





'''simple docstring'''


import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


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



@dataclass
class 							__UpperCAmelCase  (       datasets.BuilderConfig						):







		'''simple docstring'''







		__lowerCAmelCase 				=    None






class 							__UpperCAmelCase  (       datasets.ArrowBasedBuilder						):







		'''simple docstring'''







		__lowerCAmelCase 				=    ArrowConfig







		def        A     (self						:      Union[str, Any]      ):
			return datasets.DatasetInfo(features=self.config.features      )







		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      Union[str, Any]      ):





			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							  =      dl_manager.download_and_extract(self.config.data_files      )
			if isinstance(__a      ,      (str, list, tuple)      ):
				A							  =      data_files
				if isinstance(__a      ,      __a      ):
					A							  =      [files]
				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
				A							  =      [dl_manager.iter_files(__a      ) for file in files]
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"""files""": files}      )]
			A							  =      []
			for split_name, files in data_files.items():
				if isinstance(__a      ,      __a      ):
					A							  =      [files]
				# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
				A							  =      [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:
							A							  =      datasets.Features.from_arrow_schema(pa.ipc.open_stream(__a      ).schema      )
						break
				splits.append(datasets.SplitGenerator(name=__a      ,      gen_kwargs={"""files""": files}      )      )
			return splits







		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      Union[str, Any]      ):





			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							  =      table_cast(__a      ,      self.info.features.arrow_schema      )
			return pa_table







		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      Tuple      ):





			for file_idx, file in enumerate(itertools.chain.from_iterable(__a      )      ):
				with open(__a      ,      """rb"""      ) as f:
					try:
						for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__a      )      ):
							A							  =      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













'''simple docstring'''


_lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
			A							  =      name
			A							  =      []
			A							  =      []







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
			self.inbound.append(_lowerCAmelCase      )







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
			self.outbound.append(_lowerCAmelCase      )







		def __repr__(self						:      Tuple      ):
			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:



	"""simple docstring"""







	A							  =      {}
	for node in nodes:
		A							  =      1

	A							  =      {}
	for node in nodes:
		A							  =      len(node.outbound    )

	for i in range(UpperCAmelCase    ):
		print(f"""======= Iteration {i + 1} ======="""    )
		for _, node in enumerate(UpperCAmelCase    ):
			A							  =      (1 - d) + d * sum(
			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
		print(UpperCAmelCase    )




def    __a (     )      ->Any:



	"""simple docstring"""







	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )

	A							  =      [Node(UpperCAmelCase    ) for name in names]

	for ri, row in enumerate(UpperCAmelCase    ):
		for ci, col in enumerate(UpperCAmelCase    ):
			if col == 1:
				nodes[ci].add_inbound(names[ri]    )
				nodes[ri].add_outbound(names[ci]    )

	print("""======= Nodes ======="""    )
	for node in nodes:
		print(UpperCAmelCase    )

	page_rank(UpperCAmelCase    )


if __name__ == "__main__":
				main()

