docs for maze-dataset
v1.1.0
maze-datasetThis package provides utilities for generation, filtering, solving, visualizing, and processing of mazes for training ML systems. Primarily built for the maze-transformer interpretability project. You can find our paper on it here: http://arxiv.org/abs/2309.10498
This package includes a variety of maze generation algorithms, including randomized depth first search, Wilson’s algorithm for uniform spanning trees, and percolation. Datasets can be filtered to select mazes of a certain length or complexity, remove duplicates, and satisfy custom properties. A variety of output formats for visualization and training ML models are provided.
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This package is available on PyPI, and can be installed via
pip install maze-datasetThe full hosted documentation is available at https://understanding-search.github.io/maze-dataset/.
Additionally:
coverage/
folderbenchmarks/
folderTo create a MazeDataset, which inherits from
torch.utils.data.Dataset, you first create a
MazeDatasetConfig:
from maze_dataset import MazeDataset, MazeDatasetConfig
from maze_dataset.generation import LatticeMazeGenerators
cfg: MazeDatasetConfig = MazeDatasetConfig(
name="test", # name is only for you to keep track of things
grid_n=5, # number of rows/columns in the lattice
n_mazes=4, # number of mazes to generate
maze_ctor=LatticeMazeGenerators.gen_dfs, # algorithm to generate the maze
maze_ctor_kwargs=dict(do_forks=False), # additional parameters to pass to the maze generation algorithm
)and then pass this config to the MazeDataset.from_config
method:
dataset: MazeDataset = MazeDataset.from_config(cfg)This method can search for whether a dataset with matching config hash already exists on your filesystem in the expected location, and load it if so. It can also generate a dataset on the fly if needed.
The elements of the dataset are SolvedMaze
objects:
>>> m = dataset[0]
>>> type(m)
maze_dataset.maze.lattice_maze.SolvedMazeWhich can be converted to a variety of formats:
# visual representation as ascii art
m.as_ascii()
# RGB image, optionally without solution or endpoints, suitable for CNNs
m.as_pixels()
# text format for autoreregressive transformers
from maze_dataset.tokenization import MazeTokenizerModular, TokenizationMode
m.as_tokens(maze_tokenizer=MazeTokenizerModular(
tokenization_mode=TokenizationMode.AOTP_UT_rasterized, max_grid_size=100,
))
# advanced visualization with many features
from maze_dataset.plotting import MazePlot
MazePlot(maze).plot()
This project uses Poetry for development. To install with dev requirements, run
poetry install --with devA makefile is included to simplify common development tasks:
make help will print all available commandsmake test
make unitmake test_notebooksmake format
make check-formatIf you use this code in your research, please cite our paper:
@misc{maze-dataset,
title={A Configurable Library for Generating and Manipulating Maze Datasets},
author={Michael Igorevich Ivanitskiy and Rusheb Shah and Alex F. Spies and Tilman Räuker and Dan Valentine and Can Rager and Lucia Quirke and Chris Mathwin and Guillaume Corlouer and Cecilia Diniz Behn and Samy Wu Fung},
year={2023},
eprint={2309.10498},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={http://arxiv.org/abs/2309.10498}
}SolvedMazeMazeDatasetConfigMazeDatasetMazeDatasetCollectionMazeDatasetCollectionConfigTargetedLatticeMazeLatticeMazeset_serialize_minimal_thresholdLatticeMazeGeneratorsCoordCoordTupCoordListCoordArrayConnectionConnectionListConnectionArraySPECIAL_TOKENSVOCABVOCAB_LISTVOCAB_TOKEN_TO_INDEXmaze_dataset
maze-datasetThis package provides utilities for generation, filtering, solving, visualizing, and processing of mazes for training ML systems. Primarily built for the maze-transformer interpretability project. You can find our paper on it here: http://arxiv.org/abs/2309.10498
This package includes a variety of maze generation algorithms, including randomized depth first search, Wilson’s algorithm for uniform spanning trees, and percolation. Datasets can be filtered to select mazes of a certain length or complexity, remove duplicates, and satisfy custom properties. A variety of output formats for visualization and training ML models are provided.
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This package is available on PyPI, and can be installed via
pip install maze-datasetThe full hosted documentation is available at https://understanding-search.github.io/maze-dataset/.
Additionally:
coverage/
folderbenchmarks/
folderTo create a MazeDataset, which inherits from
torch.utils.data.Dataset, you first create a
MazeDatasetConfig:
from maze_dataset import MazeDataset, MazeDatasetConfig
from <a href="maze_dataset/generation.html">maze_dataset.generation</a> import LatticeMazeGenerators
cfg: MazeDatasetConfig = MazeDatasetConfig(
name="test", # name is only for you to keep track of things
grid_n=5, # number of rows/columns in the lattice
n_mazes=4, # number of mazes to generate
maze_ctor=LatticeMazeGenerators.gen_dfs, # algorithm to generate the maze
maze_ctor_kwargs=dict(do_forks=False), # additional parameters to pass to the maze generation algorithm
)and then pass this config to the
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
method:
dataset: MazeDataset = <a href="#MazeDataset.from_config">MazeDataset.from_config</a>(cfg)This method can search for whether a dataset with matching config hash already exists on your filesystem in the expected location, and load it if so. It can also generate a dataset on the fly if needed.
The elements of the dataset are SolvedMaze
objects:
>>> m = dataset[0]
>>> type(m)
<a href="#SolvedMaze">SolvedMaze</a>Which can be converted to a variety of formats:
### visual representation as ascii art
m.as_ascii()
### RGB image, optionally without solution or endpoints, suitable for CNNs
m.as_pixels()
### text format for autoreregressive transformers
from <a href="maze_dataset/tokenization.html">maze_dataset.tokenization</a> import MazeTokenizerModular, TokenizationMode
m.as_tokens(maze_tokenizer=MazeTokenizerModular(
tokenization_mode=TokenizationMode.AOTP_UT_rasterized, max_grid_size=100,
))
### advanced visualization with many features
from <a href="maze_dataset/plotting.html">maze_dataset.plotting</a> import MazePlot
MazePlot(maze).plot()This project uses Poetry for development. To install with dev requirements, run
poetry install --with devA makefile is included to simplify common development tasks:
make help will print all available commandsmake test
make unitmake test_notebooksmake format
make check-formatIf you use this code in your research, please cite our paper:
@misc{maze-dataset,
title={A Configurable Library for Generating and Manipulating Maze Datasets},
author={Michael Igorevich Ivanitskiy and Rusheb Shah and Alex F. Spies and Tilman Räuker and Dan Valentine and Can Rager and Lucia Quirke and Chris Mathwin and Guillaume Corlouer and Cecilia Diniz Behn and Samy Wu Fung},
year={2023},
eprint={2309.10498},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={http://arxiv.org/abs/2309.10498}
}class SolvedMaze(maze_dataset.maze.lattice_maze.TargetedLatticeMaze):Stores a maze and a solution
SolvedMaze(
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
solution: jaxtyping.Int8[ndarray, 'coord row_col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
end_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
allow_invalid: bool = False
)solution: jaxtyping.Int8[ndarray, 'coord row_col']def get_solution_tokens(self) -> list[str | tuple[int, int]]maze: maze_dataset.maze.lattice_maze.LatticeMazedef from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
solution: list[tuple[int, int]]
) -> maze_dataset.maze.lattice_maze.SolvedMazedef from_targeted_lattice_maze(
cls,
targeted_lattice_maze: maze_dataset.maze.lattice_maze.TargetedLatticeMaze,
solution: list[tuple[int, int]] | None = None
) -> maze_dataset.maze.lattice_maze.SolvedMazesolves the given targeted lattice maze and returns a SolvedMaze
def get_solution_forking_points(
self,
always_include_endpoints: bool = False
) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates and their indicies from the solution where a fork is present
def get_solution_path_following_points(self) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates from the solution where there is only a single (non-backtracking) point to move to
returns the complement of get_solution_forking_points
from the path
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeDatasetConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):config object which is passed to
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
to generate or load a dataset
MazeDatasetConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
grid_n: int,
n_mazes: int,
maze_ctor: Callable = <function LatticeMazeGenerators.gen_dfs>,
maze_ctor_kwargs: dict = <factory>,
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]] = <factory>
)grid_n: int
n_mazes: int
def maze_ctor(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.maze_ctor_kwargs: dict
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]]
grid_shape: tuple[int, int]
grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']max_grid_n: intdef stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def summary(self) -> dictreturn a summary of the config
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeDataset(typing.Generic[+T_co]):a maze dataset class. This is a collection of solved mazes, and
should be initialized via
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
MazeDataset(
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
mazes: Sequence[maze_dataset.maze.lattice_maze.SolvedMaze],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig
mazes: list[maze_dataset.maze.lattice_maze.SolvedMaze]
generation_metadata_collected: dict | None
def data_hash(self) -> intdef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens according to the passed
maze_tokenizer
the maze_tokenizer should be either a
MazeTokenizer or a MazeTokenizerModular
if join_tokens_individual_maze is True, then the tokens
of each maze are joined with a space, and the result is a list of
strings. i.e.:
>>> dataset.as_tokens(join_tokens_individual_maze=False)
[["a", "b", "c"], ["d", "e", "f"]]
>>> dataset.as_tokens(join_tokens_individual_maze=True)
["a b c", "d e f"]def generate(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
gen_parallel: bool = False,
pool_kwargs: dict | None = None,
verbose: bool = False
) -> maze_dataset.dataset.maze_dataset.MazeDatasetgenerate a maze dataset given a config and some generation parameters
def download(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetdef load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.maze_dataset.MazeDatasetload from zanj/json
def serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]serialize to zanj/json
def update_self_config(self)update the config to match the current state of the dataset (number of mazes, such as after filtering)
def custom_maze_filter(
self,
method: Callable[[maze_dataset.maze.lattice_maze.SolvedMaze], bool],
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetfilter the dataset using a custom method
class MazeDatasetCollection(typing.Generic[+T_co]):a collection of maze datasets
MazeDatasetCollection(
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset]
generation_metadata_collected: dict | None
dataset_lengths: list[int]
dataset_cum_lengths: jaxtyping.Int[ndarray, 'indices']mazes: list[maze_dataset.maze.lattice_maze.LatticeMaze]def generate(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef download(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]def load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens
if join_tokens_individual_maze is True, then the tokens of each maze are joined with a space, and the result is a list of strings. i.e.: >>> dataset.as_tokens(join_tokens_individual_maze=False) [[“a”, “b”, “c”], [“d”, “e”, “f”]] >>> dataset.as_tokens(join_tokens_individual_maze=True) [“a b c”, “d e f”]
def update_self_config(self) -> Noneupdate the config of the dataset to match the actual data, if needed
for example, adjust number of mazes after filtering
class MazeDatasetCollectionConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):maze dataset collection configuration, including tokenizers and shuffle
MazeDatasetCollectionConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]
)maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]def summary(self) -> dictreturn a summary of the config
n_mazes: intmax_grid_n: intmax_grid_shape: tuple[int, int]max_grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']def stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class TargetedLatticeMaze(maze_dataset.maze.lattice_maze.LatticeMaze):A LatticeMaze with a start and end position
TargetedLatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
)start_pos: jaxtyping.Int8[ndarray, 'row_col']
end_pos: jaxtyping.Int8[ndarray, 'row_col']
def get_start_pos_tokens(self) -> list[str | tuple[int, int]]def get_end_pos_tokens(self) -> list[str | tuple[int, int]]def from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.TargetedLatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class LatticeMaze(muutils.json_serialize.serializable_dataclass.SerializableDataclass):lattice maze (nodes on a lattice, connections only to neighboring nodes)
Connection List represents which nodes (N) are connected in each direction.
First and second elements represent rightward and downward connections, respectively.
Example: Connection list: [ [ # down [F T], [F F] ], [ # right [T F], [T F] ] ]
Nodes with connections N T N F F T N T N F F F
Graph: N - N | N - N
Note: the bottom row connections going down, and the right-hand connections going right, will always be False.
LatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None
)connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']
generation_meta: dict | None = None
lattice_dim
grid_shapen_connectionsgrid_n: intdef heuristic(a: tuple[int, int], b: tuple[int, int]) -> floatreturn manhattan distance between two points
def nodes_connected(
self,
a: jaxtyping.Int8[ndarray, 'row_col'],
b: jaxtyping.Int8[ndarray, 'row_col'],
/
) -> boolreturns whether two nodes are connected
def is_valid_path(
self,
path: jaxtyping.Int8[ndarray, 'coord row_col'],
empty_is_valid: bool = False
) -> boolcheck if a path is valid
def coord_degrees(self) -> jaxtyping.Int8[ndarray, 'row col']Returns an array with the connectivity degree of each coord. I.e., how many neighbors each coord has.
def get_coord_neighbors(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']Returns an array of the neighboring, connected coords of
c.
def gen_connected_component_from(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']return the connected component from a given coordinate
def find_shortest_path(
self,
c_start: tuple[int, int],
c_end: tuple[int, int]
) -> jaxtyping.Int8[ndarray, 'coord row_col']find the shortest path between two coordinates, using A*
def get_nodes(self) -> jaxtyping.Int8[ndarray, 'coord row_col']return a list of all nodes in the maze
def get_connected_component(self) -> jaxtyping.Int8[ndarray, 'coord row_col']get the largest (and assumed only nonsingular) connected component of the maze
TODO: other connected components?
def generate_random_path(
self,
except_when_invalid: bool = True,
allowed_start: list[tuple[int, int]] | None = None,
allowed_end: list[tuple[int, int]] | None = None,
deadend_start: bool = False,
deadend_end: bool = False,
endpoints_not_equal: bool = False
) -> jaxtyping.Int8[ndarray, 'coord row_col']return a path between randomly chosen start and end nodes within the connected component
Note that setting special conditions on start and end positions might cause the same position to be selected as both start and end.
except_when_invalid : bool deprecated. setting this to
False will cause an error. (defaults to
True)allowed_start : CoordList | None a list of allowed
start positions. If None, any position in the connected
component is allowed (defaults to None)allowed_end : CoordList | None a list of allowed end
positions. If None, any position in the connected component
is allowed (defaults to None)deadend_start : bool whether to
force the start position to be a deadend
(defaults to False) (defaults to False)deadend_end : bool whether to
force the end position to be a deadend
(defaults to False) (defaults to False)endpoints_not_equal : bool whether to ensure tha the
start and end point are not the same (defaults to
False)CoordArray a path between the selected start and end
positionsValueError : if the connected component has less than 2
nodes and except_when_invalid is Truedef as_adj_list(
self,
shuffle_d0: bool = True,
shuffle_d1: bool = True
) -> jaxtyping.Int8[ndarray, 'conn start_end coord']def from_adj_list(
cls,
adj_list: jaxtyping.Int8[ndarray, 'conn start_end coord']
) -> maze_dataset.maze.lattice_maze.LatticeMazecreate a LatticeMaze from a list of connections
[!NOTE] This has only been tested for square mazes. Might need to change some things if rectangular mazes are needed.
def as_adj_list_tokens(self) -> list[str | tuple[int, int]]def as_tokens(
self,
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> list[str]serialize maze and solution to tokens
def from_tokens(
cls,
tokens: list[str],
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> maze_dataset.maze.lattice_maze.LatticeMazeConstructs a maze from a tokenization. Only legacy tokenizers and
their MazeTokenizerModular analogs are supported.
def as_pixels(
self,
show_endpoints: bool = True,
show_solution: bool = True
) -> jaxtyping.Int[ndarray, 'x y rgb']def from_pixels(
cls,
pixel_grid: jaxtyping.Int[ndarray, 'x y rgb']
) -> maze_dataset.maze.lattice_maze.LatticeMazedef as_ascii(self, show_endpoints: bool = True, show_solution: bool = True) -> strreturn an ASCII grid of the maze
def from_ascii(cls, ascii_str: str) -> maze_dataset.maze.lattice_maze.LatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
def set_serialize_minimal_threshold(threshold: int | None) -> Noneclass LatticeMazeGenerators:namespace for lattice maze generation algorithms
def gen_dfs(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.def gen_prim(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedef gen_wilson(
grid_shape: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.LatticeMazeGenerate a lattice maze using Wilson’s algorithm.
Wilson’s algorithm generates an unbiased (random) maze sampled from the uniform distribution over all mazes, using loop-erased random walks. The generated maze is acyclic and all cells are part of a unique connected space. https://en.wikipedia.org/wiki/Maze_generation_algorithm#Wilson’s_algorithm
def gen_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using simple percolation
note that p in the range (0.4, 0.7) gives the most interesting mazes
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)p: float: the probability of a cell being accessible
(default: 0.5)start_coord: Coord | None: the starting coordinate for
the connected component (default: None will give a random
start)def gen_dfs_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
accessible_cells: int | None = None,
max_tree_depth: int | None = None,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedfs and then percolation (adds cycles)
Coord = <class 'jaxtyping.Int8[ndarray, 'row_col']'>
CoordTup = tuple[int, int]
CoordList = list[tuple[int, int]]
CoordArray = <class 'jaxtyping.Int8[ndarray, 'coord row_col']'>
Connection = <class 'jaxtyping.Int8[ndarray, 'coord=2 row_col=2']'>
ConnectionList = <class 'jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']'>
ConnectionArray = <class 'jaxtyping.Int8[ndarray, 'edges leading_trailing_coord=2 row_col=2']'>
SPECIAL_TOKENS = _SPECIAL_TOKENS_BASE(ADJLIST_START='<ADJLIST_START>', ADJLIST_END='<ADJLIST_END>', TARGET_START='<TARGET_START>', TARGET_END='<TARGET_END>', ORIGIN_START='<ORIGIN_START>', ORIGIN_END='<ORIGIN_END>', PATH_START='<PATH_START>', PATH_END='<PATH_END>', CONNECTOR='<-->', ADJACENCY_ENDLINE=';', PADDING='<PADDING>')
VOCAB = _VOCAB_BASE(ADJLIST_START='<ADJLIST_START>', ADJLIST_END='<ADJLIST_END>', TARGET_START='<TARGET_START>', TARGET_END='<TARGET_END>', ORIGIN_START='<ORIGIN_START>', ORIGIN_END='<ORIGIN_END>', PATH_START='<PATH_START>', PATH_END='<PATH_END>', CONNECTOR='<-->', ADJACENCY_ENDLINE=';', PADDING='<PADDING>', COORD_PRE='(', COORD_INTRA=',', COORD_POST=')', TARGET_INTRA='=', TARGET_POST='||', PATH_INTRA=':', PATH_POST='THEN', NEGATIVE='-', UNKNOWN='<UNK>', TARGET_A='TARGET_A', TARGET_B='TARGET_B', TARGET_C='TARGET_C', TARGET_D='TARGET_D', TARGET_E='TARGET_E', TARGET_F='TARGET_F', TARGET_G='TARGET_G', TARGET_H='TARGET_H', TARGET_I='TARGET_I', TARGET_J='TARGET_J', TARGET_K='TARGET_K', TARGET_L='TARGET_L', TARGET_M='TARGET_M', TARGET_N='TARGET_N', TARGET_O='TARGET_O', TARGET_P='TARGET_P', TARGET_Q='TARGET_Q', TARGET_R='TARGET_R', TARGET_S='TARGET_S', TARGET_T='TARGET_T', TARGET_U='TARGET_U', TARGET_V='TARGET_V', TARGET_W='TARGET_W', TARGET_X='TARGET_X', TARGET_Y='TARGET_Y', TARGET_Z='TARGET_Z', TARGET_NORTH='TARGET_NORTH', TARGET_SOUTH='TARGET_SOUTH', TARGET_EAST='TARGET_EAST', TARGET_WEST='TARGET_WEST', TARGET_NORTHEAST='TARGET_NORTHEAST', TARGET_NORTHWEST='TARGET_NORTHWEST', TARGET_SOUTHEAST='TARGET_SOUTHEAST', TARGET_SOUTHWEST='TARGET_SOUTHWEST', TARGET_CENTER='TARGET_CENTER', PATH_NORTH='NORTH', PATH_SOUTH='SOUTH', PATH_EAST='EAST', PATH_WEST='WEST', PATH_FORWARD='FORWARD', PATH_BACKWARD='BACKWARD', PATH_LEFT='LEFT', PATH_RIGHT='RIGHT', PATH_STAY='STAY', I_000='+0', I_001='+1', I_002='+2', I_003='+3', I_004='+4', I_005='+5', I_006='+6', I_007='+7', I_008='+8', I_009='+9', I_010='+10', I_011='+11', I_012='+12', I_013='+13', I_014='+14', I_015='+15', I_016='+16', I_017='+17', I_018='+18', I_019='+19', I_020='+20', I_021='+21', I_022='+22', I_023='+23', I_024='+24', I_025='+25', I_026='+26', I_027='+27', I_028='+28', I_029='+29', I_030='+30', I_031='+31', I_032='+32', I_033='+33', I_034='+34', I_035='+35', I_036='+36', I_037='+37', I_038='+38', I_039='+39', I_040='+40', I_041='+41', I_042='+42', I_043='+43', I_044='+44', I_045='+45', I_046='+46', I_047='+47', I_048='+48', I_049='+49', I_050='+50', I_051='+51', I_052='+52', I_053='+53', I_054='+54', I_055='+55', I_056='+56', I_057='+57', I_058='+58', I_059='+59', I_060='+60', I_061='+61', I_062='+62', I_063='+63', I_064='+64', I_065='+65', I_066='+66', I_067='+67', I_068='+68', I_069='+69', I_070='+70', I_071='+71', I_072='+72', I_073='+73', I_074='+74', I_075='+75', I_076='+76', I_077='+77', I_078='+78', I_079='+79', I_080='+80', I_081='+81', I_082='+82', I_083='+83', I_084='+84', I_085='+85', I_086='+86', I_087='+87', I_088='+88', I_089='+89', I_090='+90', I_091='+91', I_092='+92', I_093='+93', I_094='+94', I_095='+95', I_096='+96', I_097='+97', I_098='+98', I_099='+99', I_100='+100', I_101='+101', I_102='+102', I_103='+103', I_104='+104', I_105='+105', I_106='+106', I_107='+107', I_108='+108', I_109='+109', I_110='+110', I_111='+111', I_112='+112', I_113='+113', I_114='+114', I_115='+115', I_116='+116', I_117='+117', I_118='+118', I_119='+119', I_120='+120', I_121='+121', I_122='+122', I_123='+123', I_124='+124', I_125='+125', I_126='+126', I_127='+127', I_128='+128', I_129='+129', I_130='+130', I_131='+131', I_132='+132', I_133='+133', I_134='+134', I_135='+135', I_136='+136', I_137='+137', I_138='+138', I_139='+139', I_140='+140', I_141='+141', I_142='+142', I_143='+143', I_144='+144', I_145='+145', I_146='+146', I_147='+147', I_148='+148', I_149='+149', I_150='+150', I_151='+151', I_152='+152', I_153='+153', I_154='+154', I_155='+155', I_156='+156', I_157='+157', I_158='+158', I_159='+159', I_160='+160', I_161='+161', I_162='+162', I_163='+163', I_164='+164', I_165='+165', I_166='+166', I_167='+167', I_168='+168', I_169='+169', I_170='+170', I_171='+171', I_172='+172', I_173='+173', I_174='+174', I_175='+175', I_176='+176', I_177='+177', I_178='+178', I_179='+179', I_180='+180', I_181='+181', I_182='+182', I_183='+183', I_184='+184', I_185='+185', I_186='+186', I_187='+187', I_188='+188', I_189='+189', I_190='+190', I_191='+191', I_192='+192', I_193='+193', I_194='+194', I_195='+195', I_196='+196', I_197='+197', I_198='+198', I_199='+199', I_200='+200', I_201='+201', I_202='+202', I_203='+203', I_204='+204', I_205='+205', I_206='+206', I_207='+207', I_208='+208', I_209='+209', I_210='+210', I_211='+211', I_212='+212', I_213='+213', I_214='+214', I_215='+215', I_216='+216', I_217='+217', I_218='+218', I_219='+219', I_220='+220', I_221='+221', I_222='+222', I_223='+223', I_224='+224', I_225='+225', I_226='+226', I_227='+227', I_228='+228', I_229='+229', I_230='+230', I_231='+231', I_232='+232', I_233='+233', I_234='+234', I_235='+235', I_236='+236', I_237='+237', I_238='+238', I_239='+239', I_240='+240', I_241='+241', I_242='+242', I_243='+243', I_244='+244', I_245='+245', I_246='+246', I_247='+247', I_248='+248', I_249='+249', I_250='+250', I_251='+251', I_252='+252', I_253='+253', I_254='+254', I_255='+255', CTT_0='0', CTT_1='1', CTT_2='2', CTT_3='3', CTT_4='4', CTT_5='5', CTT_6='6', CTT_7='7', CTT_8='8', CTT_9='9', CTT_10='10', CTT_11='11', CTT_12='12', CTT_13='13', CTT_14='14', CTT_15='15', CTT_16='16', CTT_17='17', CTT_18='18', CTT_19='19', CTT_20='20', CTT_21='21', CTT_22='22', CTT_23='23', CTT_24='24', CTT_25='25', CTT_26='26', CTT_27='27', CTT_28='28', CTT_29='29', CTT_30='30', CTT_31='31', CTT_32='32', CTT_33='33', CTT_34='34', CTT_35='35', CTT_36='36', CTT_37='37', CTT_38='38', CTT_39='