

"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	(EulerDiscreteScheduler,)
 __UpperCAmelCase	:					Optional[Any]   		=	1_0


 def 					_lowercase (			self							:			List[str],					**UpperCAmelCase__							:			Optional[int]	):
    __lowercase								=						{
        "num_train_timesteps": 1_1_0_0,
        "beta_start": 0.0_001,
        "beta_end": 0.02,
        "beta_schedule": "linear",
    }

    config.update(**UpperCAmelCase__	)
    return config


 def 					_lowercase (			self							:			str	):
    for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict	):
    for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001],					[0.0_002, 0.002, 0.02]	):
       self.check_over_configs(beta_start=UpperCAmelCase__,					beta_end=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    __lowercase								=						torch.manual_seed(0	)

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

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

       __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						scheduler.step(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					generator=UpperCAmelCase__	)
       __lowercase								=						output.prev_sample

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

    assert abs(result_sum.item() - 10.0_807	) < 1E-2
    assert abs(result_mean.item() - 0.0_131	) < 1E-3


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config(prediction_type="v_prediction"	)
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    __lowercase								=						torch.manual_seed(0	)

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

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

       __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						scheduler.step(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					generator=UpperCAmelCase__	)
       __lowercase								=						output.prev_sample

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

    assert abs(result_sum.item() - 0.0_002	) < 1E-2
    assert abs(result_mean.item() - 2.2_676E-06	) < 1E-3


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__	)

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

    __lowercase								=						torch.manual_seed(0	)

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

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

       __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						scheduler.step(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					generator=UpperCAmelCase__	)
       __lowercase								=						output.prev_sample

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

    assert abs(result_sum.item() - 10.0_807	) < 1E-2
    assert abs(result_mean.item() - 0.0_131	) < 1E-3



 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.scheduler_classes[0]
    __lowercase								=						self.get_scheduler_config()
    __lowercase								=						scheduler_class(**UpperCAmelCase__,					use_karras_sigmas=UpperCAmelCase__	)

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

    __lowercase								=						torch.manual_seed(0	)

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

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

       __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)

       __lowercase								=						scheduler.step(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					generator=UpperCAmelCase__	)
       __lowercase								=						output.prev_sample

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

    assert abs(result_sum.item() - 124.52_299_499_511_719	) < 1E-2
    assert abs(result_mean.item() - 0.16_213_932_633_399_963	) < 1E-3






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel



