





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class       __A     (						a    ):
				__A      =				(CMStochasticIterativeScheduler,)
				__A      =				10


				def _snake_case       (		self   , **UpperCAmelCase_							):
								lowerCamelCase	 ={
								    """num_train_timesteps""": 201,
								    """sigma_min""": 0.0_0_2,
								    """sigma_max""": 80.0,
								}

								config.update(**UpperCAmelCase_							)
								return config


				def _snake_case       (		self							):
								lowerCamelCase	 =10

								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =self.scheduler_classes[0](**UpperCAmelCase_							)

								scheduler.set_timesteps(UpperCAmelCase_							)

								lowerCamelCase	 =scheduler.timesteps[0]
								lowerCamelCase	 =scheduler.timesteps[1]

								lowerCamelCase	 =self.dummy_sample
								lowerCamelCase	 =0.1 * sample

								lowerCamelCase	 =scheduler.step(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							).prev_sample
								lowerCamelCase	 =scheduler.step(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							).prev_sample

								self.assertEqual(output_a.shape   , sample.shape							)
								self.assertEqual(output_a.shape   , output_a.shape							)


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


				def _snake_case       (		self							):
								for clip_denoised in [True, False]:
												self.check_over_configs(clip_denoised=UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.scheduler_classes[0]
								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =scheduler_class(**UpperCAmelCase_							)

								lowerCamelCase	 =1
								scheduler.set_timesteps(UpperCAmelCase_							)
								lowerCamelCase	 =scheduler.timesteps

								lowerCamelCase	 =torch.manual_seed(0							)

								lowerCamelCase	 =self.dummy_model()
								lowerCamelCase	 =self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(UpperCAmelCase_							):
												# 1. scale model input
												lowerCamelCase	 =scheduler.scale_model_input(UpperCAmelCase_   , UpperCAmelCase_							)

												# 2. predict noise residual
												lowerCamelCase	 =model(UpperCAmelCase_   , UpperCAmelCase_							)

												# 3. predict previous sample x_t-1
												lowerCamelCase	 =scheduler.step(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , generator=UpperCAmelCase_							).prev_sample

												lowerCamelCase	 =pred_prev_sample

								lowerCamelCase	 =torch.sum(torch.abs(UpperCAmelCase_							)							)
								lowerCamelCase	 =torch.mean(torch.abs(UpperCAmelCase_							)							)

								assert abs(result_sum.item() - 192.7614							) < 1E-2
								assert abs(result_mean.item() - 0.2_5_1_0							) < 1E-3


				def _snake_case       (		self							):
								lowerCamelCase	 =self.scheduler_classes[0]
								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =scheduler_class(**UpperCAmelCase_							)

								lowerCamelCase	 =[106, 0]
								scheduler.set_timesteps(timesteps=UpperCAmelCase_							)
								lowerCamelCase	 =scheduler.timesteps

								lowerCamelCase	 =torch.manual_seed(0							)

								lowerCamelCase	 =self.dummy_model()
								lowerCamelCase	 =self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
												# 1. scale model input
												lowerCamelCase	 =scheduler.scale_model_input(UpperCAmelCase_   , UpperCAmelCase_							)

												# 2. predict noise residual
												lowerCamelCase	 =model(UpperCAmelCase_   , UpperCAmelCase_							)

												# 3. predict previous sample x_t-1
												lowerCamelCase	 =scheduler.step(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , generator=UpperCAmelCase_							).prev_sample

												lowerCamelCase	 =pred_prev_sample

								lowerCamelCase	 =torch.sum(torch.abs(UpperCAmelCase_							)							)
								lowerCamelCase	 =torch.mean(torch.abs(UpperCAmelCase_							)							)

								assert abs(result_sum.item() - 347.6357							) < 1E-2
								assert abs(result_mean.item() - 0.4_5_2_7							) < 1E-3


				def _snake_case       (		self							):
								lowerCamelCase	 =self.scheduler_classes[0]
								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =scheduler_class(**UpperCAmelCase_							)

								lowerCamelCase	 =[39, 30, 12, 15, 0]

								with self.assertRaises(UpperCAmelCase_   , msg="""`timesteps` must be in descending order."""							):
												scheduler.set_timesteps(timesteps=UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.scheduler_classes[0]
								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =scheduler_class(**UpperCAmelCase_							)

								lowerCamelCase	 =[39, 30, 12, 1, 0]
								lowerCamelCase	 =len(UpperCAmelCase_							)

								with self.assertRaises(UpperCAmelCase_   , msg="""Can only pass one of `num_inference_steps` or `timesteps`."""							):
												scheduler.set_timesteps(num_inference_steps=UpperCAmelCase_   , timesteps=UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.scheduler_classes[0]
								lowerCamelCase	 =self.get_scheduler_config()
								lowerCamelCase	 =scheduler_class(**UpperCAmelCase_							)

								lowerCamelCase	 =[scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    UpperCAmelCase_   , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""   , ):
												scheduler.set_timesteps(timesteps=UpperCAmelCase_							)












import os

try:
							from .build_directory_md import good_file_paths
except ImportError:
							from build_directory_md import good_file_paths  # type: ignore

UpperCAmelCase__						:			List[Any]									=list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

UpperCAmelCase__						:			Dict									=[file for file in filepaths if file != file.lower()]
if upper_files:
							print(F"{len(upper_files)} files contain uppercase characters:")
							print('''\n'''.join(upper_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if ''' ''' in file]
if space_files:
							print(F"{len(space_files)} files contain space characters:")
							print('''\n'''.join(space_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if '''-''' in file]
if hyphen_files:
							print(F"{len(hyphen_files)} files contain hyphen characters:")
							print('''\n'''.join(hyphen_files) + '''\n''')

UpperCAmelCase__						:			Optional[int]									=[file for file in filepaths if os.sep not in file]
if nodir_files:
							print(F"{len(nodir_files)} files are not in a directory:")
							print('''\n'''.join(nodir_files) + '''\n''')

UpperCAmelCase__						:			str									=len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
							import sys

							sys.exit(bad_files)





