
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class 				lowerCamelCase__							(       TensorFormatter[Mapping, 'torch.Tensor', Mapping]):




	'''simple docstring'''




	def __init__(      self	:List[Any]  ,     a	:str=None  ,     **a	:Any		)	->				Optional[int]:
								super().__init__(features=a		)
								__UpperCamelCase			:     int         =      torch_tensor_kwargs
								import torch  # noqa import torch at initialization







	def      _lowerCamelCase       (      self	:int  ,     a	:int		)	->				Dict:
								import torch

								if isinstance(a  ,     a		) and column:
															if all(
															    isinstance(a  ,     torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
															    for x in column		):
																						return torch.stack(a		)
								return column







	def      _lowerCamelCase       (      self	:Dict  ,     a	:List[str]		)	->				Union[str, Any]:
								import torch

								if isinstance(a  ,     (str, bytes, type(a		))		):
															return value
								elif isinstance(a  ,     (np.character, np.ndarray)		) and np.issubdtype(value.dtype  ,     np.character		):
															return value.tolist()

								__UpperCamelCase			:     Dict         =      {}

								if isinstance(a  ,     (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,     np.integer		):
															__UpperCamelCase			:     str         =      {"dtype": torch.intaa}
								elif isinstance(a  ,     (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,     np.floating		):
															__UpperCamelCase			:     Dict         =      {"dtype": torch.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(a  ,     PIL.Image.Image		):
																						__UpperCamelCase			:     Tuple         =      np.asarray(a		)
								return torch.tensor(a  ,     **{**default_dtype, **self.torch_tensor_kwargs}		)







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:str		)	->				List[Any]:
								import torch

								# support for torch, tf, jax etc.
								if hasattr(a  ,     "__array__"		) and not isinstance(a  ,     torch.Tensor		):
															__UpperCamelCase			:     List[Any]         =      data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(a  ,     np.ndarray		):
															if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(a		) for substruct in data_struct]		)
								elif isinstance(a  ,     (list, tuple)		):
															return self._consolidate([self.recursive_tensorize(a		) for substruct in data_struct]		)
								return self._tensorize(a		)







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:dict		)	->				Any:
								return map_nested(self._recursive_tensorize  ,     a  ,     map_list=a		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:pa.Table		)	->				Mapping:
								__UpperCamelCase			:     Tuple         =      self.numpy_arrow_extractor().extract_row(a		)
								__UpperCamelCase			:     Any         =      self.python_features_decoder.decode_row(a		)
								return self.recursive_tensorize(a		)







	def      _lowerCamelCase       (      self	:str  ,     a	:pa.Table		)	->				"torch.Tensor":
								__UpperCamelCase			:     Optional[int]         =      self.numpy_arrow_extractor().extract_column(a		)
								__UpperCamelCase			:     List[str]         =      self.python_features_decoder.decode_column(a  ,     pa_table.column_names[0]		)
								__UpperCamelCase			:     List[Any]         =      self.recursive_tensorize(a		)
								__UpperCamelCase			:     str         =      self._consolidate(a		)
								return column







	def      _lowerCamelCase       (      self	:Tuple  ,     a	:pa.Table		)	->				Mapping:
								__UpperCamelCase			:     int         =      self.numpy_arrow_extractor().extract_batch(a		)
								__UpperCamelCase			:     Tuple         =      self.python_features_decoder.decode_batch(a		)
								__UpperCamelCase			:     Tuple         =      self.recursive_tensorize(a		)
								for column_name in batch:
															__UpperCamelCase			:     str         =      self._consolidate(batch[column_name]		)
								return batch

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      Optional[int]									= [
    [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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







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




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

															if current_node.pos == self.target.pos:
																						__UpperCamelCase			:     Dict         =      True
																						return self.retrace_path(a		)

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, 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
																													__UpperCamelCase			:     Union[str, 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      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      parent.pos_x + action[1]
															__UpperCamelCase			:     List[Any]         =      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      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


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

		print('------')

		lowercase						:      Any									= GreedyBestFirst(init, goal)
		lowercase						:      List[str]									= greedy_bf.search()
		if path:
				for pos_x, pos_y in path:
						lowercase						:      Optional[int]									= 2

				for elem in grid:
						print(elem)