

from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	if len(_SCREAMING_SNAKE_CASE    ) == 0:
		return False
	UpperCAmelCase_       				=			len(_SCREAMING_SNAKE_CASE    ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]       ,     _SCREAMING_SNAKE_CASE    )
	else:
		return binary_search(a_list[midpoint + 1 :]       ,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
					_a:    List[str] 							=			input("""Enter numbers separated by comma:\n""").strip()
					_a:    Tuple 							=			[int(item.strip()) for item in user_input.split(""",""")]
					_a:    Union[str, Any] 							=			int(input("""Enter the number to be found in the list:\n""").strip())
					_a:    int 							=			'' if binary_search(sequence, target) else 'not '
					print(F'{target} was {not_str}found in {sequence}')


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

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

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

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

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

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

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

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

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3