1273	def __init__(
1274		self,
1275		connection_list: ConnectionList,
1276		solution: CoordArray,
1277		generation_meta: dict | None = None,
1278		start_pos: Coord | None = None,
1279		end_pos: Coord | None = None,
1280		allow_invalid: bool = False,
1281	) -> None:
1282		"""Create a SolvedMaze from a connection list and a solution
1283
1284		> DOCS: better documentation for this init method
1285		"""
1286		# figure out the solution
1287		solution_valid: bool = False
1288		if solution is not None:
1289			solution = np.array(solution)
1290			# note that a path length of 1 here is valid, since the start and end pos could be the same
1291			if (solution.shape[0] > 0) and (solution.shape[1] == 2):  # noqa: PLR2004
1292				solution_valid = True
1293
1294		if not solution_valid and not allow_invalid:
1295			err_msg: str = f"invalid solution: {solution.shape = } {solution = } {solution_valid = } {allow_invalid = }"
1296			raise ValueError(
1297				err_msg,
1298				f"{connection_list = }",
1299			)
1300
1301		# init the TargetedLatticeMaze
1302		super().__init__(
1303			connection_list=connection_list,
1304			generation_meta=generation_meta,
1305			# TODO: the argument type is stricter than the expected type but it still fails?
1306			# error: Argument "start_pos" to "__init__" of "TargetedLatticeMaze" has incompatible type
1307			# "ndarray[tuple[int, ...], dtype[Any]] | None"; expected "ndarray[Any, Any]"  [arg-type]
1308			start_pos=np.array(solution[0]) if solution_valid else None,  # type: ignore[arg-type]
1309			end_pos=np.array(solution[-1]) if solution_valid else None,  # type: ignore[arg-type]
1310		)
1311
1312		self.__dict__["solution"] = solution
1313
1314		# adjust the endpoints
1315		if not allow_invalid:
1316			if start_pos is not None:
1317				assert np.array_equal(np.array(start_pos), self.start_pos), (
1318					f"when trying to create a SolvedMaze, the given start_pos does not match the one in the solution: given={start_pos}, solution={self.start_pos}"
1319				)
1320			if end_pos is not None:
1321				assert np.array_equal(np.array(end_pos), self.end_pos), (
1322					f"when trying to create a SolvedMaze, the given end_pos does not match the one in the solution: given={end_pos}, solution={self.end_pos}"
1323				)
1324			# TODO: assert the path does not backtrack, walk through walls, etc?

1341	def get_solution_tokens(self) -> list[str | CoordTup]:
1342		"(deprecated!) return the solution as a list of tokens"
1343		warnings.warn(
1344			"`LatticeMaze.get_solution_tokens` is deprecated.",
1345			TokenizerDeprecationWarning,
1346		)
1347		return self._get_solution_tokens()

1350	@property
1351	def maze(self) -> LatticeMaze:
1352		"(deprecated!) return the maze without the solution"
1353		warnings.warn(
1354			"`maze` is deprecated, SolvedMaze now inherits from LatticeMaze.",
1355			DeprecationWarning,
1356		)
1357		return LatticeMaze(connection_list=self.connection_list)

1360	@classmethod
1361	def from_lattice_maze(  # type: ignore[override]
1362		cls,
1363		lattice_maze: LatticeMaze,
1364		solution: list[CoordTup] | CoordArray,
1365	) -> "SolvedMaze":
1366		"get a `SolvedMaze` from a `LatticeMaze` by specifying a solution"
1367		return cls(
1368			connection_list=lattice_maze.connection_list,
1369			solution=np.array(solution),
1370			generation_meta=lattice_maze.generation_meta,
1371		)

1373	@classmethod
1374	def from_targeted_lattice_maze(
1375		cls,
1376		targeted_lattice_maze: TargetedLatticeMaze,
1377		solution: list[CoordTup] | CoordArray | None = None,
1378	) -> "SolvedMaze":
1379		"""solves the given targeted lattice maze and returns a SolvedMaze"""
1380		if solution is None:
1381			solution = targeted_lattice_maze.find_shortest_path(
1382				targeted_lattice_maze.start_pos,
1383				targeted_lattice_maze.end_pos,
1384			)
1385		return cls(
1386			connection_list=targeted_lattice_maze.connection_list,
1387			solution=np.array(solution),
1388			generation_meta=targeted_lattice_maze.generation_meta,
1389		)

