
"""simple docstring"""

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(HeunDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							int:
			lowerCamelCase     :			Optional[int]		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							str:
			for schedule in ["linear", "scaled_linear", "exp"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			for clip_sample_range in [1.0, 2.0, 3.0]:
					self.check_over_configs(clip_sample_range=UpperCAmelCase_		, clip_sample=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							str:
			for prediction_type in ["epsilon", "v_prediction", "sample"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			int		=    self.scheduler_classes[0]
			lowerCamelCase     :			Any		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Any		=    self.dummy_model()
			lowerCamelCase     :			Optional[int]		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			int		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Any		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Optional[int]		=    output.prev_sample

			lowerCamelCase     :			int		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			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 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			List[str]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[int]		=    self.dummy_model()
			lowerCamelCase     :			int		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			int		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Optional[Any]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Union[str, Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			List[Any]		=    output.prev_sample

			lowerCamelCase     :			str		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.6_9_3_4E-0_7					) < 1E-2
					assert abs(result_mean.item() - 6.1_1_1_2E-1_0					) < 1E-3
			else:
					# CUDA
					assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7					) < 1E-2
					assert abs(result_mean.item() - 0.0002					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			str		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config()
			lowerCamelCase     :			Optional[int]		=    scheduler_class(**UpperCAmelCase_					)

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

			lowerCamelCase     :			Union[str, Any]		=    self.dummy_model()
			lowerCamelCase     :			Tuple		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

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

					lowerCamelCase     :			Union[str, Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[Any]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			List[Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			List[str]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			if str(UpperCAmelCase_					).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(UpperCAmelCase_					).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 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			str		=    self.get_scheduler_config()
			lowerCamelCase     :			Optional[int]		=    scheduler_class(**UpperCAmelCase_		, use_karras_sigmas=UpperCAmelCase_					)

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

			lowerCamelCase     :			Optional[Any]		=    self.dummy_model()
			lowerCamelCase     :			int		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma
			lowerCamelCase     :			Any		=    sample.to(UpperCAmelCase_					)

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

					lowerCamelCase     :			Union[str, Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Any		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
					lowerCamelCase     :			Tuple		=    output.prev_sample

			lowerCamelCase     :			Dict		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[int]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 0.00015					) < 1E-2
			assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7					) < 1E-2












def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  0
    for ele in arr:
        total_sum += product_sum(lowercase_    ,   depth + 1					) if isinstance(lowercase_    ,   lowercase_					) else ele
    return total_sum * depth




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return product_sum(lowercase_    ,   1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






