





'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 							__snake_case    (				unittest.TestCase       ):
	# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
	__lowerCAmelCase						=     (ScoreSdeVeScheduler,)
	__lowerCAmelCase						=     ()





	@property
	def      _snake_case      (		self		)			->		Optional[int]:
		snake_case__  					=      4
		snake_case__  					=      3
		snake_case__  					=      8
		snake_case__  					=      8

		snake_case__  					=      torch.rand((batch_size, num_channels, height, width)		)

		return sample





	@property
	def      _snake_case      (		self		)			->		Optional[int]:
		snake_case__  					=      4
		snake_case__  					=      3
		snake_case__  					=      8
		snake_case__  					=      8

		snake_case__  					=      batch_size * num_channels * height * width
		snake_case__  					=      torch.arange(UpperCamelCase_		)
		snake_case__  					=      sample.reshape(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
		snake_case__  					=      sample / num_elems
		snake_case__  					=      sample.permute(3			,					0			,					1			,					2		)

		return sample





	def      _snake_case      (		self		)			->		Union[str, Any]:
		def model(UpperCamelCase_			,					UpperCamelCase_			,					*UpperCamelCase_		):
			return sample * t / (t + 1)

		return model





	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		str:
		snake_case__  					=      {
		    'num_train_timesteps': 2000,
		    'snr': 0.1_5,
		    'sigma_min': 0.0_1,
		    'sigma_max': 1348,
		    'sampling_eps': 1E-5,
		}

		config.update(**UpperCamelCase_		)
		return config





	def      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		List[Any]:
		snake_case__  					=      dict(self.forward_default_kwargs		)

		for scheduler_class in self.scheduler_classes:
			snake_case__  					=      self.dummy_sample
			snake_case__  					=      0.1 * sample

			snake_case__  					=      self.get_scheduler_config(**UpperCamelCase_		)
			snake_case__  					=      scheduler_class(**UpperCamelCase_		)

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(UpperCamelCase_		)
				snake_case__  					=      scheduler_class.from_pretrained(UpperCamelCase_		)

			snake_case__  					=      scheduler.step_pred(
			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
			snake_case__  					=      new_scheduler.step_pred(
			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample

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

			snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
			snake_case__  					=      new_scheduler.step_correct(
			    UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample

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





	def      _snake_case      (		self			,					UpperCamelCase_=0			,					**UpperCamelCase_		)			->		Optional[int]:
		snake_case__  					=      dict(self.forward_default_kwargs		)
		kwargs.update(UpperCamelCase_		)

		for scheduler_class in self.scheduler_classes:
			snake_case__  					=      self.dummy_sample
			snake_case__  					=      0.1 * sample

			snake_case__  					=      self.get_scheduler_config()
			snake_case__  					=      scheduler_class(**UpperCamelCase_		)

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(UpperCamelCase_		)
				snake_case__  					=      scheduler_class.from_pretrained(UpperCamelCase_		)

			snake_case__  					=      scheduler.step_pred(
			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
			snake_case__  					=      new_scheduler.step_pred(
			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample

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

			snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
			snake_case__  					=      new_scheduler.step_correct(
			    UpperCamelCase_			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample

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





	def      _snake_case      (		self		)			->		Union[str, Any]:
		for timesteps in [10, 100, 1000]:
			self.check_over_configs(num_train_timesteps=UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Tuple:
		for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,					[1, 100, 1000]		):
			self.check_over_configs(sigma_min=UpperCamelCase_			,					sigma_max=UpperCamelCase_		)





	def      _snake_case      (		self		)			->		Dict:
		for t in [0.1, 0.5, 0.7_5]:
			self.check_over_forward(time_step=UpperCamelCase_		)





	def      _snake_case      (		self		)			->		str:
		snake_case__  					=      dict(self.forward_default_kwargs		)

		snake_case__  					=      self.scheduler_classes[0]
		snake_case__  					=      self.get_scheduler_config()
		snake_case__  					=      scheduler_class(**UpperCamelCase_		)

		snake_case__  					=      3

		snake_case__  					=      self.dummy_model()
		snake_case__  					=      self.dummy_sample_deter

		scheduler.set_sigmas(UpperCamelCase_		)
		scheduler.set_timesteps(UpperCamelCase_		)
		snake_case__  					=      torch.manual_seed(0		)

		for i, t in enumerate(scheduler.timesteps		):
			snake_case__  					=      scheduler.sigmas[i]

			for _ in range(scheduler.config.correct_steps		):
				with torch.no_grad():
					snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)
				snake_case__  					=      scheduler.step_correct(UpperCamelCase_			,					UpperCamelCase_			,					generator=UpperCamelCase_			,					**UpperCamelCase_		).prev_sample

			with torch.no_grad():
				snake_case__  					=      model(UpperCamelCase_			,					UpperCamelCase_		)

			snake_case__  					=      scheduler.step_pred(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					generator=UpperCamelCase_			,					**UpperCamelCase_		)
			snake_case__					,     snake_case__  					=      output.prev_sample, output.prev_sample_mean

		snake_case__  					=      torch.sum(torch.abs(UpperCamelCase_		)		)
		snake_case__  					=      torch.mean(torch.abs(UpperCamelCase_		)		)

		assert np.isclose(result_sum.item()			,					1_4_3_7_2_7_5_8_5_2_8.0		)
		assert np.isclose(result_mean.item()			,					1_8_7_1_4_5_3_0.0		)





	def      _snake_case      (		self		)			->		List[Any]:
		snake_case__  					=      dict(self.forward_default_kwargs		)

		snake_case__  					=      kwargs.pop('num_inference_steps'			,					UpperCamelCase_		)

		for scheduler_class in self.scheduler_classes:
			snake_case__  					=      self.get_scheduler_config()
			snake_case__  					=      scheduler_class(**UpperCamelCase_		)

			snake_case__  					=      self.dummy_sample
			snake_case__  					=      0.1 * sample

			if num_inference_steps is not None and hasattr(UpperCamelCase_			,					'set_timesteps'		):
				scheduler.set_timesteps(UpperCamelCase_		)
			elif num_inference_steps is not None and not hasattr(UpperCamelCase_			,					'set_timesteps'		):
				snake_case__  					=      num_inference_steps

			snake_case__  					=      scheduler.step_pred(UpperCamelCase_			,					0			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample
			snake_case__  					=      scheduler.step_pred(UpperCamelCase_			,					1			,					UpperCamelCase_			,					generator=torch.manual_seed(0		)			,					**UpperCamelCase_		).prev_sample

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










'''simple docstring'''




from typing import Callable




def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:

	def wrapper(UpperCAmelCase_       ):
		snake_case__  					=      function_with_docstring.__doc__
		return target_function

	return wrapper



