'''simple docstring'''




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   List[str]  					=							(KDPMaAncestralDiscreteScheduler,)
       __lowercase   :   List[Any]  					=							10

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           A_      =  {
               '''num_train_timesteps''': 1100,
               '''beta_start''': 0.0_001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self					)						->	Dict:
           for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Union[str, Any]:
           for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,	[0.0_002, 0.002, 0.02]					):
               self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE       ,	beta_end=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[str]:
           for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Tuple:
           if torch_device == "mps":
               return
           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           scheduler.set_timesteps(self.num_inference_steps					)

           A_      =  torch.manual_seed(0					)

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter * scheduler.init_noise_sigma
           A_      =  sample.to(_SCREAMING_SNAKE_CASE					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE					)
               A_      =  output.prev_sample

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert abs(result_sum.item() - 13_849.3_877					) < 1E-2
           assert abs(result_mean.item() - 18.0_331					) < 5E-3

       def    __A			( self					)						->	Tuple:
           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[Any]:
           if torch_device == "mps":
               return
           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config(prediction_type='''v_prediction'''					)
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           scheduler.set_timesteps(self.num_inference_steps					)

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter * scheduler.init_noise_sigma
           A_      =  sample.to(_SCREAMING_SNAKE_CASE					)

           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE					)
               A_      =  output.prev_sample

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert abs(result_sum.item() - 328.9_970					) < 1E-2
           assert abs(result_mean.item() - 0.4_284					) < 1E-3

       def    __A			( self					)						->	Optional[Any]:
           if torch_device == "mps":
               return
           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           scheduler.set_timesteps(self.num_inference_steps       ,	device=_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter.to(_SCREAMING_SNAKE_CASE					) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
               A_      =  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE					)
               A_      =  output.prev_sample

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert abs(result_sum.item() - 13_849.3_818					) < 1E-1
           assert abs(result_mean.item() - 18.0_331					) < 1E-3







'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






