



'''simple docstring'''

import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    int      							=np.inf

			def set_batch_size(__UpperCAmelCase					) -> None:
						nonlocal batch_size
						if isinstance(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    List[str]      							=min(__UpperCAmelCase				,							config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS					)
						elif isinstance(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    Tuple      							=min(__UpperCAmelCase				,							config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS					)
						elif isinstance(__UpperCAmelCase				,							__UpperCAmelCase					) and feature.dtype == "binary":
									snake_case:    Optional[int]      							=min(__UpperCAmelCase				,							config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS					)

			_visit(__UpperCAmelCase				,							__UpperCAmelCase					)

			return None if batch_size is np.inf else batch_size






class 						a_		(     snake_case    ):


							def __init__(				self   :						Optional[Any]      ,						a_   :						NestedDataStructureLike[PathLike]      ,						a_   :						Optional[NamedSplit] = None      ,						a_   :						Optional[Features] = None      ,						a_   :						str = None      ,						a_   :						bool = False      ,						a_   :						bool = False      ,						a_   :						Optional[int] = None      ,						**a_   :						Optional[Any]      ,						)			->					Any:
										super().__init__(
										    a_      ,						split=a_      ,						features=a_      ,						cache_dir=a_      ,						keep_in_memory=a_      ,						streaming=a_      ,						num_proc=a_      ,						**a_      ,						)
										snake_case:    str      							=path_or_paths if isinstance(a_      ,						a_			) else {self.split: path_or_paths}
										snake_case:    str      							=_PACKAGED_DATASETS_MODULES['parquet'][1]
										snake_case:    Tuple      							=Parquet(
										    cache_dir=a_      ,						data_files=a_      ,						features=a_      ,						hash=a_      ,						**a_      ,						)




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

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






class 						a_		:


							def __init__(				self   :						Union[str, Any]      ,						a_   :						Dataset      ,						a_   :						Union[PathLike, BinaryIO]      ,						a_   :						Optional[int] = None      ,						**a_   :						List[str]      ,						)			->					Optional[Any]:
										snake_case:    Optional[int]      							=dataset
										snake_case:    int      							=path_or_buf
										snake_case:    Dict      							=batch_size or get_writer_batch_size(dataset.features			)
										snake_case:    Optional[int]      							=parquet_writer_kwargs


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    List[Any]      							=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

										if isinstance(self.path_or_buf      ,						(str, bytes, os.PathLike)			):
													with open(self.path_or_buf      ,						'wb+'			) as buffer:
																snake_case:    Optional[int]      							=self._write(file_obj=a_      ,						batch_size=a_      ,						**self.parquet_writer_kwargs			)
										else:
													snake_case:    List[str]      							=self._write(file_obj=self.path_or_buf      ,						batch_size=a_      ,						**self.parquet_writer_kwargs			)
										return written




							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						BinaryIO      ,						a_   :						int      ,						**a_   :						Union[str, Any]			)			->					int:
										snake_case:    List[str]      							=0
										snake_case:    List[Any]      							=parquet_writer_kwargs.pop('path_or_buf'      ,						a_			)
										snake_case:    Optional[Any]      							=self.dataset.features.arrow_schema

										snake_case:    List[Any]      							=pq.ParquetWriter(a_      ,						schema=a_      ,						**a_			)

										for offset in logging.tqdm(
										    range(0      ,						len(self.dataset			)      ,						a_			)      ,						unit='ba'      ,						disable=not logging.is_progress_bar_enabled()      ,						desc='Creating parquet from Arrow format'      ,						):
													snake_case:    Optional[int]      							=query_table(
													    table=self.dataset._data      ,						key=slice(a_      ,						offset + batch_size			)      ,						indices=self.dataset._indices if self.dataset._indices is not None else None      ,						)
													writer.write_table(a_			)
													written += batch.nbytes
										writer.close()
										return written









'''simple docstring'''

from __future__ import annotations

a							   =				list[tuple[int, int]]

a							   =				[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a							   =				([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 						a_		:


							def __init__(				self   :						Tuple      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						int      ,						a_   :						float      ,						a_   :						Node | None      ,						)			->					Optional[Any]:
										snake_case:    str      							=pos_x
										snake_case:    str      							=pos_y
										snake_case:    Optional[Any]      							=(pos_y, pos_x)
										snake_case:    Union[str, Any]      							=goal_x
										snake_case:    Tuple      							=goal_y
										snake_case:    Tuple      							=g_cost
										snake_case:    Tuple      							=parent
										snake_case:    Optional[Any]      							=self.calculate_heuristic()


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					float:
										snake_case:    Optional[Any]      							=abs(self.pos_x - self.goal_x			)
										snake_case:    Optional[int]      							=abs(self.pos_y - self.goal_y			)
										return dx + dy




							def __lt__(				self   :						Union[str, Any]      ,						a_   :						Tuple			)			->					bool:
										return self.f_cost < other.f_cost






class 						a_		:


							def __init__(				self   :						Any      ,						a_   :						tuple[int, int]      ,						a_   :						tuple[int, int]			)			->					Optional[Any]:
										snake_case:    Any      							=Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						a_			)
										snake_case:    str      							=Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						9_9_9_9_9      ,						a_			)

										snake_case:    int      							=[self.start]
										snake_case:    list[Node]      							=[]

										snake_case:    Tuple      							=False


							def    UpperCamelCase     (				self   :						str			)			->					Path | None:
										while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													snake_case:    Tuple      							=self.open_nodes.pop(0			)

													if current_node.pos == self.target.pos:
																snake_case:    Tuple      							=True
																return self.retrace_path(a_			)

													self.closed_nodes.append(a_			)
													snake_case:    Optional[Any]      							=self.get_successors(a_			)

													for child_node in successors:
																if child_node in self.closed_nodes:
																			continue

																if child_node not in self.open_nodes:
																			self.open_nodes.append(a_			)
																else:
																			# retrieve the best current path
																			snake_case:    List[Any]      							=self.open_nodes.pop(self.open_nodes.index(a_			)			)

																			if child_node.g_cost < better_node.g_cost:
																						self.open_nodes.append(a_			)
																			else:
																						self.open_nodes.append(a_			)

										if not self.reached:
													return [self.start.pos]
										return None


							def    UpperCamelCase     (				self   :						str      ,						a_   :						Node			)			->					list[Node]:
										snake_case:    Any      							=[]
										for action in delta:
													snake_case:    Any      							=parent.pos_x + action[1]
													snake_case:    str      							=parent.pos_y + action[0]

													if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(a_			) - 1):
																continue

													if grid[pos_y][pos_x] != 0:
																continue

													successors.append(
													    Node(
													        a_      ,						a_      ,						self.target.pos_y      ,						self.target.pos_x      ,						parent.g_cost + 1      ,						a_      ,						)			)
										return successors




							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						Node | None			)			->					Path:
										snake_case:    Dict      							=node
										snake_case:    Optional[int]      							=[]
										while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)			)
													snake_case:    List[str]      							=current_node.parent
										path.reverse()
										return path


if __name__ == "__main__":
	a							   =				(0, 0)
	a							   =				(len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	print('------')

	a							   =				GreedyBestFirst(init, goal)
	a							   =				greedy_bf.search()
	if path:
		for pos_x, pos_y in path:
			a							   =				2

		for elem in grid:
			print(elem)