39', CTT_40='40', CTT_41='41', CTT_42='42', CTT_43='43', CTT_44='44', CTT_45='45', CTT_46='46', CTT_47='47', CTT_48='48', CTT_49='49', CTT_50='50', CTT_51='51', CTT_52='52', CTT_53='53', CTT_54='54', CTT_55='55', CTT_56='56', CTT_57='57', CTT_58='58', CTT_59='59', CTT_60='60', CTT_61='61', CTT_62='62', CTT_63='63', CTT_64='64', CTT_65='65', CTT_66='66', CTT_67='67', CTT_68='68', CTT_69='69', CTT_70='70', CTT_71='71', CTT_72='72', CTT_73='73', CTT_74='74', CTT_75='75', CTT_76='76', CTT_77='77', CTT_78='78', CTT_79='79', CTT_80='80', CTT_81='81', CTT_82='82', CTT_83='83', CTT_84='84', CTT_85='85', CTT_86='86', CTT_87='87', CTT_88='88', CTT_89='89', CTT_90='90', CTT_91='91', CTT_92='92', CTT_93='93', CTT_94='94', CTT_95='95', CTT_96='96', CTT_97='97', CTT_98='98', CTT_99='99', CTT_100='100', CTT_101='101', CTT_102='102', CTT_103='103', CTT_104='104', CTT_105='105', CTT_106='106', CTT_107='107', CTT_108='108', CTT_109='109', CTT_110='110', CTT_111='111', CTT_112='112', CTT_113='113', CTT_114='114', CTT_115='115', CTT_116='116', CTT_117='117', CTT_118='118', CTT_119='119', CTT_120='120', CTT_121='121', CTT_122='122', CTT_123='123', CTT_124='124', CTT_125='125', CTT_126='126', CTT_127='127', I_N256='-256', I_N255='-255', I_N254='-254', I_N253='-253', I_N252='-252', I_N251='-251', I_N250='-250', I_N249='-249', I_N248='-248', I_N247='-247', I_N246='-246', I_N245='-245', I_N244='-244', I_N243='-243', I_N242='-242', I_N241='-241', I_N240='-240', I_N239='-239', I_N238='-238', I_N237='-237', I_N236='-236', I_N235='-235', I_N234='-234', I_N233='-233', I_N232='-232', I_N231='-231', I_N230='-230', I_N229='-229', I_N228='-228', I_N227='-227', I_N226='-226', I_N225='-225', I_N224='-224', I_N223='-223', I_N222='-222', I_N221='-221', I_N220='-220', I_N219='-219', I_N218='-218', I_N217='-217', I_N216='-216', I_N215='-215', I_N214='-214', I_N213='-213', I_N212='-212', I_N211='-211', I_N210='-210', I_N209='-209', I_N208='-208', I_N207='-207', I_N206='-206', I_N205='-205', I_N204='-204', I_N203='-203', I_N202='-202', I_N201='-201', I_N200='-200', I_N199='-199', I_N198='-198', I_N197='-197', I_N196='-196', I_N195='-195', I_N194='-194', I_N193='-193', I_N192='-192', I_N191='-191', I_N190='-190', I_N189='-189', I_N188='-188', I_N187='-187', I_N186='-186', I_N185='-185', I_N184='-184', I_N183='-183', I_N182='-182', I_N181='-181', I_N180='-180', I_N179='-179', I_N178='-178', I_N177='-177', I_N176='-176', I_N175='-175', I_N174='-174', I_N173='-173', I_N172='-172', I_N171='-171', I_N170='-170', I_N169='-169', I_N168='-168', I_N167='-167', I_N166='-166', I_N165='-165', I_N164='-164', I_N163='-163', I_N162='-162', I_N161='-161', I_N160='-160', I_N159='-159', I_N158='-158', I_N157='-157', I_N156='-156', I_N155='-155', I_N154='-154', I_N153='-153', I_N152='-152', I_N151='-151', I_N150='-150', I_N149='-149', I_N148='-148', I_N147='-147', I_N146='-146', I_N145='-145', I_N144='-144', I_N143='-143', I_N142='-142', I_N141='-141', I_N140='-140', I_N139='-139', I_N138='-138', I_N137='-137', I_N136='-136', I_N135='-135', I_N134='-134', I_N133='-133', I_N132='-132', I_N131='-131', I_N130='-130', I_N129='-129', I_N128='-128', I_N127='-127', I_N126='-126', I_N125='-125', I_N124='-124', I_N123='-123', I_N122='-122', I_N121='-121', I_N120='-120', I_N119='-119', I_N118='-118', I_N117='-117', I_N116='-116', I_N115='-115', I_N114='-114', I_N113='-113', I_N112='-112', I_N111='-111', I_N110='-110', I_N109='-109', I_N108='-108', I_N107='-107', I_N106='-106', I_N105='-105', I_N104='-104', I_N103='-103', I_N102='-102', I_N101='-101', I_N100='-100', I_N099='-99', I_N098='-98', I_N097='-97', I_N096='-96', I_N095='-95', I_N094='-94', I_N093='-93', I_N092='-92', I_N091='-91', I_N090='-90', I_N089='-89', I_N088='-88', I_N087='-87', I_N086='-86', I_N085='-85', I_N084='-84', I_N083='-83', I_N082='-82', I_N081='-81', I_N080='-80', I_N079='-79', I_N078='-78', I_N077='-77', I_N076='-76', I_N075='-75', I_N074='-74', I_N073='-73', I_N072='-72', I_N071='-71', I_N070='-70', I_N069='-69', I_N068='-68', I_N067='-67', I_N066='-66', I_N065='-65', I_N064='-64', I_N063='-63', I_N062='-62', I_N061='-61', I_N060='-60', I_N059='-59', I_N058='-58', I_N057='-57', I_N056='-56', I_N055='-55', I_N054='-54', I_N053='-53', I_N052='-52', I_N051='-51', I_N050='-50', I_N049='-49', I_N048='-48', I_N047='-47', I_N046='-46', I_N045='-45', I_N044='-44', I_N043='-43', I_N042='-42', I_N041='-41', I_N040='-40', I_N039='-39', I_N038='-38', I_N037='-37', I_N036='-36', I_N035='-35', I_N034='-34', I_N033='-33', I_N032='-32', I_N031='-31', I_N030='-30', I_N029='-29', I_N028='-28', I_N027='-27', I_N026='-26', I_N025='-25', I_N024='-24', I_N023='-23', I_N022='-22', I_N021='-21', I_N020='-20', I_N019='-19', I_N018='-18', I_N017='-17', I_N016='-16', I_N015='-15', I_N014='-14', I_N013='-13', I_N012='-12', I_N011='-11', I_N010='-10', I_N009='-9', I_N008='-8', I_N007='-7', I_N006='-6', I_N005='-5', I_N004='-4', I_N003='-3', I_N002='-2', I_N001='-1', PATH_PRE='STEP', ADJLIST_PRE='ADJ_GROUP', ADJLIST_INTRA='&', ADJLIST_WALL='<XX>', RESERVE_708='<RESERVE_708>', RESERVE_709='<RESERVE_709>', RESERVE_710='<RESERVE_710>', RESERVE_711='<RESERVE_711>', RESERVE_712='<RESERVE_712>', RESERVE_713='<RESERVE_713>', RESERVE_714='<RESERVE_714>', RESERVE_715='<RESERVE_715>', RESERVE_716='<RESERVE_716>', RESERVE_717='<RESERVE_717>', RESERVE_718='<RESERVE_718>', RESERVE_719='<RESERVE_719>', RESERVE_720='<RESERVE_720>', RESERVE_721='<RESERVE_721>', RESERVE_722='<RESERVE_722>', RESERVE_723='<RESERVE_723>', RESERVE_724='<RESERVE_724>', RESERVE_725='<RESERVE_725>', RESERVE_726='<RESERVE_726>', RESERVE_727='<RESERVE_727>', RESERVE_728='<RESERVE_728>', RESERVE_729='<RESERVE_729>', RESERVE_730='<RESERVE_730>', RESERVE_731='<RESERVE_731>', RESERVE_732='<RESERVE_732>', RESERVE_733='<RESERVE_733>', RESERVE_734='<RESERVE_734>', RESERVE_735='<RESERVE_735>', RESERVE_736='<RESERVE_736>', RESERVE_737='<RESERVE_737>', RESERVE_738='<RESERVE_738>', RESERVE_739='<RESERVE_739>', RESERVE_740='<RESERVE_740>', RESERVE_741='<RESERVE_741>', RESERVE_742='<RESERVE_742>', RESERVE_743='<RESERVE_743>', RESERVE_744='<RESERVE_744>', RESERVE_745='<RESERVE_745>', RESERVE_746='<RESERVE_746>', RESERVE_747='<RESERVE_747>', RESERVE_748='<RESERVE_748>', RESERVE_749='<RESERVE_749>', RESERVE_750='<RESERVE_750>', RESERVE_751='<RESERVE_751>', RESERVE_752='<RESERVE_752>', RESERVE_753='<RESERVE_753>', RESERVE_754='<RESERVE_754>', RESERVE_755='<RESERVE_755>', RESERVE_756='<RESERVE_756>', RESERVE_757='<RESERVE_757>', RESERVE_758='<RESERVE_758>', RESERVE_759='<RESERVE_759>', RESERVE_760='<RESERVE_760>', RESERVE_761='<RESERVE_761>', RESERVE_762='<RESERVE_762>', RESERVE_763='<RESERVE_763>', RESERVE_764='<RESERVE_764>', RESERVE_765='<RESERVE_765>', RESERVE_766='<RESERVE_766>', RESERVE_767='<RESERVE_767>', RESERVE_768='<RESERVE_768>', RESERVE_769='<RESERVE_769>', RESERVE_770='<RESERVE_770>', RESERVE_771='<RESERVE_771>', RESERVE_772='<RESERVE_772>', RESERVE_773='<RESERVE_773>', RESERVE_774='<RESERVE_774>', RESERVE_775='<RESERVE_775>', RESERVE_776='<RESERVE_776>', RESERVE_777='<RESERVE_777>', RESERVE_778='<RESERVE_778>', RESERVE_779='<RESERVE_779>', RESERVE_780='<RESERVE_780>', RESERVE_781='<RESERVE_781>', RESERVE_782='<RESERVE_782>', RESERVE_783='<RESERVE_783>', RESERVE_784='<RESERVE_784>', RESERVE_785='<RESERVE_785>', RESERVE_786='<RESERVE_786>', RESERVE_787='<RESERVE_787>', RESERVE_788='<RESERVE_788>', RESERVE_789='<RESERVE_789>', RESERVE_790='<RESERVE_790>', 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'48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '-256', '-255', '-254', '-253', '-252', '-251', '-250', '-249', '-248', '-247', '-246', '-245', '-244', '-243', '-242', '-241', '-240', '-239', '-238', '-237', '-236', '-235', '-234', '-233', '-232', '-231', '-230', '-229', '-228', '-227', '-226', '-225', '-224', '-223', '-222', '-221', '-220', '-219', '-218', '-217', '-216', '-215', '-214', '-213', '-212', '-211', '-210', '-209', '-208', '-207', '-206', '-205', '-204', '-203', '-202', '-201', '-200', '-199', '-198', '-197', '-196', '-195', '-194', '-193', '-192', '-191', '-190', '-189', '-188', '-187', '-186', '-185', '-184', '-183', '-182', '-181', '-180', '-179', '-178', '-177', '-176', '-175', '-174', '-173', '-172', '-171', '-170', '-169', '-168', '-167', '-166', '-165', '-164', '-163', '-162', '-161', '-160', '-159', '-158', '-157', '-156', '-155', '-154', '-153', '-152', '-151', '-150', '-149', '-148', '-147', '-146', '-145', '-144', '-143', '-142', '-141', '-140', '-139', '-138', '-137', '-136', '-135', '-134', '-133', '-132', '-131', '-130', '-129', '-128', '-127', '-126', '-125', '-124', '-123', '-122', '-121', '-120', '-119', '-118', '-117', '-116', '-115', '-114', '-113', '-112', '-111', '-110', '-109', '-108', '-107', '-106', '-105', '-104', '-103', '-102', '-101', '-100', '-99', '-98', '-97', '-96', '-95', '-94', '-93', '-92', '-91', '-90', '-89', '-88', '-87', '-86', '-85', '-84', '-83', '-82', '-81', '-80', '-79', '-78', '-77', '-76', '-75', '-74', '-73', '-72', '-71', '-70', '-69', '-68', '-67', '-66', '-65', '-64', '-63', '-62', '-61', '-60', '-59', '-58', '-57', '-56', '-55', '-54', '-53', '-52', '-51', '-50', '-49', '-48', '-47', '-46', '-45', '-44', '-43', '-42', '-41', '-40', '-39', '-38', '-37', '-36', '-35', '-34', '-33', '-32', '-31', '-30', '-29', '-28', '-27', '-26', '-25', '-24', '-23', '-22', '-21', '-20', '-19', '-18', '-17', '-16', '-15', '-14', '-13', '-12', '-11', '-10', '-9', '-8', '-7', '-6', '-5', '-4', '-3', '-2', '-1', 'STEP', 'ADJ_GROUP', '&', '<XX>', '<RESERVE_708>', '<RESERVE_709>', '<RESERVE_710>', '<RESERVE_711>', '<RESERVE_712>', '<RESERVE_713>', '<RESERVE_714>', '<RESERVE_715>', '<RESERVE_716>', '<RESERVE_717>', '<RESERVE_718>', '<RESERVE_719>', '<RESERVE_720>', '<RESERVE_721>', '<RESERVE_722>', '<RESERVE_723>', '<RESERVE_724>', '<RESERVE_725>', '<RESERVE_726>', '<RESERVE_727>', '<RESERVE_728>', '<RESERVE_729>', '<RESERVE_730>', '<RESERVE_731>', '<RESERVE_732>', '<RESERVE_733>', '<RESERVE_734>', '<RESERVE_735>', '<RESERVE_736>', '<RESERVE_737>', '<RESERVE_738>', '<RESERVE_739>', '<RESERVE_740>', '<RESERVE_741>', '<RESERVE_742>', '<RESERVE_743>', '<RESERVE_744>', '<RESERVE_745>', '<RESERVE_746>', '<RESERVE_747>', '<RESERVE_748>', '<RESERVE_749>', '<RESERVE_750>', '<RESERVE_751>', '<RESERVE_752>', '<RESERVE_753>', '<RESERVE_754>', '<RESERVE_755>', '<RESERVE_756>', '<RESERVE_757>', '<RESERVE_758>', '<RESERVE_759>', '<RESERVE_760>', '<RESERVE_761>', '<RESERVE_762>', '<RESERVE_763>', '<RESERVE_764>', '<RESERVE_765>', '<RESERVE_766>', '<RESERVE_767>', '<RESERVE_768>', '<RESERVE_769>', '<RESERVE_770>', '<RESERVE_771>', '<RESERVE_772>', '<RESERVE_773>', '<RESERVE_774>', '<RESERVE_775>', '<RESERVE_776>', '<RESERVE_777>', '<RESERVE_778>', '<RESERVE_779>', '<RESERVE_780>', '<RESERVE_781>', '<RESERVE_782>', '<RESERVE_783>', '<RESERVE_784>', '<RESERVE_785>', '<RESERVE_786>', '<RESERVE_787>', '<RESERVE_788>', '<RESERVE_789>', '<RESERVE_790>', '<RESERVE_791>', '<RESERVE_792>', '<RESERVE_793>', '<RESERVE_794>', '<RESERVE_795>', '<RESERVE_796>', '<RESERVE_797>', '<RESERVE_798>', '<RESERVE_799>', '<RESERVE_800>', '<RESERVE_801>', '<RESERVE_802>', '<RESERVE_803>', '<RESERVE_804>', '<RESERVE_805>', '<RESERVE_806>', '<RESERVE_807>', '<RESERVE_808>', '<RESERVE_809>', '<RESERVE_810>', '<RESERVE_811>', '<RESERVE_812>', '<RESERVE_813>', '<RESERVE_814>', '<RESERVE_815>', '<RESERVE_816>', '<RESERVE_817>', '<RESERVE_818>', '<RESERVE_819>', '<RESERVE_820>', '<RESERVE_821>', '<RESERVE_822>', '<RESERVE_823>', '<RESERVE_824>', '<RESERVE_825>', '<RESERVE_826>', '<RESERVE_827>', '<RESERVE_828>', '<RESERVE_829>', '<RESERVE_830>', '<RESERVE_831>', '<RESERVE_832>', '<RESERVE_833>', '<RESERVE_834>', '<RESERVE_835>', '<RESERVE_836>', '<RESERVE_837>', '<RESERVE_838>', '<RESERVE_839>', '<RESERVE_840>', '<RESERVE_841>', '<RESERVE_842>', '<RESERVE_843>', '<RESERVE_844>', '<RESERVE_845>', '<RESERVE_846>', '<RESERVE_847>', '<RESERVE_848>', '<RESERVE_849>', '<RESERVE_850>', '<RESERVE_851>', '<RESERVE_852>', '<RESERVE_853>', '<RESERVE_854>', '<RESERVE_855>', '<RESERVE_856>', '<RESERVE_857>', '<RESERVE_858>', '<RESERVE_859>', '<RESERVE_860>', '<RESERVE_861>', '<RESERVE_862>', '<RESERVE_863>', '<RESERVE_864>', '<RESERVE_865>', '<RESERVE_866>', '<RESERVE_867>', '<RESERVE_868>', '<RESERVE_869>', '<RESERVE_870>', '<RESERVE_871>', '<RESERVE_872>', '<RESERVE_873>', '<RESERVE_874>', '<RESERVE_875>', '<RESERVE_876>', '<RESERVE_877>', '<RESERVE_878>', '<RESERVE_879>', '<RESERVE_880>', '<RESERVE_881>', '<RESERVE_882>', '<RESERVE_883>', '<RESERVE_884>', '<RESERVE_885>', '<RESERVE_886>', '<RESERVE_887>', '<RESERVE_888>', '<RESERVE_889>', '<RESERVE_890>', '<RESERVE_891>', '<RESERVE_892>', '<RESERVE_893>', '<RESERVE_894>', '<RESERVE_895>', '<RESERVE_896>', '<RESERVE_897>', '<RESERVE_898>', '<RESERVE_899>', '<RESERVE_900>', '<RESERVE_901>', '<RESERVE_902>', '<RESERVE_903>', '<RESERVE_904>', '<RESERVE_905>', '<RESERVE_906>', '<RESERVE_907>', '<RESERVE_908>', '<RESERVE_909>', '<RESERVE_910>', '<RESERVE_911>', '<RESERVE_912>', '<RESERVE_913>', '<RESERVE_914>', '<RESERVE_915>', '<RESERVE_916>', '<RESERVE_917>', '<RESERVE_918>', '<RESERVE_919>', '<RESERVE_920>', '<RESERVE_921>', '<RESERVE_922>', '<RESERVE_923>', '<RESERVE_924>', '<RESERVE_925>', '<RESERVE_926>', '<RESERVE_927>', '<RESERVE_928>', '<RESERVE_929>', '<RESERVE_930>', '<RESERVE_931>', '<RESERVE_932>', '<RESERVE_933>', '<RESERVE_934>', '<RESERVE_935>', '<RESERVE_936>', '<RESERVE_937>', '<RESERVE_938>', '<RESERVE_939>', '<RESERVE_940>', '<RESERVE_941>', '<RESERVE_942>', '<RESERVE_943>', '<RESERVE_944>', '<RESERVE_945>', '<RESERVE_946>', '<RESERVE_947>', '<RESERVE_948>', '<RESERVE_949>', '<RESERVE_950>', '<RESERVE_951>', '<RESERVE_952>', '<RESERVE_953>', '<RESERVE_954>', '<RESERVE_955>', '<RESERVE_956>', '<RESERVE_957>', '<RESERVE_958>', '<RESERVE_959>', '<RESERVE_960>', '<RESERVE_961>', '<RESERVE_962>', '<RESERVE_963>', '<RESERVE_964>', '<RESERVE_965>', '<RESERVE_966>', '<RESERVE_967>', '<RESERVE_968>', '<RESERVE_969>', '<RESERVE_970>', '<RESERVE_971>', '<RESERVE_972>', '<RESERVE_973>', '<RESERVE_974>', '<RESERVE_975>', '<RESERVE_976>', '<RESERVE_977>', '<RESERVE_978>', '<RESERVE_979>', '<RESERVE_980>', '<RESERVE_981>', '<RESERVE_982>', '<RESERVE_983>', '<RESERVE_984>', '<RESERVE_985>', '<RESERVE_986>', '<RESERVE_987>', '<RESERVE_988>', '<RESERVE_989>', '<RESERVE_990>', '<RESERVE_991>', '<RESERVE_992>', '<RESERVE_993>', '<RESERVE_994>', '<RESERVE_995>', '<RESERVE_996>', '<RESERVE_997>', '<RESERVE_998>', '<RESERVE_999>', '<RESERVE_1000>', '<RESERVE_1001>', '<RESERVE_1002>', '<RESERVE_1003>', '<RESERVE_1004>', '<RESERVE_1005>', '<RESERVE_1006>', '<RESERVE_1007>', '<RESERVE_1008>', '<RESERVE_1009>', '<RESERVE_1010>', '<RESERVE_1011>', '<RESERVE_1012>', '<RESERVE_1013>', '<RESERVE_1014>', '<RESERVE_1015>', '<RESERVE_1016>', '<RESERVE_1017>', '<RESERVE_1018>', '<RESERVE_1019>', '<RESERVE_1020>', '<RESERVE_1021>', '<RESERVE_1022>', '<RESERVE_1023>', '<RESERVE_1024>', '<RESERVE_1025>', '<RESERVE_1026>', '<RESERVE_1027>', '<RESERVE_1028>', '<RESERVE_1029>', '<RESERVE_1030>', '<RESERVE_1031>', '<RESERVE_1032>', '<RESERVE_1033>', '<RESERVE_1034>', '<RESERVE_1035>', '<RESERVE_1036>', '<RESERVE_1037>', '<RESERVE_1038>', '<RESERVE_1039>', '<RESERVE_1040>', '<RESERVE_1041>', '<RESERVE_1042>', '<RESERVE_1043>', '<RESERVE_1044>', '<RESERVE_1045>', '<RESERVE_1046>', '<RESERVE_1047>', '<RESERVE_1048>', '<RESERVE_1049>', '<RESERVE_1050>', '<RESERVE_1051>', '<RESERVE_1052>', '<RESERVE_1053>', '<RESERVE_1054>', '<RESERVE_1055>', '<RESERVE_1056>', '<RESERVE_1057>', '<RESERVE_1058>', '<RESERVE_1059>', '<RESERVE_1060>', '<RESERVE_1061>', '<RESERVE_1062>', '<RESERVE_1063>', '<RESERVE_1064>', '<RESERVE_1065>', '<RESERVE_1066>', '<RESERVE_1067>', '<RESERVE_1068>', '<RESERVE_1069>', '<RESERVE_1070>', '<RESERVE_1071>', '<RESERVE_1072>', '<RESERVE_1073>', '<RESERVE_1074>', '<RESERVE_1075>', '<RESERVE_1076>', '<RESERVE_1077>', '<RESERVE_1078>', '<RESERVE_1079>', '<RESERVE_1080>', '<RESERVE_1081>', '<RESERVE_1082>', '<RESERVE_1083>', '<RESERVE_1084>', '<RESERVE_1085>', '<RESERVE_1086>', '<RESERVE_1087>', '<RESERVE_1088>', '<RESERVE_1089>', '<RESERVE_1090>', '<RESERVE_1091>', '<RESERVE_1092>', '<RESERVE_1093>', '<RESERVE_1094>', '<RESERVE_1095>', '<RESERVE_1096>', '<RESERVE_1097>', '<RESERVE_1098>', '<RESERVE_1099>', '<RESERVE_1100>', '<RESERVE_1101>', '<RESERVE_1102>', '<RESERVE_1103>', '<RESERVE_1104>', '<RESERVE_1105>', '<RESERVE_1106>', '<RESERVE_1107>', '<RESERVE_1108>', '<RESERVE_1109>', '<RESERVE_1110>', '<RESERVE_1111>', '<RESERVE_1112>', '<RESERVE_1113>', '<RESERVE_1114>', '<RESERVE_1115>', '<RESERVE_1116>', '<RESERVE_1117>', '<RESERVE_1118>', '<RESERVE_1119>', '<RESERVE_1120>', '<RESERVE_1121>', '<RESERVE_1122>', '<RESERVE_1123>', '<RESERVE_1124>', '<RESERVE_1125>', '<RESERVE_1126>', '<RESERVE_1127>', '<RESERVE_1128>', '<RESERVE_1129>', '<RESERVE_1130>', '<RESERVE_1131>', '<RESERVE_1132>', '<RESERVE_1133>', '<RESERVE_1134>', '<RESERVE_1135>', '<RESERVE_1136>', '<RESERVE_1137>', '<RESERVE_1138>', '<RESERVE_1139>', '<RESERVE_1140>', '<RESERVE_1141>', '<RESERVE_1142>', '<RESERVE_1143>', '<RESERVE_1144>', '<RESERVE_1145>', '<RESERVE_1146>', '<RESERVE_1147>', '<RESERVE_1148>', '<RESERVE_1149>', '<RESERVE_1150>', '<RESERVE_1151>', '<RESERVE_1152>', '<RESERVE_1153>', '<RESERVE_1154>', '<RESERVE_1155>', '<RESERVE_1156>', '<RESERVE_1157>', '<RESERVE_1158>', '<RESERVE_1159>', '<RESERVE_1160>', '<RESERVE_1161>', '<RESERVE_1162>', '<RESERVE_1163>', '<RESERVE_1164>', '<RESERVE_1165>', '<RESERVE_1166>', '<RESERVE_1167>', '<RESERVE_1168>', '<RESERVE_1169>', '<RESERVE_1170>', '<RESERVE_1171>', '<RESERVE_1172>', '<RESERVE_1173>', '<RESERVE_1174>', '<RESERVE_1175>', '<RESERVE_1176>', '<RESERVE_1177>', '<RESERVE_1178>', '<RESERVE_1179>', '<RESERVE_1180>', '<RESERVE_1181>', '<RESERVE_1182>', '<RESERVE_1183>', '<RESERVE_1184>', '<RESERVE_1185>', '<RESERVE_1186>', '<RESERVE_1187>', '<RESERVE_1188>', '<RESERVE_1189>', '<RESERVE_1190>', '<RESERVE_1191>', '<RESERVE_1192>', '<RESERVE_1193>', '<RESERVE_1194>', '<RESERVE_1195>', '<RESERVE_1196>', '<RESERVE_1197>', '<RESERVE_1198>', '<RESERVE_1199>', '<RESERVE_1200>', '<RESERVE_1201>', '<RESERVE_1202>', '<RESERVE_1203>', '<RESERVE_1204>', '<RESERVE_1205>', '<RESERVE_1206>', '<RESERVE_1207>', '<RESERVE_1208>', '<RESERVE_1209>', '<RESERVE_1210>', '<RESERVE_1211>', '<RESERVE_1212>', '<RESERVE_1213>', '<RESERVE_1214>', '<RESERVE_1215>', '<RESERVE_1216>', '<RESERVE_1217>', '<RESERVE_1218>', '<RESERVE_1219>', '<RESERVE_1220>', '<RESERVE_1221>', '<RESERVE_1222>', '<RESERVE_1223>', '<RESERVE_1224>', '<RESERVE_1225>', '<RESERVE_1226>', '<RESERVE_1227>', '<RESERVE_1228>', '<RESERVE_1229>', '<RESERVE_1230>', '<RESERVE_1231>', '<RESERVE_1232>', '<RESERVE_1233>', '<RESERVE_1234>', '<RESERVE_1235>', '<RESERVE_1236>', '<RESERVE_1237>', '<RESERVE_1238>', '<RESERVE_1239>', '<RESERVE_1240>', '<RESERVE_1241>', '<RESERVE_1242>', '<RESERVE_1243>', '<RESERVE_1244>', '<RESERVE_1245>', '<RESERVE_1246>', 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'<RESERVE_1270>': 1270, '<RESERVE_1271>': 1271, '<RESERVE_1272>': 1272, '<RESERVE_1273>': 1273, '<RESERVE_1274>': 1274, '<RESERVE_1275>': 1275, '<RESERVE_1276>': 1276, '<RESERVE_1277>': 1277, '<RESERVE_1278>': 1278, '<RESERVE_1279>': 1279, '<RESERVE_1280>': 1280, '<RESERVE_1281>': 1281, '<RESERVE_1282>': 1282, '<RESERVE_1283>': 1283, '<RESERVE_1284>': 1284, '<RESERVE_1285>': 1285, '<RESERVE_1286>': 1286, '<RESERVE_1287>': 1287, '<RESERVE_1288>': 1288, '<RESERVE_1289>': 1289, '<RESERVE_1290>': 1290, '<RESERVE_1291>': 1291, '<RESERVE_1292>': 1292, '<RESERVE_1293>': 1293, '<RESERVE_1294>': 1294, '<RESERVE_1295>': 1295, '<RESERVE_1296>': 1296, '<RESERVE_1297>': 1297, '<RESERVE_1298>': 1298, '<RESERVE_1299>': 1299, '<RESERVE_1300>': 1300, '<RESERVE_1301>': 1301, '<RESERVE_1302>': 1302, '<RESERVE_1303>': 1303, '<RESERVE_1304>': 1304, '<RESERVE_1305>': 1305, '<RESERVE_1306>': 1306, '<RESERVE_1307>': 1307, '<RESERVE_1308>': 1308, '<RESERVE_1309>': 1309, '<RESERVE_1310>': 1310, 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'<RESERVE_1352>': 1352, '<RESERVE_1353>': 1353, '<RESERVE_1354>': 1354, '<RESERVE_1355>': 1355, '<RESERVE_1356>': 1356, '<RESERVE_1357>': 1357, '<RESERVE_1358>': 1358, '<RESERVE_1359>': 1359, '<RESERVE_1360>': 1360, '<RESERVE_1361>': 1361, '<RESERVE_1362>': 1362, '<RESERVE_1363>': 1363, '<RESERVE_1364>': 1364, '<RESERVE_1365>': 1365, '<RESERVE_1366>': 1366, '<RESERVE_1367>': 1367, '<RESERVE_1368>': 1368, '<RESERVE_1369>': 1369, '<RESERVE_1370>': 1370, '<RESERVE_1371>': 1371, '<RESERVE_1372>': 1372, '<RESERVE_1373>': 1373, '<RESERVE_1374>': 1374, '<RESERVE_1375>': 1375, '<RESERVE_1376>': 1376, '<RESERVE_1377>': 1377, '<RESERVE_1378>': 1378, '<RESERVE_1379>': 1379, '<RESERVE_1380>': 1380, '<RESERVE_1381>': 1381, '<RESERVE_1382>': 1382, '<RESERVE_1383>': 1383, '<RESERVE_1384>': 1384, '<RESERVE_1385>': 1385, '<RESERVE_1386>': 1386, '<RESERVE_1387>': 1387, '<RESERVE_1388>': 1388, '<RESERVE_1389>': 1389, '<RESERVE_1390>': 1390, '<RESERVE_1391>': 1391, '<RESERVE_1392>': 1392, 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'(21,47)': 3886, '(23,47)': 3887, '(25,47)': 3888, '(27,47)': 3889, '(29,47)': 3890, '(31,47)': 3891, '(33,47)': 3892, '(35,47)': 3893, '(37,47)': 3894, '(39,47)': 3895, '(41,47)': 3896, '(43,47)': 3897, '(45,47)': 3898, '(47,47)': 3899, '(0,48)': 3900, '(2,48)': 3901, '(4,48)': 3902, '(6,48)': 3903, '(8,48)': 3904, '(10,48)': 3905, '(12,48)': 3906, '(14,48)': 3907, '(16,48)': 3908, '(18,48)': 3909, '(20,48)': 3910, '(22,48)': 3911, '(24,48)': 3912, '(26,48)': 3913, '(28,48)': 3914, '(30,48)': 3915, '(32,48)': 3916, '(34,48)': 3917, '(36,48)': 3918, '(38,48)': 3919, '(40,48)': 3920, '(42,48)': 3921, '(44,48)': 3922, '(46,48)': 3923, '(48,0)': 3924, '(1,48)': 3925, '(48,1)': 3926, '(48,2)': 3927, '(3,48)': 3928, '(48,3)': 3929, '(48,4)': 3930, '(5,48)': 3931, '(48,5)': 3932, '(48,6)': 3933, '(7,48)': 3934, '(48,7)': 3935, '(48,8)': 3936, '(9,48)': 3937, '(48,9)': 3938, '(48,10)': 3939, '(11,48)': 3940, '(48,11)': 3941, '(48,12)': 3942, '(13,48)': 3943, '(48,13)': 3944, '(48,14)': 3945, '(15,48)': 3946, '(48,15)': 3947, '(48,16)': 3948, '(17,48)': 3949, '(48,17)': 3950, '(48,18)': 3951, '(19,48)': 3952, '(48,19)': 3953, '(48,20)': 3954, '(21,48)': 3955, '(48,21)': 3956, '(48,22)': 3957, '(23,48)': 3958, '(48,23)': 3959, '(48,24)': 3960, '(25,48)': 3961, '(48,25)': 3962, '(48,26)': 3963, '(27,48)': 3964, '(48,27)': 3965, '(48,28)': 3966, '(29,48)': 3967, '(48,29)': 3968, '(48,30)': 3969, '(31,48)': 3970, '(48,31)': 3971, '(48,32)': 3972, '(33,48)': 3973, '(48,33)': 3974, '(48,34)': 3975, '(35,48)': 3976, '(48,35)': 3977, '(48,36)': 3978, '(37,48)': 3979, '(48,37)': 3980, '(48,38)': 3981, '(39,48)': 3982, '(48,39)': 3983, '(48,40)': 3984, '(41,48)': 3985, '(48,41)': 3986, '(48,42)': 3987, '(43,48)': 3988, '(48,43)': 3989, '(48,44)': 3990, '(45,48)': 3991, '(48,45)': 3992, '(48,46)': 3993, '(47,48)': 3994, '(48,47)': 3995, '(48,48)': 3996, '(0,49)': 3997, '(49,0)': 3998, '(49,1)': 3999, '(2,49)': 4000, '(49,2)': 4001, '(49,3)': 4002, '(4,49)': 4003, '(49,4)': 4004, '(49,5)': 4005, '(6,49)': 4006, '(49,6)': 4007, '(49,7)': 4008, '(8,49)': 4009, '(49,8)': 4010, '(49,9)': 4011, '(10,49)': 4012, '(49,10)': 4013, '(49,11)': 4014, '(12,49)': 4015, '(49,12)': 4016, '(49,13)': 4017, '(14,49)': 4018, '(49,14)': 4019, '(49,15)': 4020, '(16,49)': 4021, '(49,16)': 4022, '(49,17)': 4023, '(18,49)': 4024, '(49,18)': 4025, '(49,19)': 4026, '(20,49)': 4027, '(49,20)': 4028, '(49,21)': 4029, '(22,49)': 4030, '(49,22)': 4031, '(49,23)': 4032, '(24,49)': 4033, '(49,24)': 4034, '(49,25)': 4035, '(26,49)': 4036, '(49,26)': 4037, '(49,27)': 4038, '(28,49)': 4039, '(49,28)': 4040, '(49,29)': 4041, '(30,49)': 4042, '(49,30)': 4043, '(49,31)': 4044, '(32,49)': 4045, '(49,32)': 4046, '(49,33)': 4047, '(34,49)': 4048, '(49,34)': 4049, '(49,35)': 4050, '(36,49)': 4051, '(49,36)': 4052, '(49,37)': 4053, '(38,49)': 4054, '(49,38)': 4055, '(49,39)': 4056, '(40,49)': 4057, '(49,40)': 4058, '(49,41)': 4059, '(42,49)': 4060, '(49,42)': 4061, '(49,43)': 4062, '(44,49)': 4063, '(49,44)': 4064, '(49,45)': 4065, '(46,49)': 4066, '(49,46)': 4067, '(49,47)': 4068, '(48,49)': 4069, '(49,48)': 4070, '(1,49)': 4071, '(3,49)': 4072, '(5,49)': 4073, '(7,49)': 4074, '(9,49)': 4075, '(11,49)': 4076, '(13,49)': 4077, '(15,49)': 4078, '(17,49)': 4079, '(19,49)': 4080, '(21,49)': 4081, '(23,49)': 4082, '(25,49)': 4083, '(27,49)': 4084, '(29,49)': 4085, '(31,49)': 4086, '(33,49)': 4087, '(35,49)': 4088, '(37,49)': 4089, '(39,49)': 4090, '(41,49)': 4091, '(43,49)': 4092, '(45,49)': 4093, '(47,49)': 4094, '(49,49)': 4095}
docs for maze-dataset
v1.1.0
constants and type hints used accross the package
CoordCoordTupCoordArrayCoordListConnectionConnectionListConnectionArraySpecialTokensErrorSPECIAL_TOKENSDIRECTIONS_MAPNEIGHBORS_MASKVOCABVOCAB_LISTVOCAB_TOKEN_TO_INDEXCARDINAL_MAPmaze_dataset.constantsconstants and type hints used accross the package
Coord = <class 'jaxtyping.Int8[ndarray, 'row_col']'>single coordinate as array
CoordTup = tuple[int, int]single coordinate as tuple
CoordArray = <class 'jaxtyping.Int8[ndarray, 'coord row_col']'>array of coordinates
CoordList = list[tuple[int, int]]list of tuple coordinates
Connection = <class 'jaxtyping.Int8[ndarray, 'coord=2 row_col=2']'>single connection (pair of coords) as array
ConnectionList = <class 'jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']'>internal representation used in LatticeMaze
ConnectionArray = <class 'jaxtyping.Int8[ndarray, 'edges leading_trailing_coord=2 row_col=2']'>n_edges * 2 * 2 array of connections, like an adjacency list
class SpecialTokensError(builtins.Exception):Common base class for all non-exit exceptions.
