

"""simple docstring"""

from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE_         =  20

# Time between time steps in seconds
SCREAMING_SNAKE_CASE_         =  INTERVAL / 1000
class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 1.0				,    lowercase_ = 1.0				,    lowercase_ = "blue"				,    )  ->   None:
          UpperCAmelCase       =   position_x
          UpperCAmelCase       =   position_y
          UpperCAmelCase       =   velocity_x
          UpperCAmelCase       =   velocity_y
          UpperCAmelCase       =   mass
          UpperCAmelCase       =   size
          UpperCAmelCase       =   color




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




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




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   None:
          self.velocity_x += force_x * delta_time
          self.velocity_y += force_y * delta_time




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

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_				,    lowercase_ = 1.0				,    lowercase_ = 1.0				,    lowercase_ = 0.0				,    )  ->   None:
          UpperCAmelCase       =   bodies
          UpperCAmelCase       =   gravitation_constant
          UpperCAmelCase       =   time_factor
          UpperCAmelCase       =   softening_factor




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




      def       a_ (							self				,    lowercase_							)  ->   None:
          for bodya in self.bodies:
              UpperCAmelCase       =   0.0
              UpperCAmelCase       =   0.0
              for bodya in self.bodies:
                  if bodya != bodya:
                      UpperCAmelCase       =   bodya.position_x - bodya.position_x
                      UpperCAmelCase       =   bodya.position_y - bodya.position_y

                      # Calculation of the distance using Pythagoras's theorem
                      # Extra factor due to the softening technique
                      UpperCAmelCase       =   (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(lowercase_				,    lowercase_				,    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__  (   lowerCAmelCase			:   BodySystem	,			lowerCAmelCase			:   float	,			lowerCAmelCase			:   list[plt.Circle]	)	-> None:






    """simple docstring"""




    body_system.update_system(lowerCAmelCase	)

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




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   BodySystem	,			lowerCAmelCase			:   float = -1	,			lowerCAmelCase			:   float = 1	,			lowerCAmelCase			:   float = -1	,			lowerCAmelCase			:   float = 1	,			)	-> None:






    """simple docstring"""




    UpperCAmelCase       =   plt.figure()
    fig.canvas.set_window_title(lowerCAmelCase	)
    UpperCAmelCase       =   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       =   [
        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(lowerCAmelCase	)

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

    UpperCAmelCase       =   animation.FuncAnimation(  # noqa: F841
        lowerCAmelCase	,			lowerCAmelCase	,			interval=lowerCAmelCase	,			blit=lowerCAmelCase	)

    plt.show()




def       lowercase__  (   )	-> BodySystem:






    """simple docstring"""




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

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




def       lowercase__  (   )	-> BodySystem:






    """simple docstring"""




    UpperCAmelCase       =   7.3_4_7_6E2_2
    UpperCAmelCase       =   5.9_7_2E2_4
    UpperCAmelCase       =   1_022
    UpperCAmelCase       =   384_399_000
    UpperCAmelCase       =   6.6_7_4E-1_1

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

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




def       lowercase__  (   )	-> BodySystem:






    """simple docstring"""




    UpperCAmelCase       =   []
    for _ in range(10	):
        UpperCAmelCase       =   random.uniform(-0.5	,			0.5	)
        UpperCAmelCase       =   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	)	,			lowerCAmelCase	,			lowerCAmelCase	,			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(lowerCAmelCase	,			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_3000_0000,
       4_3000_0000,
       -4_3000_0000,
       4_3000_0000,
   )
   plot('''Random system with many bodies''', example_a(), -1.5, 1.5, -1.5, 1.5)





"""simple docstring"""

import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)




def       lowercase__  (   lowerCAmelCase			:   bytes	,			lowerCAmelCase			:   int	)	-> np.array:






    """simple docstring"""




    UpperCAmelCase       =   F"{sampling_rate}"
    UpperCAmelCase       =   '1'
    UpperCAmelCase       =   'f32le'
    UpperCAmelCase       =   [
        'ffmpeg',
        '-i',
        'pipe:0',
        '-ac',
        ac,
        '-ar',
        ar,
        '-f',
        format_for_conversion,
        '-hide_banner',
        '-loglevel',
        'quiet',
        'pipe:1',
    ]

    try:
        UpperCAmelCase       =   subprocess.Popen(lowerCAmelCase	,			stdin=subprocess.PIPE	,			stdout=subprocess.PIPE	)
    except FileNotFoundError:
        raise ValueError('ffmpeg was not found but is required to load audio files from filename'	)
    UpperCAmelCase       =   ffmpeg_process.communicate(lowerCAmelCase	)
    UpperCAmelCase       =   output_stream[0]

