






"""simple docstring"""







import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map


def      UpperCamelCase       ( _A )     ->       List[Tuple[int, ...]]:
				lowercase    :      int       = []
				if isinstance(_A  ,				_A ):
								for v in tree.values():
												shapes.extend(_fetch_dims(_A ) )
				elif isinstance(_A  ,				(list, tuple) ):
								for t in tree:
												shapes.extend(_fetch_dims(_A ) )
				elif isinstance(_A  ,				torch.Tensor ):
								shapes.append(tree.shape )
				else:
								raise ValueError("""Not supported""" )

				return shapes


@torch.jit.ignore
def      UpperCamelCase       ( _A  ,				_A )     ->       Tuple[int, ...]:
				lowercase    :      int       = []
				for d in reversed(_A ):
								idx.append(flat_idx % d )
								lowercase    :      Optional[Any]       = flat_idx // d

				return tuple(reversed(_A ) )


@torch.jit.ignore
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A = None  ,				_A = None  ,				)     ->       List[Tuple[slice, ...]]:

				# start_edges and end_edges both indicate whether, starting from any given
				# dimension, the start/end index is at the top/bottom edge of the
				# corresponding tensor, modeled as a tree
				def reduce_edge_list(_A ) -> None:
								lowercase    :      Optional[Any]       = True
								for i in range(len(_A ) ):
												lowercase    :      List[Any]       = -1 * (i + 1)
												l[reversed_idx] &= tally
												lowercase    :      Dict       = l[reversed_idx]

				if start_edges is None:
								lowercase    :      int       = [s == 0 for s in start]
								reduce_edge_list(_A )
				if end_edges is None:
								lowercase    :      List[Any]       = [e == (d - 1) for e, d in zip(_A  ,				_A )]
								reduce_edge_list(_A )

				# Base cases. Either start/end are empty and we're done, or the final,
				# one-dimensional tensor can be simply sliced
				if len(_A ) == 0:
								return [()]
				elif len(_A ) == 1:
								return [(slice(start[0]  ,				end[0] + 1 ),)]

				lowercase    :      List[Tuple[slice, ...]]       = []
				lowercase    :      List[slice]       = []

				# Dimensions common to start and end can be selected directly
				for s, e in zip(_A  ,				_A ):
								if s == e:
												path_list.append(slice(_A  ,				s + 1 ) )
								else:
												break

				lowercase    :      Tuple[slice, ...]       = tuple(_A )
				lowercase    :      Union[str, Any]       = len(_A )

				# start == end, and we're done
				if divergence_idx == len(_A ):
								return [path]

				def upper() -> Tuple[Tuple[slice, ...], ...]:
								assert start_edges is not None
								assert end_edges is not None

								lowercase    :      Optional[Any]       = start[divergence_idx]
								return tuple(
								    path + (slice(_A  ,				sdi + 1 ),) + s
								    for s in _get_minimal_slice_set(
								        start[divergence_idx + 1 :]  ,				[d - 1 for d in dims[divergence_idx + 1 :]]  ,				dims[divergence_idx + 1 :]  ,				start_edges=start_edges[divergence_idx + 1 :]  ,				end_edges=[True for _ in end_edges[divergence_idx + 1 :]]  ,				) )

				def lower() -> Tuple[Tuple[slice, ...], ...]:
								assert start_edges is not None
								assert end_edges is not None

								lowercase    :      Tuple       = end[divergence_idx]
								return tuple(
								    path + (slice(_A  ,				edi + 1 ),) + s
								    for s in _get_minimal_slice_set(
								        [0 for _ in start[divergence_idx + 1 :]]  ,				end[divergence_idx + 1 :]  ,				dims[divergence_idx + 1 :]  ,				start_edges=[True for _ in start_edges[divergence_idx + 1 :]]  ,				end_edges=end_edges[divergence_idx + 1 :]  ,				) )

				# If both start and end are at the edges of the subtree rooted at
				# divergence_idx, we can just select the whole subtree at once
				if start_edges[divergence_idx] and end_edges[divergence_idx]:
								slices.append(path + (slice(start[divergence_idx]  ,				end[divergence_idx] + 1 ),) )
				# If just start is at the edge, we can grab almost all of the subtree,
				# treating only the ragged bottom edge as an edge case
				elif start_edges[divergence_idx]:
								slices.append(path + (slice(start[divergence_idx]  ,				end[divergence_idx] ),) )
								slices.extend(lower() )
				# Analogous to the previous case, but the top is ragged this time
				elif end_edges[divergence_idx]:
								slices.extend(upper() )
								slices.append(path + (slice(start[divergence_idx] + 1  ,				end[divergence_idx] + 1 ),) )
				# If both sides of the range are ragged, we need to handle both sides
				# separately. If there's contiguous meat in between them, we can index it
				# in one big chunk
				else:
								slices.extend(upper() )
								lowercase    :      int       = end[divergence_idx] - start[divergence_idx]
								if middle_ground > 1:
												slices.append(path + (slice(start[divergence_idx] + 1  ,				end[divergence_idx] ),) )
								slices.extend(lower() )

				return slices


@torch.jit.ignore
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       torch.Tensor:
				lowercase    :      Any       = t.shape[:no_batch_dims]
				lowercase    :      Optional[int]       = list(_flat_idx_to_idx(_A  ,				_A ) )
				# _get_minimal_slice_set is inclusive
				lowercase    :      Dict       = list(_flat_idx_to_idx(flat_end - 1  ,				_A ) )

