


'''simple docstring'''

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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      str				   =    (DPMSolverSDEScheduler,)
		A_     :      Optional[int]				   =    10







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					**_A		:						str      )  ->			Any:
			__magic_name__	:   str					     =					{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			    'noise_sampler_seed': 0,
			}

			config.update(**_A      )
			return config







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			List[Any]:
			for timesteps in [10, 50, 100, 1000]:
				self.check_over_configs(num_train_timesteps=_A      )







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Dict:
			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=_A  ,					beta_end=_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			for schedule in ["linear", "scaled_linear"]:
				self.check_over_configs(beta_schedule=_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Dict:
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(prediction_type=_A      )







		def        __lowerCAmelCase		( self		:						int      )  ->			int:
			__magic_name__	:   Optional[int]					     =					self.scheduler_classes[0]
			__magic_name__	:   Tuple					     =					self.get_scheduler_config()
			__magic_name__	:   List[str]					     =					scheduler_class(**_A      )

			scheduler.set_timesteps(self.num_inference_steps      )

			__magic_name__	:   Tuple					     =					self.dummy_model()
			__magic_name__	:   List[Any]					     =					self.dummy_sample_deter * scheduler.init_noise_sigma
			__magic_name__	:   Union[str, Any]					     =					sample.to(_A      )

			for i, t in enumerate(scheduler.timesteps      ):
				__magic_name__	:   Tuple					     =					scheduler.scale_model_input(_A  ,					_A      )

				__magic_name__	:   Tuple					     =					model(_A  ,					_A      )

				__magic_name__	:   str					     =					scheduler.step(_A  ,					_A  ,					_A      )
				__magic_name__	:   str					     =					output.prev_sample

			__magic_name__	:   List[Any]					     =					torch.sum(torch.abs(_A      )      )
			__magic_name__	:   List[str]					     =					torch.mean(torch.abs(_A      )      )

			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		:						Tuple      )  ->			Tuple:
			__magic_name__	:   Any					     =					self.scheduler_classes[0]
			__magic_name__	:   List[Any]					     =					self.get_scheduler_config(prediction_type='v_prediction'      )
			__magic_name__	:   Dict					     =					scheduler_class(**_A      )

			scheduler.set_timesteps(self.num_inference_steps      )

			__magic_name__	:   List[str]					     =					self.dummy_model()
			__magic_name__	:   int					     =					self.dummy_sample_deter * scheduler.init_noise_sigma
			__magic_name__	:   Union[str, Any]					     =					sample.to(_A      )

			for i, t in enumerate(scheduler.timesteps      ):
				__magic_name__	:   Optional[int]					     =					scheduler.scale_model_input(_A  ,					_A      )

				__magic_name__	:   Tuple					     =					model(_A  ,					_A      )

				__magic_name__	:   Optional[Any]					     =					scheduler.step(_A  ,					_A  ,					_A      )
				__magic_name__	:   int					     =					output.prev_sample

			__magic_name__	:   List[Any]					     =					torch.sum(torch.abs(_A      )      )
			__magic_name__	:   Tuple					     =					torch.mean(torch.abs(_A      )      )

			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		:						Optional[Any]      )  ->			Optional[Any]:
			__magic_name__	:   List[Any]					     =					self.scheduler_classes[0]
			__magic_name__	:   Optional[Any]					     =					self.get_scheduler_config()
			__magic_name__	:   Dict					     =					scheduler_class(**_A      )

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

			__magic_name__	:   Tuple					     =					self.dummy_model()
			__magic_name__	:   str					     =					self.dummy_sample_deter.to(_A      ) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
				__magic_name__	:   int					     =					scheduler.scale_model_input(_A  ,					_A      )

				__magic_name__	:   Tuple					     =					model(_A  ,					_A      )

				__magic_name__	:   Optional[int]					     =					scheduler.step(_A  ,					_A  ,					_A      )
				__magic_name__	:   Dict					     =					output.prev_sample

			__magic_name__	:   List[str]					     =					torch.sum(torch.abs(_A      )      )
			__magic_name__	:   List[str]					     =					torch.mean(torch.abs(_A      )      )

			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		:						Union[str, Any]      )  ->			Optional[Any]:
			__magic_name__	:   Tuple					     =					self.scheduler_classes[0]
			__magic_name__	:   Union[str, Any]					     =					self.get_scheduler_config()
			__magic_name__	:   int					     =					scheduler_class(**_A  ,					use_karras_sigmas=_A      )

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

			__magic_name__	:   int					     =					self.dummy_model()
			__magic_name__	:   Optional[Any]					     =					self.dummy_sample_deter.to(_A      ) * scheduler.init_noise_sigma
			__magic_name__	:   Optional[int]					     =					sample.to(_A      )

			for t in scheduler.timesteps:
				__magic_name__	:   str					     =					scheduler.scale_model_input(_A  ,					_A      )

				__magic_name__	:   List[str]					     =					model(_A  ,					_A      )

				__magic_name__	:   Optional[Any]					     =					scheduler.step(_A  ,					_A  ,					_A      )
				__magic_name__	:   Dict					     =					output.prev_sample

			__magic_name__	:   Dict					     =					torch.sum(torch.abs(_A      )      )
			__magic_name__	:   List[Any]					     =					torch.mean(torch.abs(_A      )      )

			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



'''simple docstring'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}