'''simple docstring'''

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

import numpy as np







def __UpperCamelCase(   _A					:				bytes  ,			_A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[Any]   				=  F'''{sampling_rate}'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''1'''
   UpperCAmelCase__							:				List[str]   				=  '''f32le'''
   UpperCAmelCase__							:				Any   				=  [
       '''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:
         UpperCAmelCase__							:				Union[str, Any]   				=  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
   UpperCAmelCase__							:				int   				=  output_stream[0]
   UpperCAmelCase__							:				Optional[int]   				=  np.frombuffer(_A  ,			np.floataa    )
   if audio.shape[0] == 0:
      raise ValueError('''Malformed soundfile'''    )
   return audio







def __UpperCamelCase(   _A					:				int  ,			_A					:				float  ,			_A					:				str = "f32le"  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  F'''{sampling_rate}'''
   UpperCAmelCase__							:				Optional[Any]   				=  '''1'''
   if format_for_conversion == "s16le":
      UpperCAmelCase__							:				Union[str, Any]   				=  2
   elif format_for_conversion == "f32le":
      UpperCAmelCase__							:				List[str]   				=  4
   else:
      raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''    )

   UpperCAmelCase__							:				Dict   				=  platform.system()
   if system == "Linux":
      UpperCAmelCase__							:				Optional[int]   				=  '''alsa'''
      UpperCAmelCase__							:				Optional[int]   				=  '''default'''
   elif system == "Darwin":
      UpperCAmelCase__							:				int   				=  '''avfoundation'''
      UpperCAmelCase__							:				Any   				=  ''':0'''
   elif system == "Windows":
      UpperCAmelCase__							:				List[Any]   				=  '''dshow'''
      UpperCAmelCase__							:				Dict   				=  '''default'''

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







def __UpperCamelCase(   _A					:				int  ,			_A					:				float  ,			_A					:				Optional[int] = None  ,			_A					:				Optional[Union[Tuple[float, float], float]] = None  ,			_A					:				str = "f32le"  ,			):
   '''simple docstring'''
   if stream_chunk_s is not None:
      UpperCAmelCase__							:				int   				=  stream_chunk_s
   else:
      UpperCAmelCase__							:				List[Any]   				=  chunk_length_s

   UpperCAmelCase__							:				int   				=  ffmpeg_microphone(_A  ,			_A  ,			format_for_conversion=_A    )
   if format_for_conversion == "s16le":
      UpperCAmelCase__							:				int   				=  np.intaa
      UpperCAmelCase__							:				Optional[int]   				=  2
   elif format_for_conversion == "f32le":
      UpperCAmelCase__							:				List[str]   				=  np.floataa
      UpperCAmelCase__							:				List[Any]   				=  4
   else:
      raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''    )

   if stride_length_s is None:
      UpperCAmelCase__							:				List[Any]   				=  chunk_length_s / 6
   UpperCAmelCase__							:				List[str]   				=  int(round(sampling_rate * chunk_length_s    )    ) * size_of_sample
   if isinstance(_A  ,			(int, float)    ):
      UpperCAmelCase__							:				str   				=  [stride_length_s, stride_length_s]

   UpperCAmelCase__							:				int   				=  int(round(sampling_rate * stride_length_s[0]    )    ) * size_of_sample
   UpperCAmelCase__							:				str   				=  int(round(sampling_rate * stride_length_s[1]    )    ) * size_of_sample
   UpperCAmelCase__							:				Dict   				=  datetime.datetime.now()
   UpperCAmelCase__							:				List[str]   				=  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
      UpperCAmelCase__							:				Tuple   				=  np.frombuffer(item['''raw''']  ,			dtype=_A    )
      UpperCAmelCase__							:				List[Any]   				=  (
          item['''stride'''][0] // size_of_sample,
          item['''stride'''][1] // size_of_sample,
      )
      UpperCAmelCase__							:				Optional[int]   				=  sampling_rate
      audio_time += delta
      if datetime.datetime.now() > audio_time + 10 * delta:
         # We're late !! SKIP
         continue
      yield item







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				int  ,			_A					:				Tuple[int, int]  ,			_A					:				bool = False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  B''''''
   UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  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__							:				Dict   				=  0
   for raw in iterator:
      acc += raw
      if stream and len(_A    ) < chunk_len:
         UpperCAmelCase__							:				List[str]   				=  (_stride_left, 0)
         yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
      else:
         while len(_A    ) >= chunk_len:
            # We are flushing the accumulator
            UpperCAmelCase__							:				int   				=  (_stride_left, stride_right)
            UpperCAmelCase__							:				int   				=  {'''raw''': acc[:chunk_len], '''stride''': stride}
            if stream:
               UpperCAmelCase__							:				Optional[Any]   				=  False
            yield item
            UpperCAmelCase__							:				Tuple   				=  stride_left
            UpperCAmelCase__							:				Optional[int]   				=  acc[chunk_len - stride_left - stride_right :]
    # Last chunk
   if len(_A    ) > stride_left:
      UpperCAmelCase__							:				List[Any]   				=  {'''raw''': acc, '''stride''': (_stride_left, 0)}
      if stream:
         UpperCAmelCase__							:				List[str]   				=  False
      yield item







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  2**24  # 16Mo
   try:
      with subprocess.Popen(_A  ,			stdout=subprocess.PIPE  ,			bufsize=_A    ) as ffmpeg_process:
         while True:
            UpperCAmelCase__							:				Dict   				=  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'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