				# Get an ordered list of slices to perform
				lowercase    :      int       = _get_minimal_slice_set(
				    _A  ,				_A  ,				_A  ,				)

				lowercase    :      Tuple       = [t[s] for s in slices]

				return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A = False  ,				_A = None  ,				_A = False  ,				)     ->       Any:
				if not (len(_A ) > 0):
								raise ValueError("""Must provide at least one input""" )

				lowercase    :      Dict       = [shape[:no_batch_dims] for shape in _fetch_dims(_A )]
				lowercase    :      Optional[int]       = tuple([max(_A ) for s in zip(*_A )] )

				def _prep_inputs(_A ) -> torch.Tensor:
								if not low_mem:
												if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
																lowercase    :      List[str]       = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
												lowercase    :      Optional[int]       = t.reshape(-1  ,				*t.shape[no_batch_dims:] )
								else:
												lowercase    :      Any       = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
								return t

				lowercase    :      Dict[str, Any]       = tensor_tree_map(_prep_inputs  ,				_A )
				lowercase    :      Optional[int]       = None
				if _out is not None:
								lowercase    :      Dict       = tensor_tree_map(lambda _A : t.view([-1] + list(t.shape[no_batch_dims:] ) )  ,				_out )

				lowercase    :      int       = 1
				for d in orig_batch_dims:
								flat_batch_dim *= d

				lowercase    :      Optional[int]       = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

				def _select_chunk(_A ) -> torch.Tensor:
								return t[i : i + chunk_size] if t.shape[0] != 1 else t

				lowercase    :      List[str]       = 0
				lowercase    :      str       = prepped_outputs
				for _ in range(_A ):
								# Chunk the input
								if not low_mem:
												lowercase    :      Union[str, Any]       = _select_chunk
								else:
												lowercase    :      Dict       = partial(
												    _chunk_slice  ,				flat_start=_A  ,				flat_end=min(_A  ,				i + chunk_size )  ,				no_batch_dims=len(_A )  ,				)

								lowercase    :      Dict[str, Any]       = tensor_tree_map(_A  ,				_A )

								# Run the layer on the chunk
								lowercase    :      Optional[int]       = layer(**_A )

								# Allocate space for the output
								if out is None:
												lowercase    :      List[Any]       = tensor_tree_map(lambda _A : t.new_zeros((flat_batch_dim,) + t.shape[1:] )  ,				_A )

								# Put the chunk in its pre-allocated space
								if isinstance(_A  ,				_A ):

												def assign(_A  ,				_A ) -> None:
																for k, v in da.items():
																				if isinstance(_A  ,				_A ):
																								assign(_A  ,				da[k] )
																				else:
																								if _add_into_out:
																												v[i : i + chunk_size] += da[k]
																								else:
																												lowercase    :      str       = da[k]

												assign(_A  ,				_A )
								elif isinstance(_A  ,				_A ):
												for xa, xa in zip(_A  ,				_A ):
																if _add_into_out:
																				xa[i : i + chunk_size] += xa
																else:
																				lowercase    :      int       = xa
								elif isinstance(_A  ,				torch.Tensor ):
												if _add_into_out:
																out[i : i + chunk_size] += output_chunk
												else:
																lowercase    :      Optional[int]       = output_chunk
								else:
												raise ValueError("""Not supported""" )

								i += chunk_size

				lowercase    :      Any       = tensor_tree_map(lambda _A : t.view(orig_batch_dims + t.shape[1:] )  ,				_A )

				return out


class      UpperCamelCase		:






						def __init__(		self  :List[Any]			, __magic_name__  :int = 512			, )      ->List[Any]:
										lowercase    :      int       = max_chunk_size
										lowercase    :      Optional[int]       = None
										lowercase    :      Optional[tuple]       = None






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Callable			, __magic_name__  :tuple			, __magic_name__  :int				)      ->int:
										logging.info("""Tuning chunk size..."""				)

										if min_chunk_size >= self.max_chunk_size:
														return min_chunk_size

										lowercase    :      List[int]       = [2**l for l in range(int(math.log(self.max_chunk_size			, 2				)				) + 1				)]
										lowercase    :      List[Any]       = [c for c in candidates if c > min_chunk_size]
										lowercase    :      Union[str, Any]       = [min_chunk_size] + candidates
										candidates[-1] += 4

										def test_chunk_size(__magic_name__  :int				) -> bool:
														try:
																		with torch.no_grad():
																						fn(*__magic_name__			, chunk_size=__magic_name__				)
																		return True
														except RuntimeError:
																		return False

										lowercase    :      Dict       = 0
										lowercase    :      Optional[int]       = len(__magic_name__				) - 1
										while i > min_viable_chunk_size_index:
														lowercase    :      Dict       = test_chunk_size(candidates[i]				)
														if not viable:
																		lowercase    :      int       = (min_viable_chunk_size_index + i) // 2
														else:
																		lowercase    :      Optional[Any]       = i
																		lowercase    :      List[Any]       = (i + len(__magic_name__				) - 1) // 2

										return candidates[min_viable_chunk_size_index]