SPECIAL_TOKENS: maze_dataset.constants._SPECIAL_TOKENS_BASE = _SPECIAL_TOKENS_BASE(ADJLIST_START='<ADJLIST_START>', ADJLIST_END='<ADJLIST_END>', TARGET_START='<TARGET_START>', TARGET_END='<TARGET_END>', ORIGIN_START='<ORIGIN_START>', ORIGIN_END='<ORIGIN_END>', PATH_START='<PATH_START>', PATH_END='<PATH_END>', CONNECTOR='<-->', ADJACENCY_ENDLINE=';', PADDING='<PADDING>')
special tokens
DIRECTIONS_MAP: jaxtyping.Int8[ndarray, 'direction axes'] = array([[ 0, 1], [ 0, -1], [ 1, 1], [ 1, -1]])down, up, right, left directions for when inside a
ConnectionList
NEIGHBORS_MASK: jaxtyping.Int8[ndarray, 'coord point'] = array([[ 0, 1], [ 0, -1], [ 1, 0], [-1, 0]])down, up, right, left as vectors
VOCAB: types._VOCAB_BASE = _VOCAB_BASE(ADJLIST_START='<ADJLIST_START>', ADJLIST_END='<ADJLIST_END>', TARGET_START='<TARGET_START>', TARGET_END='<TARGET_END>', ORIGIN_START='<ORIGIN_START>', ORIGIN_END='<ORIGIN_END>', PATH_START='<PATH_START>', PATH_END='<PATH_END>', CONNECTOR='<-->', ADJACENCY_ENDLINE=';', PADDING='<PADDING>', COORD_PRE='(', COORD_INTRA=',', COORD_POST=')', TARGET_INTRA='=', TARGET_POST='||', PATH_INTRA=':', PATH_POST='THEN', NEGATIVE='-', UNKNOWN='<UNK>', TARGET_A='TARGET_A', TARGET_B='TARGET_B', TARGET_C='TARGET_C', TARGET_D='TARGET_D', TARGET_E='TARGET_E', TARGET_F='TARGET_F', TARGET_G='TARGET_G', TARGET_H='TARGET_H', TARGET_I='TARGET_I', TARGET_J='TARGET_J', TARGET_K='TARGET_K', TARGET_L='TARGET_L', TARGET_M='TARGET_M', TARGET_N='TARGET_N', TARGET_O='TARGET_O', TARGET_P='TARGET_P', TARGET_Q='TARGET_Q', TARGET_R='TARGET_R', TARGET_S='TARGET_S', TARGET_T='TARGET_T', TARGET_U='TARGET_U', TARGET_V='TARGET_V', TARGET_W='TARGET_W', TARGET_X='TARGET_X', TARGET_Y='TARGET_Y', TARGET_Z='TARGET_Z', TARGET_NORTH='TARGET_NORTH', TARGET_SOUTH='TARGET_SOUTH', TARGET_EAST='TARGET_EAST', TARGET_WEST='TARGET_WEST', TARGET_NORTHEAST='TARGET_NORTHEAST', TARGET_NORTHWEST='TARGET_NORTHWEST', TARGET_SOUTHEAST='TARGET_SOUTHEAST', TARGET_SOUTHWEST='TARGET_SOUTHWEST', TARGET_CENTER='TARGET_CENTER', PATH_NORTH='NORTH', PATH_SOUTH='SOUTH', PATH_EAST='EAST', PATH_WEST='WEST', PATH_FORWARD='FORWARD', PATH_BACKWARD='BACKWARD', PATH_LEFT='LEFT', PATH_RIGHT='RIGHT', PATH_STAY='STAY', I_000='+0', I_001='+1', I_002='+2', I_003='+3', I_004='+4', I_005='+5', I_006='+6', I_007='+7', I_008='+8', I_009='+9', I_010='+10', I_011='+11', I_012='+12', I_013='+13', I_014='+14', I_015='+15', I_016='+16', I_017='+17', I_018='+18', I_019='+19', I_020='+20', I_021='+21', I_022='+22', I_023='+23', I_024='+24', I_025='+25', I_026='+26', I_027='+27', I_028='+28', I_029='+29', I_030='+30', I_031='+31', I_032='+32', I_033='+33', I_034='+34', I_035='+35', I_036='+36', I_037='+37', I_038='+38', I_039='+39', I_040='+40', I_041='+41', I_042='+42', I_043='+43', I_044='+44', I_045='+45', I_046='+46', I_047='+47', I_048='+48', I_049='+49', I_050='+50', I_051='+51', I_052='+52', I_053='+53', I_054='+54', I_055='+55', I_056='+56', I_057='+57', I_058='+58', I_059='+59', I_060='+60', I_061='+61', I_062='+62', I_063='+63', I_064='+64', I_065='+65', I_066='+66', I_067='+67', I_068='+68', I_069='+69', I_070='+70', I_071='+71', I_072='+72', I_073='+73', I_074='+74', I_075='+75', I_076='+76', I_077='+77', I_078='+78', I_079='+79', I_080='+80', I_081='+81', I_082='+82', I_083='+83', I_084='+84', I_085='+85', I_086='+86', I_087='+87', I_088='+88', I_089='+89', I_090='+90', I_091='+91', I_092='+92', I_093='+93', I_094='+94', I_095='+95', I_096='+96', I_097='+97', I_098='+98', I_099='+99', I_100='+100', I_101='+101', I_102='+102', I_103='+103', I_104='+104', I_105='+105', I_106='+106', I_107='+107', I_108='+108', I_109='+109', I_110='+110', I_111='+111', I_112='+112', I_113='+113', I_114='+114', I_115='+115', I_116='+116', I_117='+117', I_118='+118', I_119='+119', I_120='+120', I_121='+121', I_122='+122', I_123='+123', I_124='+124', I_125='+125', I_126='+126', I_127='+127', I_128='+128', I_129='+129', I_130='+130', I_131='+131', I_132='+132', I_133='+133', I_134='+134', I_135='+135', I_136='+136', I_137='+137', I_138='+138', I_139='+139', I_140='+140', I_141='+141', I_142='+142', I_143='+143', I_144='+144', I_145='+145', I_146='+146', I_147='+147', I_148='+148', I_149='+149', I_150='+150', I_151='+151', I_152='+152', I_153='+153', I_154='+154', I_155='+155', I_156='+156', I_157='+157', I_158='+158', I_159='+159', I_160='+160', I_161='+161', I_162='+162', I_163='+163', I_164='+164', I_165='+165', I_166='+166', I_167='+167', I_168='+168', I_169='+169', I_170='+170', I_171='+171', I_172='+172', I_173='+173', I_174='+174', I_175='+175', I_176='+176', I_177='+177', I_178='+178', I_179='+179', I_180='+180', I_181='+181', I_182='+182', I_183='+183', I_184='+184', I_185='+185', I_186='+186', I_187='+187', I_188='+188', I_189='+189', I_190='+190', I_191='+191', I_192='+192', I_193='+193', I_194='+194', I_195='+195', I_196='+196', I_197='+197', I_198='+198', I_199='+199', I_200='+200', I_201='+201', I_202='+202', I_203='+203', I_204='+204', I_205='+205', I_206='+206', I_207='+207', I_208='+208', I_209='+209', I_210='+210', I_211='+211', I_212='+212', I_213='+213', I_214='+214', I_215='+215', I_216='+216', I_217='+217', I_218='+218', I_219='+219', I_220='+220', I_221='+221', I_222='+222', I_223='+223', I_224='+224', I_225='+225', I_226='+226', I_227='+227', I_228='+228', I_229='+229', I_230='+230', I_231='+231', I_232='+232', I_233='+233', I_234='+234', I_235='+235', I_236='+236', I_237='+237', I_238='+238', I_239='+239', I_240='+240', I_241='+241', I_242='+242', I_243='+243', I_244='+244', I_245='+245', I_246='+246', I_247='+247', I_248='+248', I_249='+249', I_250='+250', I_251='+251', I_252='+252', I_253='+253', I_254='+254', I_255='+255', CTT_0='0', CTT_1='1', CTT_2='2', CTT_3='3', CTT_4='4', CTT_5='5', CTT_6='6', CTT_7='7', CTT_8='8', CTT_9='9', CTT_10='10', CTT_11='11', CTT_12='12', CTT_13='13', CTT_14='14', CTT_15='15', CTT_16='16', CTT_17='17', CTT_18='18', CTT_19='19', CTT_20='20', CTT_21='21', CTT_22='22', CTT_23='23', CTT_24='24', CTT_25='25', CTT_26='26', CTT_27='27', CTT_28='28', CTT_29='29', CTT_30='30', CTT_31='31', CTT_32='32', CTT_33='33', CTT_34='34', CTT_35='35', CTT_36='36', CTT_37='37', CTT_38='38', CTT_39='39', CTT_40='40', CTT_41='41', CTT_42='42', CTT_43='43', CTT_44='44', CTT_45='45', CTT_46='46', CTT_47='47', CTT_48='48', CTT_49='49', CTT_50='50', CTT_51='51', CTT_52='52', CTT_53='53', CTT_54='54', CTT_55='55', CTT_56='56', CTT_57='57', CTT_58='58', CTT_59='59', CTT_60='60', CTT_61='61', CTT_62='62', CTT_63='63', CTT_64='64', CTT_65='65', CTT_66='66', CTT_67='67', CTT_68='68', CTT_69='69', CTT_70='70', CTT_71='71', CTT_72='72', CTT_73='73', CTT_74='74', CTT_75='75', CTT_76='76', CTT_77='77', CTT_78='78', CTT_79='79', CTT_80='80', CTT_81='81', CTT_82='82', CTT_83='83', CTT_84='84', CTT_85='85', CTT_86='86', CTT_87='87', CTT_88='88', CTT_89='89', CTT_90='90', CTT_91='91', CTT_92='92', CTT_93='93', CTT_94='94', CTT_95='95', CTT_96='96', CTT_97='97', CTT_98='98', CTT_99='99', CTT_100='100', CTT_101='101', CTT_102='102', CTT_103='103', CTT_104='104', CTT_105='105', CTT_106='106', CTT_107='107', CTT_108='108', CTT_109='109', CTT_110='110', CTT_111='111', CTT_112='112', CTT_113='113', CTT_114='114', CTT_115='115', CTT_116='116', CTT_117='117', CTT_118='118', CTT_119='119', CTT_120='120', CTT_121='121', CTT_122='122', CTT_123='123', CTT_124='124', CTT_125='125', CTT_126='126', CTT_127='127', I_N256='-256', I_N255='-255', I_N254='-254', I_N253='-253', I_N252='-252', I_N251='-251', I_N250='-250', I_N249='-249', I_N248='-248', I_N247='-247', I_N246='-246', I_N245='-245', I_N244='-244', I_N243='-243', I_N242='-242', I_N241='-241', I_N240='-240', I_N239='-239', I_N238='-238', I_N237='-237', I_N236='-236', I_N235='-235', I_N234='-234', I_N233='-233', I_N232='-232', I_N231='-231', I_N230='-230', I_N229='-229', I_N228='-228', I_N227='-227', I_N226='-226', I_N225='-225', I_N224='-224', I_N223='-223', I_N222='-222', I_N221='-221', I_N220='-220', I_N219='-219', I_N218='-218', I_N217='-217', I_N216='-216', I_N215='-215', I_N214='-214', I_N213='-213', I_N212='-212', I_N211='-211', I_N210='-210', I_N209='-209', I_N208='-208', I_N207='-207', I_N206='-206', I_N205='-205', I_N204='-204', I_N203='-203', I_N202='-202', I_N201='-201', I_N200='-200', I_N199='-199', I_N198='-198', I_N197='-197', I_N196='-196', I_N195='-195', I_N194='-194', I_N193='-193', I_N192='-192', I_N191='-191', I_N190='-190', I_N189='-189', I_N188='-188', I_N187='-187', I_N186='-186', I_N185='-185', I_N184='-184', I_N183='-183', I_N182='-182', I_N181='-181', I_N180='-180', I_N179='-179', I_N178='-178', I_N177='-177', I_N176='-176', I_N175='-175', I_N174='-174', I_N173='-173', I_N172='-172', I_N171='-171', I_N170='-170', I_N169='-169', I_N168='-168', I_N167='-167', I_N166='-166', I_N165='-165', I_N164='-164', I_N163='-163', I_N162='-162', I_N161='-161', I_N160='-160', I_N159='-159', I_N158='-158', I_N157='-157', I_N156='-156', I_N155='-155', I_N154='-154', I_N153='-153', I_N152='-152', I_N151='-151', I_N150='-150', I_N149='-149', I_N148='-148', I_N147='-147', I_N146='-146', I_N145='-145', I_N144='-144', I_N143='-143', I_N142='-142', I_N141='-141', I_N140='-140', I_N139='-139', I_N138='-138', I_N137='-137', I_N136='-136', I_N135='-135', I_N134='-134', I_N133='-133', I_N132='-132', I_N131='-131', I_N130='-130', I_N129='-129', I_N128='-128', I_N127='-127', I_N126='-126', I_N125='-125', I_N124='-124', I_N123='-123', I_N122='-122', I_N121='-121', I_N120='-120', I_N119='-119', I_N118='-118', I_N117='-117', I_N116='-116', I_N115='-115', I_N114='-114', I_N113='-113', I_N112='-112', I_N111='-111', I_N110='-110', I_N109='-109', I_N108='-108', I_N107='-107', I_N106='-106', I_N105='-105', I_N104='-104', I_N103='-103', I_N102='-102', I_N101='-101', I_N100='-100', I_N099='-99', I_N098='-98', I_N097='-97', I_N096='-96', I_N095='-95', I_N094='-94', I_N093='-93', I_N092='-92', I_N091='-91', I_N090='-90', I_N089='-89', I_N088='-88', I_N087='-87', I_N086='-86', I_N085='-85', I_N084='-84', I_N083='-83', I_N082='-82', I_N081='-81', I_N080='-80', I_N079='-79', I_N078='-78', I_N077='-77', I_N076='-76', I_N075='-75', I_N074='-74', I_N073='-73', I_N072='-72', I_N071='-71', I_N070='-70', I_N069='-69', I_N068='-68', I_N067='-67', I_N066='-66', I_N065='-65', I_N064='-64', I_N063='-63', I_N062='-62', I_N061='-61', I_N060='-60', I_N059='-59', I_N058='-58', I_N057='-57', I_N056='-56', I_N055='-55', I_N054='-54', I_N053='-53', I_N052='-52', I_N051='-51', I_N050='-50', I_N049='-49', I_N048='-48', I_N047='-47', I_N046='-46', I_N045='-45', I_N044='-44', I_N043='-43', I_N042='-42', I_N041='-41', I_N040='-40', I_N039='-39', I_N038='-38', I_N037='-37', I_N036='-36', I_N035='-35', I_N034='-34', I_N033='-33', I_N032='-32', I_N031='-31', I_N030='-30', I_N029='-29', I_N028='-28', I_N027='-27', I_N026='-26', I_N025='-25', I_N024='-24', I_N023='-23', I_N022='-22', I_N021='-21', I_N020='-20', I_N019='-19', I_N018='-18', I_N017='-17', I_N016='-16', I_N015='-15', I_N014='-14', I_N013='-13', I_N012='-12', I_N011='-11', I_N010='-10', I_N009='-9', I_N008='-8', I_N007='-7', I_N006='-6', I_N005='-5', I_N004='-4', I_N003='-3', I_N002='-2', I_N001='-1', PATH_PRE='STEP', ADJLIST_PRE='ADJ_GROUP', ADJLIST_INTRA='&', ADJLIST_WALL='<XX>', RESERVE_708='<RESERVE_708>', RESERVE_709='<RESERVE_709>', RESERVE_710='<RESERVE_710>', RESERVE_711='<RESERVE_711>', RESERVE_712='<RESERVE_712>', RESERVE_713='<RESERVE_713>', RESERVE_714='<RESERVE_714>', RESERVE_715='<RESERVE_715>', RESERVE_716='<RESERVE_716>', RESERVE_717='<RESERVE_717>', RESERVE_718='<RESERVE_718>', RESERVE_719='<RESERVE_719>', RESERVE_720='<RESERVE_720>', RESERVE_721='<RESERVE_721>', RESERVE_722='<RESERVE_722>', RESERVE_723='<RESERVE_723>', RESERVE_724='<RESERVE_724>', RESERVE_725='<RESERVE_725>', RESERVE_726='<RESERVE_726>', RESERVE_727='<RESERVE_727>', RESERVE_728='<RESERVE_728>', RESERVE_729='<RESERVE_729>', RESERVE_730='<RESERVE_730>', RESERVE_731='<RESERVE_731>', RESERVE_732='<RESERVE_732>', RESERVE_733='<RESERVE_733>', RESERVE_734='<RESERVE_734>', RESERVE_735='<RESERVE_735>', RESERVE_736='<RESERVE_736>', RESERVE_737='<RESERVE_737>', RESERVE_738='<RESERVE_738>', RESERVE_739='<RESERVE_739>', RESERVE_740='<RESERVE_740>', RESERVE_741='<RESERVE_741>', RESERVE_742='<RESERVE_742>', RESERVE_743='<RESERVE_743>', RESERVE_744='<RESERVE_744>', RESERVE_745='<RESERVE_745>', RESERVE_746='<RESERVE_746>', RESERVE_747='<RESERVE_747>', RESERVE_748='<RESERVE_748>', RESERVE_749='<RESERVE_749>', RESERVE_750='<RESERVE_750>', RESERVE_751='<RESERVE_751>', RESERVE_752='<RESERVE_752>', RESERVE_753='<RESERVE_753>', RESERVE_754='<RESERVE_754>', RESERVE_755='<RESERVE_755>', RESERVE_756='<RESERVE_756>', RESERVE_757='<RESERVE_757>', RESERVE_758='<RESERVE_758>', RESERVE_759='<RESERVE_759>', RESERVE_760='<RESERVE_760>', RESERVE_761='<RESERVE_761>', RESERVE_762='<RESERVE_762>', RESERVE_763='<RESERVE_763>', RESERVE_764='<RESERVE_764>', RESERVE_765='<RESERVE_765>', RESERVE_766='<RESERVE_766>', RESERVE_767='<RESERVE_767>', RESERVE_768='<RESERVE_768>', RESERVE_769='<RESERVE_769>', RESERVE_770='<RESERVE_770>', RESERVE_771='<RESERVE_771>', RESERVE_772='<RESERVE_772>', RESERVE_773='<RESERVE_773>', RESERVE_774='<RESERVE_774>', RESERVE_775='<RESERVE_775>', RESERVE_776='<RESERVE_776>', RESERVE_777='<RESERVE_777>', RESERVE_778='<RESERVE_778>', RESERVE_779='<RESERVE_779>', RESERVE_780='<RESERVE_780>', RESERVE_781='<RESERVE_781>', RESERVE_782='<RESERVE_782>', RESERVE_783='<RESERVE_783>', RESERVE_784='<RESERVE_784>', RESERVE_785='<RESERVE_785>', RESERVE_786='<RESERVE_786>', RESERVE_787='<RESERVE_787>', RESERVE_788='<RESERVE_788>', RESERVE_789='<RESERVE_789>', RESERVE_790='<RESERVE_790>', RESERVE_791='<RESERVE_791>', RESERVE_792='<RESERVE_792>', RESERVE_793='<RESERVE_793>', RESERVE_794='<RESERVE_794>', RESERVE_795='<RESERVE_795>', RESERVE_796='<RESERVE_796>', RESERVE_797='<RESERVE_797>', RESERVE_798='<RESERVE_798>', RESERVE_799='<RESERVE_799>', RESERVE_800='<RESERVE_800>', RESERVE_801='<RESERVE_801>', RESERVE_802='<RESERVE_802>', RESERVE_803='<RESERVE_803>', RESERVE_804='<RESERVE_804>', RESERVE_805='<RESERVE_805>', RESERVE_806='<RESERVE_806>', RESERVE_807='<RESERVE_807>', RESERVE_808='<RESERVE_808>', RESERVE_809='<RESERVE_809>', RESERVE_810='<RESERVE_810>', RESERVE_811='<RESERVE_811>', RESERVE_812='<RESERVE_812>', RESERVE_813='<RESERVE_813>', RESERVE_814='<RESERVE_814>', RESERVE_815='<RESERVE_815>', RESERVE_816='<RESERVE_816>', RESERVE_817='<RESERVE_817>', RESERVE_818='<RESERVE_818>', RESERVE_819='<RESERVE_819>', RESERVE_820='<RESERVE_820>', RESERVE_821='<RESERVE_821>', RESERVE_822='<RESERVE_822>', RESERVE_823='<RESERVE_823>', RESERVE_824='<RESERVE_824>', RESERVE_825='<RESERVE_825>', RESERVE_826='<RESERVE_826>', RESERVE_827='<RESERVE_827>', RESERVE_828='<RESERVE_828>', RESERVE_829='<RESERVE_829>', RESERVE_830='<RESERVE_830>', RESERVE_831='<RESERVE_831>', RESERVE_832='<RESERVE_832>', RESERVE_833='<RESERVE_833>', RESERVE_834='<RESERVE_834>', RESERVE_835='<RESERVE_835>', RESERVE_836='<RESERVE_836>', RESERVE_837='<RESERVE_837>', RESERVE_838='<RESERVE_838>', RESERVE_839='<RESERVE_839>', RESERVE_840='<RESERVE_840>', RESERVE_841='<RESERVE_841>', RESERVE_842='<RESERVE_842>', RESERVE_843='<RESERVE_843>', RESERVE_844='<RESERVE_844>', RESERVE_845='<RESERVE_845>', RESERVE_846='<RESERVE_846>', RESERVE_847='<RESERVE_847>', RESERVE_848='<RESERVE_848>', RESERVE_849='<RESERVE_849>', RESERVE_850='<RESERVE_850>', RESERVE_851='<RESERVE_851>', RESERVE_852='<RESERVE_852>', RESERVE_853='<RESERVE_853>', RESERVE_854='<RESERVE_854>', RESERVE_855='<RESERVE_855>', RESERVE_856='<RESERVE_856>', RESERVE_857='<RESERVE_857>', RESERVE_858='<RESERVE_858>', RESERVE_859='<RESERVE_859>', RESERVE_860='<RESERVE_860>', RESERVE_861='<RESERVE_861>', RESERVE_862='<RESERVE_862>', RESERVE_863='<RESERVE_863>', RESERVE_864='<RESERVE_864>', RESERVE_865='<RESERVE_865>', RESERVE_866='<RESERVE_866>', RESERVE_867='<RESERVE_867>', RESERVE_868='<RESERVE_868>', RESERVE_869='<RESERVE_869>', RESERVE_870='<RESERVE_870>', RESERVE_871='<RESERVE_871>', RESERVE_872='<RESERVE_872>', RESERVE_873='<RESERVE_873>', RESERVE_874='<RESERVE_874>', RESERVE_875='<RESERVE_875>', RESERVE_876='<RESERVE_876>', RESERVE_877='<RESERVE_877>', RESERVE_878='<RESERVE_878>', RESERVE_879='<RESERVE_879>', RESERVE_880='<RESERVE_880>', RESERVE_881='<RESERVE_881>', RESERVE_882='<RESERVE_882>', RESERVE_883='<RESERVE_883>', RESERVE_884='<RESERVE_884>', RESERVE_885='<RESERVE_885>', RESERVE_886='<RESERVE_886>', RESERVE_887='<RESERVE_887>', RESERVE_888='<RESERVE_888>', RESERVE_889='<RESERVE_889>', RESERVE_890='<RESERVE_890>', RESERVE_891='<RESERVE_891>', RESERVE_892='<RESERVE_892>', RESERVE_893='<RESERVE_893>', RESERVE_894='<RESERVE_894>', RESERVE_895='<RESERVE_895>', RESERVE_896='<RESERVE_896>', RESERVE_897='<RESERVE_897>', RESERVE_898='<RESERVE_898>', RESERVE_899='<RESERVE_899>', RESERVE_900='<RESERVE_900>', RESERVE_901='<RESERVE_901>', RESERVE_902='<RESERVE_902>', RESERVE_903='<RESERVE_903>', RESERVE_904='<RESERVE_904>', RESERVE_905='<RESERVE_905>', RESERVE_906='<RESERVE_906>', RESERVE_907='<RESERVE_907>', RESERVE_908='<RESERVE_908>', RESERVE_909='<RESERVE_909>', RESERVE_910='<RESERVE_910>', RESERVE_911='<RESERVE_911>', RESERVE_912='<RESERVE_912>', RESERVE_913='<RESERVE_913>', RESERVE_914='<RESERVE_914>', RESERVE_915='<RESERVE_915>', RESERVE_916='<RESERVE_916>', RESERVE_917='<RESERVE_917>', RESERVE_918='<RESERVE_918>', RESERVE_919='<RESERVE_919>', RESERVE_920='<RESERVE_920>', RESERVE_921='<RESERVE_921>', RESERVE_922='<RESERVE_922>', RESERVE_923='<RESERVE_923>', RESERVE_924='<RESERVE_924>', RESERVE_925='<RESERVE_925>', RESERVE_926='<RESERVE_926>', RESERVE_927='<RESERVE_927>', RESERVE_928='<RESERVE_928>', RESERVE_929='<RESERVE_929>', RESERVE_930='<RESERVE_930>', RESERVE_931='<RESERVE_931>', RESERVE_932='<RESERVE_932>', RESERVE_933='<RESERVE_933>', RESERVE_934='<RESERVE_934>', RESERVE_935='<RESERVE_935>', RESERVE_936='<RESERVE_936>', RESERVE_937='<RESERVE_937>', RESERVE_938='<RESERVE_938>', RESERVE_939='<RESERVE_939>', RESERVE_940='<RESERVE_940>', RESERVE_941='<RESERVE_941>', RESERVE_942='<RESERVE_942>', RESERVE_943='<RESERVE_943>', RESERVE_944='<RESERVE_944>', RESERVE_945='<RESERVE_945>', RESERVE_946='<RESERVE_946>', RESERVE_947='<RESERVE_947>', RESERVE_948='<RESERVE_948>', RESERVE_949='<RESERVE_949>', RESERVE_950='<RESERVE_950>', RESERVE_951='<RESERVE_951>', RESERVE_952='<RESERVE_952>', RESERVE_953='<RESERVE_953>', RESERVE_954='<RESERVE_954>', RESERVE_955='<RESERVE_955>', RESERVE_956='<RESERVE_956>', RESERVE_957='<RESERVE_957>', RESERVE_958='<RESERVE_958>', RESERVE_959='<RESERVE_959>', RESERVE_960='<RESERVE_960>', RESERVE_961='<RESERVE_961>', RESERVE_962='<RESERVE_962>', RESERVE_963='<RESERVE_963>', RESERVE_964='<RESERVE_964>', RESERVE_965='<RESERVE_965>', RESERVE_966='<RESERVE_966>', RESERVE_967='<RESERVE_967>', RESERVE_968='<RESERVE_968>', RESERVE_969='<RESERVE_969>', RESERVE_970='<RESERVE_970>', RESERVE_971='<RESERVE_971>', RESERVE_972='<RESERVE_972>', RESERVE_973='<RESERVE_973>', RESERVE_974='<RESERVE_974>', RESERVE_975='<RESERVE_975>', RESERVE_976='<RESERVE_976>', RESERVE_977='<RESERVE_977>', RESERVE_978='<RESERVE_978>', RESERVE_979='<RESERVE_979>', RESERVE_980='<RESERVE_980>', RESERVE_981='<RESERVE_981>', RESERVE_982='<RESERVE_982>', RESERVE_983='<RESERVE_983>', RESERVE_984='<RESERVE_984>', RESERVE_985='<RESERVE_985>', RESERVE_986='<RESERVE_986>', RESERVE_987='<RESERVE_987>', RESERVE_988='<RESERVE_988>', RESERVE_989='<RESERVE_989>', RESERVE_990='<RESERVE_990>', RESERVE_991='<RESERVE_991>', RESERVE_992='<RESERVE_992>', RESERVE_993='<RESERVE_993>', RESERVE_994='<RESERVE_994>', 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UT_29_49='(29,49)', UT_31_49='(31,49)', UT_33_49='(33,49)', UT_35_49='(35,49)', UT_37_49='(37,49)', UT_39_49='(39,49)', UT_41_49='(41,49)', UT_43_49='(43,49)', UT_45_49='(45,49)', UT_47_49='(47,49)', UT_49_49='(49,49)')public access to universal vocabulary for
MazeTokenizerModular
VOCAB_LIST: list[str] = ['<ADJLIST_START>', '<ADJLIST_END>', '<TARGET_START>', '<TARGET_END>', '<ORIGIN_START>', '<ORIGIN_END>', '<PATH_START>', '<PATH_END>', '<-->', ';', '<PADDING>', '(', ',', ')', '=', '||', ':', 'THEN', '-', '<UNK>', 'TARGET_A', 'TARGET_B', 'TARGET_C', 'TARGET_D', 'TARGET_E', 'TARGET_F', 'TARGET_G', 'TARGET_H', 'TARGET_I', 'TARGET_J', 'TARGET_K', 'TARGET_L', 'TARGET_M', 'TARGET_N', 'TARGET_O', 'TARGET_P', 'TARGET_Q', 'TARGET_R', 'TARGET_S', 'TARGET_T', 'TARGET_U', 'TARGET_V', 'TARGET_W', 'TARGET_X', 'TARGET_Y', 'TARGET_Z', 'TARGET_NORTH', 'TARGET_SOUTH', 'TARGET_EAST', 'TARGET_WEST', 'TARGET_NORTHEAST', 'TARGET_NORTHWEST', 'TARGET_SOUTHEAST', 'TARGET_SOUTHWEST', 'TARGET_CENTER', 'NORTH', 'SOUTH', 'EAST', 'WEST', 'FORWARD', 'BACKWARD', 'LEFT', 'RIGHT', 'STAY', '+0', '+1', '+2', '+3', '+4', '+5', '+6', '+7', '+8', '+9', '+10', '+11', '+12', '+13', '+14', '+15', '+16', '+17', '+18', '+19', '+20', '+21', '+22', '+23', '+24', '+25', '+26', '+27', '+28', '+29', '+30', 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'<RESERVE_795>', '<RESERVE_796>', '<RESERVE_797>', '<RESERVE_798>', '<RESERVE_799>', '<RESERVE_800>', '<RESERVE_801>', '<RESERVE_802>', '<RESERVE_803>', '<RESERVE_804>', '<RESERVE_805>', '<RESERVE_806>', '<RESERVE_807>', '<RESERVE_808>', '<RESERVE_809>', '<RESERVE_810>', '<RESERVE_811>', '<RESERVE_812>', '<RESERVE_813>', '<RESERVE_814>', '<RESERVE_815>', '<RESERVE_816>', '<RESERVE_817>', '<RESERVE_818>', '<RESERVE_819>', '<RESERVE_820>', '<RESERVE_821>', '<RESERVE_822>', '<RESERVE_823>', '<RESERVE_824>', '<RESERVE_825>', '<RESERVE_826>', '<RESERVE_827>', '<RESERVE_828>', '<RESERVE_829>', '<RESERVE_830>', '<RESERVE_831>', '<RESERVE_832>', '<RESERVE_833>', '<RESERVE_834>', '<RESERVE_835>', '<RESERVE_836>', '<RESERVE_837>', '<RESERVE_838>', '<RESERVE_839>', '<RESERVE_840>', '<RESERVE_841>', '<RESERVE_842>', '<RESERVE_843>', '<RESERVE_844>', '<RESERVE_845>', '<RESERVE_846>', '<RESERVE_847>', '<RESERVE_848>', '<RESERVE_849>', '<RESERVE_850>', '<RESERVE_851>', '<RESERVE_852>', 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in order
