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


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import TvltImageProcessor





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=10		,       __lowerCAmelCase=10		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Union[str, Any]     	=[]
      for i in range(image_processor_tester.num_frames    ):
            video.append(np.random.randint(2_55		,       size=(image_processor_tester.num_channels, width, height)		,       dtype=np.uinta    )    )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            UpperCAmelCase     :			str     	=[Image.fromarray(np.moveaxis(__lowerCAmelCase		,       0		,       -1    )    ) for frame in video]

      if torchify:
            UpperCAmelCase     :			Any     	=[torch.from_numpy(__lowerCAmelCase    ) for frame in video]

      return video





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False		,       __lowerCAmelCase=False    )->	Optional[Any]:






      '''simple docstring'''





      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      UpperCAmelCase     :			str     	=[]
      for i in range(image_processor_tester.batch_size    ):
            if equal_resolution:
                  UpperCAmelCase     :			str     	=image_processor_tester.max_resolution
            else:
                  UpperCAmelCase	,    UpperCAmelCase     :			Dict     	=np.random.choice(
                      np.arange(image_processor_tester.min_resolution		,       image_processor_tester.max_resolution    )		,       2    )
                  UpperCAmelCase     :			Optional[Any]     	=prepare_video(
                      image_processor_tester=__lowerCAmelCase		,       width=__lowerCAmelCase		,       height=__lowerCAmelCase		,       numpify=__lowerCAmelCase		,       torchify=__lowerCAmelCase		,       )
            video_inputs.append(__lowerCAmelCase    )

      return video_inputs







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=7		, snake_case__=3		, snake_case__=4		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=True		, snake_case__=None		, )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=size if size is not None else {'''shortest_edge''': 18}
       UpperCAmelCase     :			List[str]     	=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       UpperCAmelCase     :			List[Any]     	=parent
       UpperCAmelCase     :			Union[str, Any]     	=batch_size
       UpperCAmelCase     :			Optional[int]     	=num_channels
       UpperCAmelCase     :			Tuple     	=num_frames
       UpperCAmelCase     :			Union[str, Any]     	=image_size
       UpperCAmelCase     :			Optional[Any]     	=min_resolution
       UpperCAmelCase     :			List[Any]     	=max_resolution
       UpperCAmelCase     :			Tuple     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Tuple     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			Dict     	=image_std
       UpperCAmelCase     :			Optional[int]     	=do_center_crop
       UpperCAmelCase     :			Tuple     	=crop_size






 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }







@require_torch
@require_vision
class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   TvltImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=TvltImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_center_crop'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL videos
       UpperCAmelCase     :			Any     	=prepare_video_inputs(self.image_processor_tester		, equal_resolution=snake_case__    )
       for video in video_inputs:
             self.assertIsInstance(snake_case__		, snake_case__    )
             self.assertIsInstance(video[0]		, Image.Image    )

       # Test not batched input
       UpperCAmelCase     :			List[Any]     	=image_processor(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     :			str     	=image_processor(snake_case__		, 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    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			Any     	=prepare_video_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, numpify=snake_case__    )
       for video in video_inputs:
             self.assertIsInstance(snake_case__		, snake_case__    )
             self.assertIsInstance(video[0]		, np.ndarray    )

       # Test not batched input
       UpperCAmelCase     :			List[str]     	=image_processor(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     :			Optional[Any]     	=image_processor(snake_case__		, 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    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			Union[str, Any]     	=prepare_video_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, torchify=snake_case__    )
       for video in video_inputs:
             self.assertIsInstance(snake_case__		, snake_case__    )
             self.assertIsInstance(video[0]		, torch.Tensor    )

       # Test not batched input
       UpperCAmelCase     :			Any     	=image_processor(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     :			Optional[Any]     	=image_processor(snake_case__		, 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'''],
           )		, )

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__snake_case         =     20

# Time between time steps in seconds
__snake_case         =     INTERVAL / 10_00







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__ = 1.0		, snake_case__ = 1.0		, snake_case__ = "blue"		, )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	=position_x
       UpperCAmelCase     :			Dict     	=position_y
       UpperCAmelCase     :			List[Any]     	=velocity_x
       UpperCAmelCase     :			Optional[int]     	=velocity_y
       UpperCAmelCase     :			List[str]     	=mass
       UpperCAmelCase     :			Any     	=size
       UpperCAmelCase     :			List[str]     	=color
 @property
 def 						UpperCAmelCase__							(	self    )						->				tuple[float, float]:
       '''simple docstring'''





