

'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (PNDMScheduler,)
						__UpperCAmelCase			     =    (("""num_inference_steps""", 50),)







						def       __magic_name__							(							self					:      List[Any]			,			**snake_case_					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       str       						=  {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**snake_case_  )
								return config







						def       __magic_name__							(							self					:      str			,			snake_case_					:      Optional[int]=0			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  dict(self.forward_default_kwargs  )
								snake_case__						:       Union[str, Any]       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )
								snake_case__						:       Dict       						=  self.dummy_sample
								snake_case__						:       Any       						=  0.1 * sample
								snake_case__						:       Any       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       Tuple       						=  self.get_scheduler_config(**snake_case_  )
										snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )
										# copy over dummy past residuals
										snake_case__						:       List[str]       						=  dummy_past_residuals[:]

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

										snake_case__						:       Optional[int]       						=  scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       List[str]       						=  new_scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

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

										snake_case__						:       Union[str, Any]       						=  scheduler.step_plms(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       Optional[int]       						=  new_scheduler.step_plms(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

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







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any=0			,			**snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  dict(self.forward_default_kwargs  )
								snake_case__						:       Tuple       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )
								snake_case__						:       Tuple       						=  self.dummy_sample
								snake_case__						:       Any       						=  0.1 * sample
								snake_case__						:       Optional[int]       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       Any       						=  self.get_scheduler_config()
										snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )
										scheduler.set_timesteps(snake_case_  )

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

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

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

										snake_case__						:       Optional[Any]       						=  scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       Any       						=  new_scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

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

										snake_case__						:       Optional[Any]       						=  scheduler.step_plms(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       str       						=  new_scheduler.step_plms(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  ).prev_sample

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







						def       __magic_name__							(							self					:      Union[str, Any]			,			**snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.scheduler_classes[0]
								snake_case__						:       Tuple       						=  self.get_scheduler_config(**snake_case_  )
								snake_case__						:       Any       						=  scheduler_class(**snake_case_  )

								snake_case__						:       Any       						=  1_0
								snake_case__						:       Optional[Any]       						=  self.dummy_model()
								snake_case__						:       Union[str, Any]       						=  self.dummy_sample_deter
								scheduler.set_timesteps(snake_case_  )

								for i, t in enumerate(scheduler.prk_timesteps  ):
										snake_case__						:       str       						=  model(snake_case_			,			snake_case_  )
										snake_case__						:       List[str]       						=  scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps  ):
										snake_case__						:       List[Any]       						=  model(snake_case_			,			snake_case_  )
										snake_case__						:       int       						=  scheduler.step_plms(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample

								return sample







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  dict(self.forward_default_kwargs  )

								snake_case__						:       Any       						=  kwargs.pop('''num_inference_steps'''			,			snake_case_  )

								for scheduler_class in self.scheduler_classes:
										snake_case__						:       Optional[Any]       						=  self.get_scheduler_config()
										snake_case__						:       List[Any]       						=  scheduler_class(**snake_case_  )

										snake_case__						:       List[str]       						=  self.dummy_sample
										snake_case__						:       List[str]       						=  0.1 * sample

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

										# copy over dummy past residuals (must be done after set_timesteps)
										snake_case__						:       Optional[Any]       						=  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
										snake_case__						:       List[str]       						=  dummy_past_residuals[:]

										snake_case__						:       str       						=  scheduler.step_prk(snake_case_			,			0			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       Optional[int]       						=  scheduler.step_prk(snake_case_			,			1			,			snake_case_			,			**snake_case_  ).prev_sample

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

										snake_case__						:       List[str]       						=  scheduler.step_plms(snake_case_			,			0			,			snake_case_			,			**snake_case_  ).prev_sample
										snake_case__						:       int       						=  scheduler.step_plms(snake_case_			,			1			,			snake_case_			,			**snake_case_  ).prev_sample

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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								for timesteps in [1_0_0, 1_0_0_0]:
										self.check_over_configs(num_train_timesteps=snake_case_  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=snake_case_  )

								snake_case__						:       List[str]       						=  self.scheduler_classes[0]
								snake_case__						:       Tuple       						=  self.get_scheduler_config(steps_offset=1  )
								snake_case__						:       Union[str, Any]       						=  scheduler_class(**snake_case_  )
								scheduler.set_timesteps(1_0  )
								assert torch.equal(
								    scheduler.timesteps			,			torch.LongTensor(
								        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]  )			,			)







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]			,			[0.0_0_2, 0.0_2]  ):
										self.check_over_configs(beta_start=snake_case_			,			beta_end=snake_case_  )







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



								'''simple docstring'''



								for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=snake_case_  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=snake_case_  )







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



								'''simple docstring'''



								for t in [1, 5, 1_0]:
										self.check_over_forward(time_step=snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								for t, num_inference_steps in zip([1, 5, 1_0]			,			[1_0, 5_0, 1_0_0]  ):
										self.check_over_forward(num_inference_steps=snake_case_  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       str       						=  2_7

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

										snake_case__						:       Dict       						=  self.get_scheduler_config()
										snake_case__						:       Tuple       						=  scheduler_class(**snake_case_  )

										scheduler.set_timesteps(snake_case_  )

										# 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__						:       Union[str, Any]       						=  scheduler.step_prk(snake_case_			,			snake_case_			,			snake_case_  ).prev_sample







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								with self.assertRaises(snake_case_  ):
										snake_case__						:       List[str]       						=  self.scheduler_classes[0]
										snake_case__						:       List[str]       						=  self.get_scheduler_config()
										snake_case__						:       int       						=  scheduler_class(**snake_case_  )

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







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.full_loop()
								snake_case__						:       Any       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       Tuple       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 1_9_8.1_3_1_8  ) < 1e-2
								assert abs(result_mean.item() - 0.2_5_8_0  ) < 1e-3







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.full_loop(prediction_type='''v_prediction'''  )
								snake_case__						:       List[str]       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       int       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 6_7.3_9_8_6  ) < 1e-2
								assert abs(result_mean.item() - 0.0_8_7_8  ) < 1e-3







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.full_loop(set_alpha_to_one=snake_case_			,			beta_start=0.0_1  )
								snake_case__						:       List[str]       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       Optional[int]       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 2_3_0.0_3_9_9  ) < 1e-2
								assert abs(result_mean.item() - 0.2_9_9_5  ) < 1e-3







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.full_loop(set_alpha_to_one=snake_case_			,			beta_start=0.0_1  )
								snake_case__						:       Any       						=  torch.sum(torch.abs(snake_case_  )  )
								snake_case__						:       Tuple       						=  torch.mean(torch.abs(snake_case_  )  )

								assert abs(result_sum.item() - 1_8_6.9_4_8_2  ) < 1e-2
								assert abs(result_mean.item() - 0.2_4_3_4  ) < 1e-3






'''simple docstring'''


from collections.abc import Generator




def 	_a       (				):

		"""simple docstring"""

		snake_case__						,       snake_case__						:       List[Any]       						=  0, 1
		while True:
				snake_case__						,       snake_case__						:       str       						=  b, a + b
				yield b




def 	_a       (				__lowerCAmelCase		:      int = 10_00     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  1
		snake_case__						:       Tuple       						=  fibonacci_generator()
		while len(str(next(__lowerCAmelCase     )     )     ) < n:
				answer += 1
		return answer + 1


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