1391	def get_solution_forking_points(
1392		self,
1393		always_include_endpoints: bool = False,
1394	) -> tuple[list[int], CoordArray]:
1395		"""coordinates and their indicies from the solution where a fork is present
1396
1397		- if the start point is not a dead end, this counts as a fork
1398		- if the end point is not a dead end, this counts as a fork
1399		"""
1400		output_idxs: list[int] = list()
1401		output_coords: list[CoordTup] = list()
1402
1403		for idx, coord in enumerate(self.solution):
1404			# more than one choice for first coord, or more than 2 for any other
1405			# since the previous coord doesn't count as a choice
1406			is_endpoint: bool = idx == 0 or idx == self.solution.shape[0] - 1
1407			theshold: int = 1 if is_endpoint else 2
1408			if self.get_coord_neighbors(coord).shape[0] > theshold or (
1409				is_endpoint and always_include_endpoints
1410			):
1411				output_idxs.append(idx)
1412				output_coords.append(coord)
1413
1414		return output_idxs, np.array(output_coords)

1416	def get_solution_path_following_points(self) -> tuple[list[int], CoordArray]:
1417		"""coordinates from the solution where there is only a single (non-backtracking) point to move to
1418
1419		returns the complement of `get_solution_forking_points` from the path
1420		"""
1421		forks_idxs, _ = self.get_solution_forking_points()
1422		# HACK: idk why type ignore here
1423		return (  # type: ignore[return-value]
1424			np.delete(np.arange(self.solution.shape[0]), forks_idxs, axis=0),
1425			np.delete(self.solution, forks_idxs, axis=0),
1426		)

714        def serialize(self) -> dict[str, Any]:
715            result: dict[str, Any] = {
716                _FORMAT_KEY: f"{self.__class__.__name__}(SerializableDataclass)"
717            }
718            # for each field in the class
719            for field in dataclasses.fields(self):  # type: ignore[arg-type]
720                # need it to be our special SerializableField
721                if not isinstance(field, SerializableField):
722                    raise NotSerializableFieldException(
723                        f"Field '{field.name}' on class {self.__class__.__module__}.{self.__class__.__name__} is not a `SerializableField`, "
724                        f"but a {type(field)} "
725                        "this state should be inaccessible, please report this bug!"
726                    )
727
728                # try to save it
729                if field.serialize:
730                    try:
731                        # get the val
732                        value = getattr(self, field.name)
733                        # if it is a serializable dataclass, serialize it
734                        if isinstance(value, SerializableDataclass):
735                            value = value.serialize()
736                        # if the value has a serialization function, use that
737                        if hasattr(value, "serialize") and callable(value.serialize):
738                            value = value.serialize()
739                        # if the field has a serialization function, use that
740                        # it would be nice to be able to override a class's `.serialize()`, but that could lead to some inconsistencies!
741                        elif field.serialization_fn:
742                            value = field.serialization_fn(value)
743
744                        # store the value in the result
745                        result[field.name] = value
746                    except Exception as e:
747                        raise FieldSerializationError(
748                            "\n".join(
749                                [
750                                    f"Error serializing field '{field.name}' on class {self.__class__.__module__}.{self.__class__.__name__}",
751                                    f"{field = }",
752                                    f"{value = }",
753                                    f"{self = }",
754                                ]
755                            )
756                        ) from e
757
758            # store each property if we can get it
759            for prop in self._properties_to_serialize:
760                if hasattr(cls, prop):
761                    value = getattr(self, prop)
762                    result[prop] = value
763                else:
764                    raise AttributeError(
765                        f"Cannot serialize property '{prop}' on class {self.__class__.__module__}.{self.__class__.__name__}"
766                        + f"but it is in {self._properties_to_serialize = }"
767                        + f"\n{self = }"
768                    )
769
770            return result

