



'''simple docstring'''


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 	UpperCAmelCase	(		__a):


	'''simple docstring'''




	__magic_name__					: List[Any]  =      (PNDMScheduler,)
	__magic_name__					: Any  =      (("num_inference_steps", 50),)




	def       lowercase_  (   self							,  **lowerCAmelCase_) ->      List[str]:



						"""simple docstring"""







						a_       		={
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCAmelCase_)
						return config




	def       lowercase_  (   self							,  lowerCAmelCase_=0							,  **lowerCAmelCase_) ->      int:



						"""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 + 0.1_5, residual + 0.1, residual + 0.0_5]

						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       lowercase_  (   self) ->      Dict:



						"""simple docstring"""







						pass




	def       lowercase_  (   self							,  lowerCAmelCase_=0							,  **lowerCAmelCase_) ->      Dict:



						"""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 + 0.1_5, residual + 0.1, residual + 0.0_5]

						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       lowercase_  (   self							,  **lowerCAmelCase_) ->      Optional[Any]:



						"""simple docstring"""







						a_       		=self.scheduler_classes[0]
						a_       		=self.get_scheduler_config(**lowerCAmelCase_)
						a_       		=scheduler_class(**lowerCAmelCase_)

						a_       		=1_0
						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       lowercase_  (   self) ->      Union[str, Any]:



						"""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 + 0.1_5, residual + 0.1, residual + 0.0_5]
											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       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







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




	def       lowercase_  (   self) ->      Dict:



						"""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(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) ->      List[str]:



						"""simple docstring"""







						for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]							,  [0.0_0_2, 0.0_2]):
											self.check_over_configs(beta_start=lowerCAmelCase_							,  beta_end=lowerCAmelCase_)




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



						"""simple docstring"""







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




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



						"""simple docstring"""







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




	def       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







						for t in [1, 5, 1_0]:
											self.check_over_forward(time_step=lowerCAmelCase_)




	def       lowercase_  (   self) ->      str:



						"""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=lowerCAmelCase_)




	def       lowercase_  (   self) ->      Any:



						"""simple docstring"""







						a_       		=2_7

						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       lowercase_  (   self) ->      Any:



						"""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       lowercase_  (   self) ->      List[Any]:



						"""simple docstring"""







						a_       		=self.full_loop()
						a_       		=torch.sum(torch.abs(lowerCAmelCase_))
						a_       		=torch.mean(torch.abs(lowerCAmelCase_))

						assert abs(result_sum.item() - 1_9_8.1_3_1_8) < 1e-2
						assert abs(result_mean.item() - 0.2_5_8_0) < 1e-3




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



						"""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() - 6_7.3_9_8_6) < 1e-2
						assert abs(result_mean.item() - 0.0_8_7_8) < 1e-3




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



						"""simple docstring"""







						a_       		=self.full_loop(set_alpha_to_one=lowerCAmelCase_							,  beta_start=0.0_1)
						a_       		=torch.sum(torch.abs(lowerCAmelCase_))
						a_       		=torch.mean(torch.abs(lowerCAmelCase_))

						assert abs(result_sum.item() - 2_3_0.0_3_9_9) < 1e-2
						assert abs(result_mean.item() - 0.2_9_9_5) < 1e-3




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



						"""simple docstring"""







						a_       		=self.full_loop(set_alpha_to_one=lowerCAmelCase_							,  beta_start=0.0_1)
						a_       		=torch.sum(torch.abs(lowerCAmelCase_))
						a_       		=torch.mean(torch.abs(lowerCAmelCase_))

						assert abs(result_sum.item() - 1_8_6.9_4_8_2) < 1e-2
						assert abs(result_mean.item() - 0.2_4_3_4) < 1e-3







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


