"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class    __UpperCAmelCase						(	unittest.TestCase	):
				# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
				A__      :   Any  							=       (ScoreSdeVeScheduler,)
				A__      :   Tuple  							=       ()

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=4
										lowerCamelCase__					=3
										lowerCamelCase__					=8
										lowerCamelCase__					=8

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

										return sample

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=4
										lowerCamelCase__					=3
										lowerCamelCase__					=8
										lowerCamelCase__					=8

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

										return sample

				def 		_a	(      self      ):
										def model(_lowerCamelCase	, _lowerCamelCase	, *_lowerCamelCase      ):
																return sample * t / (t + 1)

										return model

				def 		_a	(      self	, **_lowerCamelCase      ):
										lowerCamelCase__					={
										    "num_train_timesteps": 2000,
										    "snr": 0.1_5,
										    "sigma_min": 0.0_1,
										    "sigma_max": 1348,
										    "sampling_eps": 1E-5,
										}

										config.update(**_lowerCamelCase      )
										return config

				def 		_a	(      self	, _lowerCamelCase=0	, **_lowerCamelCase      ):
										lowerCamelCase__					=dict(self.forward_default_kwargs      )

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

																lowerCamelCase__					=self.get_scheduler_config(**_lowerCamelCase      )
																lowerCamelCase__					=scheduler_class(**_lowerCamelCase      )

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_lowerCamelCase      )
																						lowerCamelCase__					=scheduler_class.from_pretrained(_lowerCamelCase      )

																lowerCamelCase__					=scheduler.step_pred(
																    _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, generator=torch.manual_seed(0      )	, **_lowerCamelCase      ).prev_sample
																lowerCamelCase__					=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"

																lowerCamelCase__					=scheduler.step_correct(_lowerCamelCase	, _lowerCamelCase	, generator=torch.manual_seed(0      )	, **_lowerCamelCase      ).prev_sample
																lowerCamelCase__					=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 		_a	(      self	, _lowerCamelCase=0	, **_lowerCamelCase      ):
										lowerCamelCase__					=dict(self.forward_default_kwargs      )
										kwargs.update(_lowerCamelCase      )

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

																lowerCamelCase__					=self.get_scheduler_config()
																lowerCamelCase__					=scheduler_class(**_lowerCamelCase      )

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_lowerCamelCase      )
																						lowerCamelCase__					=scheduler_class.from_pretrained(_lowerCamelCase      )

																lowerCamelCase__					=scheduler.step_pred(
																    _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, generator=torch.manual_seed(0      )	, **_lowerCamelCase      ).prev_sample
																lowerCamelCase__					=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"

																lowerCamelCase__					=scheduler.step_correct(_lowerCamelCase	, _lowerCamelCase	, generator=torch.manual_seed(0      )	, **_lowerCamelCase      ).prev_sample
																lowerCamelCase__					=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 		_a	(      self      ):
										for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_lowerCamelCase      )

				def 		_a	(      self      ):
										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=_lowerCamelCase	, sigma_max=_lowerCamelCase      )

				def 		_a	(      self      ):
										for t in [0.1, 0.5, 0.7_5]:
																self.check_over_forward(time_step=_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=dict(self.forward_default_kwargs      )

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

										lowerCamelCase__					=3

										lowerCamelCase__					=self.dummy_model()
										lowerCamelCase__					=self.dummy_sample_deter

										scheduler.set_sigmas(_lowerCamelCase      )
										scheduler.set_timesteps(_lowerCamelCase      )
										lowerCamelCase__					=torch.manual_seed(0      )

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

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

																with torch.no_grad():
																						lowerCamelCase__					=model(_lowerCamelCase	, _lowerCamelCase      )

																lowerCamelCase__					=scheduler.step_pred(_lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase	, generator=_lowerCamelCase	, **_lowerCamelCase      )
																lowerCamelCase__					,      lowerCamelCase__					=output.prev_sample, output.prev_sample_mean

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

										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 		_a	(      self      ):
										lowerCamelCase__					=dict(self.forward_default_kwargs      )

										lowerCamelCase__					=kwargs.pop("num_inference_steps"	, _lowerCamelCase      )

										for scheduler_class in self.scheduler_classes:
																lowerCamelCase__					=self.get_scheduler_config()
																lowerCamelCase__					=scheduler_class(**_lowerCamelCase      )

																lowerCamelCase__					=self.dummy_sample
																lowerCamelCase__					=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"      ):
																						lowerCamelCase__					=num_inference_steps

																lowerCamelCase__					=scheduler.step_pred(_lowerCamelCase	, 0	, _lowerCamelCase	, generator=torch.manual_seed(0      )	, **_lowerCamelCase      ).prev_sample
																lowerCamelCase__					=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      )



"""simple docstring"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


