
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 			_lowerCAmelCase      (			datasets.BuilderConfig ):
				_lowercase    	=1_00_00
				_lowercase    	=None
				_lowercase    	=None





class 			_lowerCAmelCase      (			datasets.ArrowBasedBuilder ):
				_lowercase    	=ParquetConfig




				def   __a		(      self  )       ->   Any:
							return datasets.DatasetInfo(features=self.config.features  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Tuple:
							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}"""  )
							lowerCAmelCase_      			=			dl_manager.download_and_extract(self.config.data_files  )
							if isinstance(_UpperCamelCase       ,       (str, list, tuple)  ):
										lowerCAmelCase_      			=			data_files
										if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													lowerCAmelCase_      			=			[files]
										# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
										lowerCAmelCase_      			=			[dl_manager.iter_files(_UpperCamelCase  ) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,       gen_kwargs={"files": files}  )]
							lowerCAmelCase_      			=			[]
							for split_name, files in data_files.items():
										if isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													lowerCAmelCase_      			=			[files]
										# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
										lowerCAmelCase_      			=			[dl_manager.iter_files(_UpperCamelCase  ) 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(_UpperCamelCase  ):
																with open(_UpperCamelCase       ,       "rb"  ) as f:
																			lowerCAmelCase_      			=			datasets.Features.from_arrow_schema(pq.read_schema(_UpperCamelCase  )  )
																break
										splits.append(datasets.SplitGenerator(name=_UpperCamelCase       ,       gen_kwargs={"files": files}  )  )
							return splits




				def   __a		(      self       ,       _UpperCamelCase  )       ->   pa.Table:
							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
										lowerCAmelCase_      			=			table_cast(_UpperCamelCase       ,       self.info.features.arrow_schema  )
							return pa_table




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Dict:
							lowerCAmelCase_      			=			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(_UpperCamelCase  )  ):
										with open(_UpperCamelCase       ,       "rb"  ) as f:
													lowerCAmelCase_      			=			pq.ParquetFile(_UpperCamelCase  )
													try:
																for batch_idx, record_batch in enumerate(
																    parquet_file.iter_batches(batch_size=self.config.batch_size       ,       columns=self.config.columns  )  ):
																			lowerCAmelCase_      			=			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(_UpperCamelCase  )
													except ValueError as e:
																logger.error(f"""Failed to read file '{file}' with error {type(_UpperCamelCase  )}: {e}"""  )
																raise





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 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   Tuple:
							lowerCAmelCase_      			=			pos_x
							lowerCAmelCase_      			=			pos_y
							lowerCAmelCase_      			=			(pos_y, pos_x)
							lowerCAmelCase_      			=			goal_x
							lowerCAmelCase_      			=			goal_y
							lowerCAmelCase_      			=			g_cost
							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			self.calculate_heuristic()




				def   __a		(      self  )       ->   float:
							lowerCAmelCase_      			=			abs(self.pos_x - self.goal_x  )
							lowerCAmelCase_      			=			abs(self.pos_y - self.goal_y  )
							return dx + dy




				def __lt__(      self       ,       _UpperCamelCase  )       ->   bool:
							return self.f_cost < other.f_cost





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			Node(start[1]       ,       start[0]       ,       goal[1]       ,       goal[0]       ,       0       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			Node(goal[1]       ,       goal[0]       ,       goal[1]       ,       goal[0]       ,       99_999       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			[self.start]
							lowerCAmelCase_      			=			[]

							lowerCAmelCase_      			=			False




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

										if current_node.pos == self.target.pos:
													lowerCAmelCase_      			=			True
													return self.retrace_path(_UpperCamelCase  )

										self.closed_nodes.append(_UpperCamelCase  )
										lowerCAmelCase_      			=			self.get_successors(_UpperCamelCase  )

										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(_UpperCamelCase  )
													else:
																# retrieve the best current path
																lowerCAmelCase_      			=			self.open_nodes.pop(self.open_nodes.index(_UpperCamelCase  )  )

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

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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   list[Node]:
							lowerCAmelCase_      			=			[]
							for action in delta:
										lowerCAmelCase_      			=			parent.pos_x + action[1]
										lowerCAmelCase_      			=			parent.pos_y + action[0]

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

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

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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Path:
							lowerCAmelCase_      			=			node
							lowerCAmelCase_      			=			[]
							while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)  )
										lowerCAmelCase_      			=			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)