777        @classmethod  # type: ignore[misc]
778        def load(cls, data: dict[str, Any] | T) -> Type[T]:
779            # HACK: this is kind of ugly, but it fixes a lot of issues for when we do recursive loading with ZANJ
780            if isinstance(data, cls):
781                return data
782
783            assert isinstance(
784                data, typing.Mapping
785            ), f"When loading {cls.__name__ = } expected a Mapping, but got {type(data) = }:\n{data = }"
786
787            cls_type_hints: dict[str, Any] = get_cls_type_hints(cls)
788
789            # initialize dict for keeping what we will pass to the constructor
790            ctor_kwargs: dict[str, Any] = dict()
791
792            # iterate over the fields of the class
793            for field in dataclasses.fields(cls):
794                # check if the field is a SerializableField
795                assert isinstance(
796                    field, SerializableField
797                ), f"Field '{field.name}' on class {cls.__name__} is not a SerializableField, but a {type(field)}. this state should be inaccessible, please report this bug!\nhttps://github.com/mivanit/muutils/issues/new"
798
799                # check if the field is in the data and if it should be initialized
800                if (field.name in data) and field.init:
801                    # get the value, we will be processing it
802                    value: Any = data[field.name]
803
804                    # get the type hint for the field
805                    field_type_hint: Any = cls_type_hints.get(field.name, None)
806
807                    # we rely on the init of `SerializableField` to check that only one of `loading_fn` and `deserialize_fn` is set
808                    if field.deserialize_fn:
809                        # if it has a deserialization function, use that
810                        value = field.deserialize_fn(value)
811                    elif field.loading_fn:
812                        # if it has a loading function, use that
813                        value = field.loading_fn(data)
814                    elif (
815                        field_type_hint is not None
816                        and hasattr(field_type_hint, "load")
817                        and callable(field_type_hint.load)
818                    ):
819                        # if no loading function but has a type hint with a load method, use that
820                        if isinstance(value, dict):
821                            value = field_type_hint.load(value)
822                        else:
823                            raise FieldLoadingError(
824                                f"Cannot load value into {field_type_hint}, expected {type(value) = } to be a dict\n{value = }"
825                            )
826                    else:
827                        # assume no loading needs to happen, keep `value` as-is
828                        pass
829
830                    # store the value in the constructor kwargs
831                    ctor_kwargs[field.name] = value
832
833            # create a new instance of the class with the constructor kwargs
834            output: cls = cls(**ctor_kwargs)
835
836            # validate the types of the fields if needed
837            if on_typecheck_mismatch != ErrorMode.IGNORE:
838                fields_valid: dict[str, bool] = (
839                    SerializableDataclass__validate_fields_types__dict(
840                        output,
841                        on_typecheck_error=on_typecheck_error,
842                    )
843                )
844
845                # if there are any fields that are not valid, raise an error
846                if not all(fields_valid.values()):
847                    msg: str = (
848                        f"Type mismatch in fields of {cls.__name__}:\n"
849                        + "\n".join(
850                            [
851                                f"{k}:\texpected {cls_type_hints[k] = }, but got value {getattr(output, k) = }, {type(getattr(output, k)) = }"
852                                for k, v in fields_valid.items()
853                                if not v
854                            ]
855                        )
856                    )
857
858                    on_typecheck_mismatch.process(
859                        msg, except_cls=FieldTypeMismatchError
860                    )
861
862            # return the new instance
863            return output

283def SerializableDataclass__validate_fields_types(
284    self: SerializableDataclass,
285    on_typecheck_error: ErrorMode = _DEFAULT_ON_TYPECHECK_ERROR,
286) -> bool:
287    """validate the types of all the fields on a `SerializableDataclass`. calls `SerializableDataclass__validate_field_type` for each field"""
288    return all(
289        SerializableDataclass__validate_fields_types__dict(
290            self, on_typecheck_error=on_typecheck_error
291        ).values()
292    )

282	@property
283	def config_version(self) -> str:
284		"""return the version of the config. added in maze_dataset v1.3.0, previous versions had no dataset config"""
285		return "1.0"

287	@property
288	def versions(self) -> dict:
289		"""return the versions of the config and the maze_dataset"""
290		return dict(
291			config=self.config_version,
292			maze_dataset=importlib.metadata.version("maze_dataset"),
293		)

