps[pkg])
		else:
				raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def    UpperCamelCase						(	_A,   _A=None			):






						"""simple docstring"""







						require_version(deps[pkg],   _A			)
