



"""simple docstring"""





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

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      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:
            with subprocess.Popen(lowerCAmelCase__			,    stdin=subprocess.PIPE			,    stdout=subprocess.PIPE   ) as ffmpeg_process:
                  UpperCAmelCase_    				=							ffmpeg_process.communicate(lowerCAmelCase__   )
      except FileNotFoundError as error:
            raise ValueError("ffmpeg was not found but is required to load audio files from filename"   ) from error
      UpperCAmelCase_    				=							output_stream[0]
      UpperCAmelCase_    				=							np.frombuffer(lowerCAmelCase__			,    np.floataa   )
      if audio.shape[0] == 0:
            raise ValueError("Malformed soundfile"   )
      return audio
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = "f32le"			,    ):
      UpperCAmelCase_    				=							f"""{sampling_rate}"""
      UpperCAmelCase_    				=							"1"
      if format_for_conversion == "s16le":
            UpperCAmelCase_    				=							2
      elif format_for_conversion == "f32le":
            UpperCAmelCase_    				=							4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""   )

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

      UpperCAmelCase_    				=							[
          "ffmpeg",
          "-f",
          format_,
          "-i",
          input_,
          "-ac",
          ac,
          "-ar",
          ar,
          "-f",
          format_for_conversion,
          "-fflags",
          "nobuffer",
          "-hide_banner",
          "-loglevel",
          "quiet",
          "pipe:1",
      ]
      UpperCAmelCase_    				=							int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
      UpperCAmelCase_    				=							_ffmpeg_stream(lowerCAmelCase__			,    lowerCAmelCase__   )
      for item in iterator:
            yield item
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = None			,    lowerCAmelCase__ = None			,    lowerCAmelCase__ = "f32le"			,    ):
      if stream_chunk_s is not None:
            UpperCAmelCase_    				=							stream_chunk_s
      else:
            UpperCAmelCase_    				=							chunk_length_s

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

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

      UpperCAmelCase_    				=							int(round(sampling_rate * stride_length_s[0]   )   ) * size_of_sample
      UpperCAmelCase_    				=							int(round(sampling_rate * stride_length_s[1]   )   ) * size_of_sample
      UpperCAmelCase_    				=							datetime.datetime.now()
      UpperCAmelCase_    				=							datetime.timedelta(seconds=lowerCAmelCase__   )
      for item in chunk_bytes_iter(lowerCAmelCase__			,    lowerCAmelCase__			,    stride=(stride_left, stride_right)			,    stream=lowerCAmelCase__   ):
            # Put everything back in numpy scale
            UpperCAmelCase_    				=							np.frombuffer(item["raw"]			,    dtype=lowerCAmelCase__   )
            UpperCAmelCase_    				=							(
                item["stride"][0] // size_of_sample,
                item["stride"][1] // size_of_sample,
            )
            UpperCAmelCase_    				=							sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							b""
      UpperCAmelCase_				,					UpperCAmelCase_    				=							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}"""   )
      UpperCAmelCase_    				=							0
      for raw in iterator:
            acc += raw
            if stream and len(lowerCAmelCase__   ) < chunk_len:
                  UpperCAmelCase_    				=							(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(lowerCAmelCase__   ) >= chunk_len:
                        # We are flushing the accumulator
                        UpperCAmelCase_    				=							(_stride_left, stride_right)
                        UpperCAmelCase_    				=							{"raw": acc[:chunk_len], "stride": stride}
                        if stream:
                              UpperCAmelCase_    				=							False
                        yield item
                        UpperCAmelCase_    				=							stride_left
                        UpperCAmelCase_    				=							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(lowerCAmelCase__   ) > stride_left:
            UpperCAmelCase_    				=							{"raw": acc, "stride": (_stride_left, 0)}
            if stream:
                  UpperCAmelCase_    				=							False
            yield item


def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							2**24  # 16Mo
      try:
            with subprocess.Popen(lowerCAmelCase__			,    stdout=subprocess.PIPE			,    bufsize=lowerCAmelCase__   ) as ffmpeg_process:
                  while True:
                        UpperCAmelCase_    				=							ffmpeg_process.stdout.read(lowerCAmelCase__   )
                        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 heapq import heappop, heappush

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							grid.shape
      UpperCAmelCase_    				=							[-1, 1, 0, 0]
      UpperCAmelCase_    				=							[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_				,					UpperCAmelCase_    				=							[(0, source)], set()
      UpperCAmelCase_    				=							np.full((rows, cols)			,    np.inf   )
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							np.empty((rows, cols)			,    dtype=lowerCAmelCase__   )
      UpperCAmelCase_    				=							None

      while queue:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heappop(lowerCAmelCase__   )
            if (x, y) in visited:
                  continue
            visited.add((x, y)   )

            if (x, y) == destination:
                  UpperCAmelCase_    				=							[]
                  while (x, y) != source:
                        path.append((x, y)   )
                        UpperCAmelCase_				,					UpperCAmelCase_    				=							predecessors[x, y]
                  path.append(lowerCAmelCase__   )  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(lowerCAmelCase__   )   ):
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_    				=							grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(lowerCAmelCase__			,    (dist + 1, (nx, ny))   )
                              UpperCAmelCase_    				=							dist + 1
                              UpperCAmelCase_    				=							(x, y)

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()




