"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							(LMSDiscreteScheduler,)
			_UpperCamelCase     :	Optional[Any]				    =							10





			def 		lowerCamelCase				( self      ,			**snake_case_       ):







										'''simple docstring'''


										A__					:    str														= {
										    """num_train_timesteps""": 1_100,
										    """beta_start""": 0.00_01,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**snake_case_       )
										return config





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]      ,			[0.00_02, 0.0_02, 0.02]       ):
																	self.check_over_configs(beta_start=snake_case_      ,			beta_end=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										for t in [0, 500, 800]:
																	self.check_over_forward(time_step=snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= self.scheduler_classes[0]
										A__					:    Union[str, Any]														= self.get_scheduler_config()
										A__					:    List[Any]														= scheduler_class(**snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps       )

										A__					:    Union[str, Any]														= self.dummy_model()
										A__					:    str														= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    str														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[Any]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    Optional[int]														= output.prev_sample

										A__					:    Tuple														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    str														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 1_006.388       ) < 1E-2
										assert abs(result_mean.item() - 1.31       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= self.scheduler_classes[0]
										A__					:    Any														= self.get_scheduler_config(prediction_type="""v_prediction"""       )
										A__					:    Optional[int]														= scheduler_class(**snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps       )

										A__					:    Union[str, Any]														= self.dummy_model()
										A__					:    Tuple														= self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    Union[str, Any]														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Tuple														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    Any														= output.prev_sample

										A__					:    Union[str, Any]														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    Optional[int]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 0.00_17       ) < 1E-2
										assert abs(result_mean.item() - 2.2676E-06       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    List[str]														= self.scheduler_classes[0]
										A__					:    int														= self.get_scheduler_config()
										A__					:    str														= scheduler_class(**snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps      ,			device=snake_case_       )

										A__					:    str														= self.dummy_model()
										A__					:    Optional[Any]														= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										A__					:    Any														= sample.to(snake_case_       )

										for i, t in enumerate(scheduler.timesteps       ):
																	A__					:    List[Any]														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Optional[int]														= model(snake_case_      ,			snake_case_       )

																	A__					:    str														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    List[str]														= output.prev_sample

										A__					:    str														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    Union[str, Any]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 1_006.388       ) < 1E-2
										assert abs(result_mean.item() - 1.31       ) < 1E-3





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= self.scheduler_classes[0]
										A__					:    str														= self.get_scheduler_config()
										A__					:    Union[str, Any]														= scheduler_class(**snake_case_      ,			use_karras_sigmas=snake_case_       )

										scheduler.set_timesteps(self.num_inference_steps      ,			device=snake_case_       )

										A__					:    str														= self.dummy_model()
										A__					:    Union[str, Any]														= self.dummy_sample_deter.to(snake_case_       ) * scheduler.init_noise_sigma
										A__					:    Optional[int]														= sample.to(snake_case_       )

										for t in scheduler.timesteps:
																	A__					:    Tuple														= scheduler.scale_model_input(snake_case_      ,			snake_case_       )

																	A__					:    Optional[Any]														= model(snake_case_      ,			snake_case_       )

																	A__					:    List[str]														= scheduler.step(snake_case_      ,			snake_case_      ,			snake_case_       )
																	A__					:    int														= output.prev_sample

										A__					:    Dict														= torch.sum(torch.abs(snake_case_       )       )
										A__					:    List[Any]														= torch.mean(torch.abs(snake_case_       )       )

										assert abs(result_sum.item() - 3_812.9_927       ) < 2E-2
										assert abs(result_mean.item() - 4.96_48       ) < 1E-3






"""simple docstring"""



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_UpperCamelCase         =		set()
_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase = None       ):

							def decorator(lowerCAmelCase       ):
														global _emitted_deprecation_warnings

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    List[Any]														= deprecated_class_or_function.__init__
																					A__					:    Any														= deprecated_class_or_function.__name__
														else:
																					A__					:    Dict														= deprecated_class_or_function
																					A__					:    Optional[int]														= deprecated_function.__name__
																					# Support deprecating __init__ class method: class name instead
																					A__					:    List[str]														= name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

														A__					:    Union[str, Any]														= (
														    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
														    if help_message
														    else """"""
														)

														@wraps(lowerCAmelCase       )
														def wrapper(*lowerCAmelCase ,     **lowerCAmelCase       ):
																					A__					:    Tuple														= hash(lowerCAmelCase       )
																					if func_hash not in _emitted_deprecation_warnings:
																												warnings.warn(lowerCAmelCase ,     category=lowerCAmelCase ,     stacklevel=2       )
																												_emitted_deprecation_warnings.add(lowerCAmelCase       )
																					return deprecated_function(*lowerCAmelCase ,     **lowerCAmelCase       )

														A__					:    int														= """deprecated"""

														if inspect.isclass(lowerCAmelCase       ):
																					A__					:    Union[str, Any]														= wrapper
																					return deprecated_class_or_function
														else:
																					return wrapper

							return decorator
class 	__UpperCAmelCase						(enum.EnumMeta    ):







			'''simple docstring'''





			def __getattribute__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Union[str, Any]														= super().__getattribute__(snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





			def __getitem__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= super().__getitem__(snake_case_       )
										if member._on_access:
																	member._on_access()
										return member





			def __call__( cls      ,			snake_case_      ,			snake_case_=None      ,			*, snake_case_=None      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=1       ):







										'''simple docstring'''


										A__					:    List[Any]														= super().__call__(snake_case_      ,			snake_case_      ,			module=snake_case_      ,			qualname=snake_case_      ,			type=snake_case_      ,			start=snake_case_       )
										if isinstance(snake_case_      ,			enum.Enum       ) and obj._on_access:
																	obj._on_access()
										return obj





class 	__UpperCAmelCase						(enum.Enum       ,      metaclass=__A    ):







			'''simple docstring'''





			def __new__( cls      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Tuple														= object.__new__(cls       )
										A__					:    int														= value
										A__					:    Optional[int]														= member.deprecate
										return member





			@property
			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										return ""





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Tuple														= F''' {self.help_message}''' if self.help_message else """"""
										warnings.warn(
										    F'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
										    + help_message      ,			snake_case_      ,			stacklevel=3      ,			)





