




'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		(PNDMScheduler,)
				snake_case_      =		(("num_inference_steps", 50),)


				def   A_ (		self    :					Tuple       ,				**lowercase_    :					Tuple					):
									snake_case_									=				{
									    '''num_train_timesteps''': 1000,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									}

									config.update(**lowercase_					)
									return config


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[int]=0       ,				**lowercase_    :					int					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config(**lowercase_					)
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)
														# copy over dummy past residuals
														snake_case_									=				dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase_					)
																			snake_case_									=				scheduler_class.from_pretrained(lowercase_					)
																			new_scheduler.set_timesteps(lowercase_					)
																			# copy over dummy past residuals
																			snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					Any					):
									pass


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict=0       ,				**lowercase_    :					Optional[int]					):
									snake_case_									=				dict(self.forward_default_kwargs					)
									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)
									snake_case_									=				self.dummy_sample
									snake_case_									=				0.1 * sample
									snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)
														scheduler.set_timesteps(lowercase_					)

														# copy over dummy past residuals (must be after setting timesteps)
														snake_case_									=				dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase_					)
																			snake_case_									=				scheduler_class.from_pretrained(lowercase_					)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(lowercase_					)

																			# copy over dummy past residual (must be after setting timesteps)
																			snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				new_scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					Dict       ,				**lowercase_    :					str					):
									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(**lowercase_					)
									snake_case_									=				scheduler_class(**lowercase_					)

									snake_case_									=				10
									snake_case_									=				self.dummy_model()
									snake_case_									=				self.dummy_sample_deter
									scheduler.set_timesteps(lowercase_					)

									for i, t in enumerate(scheduler.prk_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps					):
														snake_case_									=				model(lowercase_       ,				lowercase_					)
														snake_case_									=				scheduler.step_plms(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample

									return sample


				def   A_ (		self    :					Dict					):
									snake_case_									=				dict(self.forward_default_kwargs					)

									snake_case_									=				kwargs.pop('''num_inference_steps'''       ,				lowercase_					)

									for scheduler_class in self.scheduler_classes:
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														snake_case_									=				self.dummy_sample
														snake_case_									=				0.1 * sample

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

														# copy over dummy past residuals (must be done after set_timesteps)
														snake_case_									=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														snake_case_									=				dummy_past_residuals[:]

														snake_case_									=				scheduler.step_prk(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_prk(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

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

														snake_case_									=				scheduler.step_plms(lowercase_       ,				0       ,				lowercase_       ,				**lowercase_					).prev_sample
														snake_case_									=				scheduler.step_plms(lowercase_       ,				1       ,				lowercase_       ,				**lowercase_					).prev_sample

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


				def   A_ (		self    :					List[Any]					):
									for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=lowercase_					)

									snake_case_									=				self.scheduler_classes[0]
									snake_case_									=				self.get_scheduler_config(steps_offset=1					)
									snake_case_									=				scheduler_class(**lowercase_					)
									scheduler.set_timesteps(10					)
									assert torch.equal(
									    scheduler.timesteps       ,				torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)       ,				)


				def   A_ (		self    :					Any					):
									for beta_start, beta_end in zip([0.0001, 0.001]       ,				[0.002, 0.02]					):
														self.check_over_configs(beta_start=lowercase_       ,				beta_end=lowercase_					)


				def   A_ (		self    :					List[str]					):
									for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=lowercase_					)


				def   A_ (		self    :					str					):
									for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=lowercase_					)


				def   A_ (		self    :					str					):
									for t in [1, 5, 10]:
														self.check_over_forward(time_step=lowercase_					)


				def   A_ (		self    :					str					):
									for t, num_inference_steps in zip([1, 5, 10]       ,				[10, 50, 100]					):
														self.check_over_forward(num_inference_steps=lowercase_					)


				def   A_ (		self    :					Any					):
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									snake_case_									=				27

									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(**lowercase_					)

														scheduler.set_timesteps(lowercase_					)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]					):
																			snake_case_									=				scheduler.step_prk(lowercase_       ,				lowercase_       ,				lowercase_					).prev_sample


				def   A_ (		self    :					Tuple					):
									with self.assertRaises(lowercase_					):
														snake_case_									=				self.scheduler_classes[0]
														snake_case_									=				self.get_scheduler_config()
														snake_case_									=				scheduler_class(**lowercase_					)

														scheduler.step_plms(self.dummy_sample       ,				1       ,				self.dummy_sample					).prev_sample


				def   A_ (		self    :					str					):
									snake_case_									=				self.full_loop()
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 198.1318					) < 1e-2
									assert abs(result_mean.item() - 0.2580					) < 1e-3


				def   A_ (		self    :					Any					):
									snake_case_									=				self.full_loop(prediction_type='''v_prediction'''					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 67.3986					) < 1e-2
									assert abs(result_mean.item() - 0.0878					) < 1e-3


				def   A_ (		self    :					Optional[Any]					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 230.0399					) < 1e-2
									assert abs(result_mean.item() - 0.2995					) < 1e-3


				def   A_ (		self    :					str					):
									# We specify different beta, so that the first alpha is 0.99
									snake_case_									=				self.full_loop(set_alpha_to_one=lowercase_       ,				beta_start=0.01					)
									snake_case_									=				torch.sum(torch.abs(lowercase_					)					)
									snake_case_									=				torch.mean(torch.abs(lowercase_					)					)

									assert abs(result_sum.item() - 186.9482					) < 1e-2
									assert abs(result_mean.item() - 0.2434					) < 1e-3









"""simple docstring"""



def 						lowercase    (				__snake_case				: int	):
		if n == 1 or not isinstance(__snake_case		,					__snake_case	):
				return 0
		elif n == 2:
				return 1
		else:
				lowercase_ : Dict							       =   [0, 1]
				for i in range(2		,					n + 1	):
						sequence.append(sequence[i - 1] + sequence[i - 2]	)

				return sequence[n]



def 						lowercase    (				__snake_case				: int	):
		lowercase_ : str							       =   0
		lowercase_ : List[str]							       =   2

		while digits < n:
				index += 1
				lowercase_ : Any							       =   len(str(fibonacci(__snake_case	)	)	)

		return index



def 						lowercase    (				__snake_case				: int = 1_0_0_0	):
		return fibonacci_digits_index(__snake_case	)


if __name__ == "__main__":
						print(solution(int(str(input()).strip())))



