


"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class 				UpperCamelCase_			(				UpperCamelCase):
	snake_case__       :						Tuple					      =    (CMStochasticIterativeScheduler,)
	snake_case__       :						str					      =    10




	def 						UpperCAmelCase_   (				self    :       Any    ,    **UpperCAmelCase__    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       {
						    "num_train_timesteps": 2_0_1,
						    "sigma_min": 0.002,
						    "sigma_max": 80.0,
						}

						config.update(**UpperCAmelCase__     )
						return config




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       1_0

						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0](**UpperCAmelCase__     )

						scheduler.set_timesteps(UpperCAmelCase__     )

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

						__SCREAMING_SNAKE_CASE		     =       self.dummy_sample
						__SCREAMING_SNAKE_CASE		     =       0.1 * sample

						__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ).prev_sample
						__SCREAMING_SNAKE_CASE		     =       scheduler.step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     ).prev_sample

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




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[Any]:
						for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			List[str]:
						for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Any:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       1
						scheduler.set_timesteps(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       scheduler.timesteps

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

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

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

											# 2. predict noise residual
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     )

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

											__SCREAMING_SNAKE_CASE		     =       pred_prev_sample

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

						assert abs(result_sum.item() - 192.7_614     ) < 1E-2
						assert abs(result_mean.item() - 0.2_510     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       [1_0_6, 0]
						scheduler.set_timesteps(timesteps=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       scheduler.timesteps

						__SCREAMING_SNAKE_CASE		     =       torch.manual_seed(0     )

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

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

											# 2. predict noise residual
											__SCREAMING_SNAKE_CASE		     =       model(UpperCAmelCase__    ,    UpperCAmelCase__     )

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

											__SCREAMING_SNAKE_CASE		     =       pred_prev_sample

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

						assert abs(result_sum.item() - 347.6_357     ) < 1E-2
						assert abs(result_mean.item() - 0.4_527     ) < 1E-3




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       [3_9, 3_0, 1_2, 1_5, 0]

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




	def 						UpperCAmelCase_   (				self    :       Any     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       [3_9, 3_0, 1_2, 1, 0]
						__SCREAMING_SNAKE_CASE		     =       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 						UpperCAmelCase_   (				self    :       Any     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       self.scheduler_classes[0]
						__SCREAMING_SNAKE_CASE		     =       self.get_scheduler_config()
						__SCREAMING_SNAKE_CASE		     =       scheduler_class(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       [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__     )




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


a__       :							List[str]        =  {}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  ['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		a__       :							Optional[Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
