





'''simple docstring'''






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 (					__lowercase		):

		def __init__(   self					:   Union[str, Any]  ,      __magic_name__					:   NestedDataStructureLike[PathLike]  ,      __magic_name__					:   Optional[NamedSplit] = None  ,      __magic_name__					:   Optional[Features] = None  ,      __magic_name__					:   str = None  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[int] = None  ,      **__magic_name__					:   int  ,      )					->    List[str]:


					"""simple docstring"""

					super().__init__(
					    __magic_name__  ,      split=__magic_name__  ,      features=__magic_name__  ,      cache_dir=__magic_name__  ,      keep_in_memory=__magic_name__  ,      streaming=__magic_name__  ,      num_proc=__magic_name__  ,      **__magic_name__  ,      )
					__snake_case					:      Union[str, Any]							      =     field
					__snake_case					:      Dict							      =     path_or_paths if isinstance(__magic_name__  ,      __magic_name__			) else {self.split: path_or_paths}
					__snake_case					:      Any							      =     Json(
					    cache_dir=__magic_name__  ,      data_files=__magic_name__  ,      features=__magic_name__  ,      field=__magic_name__  ,      **__magic_name__  ,      )

		def 	lowercase__     (   self					:   str			)					->    Optional[int]:


					"""simple docstring"""

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

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





class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   Union[PathLike, BinaryIO]  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   Optional[int] = None  ,      **__magic_name__					:   List[Any]  ,      )					->    List[str]:


					"""simple docstring"""

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

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

		def 	lowercase__     (   self					:   Tuple			)					->    int:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.to_json_kwargs.pop("""path_or_buf"""  ,      __magic_name__			)
					__snake_case					:      Optional[Any]							      =     self.to_json_kwargs.pop("""orient"""  ,      """records"""			)
					__snake_case					:      List[Any]							      =     self.to_json_kwargs.pop("""lines"""  ,      True if orient == """records""" else False			)
					__snake_case					:      Optional[Any]							      =     self.to_json_kwargs.pop("""index"""  ,      False if orient in ["""split""", """table"""] else True			)
					__snake_case					:      Dict							      =     self.to_json_kwargs.pop("""compression"""  ,      __magic_name__			)

					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=__magic_name__			) as buffer:
											__snake_case					:      Any							      =     self._write(file_obj=__magic_name__  ,      orient=__magic_name__  ,      lines=__magic_name__  ,      index=__magic_name__  ,      **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					:      Dict							      =     self._write(
								    file_obj=self.path_or_buf  ,      orient=__magic_name__  ,      lines=__magic_name__  ,      index=__magic_name__  ,      **self.to_json_kwargs			)
					return written

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Tuple			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     args

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

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   BinaryIO  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]  ,      **__magic_name__					:   int  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     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					:      Tuple							      =     self._batch_json((offset, orient, lines, index, to_json_kwargs)			)
											written += file_obj.write(__magic_name__			)
					else:
								__snake_case      ,     __snake_case					:      Any							      =     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  ,      __magic_name__  ,      __magic_name__			)]  ,      )  ,      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(__magic_name__			)

					return written









'''simple docstring'''






from __future__ import annotations

__UpperCamelCase					    =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				tuple[list[list[int]], list[list[int]]]:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the reference grid
			__snake_case					:      Tuple							      =     1
			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the action grid

			__snake_case					:      List[str]							      =     init[0]
			__snake_case					:      str							      =     init[1]
			__snake_case					:      int							      =     0
			__snake_case					:      int							      =     g + heuristic[x][y]  # cost from starting cell to destination cell
			__snake_case					:      List[str]							      =     [[f, g, x, y]]

			__snake_case					:      Any							      =     False  # flag that is set when search is complete
			__snake_case					:      int							      =     False  # flag set if we can't find expand

			while not found and not resign:
						if len(_lowerCamelCase ) == 0:
									raise ValueError("""Algorithm is unable to find solution""" )
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									__snake_case					:      Tuple							      =     cell.pop()
									__snake_case					:      Optional[int]							      =     next_cell[2]
									__snake_case					:      List[Any]							      =     next_cell[3]
									__snake_case					:      int							      =     next_cell[1]

									if x == goal[0] and y == goal[1]:
												__snake_case					:      Optional[Any]							      =     True
									else:
												for i in range(len(_lowerCamelCase ) ):  # to try out different valid actions
															__snake_case					:      Union[str, Any]							      =     x + DIRECTIONS[i][0]
															__snake_case					:      str							      =     y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(_lowerCamelCase ) and ya >= 0 and ya < len(grid[0] ):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					__snake_case					:      str							      =     g + cost
																					__snake_case					:      Tuple							      =     ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya] )
																					__snake_case					:      List[str]							      =     1
																					__snake_case					:      Optional[int]							      =     i
			__snake_case					:      List[str]							      =     []
			__snake_case					:      Optional[int]							      =     goal[0]
			__snake_case					:      List[Any]							      =     goal[1]
			invpath.append([x, y] )  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						__snake_case					:      Dict							      =     x - DIRECTIONS[action[x][y]][0]
						__snake_case					:      int							      =     y - DIRECTIONS[action[x][y]][1]
						__snake_case					:      Optional[int]							      =     xa
						__snake_case					:      int							      =     ya
						invpath.append([x, y] )

			__snake_case					:      Optional[int]							      =     []
			for i in range(len(_lowerCamelCase ) ):
						path.append(invpath[len(_lowerCamelCase ) - 1 - i] )
			return path, action


if __name__ == "__main__":
	__UpperCamelCase					    =	[
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	__UpperCamelCase					    =	[0, 0]
	# all coordinates are given in format [y,x]
	__UpperCamelCase					    =	[len(grid) - 1, len(grid[0]) - 1]
	__UpperCamelCase					    =	1

	# the cost map which pushes the path closer to the goal
	__UpperCamelCase					    =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			__UpperCamelCase					    =	abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				__UpperCamelCase					    =	99

	__UpperCamelCase    ,		__UpperCamelCase					    =	search(grid, init, goal, cost, heuristic)

	print("ACTION MAP")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])


