



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(PNDMScheduler,)
	__A							=							(("num_inference_steps", 50),)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		,       lowerCamelCase_=0		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   dict(self.forward_default_kwargs		)
						a					     =   kwargs.pop("num_inference_steps"		,       lowerCamelCase_		)
						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(**lowerCamelCase_		)
											a					     =   scheduler_class(**lowerCamelCase_		)
											scheduler.set_timesteps(lowerCamelCase_		)
											# copy over dummy past residuals
											a					     =   dummy_past_residuals[:]

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

											a					     =   scheduler.step_prk(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   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"

											a					     =   scheduler.step_plms(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		,       lowerCamelCase_=0		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   dict(self.forward_default_kwargs		)
						a					     =   kwargs.pop("num_inference_steps"		,       lowerCamelCase_		)
						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(**lowerCamelCase_		)
											scheduler.set_timesteps(lowerCamelCase_		)

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

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase_		)
																a					     =   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)
																a					     =   dummy_past_residuals[:]

											a					     =   scheduler.step_prk(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   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"

											a					     =   scheduler.step_plms(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   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      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(**lowerCamelCase_		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   10
						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						scheduler.set_timesteps(lowerCamelCase_		)

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

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

						return sample




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   dict(self.forward_default_kwargs		)

						a					     =   kwargs.pop("num_inference_steps"		,       lowerCamelCase_		)

						for scheduler_class in self.scheduler_classes:
											a					     =   self.get_scheduler_config()
											a					     =   scheduler_class(**lowerCamelCase_		)

											a					     =   self.dummy_sample
											a					     =   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"		):
																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(lowerCamelCase_		,       0		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   scheduler.step_prk(lowerCamelCase_		,       1		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample

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

											a					     =   scheduler.step_plms(lowerCamelCase_		,       0		,       lowerCamelCase_		,       **lowerCamelCase_		).prev_sample
											a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(steps_offset=1		)
						a					     =   scheduler_class(**lowerCamelCase_		)
						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		):
						"""simple docstring"""

						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      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [1, 5, 10]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t, num_inference_steps in zip([1, 5, 10]		,       [10, 50, 100]		):
											self.check_over_forward(num_inference_steps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						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(**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]		):
																a					     =   scheduler.step_prk(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						with self.assertRaises(lowerCamelCase_		):
											a					     =   self.scheduler_classes[0]
											a					     =   self.get_scheduler_config()
											a					     =   scheduler_class(**lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.full_loop()
						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.full_loop(prediction_type="v_prediction"		)
						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.full_loop(set_alpha_to_one=lowerCamelCase_		,       beta_start=0.01		)
						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.full_loop(set_alpha_to_one=lowerCamelCase_		,       beta_start=0.01		)
						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

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





import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
