


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

import fsspec

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

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						Optional[int]					,			lowerCamelCase :						NestedDataStructureLike[PathLike]					,			lowerCamelCase :						Optional[NamedSplit] = None					,			lowerCamelCase :						Optional[Features] = None					,			lowerCamelCase :						str = None					,			lowerCamelCase :						bool = False					,			lowerCamelCase :						bool = False					,			lowerCamelCase :						Optional[str] = None					,			lowerCamelCase :						Optional[int] = None					,			**lowerCamelCase :						str					,			)			->					Optional[int]:
														super().__init__(
														    lowerCamelCase					,			split=lowerCamelCase					,			features=lowerCamelCase					,			cache_dir=lowerCamelCase					,			keep_in_memory=lowerCamelCase					,			streaming=lowerCamelCase					,			num_proc=lowerCamelCase					,			**lowerCamelCase					,			)
														__snake_case	:    int											=     field
														__snake_case	:    str											=     path_or_paths if isinstance(lowerCamelCase					,			lowerCamelCase       ) else {self.split: path_or_paths}
														__snake_case	:    Dict											=     Json(
														    cache_dir=lowerCamelCase					,			data_files=lowerCamelCase					,			features=lowerCamelCase					,			field=lowerCamelCase					,			**lowerCamelCase					,			)






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

																					self.builder.download_and_prepare(
																					    download_config=lowerCamelCase					,			download_mode=lowerCamelCase					,			verification_mode=lowerCamelCase					,			base_path=lowerCamelCase					,			num_proc=self.num_proc					,			)
																					__snake_case	:    Dict											=     self.builder.as_dataset(
																					    split=self.split					,			verification_mode=lowerCamelCase					,			in_memory=self.keep_in_memory       )
														return dataset

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			lowerCamelCase :						Dataset					,			lowerCamelCase :						Union[PathLike, BinaryIO]					,			lowerCamelCase :						Optional[int] = None					,			lowerCamelCase :						Optional[int] = None					,			**lowerCamelCase :						List[str]					,			)			->					List[str]:
														if num_proc is not None and num_proc <= 0:
																					raise ValueError(F'num_proc {num_proc} must be an integer > 0.'       )

														__snake_case	:    Optional[Any]											=     dataset
														__snake_case	:    Tuple											=     path_or_buf
														__snake_case	:    Optional[int]											=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
														__snake_case	:    Optional[int]											=     num_proc
														__snake_case	:    Union[str, Any]											=     "utf-8"
														__snake_case	:    List[str]											=     to_json_kwargs

							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Any											=     self.to_json_kwargs.pop("path_or_buf"					,			lowerCamelCase       )
														__snake_case	:    Optional[Any]											=     self.to_json_kwargs.pop("orient"					,			"records"       )
														__snake_case	:    Union[str, Any]											=     self.to_json_kwargs.pop("lines"					,			True if orient == "records" else False       )
														__snake_case	:    str											=     self.to_json_kwargs.pop("index"					,			False if orient in ["split", "table"] else True       )
														__snake_case	:    Dict											=     self.to_json_kwargs.pop("compression"					,			lowerCamelCase       )

														if compression not in [None, "infer", "gzip", "bz2", "xz"]:
																					raise NotImplementedError(F'`datasets` currently does not support {compression} compression'       )

														if isinstance(self.path_or_buf					,			(str, bytes, os.PathLike)       ):
																					with fsspec.open(self.path_or_buf					,			"wb"					,			compression=lowerCamelCase       ) as buffer:
																												__snake_case	:    int											=     self._write(file_obj=lowerCamelCase					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**self.to_json_kwargs       )
														else:
																					if compression:
																												raise NotImplementedError(
																												    F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
																												    " was passed. Please provide a local path instead."       )
																					__snake_case	:    Optional[Any]											=     self._write(
																					    file_obj=self.path_or_buf					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**self.to_json_kwargs       )
														return written

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					List[str]:
														__snake_case	:    Dict											=     args

														__snake_case	:    List[str]											=     query_table(
														    table=self.dataset.data					,			key=slice(lowerCamelCase					,			offset + self.batch_size       )					,			indices=self.dataset._indices					,			)
														__snake_case	:    str											=     batch.to_pandas().to_json(
														    path_or_buf=lowerCamelCase					,			orient=lowerCamelCase					,			lines=lowerCamelCase					,			index=lowerCamelCase					,			**lowerCamelCase       )
														if not json_str.endswith("\n"       ):
																					json_str += "\n"
														return json_str.encode(self.encoding       )






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						BinaryIO					,			lowerCamelCase :						str					,			lowerCamelCase :						int					,			lowerCamelCase :						int					,			**lowerCamelCase :						Union[str, Any]					,			)			->					int:
														__snake_case	:    Union[str, Any]											=     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 json from Arrow format"					,			):
																												__snake_case	:    str											=     self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
																												written += file_obj.write(lowerCamelCase       )
														else:
																					__snake_case	:    str											=     len(self.dataset       ), self.batch_size
																					with multiprocessing.Pool(self.num_proc       ) as pool:
																												for json_str in logging.tqdm(
																												    pool.imap(
																												        self._batch_json					,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,			lowerCamelCase					,			lowerCamelCase       )]					,			)					,			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 json from Arrow format"					,			):
																																			written += file_obj.write(lowerCamelCase       )

														return written





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

