


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      List[str]      =		(ScoreSdeVeScheduler,)
				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		()


				@property
				def 		__magic_name__(	self      :Dict     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Any		      =		4
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		3
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		8
										__SCREAMING_SNAKE_CASE						:					Any		      =		8

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.rand((batch_size, num_channels, height, width)     )

										return sample


				@property
				def 		__magic_name__(	self      :Any     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		4
										__SCREAMING_SNAKE_CASE						:					Dict		      =		3
										__SCREAMING_SNAKE_CASE						:					str		      =		8
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		8

										__SCREAMING_SNAKE_CASE						:					Dict		      =		batch_size * num_channels * height * width
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		torch.arange(__lowerCamelCase     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		sample.reshape(__lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		sample / num_elems
										__SCREAMING_SNAKE_CASE						:					int		      =		sample.permute(3      ,      0      ,      1      ,      2     )

										return sample


				def 		__magic_name__(	self      :Dict     )		->      Union[str, Any]:


										def model(lowerCAmelCase__      :str      ,      lowerCAmelCase__      :int      ,      *lowerCAmelCase__      :Dict     ):
																return sample * t / (t + 1)

										return model


				def 		__magic_name__(	self      :Union[str, Any]      ,      **lowerCAmelCase__      :str     )		->      str:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		{
										    "num_train_timesteps": 2_000,
										    "snr": 0.15,
										    "sigma_min": 0.01,
										    "sigma_max": 1_348,
										    "sampling_eps": 1E-5,
										}

										config.update(**__lowerCamelCase     )
										return config


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Optional[int]=0      ,      **lowerCAmelCase__      :str     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		dict(self.forward_default_kwargs     )

										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.dummy_sample
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		0.1 * sample

																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.get_scheduler_config(**__lowerCamelCase     )
																__SCREAMING_SNAKE_CASE						:					Any		      =		scheduler_class(**__lowerCamelCase     )

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__lowerCamelCase     )
																						__SCREAMING_SNAKE_CASE						:					int		      =		scheduler_class.from_pretrained(__lowerCamelCase     )

																__SCREAMING_SNAKE_CASE						:					List[str]		      =		scheduler.step_pred(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					int		      =		new_scheduler.step_pred(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample

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

																__SCREAMING_SNAKE_CASE						:					List[str]		      =		scheduler.step_correct(__lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		new_scheduler.step_correct(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample

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


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :Optional[int]=0      ,      **lowerCAmelCase__      :List[str]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		dict(self.forward_default_kwargs     )
										kwargs.update(__lowerCamelCase     )

										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.dummy_sample
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		0.1 * sample

																__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_scheduler_config()
																__SCREAMING_SNAKE_CASE						:					Any		      =		scheduler_class(**__lowerCamelCase     )

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__lowerCamelCase     )
																						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		scheduler_class.from_pretrained(__lowerCamelCase     )

																__SCREAMING_SNAKE_CASE						:					List[str]		      =		scheduler.step_pred(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					int		      =		new_scheduler.step_pred(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample

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

																__SCREAMING_SNAKE_CASE						:					Dict		      =		scheduler.step_correct(__lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					Any		      =		new_scheduler.step_correct(
																    __lowerCamelCase      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample

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


				def 		__magic_name__(	self      :Any     )		->      Any:


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


				def 		__magic_name__(	self      :List[str]     )		->      int:


										for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,      [1, 100, 1_000]     ):
																self.check_over_configs(sigma_min=__lowerCamelCase      ,      sigma_max=__lowerCamelCase     )


				def 		__magic_name__(	self      :List[Any]     )		->      Optional[int]:


										for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=__lowerCamelCase     )


				def 		__magic_name__(	self      :Any     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		dict(self.forward_default_kwargs     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler_class(**__lowerCamelCase     )

										__SCREAMING_SNAKE_CASE						:					int		      =		3

										__SCREAMING_SNAKE_CASE						:					Tuple		      =		self.dummy_model()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.dummy_sample_deter

										scheduler.set_sigmas(__lowerCamelCase     )
										scheduler.set_timesteps(__lowerCamelCase     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.manual_seed(0     )

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

																for _ in range(scheduler.config.correct_steps     ):
																						with torch.no_grad():
																												__SCREAMING_SNAKE_CASE						:					int		      =		model(__lowerCamelCase      ,      __lowerCamelCase     )
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler.step_correct(__lowerCamelCase      ,      __lowerCamelCase      ,      generator=__lowerCamelCase      ,      **__lowerCamelCase     ).prev_sample

																with torch.no_grad():
																						__SCREAMING_SNAKE_CASE						:					str		      =		model(__lowerCamelCase      ,      __lowerCamelCase     )

																__SCREAMING_SNAKE_CASE						:					Tuple		      =		scheduler.step_pred(__lowerCamelCase      ,      __lowerCamelCase      ,      __lowerCamelCase      ,      generator=__lowerCamelCase      ,      **__lowerCamelCase     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		output.prev_sample, output.prev_sample_mean

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.sum(torch.abs(__lowerCamelCase     )     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		torch.mean(torch.abs(__lowerCamelCase     )     )

										assert np.isclose(result_sum.item()      ,      14_372_758_528.0     )
										assert np.isclose(result_mean.item()      ,      18_714_530.0     )



				def 		__magic_name__(	self      :List[str]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		dict(self.forward_default_kwargs     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		kwargs.pop('''num_inference_steps'''      ,      __lowerCamelCase     )

										for scheduler_class in self.scheduler_classes:
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_scheduler_config()
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		scheduler_class(**__lowerCamelCase     )

																__SCREAMING_SNAKE_CASE						:					str		      =		self.dummy_sample
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		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'''     ):
																						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		num_inference_steps

																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		scheduler.step_pred(__lowerCamelCase      ,      0      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		scheduler.step_pred(__lowerCamelCase      ,      1      ,      __lowerCamelCase      ,      generator=torch.manual_seed(0     )      ,      **__lowerCamelCase     ).prev_sample

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




from __future__ import annotations


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		sorted(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					str		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	_UpperCamelCase							(				):
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()
