

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  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
	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  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      str				     =       []
	for d in reversed(__A):
		idx.append(flat_idx % d)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       flat_idx // d

	return tuple(reversed(__A))

@torch.jit.ignore
def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a = None							,			_a = None							,			):

	# 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:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       True
		for i in range(len(__A)):
			SCREAMING_SNAKE_CASE   :      int				     =       -1 * (i + 1)
			l[reversed_idx] &= tally
			SCREAMING_SNAKE_CASE   :      str				     =       l[reversed_idx]

	if start_edges is None:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [s == 0 for s in start]
		reduce_edge_list(__A)
	if end_edges is None:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       [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),)]

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

	SCREAMING_SNAKE_CASE   :      Tuple[slice, ...]				     =       tuple(__A)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       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

		SCREAMING_SNAKE_CASE   :      List[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

		SCREAMING_SNAKE_CASE   :      List[str]				     =       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())
		SCREAMING_SNAKE_CASE   :      Dict				     =       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  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       t.shape[:no_batch_dims]
	SCREAMING_SNAKE_CASE   :      str				     =       list(_flat_idx_to_idx(__A							,			__A))
	# _get_minimal_slice_set is inclusive
	SCREAMING_SNAKE_CASE   :      List[str]				     =       list(_flat_idx_to_idx(flat_end - 1							,			__A))

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

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       [t[s] for s in slices]

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

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a = False							,			_a = None							,			_a = False							,			):
	if not (len(__A) > 0):
		raise ValueError("Must provide at least one input")

	SCREAMING_SNAKE_CASE   :      Dict				     =       [shape[:no_batch_dims] for shape in _fetch_dims(__A)]
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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:
				SCREAMING_SNAKE_CASE   :      Tuple				     =       t.expand(orig_batch_dims + t.shape[no_batch_dims:])
			SCREAMING_SNAKE_CASE   :      int				     =       t.reshape(-1							,			*t.shape[no_batch_dims:])
		else:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       t.expand(orig_batch_dims + t.shape[no_batch_dims:])
		return t

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

	SCREAMING_SNAKE_CASE   :      List[str]				     =       1
	for d in orig_batch_dims:
		flat_batch_dim *= d

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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

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

		SCREAMING_SNAKE_CASE   :      Dict[str, Any]				     =       tensor_tree_map(__A							,			__A)

		# Run the layer on the chunk
		SCREAMING_SNAKE_CASE   :      Dict				     =       layer(**__A)

		# Allocate space for the output
		if out is None:
			SCREAMING_SNAKE_CASE   :      Dict				     =       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:
							SCREAMING_SNAKE_CASE   :      List[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:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       xa
		elif isinstance(__A							,			torch.Tensor):
			if _add_into_out:
				out[i : i + chunk_size] += output_chunk
			else:
				SCREAMING_SNAKE_CASE   :      Tuple				     =       output_chunk
		else:
			raise ValueError("Not supported")

		i += chunk_size

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tensor_tree_map(lambda _a: t.view(orig_batch_dims + t.shape[1:])							,			__A)

	return out



class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: int = 512      ,      )							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       max_chunk_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       None
				SCREAMING_SNAKE_CASE   :      Optional[tuple]				     =       None
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Callable      ,      a							: tuple      ,      a							: int	)							->       int:

				"""simple docstring"""

				logging.info("Tuning chunk size..."	)

				if min_chunk_size >= self.max_chunk_size:
					return min_chunk_size

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

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

				SCREAMING_SNAKE_CASE   :      int				     =       0
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(_UpperCamelCase	) - 1
				while i > min_viable_chunk_size_index:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       test_chunk_size(candidates[i]	)
					if not viable:
						SCREAMING_SNAKE_CASE   :      str				     =       (min_viable_chunk_size_index + i) // 2
					else:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       i
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       (i + len(_UpperCamelCase	) - 1) // 2

				return candidates[min_viable_chunk_size_index]
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Iterable      ,      a							: Iterable	)							->       bool:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       True
				for aa, aa in zip(_UpperCamelCase      ,      _UpperCamelCase	):
					assert type(_UpperCamelCase	) == type(_UpperCamelCase	)
					if isinstance(_UpperCamelCase      ,      (list, tuple)	):
						consistent &= self._compare_arg_caches(_UpperCamelCase      ,      _UpperCamelCase	)
					elif isinstance(_UpperCamelCase      ,      _UpperCamelCase	):
						SCREAMING_SNAKE_CASE   :      Dict				     =       [v for _, v in sorted(aa.items()      ,      key=lambda a	: x[0]	)]
						SCREAMING_SNAKE_CASE   :      Any				     =       [v for _, v in sorted(aa.items()      ,      key=lambda a	: x[0]	)]
						consistent &= self._compare_arg_caches(_UpperCamelCase      ,      _UpperCamelCase	)
					else:
						consistent &= aa == aa

				return consistent






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Callable      ,      a							: tuple      ,      a							: int      ,      )							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       True
				SCREAMING_SNAKE_CASE   :      tuple				     =       tree_map(lambda a	: a.shape if isinstance(_UpperCamelCase      ,      torch.Tensor	) else a      ,      _UpperCamelCase      ,      _UpperCamelCase	)
				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(_UpperCamelCase	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._compare_arg_caches(self.cached_arg_data      ,      _UpperCamelCase	)
				else:
					# Otherwise, we can reuse the precomputed value
					SCREAMING_SNAKE_CASE   :      List[str]				     =       False

				if not consistent:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._determine_favorable_chunk_size(
					    _UpperCamelCase      ,      _UpperCamelCase      ,      _UpperCamelCase      ,      )
					SCREAMING_SNAKE_CASE   :      str				     =       arg_data

				assert self.cached_chunk_size is not None

				return self.cached_chunk_size


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _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
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


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