VOCAB_TOKEN_TO_INDEX: dict[str, int] = {'<ADJLIST_START>': 0, '<ADJLIST_END>': 1, '<TARGET_START>': 2, '<TARGET_END>': 3, '<ORIGIN_START>': 4, '<ORIGIN_END>': 5, '<PATH_START>': 6, '<PATH_END>': 7, '<-->': 8, ';': 9, '<PADDING>': 10, '(': 11, ',': 12, ')': 13, '=': 14, '||': 15, ':': 16, 'THEN': 17, '-': 18, '<UNK>': 19, 'TARGET_A': 20, 'TARGET_B': 21, 'TARGET_C': 22, 'TARGET_D': 23, 'TARGET_E': 24, 'TARGET_F': 25, 'TARGET_G': 26, 'TARGET_H': 27, 'TARGET_I': 28, 'TARGET_J': 29, 'TARGET_K': 30, 'TARGET_L': 31, 'TARGET_M': 32, 'TARGET_N': 33, 'TARGET_O': 34, 'TARGET_P': 35, 'TARGET_Q': 36, 'TARGET_R': 37, 'TARGET_S': 38, 'TARGET_T': 39, 'TARGET_U': 40, 'TARGET_V': 41, 'TARGET_W': 42, 'TARGET_X': 43, 'TARGET_Y': 44, 'TARGET_Z': 45, 'TARGET_NORTH': 46, 'TARGET_SOUTH': 47, 'TARGET_EAST': 48, 'TARGET_WEST': 49, 'TARGET_NORTHEAST': 50, 'TARGET_NORTHWEST': 51, 'TARGET_SOUTHEAST': 52, 'TARGET_SOUTHWEST': 53, 'TARGET_CENTER': 54, 'NORTH': 55, 'SOUTH': 56, 'EAST': 57, 'WEST': 58, 'FORWARD': 59, 'BACKWARD': 60, 'LEFT': 61, 'RIGHT': 62, 'STAY': 63, '+0': 64, '+1': 65, '+2': 66, '+3': 67, '+4': 68, '+5': 69, '+6': 70, '+7': 71, '+8': 72, '+9': 73, '+10': 74, '+11': 75, '+12': 76, '+13': 77, '+14': 78, '+15': 79, '+16': 80, '+17': 81, '+18': 82, '+19': 83, '+20': 84, '+21': 85, '+22': 86, '+23': 87, '+24': 88, '+25': 89, '+26': 90, '+27': 91, '+28': 92, '+29': 93, '+30': 94, '+31': 95, '+32': 96, '+33': 97, '+34': 98, '+35': 99, '+36': 100, '+37': 101, '+38': 102, '+39': 103, '+40': 104, '+41': 105, '+42': 106, '+43': 107, '+44': 108, '+45': 109, '+46': 110, '+47': 111, '+48': 112, '+49': 113, '+50': 114, '+51': 115, '+52': 116, '+53': 117, '+54': 118, '+55': 119, '+56': 120, '+57': 121, '+58': 122, '+59': 123, '+60': 124, '+61': 125, '+62': 126, '+63': 127, '+64': 128, '+65': 129, '+66': 130, '+67': 131, '+68': 132, '+69': 133, '+70': 134, '+71': 135, '+72': 136, '+73': 137, '+74': 138, '+75': 139, '+76': 140, '+77': 141, '+78': 142, '+79': 143, '+80': 144, '+81': 145, '+82': 146, '+83': 147, '+84': 148, '+85': 149, '+86': 150, '+87': 151, '+88': 152, '+89': 153, '+90': 154, '+91': 155, '+92': 156, '+93': 157, '+94': 158, '+95': 159, '+96': 160, '+97': 161, '+98': 162, '+99': 163, '+100': 164, '+101': 165, '+102': 166, '+103': 167, '+104': 168, '+105': 169, '+106': 170, '+107': 171, '+108': 172, '+109': 173, '+110': 174, '+111': 175, '+112': 176, '+113': 177, '+114': 178, '+115': 179, '+116': 180, '+117': 181, '+118': 182, '+119': 183, '+120': 184, '+121': 185, '+122': 186, '+123': 187, '+124': 188, '+125': 189, '+126': 190, '+127': 191, '+128': 192, '+129': 193, '+130': 194, '+131': 195, '+132': 196, '+133': 197, '+134': 198, '+135': 199, '+136': 200, '+137': 201, '+138': 202, '+139': 203, '+140': 204, '+141': 205, '+142': 206, '+143': 207, '+144': 208, '+145': 209, '+146': 210, '+147': 211, '+148': 212, '+149': 213, '+150': 214, '+151': 215, '+152': 216, '+153': 217, '+154': 218, '+155': 219, '+156': 220, '+157': 221, '+158': 222, '+159': 223, '+160': 224, '+161': 225, '+162': 226, '+163': 227, '+164': 228, '+165': 229, '+166': 230, '+167': 231, '+168': 232, '+169': 233, '+170': 234, '+171': 235, '+172': 236, '+173': 237, '+174': 238, '+175': 239, '+176': 240, '+177': 241, '+178': 242, '+179': 243, '+180': 244, '+181': 245, '+182': 246, '+183': 247, '+184': 248, '+185': 249, '+186': 250, '+187': 251, '+188': 252, '+189': 253, '+190': 254, '+191': 255, '+192': 256, '+193': 257, '+194': 258, '+195': 259, '+196': 260, '+197': 261, '+198': 262, '+199': 263, '+200': 264, '+201': 265, '+202': 266, '+203': 267, '+204': 268, '+205': 269, '+206': 270, '+207': 271, '+208': 272, '+209': 273, '+210': 274, '+211': 275, '+212': 276, '+213': 277, '+214': 278, '+215': 279, '+216': 280, '+217': 281, '+218': 282, '+219': 283, '+220': 284, '+221': 285, '+222': 286, '+223': 287, '+224': 288, '+225': 289, '+226': 290, '+227': 291, '+228': 292, '+229': 293, '+230': 294, '+231': 295, '+232': 296, '+233': 297, '+234': 298, '+235': 299, '+236': 300, '+237': 301, '+238': 302, '+239': 303, '+240': 304, '+241': 305, '+242': 306, '+243': 307, '+244': 308, '+245': 309, '+246': 310, '+247': 311, '+248': 312, '+249': 313, '+250': 314, '+251': 315, '+252': 316, '+253': 317, '+254': 318, '+255': 319, '0': 320, '1': 321, '2': 322, '3': 323, '4': 324, '5': 325, '6': 326, '7': 327, '8': 328, '9': 329, '10': 330, '11': 331, '12': 332, '13': 333, '14': 334, '15': 335, '16': 336, '17': 337, '18': 338, '19': 339, '20': 340, '21': 341, '22': 342, '23': 343, '24': 344, '25': 345, '26': 346, '27': 347, '28': 348, '29': 349, '30': 350, '31': 351, '32': 352, '33': 353, '34': 354, '35': 355, '36': 356, '37': 357, '38': 358, '39': 359, '40': 360, '41': 361, '42': 362, '43': 363, '44': 364, '45': 365, '46': 366, '47': 367, '48': 368, '49': 369, '50': 370, '51': 371, '52': 372, '53': 373, '54': 374, '55': 375, '56': 376, '57': 377, '58': 378, '59': 379, '60': 380, '61': 381, '62': 382, '63': 383, '64': 384, '65': 385, '66': 386, '67': 387, '68': 388, '69': 389, '70': 390, '71': 391, '72': 392, '73': 393, '74': 394, '75': 395, '76': 396, '77': 397, '78': 398, '79': 399, '80': 400, '81': 401, '82': 402, '83': 403, '84': 404, '85': 405, '86': 406, '87': 407, '88': 408, '89': 409, '90': 410, '91': 411, '92': 412, '93': 413, '94': 414, '95': 415, '96': 416, '97': 417, '98': 418, '99': 419, '100': 420, '101': 421, '102': 422, '103': 423, '104': 424, '105': 425, '106': 426, '107': 427, '108': 428, '109': 429, '110': 430, '111': 431, '112': 432, '113': 433, '114': 434, '115': 435, '116': 436, '117': 437, '118': 438, '119': 439, '120': 440, '121': 441, '122': 442, '123': 443, '124': 444, '125': 445, '126': 446, '127': 447, '-256': 448, '-255': 449, '-254': 450, '-253': 451, '-252': 452, '-251': 453, '-250': 454, '-249': 455, '-248': 456, '-247': 457, '-246': 458, '-245': 459, '-244': 460, '-243': 461, '-242': 462, '-241': 463, '-240': 464, '-239': 465, '-238': 466, '-237': 467, '-236': 468, '-235': 469, '-234': 470, '-233': 471, '-232': 472, '-231': 473, '-230': 474, '-229': 475, '-228': 476, '-227': 477, '-226': 478, '-225': 479, '-224': 480, '-223': 481, '-222': 482, '-221': 483, '-220': 484, '-219': 485, '-218': 486, '-217': 487, '-216': 488, '-215': 489, '-214': 490, '-213': 491, '-212': 492, '-211': 493, '-210': 494, '-209': 495, '-208': 496, '-207': 497, '-206': 498, '-205': 499, '-204': 500, '-203': 501, '-202': 502, '-201': 503, '-200': 504, '-199': 505, '-198': 506, '-197': 507, '-196': 508, '-195': 509, '-194': 510, '-193': 511, '-192': 512, '-191': 513, '-190': 514, '-189': 515, '-188': 516, '-187': 517, '-186': 518, '-185': 519, '-184': 520, '-183': 521, '-182': 522, '-181': 523, '-180': 524, '-179': 525, '-178': 526, '-177': 527, '-176': 528, '-175': 529, '-174': 530, '-173': 531, '-172': 532, '-171': 533, '-170': 534, '-169': 535, '-168': 536, '-167': 537, '-166': 538, '-165': 539, '-164': 540, '-163': 541, '-162': 542, '-161': 543, '-160': 544, '-159': 545, '-158': 546, '-157': 547, '-156': 548, '-155': 549, '-154': 550, '-153': 551, '-152': 552, '-151': 553, '-150': 554, '-149': 555, '-148': 556, '-147': 557, '-146': 558, '-145': 559, '-144': 560, '-143': 561, '-142': 562, '-141': 563, '-140': 564, '-139': 565, '-138': 566, '-137': 567, '-136': 568, '-135': 569, '-134': 570, '-133': 571, '-132': 572, '-131': 573, '-130': 574, '-129': 575, '-128': 576, '-127': 577, '-126': 578, '-125': 579, '-124': 580, '-123': 581, '-122': 582, '-121': 583, '-120': 584, '-119': 585, '-118': 586, '-117': 587, '-116': 588, '-115': 589, '-114': 590, '-113': 591, '-112': 592, '-111': 593, '-110': 594, '-109': 595, '-108': 596, '-107': 597, '-106': 598, '-105': 599, '-104': 600, '-103': 601, '-102': 602, '-101': 603, '-100': 604, '-99': 605, '-98': 606, '-97': 607, '-96': 608, '-95': 609, '-94': 610, '-93': 611, '-92': 612, '-91': 613, '-90': 614, '-89': 615, '-88': 616, '-87': 617, '-86': 618, '-85': 619, '-84': 620, '-83': 621, '-82': 622, '-81': 623, '-80': 624, '-79': 625, '-78': 626, '-77': 627, '-76': 628, '-75': 629, '-74': 630, '-73': 631, '-72': 632, '-71': 633, '-70': 634, '-69': 635, '-68': 636, '-67': 637, '-66': 638, '-65': 639, '-64': 640, '-63': 641, '-62': 642, '-61': 643, '-60': 644, '-59': 645, '-58': 646, '-57': 647, '-56': 648, '-55': 649, '-54': 650, '-53': 651, '-52': 652, '-51': 653, '-50': 654, '-49': 655, '-48': 656, '-47': 657, '-46': 658, '-45': 659, '-44': 660, '-43': 661, '-42': 662, '-41': 663, '-40': 664, '-39': 665, '-38': 666, '-37': 667, '-36': 668, '-35': 669, '-34': 670, '-33': 671, '-32': 672, '-31': 673, '-30': 674, '-29': 675, '-28': 676, '-27': 677, '-26': 678, '-25': 679, '-24': 680, '-23': 681, '-22': 682, '-21': 683, '-20': 684, '-19': 685, '-18': 686, '-17': 687, '-16': 688, '-15': 689, '-14': 690, '-13': 691, '-12': 692, '-11': 693, '-10': 694, '-9': 695, '-8': 696, '-7': 697, '-6': 698, '-5': 699, '-4': 700, '-3': 701, '-2': 702, '-1': 703, 'STEP': 704, 'ADJ_GROUP': 705, '&': 706, '<XX>': 707, '<RESERVE_708>': 708, '<RESERVE_709>': 709, '<RESERVE_710>': 710, '<RESERVE_711>': 711, '<RESERVE_712>': 712, '<RESERVE_713>': 713, '<RESERVE_714>': 714, '<RESERVE_715>': 715, '<RESERVE_716>': 716, '<RESERVE_717>': 717, '<RESERVE_718>': 718, '<RESERVE_719>': 719, '<RESERVE_720>': 720, '<RESERVE_721>': 721, '<RESERVE_722>': 722, '<RESERVE_723>': 723, '<RESERVE_724>': 724, '<RESERVE_725>': 725, '<RESERVE_726>': 726, '<RESERVE_727>': 727, '<RESERVE_728>': 728, '<RESERVE_729>': 729, '<RESERVE_730>': 730, '<RESERVE_731>': 731, '<RESERVE_732>': 732, '<RESERVE_733>': 733, '<RESERVE_734>': 734, '<RESERVE_735>': 735, '<RESERVE_736>': 736, '<RESERVE_737>': 737, '<RESERVE_738>': 738, '<RESERVE_739>': 739, '<RESERVE_740>': 740, '<RESERVE_741>': 741, '<RESERVE_742>': 742, '<RESERVE_743>': 743, '<RESERVE_744>': 744, '<RESERVE_745>': 745, '<RESERVE_746>': 746, '<RESERVE_747>': 747, '<RESERVE_748>': 748, '<RESERVE_749>': 749, '<RESERVE_750>': 750, '<RESERVE_751>': 751, '<RESERVE_752>': 752, '<RESERVE_753>': 753, '<RESERVE_754>': 754, '<RESERVE_755>': 755, '<RESERVE_756>': 756, '<RESERVE_757>': 757, '<RESERVE_758>': 758, '<RESERVE_759>': 759, '<RESERVE_760>': 760, '<RESERVE_761>': 761, '<RESERVE_762>': 762, '<RESERVE_763>': 763, '<RESERVE_764>': 764, '<RESERVE_765>': 765, '<RESERVE_766>': 766, '<RESERVE_767>': 767, '<RESERVE_768>': 768, '<RESERVE_769>': 769, '<RESERVE_770>': 770, '<RESERVE_771>': 771, '<RESERVE_772>': 772, '<RESERVE_773>': 773, '<RESERVE_774>': 774, '<RESERVE_775>': 775, '<RESERVE_776>': 776, '<RESERVE_777>': 777, '<RESERVE_778>': 778, '<RESERVE_779>': 779, '<RESERVE_780>': 780, '<RESERVE_781>': 781, '<RESERVE_782>': 782, '<RESERVE_783>': 783, '<RESERVE_784>': 784, '<RESERVE_785>': 785, '<RESERVE_786>': 786, '<RESERVE_787>': 787, '<RESERVE_788>': 788, '<RESERVE_789>': 789, '<RESERVE_790>': 790, '<RESERVE_791>': 791, '<RESERVE_792>': 792, '<RESERVE_793>': 793, '<RESERVE_794>': 794, '<RESERVE_795>': 795, '<RESERVE_796>': 796, '<RESERVE_797>': 797, '<RESERVE_798>': 798, '<RESERVE_799>': 799, '<RESERVE_800>': 800, '<RESERVE_801>': 801, '<RESERVE_802>': 802, '<RESERVE_803>': 803, '<RESERVE_804>': 804, '<RESERVE_805>': 805, '<RESERVE_806>': 806, '<RESERVE_807>': 807, '<RESERVE_808>': 808, '<RESERVE_809>': 809, '<RESERVE_810>': 810, '<RESERVE_811>': 811, '<RESERVE_812>': 812, '<RESERVE_813>': 813, '<RESERVE_814>': 814, '<RESERVE_815>': 815, '<RESERVE_816>': 816, '<RESERVE_817>': 817, '<RESERVE_818>': 818, '<RESERVE_819>': 819, '<RESERVE_820>': 820, '<RESERVE_821>': 821, '<RESERVE_822>': 822, '<RESERVE_823>': 823, '<RESERVE_824>': 824, '<RESERVE_825>': 825, '<RESERVE_826>': 826, '<RESERVE_827>': 827, '<RESERVE_828>': 828, '<RESERVE_829>': 829, '<RESERVE_830>': 830, '<RESERVE_831>': 831, '<RESERVE_832>': 832, '<RESERVE_833>': 833, '<RESERVE_834>': 834, '<RESERVE_835>': 835, '<RESERVE_836>': 836, '<RESERVE_837>': 837, '<RESERVE_838>': 838, '<RESERVE_839>': 839, '<RESERVE_840>': 840, '<RESERVE_841>': 841, '<RESERVE_842>': 842, '<RESERVE_843>': 843, '<RESERVE_844>': 844, 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'<RESERVE_1515>': 1515, '<RESERVE_1516>': 1516, '<RESERVE_1517>': 1517, '<RESERVE_1518>': 1518, '<RESERVE_1519>': 1519, '<RESERVE_1520>': 1520, '<RESERVE_1521>': 1521, '<RESERVE_1522>': 1522, '<RESERVE_1523>': 1523, '<RESERVE_1524>': 1524, '<RESERVE_1525>': 1525, '<RESERVE_1526>': 1526, '<RESERVE_1527>': 1527, '<RESERVE_1528>': 1528, '<RESERVE_1529>': 1529, '<RESERVE_1530>': 1530, '<RESERVE_1531>': 1531, '<RESERVE_1532>': 1532, '<RESERVE_1533>': 1533, '<RESERVE_1534>': 1534, '<RESERVE_1535>': 1535, '<RESERVE_1536>': 1536, '<RESERVE_1537>': 1537, '<RESERVE_1538>': 1538, '<RESERVE_1539>': 1539, '<RESERVE_1540>': 1540, '<RESERVE_1541>': 1541, '<RESERVE_1542>': 1542, '<RESERVE_1543>': 1543, '<RESERVE_1544>': 1544, '<RESERVE_1545>': 1545, '<RESERVE_1546>': 1546, '<RESERVE_1547>': 1547, '<RESERVE_1548>': 1548, '<RESERVE_1549>': 1549, '<RESERVE_1550>': 1550, '<RESERVE_1551>': 1551, '<RESERVE_1552>': 1552, '<RESERVE_1553>': 1553, '<RESERVE_1554>': 1554, '<RESERVE_1555>': 1555, '<RESERVE_1556>': 1556, '<RESERVE_1557>': 1557, '<RESERVE_1558>': 1558, '<RESERVE_1559>': 1559, '<RESERVE_1560>': 1560, '<RESERVE_1561>': 1561, '<RESERVE_1562>': 1562, '<RESERVE_1563>': 1563, '<RESERVE_1564>': 1564, '<RESERVE_1565>': 1565, '<RESERVE_1566>': 1566, '<RESERVE_1567>': 1567, '<RESERVE_1568>': 1568, '<RESERVE_1569>': 1569, '<RESERVE_1570>': 1570, '<RESERVE_1571>': 1571, '<RESERVE_1572>': 1572, '<RESERVE_1573>': 1573, '<RESERVE_1574>': 1574, '<RESERVE_1575>': 1575, '<RESERVE_1576>': 1576, '<RESERVE_1577>': 1577, '<RESERVE_1578>': 1578, '<RESERVE_1579>': 1579, '<RESERVE_1580>': 1580, '<RESERVE_1581>': 1581, '<RESERVE_1582>': 1582, '<RESERVE_1583>': 1583, '<RESERVE_1584>': 1584, '<RESERVE_1585>': 1585, '<RESERVE_1586>': 1586, '<RESERVE_1587>': 1587, '<RESERVE_1588>': 1588, '<RESERVE_1589>': 1589, '<RESERVE_1590>': 1590, '<RESERVE_1591>': 1591, '<RESERVE_1592>': 1592, '<RESERVE_1593>': 1593, '<RESERVE_1594>': 1594, '<RESERVE_1595>': 1595, '(0,0)': 1596, '(0,1)': 1597, '(1,0)': 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'(35,43)': 3527, '(37,43)': 3528, '(39,43)': 3529, '(41,43)': 3530, '(43,43)': 3531, '(0,44)': 3532, '(2,44)': 3533, '(4,44)': 3534, '(6,44)': 3535, '(8,44)': 3536, '(10,44)': 3537, '(12,44)': 3538, '(14,44)': 3539, '(16,44)': 3540, '(18,44)': 3541, '(20,44)': 3542, '(22,44)': 3543, '(24,44)': 3544, '(26,44)': 3545, '(28,44)': 3546, '(30,44)': 3547, '(32,44)': 3548, '(34,44)': 3549, '(36,44)': 3550, '(38,44)': 3551, '(40,44)': 3552, '(42,44)': 3553, '(44,0)': 3554, '(1,44)': 3555, '(44,1)': 3556, '(44,2)': 3557, '(3,44)': 3558, '(44,3)': 3559, '(44,4)': 3560, '(5,44)': 3561, '(44,5)': 3562, '(44,6)': 3563, '(7,44)': 3564, '(44,7)': 3565, '(44,8)': 3566, '(9,44)': 3567, '(44,9)': 3568, '(44,10)': 3569, '(11,44)': 3570, '(44,11)': 3571, '(44,12)': 3572, '(13,44)': 3573, '(44,13)': 3574, '(44,14)': 3575, '(15,44)': 3576, '(44,15)': 3577, '(44,16)': 3578, '(17,44)': 3579, '(44,17)': 3580, '(44,18)': 3581, '(19,44)': 3582, '(44,19)': 3583, '(44,20)': 3584, '(21,44)': 3585, '(44,21)': 3586, '(44,22)': 3587, '(23,44)': 3588, '(44,23)': 3589, '(44,24)': 3590, '(25,44)': 3591, '(44,25)': 3592, '(44,26)': 3593, '(27,44)': 3594, '(44,27)': 3595, '(44,28)': 3596, '(29,44)': 3597, '(44,29)': 3598, '(44,30)': 3599, '(31,44)': 3600, '(44,31)': 3601, '(44,32)': 3602, '(33,44)': 3603, '(44,33)': 3604, '(44,34)': 3605, '(35,44)': 3606, '(44,35)': 3607, '(44,36)': 3608, '(37,44)': 3609, '(44,37)': 3610, '(44,38)': 3611, '(39,44)': 3612, '(44,39)': 3613, '(44,40)': 3614, '(41,44)': 3615, '(44,41)': 3616, '(44,42)': 3617, '(43,44)': 3618, '(44,43)': 3619, '(44,44)': 3620, '(0,45)': 3621, '(45,0)': 3622, '(45,1)': 3623, '(2,45)': 3624, '(45,2)': 3625, '(45,3)': 3626, '(4,45)': 3627, '(45,4)': 3628, '(45,5)': 3629, '(6,45)': 3630, '(45,6)': 3631, '(45,7)': 3632, '(8,45)': 3633, '(45,8)': 3634, '(45,9)': 3635, '(10,45)': 3636, '(45,10)': 3637, '(45,11)': 3638, '(12,45)': 3639, '(45,12)': 3640, '(45,13)': 3641, '(14,45)': 3642, '(45,14)': 3643, '(45,15)': 3644, '(16,45)': 3645, '(45,16)': 3646, '(45,17)': 3647, '(18,45)': 3648, '(45,18)': 3649, '(45,19)': 3650, '(20,45)': 3651, '(45,20)': 3652, '(45,21)': 3653, '(22,45)': 3654, '(45,22)': 3655, '(45,23)': 3656, '(24,45)': 3657, '(45,24)': 3658, '(45,25)': 3659, '(26,45)': 3660, '(45,26)': 3661, '(45,27)': 3662, '(28,45)': 3663, '(45,28)': 3664, '(45,29)': 3665, '(30,45)': 3666, '(45,30)': 3667, '(45,31)': 3668, '(32,45)': 3669, '(45,32)': 3670, '(45,33)': 3671, '(34,45)': 3672, '(45,34)': 3673, '(45,35)': 3674, '(36,45)': 3675, '(45,36)': 3676, '(45,37)': 3677, '(38,45)': 3678, '(45,38)': 3679, '(45,39)': 3680, '(40,45)': 3681, '(45,40)': 3682, '(45,41)': 3683, '(42,45)': 3684, '(45,42)': 3685, '(45,43)': 3686, '(44,45)': 3687, '(45,44)': 3688, '(1,45)': 3689, '(3,45)': 3690, '(5,45)': 3691, '(7,45)': 3692, '(9,45)': 3693, '(11,45)': 3694, '(13,45)': 3695, '(15,45)': 3696, '(17,45)': 3697, '(19,45)': 3698, '(21,45)': 3699, '(23,45)': 3700, '(25,45)': 3701, '(27,45)': 3702, '(29,45)': 3703, '(31,45)': 3704, '(33,45)': 3705, '(35,45)': 3706, '(37,45)': 3707, '(39,45)': 3708, '(41,45)': 3709, '(43,45)': 3710, '(45,45)': 3711, '(0,46)': 3712, '(2,46)': 3713, '(4,46)': 3714, '(6,46)': 3715, '(8,46)': 3716, '(10,46)': 3717, '(12,46)': 3718, '(14,46)': 3719, '(16,46)': 3720, '(18,46)': 3721, '(20,46)': 3722, '(22,46)': 3723, '(24,46)': 3724, '(26,46)': 3725, '(28,46)': 3726, '(30,46)': 3727, '(32,46)': 3728, '(34,46)': 3729, '(36,46)': 3730, '(38,46)': 3731, '(40,46)': 3732, '(42,46)': 3733, '(44,46)': 3734, '(46,0)': 3735, '(1,46)': 3736, '(46,1)': 3737, '(46,2)': 3738, '(3,46)': 3739, '(46,3)': 3740, '(46,4)': 3741, '(5,46)': 3742, '(46,5)': 3743, '(46,6)': 3744, '(7,46)': 3745, '(46,7)': 3746, '(46,8)': 3747, '(9,46)': 3748, '(46,9)': 3749, '(46,10)': 3750, '(11,46)': 3751, '(46,11)': 3752, '(46,12)': 3753, '(13,46)': 3754, '(46,13)': 3755, '(46,14)': 3756, '(15,46)': 3757, '(46,15)': 3758, '(46,16)': 3759, '(17,46)': 3760, '(46,17)': 3761, '(46,18)': 3762, '(19,46)': 3763, '(46,19)': 3764, '(46,20)': 3765, '(21,46)': 3766, '(46,21)': 3767, '(46,22)': 3768, '(23,46)': 3769, '(46,23)': 3770, '(46,24)': 3771, '(25,46)': 3772, '(46,25)': 3773, '(46,26)': 3774, '(27,46)': 3775, '(46,27)': 3776, '(46,28)': 3777, '(29,46)': 3778, '(46,29)': 3779, '(46,30)': 3780, '(31,46)': 3781, '(46,31)': 3782, '(46,32)': 3783, '(33,46)': 3784, '(46,33)': 3785, '(46,34)': 3786, '(35,46)': 3787, '(46,35)': 3788, '(46,36)': 3789, '(37,46)': 3790, '(46,37)': 3791, '(46,38)': 3792, '(39,46)': 3793, '(46,39)': 3794, '(46,40)': 3795, '(41,46)': 3796, '(46,41)': 3797, '(46,42)': 3798, '(43,46)': 3799, '(46,43)': 3800, '(46,44)': 3801, '(45,46)': 3802, '(46,45)': 3803, '(46,46)': 3804, '(0,47)': 3805, '(47,0)': 3806, '(47,1)': 3807, '(2,47)': 3808, '(47,2)': 3809, '(47,3)': 3810, '(4,47)': 3811, '(47,4)': 3812, '(47,5)': 3813, '(6,47)': 3814, '(47,6)': 3815, '(47,7)': 3816, '(8,47)': 3817, '(47,8)': 3818, '(47,9)': 3819, '(10,47)': 3820, '(47,10)': 3821, '(47,11)': 3822, '(12,47)': 3823, '(47,12)': 3824, '(47,13)': 3825, '(14,47)': 3826, '(47,14)': 3827, '(47,15)': 3828, '(16,47)': 3829, '(47,16)': 3830, '(47,17)': 3831, '(18,47)': 3832, '(47,18)': 3833, '(47,19)': 3834, '(20,47)': 3835, '(47,20)': 3836, '(47,21)': 3837, '(22,47)': 3838, '(47,22)': 3839, '(47,23)': 3840, '(24,47)': 3841, '(47,24)': 3842, '(47,25)': 3843, '(26,47)': 3844, '(47,26)': 3845, '(47,27)': 3846, '(28,47)': 3847, '(47,28)': 3848, '(47,29)': 3849, '(30,47)': 3850, '(47,30)': 3851, '(47,31)': 3852, '(32,47)': 3853, '(47,32)': 3854, '(47,33)': 3855, '(34,47)': 3856, '(47,34)': 3857, '(47,35)': 3858, '(36,47)': 3859, '(47,36)': 3860, '(47,37)': 3861, '(38,47)': 3862, '(47,38)': 3863, '(47,39)': 3864, '(40,47)': 3865, '(47,40)': 3866, '(47,41)': 3867, '(42,47)': 3868, '(47,42)': 3869, '(47,43)': 3870, '(44,47)': 3871, '(47,44)': 3872, '(47,45)': 3873, '(46,47)': 3874, '(47,46)': 3875, '(1,47)': 3876, '(3,47)': 3877, '(5,47)': 3878, '(7,47)': 3879, '(9,47)': 3880, '(11,47)': 3881, '(13,47)': 3882, '(15,47)': 3883, '(17,47)': 3884, '(19,47)': 3885, '(21,47)': 3886, '(23,47)': 3887, '(25,47)': 3888, '(27,47)': 3889, '(29,47)': 3890, '(31,47)': 3891, '(33,47)': 3892, '(35,47)': 3893, '(37,47)': 3894, '(39,47)': 3895, '(41,47)': 3896, '(43,47)': 3897, '(45,47)': 3898, '(47,47)': 3899, '(0,48)': 3900, '(2,48)': 3901, '(4,48)': 3902, '(6,48)': 3903, '(8,48)': 3904, '(10,48)': 3905, '(12,48)': 3906, '(14,48)': 3907, '(16,48)': 3908, '(18,48)': 3909, '(20,48)': 3910, '(22,48)': 3911, '(24,48)': 3912, '(26,48)': 3913, '(28,48)': 3914, '(30,48)': 3915, '(32,48)': 3916, '(34,48)': 3917, '(36,48)': 3918, '(38,48)': 3919, '(40,48)': 3920, '(42,48)': 3921, '(44,48)': 3922, '(46,48)': 3923, '(48,0)': 3924, '(1,48)': 3925, '(48,1)': 3926, '(48,2)': 3927, '(3,48)': 3928, '(48,3)': 3929, '(48,4)': 3930, '(5,48)': 3931, '(48,5)': 3932, '(48,6)': 3933, '(7,48)': 3934, '(48,7)': 3935, '(48,8)': 3936, '(9,48)': 3937, '(48,9)': 3938, '(48,10)': 3939, '(11,48)': 3940, '(48,11)': 3941, '(48,12)': 3942, '(13,48)': 3943, '(48,13)': 3944, '(48,14)': 3945, '(15,48)': 3946, '(48,15)': 3947, '(48,16)': 3948, '(17,48)': 3949, '(48,17)': 3950, '(48,18)': 3951, '(19,48)': 3952, '(48,19)': 3953, '(48,20)': 3954, '(21,48)': 3955, '(48,21)': 3956, '(48,22)': 3957, '(23,48)': 3958, '(48,23)': 3959, '(48,24)': 3960, '(25,48)': 3961, '(48,25)': 3962, '(48,26)': 3963, '(27,48)': 3964, '(48,27)': 3965, '(48,28)': 3966, '(29,48)': 3967, '(48,29)': 3968, '(48,30)': 3969, '(31,48)': 3970, '(48,31)': 3971, '(48,32)': 3972, '(33,48)': 3973, '(48,33)': 3974, '(48,34)': 3975, '(35,48)': 3976, '(48,35)': 3977, '(48,36)': 3978, '(37,48)': 3979, '(48,37)': 3980, '(48,38)': 3981, '(39,48)': 3982, '(48,39)': 3983, '(48,40)': 3984, '(41,48)': 3985, '(48,41)': 3986, '(48,42)': 3987, '(43,48)': 3988, '(48,43)': 3989, '(48,44)': 3990, '(45,48)': 3991, '(48,45)': 3992, '(48,46)': 3993, '(47,48)': 3994, '(48,47)': 3995, '(48,48)': 3996, '(0,49)': 3997, '(49,0)': 3998, '(49,1)': 3999, '(2,49)': 4000, '(49,2)': 4001, '(49,3)': 4002, '(4,49)': 4003, '(49,4)': 4004, '(49,5)': 4005, '(6,49)': 4006, '(49,6)': 4007, '(49,7)': 4008, '(8,49)': 4009, '(49,8)': 4010, '(49,9)': 4011, '(10,49)': 4012, '(49,10)': 4013, '(49,11)': 4014, '(12,49)': 4015, '(49,12)': 4016, '(49,13)': 4017, '(14,49)': 4018, '(49,14)': 4019, '(49,15)': 4020, '(16,49)': 4021, '(49,16)': 4022, '(49,17)': 4023, '(18,49)': 4024, '(49,18)': 4025, '(49,19)': 4026, '(20,49)': 4027, '(49,20)': 4028, '(49,21)': 4029, '(22,49)': 4030, '(49,22)': 4031, '(49,23)': 4032, '(24,49)': 4033, '(49,24)': 4034, '(49,25)': 4035, '(26,49)': 4036, '(49,26)': 4037, '(49,27)': 4038, '(28,49)': 4039, '(49,28)': 4040, '(49,29)': 4041, '(30,49)': 4042, '(49,30)': 4043, '(49,31)': 4044, '(32,49)': 4045, '(49,32)': 4046, '(49,33)': 4047, '(34,49)': 4048, '(49,34)': 4049, '(49,35)': 4050, '(36,49)': 4051, '(49,36)': 4052, '(49,37)': 4053, '(38,49)': 4054, '(49,38)': 4055, '(49,39)': 4056, '(40,49)': 4057, '(49,40)': 4058, '(49,41)': 4059, '(42,49)': 4060, '(49,42)': 4061, '(49,43)': 4062, '(44,49)': 4063, '(49,44)': 4064, '(49,45)': 4065, '(46,49)': 4066, '(49,46)': 4067, '(49,47)': 4068, '(48,49)': 4069, '(49,48)': 4070, '(1,49)': 4071, '(3,49)': 4072, '(5,49)': 4073, '(7,49)': 4074, '(9,49)': 4075, '(11,49)': 4076, '(13,49)': 4077, '(15,49)': 4078, '(17,49)': 4079, '(19,49)': 4080, '(21,49)': 4081, '(23,49)': 4082, '(25,49)': 4083, '(27,49)': 4084, '(29,49)': 4085, '(31,49)': 4086, '(33,49)': 4087, '(35,49)': 4088, '(37,49)': 4089, '(39,49)': 4090, '(41,49)': 4091, '(43,49)': 4092, '(45,49)': 4093, '(47,49)': 4094, '(49,49)': 4095}map of VOCAB tokens to their indices
CARDINAL_MAP: dict[tuple[int, int], str] = {(-1, 0): 'NORTH', (1, 0): 'SOUTH', (0, -1): 'WEST', (0, 1): 'EAST'}map of cardinal directions to appropriate tokens
docs for maze-dataset
v1.1.0
MazeDatasetConfigs are used to create a
MazeDataset via
MazeDataset.from_config(cfg)
maze_dataset.datasetMazeDatasetConfigs are used to create a
MazeDataset via
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>(cfg)
class MazeDataset(typing.Generic[+T_co]):a maze dataset class. This is a collection of solved mazes, and
should be initialized via
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
MazeDataset(
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
mazes: Sequence[maze_dataset.maze.lattice_maze.SolvedMaze],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig
mazes: list[maze_dataset.maze.lattice_maze.SolvedMaze]
generation_metadata_collected: dict | None
def data_hash(self) -> intdef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens according to the passed
maze_tokenizer
the maze_tokenizer should be either a
MazeTokenizer or a MazeTokenizerModular
if join_tokens_individual_maze is True, then the tokens
of each maze are joined with a space, and the result is a list of
strings. i.e.:
>>> dataset.as_tokens(join_tokens_individual_maze=False)
[["a", "b", "c"], ["d", "e", "f"]]
>>> dataset.as_tokens(join_tokens_individual_maze=True)
["a b c", "d e f"]def generate(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
gen_parallel: bool = False,
pool_kwargs: dict | None = None,
verbose: bool = False
) -> maze_dataset.dataset.maze_dataset.MazeDatasetgenerate a maze dataset given a config and some generation parameters
def download(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetdef load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.maze_dataset.MazeDatasetload from zanj/json
def serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]serialize to zanj/json
def update_self_config(self)update the config to match the current state of the dataset (number of mazes, such as after filtering)
def custom_maze_filter(
self,
method: Callable[[maze_dataset.maze.lattice_maze.SolvedMaze], bool],
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetfilter the dataset using a custom method
class MazeDatasetConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):config object which is passed to
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
to generate or load a dataset
MazeDatasetConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
grid_n: int,
n_mazes: int,
maze_ctor: Callable = <function LatticeMazeGenerators.gen_dfs>,
maze_ctor_kwargs: dict = <factory>,
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]] = <factory>
)grid_n: int
n_mazes: int
def maze_ctor(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.maze_ctor_kwargs: dict
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]]
grid_shape: tuple[int, int]
grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']max_grid_n: intdef stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def summary(self) -> dictreturn a summary of the config
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeDatasetCollection(typing.Generic[+T_co]):a collection of maze datasets
MazeDatasetCollection(
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset]
generation_metadata_collected: dict | None
dataset_lengths: list[int]
dataset_cum_lengths: jaxtyping.Int[ndarray, 'indices']mazes: list[maze_dataset.maze.lattice_maze.LatticeMaze]def generate(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef download(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]def load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens
if join_tokens_individual_maze is True, then the tokens of each maze are joined with a space, and the result is a list of strings. i.e.: >>> dataset.as_tokens(join_tokens_individual_maze=False) [[“a”, “b”, “c”], [“d”, “e”, “f”]] >>> dataset.as_tokens(join_tokens_individual_maze=True) [“a b c”, “d e f”]
def update_self_config(self) -> Noneupdate the config of the dataset to match the actual data, if needed
for example, adjust number of mazes after filtering
class MazeDatasetCollectionConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):maze dataset collection configuration, including tokenizers and shuffle
MazeDatasetCollectionConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]
)maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]def summary(self) -> dictreturn a summary of the config
n_mazes: intmax_grid_n: intmax_grid_shape: tuple[int, int]max_grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']def stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
docs for maze-dataset
v1.1.0
collecting different maze datasets into a single dataset, for greater variety in a training or validation set
[!CAUTION]
MazeDatasetCollectionis not thoroughly tested and is not guaranteed to work.
