

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__												=						TypeVar('''T''')



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (position - 1) // 2



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (2 * position) + 1



def   __UpperCAmelCase					(      __a :     int      )     ->	int:

					"""simple docstring"""

					return (2 * position) + 2

class 							UpperCAmelCase_						(     Generic[T]  ):
							"""simple docstring"""







							def __init__(     self			)       ->       None:
												_a      :       list[tuple[T, int]]   =						[]
												_a      :       dict[T, int]   =						{}
												_a      :       int   =						0


							def __len__(     self			)       ->       int:
												return self.elements


							def __repr__(     self			)       ->       str:
												return str(self.heap			)


							def 							__lowercase							(     self			)       ->       bool:
												# Check if the priority queue is empty
												return self.elements == 0


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Add an element with given priority to the queue
												self.heap.append((elem, weight)			)
												_a      :       Union[str, Any]   =						self.elements
												self.elements += 1
												self._bubble_up(_a			)


							def 							__lowercase							(     self			)       ->       T:
												# Remove and return the element with lowest weight (highest priority)
												if self.elements > 1:
																	self._swap_nodes(0			,				self.elements - 1			)
												_a ,   _a      :       str   =						self.heap.pop()
												del self.position_map[elem]
												self.elements -= 1
												if self.elements > 0:
																	_a ,   _a      :       List[Any]   =						self.heap[0]
																	self._bubble_down(_a			)
												return elem


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Update the weight of the given key
												_a      :       Optional[int]   =						self.position_map[elem]
												_a      :       Any   =						(elem, weight)
												if position > 0:
																	_a      :       Any   =						get_parent_position(_a			)
																	_a ,   _a      :       Optional[int]   =						self.heap[parent_position]
																	if parent_weight > weight:
																						self._bubble_up(_a			)
																	else:
																						self._bubble_down(_a			)
												else:
																	self._bubble_down(_a			)


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Place a node at the proper position (upward movement) [to be used internally
												# only]
												_a      :       Union[str, Any]   =						self.position_map[elem]
												if curr_pos == 0:
																	return None
												_a      :       str   =						get_parent_position(_a			)
												_a ,   _a      :       List[str]   =						self.heap[curr_pos]
												_a ,   _a      :       List[str]   =						self.heap[parent_position]
												if parent_weight > weight:
																	self._swap_nodes(_a			,				_a			)
																	return self._bubble_up(_a			)
												return None


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Place a node at the proper position (downward movement) [to be used
												# internally only]
												_a      :       Union[str, Any]   =						self.position_map[elem]
												_a ,   _a      :       List[str]   =						self.heap[curr_pos]
												_a      :       int   =						get_child_left_position(_a			)
												_a      :       Optional[int]   =						get_child_right_position(_a			)
												if child_left_position < self.elements and child_right_position < self.elements:
																	_a ,   _a      :       Union[str, Any]   =						self.heap[child_left_position]
																	_a ,   _a      :       Tuple   =						self.heap[child_right_position]
																	if child_right_weight < child_left_weight and child_right_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												if child_left_position < self.elements:
																	_a ,   _a      :       List[str]   =						self.heap[child_left_position]
																	if child_left_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												else:
																	return None
												if child_right_position < self.elements:
																	_a ,   _a      :       Optional[Any]   =						self.heap[child_right_position]
																	if child_right_weight < weight:
																						self._swap_nodes(_a			,				_a			)
																						return self._bubble_down(_a			)
												return None




							def 							__lowercase							(     self			,				_a			,				_a			)       ->       None:
												# Swap the nodes at the given positions
												_a      :       str   =						self.heap[nodea_pos][0]
												_a      :       str   =						self.heap[nodea_pos][0]
												_a ,   _a      :       Optional[Any]   =						(
												    self.heap[nodea_pos],
												    self.heap[nodea_pos],
												)
												_a      :       List[str]   =						nodea_pos
												_a      :       List[str]   =						nodea_pos



class 							UpperCAmelCase_						(     Generic[T]  ):
							"""simple docstring"""







							def __init__(     self			)       ->       None:
												_a      :       dict[T, dict[T, int]]   =						{}
												_a      :       int   =						0


							def __repr__(     self			)       ->       str:
												return str(self.connections			)


							def __len__(     self			)       ->       int:
												return self.nodes


							def 							__lowercase							(     self			,				_a			)       ->       None:
												# Add a node in the graph if it is not in the graph
												if node not in self.connections:
																	_a      :       Optional[Any]   =						{}
																	self.nodes += 1




