



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_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViltImageProcessor



class        lowerCAmelCase_	(						unittest.TestCase							):
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
          UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
          UpperCamelCase					:						Dict					=			parent
          UpperCamelCase					:						str					=			batch_size
          UpperCamelCase					:						str					=			num_channels
          UpperCamelCase					:						Optional[int]					=			image_size
          UpperCamelCase					:						List[Any]					=			min_resolution
          UpperCamelCase					:						Tuple					=			max_resolution
          UpperCamelCase					:						Tuple					=			do_resize
          UpperCamelCase					:						Any					=			size
          UpperCamelCase					:						int					=			size_divisor
          UpperCamelCase					:						str					=			do_normalize
          UpperCamelCase					:						Optional[int]					=			image_mean
          UpperCamelCase					:						List[str]					=			image_std
       def       snake_case_ (    self )  ->	List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
          if not batched:
             UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
             UpperCamelCase					:						int					=			image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
             UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
             if h < w:
                UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
             else:
                UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size

             UpperCamelCase					:						int					=			int((1333 / 800) * size )
             if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                UpperCamelCase					:						List[Any]					=			newh * scale
                UpperCamelCase					:						Optional[int]					=			neww * scale

             UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
             UpperCamelCase			,  UpperCamelCase					:						int					=			(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

          else:
             UpperCamelCase					:						Dict					=			[]
             for image in image_inputs:
                UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                expected_values.append((expected_height, expected_width) )
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
             UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]

          return expected_height, expected_width






@require_torch
@require_vision
class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
       UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
       @property
       def       snake_case_ (    self )  ->	Dict:
          return self.image_processor_tester.prepare_image_processor_dict()
       def       snake_case_ (    self )  ->	Union[str, Any]:
          UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
       def       snake_case_ (    self )  ->	List[Any]:
          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size,    {'shortest_edge': 30} )

          UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
          self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
       def       snake_case_ (    self )  ->	Dict:
          pass
       def       snake_case_ (    self )  ->	str:
          # Initialize image_processing
          UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )

          # Test not batched input
          UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )
       def       snake_case_ (    self )  ->	List[str]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray )

          # Test not batched input
          UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )






       def       snake_case_ (    self )  ->	Optional[Any]:
          # Initialize image_processing
          UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_ )
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor )

          # Test not batched input
          UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )

          # Test batched
          UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values

          UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
          self.assertEqual(
              encoded_images.shape,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              ),    )








from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__UpperCAmelCase      = 20

# Time between time steps in seconds
__UpperCAmelCase      = INTERVAL / 1_000



class        lowerCAmelCase_	:
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = 1.0,    SCREAMING_SNAKE_CASE_ = 1.0,    SCREAMING_SNAKE_CASE_ = "blue",    )  ->	None:
          UpperCamelCase					:						int					=			position_x
          UpperCamelCase					:						List[Any]					=			position_y
          UpperCamelCase					:						Tuple					=			velocity_x
          UpperCamelCase					:						Optional[Any]					=			velocity_y
          UpperCamelCase					:						Optional[Any]					=			mass
          UpperCamelCase					:						Union[str, Any]					=			size
          UpperCamelCase					:						Any					=			color
       @property
       def       snake_case_ (    self )  ->	tuple[float, float]:
          return self.position_x, self.position_y
       @property
       def       snake_case_ (    self )  ->	tuple[float, float]:
          return self.velocity_x, self.velocity_y
       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )  ->	None:
          self.velocity_x += force_x * delta_time
          self.velocity_y += force_y * delta_time






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






class        lowerCAmelCase_	:
       def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = 1.0,    SCREAMING_SNAKE_CASE_ = 1.0,    SCREAMING_SNAKE_CASE_ = 0.0,    )  ->	None:
          UpperCamelCase					:						Tuple					=			bodies
          UpperCamelCase					:						Tuple					=			gravitation_constant
          UpperCamelCase					:						Any					=			time_factor
          UpperCamelCase					:						Any					=			softening_factor
       def __len__(    self )  ->	int:
          return len(self.bodies )






       def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	None:
          for bodya in self.bodies:
             UpperCamelCase					:						Optional[Any]					=			0.0
             UpperCamelCase					:						str					=			0.0
             for bodya in self.bodies:
                if bodya != bodya:
                   UpperCamelCase					:						List[str]					=			bodya.position_x - bodya.position_x
                   UpperCamelCase					:						Union[str, Any]					=			bodya.position_y - bodya.position_y

                   # Calculation of the distance using Pythagoras's theorem
                   # Extra factor due to the softening technique
                   UpperCamelCase					:						Dict					=			(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 	UpperCamelCase			(     snake_case__ :					BodySystem ,  snake_case__ :					float ,  snake_case__ :					list[plt.Circle]			)			->  None:
   body_system.update_system(snake_case__			)

   # Update the positions of the patches
   for patch, body in zip(snake_case__ ,  body_system.bodies			):
      UpperCamelCase					:						List[str]					=			(body.position_x, body.position_y)
def 	UpperCamelCase			(     snake_case__ :					str ,  snake_case__ :					BodySystem ,  snake_case__ :					float = -1 ,  snake_case__ :					float = 1 ,  snake_case__ :					float = -1 ,  snake_case__ :					float = 1 ,  )			->  None:
   UpperCamelCase					:						Dict					=			plt.figure()
   fig.canvas.set_window_title(snake_case__			)
   UpperCamelCase					:						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(snake_case__			)

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

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

   plt.show()
def 	UpperCamelCase			(     )			->  BodySystem:
   UpperCamelCase					:						str					=			0.9700436
   UpperCamelCase					:						Optional[Any]					=			-0.24308753
   UpperCamelCase					:						Optional[int]					=			0.466203685
   UpperCamelCase					:						int					=			0.43236573

   UpperCamelCase					:						int					=			[
       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 	UpperCamelCase			(     )			->  BodySystem:
   UpperCamelCase					:						str					=			7.3_476E22
   UpperCamelCase					:						str					=			5.972E24
   UpperCamelCase					:						Union[str, Any]					=			1022
   UpperCamelCase					:						Optional[Any]					=			384399000
   UpperCamelCase					:						str					=			6.674E-11

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

   UpperCamelCase					:						Optional[int]					=			Body(-earth_moon_distance ,  0 ,  0 ,  snake_case__ ,  snake_case__ ,  10000000 ,  'grey'			)
   UpperCamelCase					:						Dict					=			Body(0 ,  0 ,  0 ,  snake_case__ ,  snake_case__ ,  50000000 ,  'blue'			)
   return BodySystem([earth, moon] ,  snake_case__ ,  time_factor=1000000			)
def 	UpperCamelCase			(     )			->  BodySystem:
   UpperCamelCase					:						int					=			[]
   for _ in range(10			):
      UpperCamelCase					:						Optional[int]					=			random.uniform(-0.5 ,  0.5			)
      UpperCamelCase					:						Union[str, 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			) ,  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(),
        -430_000_000,
        430_000_000,
        -430_000_000,
        430_000_000,
    )
    plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)