maze_dataset.dataset.collected_datasetcollecting different maze datasets into a single dataset, for greater variety in a training or validation set
[!CAUTION]
MazeDatasetCollectionis not thoroughly tested and is not guaranteed to work.
class MazeDatasetCollectionConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):maze dataset collection configuration, including tokenizers and shuffle
MazeDatasetCollectionConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]
)maze_dataset_configs: list[maze_dataset.dataset.maze_dataset.MazeDatasetConfig]def summary(self) -> dictreturn a summary of the config
n_mazes: intmax_grid_n: intmax_grid_shape: tuple[int, int]max_grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']def stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeDatasetCollection(typing.Generic[+T_co]):a collection of maze datasets
MazeDatasetCollection(
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig
maze_datasets: list[maze_dataset.dataset.maze_dataset.MazeDataset]
generation_metadata_collected: dict | None
dataset_lengths: list[int]
dataset_cum_lengths: jaxtyping.Int[ndarray, 'indices']mazes: list[maze_dataset.maze.lattice_maze.LatticeMaze]def generate(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef download(
cls,
cfg: maze_dataset.dataset.collected_dataset.MazeDatasetCollectionConfig,
**kwargs
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]def load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.collected_dataset.MazeDatasetCollectiondef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens
if join_tokens_individual_maze is True, then the tokens of each maze are joined with a space, and the result is a list of strings. i.e.: >>> dataset.as_tokens(join_tokens_individual_maze=False) [[“a”, “b”, “c”], [“d”, “e”, “f”]] >>> dataset.as_tokens(join_tokens_individual_maze=True) [“a b c”, “d e f”]
def update_self_config(self) -> Noneupdate the config of the dataset to match the actual data, if needed
for example, adjust number of mazes after filtering
docs for maze-dataset
v1.1.0
MAZE_DATASET_CONFIGS contains some default configs for
tests and demos
maze_dataset.dataset.configsMAZE_DATASET_CONFIGS contains some default configs for
tests and demos
MAZE_DATASET_CONFIGS: maze_dataset.dataset.configs._MazeDatsetConfigsWrapper = <maze_dataset.dataset.configs._MazeDatsetConfigsWrapper object>docs for maze-dataset
v1.1.0
GPTDatasetConfig and GPTDataset are base
classes for datasets they implement some basic functionality,
saving/loading, the from_config pipeline, and filtering
[!NOTE] these should probably be moved into a different package, so don’t rely on them being here
FilterInfoMismatchErrorGPTDatasetConfigGPTDatasetregister_filter_namespace_for_datasetDatasetFilterProtocolregister_dataset_filtermaze_dataset.dataset.datasetGPTDatasetConfig and GPTDataset are base
classes for datasets they implement some basic functionality,
saving/loading, the from_config pipeline, and filtering
[!NOTE] these should probably be moved into a different package, so don’t rely on them being here
class FilterInfoMismatchError(builtins.ValueError):raised when the filter info in a dataset config does not match the filter info in the dataset
class GPTDatasetConfig(muutils.json_serialize.serializable_dataclass.SerializableDataclass):base GPTDatasetConfig class
GPTDatasetConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>
)name: str
seq_len_min: int = 1
seq_len_max: int = 512
seed: int | None = 42
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]]
def summary(self) -> dictreturn a summary of the config
def to_fname(self) -> strconvert config to a filename
def serialize(
self,
*args,
**kwargs
) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(*args, **kwargs) -> maze_dataset.dataset.dataset.GPTDatasetConfigtakes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class GPTDataset(typing.Generic[+T_co]):wrapper for torch dataset with some extra functionality
(meaning the functionality should be inherited in downstream classes)
[!NOTE]
GPTDatasetConfigshould implement ato_fnamemethod that returns a unique filename for the config
the following methods should be implemented in subclasses: -
__init__(self, cfg: GPTDatasetConfig, **kwargs) initialize
the dataset from a given config. kwargs are not passed through, the
kwargs should take the actual generated or loaded data (a list of
objects or sequences probably) -
generate(cls, cfg: GPTDatasetConfig, **kwargs) -> GPTDataset
generate the dataset from a given config. kwargs are passed through from
from_config, and should only contain things that dont
belong in the config (i.e. how many threads to use for generation) -
serialize(self) -> JSONitem serialize the dataset to a
ZANJ-serializable object, including: - config - data in formats
specified by self.save_formats -
load(cls, data: JSONitem) -> GPTDataset load the dataset
from a ZANJ-serializable object -
download(cls, cfg: GPTDatasetConfig, **kwargs) -> GPTDataset
given a config, try to download a dataset from some source. kwargs are
passed through from from_config, and should only contain
things that dont belong in the config (i.e. some kind of auth token or
source url) - __len__(self) -> int return the length of
the dataset, required for torch.utils.data.Dataset -
__getitem__(self, i: int) -> list[str] return the ith
item in the dataset, required for torch.utils.data.Dataset
return the ith item in the dataset, required for
torch.utils.data.Dataset -
update_self_config(self) -> None update the config of
the dataset to match the current state of the dataset, used primarily in
filtering and validation - decorating the appropriate filter namespace
with
register_filter_namespace_for_dataset(your_dataset_class)
if you want to use filters
- `cfg : GPTDatasetConfig`
config for the dataset, used to generate the dataset
- `do_generate : bool`
whether to generate the dataset if it isn't found
(defaults to `True`)
- `load_local : bool`
whether to try finding the dataset locally
(defaults to `True`)
- `save_local : bool`
whether to save the dataset locally if it is generated or downloaded
(defaults to `True`)
- `do_download : bool`
whether to try downloading the dataset
(defaults to `True`)
- `local_base_path : Path`
where to save the dataset
(defaults to `Path("data/maze_dataset")`)- `GPTDataset`
the dataset, as you wanted it- `save(self, file_path: str) -> None`
save the dataset to a file, using ZANJ
- `read(cls, file_path: str) -> GPTDataset`
read the dataset from a file, using ZANJ
get all items in the dataset, in the specified format
- `filter_by(self)`
returns a namespace class
- `_filter_namespace(self) -> Class`
returns a namespace class for filtering the dataset, checking that method
- `_apply_filters_from_config(self) -> None`
apply filters to the dataset, as specified in the config. used in `from_config()` but only when generatingdef from_config(
cls,
cfg: maze_dataset.dataset.dataset.GPTDatasetConfig,
do_generate: bool = True,
load_local: bool = True,
save_local: bool = True,
zanj: zanj.zanj.ZANJ | None = None,
do_download: bool = True,
local_base_path: pathlib.Path = WindowsPath('data/maze_dataset'),
except_on_config_mismatch: bool = True,
allow_generation_metadata_filter_mismatch: bool = True,
verbose: bool = False,
**kwargs
) -> maze_dataset.dataset.dataset.GPTDatasetbase class for gpt datasets
priority of loading: 1. load from local 2. download 3. generate
def save(
self,
file_path: pathlib.Path | str,
zanj: zanj.zanj.ZANJ | None = None
)def read(
cls,
file_path: str,
zanj: zanj.zanj.ZANJ | None = None
) -> maze_dataset.dataset.dataset.GPTDatasetdef serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]def data_hash(self) -> intdef load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.dataset.GPTDatasetdef generate(
cls,
cfg: maze_dataset.dataset.dataset.GPTDatasetConfig,
**kwargs
) -> maze_dataset.dataset.dataset.GPTDatasetdef download(
cls,
cfg: maze_dataset.dataset.dataset.GPTDatasetConfig,
**kwargs
) -> maze_dataset.dataset.dataset.GPTDatasetdef update_self_config(self)update the config of the dataset to match the actual data, if needed
for example, adjust number of mazes after filtering
filter_by: maze_dataset.dataset.dataset.GPTDataset.FilterByclass GPTDataset.FilterBy:thanks GPT-4
GPTDataset.FilterBy(dataset: maze_dataset.dataset.dataset.GPTDataset)dataset: maze_dataset.dataset.dataset.GPTDatasetdef register_filter_namespace_for_dataset(
dataset_cls: Type[maze_dataset.dataset.dataset.GPTDataset]
) -> Callable[[Type], Type]register the namespace class with the given dataset class
class DatasetFilterProtocol(typing.Protocol):Base class for protocol classes.
Protocol classes are defined as::
class Proto(Protocol):
def meth(self) -> int:
...Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example::
class C:
def meth(self) -> int:
return 0
def func(x: Proto) -> int:
return x.meth()
func(C()) # Passes static type checkSee PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as::
class GenProto(Protocol[T]):
def meth(self) -> T:
...DatasetFilterProtocol(*args, **kwargs)def register_dataset_filter(
method: maze_dataset.dataset.dataset.DatasetFilterProtocol
) -> maze_dataset.dataset.dataset.DatasetFilterProtocolregister a dataset filter, copying the underlying dataset and updating the config
be sure to return a COPY, not the original?
method should be a staticmethod of a namespace class registered with
register_filter_namespace_for_dataset
docs for maze-dataset
v1.1.0
MazeDatasetConfig is where you decide what your dataset
should look like, then pass it to MazeDataset.from_config
to generate or load the dataset.
SERIALIZE_MINIMAL_THRESHOLDset_serialize_minimal_thresholdEndpointKwargsTypeMazeDatasetConfigMazeDatasetregister_maze_filterMazeDatasetFiltersmaze_dataset.dataset.maze_datasetMazeDatasetConfig is where you decide what your dataset
should look like, then pass it to
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
to generate or load the dataset.
SERIALIZE_MINIMAL_THRESHOLD: int | None = 100def set_serialize_minimal_threshold(threshold: int | None) -> NoneEndpointKwargsType = dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]]type hint for
<a href="#MazeDatasetConfig.endpoint_kwargs">MazeDatasetConfig.endpoint_kwargs</a>
class MazeDatasetConfig(maze_dataset.dataset.dataset.GPTDatasetConfig):config object which is passed to
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
to generate or load a dataset
MazeDatasetConfig(
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
grid_n: int,
n_mazes: int,
maze_ctor: Callable = <function LatticeMazeGenerators.gen_dfs>,
maze_ctor_kwargs: dict = <factory>,
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]] = <factory>
)grid_n: int
n_mazes: int
def maze_ctor(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.maze_ctor_kwargs: dict
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]]
grid_shape: tuple[int, int]
grid_shape_np: jaxtyping.Int8[ndarray, 'row_col']max_grid_n: intdef stable_hash_cfg(self) -> intdef to_fname(self) -> strconvert config to a filename
def summary(self) -> dictreturn a summary of the config
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeDataset(typing.Generic[+T_co]):a maze dataset class. This is a collection of solved mazes, and
should be initialized via
<a href="#MazeDataset.from_config">MazeDataset.from_config</a>
MazeDataset(
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
mazes: Sequence[maze_dataset.maze.lattice_maze.SolvedMaze],
generation_metadata_collected: dict | None = None
)cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig
mazes: list[maze_dataset.maze.lattice_maze.SolvedMaze]
generation_metadata_collected: dict | None
def data_hash(self) -> intdef as_tokens(
self,
maze_tokenizer,
limit: int | None = None,
join_tokens_individual_maze: bool = False
) -> list[list[str]] | list[str]return the dataset as tokens according to the passed
maze_tokenizer
the maze_tokenizer should be either a
MazeTokenizer or a MazeTokenizerModular
if join_tokens_individual_maze is True, then the tokens
of each maze are joined with a space, and the result is a list of
strings. i.e.:
>>> dataset.as_tokens(join_tokens_individual_maze=False)
[["a", "b", "c"], ["d", "e", "f"]]
>>> dataset.as_tokens(join_tokens_individual_maze=True)
["a b c", "d e f"]def generate(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
gen_parallel: bool = False,
pool_kwargs: dict | None = None,
verbose: bool = False
) -> maze_dataset.dataset.maze_dataset.MazeDatasetgenerate a maze dataset given a config and some generation parameters
def download(
cls,
cfg: maze_dataset.dataset.maze_dataset.MazeDatasetConfig,
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetdef load(
cls,
data: Union[bool, int, float, str, list, Dict[str, Any], NoneType]
) -> maze_dataset.dataset.maze_dataset.MazeDatasetload from zanj/json
def serialize(self) -> Union[bool, int, float, str, list, Dict[str, Any], NoneType]serialize to zanj/json
def update_self_config(self)update the config to match the current state of the dataset (number of mazes, such as after filtering)
def custom_maze_filter(
self,
method: Callable[[maze_dataset.maze.lattice_maze.SolvedMaze], bool],
**kwargs
) -> maze_dataset.dataset.maze_dataset.MazeDatasetfilter the dataset using a custom method
def register_maze_filter(
method: Callable[[maze_dataset.maze.lattice_maze.SolvedMaze, Any], bool]
) -> maze_dataset.dataset.dataset.DatasetFilterProtocolregister a maze filter, casting it to operate over the whole list of mazes
method should be a staticmethod of a namespace class registered with
register_filter_namespace_for_dataset
this is a more restricted version of
register_dataset_filter that removes the need for
boilerplate for operating over the arrays
class MazeDatasetFilters:namespace for filters for MazeDatasets
def path_length(maze: maze_dataset.maze.lattice_maze.SolvedMaze, min_length: int) -> boolfilter out mazes with a solution length less than
min_length
def start_end_distance(
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
min_distance: int
) -> boolfilter out datasets where the start and end pos are less than
min_distance apart on the manhattan distance (ignoring
walls)
def cut_percentile_shortest(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset,
percentile: float = 10.0
) -> maze_dataset.dataset.maze_dataset.MazeDatasetcut the shortest percentile of mazes from the
dataset
percentile is 1-100, not 0-1, as this is what
np.percentile expects
def truncate_count(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset,
max_count: int
) -> maze_dataset.dataset.maze_dataset.MazeDatasettruncate the dataset to be at most max_count mazes
def remove_duplicates(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset,
minimum_difference_connection_list: int | None = 1,
minimum_difference_solution: int | None = 1,
_max_dataset_len_threshold: int = 1000
) -> maze_dataset.dataset.maze_dataset.MazeDatasetremove duplicates from a dataset, keeping the LAST unique maze
set minimum either minimum difference to None to disable
checking
if you want to avoid mazes which have more overlap, set the minimum difference to be greater
Gotchas: - if two mazes are of different sizes, they will never be considered duplicates - if two solutions are of different lengths, they will never be considered duplicates TODO: check for overlap?
def remove_duplicates_fast(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset
) -> maze_dataset.dataset.maze_dataset.MazeDatasetremove duplicates from a dataset
def strip_generation_meta(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset
) -> maze_dataset.dataset.maze_dataset.MazeDatasetstrip the generation meta from the dataset
def collect_generation_meta(
dataset: maze_dataset.dataset.maze_dataset.MazeDataset,
clear_in_mazes: bool = True,
inplace: bool = True,
allow_fail: bool = False
) -> maze_dataset.dataset.maze_dataset.MazeDatasetdocs for maze-dataset
v1.1.0
a special RasterizedMazeDataset that returns 2 images,
one for input and one for target, for each maze
this lets you match the input and target format of the easy_2_hard
dataset
see their paper:
@misc{schwarzschild2021learn,
title={Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks},
author={Avi Schwarzschild and Eitan Borgnia and Arjun Gupta and Furong Huang and Uzi Vishkin and Micah Goldblum and Tom Goldstein},
year={2021},
eprint={2106.04537},
archivePrefix={arXiv},
primaryClass={cs.LG}
}process_maze_rasterized_input_targetRasterizedMazeDatasetConfigRasterizedMazeDatasetmake_numpy_collectionmaze_dataset.dataset.rasterizeda special RasterizedMazeDataset that returns 2 images,
one for input and one for target, for each maze
this lets you match the input and target format of the easy_2_hard
dataset
see their paper:
@misc{schwarzschild2021learn,
title={Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks},
author={Avi Schwarzschild and Eitan Borgnia and Arjun Gupta and Furong Huang and Uzi Vishkin and Micah Goldblum and Tom Goldstein},
year={2021},
eprint={2106.04537},
archivePrefix={arXiv},
primaryClass={cs.LG}
}def process_maze_rasterized_input_target(
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
remove_isolated_cells: bool = True,
extend_pixels: bool = True,
endpoints_as_open: bool = False
) -> jaxtyping.Float[Tensor, 'in/tgt=2 x y rgb=3']class RasterizedMazeDatasetConfig(maze_dataset.dataset.maze_dataset.MazeDatasetConfig):remove_isolated_cells: bool whether to set isolated
cells to wallsextend_pixels: bool whether to extend pixels to match
easy_2_hard dataset (2x2 cells, extra 1 pixel row of wall around
maze)endpoints_as_open: bool whether to set endpoints to
openRasterizedMazeDatasetConfig(
remove_isolated_cells: bool = True,
extend_pixels: bool = True,
endpoints_as_open: bool = False,
*,
name: str,
seq_len_min: int = 1,
seq_len_max: int = 512,
seed: int | None = 42,
applied_filters: list[dict[typing.Literal['name', 'args', 'kwargs'], str | list | dict]] = <factory>,
grid_n: int,
n_mazes: int,
maze_ctor: Callable = <function LatticeMazeGenerators.gen_dfs>,
maze_ctor_kwargs: dict = <factory>,
endpoint_kwargs: dict[typing.Literal['except_when_invalid', 'allowed_start', 'allowed_end', 'deadend_start', 'deadend_end'], bool | None | list[tuple[int, int]]] = <factory>
)remove_isolated_cells: bool = True
extend_pixels: bool = True
endpoints_as_open: bool = False
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class RasterizedMazeDataset(typing.Generic[+T_co]):a maze dataset class. This is a collection of solved mazes, and
should be initialized via MazeDataset.from_config
def get_batch(
self,
idxs: list[int] | None
) -> jaxtyping.Float[Tensor, 'in/tgt=2 item x y rgb=3']def from_config_augmented(
cls,
cfg: maze_dataset.dataset.rasterized.RasterizedMazeDatasetConfig,
**kwargs
) -> torch.utils.data.dataset.Datasetloads either a maze transformer dataset or an easy_2_hard dataset
def from_base_MazeDataset(
cls,
base_dataset: maze_dataset.dataset.maze_dataset.MazeDataset,
added_params: dict | None = None
) -> torch.utils.data.dataset.Datasetloads either a maze transformer dataset or an easy_2_hard dataset
def plot(self, count: int | None = None, show: bool = True) -> tupledef make_numpy_collection(
base_cfg: maze_dataset.dataset.rasterized.RasterizedMazeDatasetConfig,
grid_sizes: list[int],
from_config_kwargs: dict | None = None,
verbose: bool = True,
key_fmt: str = '{size}x{size}'
) -> dict[typing.Literal['configs', 'arrays'], dict[str, maze_dataset.dataset.rasterized.RasterizedMazeDatasetConfig | numpy.ndarray]]create a collection of configs and arrays for different grid sizes, in plain tensor form
output is of structure:
{
"configs": {
"<n>x<n>": RasterizedMazeDatasetConfig,
...
},
"arrays": {
"<n>x<n>": np.ndarray,
...
},
}docs for maze-dataset
v1.1.0
generation functions have signature
(grid_shape: Coord, **kwargs) -> LatticeMaze and are
methods in LatticeMazeGenerators
DEFAULT_GENERATORS is a list of generator name,
generator kwargs pairs used in tests and demos
maze_dataset.generationgeneration functions have signature
(grid_shape: Coord, **kwargs) -> LatticeMaze and are
methods in LatticeMazeGenerators
DEFAULT_GENERATORS is a list of generator name,
generator kwargs pairs used in tests and demos
class LatticeMazeGenerators:namespace for lattice maze generation algorithms
def gen_dfs(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.def gen_prim(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedef gen_wilson(
grid_shape: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.LatticeMazeGenerate a lattice maze using Wilson’s algorithm.
Wilson’s algorithm generates an unbiased (random) maze sampled from the uniform distribution over all mazes, using loop-erased random walks. The generated maze is acyclic and all cells are part of a unique connected space. https://en.wikipedia.org/wiki/Maze_generation_algorithm#Wilson’s_algorithm
def gen_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using simple percolation
note that p in the range (0.4, 0.7) gives the most interesting mazes
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)p: float: the probability of a cell being accessible
(default: 0.5)start_coord: Coord | None: the starting coordinate for
the connected component (default: None will give a random
start)def gen_dfs_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
accessible_cells: int | None = None,
max_tree_depth: int | None = None,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedfs and then percolation (adds cycles)
GENERATORS_MAP = {'gen_dfs': <function LatticeMazeGenerators.gen_dfs>, 'gen_wilson': <function LatticeMazeGenerators.gen_wilson>, 'gen_percolation': <function LatticeMazeGenerators.gen_percolation>, 'gen_dfs_percolation': <function LatticeMazeGenerators.gen_dfs_percolation>, 'gen_prim': <function LatticeMazeGenerators.gen_prim>}def get_maze_with_solution(
gen_name: str,
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
maze_ctor_kwargs: dict | None = None
) -> maze_dataset.maze.lattice_maze.SolvedMazehelper function to get a maze already with a solution
numpy_rng = Generator(PCG64) at 0x23633FCE5E0docs for maze-dataset
v1.1.0
DEFAULT_GENERATORS is a list of generator name,
generator kwargs pairs used in tests and demos
maze_dataset.generation.default_generatorsDEFAULT_GENERATORS is a list of generator name,
generator kwargs pairs used in tests and demos
DEFAULT_GENERATORS: list[tuple[str, dict]] = [('gen_dfs', {}), ('gen_dfs', {'do_forks': False}), ('gen_dfs', {'accessible_cells': 20}), ('gen_dfs', {'max_tree_depth': 0.5}), ('gen_wilson', {}), ('gen_percolation', {'p': 1.0}), ('gen_dfs_percolation', {'p': 0.1}), ('gen_dfs_percolation', {'p': 0.4})]docs for maze-dataset
v1.1.0
generation functions have signature
(grid_shape: Coord, **kwargs) -> LatticeMaze and are
methods in LatticeMazeGenerators
maze_dataset.generation.generatorsgeneration functions have signature
(grid_shape: Coord, **kwargs) -> LatticeMaze and are
methods in LatticeMazeGenerators
numpy_rng = Generator(PCG64) at 0x23633FCE5E0def get_neighbors_in_bounds(
coord: jaxtyping.Int8[ndarray, 'row_col'],
grid_shape: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']get all neighbors of a coordinate that are within the bounds of the grid
class LatticeMazeGenerators:namespace for lattice maze generation algorithms
def gen_dfs(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
randomized_stack: bool = False,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using depth first search, iterative
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)accessible_cells: int | float |None: the number of
accessible cells in the maze. If None, defaults to the
total number of cells in the grid. if a float, asserts it is <= 1 and
treats it as a proportion of total cells (default:
None)max_tree_depth: int | float | None: the maximum depth
of the tree. If None, defaults to
2 * accessible_cells. if a float, asserts it is <= 1 and
treats it as a proportion of the sum of the grid shape
(default: None)do_forks: bool: whether to allow forks in the maze. If
False, the maze will be have no forks and will be a simple
hallway.start_coord: Coord | None: the starting coordinate of
the generation algorithm. If None, defaults to a random
coordinate.def gen_prim(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
lattice_dim: int = 2,
accessible_cells: int | float | None = None,
max_tree_depth: int | float | None = None,
do_forks: bool = True,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedef gen_wilson(
grid_shape: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.LatticeMazeGenerate a lattice maze using Wilson’s algorithm.
Wilson’s algorithm generates an unbiased (random) maze sampled from the uniform distribution over all mazes, using loop-erased random walks. The generated maze is acyclic and all cells are part of a unique connected space. https://en.wikipedia.org/wiki/Maze_generation_algorithm#Wilson’s_algorithm
def gen_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazegenerate a lattice maze using simple percolation
note that p in the range (0.4, 0.7) gives the most interesting mazes
grid_shape: Coord: the shape of the gridlattice_dim: int: the dimension of the lattice
(default: 2)p: float: the probability of a cell being accessible
(default: 0.5)start_coord: Coord | None: the starting coordinate for
the connected component (default: None will give a random
start)def gen_dfs_percolation(
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
p: float = 0.4,
lattice_dim: int = 2,
accessible_cells: int | None = None,
max_tree_depth: int | None = None,
start_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None
) -> maze_dataset.maze.lattice_maze.LatticeMazedfs and then percolation (adds cycles)
GENERATORS_MAP: dict[str, typing.Callable[[jaxtyping.Int8[ndarray, 'row_col'], typing.Any], maze_dataset.maze.lattice_maze.LatticeMaze]] = {'gen_dfs': <function LatticeMazeGenerators.gen_dfs>, 'gen_wilson': <function LatticeMazeGenerators.gen_wilson>, 'gen_percolation': <function LatticeMazeGenerators.gen_percolation>, 'gen_dfs_percolation': <function LatticeMazeGenerators.gen_dfs_percolation>, 'gen_prim': <function LatticeMazeGenerators.gen_prim>}mapping of generator names to generator functions, useful for loading
MazeDatasetConfig
def get_maze_with_solution(
gen_name: str,
grid_shape: jaxtyping.Int8[ndarray, 'row_col'],
maze_ctor_kwargs: dict | None = None
) -> maze_dataset.maze.lattice_maze.SolvedMazehelper function to get a maze already with a solution
docs for maze-dataset
v1.1.0
LatticeMaze and the classes like SolvedMaze
that inherit from it, along with a ton of helper funcs
maze_dataset.mazeLatticeMaze and the classes like SolvedMaze
that inherit from it, along with a ton of helper funcs
class SolvedMaze(maze_dataset.maze.lattice_maze.TargetedLatticeMaze):Stores a maze and a solution
SolvedMaze(
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
solution: jaxtyping.Int8[ndarray, 'coord row_col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
end_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
allow_invalid: bool = False
)solution: jaxtyping.Int8[ndarray, 'coord row_col']def get_solution_tokens(self) -> list[str | tuple[int, int]]maze: maze_dataset.maze.lattice_maze.LatticeMazedef from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
solution: list[tuple[int, int]]
) -> maze_dataset.maze.lattice_maze.SolvedMazedef from_targeted_lattice_maze(
cls,
targeted_lattice_maze: maze_dataset.maze.lattice_maze.TargetedLatticeMaze,
solution: list[tuple[int, int]] | None = None
) -> maze_dataset.maze.lattice_maze.SolvedMazesolves the given targeted lattice maze and returns a SolvedMaze
def get_solution_forking_points(
self,
always_include_endpoints: bool = False
) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates and their indicies from the solution where a fork is present
def get_solution_path_following_points(self) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates from the solution where there is only a single (non-backtracking) point to move to
returns the complement of get_solution_forking_points
from the path
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class TargetedLatticeMaze(maze_dataset.maze.lattice_maze.LatticeMaze):A LatticeMaze with a start and end position
TargetedLatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
)start_pos: jaxtyping.Int8[ndarray, 'row_col']
end_pos: jaxtyping.Int8[ndarray, 'row_col']
def get_start_pos_tokens(self) -> list[str | tuple[int, int]]def get_end_pos_tokens(self) -> list[str | tuple[int, int]]def from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.TargetedLatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class LatticeMaze(muutils.json_serialize.serializable_dataclass.SerializableDataclass):lattice maze (nodes on a lattice, connections only to neighboring nodes)
Connection List represents which nodes (N) are connected in each direction.
First and second elements represent rightward and downward connections, respectively.
Example: Connection list: [ [ # down [F T], [F F] ], [ # right [T F], [T F] ] ]
Nodes with connections N T N F F T N T N F F F
Graph: N - N | N - N
Note: the bottom row connections going down, and the right-hand connections going right, will always be False.
LatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None
)connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']
generation_meta: dict | None = None
lattice_dim
grid_shapen_connectionsgrid_n: intdef heuristic(a: tuple[int, int], b: tuple[int, int]) -> floatreturn manhattan distance between two points
def nodes_connected(
self,
a: jaxtyping.Int8[ndarray, 'row_col'],
b: jaxtyping.Int8[ndarray, 'row_col'],
/
) -> boolreturns whether two nodes are connected
def is_valid_path(
self,
path: jaxtyping.Int8[ndarray, 'coord row_col'],
empty_is_valid: bool = False
) -> boolcheck if a path is valid
def coord_degrees(self) -> jaxtyping.Int8[ndarray, 'row col']Returns an array with the connectivity degree of each coord. I.e., how many neighbors each coord has.
def get_coord_neighbors(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']Returns an array of the neighboring, connected coords of
c.
def gen_connected_component_from(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']return the connected component from a given coordinate
def find_shortest_path(
self,
c_start: tuple[int, int],
c_end: tuple[int, int]
) -> jaxtyping.Int8[ndarray, 'coord row_col']find the shortest path between two coordinates, using A*
def get_nodes(self) -> jaxtyping.Int8[ndarray, 'coord row_col']return a list of all nodes in the maze
def get_connected_component(self) -> jaxtyping.Int8[ndarray, 'coord row_col']get the largest (and assumed only nonsingular) connected component of the maze
TODO: other connected components?
def generate_random_path(
self,
except_when_invalid: bool = True,
allowed_start: list[tuple[int, int]] | None = None,
allowed_end: list[tuple[int, int]] | None = None,
deadend_start: bool = False,
deadend_end: bool = False,
endpoints_not_equal: bool = False
) -> jaxtyping.Int8[ndarray, 'coord row_col']return a path between randomly chosen start and end nodes within the connected component
Note that setting special conditions on start and end positions might cause the same position to be selected as both start and end.
except_when_invalid : bool deprecated. setting this to
False will cause an error. (defaults to
True)allowed_start : CoordList | None a list of allowed
start positions. If None, any position in the connected
component is allowed (defaults to None)allowed_end : CoordList | None a list of allowed end
positions. If None, any position in the connected component
is allowed (defaults to None)deadend_start : bool whether to
force the start position to be a deadend
(defaults to False) (defaults to False)deadend_end : bool whether to
force the end position to be a deadend
(defaults to False) (defaults to False)endpoints_not_equal : bool whether to ensure tha the
start and end point are not the same (defaults to
False)CoordArray a path between the selected start and end
positionsValueError : if the connected component has less than 2
nodes and except_when_invalid is Truedef as_adj_list(
self,
shuffle_d0: bool = True,
shuffle_d1: bool = True
) -> jaxtyping.Int8[ndarray, 'conn start_end coord']def from_adj_list(
cls,
adj_list: jaxtyping.Int8[ndarray, 'conn start_end coord']
) -> maze_dataset.maze.lattice_maze.LatticeMazecreate a LatticeMaze from a list of connections
[!NOTE] This has only been tested for square mazes. Might need to change some things if rectangular mazes are needed.
def as_adj_list_tokens(self) -> list[str | tuple[int, int]]def as_tokens(
self,
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> list[str]serialize maze and solution to tokens
def from_tokens(
cls,
tokens: list[str],
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> maze_dataset.maze.lattice_maze.LatticeMazeConstructs a maze from a tokenization. Only legacy tokenizers and
their MazeTokenizerModular analogs are supported.
def as_pixels(
self,
show_endpoints: bool = True,
show_solution: bool = True
) -> jaxtyping.Int[ndarray, 'x y rgb']def from_pixels(
cls,
pixel_grid: jaxtyping.Int[ndarray, 'x y rgb']
) -> maze_dataset.maze.lattice_maze.LatticeMazedef as_ascii(self, show_endpoints: bool = True, show_solution: bool = True) -> strreturn an ASCII grid of the maze
def from_ascii(cls, ascii_str: str) -> maze_dataset.maze.lattice_maze.LatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
ConnectionList = <class 'jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']'>
class AsciiChars:standard ascii characters for mazes
AsciiChars(
WALL: str = '#',
OPEN: str = ' ',
START: str = 'S',
END: str = 'E',
PATH: str = 'X'
)WALL: str = '#'
OPEN: str = ' '
START: str = 'S'
END: str = 'E'
PATH: str = 'X'
Coord = <class 'jaxtyping.Int8[ndarray, 'row_col']'>
CoordArray = <class 'jaxtyping.Int8[ndarray, 'coord row_col']'>
class PixelColors:standard colors for pixel grids
PixelColors(
WALL: tuple[int, int, int] = (0, 0, 0),
OPEN: tuple[int, int, int] = (255, 255, 255),
START: tuple[int, int, int] = (0, 255, 0),
END: tuple[int, int, int] = (255, 0, 0),
PATH: tuple[int, int, int] = (0, 0, 255)
)WALL: tuple[int, int, int] = (0, 0, 0)
OPEN: tuple[int, int, int] = (255, 255, 255)
START: tuple[int, int, int] = (0, 255, 0)
END: tuple[int, int, int] = (255, 0, 0)
PATH: tuple[int, int, int] = (0, 0, 255)
docs for maze-dataset
v1.1.0
RGBPixelGridBinaryPixelGridcolor_in_pixel_gridPixelColorsAsciiCharsASCII_PIXEL_PAIRINGSLatticeMazeTargetedLatticeMazeSolvedMazedetect_pixels_typemaze_dataset.maze.lattice_mazeRGB = tuple[int, int, int]rgb tuple of values 0-255
PixelGrid = <class 'jaxtyping.Int[ndarray, 'x y rgb']'>rgb grid of pixels
BinaryPixelGrid = <class 'jaxtyping.Bool[ndarray, 'x y']'>boolean grid of pixels
def color_in_pixel_grid(
pixel_grid: jaxtyping.Int[ndarray, 'x y rgb'],
color: tuple[int, int, int]
) -> boolclass PixelColors:standard colors for pixel grids
PixelColors(
WALL: tuple[int, int, int] = (0, 0, 0),
OPEN: tuple[int, int, int] = (255, 255, 255),
START: tuple[int, int, int] = (0, 255, 0),
END: tuple[int, int, int] = (255, 0, 0),
PATH: tuple[int, int, int] = (0, 0, 255)
)WALL: tuple[int, int, int] = (0, 0, 0)
OPEN: tuple[int, int, int] = (255, 255, 255)
START: tuple[int, int, int] = (0, 255, 0)
END: tuple[int, int, int] = (255, 0, 0)
PATH: tuple[int, int, int] = (0, 0, 255)
class AsciiChars:standard ascii characters for mazes
AsciiChars(
WALL: str = '#',
OPEN: str = ' ',
START: str = 'S',
END: str = 'E',
PATH: str = 'X'
)WALL: str = '#'
OPEN: str = ' '
START: str = 'S'
END: str = 'E'
PATH: str = 'X'
ASCII_PIXEL_PAIRINGS: dict[str, tuple[int, int, int]] = {'#': (0, 0, 0), ' ': (255, 255, 255), 'S': (0, 255, 0), 'E': (255, 0, 0), 'X': (0, 0, 255)}
map ascii characters to pixel colors
class LatticeMaze(muutils.json_serialize.serializable_dataclass.SerializableDataclass):lattice maze (nodes on a lattice, connections only to neighboring nodes)
Connection List represents which nodes (N) are connected in each direction.
