
from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__magic_name__  :  List[Any]     			=   2_0

# Time between time steps in seconds
__magic_name__  :  int     			=   INTERVAL / 1_0_0_0


class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase = 1.0						,    __UpperCamelCase = 1.0						,    __UpperCamelCase = "blue"						,    ):
      A_								=					position_x
      A_								=					position_y
      A_								=					velocity_x
      A_								=					velocity_y
      A_								=					mass
      A_								=					size
      A_								=					color

  @property
  def 					lowercase_						(					self					):
      return self.position_x, self.position_y

  @property
  def 					lowercase_						(					self					):
      return self.velocity_x, self.velocity_y

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase						,    __UpperCamelCase					):
      self.velocity_x += force_x * delta_time
      self.velocity_y += force_y * delta_time

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      self.position_x += self.velocity_x * delta_time
      self.position_y += self.velocity_y * delta_time



class 							lowerCamelCase       :



  """simple docstring"""





  def __init__(					self						,    __UpperCamelCase						,    __UpperCamelCase = 1.0						,    __UpperCamelCase = 1.0						,    __UpperCamelCase = 0.0						,    ):
      A_								=					bodies
      A_								=					gravitation_constant
      A_								=					time_factor
      A_								=					softening_factor

  def __len__(					self					):
      return len(self.bodies					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      for bodya in self.bodies:
          A_								=					0.0
          A_								=					0.0
          for bodya in self.bodies:
              if bodya != bodya:
                  A_								=					bodya.position_x - bodya.position_x
                  A_								=					bodya.position_y - bodya.position_y

                  # Calculation of the distance using Pythagoras's theorem
                  # Extra factor due to the softening technique
                  A_								=					(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       (				snake_case__   :      Optional[Any]				,					snake_case__   :      Tuple				,					snake_case__   :      int )->			None:
    body_system.update_system(snake_case__ )

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





def        lowerCAmelCase       (				snake_case__   :      Optional[Any]				,					snake_case__   :      Optional[Any]				,					snake_case__   :      List[Any] = -1				,					snake_case__   :      str = 1				,					snake_case__   :      str = -1				,					snake_case__   :      str = 1				,					)->			None:
    A_								=					plt.figure()
    fig.canvas.set_window_title(snake_case__ )
    A_								=					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
    A_								=					[
        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__   :      Any ) -> list[plt.Circle]:
        update_step(snake_case__				,					snake_case__				,					snake_case__ )
        return patches

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

    plt.show()





def        lowerCAmelCase       (				)->			BodySystem:
    A_								=					0.9_7_0_0_4_3_6
    A_								=					-0.2_4_3_0_8_7_5_3
    A_								=					0.4_6_6_2_0_3_6_8_5
    A_								=					0.4_3_2_3_6_5_7_3

    A_								=					[
        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        lowerCAmelCase       (				)->			BodySystem:
    A_								=					7.3_476e22
    A_								=					5.972e24
    A_								=					1022
    A_								=					384399000
    A_								=					6.674e-11

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

    A_								=					Body(-earth_moon_distance				,					0				,					0				,					snake_case__				,					snake_case__				,					10000000				,					"grey" )
    A_								=					Body(0				,					0				,					0				,					snake_case__				,					snake_case__				,					50000000				,					"blue" )
    return BodySystem([earth, moon]				,					snake_case__				,					time_factor=1000000 )





def        lowerCAmelCase       (				)->			BodySystem:
    A_								=					[]
    for _ in range(10 ):
        A_								=					random.uniform(-0.5				,					0.5 )
        A_								=					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=0.0_5				,					) )
        bodies.append(
            Body(
                random.uniform(-0.5				,					0.5 )				,					random.uniform(-0.5				,					0.5 )				,					-velocity_x				,					-velocity_y				,					size=0.0_5				,					) )
    return BodySystem(snake_case__				,					0.0_1				,					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)









'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=3     ,      _snake_case=32     ,      _snake_case=3     ,      _snake_case=10     ,      _snake_case=[10, 20, 30, 40]     ,      _snake_case=[1, 1, 2, 1]     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case="relu"     ,      _snake_case=3     ,      _snake_case=None     ,      )				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       parent
								UpperCAmelCase_		:   Union[str, Any]													=       batch_size
								UpperCAmelCase_		:   List[Any]													=       image_size
								UpperCAmelCase_		:   List[Any]													=       num_channels
								UpperCAmelCase_		:   Dict													=       embeddings_size
								UpperCAmelCase_		:   str													=       hidden_sizes
								UpperCAmelCase_		:   List[Any]													=       depths
								UpperCAmelCase_		:   Optional[Any]													=       is_training
								UpperCAmelCase_		:   str													=       use_labels
								UpperCAmelCase_		:   List[str]													=       hidden_act
								UpperCAmelCase_		:   Dict													=       num_labels
								UpperCAmelCase_		:   List[Any]													=       scope
								UpperCAmelCase_		:   Tuple													=       len(_snake_case)