							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       None:
												# Add an edge between 2 nodes in the graph
												self.add_node(_a			)
												self.add_node(_a			)
												_a      :       int   =						weight
												_a      :       int   =						weight



def   __UpperCAmelCase					(      __a :     GraphUndirectedWeighted[T]						,)     ->	tuple[dict[T, int], dict[T, T | None]]:

					"""simple docstring"""

					_a      :       dict[T, int]   =						{node: maxsize for node in graph.connections}
					_a      :       dict[T, T | None]   =						{node: None for node in graph.connections}

					_a      :       MinPriorityQueue[T]   =						MinPriorityQueue()
					for node, weight in dist.items():
										priority_queue.push(__a						,__a      )

					if priority_queue.is_empty():
										return dist, parent

					# initialization
					_a      :       List[str]   =						priority_queue.extract_min()
					_a      :       int   =						0
					for neighbour in graph.connections[node]:
										if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
															_a      :       Tuple   =						dist[node] + graph.connections[node][neighbour]
															priority_queue.update_key(__a						,dist[neighbour]      )
															_a      :       List[Any]   =						node

    # running prim's algorithm
					while not priority_queue.is_empty():
										_a      :       List[str]   =						priority_queue.extract_min()
										for neighbour in graph.connections[node]:
															if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																				_a      :       Dict   =						dist[node] + graph.connections[node][neighbour]
																				priority_queue.update_key(__a						,dist[neighbour]      )
																				_a      :       Optional[Any]   =						node
					return dist, parent






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

import numpy as np



def   __UpperCAmelCase					(      __a :     bytes						,__a :     int      )     ->	np.array:

					"""simple docstring"""

					_a      :       int   =						F"""{sampling_rate}"""
					_a      :       str   =						'''1'''
					_a      :       Optional[int]   =						'''f32le'''
					_a      :       Optional[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:
															_a      :       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
					_a      :       Optional[Any]   =						output_stream[0]
					_a      :       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"						,)     ->	str:

					"""simple docstring"""

					_a      :       Dict   =						F"""{sampling_rate}"""
					_a      :       Optional[Any]   =						'''1'''
					if format_for_conversion == "s16le":
										_a      :       Dict   =						2
					elif format_for_conversion == "f32le":
										_a      :       Optional[Any]   =						4
					else:
										raise ValueError(F"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

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

					_a      :       Tuple   =						[
					    '''ffmpeg''',
					    '''-f''',
					    format_,
					    '''-i''',
					    input_,
					    '''-ac''',
					    ac,
					    '''-ar''',
					    ar,
					    '''-f''',
					    format_for_conversion,
					    '''-fflags''',
					    '''nobuffer''',
					    '''-hide_banner''',
					    '''-loglevel''',
					    '''quiet''',
					    '''pipe:1''',
					]
					_a      :       Any   =						int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					_a      :       str   =						_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"						,)     ->	Optional[int]:

					"""simple docstring"""

					if stream_chunk_s is not None:
										_a      :       Tuple   =						stream_chunk_s
					else:
										_a      :       Tuple   =						chunk_length_s

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

					if stride_length_s is None:
										_a      :       List[Any]   =						chunk_length_s / 6
					_a      :       Optional[int]   =						int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
					if isinstance(__a						,(int, float)      ):
										_a      :       Optional[Any]   =						[stride_length_s, stride_length_s]

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



def   __UpperCAmelCase					(      __a :     Optional[int]						,__a :     int						,__a :     Tuple[int, int]						,__a :     bool = False      )     ->	Optional[int]:

					"""simple docstring"""

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







def   __UpperCAmelCase					(      __a :     int						,__a :     int      )     ->	Tuple:

					"""simple docstring"""

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