First and second elements represent rightward and downward connections, respectively.
Example: Connection list: [ [ # down [F T], [F F] ], [ # right [T F], [T F] ] ]
Nodes with connections N T N F F T N T N F F F
Graph: N - N | N - N
Note: the bottom row connections going down, and the right-hand connections going right, will always be False.
LatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None
)connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']
generation_meta: dict | None = None
lattice_dim
grid_shapen_connectionsgrid_n: intdef heuristic(a: tuple[int, int], b: tuple[int, int]) -> floatreturn manhattan distance between two points
def nodes_connected(
self,
a: jaxtyping.Int8[ndarray, 'row_col'],
b: jaxtyping.Int8[ndarray, 'row_col'],
/
) -> boolreturns whether two nodes are connected
def is_valid_path(
self,
path: jaxtyping.Int8[ndarray, 'coord row_col'],
empty_is_valid: bool = False
) -> boolcheck if a path is valid
def coord_degrees(self) -> jaxtyping.Int8[ndarray, 'row col']Returns an array with the connectivity degree of each coord. I.e., how many neighbors each coord has.
def get_coord_neighbors(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']Returns an array of the neighboring, connected coords of
c.
def gen_connected_component_from(
self,
c: jaxtyping.Int8[ndarray, 'row_col']
) -> jaxtyping.Int8[ndarray, 'coord row_col']return the connected component from a given coordinate
def find_shortest_path(
self,
c_start: tuple[int, int],
c_end: tuple[int, int]
) -> jaxtyping.Int8[ndarray, 'coord row_col']find the shortest path between two coordinates, using A*
def get_nodes(self) -> jaxtyping.Int8[ndarray, 'coord row_col']return a list of all nodes in the maze
def get_connected_component(self) -> jaxtyping.Int8[ndarray, 'coord row_col']get the largest (and assumed only nonsingular) connected component of the maze
TODO: other connected components?
def generate_random_path(
self,
except_when_invalid: bool = True,
allowed_start: list[tuple[int, int]] | None = None,
allowed_end: list[tuple[int, int]] | None = None,
deadend_start: bool = False,
deadend_end: bool = False,
endpoints_not_equal: bool = False
) -> jaxtyping.Int8[ndarray, 'coord row_col']return a path between randomly chosen start and end nodes within the connected component
Note that setting special conditions on start and end positions might cause the same position to be selected as both start and end.
except_when_invalid : bool deprecated. setting this to
False will cause an error. (defaults to
True)allowed_start : CoordList | None a list of allowed
start positions. If None, any position in the connected
component is allowed (defaults to None)allowed_end : CoordList | None a list of allowed end
positions. If None, any position in the connected component
is allowed (defaults to None)deadend_start : bool whether to
force the start position to be a deadend
(defaults to False) (defaults to False)deadend_end : bool whether to
force the end position to be a deadend
(defaults to False) (defaults to False)endpoints_not_equal : bool whether to ensure tha the
start and end point are not the same (defaults to
False)CoordArray a path between the selected start and end
positionsValueError : if the connected component has less than 2
nodes and except_when_invalid is Truedef as_adj_list(
self,
shuffle_d0: bool = True,
shuffle_d1: bool = True
) -> jaxtyping.Int8[ndarray, 'conn start_end coord']def from_adj_list(
cls,
adj_list: jaxtyping.Int8[ndarray, 'conn start_end coord']
) -> maze_dataset.maze.lattice_maze.LatticeMazecreate a LatticeMaze from a list of connections
[!NOTE] This has only been tested for square mazes. Might need to change some things if rectangular mazes are needed.
def as_adj_list_tokens(self) -> list[str | tuple[int, int]]def as_tokens(
self,
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> list[str]serialize maze and solution to tokens
def from_tokens(
cls,
tokens: list[str],
maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode | maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular
) -> maze_dataset.maze.lattice_maze.LatticeMazeConstructs a maze from a tokenization. Only legacy tokenizers and
their MazeTokenizerModular analogs are supported.
def as_pixels(
self,
show_endpoints: bool = True,
show_solution: bool = True
) -> jaxtyping.Int[ndarray, 'x y rgb']def from_pixels(
cls,
pixel_grid: jaxtyping.Int[ndarray, 'x y rgb']
) -> maze_dataset.maze.lattice_maze.LatticeMazedef as_ascii(self, show_endpoints: bool = True, show_solution: bool = True) -> strreturn an ASCII grid of the maze
def from_ascii(cls, ascii_str: str) -> maze_dataset.maze.lattice_maze.LatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class TargetedLatticeMaze(LatticeMaze):A LatticeMaze with a start and end position
TargetedLatticeMaze(
*,
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
)start_pos: jaxtyping.Int8[ndarray, 'row_col']
end_pos: jaxtyping.Int8[ndarray, 'row_col']
def get_start_pos_tokens(self) -> list[str | tuple[int, int]]def get_end_pos_tokens(self) -> list[str | tuple[int, int]]def from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
start_pos: jaxtyping.Int8[ndarray, 'row_col'],
end_pos: jaxtyping.Int8[ndarray, 'row_col']
) -> maze_dataset.maze.lattice_maze.TargetedLatticeMazedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class SolvedMaze(TargetedLatticeMaze):Stores a maze and a solution
SolvedMaze(
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
solution: jaxtyping.Int8[ndarray, 'coord row_col'],
generation_meta: dict | None = None,
start_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
end_pos: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
allow_invalid: bool = False
)solution: jaxtyping.Int8[ndarray, 'coord row_col']def get_solution_tokens(self) -> list[str | tuple[int, int]]maze: maze_dataset.maze.lattice_maze.LatticeMazedef from_lattice_maze(
cls,
lattice_maze: maze_dataset.maze.lattice_maze.LatticeMaze,
solution: list[tuple[int, int]]
) -> maze_dataset.maze.lattice_maze.SolvedMazedef from_targeted_lattice_maze(
cls,
targeted_lattice_maze: maze_dataset.maze.lattice_maze.TargetedLatticeMaze,
solution: list[tuple[int, int]] | None = None
) -> maze_dataset.maze.lattice_maze.SolvedMazesolves the given targeted lattice maze and returns a SolvedMaze
def get_solution_forking_points(
self,
always_include_endpoints: bool = False
) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates and their indicies from the solution where a fork is present
def get_solution_path_following_points(self) -> tuple[list[int], jaxtyping.Int8[ndarray, 'coord row_col']]coordinates from the solution where there is only a single (non-backtracking) point to move to
returns the complement of get_solution_forking_points
from the path
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
def detect_pixels_type(
data: jaxtyping.Int[ndarray, 'x y rgb']
) -> Type[maze_dataset.maze.lattice_maze.LatticeMaze]Detects the type of pixels data by checking for the presence of start and end pixels
docs for maze-dataset
v1.1.0
utilities for plotting mazes and printing tokens
LatticeMaze or SolvedMaze comes with a
as_pixels() method that returns a 2D numpy array of pixel
values, but this is somewhat limitedMazePlot is a class that can be used to plot mazes and
paths in a more customizable wayprint_tokens contains utilities for printing tokens,
colored by their type, position, or some custom weights (i.e. attention
weights)plot_dataset_mazesprint_dataset_mazesDEFAULT_FORMATSMazePlotPathFormatcolor_tokens_cmapcolor_maze_tokens_AOTPcolor_tokens_rgbmaze_dataset.plottingutilities for plotting mazes and printing tokens
LatticeMaze or SolvedMaze comes with a
as_pixels() method that returns a 2D numpy array of pixel
values, but this is somewhat limitedMazePlot is a class that can be used to plot mazes and
paths in a more customizable wayprint_tokens contains utilities for printing tokens,
colored by their type, position, or some custom weights (i.e. attention
weights)def plot_dataset_mazes(
ds: maze_dataset.dataset.maze_dataset.MazeDataset,
count: int | None = None,
figsize_mult: tuple[float, float] = (1.0, 2.0),
title: bool | str = True
) -> tupledef print_dataset_mazes(
ds: maze_dataset.dataset.maze_dataset.MazeDataset,
count: int | None = None
)DEFAULT_FORMATS = {'true': PathFormat(label='true path', fmt='--', color='red', cmap=None, line_width=2.5, quiver_kwargs=None), 'predicted': PathFormat(label=None, fmt=':', color=None, cmap=None, line_width=2, quiver_kwargs={'width': 0.015})}class MazePlot:Class for displaying mazes and paths
MazePlot(
maze: maze_dataset.maze.lattice_maze.LatticeMaze,
unit_length: int = 14
)UNIT_LENGTH: Set ratio between node size and wall thickness in image. Wall thickness is fixed to 1px A “unit” consists of a single node and the right and lower connection/wall. Example: ul = 14 yields 13:1 ratio between node size and wall thickness
DEFAULT_PREDICTED_PATH_COLORS: list[str] = ['tab:orange', 'tab:olive', 'sienna', 'mediumseagreen', 'tab:purple', 'slategrey']
unit_length: int
maze: maze_dataset.maze.lattice_maze.LatticeMaze
true_path: maze_dataset.plotting.plot_maze.StyledPath | None
predicted_paths: list[maze_dataset.plotting.plot_maze.StyledPath]
node_values: jaxtyping.Float[ndarray, 'grid_n grid_n']
custom_node_value_flag: bool
node_color_map: str
target_token_coord: jaxtyping.Int8[ndarray, 'row_col']
preceding_tokens_coords: jaxtyping.Int8[ndarray, 'coord row_col']
colormap_center: float | None
cbar_ax
marked_coords: list[tuple[jaxtyping.Int8[ndarray, 'row_col'], dict]]
marker_kwargs_current: dict
marker_kwargs_next: dict
solved_maze: maze_dataset.maze.lattice_maze.SolvedMaze
def add_true_path(
self,
path: list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath,
path_fmt: maze_dataset.plotting.plot_maze.PathFormat | None = None,
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotdef add_predicted_path(
self,
path: list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath,
path_fmt: maze_dataset.plotting.plot_maze.PathFormat | None = None,
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotRecieve predicted path and formatting preferences from input and save in predicted_path list. Default formatting depends on nuber of paths already saved in predicted path list.
def add_multiple_paths(
self,
path_list: list[list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath]
)Function for adding multiple paths to MazePlot at once. This can be done in two ways: 1. Passing a list of
def add_node_values(
self,
node_values: jaxtyping.Float[ndarray, 'grid_n grid_n'],
color_map: str = 'Blues',
target_token_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
preceeding_tokens_coords: jaxtyping.Int8[ndarray, 'coord row_col'] = None,
colormap_center: float | None = None,
colormap_max: float | None = None,
hide_colorbar: bool = False
) -> maze_dataset.plotting.plot_maze.MazePlotdef plot(
self,
dpi: int = 100,
title: str = '',
fig_ax: tuple | None = None,
plain: bool = False
) -> maze_dataset.plotting.plot_maze.MazePlotPlot the maze and paths.
def mark_coords(
self,
coords: jaxtyping.Int8[ndarray, 'coord row_col'] | list[jaxtyping.Int8[ndarray, 'row_col']],
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotdef to_ascii(self, show_endpoints: bool = True, show_solution: bool = True) -> strclass PathFormat:formatting options for path plot
PathFormat(
*,
label: str | None = None,
fmt: str = 'o',
color: str | None = None,
cmap: str | None = None,
line_width: float | None = None,
quiver_kwargs: dict | None = None
)label: str | None = None
fmt: str = 'o'
color: str | None = None
cmap: str | None = None
line_width: float | None = None
quiver_kwargs: dict | None = None
def combine(
self,
other: maze_dataset.plotting.plot_maze.PathFormat
) -> maze_dataset.plotting.plot_maze.PathFormatcombine with other PathFormat object, overwriting attributes with non-None values.
returns a modified copy of self.
def color_tokens_cmap(
tokens: list[str],
weights: Sequence[float],
cmap: str | matplotlib.colors.Colormap = 'Blues',
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
labels: bool = False
)color tokens given a list of weights and a colormap
def color_maze_tokens_AOTP(
tokens: list[str],
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
**kwargs
) -> strcolor tokens assuming AOTP format
i.e: adjaceny list, origin, target, path
def color_tokens_rgb(
tokens: list,
colors: Sequence[Sequence[int]],
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
clr_join: str | None = None,
max_length: int | None = None
) -> strcolor tokens from a list with an RGB color array
tokens will not be escaped if fmt is None
max_length: int | None: Max number of characters before
triggering a line wrap, i.e., making a new colorbox. If
None, no limit on max length.docs for maze-dataset
v1.1.0
plot_dataset_mazes will plot several mazes using
as_pixels
print_dataset_mazes will use as_ascii to
print several mazes
maze_dataset.plotting.plot_datasetplot_dataset_mazes will plot several mazes using
as_pixels
print_dataset_mazes will use as_ascii to
print several mazes
def plot_dataset_mazes(
ds: maze_dataset.dataset.maze_dataset.MazeDataset,
count: int | None = None,
figsize_mult: tuple[float, float] = (1.0, 2.0),
title: bool | str = True
) -> tupledef print_dataset_mazes(
ds: maze_dataset.dataset.maze_dataset.MazeDataset,
count: int | None = None
)docs for maze-dataset
v1.1.0
provides MazePlot, which has many tools for plotting
mazes with multiple paths, colored nodes, and more
maze_dataset.plotting.plot_mazeprovides MazePlot, which has many tools for plotting
mazes with multiple paths, colored nodes, and more
LARGE_NEGATIVE_NUMBER: float = -10000000000.0class PathFormat:formatting options for path plot
PathFormat(
*,
label: str | None = None,
fmt: str = 'o',
color: str | None = None,
cmap: str | None = None,
line_width: float | None = None,
quiver_kwargs: dict | None = None
)label: str | None = None
fmt: str = 'o'
color: str | None = None
cmap: str | None = None
line_width: float | None = None
quiver_kwargs: dict | None = None
def combine(
self,
other: maze_dataset.plotting.plot_maze.PathFormat
) -> maze_dataset.plotting.plot_maze.PathFormatcombine with other PathFormat object, overwriting attributes with non-None values.
returns a modified copy of self.
class StyledPath(PathFormat):StyledPath(
path: jaxtyping.Int8[ndarray, 'coord row_col'],
*,
label: str | None = None,
fmt: str = 'o',
color: str | None = None,
cmap: str | None = None,
line_width: float | None = None,
quiver_kwargs: dict | None = None
)path: jaxtyping.Int8[ndarray, 'coord row_col']DEFAULT_FORMATS: dict[str, maze_dataset.plotting.plot_maze.PathFormat] = {'true': PathFormat(label='true path', fmt='--', color='red', cmap=None, line_width=2.5, quiver_kwargs=None), 'predicted': PathFormat(label=None, fmt=':', color=None, cmap=None, line_width=2, quiver_kwargs={'width': 0.015})}
def process_path_input(
path: list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath,
_default_key: str,
path_fmt: maze_dataset.plotting.plot_maze.PathFormat | None = None,
**kwargs
) -> maze_dataset.plotting.plot_maze.StyledPathclass MazePlot:Class for displaying mazes and paths
MazePlot(
maze: maze_dataset.maze.lattice_maze.LatticeMaze,
unit_length: int = 14
)UNIT_LENGTH: Set ratio between node size and wall thickness in image. Wall thickness is fixed to 1px A “unit” consists of a single node and the right and lower connection/wall. Example: ul = 14 yields 13:1 ratio between node size and wall thickness
DEFAULT_PREDICTED_PATH_COLORS: list[str] = ['tab:orange', 'tab:olive', 'sienna', 'mediumseagreen', 'tab:purple', 'slategrey']
unit_length: int
maze: maze_dataset.maze.lattice_maze.LatticeMaze
true_path: maze_dataset.plotting.plot_maze.StyledPath | None
predicted_paths: list[maze_dataset.plotting.plot_maze.StyledPath]
node_values: jaxtyping.Float[ndarray, 'grid_n grid_n']
custom_node_value_flag: bool
node_color_map: str
target_token_coord: jaxtyping.Int8[ndarray, 'row_col']
preceding_tokens_coords: jaxtyping.Int8[ndarray, 'coord row_col']
colormap_center: float | None
cbar_ax
marked_coords: list[tuple[jaxtyping.Int8[ndarray, 'row_col'], dict]]
marker_kwargs_current: dict
marker_kwargs_next: dict
solved_maze: maze_dataset.maze.lattice_maze.SolvedMaze
def add_true_path(
self,
path: list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath,
path_fmt: maze_dataset.plotting.plot_maze.PathFormat | None = None,
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotdef add_predicted_path(
self,
path: list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath,
path_fmt: maze_dataset.plotting.plot_maze.PathFormat | None = None,
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotRecieve predicted path and formatting preferences from input and save in predicted_path list. Default formatting depends on nuber of paths already saved in predicted path list.
def add_multiple_paths(
self,
path_list: list[list[tuple[int, int]] | jaxtyping.Int8[ndarray, 'coord row_col'] | maze_dataset.plotting.plot_maze.StyledPath]
)Function for adding multiple paths to MazePlot at once. This can be done in two ways: 1. Passing a list of
def add_node_values(
self,
node_values: jaxtyping.Float[ndarray, 'grid_n grid_n'],
color_map: str = 'Blues',
target_token_coord: jaxtyping.Int8[ndarray, 'row_col'] | None = None,
preceeding_tokens_coords: jaxtyping.Int8[ndarray, 'coord row_col'] = None,
colormap_center: float | None = None,
colormap_max: float | None = None,
hide_colorbar: bool = False
) -> maze_dataset.plotting.plot_maze.MazePlotdef plot(
self,
dpi: int = 100,
title: str = '',
fig_ax: tuple | None = None,
plain: bool = False
) -> maze_dataset.plotting.plot_maze.MazePlotPlot the maze and paths.
def mark_coords(
self,
coords: jaxtyping.Int8[ndarray, 'coord row_col'] | list[jaxtyping.Int8[ndarray, 'row_col']],
**kwargs
) -> maze_dataset.plotting.plot_maze.MazePlotdef to_ascii(self, show_endpoints: bool = True, show_solution: bool = True) -> strdocs for maze-dataset
v1.1.0
plot_colored_text function to plot tokens on a
matplotlib axis with colored backgrounds
maze_dataset.plotting.plot_tokensplot_colored_text function to plot tokens on a
matplotlib axis with colored backgrounds
def plot_colored_text(
tokens: Sequence[str],
weights: Sequence[float],
cmap: str | typing.Any,
ax: matplotlib.axes._axes.Axes = None,
width_scale: float = 0.023,
width_offset: float = 0.005,
height_offset: float = 0.1,
rect_height: float = 0.7,
token_height: float = 0.7,
label_height: float = 0.3,
word_gap: float = 0.01,
fontsize: int = 12,
fig_height: float = 0.7,
fig_width_scale: float = 0.25,
char_min: int = 4
)hacky function to plot tokens on a matplotlib axis with colored backgrounds
docs for maze-dataset
v1.1.0
Functions to print tokens with colors in different formats
you can color the tokens by their:
color_maze_tokens_AOTPcolor_tokens_cmapcolor_tokens_rgband the output can be in different formats, specified by
FormatType (html, latex, terminal)
RGBArrayFormatTypeTEMPLATEScolor_tokens_rgbcolor_tokens_cmapcolor_maze_tokens_AOTPdisplay_htmldisplay_color_tokens_rgbdisplay_color_tokens_cmapdisplay_color_maze_tokens_AOTPmaze_dataset.plotting.print_tokensFunctions to print tokens with colors in different formats
you can color the tokens by their:
color_maze_tokens_AOTPcolor_tokens_cmapcolor_tokens_rgband the output can be in different formats, specified by
FormatType (html, latex, terminal)
RGBArray = <class 'jaxtyping.UInt8[ndarray, 'n 3']'>1D array of RGB values
FormatType = typing.Literal['html', 'latex', 'terminal', None]output format for the tokens
TEMPLATES: dict[typing.Literal['html', 'latex', 'terminal', None], str] = {'html': '<span style="color: black; background-color: rgb({clr})"> {tok} </span>', 'latex': '\\colorbox[RGB]{{ {clr} }}{{ \\texttt{{ {tok} }} }}', 'terminal': '\x1b[30m\x1b[48;2;{clr}m{tok}\x1b[0m'}templates of printing tokens in different formats
def color_tokens_rgb(
tokens: list,
colors: Sequence[Sequence[int]],
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
clr_join: str | None = None,
max_length: int | None = None
) -> strcolor tokens from a list with an RGB color array
tokens will not be escaped if fmt is None
max_length: int | None: Max number of characters before
triggering a line wrap, i.e., making a new colorbox. If
None, no limit on max length.def color_tokens_cmap(
tokens: list[str],
weights: Sequence[float],
cmap: str | matplotlib.colors.Colormap = 'Blues',
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
labels: bool = False
)color tokens given a list of weights and a colormap
def color_maze_tokens_AOTP(
tokens: list[str],
fmt: Literal['html', 'latex', 'terminal', None] = 'html',
template: str | None = None,
**kwargs
) -> strcolor tokens assuming AOTP format
i.e: adjaceny list, origin, target, path
def display_html(html: str)def display_color_tokens_rgb(tokens: list[str], colors: jaxtyping.UInt8[ndarray, 'n 3']) -> Nonedef display_color_tokens_cmap(
tokens: list[str],
weights: Sequence[float],
cmap: str | matplotlib.colors.Colormap = 'Blues'
) -> Nonedef display_color_maze_tokens_AOTP(tokens: list[str]) -> Nonedocs for maze-dataset
v1.1.0
Shared utilities for tests only. Do not import into any module outside of the tests directory
GRID_NN_MAZESCFGMAZE_DATASETLATTICE_MAZESTARGETED_MAZESMIXED_MAZESMANUAL_MAZEASCII_MAZESLEGACY_AND_EQUIVALENT_TOKENIZERSmaze_dataset.testing_utilsShared utilities for tests only. Do not import into any module outside of the tests directory
GRID_N: Final[int] = 5
N_MAZES: Final[int] = 5
CFG: Final[maze_dataset.dataset.maze_dataset.MazeDatasetConfig] = MazeDatasetConfig(name='test', seq_len_min=1, seq_len_max=512, seed=42, applied_filters=[], grid_n=5, n_mazes=5, maze_ctor=<function LatticeMazeGenerators.gen_dfs>, maze_ctor_kwargs={}, endpoint_kwargs={})
MAZE_DATASET: Final[maze_dataset.dataset.maze_dataset.MazeDataset] = <maze_dataset.dataset.maze_dataset.MazeDataset object>
`LATTICE_MAZES: Final[tuple[maze_dataset.maze.lattice_maze.LatticeMaze]] = (LatticeMaze(connection_list=array([[[ True, True, True, True, False], [ True, False, True, False, True], [ True, False, False, True, True], [False, False, True, True, True], [False, False, False, False, False]],
[[ True, False, True, True, False],
[False, False, False, True, False],
[ True, True, False, False, False],
[ True, True, False, False, False],
[ True, True, False, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), LatticeMaze(connection_list=array([[[ True, False, False, True, True],
[ True, False, True, True, True],
[False, True, True, False, True],
[ True, False, True, True, False],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[ True, True, False, False, False],
[ True, False, False, True, False],
[ True, False, False, True, False],
[ True, False, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 2]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), LatticeMaze(connection_list=array([[[False, False, True, True, True],
[ True, False, False, False, True],
[False, False, False, False, True],
[ True, True, True, False, False],
[False, False, False, False, False]],
[[ True, True, True, False, False],
[ True, True, False, True, False],
[ True, True, True, False, False],
[ True, False, True, True, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), LatticeMaze(connection_list=array([[[False, True, True, True, True],
[ True, True, True, False, True],
[ True, False, False, True, True],
[False, False, False, False, True],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[False, False, False, True, False],
[ True, False, True, False, False],
[ True, True, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 0]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), LatticeMaze(connection_list=array([[[ True, False, False, False, True],
[False, True, True, True, False],
[ True, False, True, False, True],
[ True, True, False, True, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[ True, False, False, True, False],
[False, False, True, False, False],
[False, True, False, True, False],
[ True, True, True, False, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 1]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}))``TARGETED_MAZES: Final[tuple[maze_dataset.maze.lattice_maze.TargetedLatticeMaze]] = (TargetedLatticeMaze(connection_list=array([[[ True, True, True, True, False], [ True, False, True, False, True], [ True, False, False, True, True], [False, False, True, True, True], [False, False, False, False, False]],
[[ True, False, True, True, False],
[False, False, False, True, False],
[ True, True, False, False, False],
[ True, True, False, False, False],
[ True, True, False, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([2, 4]), end_pos=array([3, 0])), TargetedLatticeMaze(connection_list=array([[[ True, False, False, True, True],
[ True, False, True, True, True],
[False, True, True, False, True],
[ True, False, True, True, False],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[ True, True, False, False, False],
[ True, False, False, True, False],
[ True, False, False, True, False],
[ True, False, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 2]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 0]), end_pos=array([1, 0])), TargetedLatticeMaze(connection_list=array([[[False, False, True, True, True],
[ True, False, False, False, True],
[False, False, False, False, True],
[ True, True, True, False, False],
[False, False, False, False, False]],
[[ True, True, True, False, False],
[ True, True, False, True, False],
[ True, True, True, False, False],
[ True, False, True, True, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 0]), end_pos=array([4, 0])), TargetedLatticeMaze(connection_list=array([[[False, True, True, True, True],
[ True, True, True, False, True],
[ True, False, False, True, True],
[False, False, False, False, True],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[False, False, False, True, False],
[ True, False, True, False, False],
[ True, True, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 0]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([3, 2]), end_pos=array([3, 4])), TargetedLatticeMaze(connection_list=array([[[ True, False, False, False, True],
[False, True, True, True, False],
[ True, False, True, False, True],
[ True, True, False, True, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[ True, False, False, True, False],
[False, False, True, False, False],
[False, True, False, True, False],
[ True, True, True, False, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 1]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 3]), end_pos=array([2, 3])))``MIXED_MAZES: Final[tuple[maze_dataset.maze.lattice_maze.LatticeMaze | maze_dataset.maze.lattice_maze.TargetedLatticeMaze | maze_dataset.maze.lattice_maze.SolvedMaze]] = (SolvedMaze(connection_list=array([[[ True, False, True, True, True], [False, False, True, False, False], [ True, False, True, False, True], [ True, False, False, False, True], [False, False, False, False, False]],
[[ True, True, True, False, False],
[ True, False, False, True, False],
[ True, True, False, True, False],
[False, True, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([1, 3]), end_pos=array([2, 3]), solution=array([[1, 3],
[0, 3],
[0, 2],
[1, 2],
[2, 2],
[2, 1],
[2, 0],
[3, 0],
[4, 0],
[4, 1],
[4, 2],
[4, 3],
[4, 4],
[3, 4],
[2, 4],
[2, 3]])), TargetedLatticeMaze(connection_list=array([[[ True, True, True, True, False],
[ True, False, True, False, True],
[ True, False, False, True, True],
[False, False, True, True, True],
[False, False, False, False, False]],
[[ True, False, True, True, False],
[False, False, False, True, False],
[ True, True, False, False, False],
[ True, True, False, False, False],
[ True, True, False, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([2, 4]), end_pos=array([3, 0])), LatticeMaze(connection_list=array([[[ True, True, True, True, False],
[ True, False, True, False, True],
[ True, False, False, True, True],
[False, False, True, True, True],
[False, False, False, False, False]],
[[ True, False, True, True, False],
[False, False, False, True, False],
[ True, True, False, False, False],
[ True, True, False, False, False],
[ True, True, False, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), SolvedMaze(connection_list=array([[[ True, True, True, False, True],
[ True, False, False, False, True],
[ True, True, True, True, False],
[ True, True, False, False, True],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[False, False, True, True, False],
[ True, True, False, True, False],
[False, False, False, True, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([3, 4]), end_pos=array([2, 0]), solution=array([[3, 4],
[4, 4],
[4, 3],
[4, 2],
[4, 1],
[3, 1],
[2, 1],
[2, 0]])), TargetedLatticeMaze(connection_list=array([[[ True, False, False, True, True],
[ True, False, True, True, True],
[False, True, True, False, True],
[ True, False, True, True, False],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[ True, True, False, False, False],
[ True, False, False, True, False],
[ True, False, False, True, False],
[ True, False, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 2]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 0]), end_pos=array([1, 0])), LatticeMaze(connection_list=array([[[ True, False, False, True, True],
[ True, False, True, True, True],
[False, True, True, False, True],
[ True, False, True, True, False],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[ True, True, False, False, False],
[ True, False, False, True, False],
[ True, False, False, True, False],
[ True, False, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 2]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), SolvedMaze(connection_list=array([[[ True, False, False, True, True],
[False, False, True, False, True],
[ True, True, False, True, True],
[ True, True, False, True, True],
[False, False, False, False, False]],
[[ True, False, True, True, False],
[ True, True, False, False, False],
[False, True, False, True, False],
[False, False, True, False, False],
[ True, True, True, False, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 1]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 0]), end_pos=array([3, 3]), solution=array([[0, 0],
[1, 0],
[1, 1],
[1, 2],
[2, 2],
[2, 1],
[3, 1],
[4, 1],
[4, 2],
[4, 3],
[3, 3]])), TargetedLatticeMaze(connection_list=array([[[False, False, True, True, True],
[ True, False, False, False, True],
[False, False, False, False, True],
[ True, True, True, False, False],
[False, False, False, False, False]],
[[ True, True, True, False, False],
[ True, True, False, True, False],
[ True, True, True, False, False],
[ True, False, True, True, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 0]), end_pos=array([4, 0])), LatticeMaze(connection_list=array([[[False, False, True, True, True],
[ True, False, False, False, True],
[False, False, False, False, True],
[ True, True, True, False, False],
[False, False, False, False, False]],
[[ True, True, True, False, False],
[ True, True, False, True, False],
[ True, True, True, False, False],
[ True, False, True, True, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), SolvedMaze(connection_list=array([[[ True, False, True, False, False],
[ True, True, True, False, True],
[ True, True, False, True, False],
[ True, True, True, False, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[ True, False, False, True, False],
[False, False, False, True, False],
[False, False, True, False, False],
[False, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([1, 3]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 1]), end_pos=array([1, 0]), solution=array([[0, 1],
[0, 0],
[1, 0]])), TargetedLatticeMaze(connection_list=array([[[False, True, True, True, True],
[ True, True, True, False, True],
[ True, False, False, True, True],
[False, False, False, False, True],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[False, False, False, True, False],
[ True, False, True, False, False],
[ True, True, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 0]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([3, 2]), end_pos=array([3, 4])), LatticeMaze(connection_list=array([[[False, True, True, True, True],
[ True, True, True, False, True],
[ True, False, False, True, True],
[False, False, False, False, True],
[False, False, False, False, False]],
