




"""simple docstring"""






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    None
							_lowerCamelCase     :			Dict    =    None
							_lowerCamelCase     :			List[str]    =    graph

							self._normalize_graph(lowercase  ,      lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    len(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    None
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							if sources is int:
											_lowerCamelCase     :			List[str]    =    [sources]
							if sinks is int:
											_lowerCamelCase     :			Dict    =    [sinks]

							if len(lowercase  ) == 0 or len(lowercase  ) == 0:
											return

							_lowerCamelCase     :			Union[str, Any]    =    sources[0]
							_lowerCamelCase     :			Tuple    =    sinks[0]

							# make fake vertex if there are more
							# than one source or sink
							if len(lowercase  ) > 1 or len(lowercase  ) > 1:
											_lowerCamelCase     :			Tuple    =    0
											for i in sources:
															max_input_flow += sum(self.graph[i]  )

											_lowerCamelCase     :			Tuple    =    len(self.graph  ) + 1
											for room in self.graph:
															room.insert(0  ,      0  )
											self.graph.insert(0  ,      [0] * size  )
											for i in sources:
															_lowerCamelCase     :			List[Any]    =    max_input_flow
											_lowerCamelCase     :			Any    =    0

											_lowerCamelCase     :			Tuple    =    len(self.graph  ) + 1
											for room in self.graph:
															room.append(0  )
											self.graph.append([0] * size  )
											for i in sinks:
															_lowerCamelCase     :			int    =    max_input_flow
											_lowerCamelCase     :			List[str]    =    size - 1
			def 					A_    (  self  ):
							if self.maximum_flow_algorithm is None:
											raise Exception('You need to set maximum flow algorithm before.'  )
							if self.source_index is None or self.sink_index is None:
											return 0

							self.maximum_flow_algorithm.execute()
							return self.maximum_flow_algorithm.getMaximumFlow()






			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			List[Any]    =    algorithm(self  )






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    flow_network
							_lowerCamelCase     :			Optional[int]    =    flow_network.verticesCount
							_lowerCamelCase     :			Tuple    =    flow_network.sourceIndex
							_lowerCamelCase     :			int    =    flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							_lowerCamelCase     :			Dict    =    flow_network.graph
							_lowerCamelCase     :			Tuple    =    False
			def 					A_    (  self  ):
							if not self.executed:
											self._algorithm()
											_lowerCamelCase     :			Any    =    True






			def 					A_    (  self  ):
							pass






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__(lowercase  )
							# use this to save your result
							_lowerCamelCase     :			str    =    -1






			def 					A_    (  self  ):
							if not self.executed:
											raise Exception('You should execute algorithm before using its result!'  )

							return self.maximum_flow






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__(lowercase  )

							_lowerCamelCase     :			Dict    =    [[0] * self.verticies_count for i in range(self.verticies_count  )]

							_lowerCamelCase     :			List[str]    =    [0] * self.verticies_count
							_lowerCamelCase     :			int    =    [0] * self.verticies_count
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    self.verticies_count

							# push some substance to graph
							for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]  ):
											self.preflow[self.source_index][nextvertex_index] += bandwidth
											self.preflow[nextvertex_index][self.source_index] -= bandwidth
											self.excesses[nextvertex_index] += bandwidth

							# Relabel-to-front selection rule
							_lowerCamelCase     :			int    =    [
							    i
							    for i in range(self.verticies_count  )
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							_lowerCamelCase     :			int    =    0
							while i < len(lowercase  ):
											_lowerCamelCase     :			Any    =    vertices_list[i]
											_lowerCamelCase     :			Union[str, Any]    =    self.heights[vertex_index]
											self.process_vertex(lowercase  )
											if self.heights[vertex_index] > previous_height:
															# if it was relabeled, swap elements
															# and start from 0 index
															vertices_list.insert(0  ,      vertices_list.pop(lowercase  )  )
															_lowerCamelCase     :			List[Any]    =    0
											else:
															i += 1

