

"""simple docstring"""






import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


snake_case_   :   Optional[int]        =       datasets.utils.logging.get_logger(__name__)







@dataclass
class       snake_case__  ( datasets.BuilderConfig     ):
			SCREAMING_SNAKE_CASE__     				=						None
			SCREAMING_SNAKE_CASE__     				=						'''utf-8'''
			SCREAMING_SNAKE_CASE__     				=						None
			SCREAMING_SNAKE_CASE__     				=						None
			SCREAMING_SNAKE_CASE__     				=						True  # deprecated
			SCREAMING_SNAKE_CASE__     				=						None  # deprecated
			SCREAMING_SNAKE_CASE__     				=						10 << 20  # 10MB
			SCREAMING_SNAKE_CASE__     				=						None







class       snake_case__  ( datasets.ArrowBasedBuilder     ):
			SCREAMING_SNAKE_CASE__     				=						JsonConfig







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								if self.config.block_size is not None:
													logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"  )
													UpperCAmelCase			:   Tuple      =				self.config.block_size
								if self.config.use_threads is not True:
													logger.warning(
													    "The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore."  )
								if self.config.newlines_in_values is not None:
													raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"  )
								return datasets.DatasetInfo(features=self.config.features  )







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: Any  ):





								'''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}"""  )
								UpperCAmelCase			:   Optional[int]      =				dl_manager.download_and_extract(self.config.data_files  )
								if isinstance(__a			,  (str, list, tuple)  ):
													UpperCAmelCase			:   Tuple      =				data_files
													if isinstance(__a			,  __a  ):
																		UpperCAmelCase			:   Tuple      =				[files]
													UpperCAmelCase			:   Optional[int]      =				[dl_manager.iter_files(__a  ) for file in files]
													return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,  gen_kwargs={"files": files}  )]
								UpperCAmelCase			:   Dict      =				[]
								for split_name, files in data_files.items():
													if isinstance(__a			,  __a  ):
																		UpperCAmelCase			:   List[str]      =				[files]
													UpperCAmelCase			:   Any      =				[dl_manager.iter_files(__a  ) for file in files]
													splits.append(datasets.SplitGenerator(name=__a			,  gen_kwargs={"files": files}  )  )
								return splits







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								if self.config.features is not None:
													# adding missing columns
													for column_name in set(self.config.features  ) - set(pa_table.column_names  ):
																		UpperCAmelCase			:   Any      =				self.config.features.arrow_schema.field(__a  ).type
																		UpperCAmelCase			:   Any      =				pa_table.append_column(__a			,  pa.array([None] * len(__a  )			,  type=__a  )  )
													# more expensive cast to support nested structures with keys in a different order
													# allows str <-> int/float or str to Audio for example
													UpperCAmelCase			:   Optional[Any]      =				table_cast(__a			,  self.config.features.arrow_schema  )
								return pa_table







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: Dict  ):





								'''simple docstring'''



								for file_idx, file in enumerate(itertools.chain.from_iterable(__a  )  ):
													# If the file is one json object and if we need to look at the list of items in one specific field
													if self.config.field is not None:
																		with open(__a			,  encoding=self.config.encoding			,  errors=self.config.encoding_errors  ) as f:
																							UpperCAmelCase			:   Union[str, Any]      =				json.load(__a  )

																		# We keep only the field we are interested in
																		UpperCAmelCase			:   Tuple      =				dataset[self.config.field]

																		# We accept two format: a list of dicts or a dict of lists
																		if isinstance(__a			,  (list, tuple)  ):
																							UpperCAmelCase			:   Optional[Any]      =				set().union(*[row.keys() for row in dataset]  )
																							UpperCAmelCase			:   List[str]      =				{col: [row.get(__a  ) for row in dataset] for col in keys}
																		else:
																							UpperCAmelCase			:   Tuple      =				dataset
																		UpperCAmelCase			:   Optional[int]      =				pa.Table.from_pydict(__a  )
																		yield file_idx, self._cast_table(__a  )

