





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(DPMSolverSDEScheduler,)
			lowerCAmelCase_							  =		10





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	{
				    '''num_train_timesteps''': 1_1_0_0,
				    '''beta_start''': 0.0001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''noise_sampler_seed''': 0,
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,						[0.0002, 0.002, 0.02]							):
					self.check_over_configs(beta_start=__lowerCAmelCase ,						beta_end=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				scheduler.set_timesteps(self.num_inference_steps							)

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

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

					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

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

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

				if torch_device in ["mps"]:
					assert abs(result_sum.item() - 167.47_8210_4492_1875							) < 1E-2
					assert abs(result_mean.item() - 0.2178_7059_6456_5277							) < 1E-3
				elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 171.59_3521_1181_6406							) < 1E-2
					assert abs(result_mean.item() - 0.2_2342_9068_9229_9652							) < 1E-3
				else:
					assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
					assert abs(result_mean.item() - 0.211_6195_7085_1326							) < 1E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				scheduler.set_timesteps(self.num_inference_steps							)

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

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

					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

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

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

				if torch_device in ["mps"]:
					assert abs(result_sum.item() - 124.77_1492_0043_9453							) < 1E-2
					assert abs(result_mean.item() - 0.1_6226_2890_1481_6284							) < 1E-3
				elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 128.1_6633_6059_5703							) < 1E-2
					assert abs(result_mean.item() - 0.1_6688_3260_0116_7297							) < 1E-3
				else:
					assert abs(result_sum.item() - 119.8_4875_4882_8125							) < 1E-2
					assert abs(result_mean.item() - 0.1560_5306_6253_6621							) < 1E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

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

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

					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

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

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

				if torch_device in ["mps"]:
					assert abs(result_sum.item() - 167.46_9573_9746_0938							) < 1E-2
					assert abs(result_mean.item() - 0.2_1805_9346_0798_2635							) < 1E-3
				elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 171.59_3536_3769_5312							) < 1E-2
					assert abs(result_mean.item() - 0.2_2342_9083_8241_5771							) < 1E-3
				else:
					assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
					assert abs(result_mean.item() - 0.211_6195_7085_1326							) < 1E-3





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

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

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

					lowerCamelCase__              =	model(__lowerCAmelCase ,						__lowerCAmelCase							)

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

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

				if torch_device in ["mps"]:
					assert abs(result_sum.item() - 176.66_9741_3574_2188							) < 1E-2
					assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2
				elif torch_device in ["cuda"]:
					assert abs(result_sum.item() - 177.63_6535_6445_3125							) < 1E-2
					assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2
				else:
					assert abs(result_sum.item() - 170.3_1352_2338_8672							) < 1E-2
					assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



