





"""simple docstring"""





from __future__ import annotations
class 		SCREAMING_SNAKE_CASE_				:
				"""simple docstring"""




				def __init__(  self	:Optional[Any]    ,     __lowercase	:list[list[int]]      ):
											__lowerCamelCase   :   List[str]       =TypeError(
											    '''Matrices must be formed from a list of zero or more lists containing at '''
											    '''least one and the same number of values, each of which must be of type '''
											    '''int or float.'''      )
											if len(__lowercase      ) != 0:
																		__lowerCamelCase   :   Tuple       =len(rows[0]      )
																		if cols == 0:
																									raise error
																		for row in rows:
																									if len(__lowercase      ) != cols:
																																raise error
																									for value in row:
																																if not isinstance(__lowercase    ,     (int, float)      ):
																																							raise error
																		__lowerCamelCase   :   Optional[int]       =rows
											else:
																		__lowerCamelCase   :   List[str]       =[]




				def        __lowercase	(  self	:List[Any]      ):
											return [[row[i] for row in self.rows] for i in range(len(self.rows[0]      )      )]




				@property
				def        __lowercase	(  self	:str      ):
											return len(self.rows      )




				@property
				def        __lowercase	(  self	:int      ):
											return len(self.rows[0]      )




				@property
				def        __lowercase	(  self	:Optional[int]      ):
											return (self.num_rows, self.num_columns)




				@property
				def        __lowercase	(  self	:List[Any]      ):
											return self.order[0] == self.order[1]




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Union[str, Any]       =[
											    [0 if column_num != row_num else 1 for column_num in range(self.num_rows      )]
											    for row_num in range(self.num_rows      )
											]
											return Matrix(__lowercase      )




				def        __lowercase	(  self	:str      ):
											if not self.is_square:
																		return 0
											if self.order == (0, 0):
																		return 1
											if self.order == (1, 1):
																		return int(self.rows[0][0]      )
											if self.order == (2, 2):
																		return int(
																		    (self.rows[0][0] * self.rows[1][1])
																		    - (self.rows[0][1] * self.rows[1][0])      )
											else:
																		return sum(
																		    self.rows[0][column] * self.cofactors().rows[0][column]
																		    for column in range(self.num_columns      )      )




				def        __lowercase	(  self	:Tuple      ):
											return bool(self.determinant()      )




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:int    ,     __lowercase	:int      ):
											__lowerCamelCase   :   str       =[
											    [
											        self.rows[other_row][other_column]
											        for other_column in range(self.num_columns      )
											        if other_column != column
											    ]
											    for other_row in range(self.num_rows      )
											    if other_row != row
											]
											return Matrix(__lowercase      ).determinant()




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:int    ,     __lowercase	:int      ):
											if (row + column) % 2 == 0:
																		return self.get_minor(__lowercase    ,     __lowercase      )
											return -1 * self.get_minor(__lowercase    ,     __lowercase      )




				def        __lowercase	(  self	:Any      ):
											return Matrix(
											    [
											        [self.get_minor(__lowercase    ,     __lowercase      ) for column in range(self.num_columns      )]
											        for row in range(self.num_rows      )
											    ]      )




				def        __lowercase	(  self	:List[Any]      ):
											return Matrix(
											    [
											        [
											            self.minors().rows[row][column]
											            if (row + column) % 2 == 0
											            else self.minors().rows[row][column] * -1
											            for column in range(self.minors().num_columns      )
											        ]
											        for row in range(self.minors().num_rows      )
											    ]      )




				def        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   str       =[
											    [self.cofactors().rows[column][row] for column in range(self.num_columns      )]
											    for row in range(self.num_rows      )
											]
											return Matrix(__lowercase      )




				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   Dict       =self.determinant()
											if not determinant:
																		raise TypeError('''Only matrices with a non-zero determinant have an inverse'''      )
											return self.adjugate() * (1 / determinant)




				def __repr__(  self	:Tuple      ):
											return str(self.rows      )




				def __str__(  self	:Optional[int]      ):
											if self.num_rows == 0:
																		return "[]"
											if self.num_rows == 1:
																		return "[[" + ". ".join(str(self.rows[0]      )      ) + "]]"
											return (
											    "["
											    + "\n ".join(
											        [
											            '''[''' + '''. '''.join([str(__lowercase      ) for value in row]      ) + '''.]'''
											            for row in self.rows
											        ]      )
											    + "]"
											)




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:list[int]    ,     __lowercase	:int | None = None      ):
											__lowerCamelCase   :   Tuple       =TypeError('''Row must be a list containing all ints and/or floats'''      )
											if not isinstance(__lowercase    ,     __lowercase      ):
																		raise type_error
											for value in row:
																		if not isinstance(__lowercase    ,     (int, float)      ):
																									raise type_error
											if len(__lowercase      ) != self.num_columns:
																		raise ValueError(
																		    '''Row must be equal in length to the other rows in the matrix'''      )
											if position is None:
																		self.rows.append(__lowercase      )
											else:
																		__lowerCamelCase   :   Optional[int]       =self.rows[0:position] + [row] + self.rows[position:]




