




'''simple docstring'''
import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A							:      str 	=       set()
A							:      Optional[Any] 	=       get_logger(__name__)
def 				snake_case_  (   a__		:	Optional[str] = None			):



    """simple docstring"""




    def decorator(a__		:	Callable			):
        global _emitted_deprecation_warnings

        if inspect.isclass(a__			):
            __lowercase							=						deprecated_class_or_function.__init__
            __lowercase							=						deprecated_class_or_function.__name__
        else:
            __lowercase							=						deprecated_class_or_function
            __lowercase							=						deprecated_function.__name__
            # Support deprecating __init__ class method: class name instead
            __lowercase							=						name if name != """__init__""" else deprecated_function.__qualname__.split("""."""			)[-2]

        __lowercase							=						(
            f'{name} is deprecated and will be removed in the next major version of datasets.' + f' {help_message}'
            if help_message
            else """"""
        )

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

        __lowercase							=						"""deprecated"""

        if inspect.isclass(a__			):
            __lowercase							=						wrapper
            return deprecated_class_or_function
        else:
            return wrapper

    return decorator






class        SCREAMING_SNAKE_CASE(				enum.EnumMeta     ):

       def __getattribute__(    cls					,		lowerCamelCase__     )    ->					Optional[Any]:

           """simple docstring"""



           __lowercase							=						super().__getattribute__(lowerCamelCase__     )
           if isinstance(lowerCamelCase__					,		enum.Enum     ) and obj._on_access:
               obj._on_access()
           return obj

       def __getitem__(    cls					,		lowerCamelCase__     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						super().__getitem__(lowerCamelCase__     )
           if member._on_access:
               member._on_access()
           return member




       def __call__(    cls					,		lowerCamelCase__					,		lowerCamelCase__=None					,		*, lowerCamelCase__=None					,		lowerCamelCase__=None					,		lowerCamelCase__=None					,		lowerCamelCase__=1     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						super().__call__(lowerCamelCase__					,		lowerCamelCase__					,		module=lowerCamelCase__					,		qualname=lowerCamelCase__					,		type=lowerCamelCase__					,		start=lowerCamelCase__     )
           if isinstance(lowerCamelCase__					,		enum.Enum     ) and obj._on_access:
               obj._on_access()
           return obj






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

       def __new__(    cls					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						object.__new__(cls     )
           __lowercase							=						value
           __lowercase							=						member.deprecate
           return member

       @property
       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           return ""




       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						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					,		lowerCamelCase__					,		stacklevel=3					,		)










'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Any    =					(HeunDiscreteScheduler,)
       snake_case_ :     Any    =					10

       def     snake_case__			(    self					,		**lowerCamelCase__     )    ->					int:

           """simple docstring"""



           __lowercase							=						{
               """num_train_timesteps""": 1100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**lowerCamelCase__     )
           return config

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

           """simple docstring"""



           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Dict:

           """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=lowerCamelCase__					,		beta_end=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCamelCase__					,		clip_sample=lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



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

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config(prediction_type="""v_prediction"""     )
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for i, t in enumerate(scheduler.timesteps     ):
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6_9_3_4E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 6.1_1_1_2E-1_0     ) < 1E-3
           else:
               # CUDA
               assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__     )

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

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           if str(lowerCamelCase__     ).startswith("""cpu"""     ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3
           elif str(lowerCamelCase__     ).startswith("""mps"""     ):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.00_02     ) < 1E-2
           else:
               # CUDA
               assert abs(result_sum.item() - 0.12_33     ) < 1E-2
               assert abs(result_mean.item() - 0.00_02     ) < 1E-3




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

           """simple docstring"""



           __lowercase							=						self.scheduler_classes[0]
           __lowercase							=						self.get_scheduler_config()
           __lowercase							=						scheduler_class(**lowerCamelCase__					,		use_karras_sigmas=lowerCamelCase__     )

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

           __lowercase							=						self.dummy_model()
           __lowercase							=						self.dummy_sample_deter.to(lowerCamelCase__     ) * scheduler.init_noise_sigma
           __lowercase							=						sample.to(lowerCamelCase__     )

           for t in scheduler.timesteps:
               __lowercase							=						scheduler.scale_model_input(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						model(lowerCamelCase__					,		lowerCamelCase__     )

               __lowercase							=						scheduler.step(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
               __lowercase							=						output.prev_sample

           __lowercase							=						torch.sum(torch.abs(lowerCamelCase__     )     )
           __lowercase							=						torch.mean(torch.abs(lowerCamelCase__     )     )

           assert abs(result_sum.item() - 0.0_00_15     ) < 1E-2
           assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7     ) < 1E-2




