


'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3








'''simple docstring'''




import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

_a      :      Optional[int]       				=      0.25 + 0.0J
_a      :      Dict       				=      -0.4 + 0.6J
_a      :      List[str]       				=      -0.1 + 0.651J
_a      :      Optional[int]       				=      -2.0
_a      :      List[str]       				=      56
_a      :      Tuple       				=      2.0
_a      :      List[str]       				=      666





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return numpy.exp(lowercase__	) + c_parameter





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return z_values * z_values + c_parameter





def      _a		(lowercase__ :      float       ,      lowercase__ :      int	) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	x.reshape((nb_pixels, 1)	)
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	y.reshape((1, nb_pixels)	)
     return x + 1.0J * y





def      _a		(lowercase__ :      Callable[[Any, numpy.ndarray], numpy.ndarray]       ,      lowercase__ :      Any       ,      lowercase__ :      int       ,      lowercase__ :      numpy.ndarray       ,      lowercase__ :      float | None = None       ,      ) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	z_a.astype('complex64'	)
     for _ in range(lowercase__	):
          __snake_case    			=	eval_function(lowercase__       ,      lowercase__	)
          if infinity is not None:
               numpy.nan_to_num(lowercase__       ,      copy=lowercase__       ,      nan=lowercase__	)
               __snake_case    			=	infinity
     return z_n





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      float       ,      lowercase__ :      numpy.ndarray       ,      ) -> None:






     """simple docstring"""
     __snake_case    			=	(abs(lowercase__	)).transpose()
     __snake_case    			=	abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius	)
     pyplot.title(f'Julia set of ${function_label}$, $c={function_params}$'	)
     pyplot.show()





def      _a		() -> None:






     """simple docstring"""
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in multiply'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='invalid value encountered in multiply'       ,      )
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in absolute'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in exp'	)


if __name__ == "__main__":
       _a      :      List[Any]       				=      prepare_grid(window_size, nb_pixels)

       ignore_overflow_warnings()  # See file header for explanations

       _a      :      Optional[Any]       				=      24
       _a      :      Optional[int]       				=      2 * abs(c_cauliflower) + 1
       _a      :      List[str]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_cauliflower,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_cauliflower, escape_radius, z_final)

       _a      :      str       				=      64
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      List[Any]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      List[Any]       				=      161
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      int       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      str       				=      12
       _a      :      Optional[Any]       				=      10_000.0
       _a      :      Dict       				=      iterate_function(
           eval_exponential,
           c_exponential,
           nb_iterations,
           z_a + 2,
           infinity=1.0e1_0,
       )
       show_results("e^z+c", c_exponential, escape_radius, z_final)