[[ True, False, True, False, False],
[False, False, False, True, False],
[ True, False, True, False, False],
[ True, True, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([0, 0]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}), SolvedMaze(connection_list=array([[[ True, False, False, False, True],
[ True, True, True, False, True],
[False, True, True, True, False],
[ True, True, False, False, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[False, True, True, False, False],
[False, False, False, True, False],
[False, False, True, False, False],
[ True, True, True, True, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 0]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([2, 2]), end_pos=array([1, 0]), solution=array([[2, 2],
[3, 2],
[3, 3],
[2, 3],
[2, 4],
[1, 4],
[0, 4],
[0, 3],
[0, 2],
[0, 1],
[0, 0],
[1, 0]])), TargetedLatticeMaze(connection_list=array([[[ True, False, False, False, True],
[False, True, True, True, False],
[ True, False, True, False, True],
[ True, True, False, True, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[ True, False, False, True, False],
[False, False, True, False, False],
[False, True, False, True, False],
[ True, True, True, False, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 1]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}, start_pos=array([0, 3]), end_pos=array([2, 3])), LatticeMaze(connection_list=array([[[ True, False, False, False, True],
[False, True, True, True, False],
[ True, False, True, False, True],
[ True, True, False, True, True],
[False, False, False, False, False]],
[[ True, True, True, True, False],
[ True, False, False, True, False],
[False, False, True, False, False],
[False, True, False, True, False],
[ True, True, True, False, False]]]), generation_meta={'func_name': 'gen_dfs', 'grid_shape': array([5, 5]), 'start_coord': array([2, 1]), 'n_accessible_cells': 25, 'max_tree_depth': 50, 'fully_connected': True, 'visited_cells': {(4, 0), (3, 4), (4, 3), (3, 1), (0, 2), (2, 2), (1, 0), (1, 3), (4, 2), (3, 0), (3, 3), (0, 1), (2, 4), (1, 2), (0, 4), (2, 1), (3, 2), (4, 1), (4, 4), (0, 0), (1, 1), (0, 3), (2, 0), (1, 4), (2, 3)}}))`class MANUAL_MAZE(typing.NamedTuple):MANUAL_MAZE(tokens, ascii, straightaway_footprints)
MANUAL_MAZE(
tokens: str,
ascii: tuple[str],
straightaway_footprints: jaxtyping.Int8[ndarray, 'coord row_col']
)Create new instance of MANUAL_MAZE(tokens, ascii, straightaway_footprints)
tokens: strAlias for field number 0
ascii: tuple[str]Alias for field number 1
straightaway_footprints: jaxtyping.Int8[ndarray, 'coord row_col']Alias for field number 2
ASCII_MAZES: Final[frozendict.frozendict[str, maze_dataset.testing_utils.MANUAL_MAZE]] = frozendict.frozendict({'small_3x3': MANUAL_MAZE(tokens='<ADJLIST_START> (2,0) <--> (2,1) ; (0,0) <--> (0,1) ; (0,0) <--> (1,0) ; (0,2) <--> (1,2) ; (1,0) <--> (2,0) ; (0,2) <--> (0,1) ; (2,2) <--> (2,1) ; (1,1) <--> (2,1) ; <ADJLIST_END> <ORIGIN_START> (0,0) <ORIGIN_END> <TARGET_START> (2,1) <TARGET_END> <PATH_START> (0,0) (1,0) (2,0) (2,1) <PATH_END>', ascii=('#######', '#S #', '#X### #', '#X# # #', '#X# ###', '#XXE #', '#######'), straightaway_footprints=array([[0, 0], [2, 0], [2, 1]])), 'big_10x10': MANUAL_MAZE(tokens='<ADJLIST_START> (8,2) <--> (8,3) ; (3,7) <--> (3,6) ; (6,7) <--> (6,8) ; (4,6) <--> (5,6) ; (9,5) <--> (9,4) ; (3,3) <--> (3,4) ; (5,1) <--> (4,1) ; (2,6) <--> (2,7) ; (8,5) <--> (8,4) ; (1,9) <--> (2,9) ; (4,1) <--> (4,2) ; (0,8) <--> (0,7) ; (5,4) <--> (5,3) ; (6,3) <--> (6,4) ; (5,0) <--> (4,0) ; (5,3) <--> (5,2) ; (3,1) <--> (2,1) ; (9,1) <--> (9,0) ; (3,5) <--> (3,6) ; (5,5) <--> (6,5) ; (7,1) <--> (7,2) ; (0,1) <--> (1,1) ; (7,8) <--> (8,8) ; (3,9) <--> (4,9) ; (4,6) <--> (4,7) ; (0,6) <--> (0,7) ; (3,4) <--> (3,5) ; (6,0) <--> (5,0) ; (7,7) <--> (7,6) ; (1,6) <--> (0,6) ; (6,1) <--> (6,0) ; (8,6) <--> (8,7) ; (9,9) <--> (9,8) ; (1,8) <--> (1,9) ; (2,1) <--> (2,2) ; (9,2) <--> (9,3) ; (5,9) <--> (6,9) ; (3,2) <--> (2,2) ; (0,8) <--> (0,9) ; (5,6) <--> (5,7) ; (2,3) <--> (2,4) ; (4,5) <--> (4,4) ; (8,9) <--> (8,8) ; (9,6) <--> (8,6) ; (3,7) <--> (3,8) ; (8,0) <--> (7,0) ; (6,1) <--> (6,2) ; (0,1) <--> (0,0) ; (7,3) <--> (7,4) ; (9,4) <--> (9,3) ; (9,6) <--> (9,5) ; (8,7) <--> (7,7) ; (5,2) <--> (5,1) ; (0,0) <--> (1,0) ; (7,2) <--> (7,3) ; (2,5) <--> (2,6) ; (4,9) <--> (5,9) ; (5,5) <--> (5,4) ; (5,6) <--> (6,6) ; (7,8) <--> (7,9) ; (1,7) <--> (2,7) ; (4,6) <--> (4,5) ; (1,1) <--> (1,2) ; (3,1) <--> (3,0) ; (1,5) <--> (1,6) ; (8,3) <--> (8,4) ; (9,9) <--> (8,9) ; (8,5) <--> (7,5) ; (1,4) <--> (2,4) ; (3,0) <--> (4,0) ; (3,3) <--> (4,3) ; (6,9) <--> (6,8) ; (1,0) <--> (2,0) ; (6,0) <--> (7,0) ; (8,0) <--> (9,0) ; (2,3) <--> (2,2) ; (2,8) <--> (3,8) ; (5,7) <--> (6,7) ; (1,3) <--> (0,3) ; (9,7) <--> (9,8) ; (7,5) <--> (7,4) ; (1,8) <--> (2,8) ; (6,5) <--> (6,4) ; (0,2) <--> (1,2) ; (0,7) <--> (1,7) ; (0,3) <--> (0,2) ; (4,3) <--> (4,2) ; (5,8) <--> (4,8) ; (9,1) <--> (8,1) ; (9,2) <--> (8,2) ; (1,3) <--> (1,4) ; (2,9) <--> (3,9) ; (4,8) <--> (4,7) ; (0,5) <--> (0,4) ; (8,1) <--> (7,1) ; (0,3) <--> (0,4) ; (9,7) <--> (9,6) ; (7,6) <--> (6,6) ; (1,5) <--> (0,5) ; <ADJLIST_END> <ORIGIN_START> (6,2) <ORIGIN_END> <TARGET_START> (2,1) <TARGET_END> <PATH_START> (6,2) (6,1) (6,0) (5,0) (4,0) (3,0) (3,1) (2,1) <PATH_END>', ascii=('#####################', '# # # #', '# # # # ### # # #####', '# # # # # # #', '# ####### ##### # # #', '# #E # # # #', '###X# ########### # #', '#XXX# # # #', '#X##### ########### #', '#X# # # #', '#X# ######### ### # #', '#X# # # # #', '#X######### # # ### #', '#XXXXS# # # #', '# ########### #######', '# # # # #', '# # ####### ### # ###', '# # # # # #', '# # # ####### ##### #', '# # #', '#####################'), straightaway_footprints=array([[6, 2], [6, 0], [3, 0], [3, 1], [2, 1]])), 'longer_10x10': MANUAL_MAZE(tokens='<ADJLIST_START> (8,2) <--> (8,3) ; (3,7) <--> (3,6) ; (6,7) <--> (6,8) ; (4,6) <--> (5,6) ; (9,5) <--> (9,4) ; (3,3) <--> (3,4) ; (5,1) <--> (4,1) ; (2,6) <--> (2,7) ; (8,5) <--> (8,4) ; (1,9) <--> (2,9) ; (4,1) <--> (4,2) ; (0,8) <--> (0,7) ; (5,4) <--> (5,3) ; (6,3) <--> (6,4) ; (5,0) <--> (4,0) ; (5,3) <--> (5,2) ; (3,1) <--> (2,1) ; (9,1) <--> (9,0) ; (3,5) <--> (3,6) ; (5,5) <--> (6,5) ; (7,1) <--> (7,2) ; (0,1) <--> (1,1) ; (7,8) <--> (8,8) ; (3,9) <--> (4,9) ; (4,6) <--> (4,7) ; (0,6) <--> (0,7) ; (3,4) <--> (3,5) ; (6,0) <--> (5,0) ; (7,7) <--> (7,6) ; (1,6) <--> (0,6) ; (6,1) <--> (6,0) ; (8,6) <--> (8,7) ; (9,9) <--> (9,8) ; (1,8) <--> (1,9) ; (2,1) <--> (2,2) ; (9,2) <--> (9,3) ; (5,9) <--> (6,9) ; (3,2) <--> (2,2) ; (0,8) <--> (0,9) ; (5,6) <--> (5,7) ; (2,3) <--> (2,4) ; (4,5) <--> (4,4) ; (8,9) <--> (8,8) ; (9,6) <--> (8,6) ; (3,7) <--> (3,8) ; (8,0) <--> (7,0) ; (6,1) <--> (6,2) ; (0,1) <--> (0,0) ; (7,3) <--> (7,4) ; (9,4) <--> (9,3) ; (9,6) <--> (9,5) ; (8,7) <--> (7,7) ; (5,2) <--> (5,1) ; (0,0) <--> (1,0) ; (7,2) <--> (7,3) ; (2,5) <--> (2,6) ; (4,9) <--> (5,9) ; (5,5) <--> (5,4) ; (5,6) <--> (6,6) ; (7,8) <--> (7,9) ; (1,7) <--> (2,7) ; (4,6) <--> (4,5) ; (1,1) <--> (1,2) ; (3,1) <--> (3,0) ; (1,5) <--> (1,6) ; (8,3) <--> (8,4) ; (9,9) <--> (8,9) ; (8,5) <--> (7,5) ; (1,4) <--> (2,4) ; (3,0) <--> (4,0) ; (3,3) <--> (4,3) ; (6,9) <--> (6,8) ; (1,0) <--> (2,0) ; (6,0) <--> (7,0) ; (8,0) <--> (9,0) ; (2,3) <--> (2,2) ; (2,8) <--> (3,8) ; (5,7) <--> (6,7) ; (1,3) <--> (0,3) ; (9,7) <--> (9,8) ; (7,5) <--> (7,4) ; (1,8) <--> (2,8) ; (6,5) <--> (6,4) ; (0,2) <--> (1,2) ; (0,7) <--> (1,7) ; (0,3) <--> (0,2) ; (4,3) <--> (4,2) ; (5,8) <--> (4,8) ; (9,1) <--> (8,1) ; (9,2) <--> (8,2) ; (1,3) <--> (1,4) ; (2,9) <--> (3,9) ; (4,8) <--> (4,7) ; (0,5) <--> (0,4) ; (8,1) <--> (7,1) ; (0,3) <--> (0,4) ; (9,7) <--> (9,6) ; (7,6) <--> (6,6) ; (1,5) <--> (0,5) ; <ADJLIST_END> <ORIGIN_START> (6,2) <ORIGIN_END> <TARGET_START> (2,1) <TARGET_END> <PATH_START> (6,2) (6,1) (6,0) (5,0) (4,0) (3,0) (3,1) (2,1) (2,2) (2,3) (2,4) (1,4) (1,3) (0,3) (0,4) (0,5) (1,5) (1,6) (0,6) (0,7) (0,8) <PATH_END>', ascii=('#####################', '# # XXXXX#XXXXE #', '# # # #X###X#X# #####', '# # #XXX#XXX# # #', '# #######X##### # # #', '# #XXXXXXX# # # #', '###X# ########### # #', '#XXX# # # #', '#X##### ########### #', '#X# # # #', '#X# ######### ### # #', '#X# # # # #', '#X######### # # ### #', '#XXXXS# # # #', '# ########### #######', '# # # # #', '# # ####### ### # ###', '# # # # # #', '# # # ####### ##### #', '# # #', '#####################'), straightaway_footprints=array([[6, 2], [6, 0], [3, 0], [3, 1], [2, 1], [2, 4], [1, 4], [1, 3], [0, 3], [0, 5], [1, 5], [1, 6], [0, 6], [0, 8]]))})
LEGACY_AND_EQUIVALENT_TOKENIZERS: list[maze_dataset.tokenization.maze_tokenizer.MazeTokenizer, maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular] = [MazeTokenizer(tokenization_mode=<TokenizationMode.AOTP_UT_rasterized: 'AOTP_UT_rasterized'>, max_grid_size=20), MazeTokenizer(tokenization_mode=<TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>, max_grid_size=20), MazeTokenizer(tokenization_mode=<TokenizationMode.AOTP_CTT_indexed: 'AOTP_CTT_indexed'>, max_grid_size=20), MazeTokenizerModular(prompt_sequencer=PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))), MazeTokenizerModular(prompt_sequencer=PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))), MazeTokenizerModular(prompt_sequencer=PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.CTT(pre=True, intra=True, post=True), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)))]
docs for maze-dataset
v1.1.0
a whole bunch of utilities for tokenization
remove_padding_from_token_strtokens_betweenget_adj_list_tokensget_path_tokensget_context_tokensget_origin_tokensget_target_tokensget_cardinal_directionget_relative_directionTokenizerPendingDeprecationWarningstr_is_coordTokenizerDeprecationWarningcoord_str_to_tuplecoord_str_to_coord_npcoord_str_to_tuple_noneablecoords_string_split_UTstrings_to_coordscoords_to_stringsget_token_regionsequal_except_adj_list_sequenceconnection_list_to_adj_listis_connectionmaze_dataset.token_utilsa whole bunch of utilities for tokenization
def remove_padding_from_token_str(token_str: str) -> strdef tokens_between(
tokens: list[str],
start_value: str,
end_value: str,
include_start: bool = False,
include_end: bool = False,
except_when_tokens_not_unique: bool = False
) -> list[str]def get_adj_list_tokens(tokens: list[str]) -> list[str]def get_path_tokens(tokens: list[str], trim_end: bool = False) -> list[str]The path is considered everything from the first path coord to the path_end token, if it exists.
def get_context_tokens(tokens: list[str]) -> list[str]def get_origin_tokens(tokens: list[str]) -> list[str]def get_target_tokens(tokens: list[str]) -> list[str]def get_cardinal_direction(coords: jaxtyping.Int[ndarray, 'start_end=2 row_col=2']) -> strReturns the cardinal direction token corresponding to traveling from
coords[0] to coords[1].
def get_relative_direction(coords: jaxtyping.Int[ndarray, 'prev_cur_next=3 row_col=2']) -> strReturns the relative first-person direction token corresponding to
traveling from coords[1] to coords[2]. ###
Parameters - coords: Contains 3 Coords, each of which must
neighbor the previous Coord. - coords[0]: The previous
location, used to determine the current absolute direction that the
“agent” is facing. - coords[1]: The current location -
coords[2]: The next location. May be equal to the current
location.
class TokenizerPendingDeprecationWarning(builtins.PendingDeprecationWarning):Pending deprecation warnings related to the
MazeTokenizerModular upgrade.
def str_is_coord(coord_str: str, allow_whitespace: bool = True) -> boolreturn True if the string represents a coordinate, False otherwise
class TokenizerDeprecationWarning(builtins.DeprecationWarning):Deprecation warnings related to the MazeTokenizerModular
upgrade.
def coord_str_to_tuple(coord_str: str, allow_whitespace: bool = True) -> tuple[int, ...]convert a coordinate string to a tuple
def coord_str_to_coord_np(coord_str: str, allow_whitespace: bool = True) -> numpy.ndarrayconvert a coordinate string to a numpy array
def coord_str_to_tuple_noneable(coord_str: str) -> tuple[int, int] | Noneconvert a coordinate string to a tuple, or None if the string is not a coordinate string
def coords_string_split_UT(coords: str) -> list[str]Splits a string of tokens into a list containing the UT tokens for each coordinate.
Not capable of producing indexed tokens (“(”, “1”, “,”, “2”, “)”),
only unique tokens (“(1,2)”). Non-whitespace portions of the input
string not matched are preserved in the same list: “(1,2)
def strings_to_coords(
text: str | list[str],
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str | tuple[int, int]]converts a list of tokens to a list of coordinates
returns list[CoordTup] if when_noncoord is “skip” or
“error” returns list[str | CoordTup] if when_noncoord is
“include”
def coords_to_strings(
coords: list[str | tuple[int, int]],
coord_to_strings_func: Callable[[tuple[int, int]], list[str]],
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str]converts a list of coordinates to a list of strings (tokens)
expects list[CoordTup] if when_noncoord is “error”
expects list[str | CoordTup] if when_noncoord is “include”
or “skip”
def get_token_regions(toks: list[str]) -> tuple[list[str], list[str]]def equal_except_adj_list_sequence(
rollout1: list[str],
rollout2: list[str],
do_except: bool = False,
when_counter_mismatch: muutils.errormode.ErrorMode = ErrorMode.Except,
when_len_mismatch: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolReturns if the rollout strings are equal, allowing for differently
sequenced adjacency lists. LatticeMaze object.
This function is not robustly correct for some corner cases using
CoordTokenizers.CTT. If rollouts are passed for identical
tokenizers processing two slightly different mazes, a false positive is
possible. More specifically, some cases of zero-sum adding and removing
of connections in a maze within square regions along the diagonal will
produce a false positive.
def connection_list_to_adj_list(
conn_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col'],
shuffle_d0: bool = True,
shuffle_d1: bool = True
) -> jaxtyping.Int8[ndarray, 'conn start_end=2 coord=2']converts a ConnectionList (special lattice format) to a
shuffled adjacency list
conn_list: ConnectionList special internal format for
graphs which are subgraphs of a latticeshuffle_d0: bool shuffle the adjacency list along the
0th axis (order of pairs)shuffle_d1: bool shuffle the adjacency list along the
1st axis (order of coordinates in each pair). If False, all
pairs have the smaller coord first.Int8[np.ndarray, "conn start_end=2 coord=2"] adjacency
list in the shape (n_connections, 2, 2)def is_connection(
edges: jaxtyping.Int8[ndarray, 'edges leading_trailing_coord=2 row_col=2'],
connection_list: jaxtyping.Bool[ndarray, 'lattice_dim=2 row col']
) -> jaxtyping.Bool[ndarray, 'is_connection=edges']Returns if each edge in edges is a connection
(True) or wall (False) in
connection_list.
docs for maze-dataset
v1.1.0
turning a maze into text
MazeTokenizerModular is the new recommended way to do
this as of 1.0.0TokenizationMode enum and
MazeTokenizer class for supporting existing codeTokenizationMode_TokenizerElementMazeTokenizerModularPromptSequencersCoordTokenizersAdjListTokenizersEdgeGroupingsEdgePermutersEdgeSubsetsTargetTokenizersStepSizesStepTokenizersPathTokenizerscoord_str_to_tupleget_tokens_up_to_path_startMazeTokenizermaze_dataset.tokenizationturning a maze into text
MazeTokenizerModular is the new recommended way to do
this as of 1.0.0TokenizationMode enum and
MazeTokenizer class for supporting existing codeclass TokenizationMode(enum.Enum):legacy tokenization modes
[!CAUTION] Legacy mode of tokenization. will still be around in future releases, but is no longer recommended for use. Use
MazeTokenizerModularinstead.
AOTP: Ajacency list, Origin, Target, PathUT: Unique Token (for each coordiate)CTT: Coordinate Tuple Tokens (each coordinate is
tokenized as a tuple of integers)AOTP_UT_rasterized: the “classic” mode: assigning
tokens to each coordinate is done via rasterization example: for a 3x3
maze, token order is
(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)
AOTP_UT_uniform: new mode, where a 3x3 tokenization
scheme and 5x5 tokenizations scheme are compatible uses
corner_first_ndindex function to order the tokens
AOTP_CTT_indexed: each coordinate is a tuple of
integers
AOTP_UT_rasterized = <TokenizationMode.AOTP_UT_rasterized: 'AOTP_UT_rasterized'>
AOTP_UT_uniform = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
AOTP_CTT_indexed = <TokenizationMode.AOTP_CTT_indexed: 'AOTP_CTT_indexed'>
def to_legacy_tokenizer(self, max_grid_size: int | None = None)class _TokenizerElement(muutils.json_serialize.serializable_dataclass.SerializableDataclass, abc.ABC):Superclass for tokenizer elements. Subclasses contain modular functionality for maze tokenization.
[!TIP] Due to the functionality of
get_all_tokenizers(),_TokenizerElementsubclasses may only contain fields of typeutils.FiniteValued. Implementing a subclass with anintorfloat-typed field, for example, is not supported. In the event that adding such fields is deemed necessary,get_all_tokenizers()must be updated.
name: strdef tokenizer_elements(
self,
deep: bool = True
) -> list[maze_dataset.tokenization.maze_tokenizer._TokenizerElement]Returns a list of all _TokenizerElement instances
contained in the subtree. Currently only detects
_TokenizerElement instances which are either direct
attributes of another instance or which sit inside a tuple
without further nesting.
deep: bool: Whether to return elements nested
arbitrarily deeply or just a single layer.def tokenizer_element_tree(self, depth: int = 0, abstract: bool = False) -> strReturns a string representation of the tree of tokenizer elements
contained in self.
depth: int: Current depth in the tree. Used internally
for recursion, no need to specify.abstract: bool: Whether to print the name of the
abstract base class or the concrete class for each
_TokenizerElement instance.def tokenizer_element_dict(self) -> dictReturns a dictionary representation of the tree of tokenizer elements
contained in self.
def attribute_key(cls) -> strReturns the binding used in MazeTokenizerModular for
that type of _TokenizerElement.
def to_tokens(self, *args, **kwargs) -> list[str]Converts a maze element into a list of tokens. Not all
_TokenizerElement subclasses produce tokens, so this is not
an abstract method. Those subclasses which do produce tokens should
override this method.
def is_valid(self) -> boolReturns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeTokenizerModular(muutils.json_serialize.serializable_dataclass.SerializableDataclass):Tokenizer for mazes
prompt_sequencer: Tokenizer element which assembles
token regions (adjacency list, origin, target, path) into a complete
prompt.TokenizationMode.AOTP_UT_Uniform.from_legacy must
also be maintained.MazeTokenizerModular or the
_TokenizerElement hierarchy must maintain that
behavior.MazeTokenizerModular(
*,
prompt_sequencer: maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer = PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))
)prompt_sequencer: maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer = PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))def hash_int(self) -> intdef hash_b64(self, n_bytes: int = 8) -> strfilename-safe base64 encoding of the hash
tokenizer_elements: list[maze_dataset.tokenization.maze_tokenizer._TokenizerElement]def tokenizer_element_tree(self, abstract: bool = False) -> strReturns a string representation of the tree of tokenizer elements
contained in self.
abstract: bool: Whether to print the name of the
abstract base class or the concrete class for each
_TokenizerElement instance.
tokenizer_element_tree_concrete
Property wrapper for tokenizer_element_tree so that it
can be used in properties_to_serialize.
def tokenizer_element_dict(self) -> dictNested dictionary of the internal TokenizerElements.
name: strSerializes MazeTokenizer into a key for encoding in zanj
def summary(self) -> dict[str, str]Single-level dictionary of the internal
TokenizerElements.
def has_element(
self,
*elements: Sequence[type[maze_dataset.tokenization.maze_tokenizer._TokenizerElement] | maze_dataset.tokenization.maze_tokenizer._TokenizerElement]
) -> boolReturns True if the MazeTokenizerModular instance
contains ALL of the items specified in elements.
Querying with a partial subset of _TokenizerElement
fields is not currently supported. To do such a query, assemble multiple
calls to has_elements.
elements: Singleton or iterable of
_TokenizerElement instances or classes. If an instance is
provided, then comparison is done via instance equality. If a class is
provided, then comparison isdone via isinstance. I.e., any
instance of that class is accepted.def is_valid(self)Returns True if self is a valid tokenizer.
Evaluates the validity of all of self.tokenizer_elements
according to each one’s method.
def is_legacy_equivalent(self) -> boolReturns if self has identical stringification behavior
as any legacy MazeTokenizer.
def is_tested_tokenizer(self, do_assert: bool = False) -> boolReturns if the tokenizer is returned by
all_tokenizers.get_all_tokenizers, the set of tested and
reliable tokenizers.
Since evaluating all_tokenizers.get_all_tokenizers is
expensive, instead checks for membership of self’s hash in
get_all_tokenizer_hashes().
if do_assert is True, raises an
AssertionError if the tokenizer is not tested.
def is_AOTP(self) -> booldef is_UT(self) -> booldef from_legacy(
cls,
legacy_maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode
) -> maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModularMaps a legacy MazeTokenizer or
TokenizationMode to its equivalent
MazeTokenizerModular instance.
def from_tokens(
cls,
tokens: str | list[str]
) -> maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModularInfers most MazeTokenizerModular parameters from a full
sequence of tokens.
token_arr: list[str] | Nonemap from index to token
tokenizer_map: dict[str, int]map from token to index
vocab_size: intNumber of tokens in the static vocab
n_tokens: intpadding_token_index: intdef to_tokens(self, maze: maze_dataset.maze.lattice_maze.LatticeMaze) -> list[str]Converts maze into a list of tokens.
def coords_to_strings(
self,
coords: list[tuple[int, int] | jaxtyping.Int8[ndarray, 'row_col']]
) -> list[str]def strings_to_coords(
text: str,
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str | tuple[int, int]]def encode(text: str | list[str]) -> list[int]encode a string or list of strings into a list of tokens
def decode(token_ids: Sequence[int], joined_tokens: bool = False) -> list[str] | strdecode a list of tokens into a string or list of strings
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PromptSequencers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _PromptSequencer subclass hierarchy used
by MazeTokenizerModular.
key = 'prompt_sequencer'class PromptSequencers.AOTP(maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer):Sequences a prompt as [adjacency list, origin, target, path].
target_tokenizer: Tokenizer element which tokenizes the
target(s) of a TargetedLatticeMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that TargetTokenizer.path_tokenizer: Tokenizer element which tokenizes the
solution path of a SolvedMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that PathTokenizer.PromptSequencers.AOTP(
*,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer = CoordTokenizers.UT(),
adj_list_tokenizer: maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer = AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOTP'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOTP'>",
target_tokenizer: maze_dataset.tokenization.maze_tokenizer.TargetTokenizers._TargetTokenizer = TargetTokenizers.Unlabeled(post=False),
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
)target_tokenizer: maze_dataset.tokenization.maze_tokenizer.TargetTokenizers._TargetTokenizer = TargetTokenizers.Unlabeled(post=False)
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PromptSequencers.AOP(maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer):Sequences a prompt as [adjacency list, origin, path]. Still includes
“
path_tokenizer: Tokenizer element which tokenizes the
solution path of a SolvedMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that PathTokenizer.PromptSequencers.AOP(
*,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer = CoordTokenizers.UT(),
adj_list_tokenizer: maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer = AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOP'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOP'>",
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
)path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class CoordTokenizers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _CoordTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'coord_tokenizer'class CoordTokenizers.UT(maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer):Unique token coordinate tokenizer.
CoordTokenizers.UT(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.UT'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.UT'>"
)def to_tokens(
self,
coord: jaxtyping.Int8[ndarray, 'row_col'] | tuple[int, int]
) -> list[str]Converts a maze element into a list of tokens. Not all
_TokenizerElement subclasses produce tokens, so this is not
an abstract method. Those subclasses which do produce tokens should
override this method.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class CoordTokenizers.CTT(maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer):Coordinate tuple tokenizer
pre: Whether all coords include an integral preceding
delimiter tokenintra: Whether all coords include a delimiter token
between coordinatespost: Whether all coords include an integral following
delimiter tokenCoordTokenizers.CTT(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.CTT'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.CTT'>",
pre: bool = True,
intra: bool = True,
post: bool = True
)pre: bool = True
intra: bool = True
post: bool = True
def to_tokens(
self,
coord: jaxtyping.Int8[ndarray, 'row_col'] | tuple[int, int]
) -> list[str]Converts a maze element into a list of tokens. Not all
_TokenizerElement subclasses produce tokens, so this is not
an abstract method. Those subclasses which do produce tokens should
override this method.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class AdjListTokenizers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _AdjListTokenizer subclass hierarchy used
by MazeTokenizerModular.
key = 'adj_list_tokenizer'class AdjListTokenizers.AdjListCoord(maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer):Represents an edge group as tokens for the leading coord followed by coord tokens for the other group members.
AdjListTokenizers.AdjListCoord(
*,
pre: bool = False,
post: bool = True,
shuffle_d0: bool = True,
edge_grouping: maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping = EdgeGroupings.Ungrouped(connection_token_ordinal=1),
edge_subset: maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset = EdgeSubsets.ConnectionEdges(walls=False),
edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.RandomCoords(),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCoord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCoord'>"
)edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.RandomCoords()def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class AdjListTokenizers.AdjListCardinal(maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer):Represents an edge group as coord tokens for the leading coord and cardinal tokens relative to the leading coord for the other group members.
coord_first: Whether the leading coord token(s) should
come before or after the sequence of cardinal tokens.AdjListTokenizers.AdjListCardinal(
*,
pre: bool = False,
post: bool = True,
shuffle_d0: bool = True,
edge_grouping: maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping = EdgeGroupings.Ungrouped(connection_token_ordinal=1),
edge_subset: maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset = EdgeSubsets.ConnectionEdges(walls=False),
edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.BothCoords(),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCardinal'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCardinal'>"
)edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.BothCoords()def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeGroupings(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _EdgeGrouping subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_grouping'class EdgeGroupings.Ungrouped(maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping):No grouping occurs, each edge is tokenized individually.
connection_token_ordinal: At which index in the edge
tokenization the connector (or wall) token appears. Edge tokenizations
contain 3 parts: a leading coord, a connector (or wall) token, and
either a second coord or cardinal direction tokenization.EdgeGroupings.Ungrouped(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.Ungrouped'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.Ungrouped'>",
connection_token_ordinal: Literal[0, 1, 2] = 1
)connection_token_ordinal: Literal[0, 1, 2] = 1def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeGroupings.ByLeadingCoord(maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping):All edges with the same leading coord are grouped together.
intra: Whether all edge groupings include a delimiter
token between individual edge representations. Note that each edge
representation will already always include a connector token
(VOCAB.CONNECTOR, or possibly `)shuffle_group: Whether the sequence of edges within the
group should be shuffled or appear in a fixed order. If false, the fixed
order is lexicographical by (row, col). In effect, lexicographical
sorting sorts edges by their cardinal direction in the sequence NORTH,
WEST, EAST, SOUTH, where the directions indicate the position of the
trailing coord relative to the leading coord.connection_token_ordinal: At which index in token
sequence representing a single edge the connector (or wall) token
appears. Edge tokenizations contain 2 parts: a connector (or wall) token
and a coord or cardinal tokenization.EdgeGroupings.ByLeadingCoord(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.ByLeadingCoord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.ByLeadingCoord'>",
intra: bool = True,
shuffle_group: bool = True,
connection_token_ordinal: Literal[0, 1] = 0
)intra: bool = True
shuffle_group: bool = True
connection_token_ordinal: Literal[0, 1] = 0
def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _EdgePermuter subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_permuter'class EdgePermuters.SortedCoords(maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter):returns a sorted representation. useful for checking consistency
EdgePermuters.SortedCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.SortedCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.SortedCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters.RandomCoords(maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter):Permutes each edge randomly.
EdgePermuters.RandomCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.RandomCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.RandomCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters.BothCoords(maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter):Includes both possible permutations of every edge in the output.
Since input ConnectionList has only 1 instance of each edge, a call to
BothCoords._permute will modify lattice_edges
in-place, doubling shape[0].
EdgePermuters.BothCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.BothCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.BothCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeSubsets(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _EdgeSubset subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_subset'class EdgeSubsets.AllLatticeEdges(maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset):All 2n**2-2n edges of the lattice are tokenized. If a wall exists on
that edge, the edge is tokenized in the same manner, using
VOCAB.ADJLIST_WALL in place of
VOCAB.CONNECTOR.
