


import colorsys

from PIL import Image  # type: ignore



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							str      		=		x
    _lowercase       :							int      		=		y
    for step in range(lowerCamelCase_ ):  # noqa: B007
        _lowercase       :							Optional[Any]      		=		a * a - b * b + x
        _lowercase       :							List[str]      		=		2 * a * b + y
        _lowercase       :							Optional[Any]      		=		a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(lowerCamelCase_							, 1							, 1 ) )



def 				UpperCamelCase_(      lowerCamelCase_ = 800							, lowerCamelCase_ = 600							, lowerCamelCase_ = -0.6							, lowerCamelCase_ = 0							, lowerCamelCase_ = 3.2							, lowerCamelCase_ = 50							, lowerCamelCase_ = True							, )    ->						Image.Image:
    _lowercase       :							Optional[Any]      		=		Image.new('RGB'							, (image_width, image_height) )
    _lowercase       :							str      		=		img.load()

    # loop through the image-coordinates
    for image_x in range(lowerCamelCase_ ):
        for image_y in range(lowerCamelCase_ ):
            # determine the figure-coordinates based on the image-coordinates
            _lowercase       :							Dict      		=		figure_width / image_width * image_height
            _lowercase       :							Union[str, Any]      		=		figure_center_x + (image_x / image_width - 0.5) * figure_width
            _lowercase       :							List[Any]      		=		figure_center_y + (image_y / image_height - 0.5) * figure_height

            _lowercase       :							Any      		=		get_distance(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                _lowercase       :							Dict      		=		get_color_coded_rgb(lowerCamelCase_ )
            else:
                _lowercase       :							Any      		=		get_black_and_white_rgb(lowerCamelCase_ )

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 SCREAMING_SNAKE_CASE       :      int												= get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()






import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class _lowerCamelCase(						_a, unittest.TestCase   ):
       # TODO: is there an appropriate internal test set?
       lowercase_ :	int     =			"""ssube/stable-diffusion-x4-upscaler-onnx"""
       def 			UpperCamelCase     (					self,      lowerCamelCase=0)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		floats_tensor((1, 3, 1_28, 1_28),      rng=random.Random(lowerCamelCase))
           _lowercase       :							Union[str, Any]      		=		torch.manual_seed(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		{
               'prompt': 'A painting of a squirrel eating a burger',
               'image': image,
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Dict      		=		self.get_dummy_inputs()
           _lowercase       :							Optional[int]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3])
           assert np.abs(image_slice - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							str      		=		PNDMScheduler.from_config(pipe.scheduler.config,      skip_prk_steps=lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		self.get_dummy_inputs()
           _lowercase       :							List[Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							str      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							int      		=		np.array(
               [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           _lowercase       :							Dict      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Optional[int]      		=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		self.get_dummy_inputs()
           _lowercase       :							Union[str, Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							str      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Optional[int]      		=		np.array(
               [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							List[str]      		=		EulerDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Dict      		=		self.get_dummy_inputs()
           _lowercase       :							Optional[Any]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1




       def 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,      provider='CPUExecutionProvider')
           _lowercase       :							Any      		=		EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Any      		=		self.get_dummy_inputs()
           _lowercase       :							List[str]      		=		pipe(**lowerCamelCase).images
           _lowercase       :							Any      		=		image[0, -3:, -3:, -1]

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3])

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






@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		ort.SessionOptions()
           _lowercase       :							str      		=		False
           return options
       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           # using the PNDM scheduler by default
           _lowercase       :							Tuple      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=10,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[Any]      		=		output.images
           _lowercase       :							List[Any]      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							List[Any]      		=		np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




       def 			UpperCamelCase     (					self)       ->   Any:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							int      		=		init_image.resize((1_28, 1_28))
           _lowercase       :							str      		=		LMSDiscreteScheduler.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      subfolder='scheduler')
           _lowercase       :							Dict      		=		OnnxStableDiffusionUpscalePipeline.from_pretrained(
               'ssube/stable-diffusion-x4-upscaler-onnx',      scheduler=lowerCamelCase,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[int]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							str      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      guidance_scale=7.5,      num_inference_steps=20,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							str      		=		output.images
           _lowercase       :							str      		=		images[0, 2_55:2_58, 3_83:3_86, -1]

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase       :							Union[str, Any]      		=		np.array(
               [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


