


'''simple docstring'''



from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__UpperCAmelCase     :Dict												=  2_0

# Time between time steps in seconds
__UpperCAmelCase     :str												=  INTERVAL / 1_0_0_0



class       a							:


     """simple docstring"""






     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"				,					)		->				List[str]:
          __UpperCAmelCase      :					Dict    				=     position_x
          __UpperCAmelCase      :					Tuple    				=     position_y
          __UpperCAmelCase      :					Any    				=     velocity_x
          __UpperCAmelCase      :					str    				=     velocity_y
          __UpperCAmelCase      :					List[Any]    				=     mass
          __UpperCAmelCase      :					int    				=     size
          __UpperCAmelCase      :					Union[str, Any]    				=     color

     @property
     def 					lowerCamelCase__					(							self	:						Optional[int]     )		->				List[Any]:
          return self.position_x, self.position_y

     @property
     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				List[str]:
          return self.velocity_x, self.velocity_y

     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						float				,					snake_case	:						float				,					snake_case	:						float     )		->				List[str]:
          self.velocity_x += force_x * delta_time
          self.velocity_y += force_y * delta_time






     def 					lowerCamelCase__					(							self	:						Any				,					snake_case	:						float     )		->				Dict:
          self.position_x += self.velocity_x * delta_time
          self.position_y += self.velocity_y * delta_time



class       a							:


     """simple docstring"""






     def __init__(							self	:						Tuple				,					snake_case	:						list[Body]				,					snake_case	:						float = 1.0				,					snake_case	:						float = 1.0				,					snake_case	:						float = 0.0				,					)		->				Tuple:
          __UpperCAmelCase      :					Any    				=     bodies
          __UpperCAmelCase      :					Union[str, Any]    				=     gravitation_constant
          __UpperCAmelCase      :					Any    				=     time_factor
          __UpperCAmelCase      :					List[Any]    				=     softening_factor

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






     def 					lowerCamelCase__					(							self	:						int				,					snake_case	:						float     )		->				int:


          for bodya in self.bodies:
               __UpperCAmelCase      :					Tuple    				=     0.0
               __UpperCAmelCase      :					List[str]    				=     0.0
               for bodya in self.bodies:
                    if bodya != bodya:
                         __UpperCAmelCase      :					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      :					int    				=     (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      (		_lowercase   :      BodySystem     ,   _lowercase   :      float     ,   _lowercase   :      list[plt.Circle]    ):





     '''simple docstring'''






     body_system.update_system(__A    )

     # Update the positions of the patches
     for patch, body in zip(__A     ,   body_system.bodies    ):
          __UpperCAmelCase      :					Optional[int]    				=     (body.position_x, body.position_y)




def       _a      (		_lowercase   :      str     ,   _lowercase   :      BodySystem     ,   _lowercase   :      float = -1     ,   _lowercase   :      float = 1     ,   _lowercase   :      float = -1     ,   _lowercase   :      float = 1     ,   ):





     '''simple docstring'''






     __UpperCAmelCase      :					List[Any]    				=     plt.figure()
     fig.canvas.set_window_title(__A    )
     __UpperCAmelCase      :					Optional[Any]    				=     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      :					List[Any]    				=     [
         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(_lowercase   :      int    ) -> list[plt.Circle]:
          update_step(__A     ,   __A     ,   __A    )
          return patches

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

     plt.show()




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					List[str]    				=     0.970_0436
     __UpperCAmelCase      :					Any    				=     -0.2430_8753
     __UpperCAmelCase      :					Dict    				=     0.4_6620_3685
     __UpperCAmelCase      :					Optional[int]    				=     0.4323_6573

     __UpperCAmelCase      :					Dict    				=     [
         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      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     7.34_76E22
     __UpperCAmelCase      :					Optional[Any]    				=     5.9_72E24
     __UpperCAmelCase      :					Any    				=     1022
     __UpperCAmelCase      :					str    				=     384399000
     __UpperCAmelCase      :					int    				=     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      :					Union[str, Any]    				=     earth_mass * velocity_dif / (earth_mass + moon_mass)
     __UpperCAmelCase      :					str    				=     moon_velocity - velocity_dif

     __UpperCAmelCase      :					Dict    				=     Body(-earth_moon_distance     ,   0     ,   0     ,   __A     ,   __A     ,   10000000     ,   '''grey'''    )
     __UpperCAmelCase      :					Optional[Any]    				=     Body(0     ,   0     ,   0     ,   __A     ,   __A     ,   50000000     ,   '''blue'''    )
     return BodySystem([earth, moon]     ,   __A     ,   time_factor=1000000    )




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     []
     for _ in range(10    ):
          __UpperCAmelCase      :					List[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    )     ,   __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(),
          -4_3_0_0_0_0_0_0_0,
          4_3_0_0_0_0_0_0_0,
          -4_3_0_0_0_0_0_0_0,
          4_3_0_0_0_0_0_0_0,
      )
      plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)





import unittest

import numpy as np
from datasets import load_dataset

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_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor







class __snake_case     (     unittest.TestCase						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any=7   ,		_snake_case						:		Any=3   ,		_snake_case						:		str=30   ,		_snake_case						:		str=400   ,		_snake_case						:		Any=True   ,		_snake_case						:		Dict=None   ,		_snake_case						:		List[str]=True   ,		_snake_case						:		Union[str, Any]=[0.5, 0.5, 0.5]   ,		_snake_case						:		Tuple=[0.5, 0.5, 0.5]   ,		_snake_case						:		str=False   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				size if size is not None else {'''height''': 30, '''width''': 30}
        UpperCAmelCase_		=				parent
        UpperCAmelCase_		=				batch_size
        UpperCAmelCase_		=				num_channels
        UpperCAmelCase_		=				min_resolution
        UpperCAmelCase_		=				max_resolution
        UpperCAmelCase_		=				do_resize
        UpperCAmelCase_		=				size
        UpperCAmelCase_		=				do_normalize
        UpperCAmelCase_		=				image_mean
        UpperCAmelCase_		=				image_std
        UpperCAmelCase_		=				do_reduce_labels







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


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







def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)

     return image, map


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[2]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       int		=    SegformerImageProcessor if is_vision_available() else None







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				SegformerImageProcessingTester(self)







   @property
   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(_snake_case   ,		'''do_resize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''size'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_normalize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_mean'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_std'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_reduce_labels'''))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size   ,		{'''height''': 30, '''width''': 30})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)

        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		reduce_labels=_snake_case)
        self.assertEqual(image_processor.size   ,		{'''height''': 42, '''width''': 42})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        pass







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		Image.Image)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		numpify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		np.ndarray)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







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

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)







   def lowerCamelCase		( self						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        UpperCAmelCase_		=				[]
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)
             maps.append(torch.zeros(image.shape[-2:]).long())

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		maps[0]   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test not batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_batch_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 150)

        UpperCAmelCase_		=				True
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)