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   int													=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:   Optional[int]													=       self.get_config()

								return config, pixel_values


							def     _snake_case				(      self)				->					Union[str, Any]:
								return ResNetConfig(
								    num_channels=self.num_channels     ,      embeddings_size=self.embeddings_size     ,      hidden_sizes=self.hidden_sizes     ,      depths=self.depths     ,      hidden_act=self.hidden_act     ,      num_labels=self.num_labels     ,      image_size=self.image_size     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   Any													=       FlaxResNetModel(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       FlaxResNetForImageClassification(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.prepare_config_and_inputs()
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       config_and_inputs
								UpperCAmelCase_		:   str													=       {'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Dict= (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_lowerCamelCase		:   Any= False
							_lowerCamelCase		:   List[Any]= False
							_lowerCamelCase		:   str= False


							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Any													=       FlaxResNetModelTester(self)
								UpperCAmelCase_		:   Optional[int]													=       ConfigTester(self     ,      config_class=_snake_case     ,      has_text_modality=_snake_case)


							def     _snake_case				(      self)				->					Tuple:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


							def     _snake_case				(      self)				->					int:
								return


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Optional[Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   Union[str, Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)


							@unittest.skip(reason='ResNet does not use inputs_embeds')
							def     _snake_case				(      self)				->					Optional[Any]:
								pass


							@unittest.skip(reason='ResNet does not support input and output embeddings')
							def     _snake_case				(      self)				->					Union[str, Any]:
								pass


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:   Any													=       model_class(_snake_case)
									UpperCAmelCase_		:   str													=       inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_		:   Tuple													=       [*signature.parameters.keys()]

									UpperCAmelCase_		:   List[str]													=       ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,      _snake_case)


							def     _snake_case				(      self)				->					List[Any]:
								def check_hidden_states_output(_snake_case     ,      _snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       model_class(_snake_case)

									UpperCAmelCase_		:   Any													=       model(**self._prepare_for_class(_snake_case     ,      _snake_case))

									UpperCAmelCase_		:   List[Any]													=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_		:   int													=       self.model_tester.num_stages
									self.assertEqual(len(_snake_case)     ,      expected_num_stages + 1)


							@unittest.skip(reason='ResNet does not use feedforward chunking')
							def     _snake_case				(      self)				->					List[Any]:
								pass





							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:   str													=       self._prepare_for_class(_snake_case     ,      _snake_case)
										UpperCAmelCase_		:   str													=       model_class(_snake_case)

										@jax.jit
										def model_jitted(_snake_case     ,      **_snake_case):
											return model(pixel_values=_snake_case     ,      **_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:   Optional[Any]													=       model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   Union[str, Any]													=       model_jitted(**_snake_case).to_tuple()

										self.assertEqual(len(_snake_case)     ,      len(_snake_case))
										for jitted_output, output in zip(_snake_case     ,      _snake_case):
											self.assertEqual(jitted_output.shape     ,      output.shape)







def     SCREAMING_SNAKE_CASE(	)     ->     Dict:
	UpperCAmelCase_		:   Tuple													=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_flax
class        lowercase    (	unittest.TestCase			):


							@cached_property
							def     _snake_case				(      self)				->					List[str]:
								return AutoImageProcessor.from_pretrained('microsoft/resnet-50') if is_vision_available() else None





							@slow
							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50')

								UpperCAmelCase_		:   List[str]													=       self.default_image_processor
								UpperCAmelCase_		:   Union[str, Any]													=       prepare_img()
								UpperCAmelCase_		:   int													=       image_processor(images=_snake_case     ,      return_tensors='np')

								UpperCAmelCase_		:   Any													=       model(**_snake_case)

								# verify the logits
								UpperCAmelCase_		:   Optional[int]													=       (1, 1000)
								self.assertEqual(outputs.logits.shape     ,      _snake_case)

								UpperCAmelCase_		:   Tuple													=       jnp.array([-11.1_069, -9.7_877, -8.3_777])

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]     ,      _snake_case     ,      atol=1e-4))