							_lowerCamelCase     :			Any    =    sum(self.preflow[self.source_index]  )
			def 					A_    (  self  ,      lowercase  ):
							while self.excesses[vertex_index] > 0:
											for neighbour_index in range(self.verticies_count  ):
															# if it's neighbour and current vertex is higher
															if (
															    self.graph[vertex_index][neighbour_index]
															    - self.preflow[vertex_index][neighbour_index]
															    > 0
															    and self.heights[vertex_index] > self.heights[neighbour_index]
															):
																			self.push(lowercase  ,      lowercase  )

											self.relabel(lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Tuple    =    min(
							    self.excesses[from_index]  ,      self.graph[from_index][to_index] - self.preflow[from_index][to_index]  ,      )
							self.preflow[from_index][to_index] += preflow_delta
							self.preflow[to_index][from_index] -= preflow_delta
							self.excesses[from_index] -= preflow_delta
							self.excesses[to_index] += preflow_delta






			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			Dict    =    None
							for to_index in range(self.verticies_count  ):
											if (
											    self.graph[vertex_index][to_index]
											    - self.preflow[vertex_index][to_index]
											    > 0
											) and (min_height is None or self.heights[to_index] < min_height):
															_lowerCamelCase     :			Any    =    self.heights[to_index]

							if min_height is not None:
											_lowerCamelCase     :			Optional[Any]    =    min_height + 1


if __name__ == "__main__":
			lowercase__     						=						[0]
			lowercase__     						=						[3]
			# graph = [
			#     [0, 0, 4, 6, 0, 0],
			#     [0, 0, 5, 2, 0, 0],
			#     [0, 0, 0, 0, 4, 4],
			#     [0, 0, 0, 0, 6, 6],
			#     [0, 0, 0, 0, 0, 0],
			#     [0, 0, 0, 0, 0, 0],
			# ]
			lowercase__     						=						[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

			# prepare our network
			lowercase__     						=						FlowNetwork(graph, entrances, exits)
			# set algorithm
			flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
			# and calculate
			lowercase__     						=						flow_network.find_maximum_flow()

			print(F"maximum flow is {maximum_flow}")





"""simple docstring"""






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

import numpy as np







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			str    =    '1'
				_lowerCamelCase     :			str    =    'f32le'
				_lowerCamelCase     :			Union[str, Any]    =    [
				    'ffmpeg',
				    '-i',
				    'pipe:0',
				    '-ac',
				    ac,
				    '-ar',
				    ar,
				    '-f',
				    format_for_conversion,
				    '-hide_banner',
				    '-loglevel',
				    'quiet',
				    'pipe:1',
				]

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = "f32le"							,    ):
				_lowerCamelCase     :			Optional[Any]    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			List[str]    =    '1'
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    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     :			Dict    =    platform.system()
				if system == "Linux":
								_lowerCamelCase     :			Optional[int]    =    'alsa'
								_lowerCamelCase     :			Optional[Any]    =    'default'
				elif system == "Darwin":
								_lowerCamelCase     :			Optional[int]    =    'avfoundation'
								_lowerCamelCase     :			Any    =    ':0'
				elif system == "Windows":
								_lowerCamelCase     :			Tuple    =    'dshow'
								_lowerCamelCase     :			Tuple    =    'default'

				_lowerCamelCase     :			Optional[int]    =    [
				    '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     :			List[Any]    =    _ffmpeg_stream(lowercase__							,    lowercase__		)
				for item in iterator:
								yield item







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "f32le"							,    ):
				if stream_chunk_s is not None:
								_lowerCamelCase     :			int    =    stream_chunk_s
				else:
								_lowerCamelCase     :			Optional[Any]    =    chunk_length_s

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

				if stride_length_s is None:
								_lowerCamelCase     :			Union[str, Any]    =    chunk_length_s / 6
				_lowerCamelCase     :			Optional[int]    =    int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
				if isinstance(lowercase__							,    (int, float)		):
								_lowerCamelCase     :			Any    =    [stride_length_s, stride_length_s]

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







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







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    2**24  # 16Mo
				try:
								with subprocess.Popen(lowercase__							,    stdout=subprocess.PIPE							,    bufsize=lowercase__		) as ffmpeg_process:
												while True:
																_lowerCamelCase     :			Optional[Any]    =    ffmpeg_process.stdout.read(lowercase__		)
																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