

from collections import deque

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       len(_a)
	SCREAMING_SNAKE_CASE   :      int				     =       deque()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       [False for _ in range(_a)]
	SCREAMING_SNAKE_CASE   :      List[str]				     =       [-1 for _ in range(_a)]
	SCREAMING_SNAKE_CASE   :      str				     =       index_of[:]

	def strong_connect(_a							,			_a							,			_a):
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       index  # the number when this node is seen
		SCREAMING_SNAKE_CASE   :      List[str]				     =       index  # lowest rank node reachable from here
		index += 1
		stack.append(_a)
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       True

		for w in g[v]:
			if index_of[w] == -1:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       strong_connect(_a							,			_a							,			_a)
				SCREAMING_SNAKE_CASE   :      Any				     =       (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)
			elif on_stack[w]:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       (
				    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
				)

		if lowlink_of[v] == index_of[v]:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       []
			SCREAMING_SNAKE_CASE   :      Dict				     =       stack.pop()
			SCREAMING_SNAKE_CASE   :      str				     =       False
			component.append(_a)
			while w != v:
				SCREAMING_SNAKE_CASE   :      Dict				     =       stack.pop()
				SCREAMING_SNAKE_CASE   :      Tuple				     =       False
				component.append(_a)
			components.append(_a)
		return index

	SCREAMING_SNAKE_CASE   :      Any				     =       []
	for v in range(_a):
		if index_of[v] == -1:
			strong_connect(_a							,			0							,			_a)

	return components

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       [[] for _ in range(_a)]
	for u, v in edges:
		g[u].append(_a)
	return g


if __name__ == "__main__":
			# Test
			a_						     =    7
			a_						     =    [0, 0, 1, 2, 3, 3, 4, 4, 6]
			a_						     =    [1, 3, 2, 0, 1, 4, 5, 6, 5]
			a_						     =    [(u, v) for u, v in zip(source, target)]
			a_						     =    create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""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}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) 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(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       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
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       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(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise