
"""simple docstring"""




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


A__							=   False


class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self ,				A_=32       )     ->	Tuple:





         """simple docstring"""




         set_seed(0       )
         _lowercase:			str       =     UNetaDModel(sample_size=A_ ,				in_channels=3 ,				out_channels=3       )
         _lowercase:			Tuple       =     torch.optim.SGD(model.parameters() ,				lr=0.00_01       )
         return model, optimizer



       @slow
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Any       =     '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
         _lowercase:			List[Any]       =     DDPMScheduler(
             num_train_timesteps=1000 ,				beta_start=0.00_01 ,				beta_end=0.02 ,				beta_schedule='''linear''' ,				clip_sample=A_ ,				)
         _lowercase:			Dict       =     DDIMScheduler(
             num_train_timesteps=1000 ,				beta_start=0.00_01 ,				beta_end=0.02 ,				beta_schedule='''linear''' ,				clip_sample=A_ ,				)

         assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

         # shared batches for DDPM and DDIM
         set_seed(0       )
         _lowercase:			int       =     [torch.randn((4, 3, 32, 32)       ).clip(-1 ,				1       ).to(A_       ) for _ in range(4       )]
         _lowercase:			str       =     [torch.randn((4, 3, 32, 32)       ).to(A_       ) for _ in range(4       )]
         _lowercase:			List[str]       =     [torch.randint(0 ,				1000 ,				(4,)       ).long().to(A_       ) for _ in range(4       )]

         # train with a DDPM scheduler
         _lowercase:			Any       =     self.get_model_optimizer(resolution=32       )
         model.train().to(A_       )
         for i in range(4       ):
           optimizer.zero_grad()
           _lowercase:			Any       =     ddpm_scheduler.add_noise(clean_images[i] ,				noise[i] ,				timesteps[i]       )
           _lowercase:			str       =     model(A_ ,				timesteps[i]       ).sample
           _lowercase:			Dict       =     torch.nn.functional.mse_loss(A_ ,				noise[i]       )
           loss.backward()
           optimizer.step()
         del model, optimizer

         # recreate the model and optimizer, and retry with DDIM
         _lowercase:			Optional[Any]       =     self.get_model_optimizer(resolution=32       )
         model.train().to(A_       )
         for i in range(4       ):
           optimizer.zero_grad()
           _lowercase:			int       =     ddim_scheduler.add_noise(clean_images[i] ,				noise[i] ,				timesteps[i]       )
           _lowercase:			List[str]       =     model(A_ ,				timesteps[i]       ).sample
           _lowercase:			Union[str, Any]       =     torch.nn.functional.mse_loss(A_ ,				noise[i]       )
           loss.backward()
           optimizer.step()
         del model, optimizer

         self.assertTrue(torch.allclose(A_ ,				A_ ,				atol=1E-5       )       )
         self.assertTrue(torch.allclose(A_ ,				A_ ,				atol=1E-5       )       )


"""simple docstring"""




from typing import Any

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     v.conjugate().T
  _lowercase:			List[Any]       =     v_star.dot(_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				np.ndarray						)
  return (v_star_dot.dot(_UpperCamelCase						)) / (v_star.dot(_UpperCamelCase						))




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]						)
  _lowercase:			str       =     np.array([[1], [2], [3]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  print(rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						)						)

  _lowercase:			Any       =     np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  assert rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						) == float(3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()
