






"""simple docstring"""







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class        _SCREAMING_SNAKE_CASE						(  UpperCamelCase_							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    =(EulerDiscreteScheduler,)
       _SCREAMING_SNAKE_CASE    =10




       def  lowercase		(   self:     Any				,		**__A:     Union[str, Any]    ):

            '''simple docstring'''

            a__          =  {
                """num_train_timesteps""": 1100,
                """beta_start""": 0.0_0_0_1,
                """beta_end""": 0.0_2,
                """beta_schedule""": """linear""",
            }

            config.update(**_a    )
            return config




       def  lowercase		(   self:     List[str]    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     str    ):

            '''simple docstring'''

            for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,		[0.0_0_0_2, 0.0_0_2, 0.0_2]    ):
                 self.check_over_configs(beta_start=_a				,		beta_end=_a    )




       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     str    ):

            '''simple docstring'''

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




       def  lowercase		(   self:     Optional[Any]    ):

            '''simple docstring'''

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

            scheduler.set_timesteps(self.num_inference_steps    )

            a__          =  torch.manual_seed(0    )

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

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

                 a__          =  model(_a				,		_a    )

                 a__          =  scheduler.step(_a				,		_a				,		_a				,		generator=_a    )
                 a__          =  output.prev_sample

            a__          =  torch.sum(torch.abs(_a    )    )
            a__          =  torch.mean(torch.abs(_a    )    )

            assert abs(result_sum.item() - 1_0.0_8_0_7    ) < 1e-2
            assert abs(result_mean.item() - 0.0_1_3_1    ) < 1e-3




       def  lowercase		(   self:     List[str]    ):

            '''simple docstring'''

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

            scheduler.set_timesteps(self.num_inference_steps    )

            a__          =  torch.manual_seed(0    )

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

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

                 a__          =  model(_a				,		_a    )

                 a__          =  scheduler.step(_a				,		_a				,		_a				,		generator=_a    )
                 a__          =  output.prev_sample

            a__          =  torch.sum(torch.abs(_a    )    )
            a__          =  torch.mean(torch.abs(_a    )    )

            assert abs(result_sum.item() - 0.0_0_0_2    ) < 1e-2
            assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3




       def  lowercase		(   self:     Tuple    ):

            '''simple docstring'''

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

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

            a__          =  torch.manual_seed(0    )

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

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

                 a__          =  model(_a				,		_a    )

                 a__          =  scheduler.step(_a				,		_a				,		_a				,		generator=_a    )
                 a__          =  output.prev_sample

            a__          =  torch.sum(torch.abs(_a    )    )
            a__          =  torch.mean(torch.abs(_a    )    )

            assert abs(result_sum.item() - 1_0.0_8_0_7    ) < 1e-2
            assert abs(result_mean.item() - 0.0_1_3_1    ) < 1e-3





       def  lowercase		(   self:     Dict    ):

            '''simple docstring'''

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

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

            a__          =  torch.manual_seed(0    )

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

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

                 a__          =  model(_a				,		_a    )

                 a__          =  scheduler.step(_a				,		_a				,		_a				,		generator=_a    )
                 a__          =  output.prev_sample

            a__          =  torch.sum(torch.abs(_a    )    )
            a__          =  torch.mean(torch.abs(_a    )    )

            assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9    ) < 1e-2
            assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3    ) < 1e-3










"""simple docstring"""







__a       :	List[Any]             = frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'negative_prompt'])

__a       :	Any             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Dict             = frozenset(['image'])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Optional[Any]             = frozenset(['prompt', 'image', 'negative_prompt'])

__a       :	List[Any]             = frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

__a       :	Union[str, Any]             = frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

__a       :	Optional[Any]             = frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	int             = frozenset(['image', 'mask_image'])

__a       :	Tuple             = frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

__a       :	Optional[Any]             = frozenset(['example_image', 'image', 'mask_image'])

__a       :	Optional[Any]             = frozenset(['class_labels'])

__a       :	Tuple             = frozenset(['class_labels'])

__a       :	int             = frozenset(['batch_size'])

__a       :	int             = frozenset([])

__a       :	Union[str, Any]             = frozenset(['batch_size'])

__a       :	Tuple             = frozenset([])

__a       :	Dict             = frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

__a       :	Dict             = frozenset(['prompt', 'negative_prompt'])
__a       :	Optional[int]             = frozenset(['input_tokens'])

__a       :	str             = frozenset(['input_tokens'])


