


SCREAMING_SNAKE_CASE : str          =    float("inf")





class 					snake_case__  :





	def __init__(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								a_       :   Any			=	[0] * n
								a_       :   Union[str, Any]			=	[0] * n
								a_       :   Tuple			=	[0] * n
								a_       :   Dict			=	[[] for _ in range(UpperCamelCase_							)]





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_=0							)		->			str:
								"""simple docstring"""






								self.adj[a].append([b, len(self.adj[b]							), c, 0]							)
								self.adj[b].append([a, len(self.adj[a]							) - 1, rcap, 0]							)





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Optional[int]:
								"""simple docstring"""






								if vertex == sink or not flow:
															return flow

								for i in range(self.ptr[vertex]				,    len(self.adj[vertex]							)							):
															a_       :   Dict			=	self.adj[vertex][i]
															if self.lvl[e[0]] == self.lvl[vertex] + 1:
																						a_       :   Optional[int]			=	self.depth_first_search(e[0]				,    UpperCamelCase_				,    min(UpperCamelCase_				,    e[2] - e[3]							)							)
																						if p:
																													self.adj[vertex][i][3] += p
																													self.adj[e[0]][e[1]][3] -= p
																													return p
															a_       :   Any			=	self.ptr[vertex] + 1
								return 0





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Dict			=	0, source
								for l in range(31							):  # noqa: E741  l = 30 maybe faster for random data
															while True:
																						a_       :   Any			=	[0] * len(self.q							), [0] * len(self.q							)
																						a_       :   Any			=	0, 1, 1
																						while qi < qe and not self.lvl[sink]:
																													a_       :   str			=	self.q[qi]
																													qi += 1
																													for e in self.adj[v]:
																																				if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
																																											a_       :   List[Any]			=	e[0]
																																											qe += 1
																																											a_       :   str			=	self.lvl[v] + 1

																						a_       :   Union[str, Any]			=	self.depth_first_search(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)
																						while p:
																													flow += p
																													a_       :   Optional[int]			=	self.depth_first_search(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)

																						if not self.lvl[sink]:
																													break

								return flow





SCREAMING_SNAKE_CASE : Optional[Any]          =    Dinic(10)
SCREAMING_SNAKE_CASE : List[str]          =    0
SCREAMING_SNAKE_CASE : List[Any]          =    9
for vertex in range(1, 5):
	graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
	graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
	graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)





@dataclass
class 					snake_case__  ( datasets.BuilderConfig       ):
	UpperCAmelCase   :							Optional[datasets.Features]         = None





class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
	UpperCAmelCase   :							Optional[int]         = ArrowConfig





	def     A				(  self							)		->			str:
								"""simple docstring"""






								return datasets.DatasetInfo(features=self.config.features							)





	def     A				(  self				,    UpperCamelCase_							)		->			str:
								"""simple docstring"""






								if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
															a_       :   Dict			=	data_files
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Optional[Any]			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
								a_       :   Any			=	[]
								for split_name, files in data_files.items():
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Dict			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
																													with open(UpperCamelCase_				,    """rb"""							) as f:
																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
																													break
															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
								return splits





	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
								"""simple docstring"""






								if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
								return pa_table





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
															with open(UpperCamelCase_				,    """rb"""							) as f:
																						try:
																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
																																				# Uncomment for debugging (will print the Arrow table size and elements)
																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
																						except ValueError as e:
																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
																													raise


