




'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Optional[int]			  =      (PNDMScheduler,)
	lowercase_       :     Optional[int]			  =      (('''num_inference_steps''', 50),)




	def   lowercase__   (       self     :  Tuple       ,		**__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   {
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.0001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**__lowerCAmelCase      )
							return config




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  int=0       ,		**__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   dict(self.forward_default_kwargs      )
							__snake_case						 =   kwargs.pop('num_inference_steps'       ,		__lowerCAmelCase      )
							__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(**__lowerCAmelCase      )
													__snake_case						 =   scheduler_class(**__lowerCAmelCase      )
													scheduler.set_timesteps(__lowerCAmelCase      )
													# copy over dummy past residuals
													__snake_case						 =   dummy_past_residuals[:]

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

													__snake_case						 =   scheduler.step_prk(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   new_scheduler.step_prk(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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

													__snake_case						 =   scheduler.step_plms(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   new_scheduler.step_plms(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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




	def   lowercase__   (       self     :  str      ):
							pass




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  List[str]=0       ,		**__lowerCAmelCase     :  int      ):
							__snake_case						 =   dict(self.forward_default_kwargs      )
							__snake_case						 =   kwargs.pop('num_inference_steps'       ,		__lowerCAmelCase      )
							__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(**__lowerCAmelCase      )
													scheduler.set_timesteps(__lowerCAmelCase      )

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

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

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

													__snake_case						 =   scheduler.step_prk(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   new_scheduler.step_prk(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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

													__snake_case						 =   scheduler.step_plms(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   new_scheduler.step_plms(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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




	def   lowercase__   (       self     :  Optional[int]       ,		**__lowerCAmelCase     :  Dict      ):
							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config(**__lowerCAmelCase      )
							__snake_case						 =   scheduler_class(**__lowerCAmelCase      )

							__snake_case						 =   1_0
							__snake_case						 =   self.dummy_model()
							__snake_case						 =   self.dummy_sample_deter
							scheduler.set_timesteps(__lowerCAmelCase      )

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

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

							return sample




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   dict(self.forward_default_kwargs      )

							__snake_case						 =   kwargs.pop('num_inference_steps'       ,		__lowerCAmelCase      )

							for scheduler_class in self.scheduler_classes:
													__snake_case						 =   self.get_scheduler_config()
													__snake_case						 =   scheduler_class(**__lowerCAmelCase      )

													__snake_case						 =   self.dummy_sample
													__snake_case						 =   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'      ):
																			__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(__lowerCAmelCase       ,		0       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   scheduler.step_prk(__lowerCAmelCase       ,		1       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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

													__snake_case						 =   scheduler.step_plms(__lowerCAmelCase       ,		0       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample
													__snake_case						 =   scheduler.step_plms(__lowerCAmelCase       ,		1       ,		__lowerCAmelCase       ,		**__lowerCAmelCase      ).prev_sample

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




	def   lowercase__   (       self     :  Union[str, Any]      ):
							for timesteps in [1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[int]      ):
							for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=__lowerCAmelCase      )

							__snake_case						 =   self.scheduler_classes[0]
							__snake_case						 =   self.get_scheduler_config(steps_offset=1      )
							__snake_case						 =   scheduler_class(**__lowerCAmelCase      )
							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   lowercase__   (       self     :  Any      ):
							for beta_start, beta_end in zip([0.0001, 0.001]       ,		[0.002, 0.02]      ):
													self.check_over_configs(beta_start=__lowerCAmelCase       ,		beta_end=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Optional[int]      ):
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							for t in [1, 5, 1_0]:
													self.check_over_forward(time_step=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							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=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							__snake_case						 =   2_7

							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(**__lowerCAmelCase      )

													scheduler.set_timesteps(__lowerCAmelCase      )

													# 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(__lowerCAmelCase       ,		__lowerCAmelCase       ,		__lowerCAmelCase      ).prev_sample




	def   lowercase__   (       self     :  Any      ):
							with self.assertRaises(__lowerCAmelCase      ):
													__snake_case						 =   self.scheduler_classes[0]
													__snake_case						 =   self.get_scheduler_config()
													__snake_case						 =   scheduler_class(**__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.full_loop()
							__snake_case						 =   torch.sum(torch.abs(__lowerCAmelCase      )      )
							__snake_case						 =   torch.mean(torch.abs(__lowerCAmelCase      )      )

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




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.full_loop(prediction_type='v_prediction'      )
							__snake_case						 =   torch.sum(torch.abs(__lowerCAmelCase      )      )
							__snake_case						 =   torch.mean(torch.abs(__lowerCAmelCase      )      )

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




	def   lowercase__   (       self     :  Optional[int]      ):
							# We specify different beta, so that the first alpha is 0.99
							__snake_case						 =   self.full_loop(set_alpha_to_one=__lowerCAmelCase       ,		beta_start=0.01      )
							__snake_case						 =   torch.sum(torch.abs(__lowerCAmelCase      )      )
							__snake_case						 =   torch.mean(torch.abs(__lowerCAmelCase      )      )

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




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

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







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						__snake_case						 =   int(a							)
						if n_element < 1:
												__snake_case						 =   ValueError('a should be a positive number'							)
												raise my_error

						__snake_case						 =   [1]
						__snake_case ,       __snake_case ,       __snake_case						 =   (0, 0, 0)
						__snake_case						 =   1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2    ,	hamming_list[j] * 3    ,	hamming_list[k] * 5							)							)
												index += 1
						return hamming_list


if __name__ == "__main__":
	_lowercase  =			input("""Enter the last number (nth term) of the Hamming Number Series: """)
	print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
	_lowercase  =			hamming(int(n))
	print("""-----------------------------------------------------""")
	print(f'''The list with nth numbers is: {hamming_numbers}''')
	print("""-----------------------------------------------------""")