295	def serialize(self) -> dict:
296		"serialize the MazeDatasetConfig with all fields and fname"
297		return {
298			**self._serialize_base(
299				applied_filters__skip__collect_generation_meta=False
300			),
301			"fname": self.to_fname(),
302			"versions": self.versions,
303		}

305	def summary(self) -> dict:
306		"""return a summary of the config"""
307		# do we run this to make sure it doesn't error?
308		super_summary: dict = super().summary()
309		assert super_summary
310		self_ser: dict = self.serialize()
311		return dict(
312			name=self.name,
313			fname=self.to_fname(),
314			sdc_hash=self.stable_hash_cfg(),
315			seed=self.seed,
316			seq_len_min=self.seq_len_min,
317			seq_len_max=self.seq_len_max,
318			applied_filters=self.applied_filters,
319			grid_n=self_ser["grid_n"],
320			n_mazes=self_ser["n_mazes"],
321			maze_ctor_name=self_ser["maze_ctor"]["__name__"],
322			maze_ctor_kwargs=self_ser["maze_ctor_kwargs"],
323			endpoint_kwargs=self_ser["endpoint_kwargs"],
324		)

407	def success_fraction_estimate(
408		self,
409		except_if_all_success_expected: bool = False,
410	) -> float:
411		"""Estimate the success fraction of this config.
412
413		only valid when the generator is a percolation generator,
414		and endpoints are enforced to be dead ends
415
416		more information on where this comes from can be found in
417		- `cfg_success_predict_fn()` from `maze_dataset.dataset.success_predict_math`
418		- `estimate_dataset_fractions.ipynb`
419		- `maze_dataset.benchmarks.sweep_fit`
420
421		# Parameters:
422		- `except_if_all_success_expected : bool`
423			if `True`, don't raise an error if the success fraction is below the threshold.
424			will always return `1.0` if the config is not expected to fail
425
426		# Returns:
427		- `float`
428			estimated success fraction
429
430		# Raises:
431		- `NoPercolationInConfigError` : if the config is not expected to fail, and `except_if_all_success_expected` is `False`
432		"""
433		try:
434			return cfg_success_predict_fn(self)
435
436		except NoPercolationInConfigError as e:
437			if except_if_all_success_expected:
438				raise e  # noqa: TRY201
439			return 1.0

441	def success_fraction_compensate(
442		self,
443		safety_margin: float = 1.2,
444		except_if_all_success_expected: bool = False,
445		epsilon: float = 1e-2,
446	) -> "MazeDatasetConfig":
447		"""return a new `MazeDatasetConfig` like this one with `n_mazes` adjusted to compensate for the success fraction
448
449		calls `MazeDatasetConfig.success_fraction_estimate()` to get the success fraction, and then
450		computes the new number of mazes as `n_mazes = n_mazes * safety_margin / success_fraction + 1`
451
452		more information on where this comes from can be found in
453		- `cfg_success_predict_fn()` from `maze_dataset.dataset.success_predict_math`
454		- `estimate_dataset_fractions.ipynb`
455		- `maze_dataset.benchmarks.sweep_fit`
456
457		# Parameters:
458		- `safety_margin : float`
459			safety margin to apply to the success fraction estimate
460			(defaults to `1.2`, or 20% more mazes than estimated)
461		- `except_if_all_success_expected : bool`
462			if `True`, don't raise an error if the success fraction is below the threshold.
463			this is passed to `MazeDatasetConfig.success_fraction_estimate`.
464			if your config isn't expected to fail, passing this might mean you generate more mazes than needed
465			since `safety_margin` is still applied.
466			(defaults to `False`)
467		- `epsilon : float`
468			raise `SuccessChanceTooSmallError` if the success fraction is below this threshold
469			(defaults to `1e-2`)
470
471		# Returns:
472		- `MazeDatasetConfig`
473			new config with adjusted `n_mazes`
474
475		# Raises:
476		- `SuccessChanceTooSmallError` : if the computed success fraction is below `epsilon`
477		"""
478		# compute and check the success fraction
479		success_fraction: float = self.success_fraction_estimate(
480			except_if_all_success_expected=except_if_all_success_expected,
481		)
482		if success_fraction < epsilon:
483			err_msg: str = (
484				f"{success_fraction = } is below the threshold of {epsilon = }"
485			)
486			raise SuccessChanceTooSmallError(
487				err_msg,
488			)
489
490		# compute the new number of mazes
491		n_mazes: int = self.n_mazes
492		new_n_mazes: int = int((n_mazes * safety_margin) / success_fraction) + 1
493
494		# put it in a new config and return
495		cfg_dict: dict = self.serialize()
496		cfg_dict["n_mazes"] = new_n_mazes
497		return MazeDatasetConfig.load(cfg_dict)

