



"""simple docstring"""







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class        A__(  __magic_name__			):
      lowerCAmelCase										=				(EulerDiscreteScheduler,)
      lowerCAmelCase										=				10







      def  _a   (					self		:     int				,				**__SCREAMING_SNAKE_CASE		:     Dict			)							->		int:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   {
                '''num_train_timesteps''': 11_00,
                '''beta_start''': 0.00_01,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**__SCREAMING_SNAKE_CASE			)
            return config







      def  _a   (					self		:     int			)							->		int:






            """simple docstring"""

            for timesteps in [10, 50, 1_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE			)







      def  _a   (					self		:     Any			)							->		int:






            """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=__SCREAMING_SNAKE_CASE				,				beta_end=__SCREAMING_SNAKE_CASE			)







      def  _a   (					self		:     Any			)							->		str:






            """simple docstring"""

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







      def  _a   (					self		:     str			)							->		List[str]:






            """simple docstring"""

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







      def  _a   (					self		:     Optional[Any]			)							->		Dict:






            """simple docstring"""

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

            scheduler.set_timesteps(self.num_inference_steps			)

            __SCREAMING_SNAKE_CASE       		=   torch.manual_seed(0			)

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

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

                  __SCREAMING_SNAKE_CASE       		=   model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE			)

                  __SCREAMING_SNAKE_CASE       		=   scheduler.step(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				generator=__SCREAMING_SNAKE_CASE			)
                  __SCREAMING_SNAKE_CASE       		=   output.prev_sample

            __SCREAMING_SNAKE_CASE       		=   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE			)			)
            __SCREAMING_SNAKE_CASE       		=   torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

            assert abs(result_sum.item() - 10.08_07			) < 1E-2
            assert abs(result_mean.item() - 0.01_31			) < 1E-3







      def  _a   (					self		:     List[str]			)							->		Optional[int]:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   self.scheduler_classes[0]
            __SCREAMING_SNAKE_CASE       		=   self.get_scheduler_config(prediction_type='''v_prediction'''			)
            __SCREAMING_SNAKE_CASE       		=   scheduler_class(**__SCREAMING_SNAKE_CASE			)

            scheduler.set_timesteps(self.num_inference_steps			)

            __SCREAMING_SNAKE_CASE       		=   torch.manual_seed(0			)

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

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

                  __SCREAMING_SNAKE_CASE       		=   model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE			)

                  __SCREAMING_SNAKE_CASE       		=   scheduler.step(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				generator=__SCREAMING_SNAKE_CASE			)
                  __SCREAMING_SNAKE_CASE       		=   output.prev_sample

            __SCREAMING_SNAKE_CASE       		=   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE			)			)
            __SCREAMING_SNAKE_CASE       		=   torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

            assert abs(result_sum.item() - 0.00_02			) < 1E-2
            assert abs(result_mean.item() - 2.2_6_7_6E-0_6			) < 1E-3







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






            """simple docstring"""

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

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

            __SCREAMING_SNAKE_CASE       		=   torch.manual_seed(0			)

            __SCREAMING_SNAKE_CASE       		=   self.dummy_model()
            __SCREAMING_SNAKE_CASE       		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            __SCREAMING_SNAKE_CASE       		=   sample.to(__SCREAMING_SNAKE_CASE			)

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

                  __SCREAMING_SNAKE_CASE       		=   model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE			)

                  __SCREAMING_SNAKE_CASE       		=   scheduler.step(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				generator=__SCREAMING_SNAKE_CASE			)
                  __SCREAMING_SNAKE_CASE       		=   output.prev_sample

            __SCREAMING_SNAKE_CASE       		=   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE			)			)
            __SCREAMING_SNAKE_CASE       		=   torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

            assert abs(result_sum.item() - 10.08_07			) < 1E-2
            assert abs(result_mean.item() - 0.01_31			) < 1E-3







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






            """simple docstring"""

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

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

            __SCREAMING_SNAKE_CASE       		=   torch.manual_seed(0			)

            __SCREAMING_SNAKE_CASE       		=   self.dummy_model()
            __SCREAMING_SNAKE_CASE       		=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            __SCREAMING_SNAKE_CASE       		=   sample.to(__SCREAMING_SNAKE_CASE			)

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

                  __SCREAMING_SNAKE_CASE       		=   model(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE			)

                  __SCREAMING_SNAKE_CASE       		=   scheduler.step(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				generator=__SCREAMING_SNAKE_CASE			)
                  __SCREAMING_SNAKE_CASE       		=   output.prev_sample

            __SCREAMING_SNAKE_CASE       		=   torch.sum(torch.abs(__SCREAMING_SNAKE_CASE			)			)
            __SCREAMING_SNAKE_CASE       		=   torch.mean(torch.abs(__SCREAMING_SNAKE_CASE			)			)

            assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19			) < 1E-2
            assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63			) < 1E-3






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

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(args,)

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

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

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

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

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

