

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__A     						=      20

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



class     lowercase_       :



      def __init__(     self :							Tuple     ,   A__ :							float     ,   A__ :							float     ,   A__ :							float     ,   A__ :							float     ,   A__ :							float = 1.0     ,   A__ :							float = 1.0     ,   A__ :							str = "blue"     ,   )       ->	Optional[int]:
        _snake_case		=				position_x
        _snake_case		=				position_y
        _snake_case		=				velocity_x
        _snake_case		=				velocity_y
        _snake_case		=				mass
        _snake_case		=				size
        _snake_case		=				color



      @property
      def 		UpperCamelCase_						(     self :							str						)       ->	Dict:
        return self.position_x, self.position_y



      @property
      def 		UpperCamelCase_						(     self :							Dict						)       ->	Optional[Any]:
        return self.velocity_x, self.velocity_y



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							float     ,   A__ :							float     ,   A__ :							float						)       ->	Any:
        self.velocity_x += force_x * delta_time
        self.velocity_y += force_y * delta_time







      def 		UpperCamelCase_						(     self :							Tuple     ,   A__ :							float						)       ->	int:
        self.position_x += self.velocity_x * delta_time
        self.position_y += self.velocity_y * delta_time



class     lowercase_       :



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



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







      def 		UpperCamelCase_						(     self :							List[str]     ,   A__ :							float						)       ->	Union[str, Any]:
        for bodya in self.bodies:
          _snake_case		=				0.0
          _snake_case		=				0.0
          for bodya in self.bodies:
            if bodya != bodya:
              _snake_case		=				bodya.position_x - bodya.position_x
              _snake_case		=				bodya.position_y - bodya.position_y

              # Calculation of the distance using Pythagoras's theorem
              # Extra factor due to the softening technique
              _snake_case		=				(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(_A     ,   _A     ,   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_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      None:



  """simple docstring"""






  body_system.update_system(__lowercase	)

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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase = -1					,			_UpperCamelCase = 1					,			_UpperCamelCase = -1					,			_UpperCamelCase = 1					,			)				->      None:



  """simple docstring"""






  _snake_case		=				plt.figure()
  fig.canvas.set_window_title(__lowercase	)
  _snake_case		=				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
  _snake_case		=				[
      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(__lowercase	)

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

  _snake_case		=				animation.FuncAnimation(  # noqa: F841
      __lowercase					,			__lowercase					,			interval=__lowercase					,			blit=__lowercase	)

  plt.show()



def  snake_case_()				->      BodySystem:



  """simple docstring"""






  _snake_case		=				0.9700436
  _snake_case		=				-0.24308753
  _snake_case		=				0.466203685
  _snake_case		=				0.43236573

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



def  snake_case_()				->      BodySystem:



  """simple docstring"""






  _snake_case		=				7.3476E22
  _snake_case		=				5.972E24
  _snake_case		=				1_022
  _snake_case		=				384_399_000
  _snake_case		=				6.674E-11

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

  _snake_case		=				Body(-earth_moon_distance					,			0					,			0					,			__lowercase					,			__lowercase					,			10_000_000					,			'''grey'''	)
  _snake_case		=				Body(0					,			0					,			0					,			__lowercase					,			__lowercase					,			50_000_000					,			'''blue'''	)
  return BodySystem([earth, moon]					,			__lowercase					,			time_factor=1_000_000	)



def  snake_case_()				->      BodySystem:



  """simple docstring"""






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






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 				lowerCAmelCase__	(						unittest.TestCase    ):







					def __init__(							self   :  int      ,		_A   :  Optional[Any]      ,		_A   :  Optional[int]=7      ,		_A   :  str=3      ,		_A   :  Optional[int]=30      ,		_A   :  List[str]=400      ,		_A   :  Tuple=True      ,		_A   :  List[Any]=None      ,		_A   :  Dict=True      ,		_A   :  Tuple=[0.5, 0.5, 0.5]      ,		_A   :  List[str]=[0.5, 0.5, 0.5]      ,		_A   :  int=False      ,		):
											A__						:				Tuple         =			size if size is not None else {"height": 30, "width": 30}
											A__						:				Tuple         =			parent
											A__						:				str         =			batch_size
											A__						:				int         =			num_channels
											A__						:				Union[str, Any]         =			min_resolution
											A__						:				List[str]         =			max_resolution
											A__						:				int         =			do_resize
											A__						:				Tuple         =			size
											A__						:				Union[str, Any]         =			do_normalize
											A__						:				Optional[int]         =			image_mean
											A__						:				List[Any]         =			image_std
											A__						:				Dict         =			do_reduce_labels







					def 			_lowercase      (							self   :  Optional[int]):
											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 						snake_case__							(       )		->		Tuple:







