



"""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:				List[str]    							=  INTERVAL / 1_0_0_0


class   a__		:


   def __init__(							self      :							str,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float = 1.0,     lowerCAmelCase      :							float = 1.0,     lowerCAmelCase      :							str = "blue",     )    ->       None:
         lowercase  :      List[Any]					=   position_x
         lowercase  :      int					=   position_y
         lowercase  :      int					=   velocity_x
         lowercase  :      Optional[int]					=   velocity_y
         lowercase  :      int					=   mass
         lowercase  :      List[str]					=   size
         lowercase  :      Tuple					=   color


   @property
   def        lowercase				(							self      :							List[str]			)    ->       tuple[float, float]:
         return self.position_x, self.position_y


   @property
   def        lowercase				(							self      :							int			)    ->       tuple[float, float]:
         return self.velocity_x, self.velocity_y


   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float,     lowerCAmelCase      :							float			)    ->       None:
         self.velocity_x += force_x * delta_time
         self.velocity_y += force_y * delta_time






   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							float			)    ->       None:
         self.position_x += self.velocity_x * delta_time
         self.position_y += self.velocity_y * delta_time




class   a__		:


   def __init__(							self      :							Any,     lowerCAmelCase      :							list[Body],     lowerCAmelCase      :							float = 1.0,     lowerCAmelCase      :							float = 1.0,     lowerCAmelCase      :							float = 0.0,     )    ->       None:
         lowercase  :      Dict					=   bodies
         lowercase  :      Tuple					=   gravitation_constant
         lowercase  :      Optional[Any]					=   time_factor
         lowercase  :      Optional[Any]					=   softening_factor


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






   def        lowercase				(							self      :							str,     lowerCAmelCase      :							float			)    ->       None:
         for bodya in self.bodies:
               lowercase  :      List[Any]					=   0.0
               lowercase  :      str					=   0.0
               for bodya in self.bodies:
                     if bodya != bodya:
                           lowercase  :      Any					=   bodya.position_x - bodya.position_x
                           lowercase  :      str					=   bodya.position_y - bodya.position_y

                           # Calculation of the distance using Pythagoras's theorem
                           # Extra factor due to the softening technique
                           lowercase  :      Any					=   (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(lowerCAmelCase,     lowerCAmelCase,     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   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			None:






      '''simple docstring'''


      body_system.update_system(_UpperCAmelCase				)

      # Update the positions of the patches
      for patch, body in zip(_UpperCAmelCase	,      body_system.bodies				):
            lowercase  :      Union[str, Any]					=   (body.position_x, body.position_y)


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase = -1	,      _UpperCAmelCase = 1	,      _UpperCAmelCase = -1	,      _UpperCAmelCase = 1	,      )     ->			None:






      '''simple docstring'''


      lowercase  :      str					=   plt.figure()
      fig.canvas.set_window_title(_UpperCAmelCase				)
      lowercase  :      Dict					=   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
      lowercase  :      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(_UpperCAmelCase				)

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

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

      plt.show()


def   lowercase__     (			)     ->			BodySystem:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   0.9_7_0_0_4_3_6
      lowercase  :      Optional[int]					=   -0.2_4_3_0_8_7_5_3
      lowercase  :      Any					=   0.4_6_6_2_0_3_6_8_5
      lowercase  :      List[str]					=   0.4_3_2_3_6_5_7_3

      lowercase  :      Optional[int]					=   [
          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   lowercase__     (			)     ->			BodySystem:






      '''simple docstring'''


      lowercase  :      str					=   7.3476e22
      lowercase  :      List[Any]					=   5.972e24
      lowercase  :      Optional[int]					=   10_22
      lowercase  :      Optional[Any]					=   3_84_39_90_00
      lowercase  :      List[str]					=   6.674e-11

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

      lowercase  :      List[Any]					=   Body(-earth_moon_distance	,      0	,      0	,      _UpperCAmelCase	,      _UpperCAmelCase	,      10_00_00_00	,      'grey'				)
      lowercase  :      Optional[int]					=   Body(0	,      0	,      0	,      _UpperCAmelCase	,      _UpperCAmelCase	,      50_00_00_00	,      'blue'				)
      return BodySystem([earth, moon]	,      _UpperCAmelCase	,      time_factor=1_00_00_00				)


def   lowercase__     (			)     ->			BodySystem:






      '''simple docstring'''


      lowercase  :      Dict					=   []
      for _ in range(10				):
            lowercase  :      str					=   random.uniform(-0.5	,      0.5				)
            lowercase  :      List[str]					=   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=0.0_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(_UpperCAmelCase	,      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 warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'AutoFeatureExtractor'
   _lowerCamelCase												=				'Speech2Text2Tokenizer'


   def __init__(							self      :							str,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Optional[Any]			)    ->       Dict:
         super().__init__(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Tuple					=   self.feature_extractor
         lowercase  :      Any					=   False


   def __call__(							self      :							Tuple,     *lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							Tuple			)    ->       Dict:
         # For backward compatibility
         if self._in_target_context_manager:
               return self.current_processor(*lowerCAmelCase,     **lowerCAmelCase			)

         if "raw_speech" in kwargs:
               warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
               lowercase  :      str					=   kwargs.pop('raw_speech'			)
         else:
               lowercase  :      str					=   kwargs.pop('audio',     lowerCAmelCase			)
         lowercase  :      Dict					=   kwargs.pop('sampling_rate',     lowerCAmelCase			)
         lowercase  :      Optional[Any]					=   kwargs.pop('text',     lowerCAmelCase			)
         if len(lowerCAmelCase			) > 0:
               lowercase  :      Any					=   args[0]
               lowercase  :      int					=   args[1:]

         if audio is None and text is None:
               raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

         if audio is not None:
               lowercase  :      List[Any]					=   self.feature_extractor(lowerCAmelCase,     *lowerCAmelCase,     sampling_rate=lowerCAmelCase,     **lowerCAmelCase			)
         if text is not None:
               lowercase  :      str					=   self.tokenizer(lowerCAmelCase,     **lowerCAmelCase			)

         if text is None:
               return inputs
         elif audio is None:
               return encodings
         else:
               lowercase  :      Tuple					=   encodings['input_ids']
               return inputs


   def        lowercase				(							self      :							str,     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         return self.tokenizer.batch_decode(*lowerCAmelCase,     **lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     *lowerCAmelCase      :							Union[str, Any],     **lowerCAmelCase      :							int			)    ->       Dict:
         return self.tokenizer.decode(*lowerCAmelCase,     **lowerCAmelCase			)






   @contextmanager
   def        lowercase				(							self      :							str			)    ->       Any:
         warnings.warn(
             '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
             'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
             'your audio inputs, or in a separate call.'			)
         lowercase  :      Optional[Any]					=   True
         lowercase  :      Any					=   self.tokenizer
         yield
         lowercase  :      Any					=   self.feature_extractor
         lowercase  :      Any					=   False
