
import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 		__magic_name__				( __lowerCamelCase					):


  """simple docstring"""





  _UpperCamelCase						 = (LMSDiscreteScheduler,)
  _UpperCamelCase						 = 10


  def     _UpperCAmelCase  (					self	,		**a__		):
    _lowerCamelCase      =						{
        '''num_train_timesteps''': 11_00,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**__lowerCAmelCase		)
    return config


  def     _UpperCAmelCase  (					self		):
    for timesteps in [10, 50, 1_00, 10_00]:
      self.check_over_configs(num_train_timesteps=__lowerCAmelCase		)


  def     _UpperCAmelCase  (					self		):
    for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,		[0.0002, 0.002, 0.02]		):
      self.check_over_configs(beta_start=__lowerCAmelCase	,		beta_end=__lowerCAmelCase		)


  def     _UpperCAmelCase  (					self		):
    for schedule in ["linear", "scaled_linear"]:
      self.check_over_configs(beta_schedule=__lowerCAmelCase		)


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


  def     _UpperCAmelCase  (					self		):
    for t in [0, 5_00, 8_00]:
      self.check_over_forward(time_step=__lowerCAmelCase		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config()
    _lowerCamelCase      =						scheduler_class(**__lowerCAmelCase		)

    scheduler.set_timesteps(self.num_inference_steps		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps		):
      _lowerCamelCase      =						scheduler.scale_model_input(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						model(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						scheduler.step(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(__lowerCAmelCase		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(__lowerCAmelCase		)		)

    assert abs(result_sum.item() - 1006.388		) < 1E-2
    assert abs(result_mean.item() - 1.31		) < 1E-3


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config(prediction_type='''v_prediction'''		)
    _lowerCamelCase      =						scheduler_class(**__lowerCAmelCase		)

    scheduler.set_timesteps(self.num_inference_steps		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(scheduler.timesteps		):
      _lowerCamelCase      =						scheduler.scale_model_input(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						model(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						scheduler.step(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(__lowerCAmelCase		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(__lowerCAmelCase		)		)

    assert abs(result_sum.item() - 0.0017		) < 1E-2
    assert abs(result_mean.item() - 2.2676E-06		) < 1E-3


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config()
    _lowerCamelCase      =						scheduler_class(**__lowerCAmelCase		)

    scheduler.set_timesteps(self.num_inference_steps	,		device=__lowerCAmelCase		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    _lowerCamelCase      =						sample.to(__lowerCAmelCase		)

    for i, t in enumerate(scheduler.timesteps		):
      _lowerCamelCase      =						scheduler.scale_model_input(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						model(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						scheduler.step(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(__lowerCAmelCase		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(__lowerCAmelCase		)		)

    assert abs(result_sum.item() - 1006.388		) < 1E-2
    assert abs(result_mean.item() - 1.31		) < 1E-3


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.scheduler_classes[0]
    _lowerCamelCase      =						self.get_scheduler_config()
    _lowerCamelCase      =						scheduler_class(**__lowerCAmelCase	,		use_karras_sigmas=__lowerCAmelCase		)

    scheduler.set_timesteps(self.num_inference_steps	,		device=__lowerCAmelCase		)

    _lowerCamelCase      =						self.dummy_model()
    _lowerCamelCase      =						self.dummy_sample_deter.to(__lowerCAmelCase		) * scheduler.init_noise_sigma
    _lowerCamelCase      =						sample.to(__lowerCAmelCase		)

    for t in scheduler.timesteps:
      _lowerCamelCase      =						scheduler.scale_model_input(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						model(__lowerCAmelCase	,		__lowerCAmelCase		)

      _lowerCamelCase      =						scheduler.step(__lowerCAmelCase	,		__lowerCAmelCase	,		__lowerCAmelCase		)
      _lowerCamelCase      =						output.prev_sample

    _lowerCamelCase      =						torch.sum(torch.abs(__lowerCAmelCase		)		)
    _lowerCamelCase      =						torch.mean(torch.abs(__lowerCAmelCase		)		)

    assert abs(result_sum.item() - 3812.9927		) < 2E-2
    assert abs(result_mean.item() - 4.9648		) < 1E-3





'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