						"""simple docstring"""

						A__						:				Union[str, Any]         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Any         =			Image.open(dataset[0]["file"]							)
						A__						:				Any         =			Image.open(dataset[1]["file"]							)

						return image, map
def 						snake_case__							(       )		->		List[str]:







						"""simple docstring"""

						A__						:				Tuple         =			load_dataset("hf-internal-testing/fixtures_ade20k"				,  split="test"							)

						A__						:				Tuple         =			Image.open(dataset[0]["file"]							)
						A__						:				int         =			Image.open(dataset[1]["file"]							)
						A__						:				str         =			Image.open(dataset[2]["file"]							)
						A__						:				List[Any]         =			Image.open(dataset[3]["file"]							)

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




@require_torch
@require_vision
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     int     =    SegformerImageProcessor if is_vision_available() else None







					def 			_lowercase      (							self   :  int):
											A__						:				int         =			SegformerImageProcessingTester(self)







					@property
					def 			_lowercase      (							self   :  str):
											return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowercase      (							self   :  Optional[Any]):
											A__						:				Any         =			self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(_A      ,		"do_resize"))
											self.assertTrue(hasattr(_A      ,		"size"))
											self.assertTrue(hasattr(_A      ,		"do_normalize"))
											self.assertTrue(hasattr(_A      ,		"image_mean"))
											self.assertTrue(hasattr(_A      ,		"image_std"))
											self.assertTrue(hasattr(_A      ,		"do_reduce_labels"))







					def 			_lowercase      (							self   :  Tuple):
											A__						:				Union[str, Any]         =			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      ,		_A)

											A__						:				Union[str, Any]         =			self.image_processing_class.from_dict(self.image_processor_dict      ,		size=42      ,		reduce_labels=_A)
											self.assertEqual(image_processor.size      ,		{"height": 42, "width": 42})
											self.assertEqual(image_processor.do_reduce_labels      ,		_A)







					def 			_lowercase      (							self   :  Union[str, Any]):
											pass







					def 			_lowercase      (							self   :  Dict):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											A__						:				Union[str, Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		Image.Image)

											# Test not batched input
											A__						:				Dict         =			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
											A__						:				List[Any]         =			image_processing(_A      ,		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 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				Tuple         =			self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											A__						:				Tuple         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		np.ndarray)

											# Test not batched input
											A__						:				Optional[Any]         =			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
											A__						:				Optional[int]         =			image_processing(_A      ,		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 			_lowercase      (							self   :  Tuple):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				str         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)

											# Test not batched input
											A__						:				int         =			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
											A__						:				List[str]         =			image_processing(_A      ,		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 			_lowercase      (							self   :  Optional[int]):
											# Initialize image_processing
											A__						:				str         =			self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											A__						:				Optional[Any]         =			prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A)
											A__						:				List[str]         =			[]
											for image in image_inputs:
																	self.assertIsInstance(_A      ,		torch.Tensor)
																	maps.append(torch.zeros(image.shape[-2:]).long())

											# Test not batched input
											A__						:				Any         =			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
											A__						:				int         =			image_processing(_A      ,		_A      ,		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)
											A__     ,							A__						:				List[str]         =			prepare_semantic_single_inputs()

											A__						:				Any         =			image_processing(_A      ,		_A      ,		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)
											A__     ,							A__						:				Union[str, Any]         =			prepare_semantic_batch_inputs()

											A__						:				str         =			image_processing(_A      ,		_A      ,		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 			_lowercase      (							self   :  Union[str, Any]):
											# Initialize image_processing
											A__						:				List[str]         =			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
											A__     ,							A__						:				Tuple         =			prepare_semantic_single_inputs()
											A__						:				Dict         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 150)

											A__						:				str         =			True
											A__						:				int         =			image_processing(_A      ,		_A      ,		return_tensors="pt")
											self.assertTrue(encoding["labels"].min().item() >= 0)
											self.assertTrue(encoding["labels"].max().item() <= 255)