777        @classmethod  # type: ignore[misc]
778        def load(cls, data: dict[str, Any] | T) -> Type[T]:
779            # HACK: this is kind of ugly, but it fixes a lot of issues for when we do recursive loading with ZANJ
780            if isinstance(data, cls):
781                return data
782
783            assert isinstance(
784                data, typing.Mapping
785            ), f"When loading {cls.__name__ = } expected a Mapping, but got {type(data) = }:\n{data = }"
786
787            cls_type_hints: dict[str, Any] = get_cls_type_hints(cls)
788
789            # initialize dict for keeping what we will pass to the constructor
790            ctor_kwargs: dict[str, Any] = dict()
791
792            # iterate over the fields of the class
793            for field in dataclasses.fields(cls):
794                # check if the field is a SerializableField
795                assert isinstance(
796                    field, SerializableField
797                ), f"Field '{field.name}' on class {cls.__name__} is not a SerializableField, but a {type(field)}. this state should be inaccessible, please report this bug!\nhttps://github.com/mivanit/muutils/issues/new"
798
799                # check if the field is in the data and if it should be initialized
800                if (field.name in data) and field.init:
801                    # get the value, we will be processing it
802                    value: Any = data[field.name]
803
804                    # get the type hint for the field
805                    field_type_hint: Any = cls_type_hints.get(field.name, None)
806
807                    # we rely on the init of `SerializableField` to check that only one of `loading_fn` and `deserialize_fn` is set
808                    if field.deserialize_fn:
809                        # if it has a deserialization function, use that
810                        value = field.deserialize_fn(value)
811                    elif field.loading_fn:
812                        # if it has a loading function, use that
813                        value = field.loading_fn(data)
814                    elif (
815                        field_type_hint is not None
816                        and hasattr(field_type_hint, "load")
817                        and callable(field_type_hint.load)
818                    ):
819                        # if no loading function but has a type hint with a load method, use that
820                        if isinstance(value, dict):
821                            value = field_type_hint.load(value)
822                        else:
823                            raise FieldLoadingError(
824                                f"Cannot load value into {field_type_hint}, expected {type(value) = } to be a dict\n{value = }"
825                            )
826                    else:
827                        # assume no loading needs to happen, keep `value` as-is
828                        pass
829
830                    # store the value in the constructor kwargs
831                    ctor_kwargs[field.name] = value
832
833            # create a new instance of the class with the constructor kwargs
834            output: cls = cls(**ctor_kwargs)
835
836            # validate the types of the fields if needed
837            if on_typecheck_mismatch != ErrorMode.IGNORE:
838                fields_valid: dict[str, bool] = (
839                    SerializableDataclass__validate_fields_types__dict(
840                        output,
841                        on_typecheck_error=on_typecheck_error,
842                    )
843                )
844
845                # if there are any fields that are not valid, raise an error
846                if not all(fields_valid.values()):
847                    msg: str = (
848                        f"Type mismatch in fields of {cls.__name__}:\n"
849                        + "\n".join(
850                            [
851                                f"{k}:\texpected {cls_type_hints[k] = }, but got value {getattr(output, k) = }, {type(getattr(output, k)) = }"
852                                for k, v in fields_valid.items()
853                                if not v
854                            ]
855                        )
856                    )
857
858                    on_typecheck_mismatch.process(
859                        msg, except_cls=FieldTypeMismatchError
860                    )
861
862            # return the new instance
863            return output