




'''simple docstring'''
import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A				: Any					      =			0.25 + 0.0j
A				: int					      =			-0.4 + 0.6j
A				: str					      =			-0.1 + 0.651j
A				: Dict					      =			-2.0
A				: Dict					      =			56
A				: Tuple					      =			2.0
A				: Dict					      =			666
def 							snake_case_    (  a__						:      Any       ,a__						:      Optional[int]     ):



    """simple docstring"""




    return numpy.exp(A__     ) + c_parameter
def 							snake_case_    (  a__						:      Union[str, Any]       ,a__						:      str     ):



    """simple docstring"""




    return z_values * z_values + c_parameter
def 							snake_case_    (  a__						:      int       ,a__						:      Dict     ):



    """simple docstring"""




    __lowercase     							=		numpy.linspace(-window_size       ,A__       ,A__     )
    __lowercase     							=		x.reshape((nb_pixels, 1)     )
    __lowercase     							=		numpy.linspace(-window_size       ,A__       ,A__     )
    __lowercase     							=		y.reshape((1, nb_pixels)     )
    return x + 1.0J * y
def 							snake_case_    (  a__						:      Optional[int]       ,a__						:      Optional[int]       ,a__						:      List[Any]       ,a__						:      str       ,a__						:      Any = None       ,):



    """simple docstring"""




    __lowercase     							=		z_a.astype("""complex64"""     )
    for _ in range(A__     ):
        __lowercase     							=		eval_function(A__       ,A__     )
        if infinity is not None:
            numpy.nan_to_num(A__       ,copy=A__       ,nan=A__     )
            __lowercase     							=		infinity
    return z_n
def 							snake_case_    (  a__						:      List[str]       ,a__						:      Optional[int]       ,a__						:      List[Any]       ,a__						:      Any       ,):



    """simple docstring"""




    __lowercase     							=		(abs(A__     )).transpose()
    __lowercase     							=		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 							snake_case_    (  ):



    """simple docstring"""




    warnings.filterwarnings(
        """ignore"""       ,category=A__       ,message="""overflow encountered in multiply"""     )
    warnings.filterwarnings(
        """ignore"""       ,category=A__       ,message="""invalid value encountered in multiply"""       ,)
    warnings.filterwarnings(
        """ignore"""       ,category=A__       ,message="""overflow encountered in absolute"""     )
    warnings.filterwarnings(
        """ignore"""       ,category=A__       ,message="""overflow encountered in exp"""     )


if __name__ == "__main__":
  A				: Optional[int]					      =			prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  A				: Optional[int]					      =			24
  A				: Optional[Any]					      =			2 * abs(c_cauliflower) + 1
  A				: 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				: int					      =			64
  A				: Optional[int]					      =			2 * abs(c_polynomial_a) + 1
  A				: Optional[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				: Any					      =			161
  A				: int					      =			2 * abs(c_polynomial_a) + 1
  A				: Union[str, 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				: int					      =			12
  A				: Tuple					      =			10_000.0
  A				: Union[str, Any]					      =			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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__	:  Union[str, Any]						=  False





@nightly
@require_torch_gpu
class 					__snake_case    (     unittest.TestCase      ):




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


