





"""simple docstring"""







from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE_      =					20

# Time between time steps in seconds
SCREAMING_SNAKE_CASE_      =					INTERVAL / 1000

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = 1.0					,				lowerCamelCase_ = 1.0					,				lowerCamelCase_ = "blue"					,				)							->      None:
       UpperCamelCase										=  position_x
       UpperCamelCase										=  position_y
       UpperCamelCase										=  velocity_x
       UpperCamelCase										=  velocity_y
       UpperCamelCase										=  mass
       UpperCamelCase										=  size
       UpperCamelCase										=  color







   @property
   def      UpperCAmelCase__ (		self)							->      tuple[float, float]:
       return self.position_x, self.position_y







   @property
   def      UpperCAmelCase__ (		self)							->      tuple[float, float]:
       return self.velocity_x, self.velocity_y







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      None:
       self.velocity_x += force_x * delta_time
       self.velocity_y += force_y * delta_time







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      None:
       self.position_x += self.velocity_x * delta_time
       self.position_y += self.velocity_y * delta_time

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_ = 1.0					,				lowerCamelCase_ = 1.0					,				lowerCamelCase_ = 0.0					,				)							->      None:
       UpperCamelCase										=  bodies
       UpperCamelCase										=  gravitation_constant
       UpperCamelCase										=  time_factor
       UpperCamelCase										=  softening_factor







   def __len__(		self)							->      int:
       return len(self.bodies)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      None:
       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(SCREAMING_SNAKE_CASE_					,				SCREAMING_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     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    body_system.update_system(snake_case__						)

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





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase = -1 ,_lowercase = 1 ,_lowercase = -1 ,_lowercase = 1 ,):


    """simple docstring"""







    UpperCamelCase										=  plt.figure()
    fig.canvas.set_window_title(snake_case__						)
    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(snake_case__						)

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

    UpperCamelCase										=  animation.FuncAnimation(  # noqa: F841
        snake_case__ ,snake_case__ ,interval=snake_case__ ,blit=snake_case__						)

    plt.show()





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  0.9700436
    UpperCamelCase										=  -0.24308753
    UpperCamelCase										=  0.466203685
    UpperCamelCase										=  0.43236573

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





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  7.3476e22
    UpperCamelCase										=  5.972e24
    UpperCamelCase										=  1022
    UpperCamelCase										=  3_8439_9000
    UpperCamelCase										=  6.674e-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 ,snake_case__ ,snake_case__ ,1000_0000 ,'''grey'''						)
    UpperCamelCase										=  Body(0 ,0 ,0 ,snake_case__ ,snake_case__ ,5000_0000 ,'''blue'''						)
    return BodySystem([earth, moon] ,snake_case__ ,time_factor=100_0000						)





def     __snake_case   (							):


    """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						) ,snake_case__ ,snake_case__ ,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(snake_case__ ,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)





"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  num_frames
       UpperCamelCase										=  image_size
       UpperCamelCase										=  min_resolution
       UpperCamelCase										=  max_resolution
       UpperCamelCase										=  do_resize
       UpperCamelCase										=  size
       UpperCamelCase										=  do_normalize
       UpperCamelCase										=  image_mean
       UpperCamelCase										=  image_std
       UpperCamelCase										=  crop_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }

@require_torch
@require_vision
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict					,				size=4_2					,				crop_size=8_4)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 4_2})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 8_4, '''width''': 8_4})







   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				Image.Image)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				np.ndarray)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				torch.Tensor)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)