													# If the file has one json object per line
													else:
																		with open(__a			,  "rb"  ) as f:
																							UpperCAmelCase			:   Any      =				0
																							# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																							# Set a default minimum value of 16kB if the chunk size is really small
																							UpperCAmelCase			:   List[Any]      =				max(self.config.chunksize // 32			,  16 << 10  )
																							UpperCAmelCase			:   Union[str, Any]      =				(
																							    self.config.encoding_errors if self.config.encoding_errors is not None else 'strict'
																							)
																							while True:
																												UpperCAmelCase			:   str      =				f.read(self.config.chunksize  )
																												if not batch:
																																	break
																												# Finish current line
																												try:
																																	batch += f.readline()
																												except (AttributeError, io.UnsupportedOperation):
																																	batch += readline(__a  )
																												# PyArrow only accepts utf-8 encoded bytes
																												if self.config.encoding != "utf-8":
																																	UpperCAmelCase			:   int      =				batch.decode(self.config.encoding			,  errors=__a  ).encode("utf-8"  )
																												try:
																																	while True:
																																						try:
																																											UpperCAmelCase			:   Tuple      =				paj.read_json(
																																											    io.BytesIO(__a  )			,  read_options=paj.ReadOptions(block_size=__a  )  )
																																											break
																																						except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																											if (
																																											    isinstance(__a			,  pa.ArrowInvalid  )
																																											    and "straddling" not in str(__a  )
																																											    or block_size > len(__a  )
																																											):
																																																raise
																																											else:
																																																# Increase the block size in case it was too small.
																																																# The block size will be reset for the next file.
																																																logger.debug(
																																																    f"""Batch of {len(__a  )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""  )
																																																block_size *= 2
																												except pa.ArrowInvalid as e:
																																	try:
																																						with open(
																																						    __a			,  encoding=self.config.encoding			,  errors=self.config.encoding_errors  ) as f:
																																											UpperCAmelCase			:   str      =				json.load(__a  )
																																	except json.JSONDecodeError:
																																						logger.error(f"""Failed to read file '{file}' with error {type(__a  )}: {e}"""  )
																																						raise e
																																	# If possible, parse the file as a list of json objects and exit the loop
																																	if isinstance(__a			,  __a  ):  # list is the only sequence type supported in JSON
																																						try:
																																											UpperCAmelCase			:   List[Any]      =				set().union(*[row.keys() for row in dataset]  )
																																											UpperCAmelCase			:   Union[str, Any]      =				{col: [row.get(__a  ) for row in dataset] for col in keys}
																																											UpperCAmelCase			:   List[Any]      =				pa.Table.from_pydict(__a  )
																																						except (pa.ArrowInvalid, AttributeError) as e:
																																											logger.error(f"""Failed to read file '{file}' with error {type(__a  )}: {e}"""  )
																																											raise ValueError(f"""Not able to read records in the JSON file at {file}."""  ) from None
																																						yield file_idx, self._cast_table(__a  )
																																						break
																																	else:
																																						logger.error(f"""Failed to read file '{file}' with error {type(__a  )}: {e}"""  )
																																						raise ValueError(
																																						    f"""Not able to read records in the JSON file at {file}. """
																																						    f"""You should probably indicate the field of the JSON file containing your records. """
																																						    f"""This JSON file contain the following fields: {str(list(dataset.keys()  )  )}. """
																																						    f"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """  ) from None
																																						# 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 (file_idx, batch_idx), self._cast_table(__a  )
																												batch_idx += 1





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

					while queue:
										UpperCAmelCase			:   Any      =				queue.pop(0	)
										for ind in range(len(graph[u]	)	):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(_lowercase	)
																				UpperCAmelCase			:   Optional[int]      =				True
																				UpperCAmelCase			:   int      =				u

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

										while s != source:
															# Find the minimum value in select path
															UpperCAmelCase			:   Tuple      =				min(_lowercase							,      graph[parent[s]][s]	)
															UpperCAmelCase			:   Optional[Any]      =				parent[s]

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				sink

										while v != source:
															UpperCAmelCase			:   Dict      =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															UpperCAmelCase			:   Dict      =				parent[v]
					return max_flow


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


