

"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  (IPNDMScheduler,)
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  (("""num_inference_steps""", 50),)

				def 							lowercase_					(     self							:    Optional[int]					,  **UpperCamelCase__							:    Union[str, Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      {"num_train_timesteps": 1_0_0_0}
						config.update(**UpperCamelCase__)
						return config

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Optional[Any]=0					,  **UpperCamelCase__							:    int)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      dict(self.forward_default_kwargs)
						__lowerCAmelCase:	Dict   				=      kwargs.pop("num_inference_steps"					,  UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.dummy_sample
						__lowerCAmelCase:	List[str]   				=      0.1 * sample
						__lowerCAmelCase:	int   				=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

								if time_step is None:
										__lowerCAmelCase:	Optional[Any]   				=      scheduler.timesteps[len(scheduler.timesteps) // 2]

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

								__lowerCAmelCase:	Union[str, Any]   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	int   				=      new_scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

								__lowerCAmelCase:	List[str]   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	Optional[int]   				=      new_scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

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



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Union[str, Any]=0					,  **UpperCamelCase__							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      dict(self.forward_default_kwargs)
						__lowerCAmelCase:	Dict   				=      kwargs.pop("num_inference_steps"					,  UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      self.dummy_sample
						__lowerCAmelCase:	Optional[Any]   				=      0.1 * sample
						__lowerCAmelCase:	Any   				=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
								__lowerCAmelCase:	List[Any]   				=      self.get_scheduler_config()
								__lowerCAmelCase:	Any   				=      scheduler_class(**UpperCamelCase__)
								scheduler.set_timesteps(UpperCamelCase__)

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

								if time_step is None:
										__lowerCAmelCase:	Optional[Any]   				=      scheduler.timesteps[len(scheduler.timesteps) // 2]

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

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

								__lowerCAmelCase:	Any   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	int   				=      new_scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

								__lowerCAmelCase:	Tuple   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	List[Any]   				=      new_scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

				def 							lowercase_					(     self							:    Dict					,  **UpperCamelCase__							:    Tuple)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.scheduler_classes[0]
						__lowerCAmelCase:	Optional[int]   				=      self.get_scheduler_config(**UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      scheduler_class(**UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      1_0
						__lowerCAmelCase:	Any   				=      self.dummy_model()
						__lowerCAmelCase:	Any   				=      self.dummy_sample_deter
						scheduler.set_timesteps(UpperCamelCase__)

						for i, t in enumerate(scheduler.timesteps):
								__lowerCAmelCase:	List[Any]   				=      model(UpperCamelCase__					,  UpperCamelCase__)
								__lowerCAmelCase:	Optional[int]   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__).prev_sample

						for i, t in enumerate(scheduler.timesteps):
								__lowerCAmelCase:	List[Any]   				=      model(UpperCamelCase__					,  UpperCamelCase__)
								__lowerCAmelCase:	str   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__).prev_sample

						return sample

				def 							lowercase_					(     self							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      dict(self.forward_default_kwargs)

						__lowerCAmelCase:	Optional[Any]   				=      kwargs.pop("num_inference_steps"					,  UpperCamelCase__)

						for scheduler_class in self.scheduler_classes:
								__lowerCAmelCase:	Dict   				=      self.get_scheduler_config()
								__lowerCAmelCase:	Any   				=      scheduler_class(**UpperCamelCase__)

								__lowerCAmelCase:	Optional[int]   				=      self.dummy_sample
								__lowerCAmelCase:	Optional[Any]   				=      0.1 * sample

								if num_inference_steps is not None and hasattr(UpperCamelCase__					,  "set_timesteps"):
										scheduler.set_timesteps(UpperCamelCase__)
								elif num_inference_steps is not None and not hasattr(UpperCamelCase__					,  "set_timesteps"):
										__lowerCAmelCase:	Union[str, Any]   				=      num_inference_steps

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

								__lowerCAmelCase:	int   				=      scheduler.timesteps[5]
								__lowerCAmelCase:	List[str]   				=      scheduler.timesteps[6]

								__lowerCAmelCase:	str   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	int   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

								__lowerCAmelCase:	Union[str, Any]   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample
								__lowerCAmelCase:	Dict   				=      scheduler.step(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__).prev_sample

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

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



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Any)->       Any:



						'''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=UpperCamelCase__					,  time_step=UpperCamelCase__)




				def 							lowercase_					(     self							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.full_loop()
						__lowerCAmelCase:	str   				=      torch.mean(torch.abs(UpperCamelCase__))

						assert abs(result_mean.item() - 2_5_4_0_5_2_9) < 1_0





"""simple docstring"""


import math
import sys






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if number != int(__SCREAMING_SNAKE_CASE			):
				raise ValueError("the value of input must be a natural number"			)
		if number < 0:
				raise ValueError("the value of input must not be a negative number"			)
		if number == 0:
				return 1
		__lowerCAmelCase:	int   				=      [-1] * (number + 1)
		__lowerCAmelCase:	Optional[Any]   				=      0
		for i in range(1 ,			number + 1			):
				__lowerCAmelCase:	List[str]   				=      sys.maxsize
				__lowerCAmelCase:	int   				=      int(math.sqrt(__SCREAMING_SNAKE_CASE			)			)
				for j in range(1 ,			root + 1			):
						__lowerCAmelCase:	Optional[Any]   				=      1 + answers[i - (j**2)]
						__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[Any]   				=      answer
		return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


