






'''simple docstring'''





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   (EulerDiscreteScheduler,)
		snake_case    =   1_0
		def       A(  self		:       List[str]						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      {
						    'num_train_timesteps': 1_1_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**_SCREAMING_SNAKE_CASE		)
						return config
		def       A(  self		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]						,[0.0002, 0.002, 0.02]		):
										self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE						,beta_end=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Any		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

						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(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

										A	 =      scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE		)
										A	 =      output.prev_sample

						A	 =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
						A	 =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

						assert abs(result_sum.item() - 10.0807		) < 1E-2
						assert abs(result_mean.item() - 0.0131		) < 1E-3
		def       A(  self		:       Union[str, Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config(prediction_type='v_prediction'		)
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

						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(_SCREAMING_SNAKE_CASE		)

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

										A	 =      scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE		)
										A	 =      output.prev_sample

						A	 =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
						A	 =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

						assert abs(result_sum.item() - 0.0002		) < 1E-2
						assert abs(result_mean.item() - 2.2676E-06		) < 1E-3
		def       A(  self		:       int		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE		)

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

						A	 =      torch.manual_seed(0		)

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

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

										A	 =      scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE		)
										A	 =      output.prev_sample

						A	 =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
						A	 =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

						assert abs(result_sum.item() - 10.0807		) < 1E-2
						assert abs(result_mean.item() - 0.0131		) < 1E-3







		def       A(  self		:       str		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      self.scheduler_classes[0]
						A	 =      self.get_scheduler_config()
						A	 =      scheduler_class(**_SCREAMING_SNAKE_CASE						,use_karras_sigmas=_SCREAMING_SNAKE_CASE		)

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

						A	 =      torch.manual_seed(0		)

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

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

										A	 =      model(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

										A	 =      scheduler.step(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE		)
										A	 =      output.prev_sample

						A	 =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
						A	 =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

						assert abs(result_sum.item() - 124.52299499511719		) < 1E-2
						assert abs(result_mean.item() - 0.16213932633399963		) < 1E-3













import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





