


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class       lowercase_					(lowercase__   ):
				snake_case	    =(PNDMScheduler,)
				snake_case	    =(('num_inference_steps', 50),)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> Optional[int]:
											a__      ={
											    'num_train_timesteps': 1000,
											    'beta_start': 0.00_01,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**lowercase_)
											return config





				def 						__UpperCamelCase    (				self       ,  lowercase_=0       ,  **lowercase_)  -> List[Any]:
											a__      =dict(self.forward_default_kwargs)
											a__      =kwargs.pop('num_inference_steps'       ,  lowercase_)
											a__      =self.dummy_sample
											a__      =0.1 * sample
											a__      =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																		a__      =scheduler.step_prk(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_).prev_sample
																		a__      =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"

																		a__      =scheduler.step_plms(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_).prev_sample
																		a__      =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 						__UpperCamelCase    (				self)  -> Tuple:
											pass





				def 						__UpperCamelCase    (				self       ,  lowercase_=0       ,  **lowercase_)  -> str:
											a__      =dict(self.forward_default_kwargs)
											a__      =kwargs.pop('num_inference_steps'       ,  lowercase_)
											a__      =self.dummy_sample
											a__      =0.1 * sample
											a__      =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(lowercase_)
																									a__      =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)
																									a__      =dummy_past_residuals[:]

																		a__      =scheduler.step_prk(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_).prev_sample
																		a__      =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"

																		a__      =scheduler.step_plms(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_).prev_sample
																		a__      =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 						__UpperCamelCase    (				self       ,  **lowercase_)  -> int:
											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config(**lowercase_)
											a__      =scheduler_class(**lowercase_)

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

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

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

											return sample





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =dict(self.forward_default_kwargs)

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

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

																		a__      =self.dummy_sample
																		a__      =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'):
																									a__      =num_inference_steps

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

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

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

																		a__      =scheduler.step_plms(lowercase_       ,  0       ,  lowercase_       ,  **lowercase_).prev_sample
																		a__      =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 						__UpperCamelCase    (				self)  -> str:
											for timesteps in [100, 1000]:
																		self.check_over_configs(num_train_timesteps=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=lowercase_)

											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config(steps_offset=1)
											a__      =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 						__UpperCamelCase    (				self)  -> List[str]:
											for beta_start, beta_end in zip([0.00_01, 0.0_01]       ,  [0.0_02, 0.02]):
																		self.check_over_configs(beta_start=lowercase_       ,  beta_end=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											for t in [1, 5, 10]:
																		self.check_over_forward(time_step=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											for t, num_inference_steps in zip([1, 5, 10]       ,  [10, 50, 100]):
																		self.check_over_forward(num_inference_steps=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
											a__      =27

											for scheduler_class in self.scheduler_classes:
																		a__      =self.dummy_sample
																		a__      =0.1 * sample

																		a__      =self.get_scheduler_config()
																		a__      =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]):
																									a__      =scheduler.step_prk(lowercase_       ,  lowercase_       ,  lowercase_).prev_sample





				def 						__UpperCamelCase    (				self)  -> Dict:
											with self.assertRaises(lowercase_):
																		a__      =self.scheduler_classes[0]
																		a__      =self.get_scheduler_config()
																		a__      =scheduler_class(**lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.full_loop()
											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 1_98.13_18) < 1e-2
											assert abs(result_mean.item() - 0.25_80) < 1e-3





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.full_loop(prediction_type='v_prediction')
											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 67.39_86) < 1e-2
											assert abs(result_mean.item() - 0.08_78) < 1e-3





				def 						__UpperCamelCase    (				self)  -> Tuple:
											# We specify different beta, so that the first alpha is 0.99
											a__      =self.full_loop(set_alpha_to_one=lowercase_       ,  beta_start=0.01)
											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 2_30.03_99) < 1e-2
											assert abs(result_mean.item() - 0.29_95) < 1e-3





				def 						__UpperCamelCase    (				self)  -> List[str]:
											# We specify different beta, so that the first alpha is 0.99
											a__      =self.full_loop(set_alpha_to_one=lowercase_       ,  beta_start=0.01)
											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 1_86.94_82) < 1e-2
											assert abs(result_mean.item() - 0.24_34) < 1e-3




from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							2
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[]
	while i * i <= n:
		if n % i:
			i += 1
		else:
			n //= i
			factors.append(_a)
	if n > 1:
		factors.append(_a)
	return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()