





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						(HeunDiscreteScheduler,)
			lowerCAmelCase_												=						10





			def 		__lowerCamelCase    (   self	,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		{
				    '''num_train_timesteps''': 1_1_0_0,
				    '''beta_start''': 0.0001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,  [0.0002, 0.002, 0.02]							):
					self.check_over_configs(beta_start=__lowerCAmelCase	,  beta_end=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				for clip_sample_range in [1.0, 2.0, 3.0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase	,  clip_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.scheduler_classes[0]
				lowerCamelCase__        =		self.get_scheduler_config()
				lowerCamelCase__        =		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(self.num_inference_steps							)

				lowerCamelCase__        =		self.dummy_model()
				lowerCamelCase__        =		self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCamelCase__        =		sample.to(__lowerCAmelCase							)

				for i, t in enumerate(scheduler.timesteps							):
					lowerCamelCase__        =		scheduler.scale_model_input(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		model(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		scheduler.step(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					lowerCamelCase__        =		output.prev_sample

				lowerCamelCase__        =		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__        =		torch.mean(torch.abs(__lowerCAmelCase							)							)

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 0.1233							) < 1E-2
					assert abs(result_mean.item() - 0.0002							) < 1E-3
				else:
					# CUDA
					assert abs(result_sum.item() - 0.1233							) < 1E-2
					assert abs(result_mean.item() - 0.0002							) < 1E-3





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.scheduler_classes[0]
				lowerCamelCase__        =		self.get_scheduler_config(prediction_type='''v_prediction'''							)
				lowerCamelCase__        =		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(self.num_inference_steps							)

				lowerCamelCase__        =		self.dummy_model()
				lowerCamelCase__        =		self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCamelCase__        =		sample.to(__lowerCAmelCase							)

				for i, t in enumerate(scheduler.timesteps							):
					lowerCamelCase__        =		scheduler.scale_model_input(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		model(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		scheduler.step(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					lowerCamelCase__        =		output.prev_sample

				lowerCamelCase__        =		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__        =		torch.mean(torch.abs(__lowerCAmelCase							)							)

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.6934E-07							) < 1E-2
					assert abs(result_mean.item() - 6.1112E-10							) < 1E-3
				else:
					# CUDA
					assert abs(result_sum.item() - 4.693428650170972E-07							) < 1E-2
					assert abs(result_mean.item() - 0.0002							) < 1E-3





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.scheduler_classes[0]
				lowerCamelCase__        =		self.get_scheduler_config()
				lowerCamelCase__        =		scheduler_class(**__lowerCAmelCase							)

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

				lowerCamelCase__        =		self.dummy_model()
				lowerCamelCase__        =		self.dummy_sample_deter.to(__lowerCAmelCase							) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					lowerCamelCase__        =		scheduler.scale_model_input(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		model(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		scheduler.step(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					lowerCamelCase__        =		output.prev_sample

				lowerCamelCase__        =		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__        =		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() - 0.1233							) < 1E-2
					assert abs(result_mean.item() - 0.0002							) < 1E-3
				elif str(__lowerCAmelCase							).startswith('''mps'''							):
					# Larger tolerance on mps
					assert abs(result_mean.item() - 0.0002							) < 1E-2
				else:
					# CUDA
					assert abs(result_sum.item() - 0.1233							) < 1E-2
					assert abs(result_mean.item() - 0.0002							) < 1E-3





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.scheduler_classes[0]
				lowerCamelCase__        =		self.get_scheduler_config()
				lowerCamelCase__        =		scheduler_class(**__lowerCAmelCase	,  use_karras_sigmas=__lowerCAmelCase							)

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

				lowerCamelCase__        =		self.dummy_model()
				lowerCamelCase__        =		self.dummy_sample_deter.to(__lowerCAmelCase							) * scheduler.init_noise_sigma
				lowerCamelCase__        =		sample.to(__lowerCAmelCase							)

				for t in scheduler.timesteps:
					lowerCamelCase__        =		scheduler.scale_model_input(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		model(__lowerCAmelCase	,  __lowerCAmelCase							)

					lowerCamelCase__        =		scheduler.step(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
					lowerCamelCase__        =		output.prev_sample

				lowerCamelCase__        =		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__        =		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 0.0_0015							) < 1E-2
				assert abs(result_mean.item() - 1.9869554535034695E-07							) < 1E-2










def lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



