



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 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=10      ,					_UpperCamelCase=10      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False			):
				_UpperCAmelCase						:  Union[str, Any]     					=	[]
				for i in range(image_processor_tester.num_frames			):
								video.append(np.random.randint(255      ,					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 	_A	(   _UpperCamelCase      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False      ,					_UpperCamelCase=False			):
				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						:  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 							lowerCAmelCase_		(		unittest.TestCase     ):







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   List[Any]       ,		UpperCAmelCase_  :   Optional[int]=7       ,		UpperCAmelCase_  :   Union[str, Any]=3       ,		UpperCAmelCase_  :   Dict=4       ,		UpperCAmelCase_  :   Union[str, Any]=18       ,		UpperCAmelCase_  :   int=30       ,		UpperCAmelCase_  :   str=400       ,		UpperCAmelCase_  :   Any=True       ,		UpperCAmelCase_  :   Dict=None       ,		UpperCAmelCase_  :   List[Any]=True       ,		UpperCAmelCase_  :   int=[0.5, 0.5, 0.5]       ,		UpperCAmelCase_  :   Dict=[0.5, 0.5, 0.5]       ,		UpperCAmelCase_  :   Union[str, Any]=True       ,		UpperCAmelCase_  :   Union[str, Any]=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 					a_  (    self  :   Tuple					)       ->		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 							lowerCAmelCase_		(		lowerCamelCase__							,		unittest.TestCase     ):
		SCREAMING_SNAKE_CASE_       :			Any					       =	TvltImageProcessor if is_vision_available() else None







		def 					a_  (    self  :   Optional[int]					)       ->		List[str]:


						'''simple docstring'''







						_UpperCAmelCase						:  Union[str, Any]     					=	TvltImageProcessorTester(self					)







		@property
		def 					a_  (    self  :   Tuple					)       ->		Any:


						'''simple docstring'''







						return self.image_processor_tester.prepare_image_processor_dict()







		def 					a_  (    self  :   Tuple					)       ->		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 					a_  (    self  :   Dict					)       ->		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 					a_  (    self  :   str					)       ->		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 					a_  (    self  :   List[Any]					)       ->		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)