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Iterable			, __magic_name__  :Iterable				)      ->bool:
										lowercase    :      List[str]       = True
										for aa, aa in zip(__magic_name__			, __magic_name__				):
														assert type(__magic_name__				) == type(__magic_name__				)
														if isinstance(__magic_name__			, (list, tuple)				):
																		consistent &= self._compare_arg_caches(__magic_name__			, __magic_name__				)
														elif isinstance(__magic_name__			, __magic_name__				):
																		lowercase    :      Tuple       = [v for _, v in sorted(aa.items()			, key=lambda __magic_name__				: x[0]				)]
																		lowercase    :      Any       = [v for _, v in sorted(aa.items()			, key=lambda __magic_name__				: x[0]				)]
																		consistent &= self._compare_arg_caches(__magic_name__			, __magic_name__				)
														else:
																		consistent &= aa == aa

										return consistent






						def      __snake_case      (		self  :List[str]			, __magic_name__  :Callable			, __magic_name__  :tuple			, __magic_name__  :int			, )      ->int:
										lowercase    :      Dict       = True
										lowercase    :      tuple       = tree_map(lambda __magic_name__				: a.shape if isinstance(__magic_name__			, torch.Tensor				) else a			, __magic_name__			, __magic_name__				)
										if self.cached_arg_data is not None:
														# If args have changed shape/value, we need to re-tune
														assert len(self.cached_arg_data				) == len(__magic_name__				)
														lowercase    :      Dict       = self._compare_arg_caches(self.cached_arg_data			, __magic_name__				)
										else:
														# Otherwise, we can reuse the precomputed value
														lowercase    :      Optional[int]       = False

										if not consistent:
														lowercase    :      Union[str, Any]       = self._determine_favorable_chunk_size(
														    __magic_name__			, __magic_name__			, __magic_name__			, )
														lowercase    :      Any       = arg_data

										assert self.cached_chunk_size is not None

										return self.cached_chunk_size








"""simple docstring"""







import numpy as np


class      UpperCamelCase		:






						def __init__(		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Dict       = (0, 0)
										lowercase    :      str       = None
										lowercase    :      Any       = 0
										lowercase    :      List[Any]       = 0
										lowercase    :      Union[str, Any]       = 0






						def __eq__(		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->Tuple:
										return self.position == cell.position






						def      __snake_case      (		self  :Any				)      ->List[Any]:
										print(self.position				)


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :int=(5, 5)				)      ->Dict:
										lowercase    :      Tuple       = np.zeros(__magic_name__				)
										lowercase    :      List[str]       = world_size[0]
										lowercase    :      str       = world_size[1]






						def      __snake_case      (		self  :str				)      ->int:
										print(self.w				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Any				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										lowercase    :      List[str]       = cell.position[0]
										lowercase    :      List[str]       = cell.position[1]
										lowercase    :      Optional[int]       = []
										for n in neughbour_cord:
														lowercase    :      List[Any]       = current_x + n[0]
														lowercase    :      List[Any]       = current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		lowercase    :      Tuple       = Cell()
																		lowercase    :      Union[str, Any]       = (x, y)
																		lowercase    :      Dict       = cell
																		neighbours.append(__magic_name__				)
										return neighbours


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Tuple:
				lowercase    :      Dict       = []
				lowercase    :      List[str]       = []
				_open.append(_A )

				while _open:
								lowercase    :      List[str]       = np.argmin([n.f for n in _open] )
								lowercase    :      int       = _open[min_f]
								_closed.append(_open.pop(_A ) )
								if current == goal:
												break
								for n in world.get_neigbours(_A ):
												for c in _closed:
																if c == n:
																				continue
												lowercase    :      List[Any]       = current.g + 1
												lowercase      ,      lowercase    :      Any       = n.position
												lowercase      ,      lowercase    :      List[str]       = goal.position
												lowercase    :      Any       = (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase    :      Union[str, Any]       = n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(_A )
				lowercase    :      List[str]       = []
				while current.parent is not None:
								path.append(current.position )
								lowercase    :      int       = current.parent
				path.append(current.position )
				return path[::-1]


if __name__ == "__main__":
				_lowerCAmelCase            =	Gridworld()
				# Start position and goal
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(0, 0)
				_lowerCAmelCase            =	Cell()
				_lowerCAmelCase            =	(4, 4)
				print(F'path from {start.position} to {goal.position}')
				_lowerCAmelCase            =	astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								_lowerCAmelCase            =	1
				print(world.w)
