


def      UpperCamelCase     (   _a							,		_a		)       ->				Dict:
     '''simple docstring'''





     while b:
          lowercase_							:List[Any]			     =							b, a % b
     return a




def      UpperCamelCase     (   _a							,		_a		)       ->				Dict:
     '''simple docstring'''





     return a if b == 0 else euclidean_gcd_recursive(snake_case__							,		a % b		)




def      UpperCamelCase     (   )       ->				str:
     '''simple docstring'''





     print(f"euclidean_gcd(3, 5) = {euclidean_gcd(3							,		5		)}"		)
     print(f"euclidean_gcd(5, 3) = {euclidean_gcd(5							,		3		)}"		)
     print(f"euclidean_gcd(1, 3) = {euclidean_gcd(1							,		3		)}"		)
     print(f"euclidean_gcd(3, 6) = {euclidean_gcd(3							,		6		)}"		)
     print(f"euclidean_gcd(6, 3) = {euclidean_gcd(6							,		3		)}"		)

     print(f"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3							,		5		)}"		)
     print(f"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5							,		3		)}"		)
     print(f"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1							,		3		)}"		)
     print(f"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3							,		6		)}"		)
     print(f"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6							,		3		)}"		)


if __name__ == "__main__":
   main()









"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					(UniPCMultistepScheduler,)
	_A							:					Optional[Any] 			=					(("""num_inference_steps""", 25),)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		A_		:    str						     = {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_				)
		return config





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Tuple						     = dict(self.forward_default_kwargs				)
		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    str						     = self.dummy_sample
		A_		:    Dict						     = 0.1 * sample
		A_		:    int						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    List[Any]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Dict						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)
			# copy over dummy past residuals
			A_		:    Tuple						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Any						     = scheduler_class.from_pretrained(lowerCAmelCase_				)
				new_scheduler.set_timesteps(lowerCAmelCase_				)
				# copy over dummy past residuals
				A_		:    Any						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_     ,  A_		:    Optional[int]						     = sample, sample
			for t in range(lowerCAmelCase_  ,  time_step + scheduler.config.solver_order + 1				):
				A_		:    Any						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
				A_		:    Dict						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

				assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Optional[int]						     = dict(self.forward_default_kwargs				)
		A_		:    int						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    Optional[Any]						     = self.dummy_sample
		A_		:    List[Any]						     = 0.1 * sample
		A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    str						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:    Union[str, Any]						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Tuple						     = scheduler_class.from_pretrained(lowerCAmelCase_				)
				# copy over dummy past residuals
				new_scheduler.set_timesteps(lowerCAmelCase_				)

				# copy over dummy past residual (must be after setting timesteps)
				A_		:    Optional[int]						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    str						     = new_scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

			assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"





	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if scheduler is None:
			A_		:    int						     = self.scheduler_classes[0]
			A_		:    List[str]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Any						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    int						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(**lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Optional[int]						     = 10
		A_		:    Optional[Any]						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[int]						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		return sample





	def  lowerCamelCase(self				):
		A_		:    Any						     = dict(self.forward_default_kwargs				)

		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    List[Any]						     = scheduler_class(**lowerCAmelCase_				)

			A_		:    int						     = self.dummy_sample
			A_		:    str						     = 0.1 * sample

			if num_inference_steps is not None and hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				scheduler.set_timesteps(lowerCAmelCase_				)
			elif num_inference_steps is not None and not hasattr(lowerCAmelCase_  ,  """set_timesteps"""				):
				A_		:    Tuple						     = num_inference_steps

			# copy over dummy past residuals (must be done after set_timesteps)
			A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]
			A_		:    Dict						     = dummy_past_residuals[: scheduler.config.solver_order]

			A_		:    str						     = scheduler.timesteps[5]
			A_		:    Any						     = scheduler.timesteps[6]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    Dict						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self				):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:    Tuple						     = UniPCMultistepScheduler(**self.get_scheduler_config()				)
		A_		:    List[str]						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    int						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3

		A_		:    List[str]						     = DPMSolverSinglestepScheduler.from_config(scheduler.config				)
		A_		:    List[str]						     = DEISMultistepScheduler.from_config(scheduler.config				)
		A_		:    Optional[int]						     = DPMSolverMultistepScheduler.from_config(scheduler.config				)
		A_		:    str						     = UniPCMultistepScheduler.from_config(scheduler.config				)

		A_		:    Dict						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    List[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.check_over_configs(thresholding=lowerCAmelCase_				)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    thresholding=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  sample_max_value=lowerCAmelCase_  ,  solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  )





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





	def  lowerCamelCase(self				):
		for solver_type in ["bh1", "bh2"]:
			for order in [1, 2, 3]:
				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					A_		:    Union[str, Any]						     = self.full_loop(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					assert not torch.isnan(lowerCAmelCase_				).any(), "Samples have nan numbers"





	def  lowerCamelCase(self				):
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=lowerCAmelCase_  ,  time_step=0				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.full_loop()
		A_		:    Optional[int]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    str						     = self.full_loop(prediction_type="""v_prediction"""				)
		A_		:    Optional[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.1014				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(thresholding=lowerCAmelCase_  ,  dynamic_thresholding_ratio=0				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Any						     = 10
		A_		:    int						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Dict						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		assert sample.dtype == torch.floataa





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		for scheduler_class in self.scheduler_classes:
			A_		:    Tuple						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    int						     = scheduler_class(**lowerCAmelCase_				)

			scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
			assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps

