






'''simple docstring'''







from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
__lowerCAmelCase					   =       20

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




class      __SCREAMING_SNAKE_CASE					:


		"""simple docstring"""


		def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = 1.0      ,  UpperCamelCase__ = 1.0      ,  UpperCamelCase__ = "blue"      ,  ):






									"""simple docstring"""


									a_	    = position_x
									a_	    = position_y
									a_	    = velocity_x
									a_	    = velocity_y
									a_	    = mass
									a_	    = size
									a_	    = color







		@property
		def       _a					( self  ):






									"""simple docstring"""


									return self.position_x, self.position_y







		@property
		def       _a					( self  ):






									"""simple docstring"""


									return self.velocity_x, self.velocity_y







		def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






									"""simple docstring"""


									self.velocity_x += force_x * delta_time
									self.velocity_y += force_y * delta_time







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									self.position_x += self.velocity_x * delta_time
									self.position_y += self.velocity_y * delta_time





class      __SCREAMING_SNAKE_CASE					:


		"""simple docstring"""


		def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__ = 1.0      ,  UpperCamelCase__ = 1.0      ,  UpperCamelCase__ = 0.0      ,  ):






									"""simple docstring"""


									a_	    = bodies
									a_	    = gravitation_constant
									a_	    = time_factor
									a_	    = softening_factor







		def __len__( self  ):






									"""simple docstring"""


									return len(self.bodies  )







		def       _a					( self      ,  UpperCamelCase__  ):






									"""simple docstring"""


									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(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				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     str      ,					lowercase_					:     Dict      ):
							"""simple docstring"""




							body_system.update_system(_UpperCAmelCase      )

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


def 			__UpperCamelCase				(			lowercase_					:     Dict      ,					lowercase_					:     Dict      ,					lowercase_					:     Tuple = -1      ,					lowercase_					:     Dict = 1      ,					lowercase_					:     Optional[Any] = -1      ,					lowercase_					:     int = 1      ,					):
							"""simple docstring"""




							a_	    = plt.figure()
							fig.canvas.set_window_title(_UpperCAmelCase      )
							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(_UpperCAmelCase      )

							# Function called at each step of the animation
							def update(lowercase_					:     Union[str, Any]      ) -> list[plt.Circle]:
														update_step(_UpperCAmelCase      ,					_UpperCAmelCase      ,					_UpperCAmelCase      )
														return patches

							a_	    = animation.FuncAnimation(  # noqa: F841
							    _UpperCAmelCase      ,					_UpperCAmelCase      ,					interval=_UpperCAmelCase      ,					blit=_UpperCAmelCase      )

							plt.show()


def 			__UpperCamelCase				(			):
							"""simple docstring"""




							a_	    = 0.970_0436
							a_	    = -0.2430_8753
							a_	    = 0.4_6620_3685
							a_	    = 0.4323_6573

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


def 			__UpperCamelCase				(			):
							"""simple docstring"""




							a_	    = 7.3476E22
							a_	    = 5.972E24
							a_	    = 1_022
							a_	    = 384_399_000
							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      ,					_UpperCAmelCase      ,					_UpperCAmelCase      ,					10_000_000      ,					'grey'      )
							a_	    = Body(0      ,					0      ,					0      ,					_UpperCAmelCase      ,					_UpperCAmelCase      ,					50_000_000      ,					'blue'      )
							return BodySystem([earth, moon]      ,					_UpperCAmelCase      ,					time_factor=1_000_000      )


def 			__UpperCamelCase				(			):
							"""simple docstring"""




							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      )      ,					_UpperCAmelCase      ,					_UpperCAmelCase      ,					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(_UpperCAmelCase      ,					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)












'''simple docstring'''







import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=12      ,  UpperCamelCase__=7      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=99      ,  UpperCamelCase__=32      ,  UpperCamelCase__=32      ,  UpperCamelCase__=5      ,  UpperCamelCase__=4      ,  UpperCamelCase__=37      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=512      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=0      ,  UpperCamelCase__=None      ,  ):






         """simple docstring"""


         a_	    = parent
         a_	    = batch_size
         a_	    = seq_length
         a_	    = is_training
         a_	    = use_input_mask
         a_	    = use_labels
         a_	    = vocab_size
         a_	    = hidden_size
         a_	    = projection_dim
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = dropout
         a_	    = attention_dropout
         a_	    = max_position_embeddings
         a_	    = initializer_range
         a_	    = scope
         a_	    = bos_token_id







  def       _a					( self  ):






         """simple docstring"""


         a_	    = ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size  )

         a_	    = None
         if self.use_input_mask:
                a_	    = random_attention_mask([self.batch_size, self.seq_length]  )

         if input_mask is not None:
                a_    ,  a_	    = input_mask.shape
                a_	    = np.random.randint(1      ,  seq_length - 1      ,  size=(batch_size,)  )
                for batch_idx, start_index in enumerate(UpperCamelCase__  ):
                       a_	    = 1
                       a_	    = 0

         a_	    = self.get_config()

         return config, input_ids, input_mask







  def       _a					( self  ):






         """simple docstring"""


         return BlipTextConfig(
             vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  projection_dim=self.projection_dim      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  dropout=self.dropout      ,  attention_dropout=self.attention_dropout      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  bos_token_id=self.bos_token_id      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = BlipTextModel(config=UpperCamelCase__  )
         model.to(UpperCamelCase__  )
         model.eval()
         with torch.no_grad():
                a_	    = model(UpperCamelCase__      ,  attention_mask=UpperCamelCase__  )
                a_	    = model(UpperCamelCase__  )
         self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.prepare_config_and_inputs()
         a_    ,  a_    ,  a_	    = config_and_inputs
         a_	    = {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Dict			 =    (BlipTextModel,) if is_torch_available() else ()
  _a     :   str			 =    False
  _a     :   str			 =    False
  _a     :   str			 =    False







  def       _a					( self  ):






         """simple docstring"""


         a_	    = BlipTextModelTester(self  )
         a_	    = ConfigTester(self      ,  config_class=UpperCamelCase__      ,  hidden_size=37  )







  def       _a					( self  ):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='Blip does not use inputs_embeds'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @slow
  def       _a					( self  ):






         """simple docstring"""


         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_	    = BlipTextModel.from_pretrained(UpperCamelCase__  )
                self.assertIsNotNone(UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__  )