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





       return self.velocity_x, self.velocity_y
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				None:
       '''simple docstring'''





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






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





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







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__ = 1.0		, snake_case__ = 1.0		, snake_case__ = 0.0		, )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=bodies
       UpperCAmelCase     :			List[str]     	=gravitation_constant
       UpperCAmelCase     :			int     	=time_factor
       UpperCAmelCase     :			List[Any]     	=softening_factor
 def __len__(	self    )						->				int:
       '''simple docstring'''





       return len(self.bodies    )






 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       for bodya in self.bodies:
             UpperCAmelCase     :			Tuple     	=0.0
             UpperCAmelCase     :			Union[str, Any]     	=0.0
             for bodya in self.bodies:
                   if bodya != bodya:
                         UpperCAmelCase     :			Optional[Any]     	=bodya.position_x - bodya.position_x
                         UpperCAmelCase     :			Any     	=bodya.position_y - bodya.position_y

                         # Calculation of the distance using Pythagoras's theorem
                         # Extra factor due to the softening technique
                         UpperCAmelCase     :			Optional[Any]     	=(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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	None:






      '''simple docstring'''





      body_system.update_system(__lowerCAmelCase    )

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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = 1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = 1		,       )->	None:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[Any]     	=plt.figure()
      fig.canvas.set_window_title(__lowerCAmelCase    )
      UpperCAmelCase     :			Dict     	=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     :			Tuple     	=[
          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(__lowerCAmelCase    )

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

      UpperCAmelCase     :			List[Any]     	=animation.FuncAnimation(  # noqa: F841
          __lowerCAmelCase		,       __lowerCAmelCase		,       interval=__lowerCAmelCase		,       blit=__lowerCAmelCase    )

      plt.show()





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=0.9700436
      UpperCAmelCase     :			Dict     	=-0.24308753
      UpperCAmelCase     :			List[Any]     	=0.466203685
      UpperCAmelCase     :			Dict     	=0.43236573

      UpperCAmelCase     :			Optional[Any]     	=[
          Body(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       size=0.2		,       color='''red'''    ),
          Body(-position_x		,       -position_y		,       __lowerCAmelCase		,       __lowerCAmelCase		,       size=0.2		,       color='''green'''    ),
          Body(0		,       0		,       -2 * velocity_x		,       -2 * velocity_y		,       size=0.2		,       color='''blue'''    ),
      ]
      return BodySystem(__lowerCAmelCase		,       time_factor=3    )





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			List[str]     	=7.3_4_7_6e2_2
      UpperCAmelCase     :			Tuple     	=5.9_7_2e2_4
      UpperCAmelCase     :			str     	=10_22
      UpperCAmelCase     :			Any     	=3_84_39_90_00
      UpperCAmelCase     :			List[Any]     	=6.6_7_4e-1_1

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

      UpperCAmelCase     :			Optional[int]     	=Body(-earth_moon_distance		,       0		,       0		,       __lowerCAmelCase		,       __lowerCAmelCase		,       10_00_00_00		,       '''grey'''    )
      UpperCAmelCase     :			Dict     	=Body(0		,       0		,       0		,       __lowerCAmelCase		,       __lowerCAmelCase		,       50_00_00_00		,       '''blue'''    )
      return BodySystem([earth, moon]		,       __lowerCAmelCase		,       time_factor=1_00_00_00    )





def 	lowerCAmelCase_			(					)->	BodySystem:






      '''simple docstring'''





      UpperCAmelCase     :			Optional[int]     	=[]
      for _ in range(10    ):
            UpperCAmelCase     :			Union[str, Any]     	=random.uniform(-0.5		,       0.5    )
            UpperCAmelCase     :			List[Any]     	=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    )		,       __lowerCAmelCase		,       __lowerCAmelCase		,       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(__lowerCAmelCase		,       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(),
         -4_30_00_00_00,
         4_30_00_00_00,
         -4_30_00_00_00,
         4_30_00_00_00,
     )
     plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)
