





'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

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

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

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

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


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

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

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

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

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

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

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

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

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

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





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

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

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

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

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

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

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










'''simple docstring'''

import timeit


class  a_					:

    '''simple docstring'''


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





    def __mul__(		self			,		A	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      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 lowerCamelCase						(					__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Optional[int]   )						->int:
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 0], [0, 1]]   )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix





def lowerCamelCase						(					__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Tuple   )						->Dict:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    _SCREAMING_SNAKE_CASE     =      Matrix([[1, 1], [1, 0]]   )
    _SCREAMING_SNAKE_CASE     =      modular_exponentiation(__lowerCamelCase ,	n - 2   )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   Tuple   )						->str:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    _SCREAMING_SNAKE_CASE     =      fa
    _SCREAMING_SNAKE_CASE     =      fa
    n -= 2

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

    return fn_a





def lowerCamelCase						(					)						->str:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
    _SCREAMING_SNAKE_CASE     =      """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print("""With matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      """
from random import randint
from __main__ import simple_fibonacci
"""
    _SCREAMING_SNAKE_CASE     =      """simple_fibonacci(randint(1,70000), 1, 1)"""
    _SCREAMING_SNAKE_CASE     =      timeit.timeit(setup=__lowerCamelCase ,	stmt=__lowerCamelCase ,	number=100   )
    print(
        """Without matrix exponentiation the average execution time is """ ,	exec_time / 100   )
    return exec_time





def lowerCamelCase						(					)						->List[str]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
      main()



