






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

import numpy
from matplotlib import pyplot

a__      =		0.25 + 0.0J
a__      =		-0.4 + 0.6J
a__      =		-0.1 + 0.651J
a__      =		-2.0
a__      =		56
a__      =		2.0
a__      =		6_66



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		complex			,    SCREAMING_SNAKE_CASE__    :		numpy.ndarray			)					->       numpy.ndarray:
 return numpy.exp(SCREAMING_SNAKE_CASE__			) + c_parameter



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		complex			,    SCREAMING_SNAKE_CASE__    :		numpy.ndarray			)					->       numpy.ndarray:
 return z_values * z_values + c_parameter



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		float			,    SCREAMING_SNAKE_CASE__    :		int			)					->       numpy.ndarray:
 _snake_case      :					str					=   numpy.linspace(-window_size			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 _snake_case      :					Any					=   x.reshape((nb_pixels, 1)			)
 _snake_case      :					Tuple					=   numpy.linspace(-window_size			,    SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
 _snake_case      :					List[Any]					=   y.reshape((1, nb_pixels)			)
 return x + 1.0j * y



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Callable[[Any, numpy.ndarray], numpy.ndarray]			,    SCREAMING_SNAKE_CASE__    :		Any			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		numpy.ndarray			,    SCREAMING_SNAKE_CASE__    :		float | None = None			,    )					->       numpy.ndarray:
 _snake_case      :					Optional[Any]					=   z_a.astype("""complex64"""			)
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case      :					str					=   eval_function(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
  if infinity is not None:
   numpy.nan_to_num(SCREAMING_SNAKE_CASE__			,    copy=SCREAMING_SNAKE_CASE__			,    nan=SCREAMING_SNAKE_CASE__			)
   _snake_case      :					List[str]					=   infinity
 return z_n



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		str			,    SCREAMING_SNAKE_CASE__    :		Any			,    SCREAMING_SNAKE_CASE__    :		float			,    SCREAMING_SNAKE_CASE__    :		numpy.ndarray			,    )					->       None:
 _snake_case      :					Optional[int]					=   (abs(SCREAMING_SNAKE_CASE__			)).transpose()
 _snake_case      :					Dict					=   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  lowercase    (				)					->       None:
 warnings.filterwarnings(
     """ignore"""			,    category=SCREAMING_SNAKE_CASE__			,    message="""overflow encountered in multiply"""			)
 warnings.filterwarnings(
     """ignore"""			,    category=SCREAMING_SNAKE_CASE__			,    message="""invalid value encountered in multiply"""			,    )
 warnings.filterwarnings(
     """ignore"""			,    category=SCREAMING_SNAKE_CASE__			,    message="""overflow encountered in absolute"""			)
 warnings.filterwarnings(
     """ignore"""			,    category=SCREAMING_SNAKE_CASE__			,    message="""overflow encountered in exp"""			)


if __name__ == "__main__":
  a__      =		prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  a__      =		24
  a__      =		2 * abs(c_cauliflower) + 1
  a__      =		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__      =		64
  a__      =		2 * abs(c_polynomial_a) + 1
  a__      =		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__      =		1_61
  a__      =		2 * abs(c_polynomial_a) + 1
  a__      =		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__      =		12
  a__      =		1_00_00.0
  a__      =		iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0E10,
  )
  show_results("""e^z+c""", c_exponential, escape_radius, z_final)





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







@slow
@require_torch_gpu
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
