





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 	UpperCamelCase_							(  __lowercase     ):
							lowerCamelCase_							     =       (UnCLIPScheduler,)


							def   _snake_case	(				self     :Any				,					**__A     :int		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {
									    """num_train_timesteps""": 1000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**_A		)
									return config


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=_A		)


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=_A		)


							def   _snake_case	(				self     :Tuple		) -> List[str]:



									"""simple docstring"""



									for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=_A		)


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=_A		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=_A		)


							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=_A				,					prev_timestep=_A		)


							def   _snake_case	(				self     :Optional[Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config(variance_type="""fixed_small_log"""		)
									SCREAMING_SNAKE_CASE__								=   scheduler_class(**_A		)

									assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0_000E-10		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_5_4_9_6_2_5		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.9_9_9_4_9_8_7		)		) < 1E-5


							def   _snake_case	(				self     :List[str]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config(variance_type="""learned_range"""		)
									SCREAMING_SNAKE_CASE__								=   scheduler_class(**_A		)

									SCREAMING_SNAKE_CASE__								=   0.5

									assert scheduler._get_variance(1				,					predicted_variance=_A		) - -1_0.1_7_1_2_7_9_0 < 1E-5
									assert scheduler._get_variance(487				,					predicted_variance=_A		) - -5.7_9_9_8_0_5_2 < 1E-5
									assert scheduler._get_variance(999				,					predicted_variance=_A		) - -0.0_0_1_0_0_1_1 < 1E-5


							def   _snake_case	(				self     :str		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								=   scheduler_class(**_A		)

									SCREAMING_SNAKE_CASE__								=   scheduler.timesteps

									SCREAMING_SNAKE_CASE__								=   self.dummy_model()
									SCREAMING_SNAKE_CASE__								=   self.dummy_sample_deter
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)

									for i, t in enumerate(_A		):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE__								=   model(_A				,					_A		)

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE__								=   scheduler.step(_A				,					_A				,					_A				,					generator=_A		).prev_sample

											SCREAMING_SNAKE_CASE__								=   pred_prev_sample

									SCREAMING_SNAKE_CASE__								=   torch.sum(torch.abs(_A		)		)
									SCREAMING_SNAKE_CASE__								=   torch.mean(torch.abs(_A		)		)

									assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5		) < 1E-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3		) < 1E-3


							def   _snake_case	(				self     :List[Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE__								=   self.get_scheduler_config()
									SCREAMING_SNAKE_CASE__								=   scheduler_class(**_A		)

									scheduler.set_timesteps(25		)

									SCREAMING_SNAKE_CASE__								=   scheduler.timesteps

									SCREAMING_SNAKE_CASE__								=   self.dummy_model()
									SCREAMING_SNAKE_CASE__								=   self.dummy_sample_deter
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)

									for i, t in enumerate(_A		):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE__								=   model(_A				,					_A		)

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE__								=   None
											else:
													SCREAMING_SNAKE_CASE__								=   timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE__								=   scheduler.step(
											    _A				,					_A				,					_A				,					prev_timestep=_A				,					generator=_A		).prev_sample

											SCREAMING_SNAKE_CASE__								=   pred_prev_sample

									SCREAMING_SNAKE_CASE__								=   torch.sum(torch.abs(_A		)		)
									SCREAMING_SNAKE_CASE__								=   torch.mean(torch.abs(_A		)		)

									assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3		) < 1E-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8		) < 1E-3


							def   _snake_case	(				self     :Any		) -> Optional[int]:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :Dict		) -> List[str]:



									"""simple docstring"""



									pass
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version
def 					_snake_case	(  *__snake_case  ,     __snake_case = None  ,     __snake_case=True  ,     __snake_case=2						):
					from .. import __version__

					_UpperCamelCase			     =     take_from
					_UpperCamelCase			     =     ()
					if not isinstance(args[0]  ,     __snake_case						):
										_UpperCamelCase			     =     (args,)

					for attribute, version_name, message in args:
										if version.parse(version.parse(__snake_case						).base_version						) >= version.parse(__snake_case						):
															raise ValueError(
															    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
															    f""" version {__version__} is >= {version_name}"""						)

										_UpperCamelCase			     =     None
										if isinstance(__snake_case  ,     __snake_case						) and attribute in deprecated_kwargs:
															values += (deprecated_kwargs.pop(__snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
										elif hasattr(__snake_case  ,     __snake_case						):
															values += (getattr(__snake_case  ,     __snake_case						),)
															_UpperCamelCase			     =     f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
										elif deprecated_kwargs is None:
															_UpperCamelCase			     =     f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

										if warning is not None:
															_UpperCamelCase			     =     warning + ''' ''' if standard_warn else ''''''
															warnings.warn(warning + message  ,     __snake_case  ,     stacklevel=__snake_case						)

					if isinstance(__snake_case  ,     __snake_case						) and len(__snake_case						) > 0:
										_UpperCamelCase			     =     inspect.getouterframes(inspect.currentframe()						)[1]
										_UpperCamelCase			     =     call_frame.filename
										_UpperCamelCase			     =     call_frame.lineno
										_UpperCamelCase			     =     call_frame.function
										_UpperCamelCase     ,    _UpperCamelCase			     =     next(iter(deprecated_kwargs.items()						)						)
										raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""						)

					if len(__snake_case						) == 0:
										return
					elif len(__snake_case						) == 1:
										return values[0]
					return values