    UpperCAmelCase       =   np.frombuffer(lowerCAmelCase	,			np.floataa	)
    if audio.shape[0] == 0:
        raise ValueError('Malformed soundfile'	)
    return audio







@add_end_docstrings(SCREAMING_SNAKE_CASE_							)
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      def __init__(							self				,    *lowercase_				,    **lowercase_							)  ->   List[Any]:
          # Default, might be overriden by the model.config.
          UpperCAmelCase       =   5
          super().__init__(*lowercase_				,    **lowercase_							)

          if self.framework != "pt":
              raise ValueError(F"The {self.__class__} is only available in PyTorch."							)

          self.check_model_type(lowercase_							)




      def __call__(							self				,    lowercase_				,    **lowercase_				,    )  ->   Tuple:
          return super().__call__(lowercase_				,    **lowercase_							)




      def       a_ (							self				,    lowercase_=None				,    **lowercase_							)  ->   Optional[Any]:
          # No parameters on this pipeline right now
          UpperCAmelCase       =   {}
          if top_k is not None:
              if top_k > self.model.config.num_labels:
                  UpperCAmelCase       =   self.model.config.num_labels
              UpperCAmelCase       =   top_k
          return {}, {}, postprocess_params




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          if isinstance(lowercase_				,    lowercase_							):
              if inputs.startswith('http://'							) or inputs.startswith('https://'							):
                  # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                  # like http_huggingface_co.png
                  UpperCAmelCase       =   requests.get(lowercase_							).content
              else:
                  with open(lowercase_				,    'rb'							) as f:
                      UpperCAmelCase       =   f.read()

          if isinstance(lowercase_				,    lowercase_							):
              UpperCAmelCase       =   ffmpeg_read(lowercase_				,    self.feature_extractor.sampling_rate							)

          if isinstance(lowercase_				,    lowercase_							):
              # Accepting `"array"` which is the key defined in `datasets` for
              # better integration
              if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                  raise ValueError(
                      'When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '
                      '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
                      'containing the sampling_rate associated with that array'							)

              UpperCAmelCase       =   inputs.pop('raw'				,    lowercase_							)
              if _inputs is None:
                  # Remove path which will not be used from `datasets`.
                  inputs.pop('path'				,    lowercase_							)
                  UpperCAmelCase       =   inputs.pop('array'				,    lowercase_							)
              UpperCAmelCase       =   inputs.pop('sampling_rate'							)
              UpperCAmelCase       =   _inputs
              if in_sampling_rate != self.feature_extractor.sampling_rate:
                  import torch

                  if is_torchaudio_available():
                      from torchaudio import functional as F
                  else:
                      raise ImportError(
                          'torchaudio is required to resample audio samples in AudioClassificationPipeline. '
                          'The torchaudio package can be installed through: `pip install torchaudio`.'							)

                  UpperCAmelCase       =   F.resample(
                      torch.from_numpy(lowercase_							)				,    lowercase_				,    self.feature_extractor.sampling_rate							).numpy()

          if not isinstance(lowercase_				,    np.ndarray							):
              raise ValueError('We expect a numpy ndarray as input'							)
          if len(inputs.shape							) != 1:
              raise ValueError('We expect a single channel audio input for AudioClassificationPipeline'							)

          UpperCAmelCase       =   self.feature_extractor(
              lowercase_				,    sampling_rate=self.feature_extractor.sampling_rate				,    return_tensors='pt'							)
          return processed




      def       a_ (							self				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   self.model(**lowercase_							)
          return model_outputs




      def       a_ (							self				,    lowercase_				,    lowercase_=5							)  ->   int:
          UpperCAmelCase       =   model_outputs.logits[0].softmax(-1							)
          UpperCAmelCase     ,							UpperCAmelCase       =   probs.topk(lowercase_							)

          UpperCAmelCase       =   scores.tolist()
          UpperCAmelCase       =   ids.tolist()

          UpperCAmelCase       =   [{'score': score, 'label': self.model.config.idalabel[_id]} for score, _id in zip(lowercase_				,    lowercase_							)]

          return labels



