

'''simple docstring'''

def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					print("\nVertex Distance"							)
					for i in range(__magic_name__							):
										if dist[i] != float("inf"							):
															print(__magic_name__			,   "\t"			,   int(dist[i]							)			,   end="\t"							)
										else:
															print(__magic_name__			,   "\t"			,   "INF"			,   end="\t"							)
										print()





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       float("inf"							)
					UpperCAmelCase		:							Tuple        =       -1
					for i in range(__magic_name__							):
										if (not vset[i]) and mdist[i] < min_val:
															UpperCAmelCase		:							Any        =       i
															UpperCAmelCase		:							str        =       mdist[i]
					return min_ind





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Dict        =       [float("inf"							) for _ in range(__magic_name__							)]
					UpperCAmelCase		:							Optional[int]        =       [False for _ in range(__magic_name__							)]
					UpperCAmelCase		:							str        =       0.0

					for _ in range(v - 1							):
										UpperCAmelCase		:							Dict        =       min_dist(__magic_name__			,   __magic_name__			,   __magic_name__							)
										UpperCAmelCase		:							Tuple        =       True

										for i in range(__magic_name__							):
															if (
															    (not vset[i])
															    and graph[u][i] != float("inf"							)
															    and mdist[u] + graph[u][i] < mdist[i]
															):
																				UpperCAmelCase		:							Any        =       mdist[u] + graph[u][i]

					print_dist(__magic_name__			,   __magic_name__							)


if __name__ == "__main__":
				a       :      List[Any]  	=			int(input("Enter number of vertices: ").strip())
				a       :      Tuple  	=			int(input("Enter number of edges: ").strip())

				a       :      Any  	=			[[float("inf") for i in range(V)] for j in range(V)]

				for i in range(V):
								a       :      Optional[int]  	=			0.0

				for i in range(E):
								print("\nEdge ", i + 1)
								a       :      Optional[int]  	=			int(input("Enter source:").strip())
								a       :      Union[str, Any]  	=			int(input("Enter destination:").strip())
								a       :      Any  	=			float(input("Enter weight:").strip())
								a       :      Optional[Any]  	=			weight

				a       :      Dict  	=			int(input("\nEnter shortest path source:").strip())
				dijkstra(graph, V, gsrc)



'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      snake_case = None   ,      snake_case = False   ,      snake_case = False   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(
							    snake_case   ,      split=snake_case   ,      features=snake_case   ,      cache_dir=snake_case   ,      keep_in_memory=snake_case   ,      streaming=snake_case   ,      num_proc=snake_case   ,      **snake_case   ,      )
							UpperCAmelCase		:							str        =       path_or_paths if isinstance(snake_case   ,      snake_case      ) else {self.split: path_or_paths}
							UpperCAmelCase		:							int        =       Csv(
							    cache_dir=snake_case   ,      data_files=snake_case   ,      features=snake_case   ,      **snake_case   ,      )







		def 				A_	(			self      ):


							'''simple docstring'''

							if self.streaming:
												UpperCAmelCase		:							Optional[Any]        =       self.builder.as_streaming_dataset(split=self.split      )
							# Build regular (map-style) dataset
							else:
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							str        =       None
												UpperCAmelCase		:							Union[str, Any]        =       None
												UpperCAmelCase		:							int        =       None

												self.builder.download_and_prepare(
												    download_config=snake_case   ,      download_mode=snake_case   ,      verification_mode=snake_case   ,      base_path=snake_case   ,      num_proc=self.num_proc   ,      )
												UpperCAmelCase		:							Tuple        =       self.builder.as_dataset(
												    split=self.split   ,      verification_mode=snake_case   ,      in_memory=self.keep_in_memory      )
							return dataset







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case   ,      snake_case = None   ,      snake_case = None   ,      **snake_case   ,      ):


							'''simple docstring'''

							if num_proc is not None and num_proc <= 0:
												raise ValueError(f"num_proc {num_proc} must be an integer > 0."      )

							UpperCAmelCase		:							Tuple        =       dataset
							UpperCAmelCase		:							Dict        =       path_or_buf
							UpperCAmelCase		:							List[Any]        =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							str        =       num_proc
							UpperCAmelCase		:							List[Any]        =       "utf-8"
							UpperCAmelCase		:							Tuple        =       to_csv_kwargs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.to_csv_kwargs.pop("path_or_buf"   ,      snake_case      )
							UpperCAmelCase		:							Any        =       self.to_csv_kwargs.pop("header"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       self.to_csv_kwargs.pop("index"   ,      snake_case      )

							if isinstance(self.path_or_buf   ,      (str, bytes, os.PathLike)      ):
												with open(self.path_or_buf   ,      "wb+"      ) as buffer:
																	UpperCAmelCase		:							Tuple        =       self._write(file_obj=snake_case   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							else:
												UpperCAmelCase		:							Any        =       self._write(file_obj=self.path_or_buf   ,      header=snake_case   ,      index=snake_case   ,      **self.to_csv_kwargs      )
							return written







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase		:							Optional[int]        =       args

							UpperCAmelCase		:							List[str]        =       query_table(
							    table=self.dataset.data   ,      key=slice(snake_case   ,      offset + self.batch_size      )   ,      indices=self.dataset._indices   ,      )
							UpperCAmelCase		:							int        =       batch.to_pandas().to_csv(
							    path_or_buf=snake_case   ,      header=header if (offset == 0) else False   ,      index=snake_case   ,      **snake_case      )
							return csv_str.encode(self.encoding      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       0

							if self.num_proc is None or self.num_proc == 1:
												for offset in logging.tqdm(
												    range(0   ,      len(self.dataset      )   ,      self.batch_size      )   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																	UpperCAmelCase		:							Any        =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																	written += file_obj.write(snake_case      )

							else:
												UpperCAmelCase ,			UpperCAmelCase		:							Union[str, Any]        =       len(self.dataset      ), self.batch_size
												with multiprocessing.Pool(self.num_proc      ) as pool:
																	for csv_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_csv   ,      [(offset, header, index, to_csv_kwargs) for offset in range(0   ,      snake_case   ,      snake_case      )]   ,      )   ,      total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,      unit="ba"   ,      disable=not logging.is_progress_bar_enabled()   ,      desc="Creating CSV from Arrow format"   ,      ):
																						written += file_obj.write(snake_case      )

							return written
