




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
snake_case_					:					Dict       						=			20

# Time between time steps in seconds
snake_case_					:					str       						=			INTERVAL / 1000







class __snake_case     :







   def __init__( self						:		List[Any]   ,		_snake_case						:		float   ,		_snake_case						:		float   ,		_snake_case						:		float   ,		_snake_case						:		float   ,		_snake_case						:		float = 1.0   ,		_snake_case						:		float = 1.0   ,		_snake_case						:		str = "blue"   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				position_x
        UpperCAmelCase_		=				position_y
        UpperCAmelCase_		=				velocity_x
        UpperCAmelCase_		=				velocity_y
        UpperCAmelCase_		=				mass
        UpperCAmelCase_		=				size
        UpperCAmelCase_		=				color







   @property
   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        return self.position_x, self.position_y







   @property
   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        return self.velocity_x, self.velocity_y







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		float   ,		_snake_case						:		float   ,		_snake_case						:		float):

        """simple docstring"""


        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		float):

        """simple docstring"""


        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time







class __snake_case     :







   def __init__( self						:		Tuple   ,		_snake_case						:		list[Body]   ,		_snake_case						:		float = 1.0   ,		_snake_case						:		float = 1.0   ,		_snake_case						:		float = 0.0   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				bodies
        UpperCAmelCase_		=				gravitation_constant
        UpperCAmelCase_		=				time_factor
        UpperCAmelCase_		=				softening_factor







   def __len__( self						:		Tuple):

        """simple docstring"""


        return len(self.bodies)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		float):

        """simple docstring"""


        for bodya in self.bodies:
             UpperCAmelCase_		=				0.0
             UpperCAmelCase_		=				0.0
             for bodya in self.bodies:
                  if bodya != bodya:
                       UpperCAmelCase_		=				bodya.position_x - bodya.position_x
                       UpperCAmelCase_		=				bodya.position_y - bodya.position_y

                       # Calculation of the distance using Pythagoras's theorem
                       # Extra factor due to the softening technique
                       UpperCAmelCase_		=				(dif_x**2 + dif_y**2 + self.softening_factor) ** (
                           1 / 2
                       )

                       # Newton's law of universal gravitation.
                       force_x += (
                           self.gravitation_constant * bodya.mass * dif_x / distance**3
                       )
                       force_y += (
                           self.gravitation_constant * bodya.mass * dif_y / distance**3
                       )

            # Update the body's velocity once all the force components have been added
             bodya.update_velocity(_snake_case   ,		_snake_case   ,		delta_time * self.time_factor)

        # Update the positions only after all the velocities have been updated
        for body in self.bodies:
             body.update_position(delta_time * self.time_factor)







def   A		(__A  :       BodySystem  ,							__A  :       float  ,							__A  :       list[plt.Circle]			)  -> None:







     """simple docstring"""


     body_system.update_system(__A			)

     # Update the positions of the patches
     for patch, body in zip(__A  ,							body_system.bodies			):
          UpperCAmelCase_		=				(body.position_x, body.position_y)


def   A		(__A  :       str  ,							__A  :       BodySystem  ,							__A  :       float = -1  ,							__A  :       float = 1  ,							__A  :       float = -1  ,							__A  :       float = 1  ,							)  -> None:







     """simple docstring"""


     UpperCAmelCase_		=				plt.figure()
     fig.canvas.set_window_title(__A			)
     UpperCAmelCase_		=				plt.axes(
         xlim=(x_start, x_end)  ,							ylim=(y_start, y_end)			)  # Set section to be plotted
     plt.gca().set_aspect('''equal'''			)  # Fix aspect ratio

     # Each body is drawn as a patch by the plt-function
     UpperCAmelCase_		=				[
         plt.Circle((body.position_x, body.position_y)  ,							body.size  ,							fc=body.color			)
         for body in body_system.bodies
     ]

     for patch in patches:
          ax.add_patch(__A			)

     # Function called at each step of the animation
     def update(__A  :       int			) -> list[plt.Circle]:
          update_step(__A  ,							__A  ,							__A			)
          return patches

     UpperCAmelCase_		=				animation.FuncAnimation(  # noqa: F841
         __A  ,							__A  ,							interval=__A  ,							blit=__A			)

     plt.show()


def   A		()  -> BodySystem:







     """simple docstring"""


     UpperCAmelCase_		=				0.9_700_436
     UpperCAmelCase_		=				-0.24_308_753
     UpperCAmelCase_		=				0.466_203_685
     UpperCAmelCase_		=				0.43_236_573

     UpperCAmelCase_		=				[
         Body(__A  ,							__A  ,							__A  ,							__A  ,							size=0.2  ,							color='''red'''			),
         Body(-position_x  ,							-position_y  ,							__A  ,							__A  ,							size=0.2  ,							color='''green'''			),
         Body(0  ,							0  ,							-2 * velocity_x  ,							-2 * velocity_y  ,							size=0.2  ,							color='''blue'''			),
     ]
     return BodySystem(__A  ,							time_factor=3			)


def   A		()  -> BodySystem:







     """simple docstring"""


     UpperCAmelCase_		=				7.34_76E22
     UpperCAmelCase_		=				5.9_72E24
     UpperCAmelCase_		=				1022
     UpperCAmelCase_		=				384399000
     UpperCAmelCase_		=				6.6_74E-11

     # Calculation of the respective velocities so that total impulse is zero,
     # i.e. the two bodies together don't move
     UpperCAmelCase_		=				earth_mass * velocity_dif / (earth_mass + moon_mass)
     UpperCAmelCase_		=				moon_velocity - velocity_dif

     UpperCAmelCase_		=				Body(-earth_moon_distance  ,							0  ,							0  ,							__A  ,							__A  ,							10000000  ,							'''grey'''			)
     UpperCAmelCase_		=				Body(0  ,							0  ,							0  ,							__A  ,							__A  ,							50000000  ,							'''blue'''			)
     return BodySystem([earth, moon]  ,							__A  ,							time_factor=1000000			)


def   A		()  -> BodySystem:







     """simple docstring"""


     UpperCAmelCase_		=				[]
     for _ in range(10			):
          UpperCAmelCase_		=				random.uniform(-0.5  ,							0.5			)
          UpperCAmelCase_		=				random.uniform(-0.5  ,							0.5			)

          # Bodies are created pairwise with opposite velocities so that the
          # total impulse remains zero
          bodies.append(
              Body(
                  random.uniform(-0.5  ,							0.5			)  ,							random.uniform(-0.5  ,							0.5			)  ,							__A  ,							__A  ,							size=0.05  ,							)			)
          bodies.append(
              Body(
                  random.uniform(-0.5  ,							0.5			)  ,							random.uniform(-0.5  ,							0.5			)  ,							-velocity_x  ,							-velocity_y  ,							size=0.05  ,							)			)
     return BodySystem(__A  ,							0.01  ,							10  ,							0.1			)


if __name__ == "__main__":
    plot("Figure-8 solution to the 3-body-problem", example_a(), -2, 2, -2, 2)
    plot(
        "Moon's orbit around the earth",
        example_a(),
        -430000000,
        430000000,
        -430000000,
        430000000,
    )
    plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)












import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       str		=    PaintByExamplePipeline
   UpperCAmelCase__    :       Tuple		=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCAmelCase__    :       Any		=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCAmelCase__    :       Union[str, Any]		=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_		=				UNetaDConditionModel(
            block_out_channels=(32, 64)   ,		layers_per_block=2   ,		sample_size=32   ,		in_channels=9   ,		out_channels=4   ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,		cross_attention_dim=32   ,		)
        UpperCAmelCase_		=				PNDMScheduler(skip_prk_steps=_snake_case)
        torch.manual_seed(0)
        UpperCAmelCase_		=				AutoencoderKL(
            block_out_channels=[32, 64]   ,		in_channels=3   ,		out_channels=3   ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,		latent_channels=4   ,		)
        torch.manual_seed(0)
        UpperCAmelCase_		=				CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        UpperCAmelCase_		=				PaintByExampleImageEncoder(_snake_case   ,		proj_size=32)
        UpperCAmelCase_		=				CLIPImageProcessor(crop_size=32   ,		size=32)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				np.array(image.convert('''RGB'''))
        UpperCAmelCase_		=				image[None].transpose(0   ,		3   ,		1   ,		2)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
        return image







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Dict="cpu"   ,		_snake_case						:		int=0):

        """simple docstring"""


        UpperCAmelCase_		=				floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(image + 4)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((32, 32))

        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_		=				torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_		=				torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_		=				{
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**_snake_case)
        UpperCAmelCase_		=				pipe.to('''cpu''')
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				self.get_dummy_inputs()
        UpperCAmelCase_		=				pipe(**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_		=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3])

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







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				'''cpu'''
        UpperCAmelCase_		=				self.get_dummy_inputs()
        inputs.pop('''mask_image''')
        UpperCAmelCase_		=				self.convert_to_pt(inputs.pop('''image'''))
        UpperCAmelCase_		=				image.clamp(0   ,		1) / 2

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**self.get_dummy_components())
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				pipe(image=_snake_case   ,		mask_image=mask_image[:, 0]   ,		**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				mask_image.cpu().permute(0   ,		2   ,		3   ,		1)[0]

        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')

        UpperCAmelCase_		=				pipe(**self.get_dummy_inputs())
        UpperCAmelCase_		=				output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''')

        UpperCAmelCase_		=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(321)
        UpperCAmelCase_		=				pipe(
            image=_snake_case   ,		mask_image=_snake_case   ,		example_image=_snake_case   ,		generator=_snake_case   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_		=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4])

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






