




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


lowerCAmelCase_						=			datasets.utils.logging.get_logger(__name__)
@dataclass
class 						_A (  datasets.BuilderConfig				):






  """simple docstring"""




  _UpperCamelCase       :      List[Any]			=      1_0_0_0_0
  _UpperCamelCase       :      Optional[int]			=      None
  _UpperCamelCase       :      List[Any]			=      None


class 						_A (  datasets.ArrowBasedBuilder				):






  """simple docstring"""




  _UpperCamelCase       :      Tuple			=      ParquetConfig






  def  __a      (  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       return datasets.DatasetInfo(features=self.config.features				)






  def  __a      (  self		:     List[Any]				,						_A		:     Optional[int]				)		->   Tuple:






       """simple docstring"""







       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}"""				)
       lowercase :       Any       =			dl_manager.download_and_extract(self.config.data_files				)
       if isinstance(_A				,						(str, list, tuple)				):
            lowercase :       Optional[int]       =			data_files
            if isinstance(_A				,						_A				):
                 lowercase :       Optional[int]       =			[files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            lowercase :       Tuple       =			[dl_manager.iter_files(_A				) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,						gen_kwargs={'''files''': files}				)]
       lowercase :       int       =			[]
       for split_name, files in data_files.items():
            if isinstance(_A				,						_A				):
                 lowercase :       Optional[int]       =			[files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            lowercase :       Tuple       =			[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:
                           lowercase :       Optional[Any]       =			datasets.Features.from_arrow_schema(pq.read_schema(_A				)				)
                      break
            splits.append(datasets.SplitGenerator(name=_A				,						gen_kwargs={'''files''': files}				)				)
       return splits






  def  __a      (  self		:     Optional[Any]				,						_A		:     pa.Table				)		->   Tuple:






       """simple docstring"""







       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
            lowercase :       int       =			table_cast(_A				,						self.info.features.arrow_schema				)
       return pa_table






  def  __a      (  self		:     List[Any]				,						_A		:     List[str]				)		->   str:






       """simple docstring"""







       lowercase :       str       =			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(_A				)				):
            with open(_A				,						'''rb'''				) as f:
                 lowercase :       List[Any]       =			pq.ParquetFile(_A				)
                 try:
                      for batch_idx, record_batch in enumerate(
                          parquet_file.iter_batches(batch_size=self.config.batch_size				,						columns=self.config.columns				)				):
                           lowercase :       int       =			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





from __future__ import annotations

import time

lowerCAmelCase_						=			list[tuple[int, int]]

lowerCAmelCase_						=			[
    [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],
]

lowerCAmelCase_						=			[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Node | None				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			pos_x
       lowercase :       Union[str, Any]       =			pos_y
       lowercase :       int       =			(pos_y, pos_x)
       lowercase :       List[str]       =			goal_x
       lowercase :       Optional[int]       =			goal_y
       lowercase :       Union[str, Any]       =			parent


class 						_A :






  def __init__(  self		:     Optional[Any]				,						_A		:     tuple[int, int]				,						_A		:     tuple[int, int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						_A				)
       lowercase :       int       =			Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						_A				)

       lowercase :       List[str]       =			[self.start]
       lowercase :       Optional[int]       =			False






  def  __a      (  self		:     Dict				)		->   Path | None:






       """simple docstring"""







       while self.node_queue:
            lowercase :       Optional[Any]       =			self.node_queue.pop(0				)

            if current_node.pos == self.target.pos:
                 lowercase :       int       =			True
                 return self.retrace_path(_A				)

            lowercase :       Union[str, Any]       =			self.get_successors(_A				)

            for node in successors:
                 self.node_queue.append(_A				)

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






  def  __a      (  self		:     str				,						_A		:     Node				)		->   list[Node]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			[]
       for action in delta:
            lowercase :       List[str]       =			parent.pos_x + action[1]
            lowercase :       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				,						_A				)				)
       return successors






  def  __a      (  self		:     Any				,						_A		:     Node | None				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[int]       =			node
       lowercase :       Dict       =			[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)				)
            lowercase :       Tuple       =			current_node.parent
       path.reverse()
       return path


class 						_A :






  def __init__(  self		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       Optional[Any]       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       List[str]       =			False






  def  __a      (  self		:     Tuple				)		->   Path | None:






       """simple docstring"""







       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            lowercase :       str       =			self.fwd_bfs.node_queue.pop(0				)
            lowercase :       Tuple       =			self.bwd_bfs.node_queue.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
                 lowercase :       Optional[Any]       =			True
                 return self.retrace_bidirectional_path(
                     _A				,						_A				)

            lowercase :       Optional[int]       =			current_bwd_node
            lowercase :       Optional[int]       =			current_fwd_node

            lowercase :       Union[str, Any]       =			{
                self.fwd_bfs: self.fwd_bfs.get_successors(_A				),
                self.bwd_bfs: self.bwd_bfs.get_successors(_A				),
            }

            for bfs in [self.fwd_bfs, self.bwd_bfs]:
                 for node in successors[bfs]:
                      bfs.node_queue.append(_A				)

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






  def  __a      (  self		:     List[Any]				,						_A		:     Node				,						_A		:     Node				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.fwd_bfs.retrace_path(_A				)
       lowercase :       Union[str, Any]       =			self.bwd_bfs.retrace_path(_A				)
       bwd_path.pop()
       bwd_path.reverse()
       lowercase :       Optional[int]       =			fwd_path + bwd_path
       return path


if __name__ == "__main__":
 # all coordinates are given in format [y,x]
 import doctest

 doctest.testmod()
 lowerCAmelCase_						=			(0, 0)
 lowerCAmelCase_						=			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bfs.search()
 lowerCAmelCase_						=			time.time() - start_bfs_time

 print('Unidirectional BFS computation time : ', bfs_time)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BidirectionalBreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bd_bfs.search()
 lowerCAmelCase_						=			time.time() - start_bd_bfs_time

 print('Bidirectional BFS computation time : ', bd_bfs_time)