"""simple docstring"""





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    int   =  (IPNDMScheduler,)
      lowerCamelCase__		:    str   =  (("num_inference_steps", 5_0),)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					List[str]:
             lowercase__			:		int       =	{'num_train_timesteps': 1_0_0_0}
             config.update(**a							)
             return config


      def 		_UpperCAmelCase       (   self		,			a=0		,			**a							)       ->					Union[str, Any]:
             lowercase__			:		Tuple       =	dict(self.forward_default_kwargs							)
             lowercase__			:		List[Any]       =	kwargs.pop('num_inference_steps'		,			a							)
             lowercase__			:		Dict       =	self.dummy_sample
             lowercase__			:		Union[str, Any]       =	0.1 * sample
             lowercase__			:		Optional[Any]       =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		List[Any]       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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

                    lowercase__			:		Tuple       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		List[str]       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             pass


      def 		_UpperCAmelCase       (   self		,			a=0		,			**a							)       ->					List[Any]:
             lowercase__			:		List[str]       =	dict(self.forward_default_kwargs							)
             lowercase__			:		List[str]       =	kwargs.pop('num_inference_steps'		,			a							)
             lowercase__			:		Union[str, Any]       =	self.dummy_sample
             lowercase__			:		Tuple       =	0.1 * sample
             lowercase__			:		Optional[Any]       =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(a							)
                           lowercase__			:		Optional[Any]       =	scheduler_class.from_pretrained(a							)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(a							)

                           # copy over dummy past residual (must be after setting timesteps)
                           lowercase__			:		Union[str, Any]       =	dummy_past_residuals[:]

                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		int       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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

                    lowercase__			:		List[str]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		int       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Tuple:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		Tuple       =	self.get_scheduler_config(**a							)
             lowercase__			:		str       =	scheduler_class(**a							)

             lowercase__			:		Dict       =	1_0
             lowercase__			:		str       =	self.dummy_model()
             lowercase__			:		Optional[Any]       =	self.dummy_sample_deter
             scheduler.set_timesteps(a							)

             for i, t in enumerate(scheduler.timesteps							):
                    lowercase__			:		Any       =	model(a		,			a							)
                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a							).prev_sample

             for i, t in enumerate(scheduler.timesteps							):
                    lowercase__			:		str       =	model(a		,			a							)
                    lowercase__			:		Tuple       =	scheduler.step(a		,			a		,			a							).prev_sample

             return sample


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	dict(self.forward_default_kwargs							)

             lowercase__			:		Tuple       =	kwargs.pop('num_inference_steps'		,			a							)

             for scheduler_class in self.scheduler_classes:
                    lowercase__			:		List[Any]       =	self.get_scheduler_config()
                    lowercase__			:		Optional[int]       =	scheduler_class(**a							)

                    lowercase__			:		List[Any]       =	self.dummy_sample
                    lowercase__			:		int       =	0.1 * sample

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

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

                    lowercase__			:		Any       =	scheduler.timesteps[5]
                    lowercase__			:		str       =	scheduler.timesteps[6]

                    lowercase__			:		Optional[int]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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

                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample

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


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             for timesteps in [1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=a		,			time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             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=a		,			time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[str]       =	self.full_loop()
             lowercase__			:		Tuple       =	torch.mean(torch.abs(a							)							)

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



'''simple docstring'''







_lowercase :  int    							=       [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       0
						while number:
												# Increased Speed Slightly by checking every 5 digits together.
												sum_of_digits_squared += DIGITS_SQUARED[number % 100000]
												number //= 100000

						return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
_lowercase :  list[bool | None]    							=       [None] * 1_0_0_0_0_0_0_0
_lowercase :  List[str]    							=       True
_lowercase :  Optional[int]    							=       False
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if CHAINS[number - 1] is not None:
												return CHAINS[number - 1]  # type: ignore

						lowercase_	:     Tuple													=       chain(next_number(__SCREAMING_SNAKE_CASE							)							)
						lowercase_	:     Union[str, Any]													=       number_chain

						while number < 10000000:
												lowercase_	:     int													=       number_chain
												number *= 10

						return number_chain
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 10000000							):



						"""simple docstring"""





						for i in range(1      ,      __SCREAMING_SNAKE_CASE							):
												if CHAINS[i] is None:
																		chain(i + 1							)

						return CHAINS[:number].count(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"""{solution() = }""")



