

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

 try:
  with subprocess.Popen(A__  ,       stdin=subprocess.PIPE  ,       stdout=subprocess.PIPE   ) as ffmpeg_process:
   __lowercase													=				ffmpeg_process.communicate(A__   )
 except FileNotFoundError as error:
  raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''   ) from error
 __lowercase													=				output_stream[0]
 __lowercase													=				np.frombuffer(A__  ,       np.floataa   )
 if audio.shape[0] == 0:
  raise ValueError('''Malformed soundfile'''   )
 return audio


def 	_A  (					A__  ,       A__  ,       A__ = "f32le"  ,       ):






 """simple docstring"""



 __lowercase													=				F"{sampling_rate}"
 __lowercase													=				"1"
 if format_for_conversion == "s16le":
  __lowercase													=				2
 elif format_for_conversion == "f32le":
  __lowercase													=				4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"   )

 __lowercase													=				platform.system()
 if system == "Linux":
  __lowercase													=				"alsa"
  __lowercase													=				"default"
 elif system == "Darwin":
  __lowercase													=				"avfoundation"
  __lowercase													=				":0"
 elif system == "Windows":
  __lowercase													=				"dshow"
  __lowercase													=				"default"

 __lowercase													=				[
     "ffmpeg",
     "-f",
     format_,
     "-i",
     input_,
     "-ac",
     ac,
     "-ar",
     ar,
     "-f",
     format_for_conversion,
     "-fflags",
     "nobuffer",
     "-hide_banner",
     "-loglevel",
     "quiet",
     "pipe:1",
 ]
 __lowercase													=				int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
 __lowercase													=				_ffmpeg_stream(A__  ,       A__   )
 for item in iterator:
  yield item


def 	_A  (					A__  ,       A__  ,       A__ = None  ,       A__ = None  ,       A__ = "f32le"  ,       ):






 """simple docstring"""



 if stream_chunk_s is not None:
  __lowercase													=				stream_chunk_s
 else:
  __lowercase													=				chunk_length_s

 __lowercase													=				ffmpeg_microphone(A__  ,       A__  ,       format_for_conversion=A__   )
 if format_for_conversion == "s16le":
  __lowercase													=				np.intaa
  __lowercase													=				2
 elif format_for_conversion == "f32le":
  __lowercase													=				np.floataa
  __lowercase													=				4
 else:
  raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"   )

 if stride_length_s is None:
  __lowercase													=				chunk_length_s / 6
 __lowercase													=				int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
 if isinstance(A__  ,       (int, float)   ):
  __lowercase													=				[stride_length_s, stride_length_s]

 __lowercase													=				int(round(sampling_rate * stride_length_s[0]   )   ) * size_of_sample
 __lowercase													=				int(round(sampling_rate * stride_length_s[1]   )   ) * size_of_sample
 __lowercase													=				datetime.datetime.now()
 __lowercase													=				datetime.timedelta(seconds=A__   )
 for item in chunk_bytes_iter(A__  ,       A__  ,       stride=(stride_left, stride_right)  ,       stream=A__   ):
  # Put everything back in numpy scale
  __lowercase													=				np.frombuffer(item['''raw''']  ,       dtype=A__   )
  __lowercase													=				(
      item["stride"][0] // size_of_sample,
      item["stride"][1] // size_of_sample,
  )
  __lowercase													=				sampling_rate
  audio_time += delta
  if datetime.datetime.now() > audio_time + 10 * delta:
   # We're late !! SKIP
   continue
  yield item


def 	_A  (					A__  ,       A__  ,       A__  ,       A__ = False   ):






 """simple docstring"""



 __lowercase													=				b""
 __lowercase													=				stride
 if stride_left + stride_right >= chunk_len:
  raise ValueError(
      F"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"   )
 __lowercase													=				0
 for raw in iterator:
  acc += raw
  if stream and len(A__   ) < chunk_len:
   __lowercase													=				(_stride_left, 0)
   yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
  else:
   while len(A__   ) >= chunk_len:
    # We are flushing the accumulator
    __lowercase													=				(_stride_left, stride_right)
    __lowercase													=				{"raw": acc[:chunk_len], "stride": stride}
    if stream:
     __lowercase													=				False
    yield item
    __lowercase													=				stride_left
    __lowercase													=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
 if len(A__   ) > stride_left:
  __lowercase													=				{"raw": acc, "stride": (_stride_left, 0)}
  if stream:
   __lowercase													=				False
  yield item






def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				2**24  # 16Mo
 try:
  with subprocess.Popen(A__  ,       stdout=subprocess.PIPE  ,       bufsize=A__   ) as ffmpeg_process:
   while True:
    __lowercase													=				ffmpeg_process.stdout.read(A__   )
    if raw == b"":
     break
    yield raw
 except FileNotFoundError as error:
  raise ValueError('''ffmpeg was not found but is required to stream audio files from filename'''   ) from error




'''simple docstring'''






from collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