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:list[int]    ,     __lowercase	:int | None = None      ):
											__lowerCamelCase   :   Any       =TypeError(
											    '''Column must be a list containing all ints and/or floats'''      )
											if not isinstance(__lowercase    ,     __lowercase      ):
																		raise type_error
											for value in column:
																		if not isinstance(__lowercase    ,     (int, float)      ):
																									raise type_error
											if len(__lowercase      ) != self.num_rows:
																		raise ValueError(
																		    '''Column must be equal in length to the other columns in the matrix'''      )
											if position is None:
																		__lowerCamelCase   :   Optional[int]       =[self.rows[i] + [column[i]] for i in range(self.num_rows      )]
											else:
																		__lowerCamelCase   :   Dict       =[
																		    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																		    for i in range(self.num_rows      )
																		]




				def __eq__(  self	:List[str]    ,     __lowercase	:object      ):
											if not isinstance(__lowercase    ,     __lowercase      ):
																		return NotImplemented
											return self.rows == other.rows




				def __ne__(  self	:List[str]    ,     __lowercase	:object      ):
											return not self == other




				def __neg__(  self	:Tuple      ):
											return self * -1




				def __add__(  self	:List[str]    ,     __lowercase	:Matrix      ):
											if self.order != other.order:
																		raise ValueError('''Addition requires matrices of the same order'''      )
											return Matrix(
											    [
											        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns      )]
											        for i in range(self.num_rows      )
											    ]      )




				def __sub__(  self	:Tuple    ,     __lowercase	:Matrix      ):
											if self.order != other.order:
																		raise ValueError('''Subtraction requires matrices of the same order'''      )
											return Matrix(
											    [
											        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns      )]
											        for i in range(self.num_rows      )
											    ]      )




				def __mul__(  self	:int    ,     __lowercase	:Matrix | int | float      ):
											if isinstance(__lowercase    ,     (int, float)      ):
																		return Matrix(
																		    [[int(element * other      ) for element in row] for row in self.rows]      )
											elif isinstance(__lowercase    ,     __lowercase      ):
																		if self.num_columns != other.num_rows:
																									raise ValueError(
																									    '''The number of columns in the first matrix must '''
																									    '''be equal to the number of rows in the second'''      )
																		return Matrix(
																		    [
																		        [Matrix.dot_product(__lowercase    ,     __lowercase      ) for column in other.columns()]
																		        for row in self.rows
																		    ]      )
											else:
																		raise TypeError(
																		    '''A Matrix can only be multiplied by an int, float, or another matrix'''      )




				def __pow__(  self	:int    ,     __lowercase	:int      ):
											if not isinstance(__lowercase    ,     __lowercase      ):
																		raise TypeError('''A Matrix can only be raised to the power of an int'''      )
											if not self.is_square:
																		raise ValueError('''Only square matrices can be raised to a power'''      )
											if other == 0:
																		return self.identity()
											if other < 0:
																		if self.is_invertable():
																									return self.inverse() ** (-other)
																		raise ValueError(
																		    '''Only invertable matrices can be raised to a negative power'''      )
											__lowerCamelCase   :   Dict       =self
											for _ in range(other - 1      ):
																		result *= self
											return result




				@classmethod
				def        __lowercase	(  cls	:str    ,     __lowercase	:list[int]    ,     __lowercase	:list[int]      ):
											return sum(row[i] * column[i] for i in range(len(__lowercase      )      )      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()











"""simple docstring"""





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

import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				bytes     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =F'{sampling_rate}'
							__lowerCamelCase   :   Union[str, Any]       ='''1'''
							__lowerCamelCase   :   List[Any]       ='''f32le'''
							__lowerCamelCase   :   Optional[int]       =[
							    '''ffmpeg''',
							    '''-i''',
							    '''pipe:0''',
							    '''-ac''',
							    ac,
							    '''-ar''',
							    ar,
							    '''-f''',
							    format_for_conversion,
							    '''-hide_banner''',
							    '''-loglevel''',
							    '''quiet''',
							    '''pipe:1''',
							]

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







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

							__lowerCamelCase   :   Union[str, Any]       =platform.system()
							if system == "Linux":
														__lowerCamelCase   :   Tuple       ='''alsa'''
														__lowerCamelCase   :   int       ='''default'''
							elif system == "Darwin":
														__lowerCamelCase   :   Any       ='''avfoundation'''
														__lowerCamelCase   :   List[str]       =''':0'''
							elif system == "Windows":
														__lowerCamelCase   :   Union[str, Any]       ='''dshow'''
														__lowerCamelCase   :   Dict       ='''default'''

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				float     ,    SCREAMING_SNAKE_CASE							:				Optional[int] = None     ,    SCREAMING_SNAKE_CASE							:				Optional[Union[Tuple[float, float], float]] = None     ,    SCREAMING_SNAKE_CASE							:				str = "f32le"     ,    ):


							'''simple docstring'''







							if stream_chunk_s is not None:
														__lowerCamelCase   :   Optional[int]       =stream_chunk_s
							else:
														__lowerCamelCase   :   Tuple       =chunk_length_s

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

							if stride_length_s is None:
														__lowerCamelCase   :   Tuple       =chunk_length_s / 6
							__lowerCamelCase   :   str       =int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
							if isinstance(SCREAMING_SNAKE_CASE     ,    (int, float) ):
														__lowerCamelCase   :   List[str]       =[stride_length_s, stride_length_s]

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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				Tuple[int, int]     ,    SCREAMING_SNAKE_CASE							:				bool = False ):


							'''simple docstring'''







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



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''simple docstring'''







							__lowerCamelCase   :   Dict       =2**24  # 16Mo
							try:
														with subprocess.Popen(SCREAMING_SNAKE_CASE     ,    stdout=subprocess.PIPE     ,    bufsize=SCREAMING_SNAKE_CASE ) as ffmpeg_process:
																					while True:
																												__lowerCamelCase   :   Dict       =ffmpeg_process.stdout.read(SCREAMING_SNAKE_CASE )
																												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