EdgeSubsets.AllLatticeEdges(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.AllLatticeEdges'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.AllLatticeEdges'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeSubsets.ConnectionEdges(maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset):Only edges which contain a connection are tokenized. Alternatively, only edges which contain a wall are tokenized.
walls: Whether wall edges or connection edges are
tokenized. If true, VOCAB.ADJLIST_WALL is used in place of
VOCAB.CONNECTOR.EdgeSubsets.ConnectionEdges(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.ConnectionEdges'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.ConnectionEdges'>",
walls: bool = False
)walls: bool = Falsedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class TargetTokenizers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _TargetTokenizer subclass hierarchy used
by MazeTokenizerModular.
key = 'target_tokenizer'class TargetTokenizers.Unlabeled(maze_dataset.tokenization.maze_tokenizer.TargetTokenizers._TargetTokenizer):Targets are simply listed as coord tokens. - post:
Whether all coords include an integral following delimiter token
TargetTokenizers.Unlabeled(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.TargetTokenizers.Unlabeled'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.TargetTokenizers.Unlabeled'>",
post: bool = False
)post: bool = Falsedef to_tokens(
self,
targets: Sequence[jaxtyping.Int8[ndarray, 'row_col']],
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Returns tokens representing the target.
def is_valid(self) -> boolReturns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _StepSize subclass hierarchy used by
MazeTokenizerModular.
key = 'step_size'class StepSizes.Singles(maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize):Every coord in maze.solution is represented. Legacy
tokenizers all use this behavior.
StepSizes.Singles(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Singles'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Singles'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.Straightaways(maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize):Only coords where the path turns are represented in the path. I.e., the path is represented as a sequence of straightaways, specified by the coords at the turns.
StepSizes.Straightaways(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Straightaways'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Straightaways'>"
)def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.Forks(maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize):Only coords at forks, where the path has >=2 options for the next step are included. Excludes the option of backtracking. The starting and ending coords are always included.
StepSizes.Forks(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Forks'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Forks'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.ForksAndStraightaways(maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize):Includes the union of the coords included by Forks and
Straightaways. See documentation for those classes for
details.
StepSizes.ForksAndStraightaways(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.ForksAndStraightaways'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.ForksAndStraightaways'>"
)def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _StepTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'step_tokenizers'
StepTokenizerPermutation: type = tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer]
class StepTokenizers.Coord(maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer):A direct tokenization of the end position coord represents the step.
StepTokenizers.Coord(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Coord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Coord'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Cardinal(maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer):A step is tokenized with a cardinal direction token. It is the direction of the step from the starting position along the solution.
StepTokenizers.Cardinal(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Cardinal'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Cardinal'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Relative(maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer):Tokenizes a solution step using relative first-person directions
(right, left, forward, etc.). To simplify the indeterminacy, at the
start of a solution the “agent” solving the maze is assumed to be facing
NORTH. Similarly to Cardinal, the direction is that of the
step from the starting position.
StepTokenizers.Relative(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Relative'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Relative'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Distance(maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer):A count of the number of individual steps from the starting point to
the end point. Contains no information about directionality, only the
distance traveled in the step. Distance must be combined
with at least one other _StepTokenizer in a
StepTokenizerPermutation. This constraint is enforced in
_PathTokenizer.is_valid.
StepTokenizers.Distance(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Distance'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Distance'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PathTokenizers(maze_dataset.tokenization.maze_tokenizer.__TokenizerElementNamespace):Namespace for _PathTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'path_tokenizer'class PathTokenizers.StepSequence(maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer, abc.ABC):Any PathTokenizer where the tokenization may be
assembled from token subsequences, each of which represents a step along
the path. Allows for a sequence of leading and trailing tokens which
don’t fit the step pattern.
step_size: Selects the size of a single step in the
sequencestep_tokenizers: Selects the combination and
permutation of tokenspre: Whether all steps include an integral preceding
delimiter tokenintra: Whether all steps include a delimiter token
after each individual _StepTokenizer tokenization.post: Whether all steps include an integral following
delimiter tokenPathTokenizers.StepSequence(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PathTokenizers.StepSequence'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PathTokenizers.StepSequence'>",
step_size: maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize = StepSizes.Singles(),
step_tokenizers: tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] = (StepTokenizers.Coord(),),
pre: bool = False,
intra: bool = False,
post: bool = False
)step_size: maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize = StepSizes.Singles()
step_tokenizers: tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] = (StepTokenizers.Coord(),)
pre: bool = False
intra: bool = False
post: bool = False
def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Returns tokens representing the solution path.
def is_valid(self) -> boolReturns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
coord_str_to_tuple
def get_tokens_up_to_path_start(
tokens: list[str],
include_start_coord: bool = True,
tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
) -> list[str]class MazeTokenizer(muutils.json_serialize.serializable_dataclass.SerializableDataclass):LEGACY Tokenizer for mazes
[!CAUTION]
MazeTokenizerModularis the new standard for tokenization. This class is no longer recommended for use, but will remain for compatibility with existing code.
tokenization_mode: TokenizationMode mode of
tokenization. required.max_grid_size: int | None maximum grid size. required
for actually turning text tokens to numerical tokens, but not for moving
between coordinates/mazes and textname: str auto-generated name of the tokenizer from
mode and sizenode_strings_map: Mapping[CoordTup, str] map from node
to string. This returns a muutils.kappa.Kappa object which
you can use like a dictionary. returns None if not a
UT modethese all return None if max_grid_size is
None. Prepend _ to the name to get a
guaranteed type, and cause an exception if max_grid_size is
None
token_arr: list[str] list of tokens, in order of their
indices in the vocabularytokenizer_map: Mapping[str, int] map from token to
indexvocab_size: int size of the vocabularypadding_token_index: int index of the padding
tokencoords_to_strings(coords: list[CoordTup]) -> list[str]
convert a list of coordinates to a list of tokens. Optionally except,
skip, or ignore non-coordinatesstrings_to_coords(strings: list[str]) -> list[CoordTup]
convert a list of tokens to a list of coordinates. Optionally except,
skip, or ignore non-coordinatesMazeTokenizer(
*,
tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>,
max_grid_size: int | None = None
)tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
max_grid_size: int | None = None
name: str
node_strings_map: Optional[Mapping[tuple[int, int], list[str]]]map a coordinate to a token
token_arr: list[str] | Nonetokenizer_map: dict[str, int] | Nonevocab_size: int | Nonen_tokens: int | Nonepadding_token_index: int | Nonedef coords_to_strings(
self,
coords: list[tuple[int, int]],
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str]def strings_to_coords(
text: str,
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str | tuple[int, int]]def encode(self, text: str | list[str]) -> list[int]encode a string or list of strings into a list of tokens
def decode(
self,
tokens: Sequence[int],
joined_tokens: bool = False
) -> list[str] | strdecode a list of tokens into a string or list of strings
coordinate_tokens_coords: dict[tuple[int, int], int]coordinate_tokens_ids: dict[str, int]def summary(self) -> dictreturns a summary of the tokenization mode
def is_AOTP(self) -> boolreturns true if a tokenization mode is Adjacency list, Origin, Target, Path
def is_UT(self) -> booldef clear_cache(self)clears all cached properties
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
docs for maze-dataset
v1.1.0
Contains get_all_tokenizers() and supporting limited-use
functions.
get_all_tokenizers()returns a comprehensive collection of all valid
MazeTokenizerModular objects. This is an overwhelming
majority subset of the set of all possible
MazeTokenizerModular objects. Other tokenizers not
contained in get_all_tokenizers() may be possible to
construct, but they are untested and not guaranteed to work. This
collection is in a separate module since it is expensive to compute and
will grow more expensive as features are added to
MazeTokenizerModular.
In general, uses for this module are limited to development of the
library and specific research studying many tokenization behaviors. -
Unit testing: - Tokenizers to use in unit tests are sampled from
get_all_tokenizers() - Large-scale tokenizer research: -
Specific research training models on many tokenization behaviors can use
get_all_tokenizers() as the maximally inclusive collection
- get_all_tokenizers() may be subsequently filtered using
MazeTokenizerModular.has_element For other uses, it’s
likely that the computational expense can be avoided by using -
maze_tokenizer.get_all_tokenizer_hashes() for membership
checks - utils.all_instances for generating smaller subsets
of MazeTokenizerModular or _TokenizerElement
objects
EVERY_TEST_TOKENIZERSA collection of the tokenizers which should always be included in unit tests when test fuzzing is used. This collection should be expanded as specific tokenizers become canonical or popular.
MAZE_TOKENIZER_MODULAR_DEFAULT_VALIDATION_FUNCSget_all_tokenizersEVERY_TEST_TOKENIZERSall_tokenizers_setsample_all_tokenizerssample_tokenizers_for_testsave_hashesmaze_dataset.tokenization.all_tokenizersContains get_all_tokenizers() and supporting limited-use
functions.
get_all_tokenizers()returns a comprehensive collection of all valid
MazeTokenizerModular objects. This is an overwhelming
majority subset of the set of all possible
MazeTokenizerModular objects. Other tokenizers not
contained in get_all_tokenizers() may be possible to
construct, but they are untested and not guaranteed to work. This
collection is in a separate module since it is expensive to compute and
will grow more expensive as features are added to
MazeTokenizerModular.
In general, uses for this module are limited to development of the
library and specific research studying many tokenization behaviors. -
Unit testing: - Tokenizers to use in unit tests are sampled from
get_all_tokenizers() - Large-scale tokenizer research: -
Specific research training models on many tokenization behaviors can use
get_all_tokenizers() as the maximally inclusive collection
- get_all_tokenizers() may be subsequently filtered using
MazeTokenizerModular.has_element For other uses, it’s
likely that the computational expense can be avoided by using -
maze_tokenizer.get_all_tokenizer_hashes() for membership
checks - utils.all_instances for generating smaller subsets
of MazeTokenizerModular or _TokenizerElement
objects
EVERY_TEST_TOKENIZERSA collection of the tokenizers which should always be included in unit tests when test fuzzing is used. This collection should be expanded as specific tokenizers become canonical or popular.
MAZE_TOKENIZER_MODULAR_DEFAULT_VALIDATION_FUNCS: frozendict.frozendict[type[~FiniteValued], typing.Callable[[~FiniteValued], bool]] = frozendict.frozendict({<class 'maze_dataset.tokenization.maze_tokenizer._TokenizerElement'>: <function <lambda>>, tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer]: <function <lambda>>})def get_all_tokenizers() -> list[maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular]Computes a complete list of all valid tokenizers. Warning: This is an expensive function.
EVERY_TEST_TOKENIZERS: list[maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular] = [MazeTokenizerModular(prompt_sequencer=PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))), MazeTokenizerModular(prompt_sequencer=PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.CTT(pre=True, intra=True, post=True), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)))]def all_tokenizers_set() -> set[maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular]Casts get_all_tokenizers() to a set.
def sample_all_tokenizers(
n: int
) -> list[maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular]Samples n tokenizers from
get_all_tokenizers().
def sample_tokenizers_for_test(
n: int | None
) -> list[maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModular]Returns a sample of size n of unique elements from
get_all_tokenizers(), always including every element in
EVERY_TEST_TOKENIZERS.
def save_hashes(
path: pathlib.Path | None = None,
verbose: bool = False,
parallelize: bool | int = False
) -> jaxtyping.Int64[ndarray, 'tokenizers']Computes, sorts, and saves the hashes of every member of
get_all_tokenizers().
docs for maze-dataset
v1.1.0
turning a maze into text: MazeTokenizerModular and the
legacy TokenizationMode enum and MazeTokenizer
class
TokenErrorTokenizationModeis_UTget_tokens_up_to_path_startMazeTokenizermark_as_unsupportedCoordTokenizersEdgeGroupingsEdgePermutersEdgeSubsetsAdjListTokenizersTargetTokenizersStepSizesStepTokenizersPathTokenizersPromptSequencersMazeTokenizerModularset_tokenizer_hashes_pathget_all_tokenizer_hashesmaze_dataset.tokenization.maze_tokenizerturning a maze into text: MazeTokenizerModular and the
legacy TokenizationMode enum and MazeTokenizer
class
class TokenError(builtins.ValueError):error for tokenization
class TokenizationMode(enum.Enum):legacy tokenization modes
[!CAUTION] Legacy mode of tokenization. will still be around in future releases, but is no longer recommended for use. Use
MazeTokenizerModularinstead.
AOTP: Ajacency list, Origin, Target, PathUT: Unique Token (for each coordiate)CTT: Coordinate Tuple Tokens (each coordinate is
tokenized as a tuple of integers)AOTP_UT_rasterized: the “classic” mode: assigning
tokens to each coordinate is done via rasterization example: for a 3x3
maze, token order is
(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)
AOTP_UT_uniform: new mode, where a 3x3 tokenization
scheme and 5x5 tokenizations scheme are compatible uses
corner_first_ndindex function to order the tokens
AOTP_CTT_indexed: each coordinate is a tuple of
integers
AOTP_UT_rasterized = <TokenizationMode.AOTP_UT_rasterized: 'AOTP_UT_rasterized'>
AOTP_UT_uniform = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
AOTP_CTT_indexed = <TokenizationMode.AOTP_CTT_indexed: 'AOTP_CTT_indexed'>
def to_legacy_tokenizer(self, max_grid_size: int | None = None)def is_UT(
tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode
) -> booldef get_tokens_up_to_path_start(
tokens: list[str],
include_start_coord: bool = True,
tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
) -> list[str]class MazeTokenizer(muutils.json_serialize.serializable_dataclass.SerializableDataclass):LEGACY Tokenizer for mazes
[!CAUTION]
MazeTokenizerModularis the new standard for tokenization. This class is no longer recommended for use, but will remain for compatibility with existing code.
tokenization_mode: TokenizationMode mode of
tokenization. required.max_grid_size: int | None maximum grid size. required
for actually turning text tokens to numerical tokens, but not for moving
between coordinates/mazes and textname: str auto-generated name of the tokenizer from
mode and sizenode_strings_map: Mapping[CoordTup, str] map from node
to string. This returns a muutils.kappa.Kappa object which
you can use like a dictionary. returns None if not a
UT modethese all return None if max_grid_size is
None. Prepend _ to the name to get a
guaranteed type, and cause an exception if max_grid_size is
None
token_arr: list[str] list of tokens, in order of their
indices in the vocabularytokenizer_map: Mapping[str, int] map from token to
indexvocab_size: int size of the vocabularypadding_token_index: int index of the padding
tokencoords_to_strings(coords: list[CoordTup]) -> list[str]
convert a list of coordinates to a list of tokens. Optionally except,
skip, or ignore non-coordinatesstrings_to_coords(strings: list[str]) -> list[CoordTup]
convert a list of tokens to a list of coordinates. Optionally except,
skip, or ignore non-coordinatesMazeTokenizer(
*,
tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>,
max_grid_size: int | None = None
)tokenization_mode: maze_dataset.tokenization.maze_tokenizer.TokenizationMode = <TokenizationMode.AOTP_UT_uniform: 'AOTP_UT_uniform'>
max_grid_size: int | None = None
name: str
node_strings_map: Optional[Mapping[tuple[int, int], list[str]]]map a coordinate to a token
token_arr: list[str] | Nonetokenizer_map: dict[str, int] | Nonevocab_size: int | Nonen_tokens: int | Nonepadding_token_index: int | Nonedef coords_to_strings(
self,
coords: list[tuple[int, int]],
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str]def strings_to_coords(
text: str,
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str | tuple[int, int]]def encode(self, text: str | list[str]) -> list[int]encode a string or list of strings into a list of tokens
def decode(
self,
tokens: Sequence[int],
joined_tokens: bool = False
) -> list[str] | strdecode a list of tokens into a string or list of strings
coordinate_tokens_coords: dict[tuple[int, int], int]coordinate_tokens_ids: dict[str, int]def summary(self) -> dictreturns a summary of the tokenization mode
def is_AOTP(self) -> boolreturns true if a tokenization mode is Adjacency list, Origin, Target, Path
def is_UT(self) -> booldef clear_cache(self)clears all cached properties
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
def mark_as_unsupported(is_valid: Callable[[~T], bool], *args) -> ~Tmark a _TokenizerElement as unsupported.
Classes marked with this decorator won’t show up in
get_all_tokenizers() and thus wont be tested. The classes
marked in release 1.0.0 did work reliably before being marked, but they
can’t be instantiated since the decorator adds an abstract method. The
decorator exists to prune the space of tokenizers returned by
all_instances both for testing and usage. Previously, the
space was too large, resulting in impractical runtimes. These decorators
could be removed in future releases to expand the space of possible
tokenizers.
class CoordTokenizers(__TokenizerElementNamespace):Namespace for _CoordTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'coord_tokenizer'class CoordTokenizers.UT(CoordTokenizers._CoordTokenizer):Unique token coordinate tokenizer.
CoordTokenizers.UT(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.UT'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.UT'>"
)def to_tokens(
self,
coord: jaxtyping.Int8[ndarray, 'row_col'] | tuple[int, int]
) -> list[str]Converts a maze element into a list of tokens. Not all
_TokenizerElement subclasses produce tokens, so this is not
an abstract method. Those subclasses which do produce tokens should
override this method.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class CoordTokenizers.CTT(CoordTokenizers._CoordTokenizer):Coordinate tuple tokenizer
pre: Whether all coords include an integral preceding
delimiter tokenintra: Whether all coords include a delimiter token
between coordinatespost: Whether all coords include an integral following
delimiter tokenCoordTokenizers.CTT(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.CTT'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.CoordTokenizers.CTT'>",
pre: bool = True,
intra: bool = True,
post: bool = True
)pre: bool = True
intra: bool = True
post: bool = True
def to_tokens(
self,
coord: jaxtyping.Int8[ndarray, 'row_col'] | tuple[int, int]
) -> list[str]Converts a maze element into a list of tokens. Not all
_TokenizerElement subclasses produce tokens, so this is not
an abstract method. Those subclasses which do produce tokens should
override this method.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeGroupings(__TokenizerElementNamespace):Namespace for _EdgeGrouping subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_grouping'class EdgeGroupings.Ungrouped(EdgeGroupings._EdgeGrouping):No grouping occurs, each edge is tokenized individually.
connection_token_ordinal: At which index in the edge
tokenization the connector (or wall) token appears. Edge tokenizations
contain 3 parts: a leading coord, a connector (or wall) token, and
either a second coord or cardinal direction tokenization.EdgeGroupings.Ungrouped(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.Ungrouped'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.Ungrouped'>",
connection_token_ordinal: Literal[0, 1, 2] = 1
)connection_token_ordinal: Literal[0, 1, 2] = 1def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeGroupings.ByLeadingCoord(EdgeGroupings._EdgeGrouping):All edges with the same leading coord are grouped together.
intra: Whether all edge groupings include a delimiter
token between individual edge representations. Note that each edge
representation will already always include a connector token
(VOCAB.CONNECTOR, or possibly `)shuffle_group: Whether the sequence of edges within the
group should be shuffled or appear in a fixed order. If false, the fixed
order is lexicographical by (row, col). In effect, lexicographical
sorting sorts edges by their cardinal direction in the sequence NORTH,
WEST, EAST, SOUTH, where the directions indicate the position of the
trailing coord relative to the leading coord.connection_token_ordinal: At which index in token
sequence representing a single edge the connector (or wall) token
appears. Edge tokenizations contain 2 parts: a connector (or wall) token
and a coord or cardinal tokenization.EdgeGroupings.ByLeadingCoord(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.ByLeadingCoord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeGroupings.ByLeadingCoord'>",
intra: bool = True,
shuffle_group: bool = True,
connection_token_ordinal: Literal[0, 1] = 0
)intra: bool = True
shuffle_group: bool = True
connection_token_ordinal: Literal[0, 1] = 0
def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters(__TokenizerElementNamespace):Namespace for _EdgePermuter subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_permuter'class EdgePermuters.SortedCoords(EdgePermuters._EdgePermuter):returns a sorted representation. useful for checking consistency
EdgePermuters.SortedCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.SortedCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.SortedCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters.RandomCoords(EdgePermuters._EdgePermuter):Permutes each edge randomly.
EdgePermuters.RandomCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.RandomCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.RandomCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgePermuters.BothCoords(EdgePermuters._EdgePermuter):Includes both possible permutations of every edge in the output.
Since input ConnectionList has only 1 instance of each edge, a call to
BothCoords._permute will modify lattice_edges
in-place, doubling shape[0].
EdgePermuters.BothCoords(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.BothCoords'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgePermuters.BothCoords'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeSubsets(__TokenizerElementNamespace):Namespace for _EdgeSubset subclass hierarchy used by
_AdjListTokenizer.
key = 'edge_subset'class EdgeSubsets.AllLatticeEdges(EdgeSubsets._EdgeSubset):All 2n**2-2n edges of the lattice are tokenized. If a wall exists on
that edge, the edge is tokenized in the same manner, using
VOCAB.ADJLIST_WALL in place of
VOCAB.CONNECTOR.
EdgeSubsets.AllLatticeEdges(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.AllLatticeEdges'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.AllLatticeEdges'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class EdgeSubsets.ConnectionEdges(EdgeSubsets._EdgeSubset):Only edges which contain a connection are tokenized. Alternatively, only edges which contain a wall are tokenized.
walls: Whether wall edges or connection edges are
tokenized. If true, VOCAB.ADJLIST_WALL is used in place of
VOCAB.CONNECTOR.EdgeSubsets.ConnectionEdges(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.ConnectionEdges'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.EdgeSubsets.ConnectionEdges'>",
walls: bool = False
)walls: bool = Falsedef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class AdjListTokenizers(__TokenizerElementNamespace):Namespace for _AdjListTokenizer subclass hierarchy used
by MazeTokenizerModular.
key = 'adj_list_tokenizer'class AdjListTokenizers.AdjListCoord(AdjListTokenizers._AdjListTokenizer):Represents an edge group as tokens for the leading coord followed by coord tokens for the other group members.
AdjListTokenizers.AdjListCoord(
*,
pre: bool = False,
post: bool = True,
shuffle_d0: bool = True,
edge_grouping: maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping = EdgeGroupings.Ungrouped(connection_token_ordinal=1),
edge_subset: maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset = EdgeSubsets.ConnectionEdges(walls=False),
edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.RandomCoords(),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCoord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCoord'>"
)edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.RandomCoords()def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class AdjListTokenizers.AdjListCardinal(AdjListTokenizers._AdjListTokenizer):Represents an edge group as coord tokens for the leading coord and cardinal tokens relative to the leading coord for the other group members.
coord_first: Whether the leading coord token(s) should
come before or after the sequence of cardinal tokens.AdjListTokenizers.AdjListCardinal(
*,
pre: bool = False,
post: bool = True,
shuffle_d0: bool = True,
edge_grouping: maze_dataset.tokenization.maze_tokenizer.EdgeGroupings._EdgeGrouping = EdgeGroupings.Ungrouped(connection_token_ordinal=1),
edge_subset: maze_dataset.tokenization.maze_tokenizer.EdgeSubsets._EdgeSubset = EdgeSubsets.ConnectionEdges(walls=False),
edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.BothCoords(),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCardinal'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers.AdjListCardinal'>"
)edge_permuter: maze_dataset.tokenization.maze_tokenizer.EdgePermuters._EdgePermuter = EdgePermuters.BothCoords()def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class TargetTokenizers(__TokenizerElementNamespace):Namespace for _TargetTokenizer subclass hierarchy used
by MazeTokenizerModular.
key = 'target_tokenizer'class TargetTokenizers.Unlabeled(TargetTokenizers._TargetTokenizer):Targets are simply listed as coord tokens. - post:
Whether all coords include an integral following delimiter token
TargetTokenizers.Unlabeled(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.TargetTokenizers.Unlabeled'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.TargetTokenizers.Unlabeled'>",
post: bool = False
)post: bool = Falsedef to_tokens(
self,
targets: Sequence[jaxtyping.Int8[ndarray, 'row_col']],
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Returns tokens representing the target.
def is_valid(self) -> boolReturns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes(__TokenizerElementNamespace):Namespace for _StepSize subclass hierarchy used by
MazeTokenizerModular.
key = 'step_size'class StepSizes.Singles(StepSizes._StepSize):Every coord in maze.solution is represented. Legacy
tokenizers all use this behavior.
StepSizes.Singles(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Singles'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Singles'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.Straightaways(StepSizes._StepSize):Only coords where the path turns are represented in the path. I.e., the path is represented as a sequence of straightaways, specified by the coords at the turns.
StepSizes.Straightaways(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Straightaways'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Straightaways'>"
)def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.Forks(StepSizes._StepSize):Only coords at forks, where the path has >=2 options for the next step are included. Excludes the option of backtracking. The starting and ending coords are always included.
StepSizes.Forks(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Forks'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.Forks'>"
)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepSizes.ForksAndStraightaways(StepSizes._StepSize):Includes the union of the coords included by Forks and
Straightaways. See documentation for those classes for
details.
StepSizes.ForksAndStraightaways(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.ForksAndStraightaways'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepSizes.ForksAndStraightaways'>"
)def is_valid(self_)Returns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers(__TokenizerElementNamespace):Namespace for _StepTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'step_tokenizers'
StepTokenizerPermutation: type = tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer]
class StepTokenizers.Coord(StepTokenizers._StepTokenizer):A direct tokenization of the end position coord represents the step.
StepTokenizers.Coord(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Coord'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Coord'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Cardinal(StepTokenizers._StepTokenizer):A step is tokenized with a cardinal direction token. It is the direction of the step from the starting position along the solution.
StepTokenizers.Cardinal(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Cardinal'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Cardinal'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Relative(StepTokenizers._StepTokenizer):Tokenizes a solution step using relative first-person directions
(right, left, forward, etc.). To simplify the indeterminacy, at the
start of a solution the “agent” solving the maze is assumed to be facing
NORTH. Similarly to Cardinal, the direction is that of the
step from the starting position.
StepTokenizers.Relative(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Relative'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Relative'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class StepTokenizers.Distance(StepTokenizers._StepTokenizer):A count of the number of individual steps from the starting point to
the end point. Contains no information about directionality, only the
distance traveled in the step. Distance must be combined
with at least one other _StepTokenizer in a
StepTokenizerPermutation. This constraint is enforced in
_PathTokenizer.is_valid.
StepTokenizers.Distance(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Distance'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.StepTokenizers.Distance'>"
)def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
start_index: int,
end_index: int,
**kwargs
) -> list[str]Tokenizes a single step in the solution.
maze: Maze to be tokenizedstart_index: The index of the Coord in
maze.solution at which the current step startsend_index: The index of the Coord in
maze.solution at which the current step endsdef serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PathTokenizers(__TokenizerElementNamespace):Namespace for _PathTokenizer subclass hierarchy used by
MazeTokenizerModular.
key = 'path_tokenizer'class PathTokenizers.StepSequence(PathTokenizers._PathTokenizer, abc.ABC):Any PathTokenizer where the tokenization may be
assembled from token subsequences, each of which represents a step along
the path. Allows for a sequence of leading and trailing tokens which
don’t fit the step pattern.
step_size: Selects the size of a single step in the
sequencestep_tokenizers: Selects the combination and
permutation of tokenspre: Whether all steps include an integral preceding
delimiter tokenintra: Whether all steps include a delimiter token
after each individual _StepTokenizer tokenization.post: Whether all steps include an integral following
delimiter tokenPathTokenizers.StepSequence(
*,
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PathTokenizers.StepSequence'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PathTokenizers.StepSequence'>",
step_size: maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize = StepSizes.Singles(),
step_tokenizers: tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] = (StepTokenizers.Coord(),),
pre: bool = False,
intra: bool = False,
post: bool = False
)step_size: maze_dataset.tokenization.maze_tokenizer.StepSizes._StepSize = StepSizes.Singles()
step_tokenizers: tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] | tuple[maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer, maze_dataset.tokenization.maze_tokenizer.StepTokenizers._StepTokenizer] = (StepTokenizers.Coord(),)
pre: bool = False
intra: bool = False
post: bool = False
def to_tokens(
self,
maze: maze_dataset.maze.lattice_maze.SolvedMaze,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer
) -> list[str]Returns tokens representing the solution path.
def is_valid(self) -> boolReturns if self contains data members capable of
producing an overall valid MazeTokenizerModular. Some
_TokenizerElement instances may be created which are not
useful despite obeying data member type hints. is_valid
allows for more precise detection of invalid
_TokenizerElements beyond type hinting alone. If type hints
are sufficient to constrain the possible instances of some subclass,
then this method may simply return True for that
subclass.
In nontrivial implementations of this method, each conditional clause
should contain a comment classifying the reason for invalidity and one
of the types below. Invalidity types, in ascending order of invalidity:
- Uninteresting: These tokenizers might be used to train functional
models, but the schemes are not interesting to study. E.g.,
_TokenizerElements which are strictly worse than some
alternative. - Duplicate: These tokenizers have identical tokenization
behavior as some other valid tokenizers. - Untrainable: Training
functional models using these tokenizers would be (nearly) impossible. -
Erroneous: These tokenizers might raise exceptions during use.
is_invalid is implemented to always return
True in some abstract classes where all currently possible
subclass instances are valid. When adding new subclasses or data
members, the developer should check if any such blanket statement of
validity still holds and update it as neccesary.
In general, when implementing this method, there is no need to
recursively call is_valid on nested
_TokenizerElements contained in the class. In other words,
failures of is_valid need not bubble up to the top of the
nested _TokenizerElement tree.
<a href="#MazeTokenizerModular.is_valid">MazeTokenizerModular.is_valid</a>
calls is_valid on each of its
_TokenizerElements individually, so failure at any level
will be detected.
If it’s judged to be useful, the types of invalidity could be
implemented with an Enum or similar rather than only living in comments.
This could be used to create more or less stringent filters on the valid
_TokenizerElement instances.
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PromptSequencers(__TokenizerElementNamespace):Namespace for _PromptSequencer subclass hierarchy used
by MazeTokenizerModular.
key = 'prompt_sequencer'class PromptSequencers.AOTP(PromptSequencers._PromptSequencer):Sequences a prompt as [adjacency list, origin, target, path].
target_tokenizer: Tokenizer element which tokenizes the
target(s) of a TargetedLatticeMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that TargetTokenizer.path_tokenizer: Tokenizer element which tokenizes the
solution path of a SolvedMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that PathTokenizer.PromptSequencers.AOTP(
*,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer = CoordTokenizers.UT(),
adj_list_tokenizer: maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer = AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOTP'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOTP'>",
target_tokenizer: maze_dataset.tokenization.maze_tokenizer.TargetTokenizers._TargetTokenizer = TargetTokenizers.Unlabeled(post=False),
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
)target_tokenizer: maze_dataset.tokenization.maze_tokenizer.TargetTokenizers._TargetTokenizer = TargetTokenizers.Unlabeled(post=False)
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class PromptSequencers.AOP(PromptSequencers._PromptSequencer):Sequences a prompt as [adjacency list, origin, path]. Still includes
“
path_tokenizer: Tokenizer element which tokenizes the
solution path of a SolvedMaze. Uses
coord_tokenizer to tokenize coords if that is part of the
design of that PathTokenizer.PromptSequencers.AOP(
*,
coord_tokenizer: maze_dataset.tokenization.maze_tokenizer.CoordTokenizers._CoordTokenizer = CoordTokenizers.UT(),
adj_list_tokenizer: maze_dataset.tokenization.maze_tokenizer.AdjListTokenizers._AdjListTokenizer = AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()),
_type_: Literal["<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOP'>"] = "<class 'maze_dataset.tokenization.maze_tokenizer.PromptSequencers.AOP'>",
path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)
)path_tokenizer: maze_dataset.tokenization.maze_tokenizer.PathTokenizers._PathTokenizer = PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False)def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
class MazeTokenizerModular(muutils.json_serialize.serializable_dataclass.SerializableDataclass):Tokenizer for mazes
prompt_sequencer: Tokenizer element which assembles
token regions (adjacency list, origin, target, path) into a complete
prompt.TokenizationMode.AOTP_UT_Uniform.from_legacy must
also be maintained.MazeTokenizerModular or the
_TokenizerElement hierarchy must maintain that
behavior.MazeTokenizerModular(
*,
prompt_sequencer: maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer = PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))
)prompt_sequencer: maze_dataset.tokenization.maze_tokenizer.PromptSequencers._PromptSequencer = PromptSequencers.AOTP(coord_tokenizer=CoordTokenizers.UT(), adj_list_tokenizer=AdjListTokenizers.AdjListCoord(pre=False, post=True, shuffle_d0=True, edge_grouping=EdgeGroupings.Ungrouped(connection_token_ordinal=1), edge_subset=EdgeSubsets.ConnectionEdges(walls=False), edge_permuter=EdgePermuters.RandomCoords()), target_tokenizer=TargetTokenizers.Unlabeled(post=False), path_tokenizer=PathTokenizers.StepSequence(step_size=StepSizes.Singles(), step_tokenizers=(StepTokenizers.Coord(),), pre=False, intra=False, post=False))def hash_int(self) -> intdef hash_b64(self, n_bytes: int = 8) -> strfilename-safe base64 encoding of the hash
tokenizer_elements: list[maze_dataset.tokenization.maze_tokenizer._TokenizerElement]def tokenizer_element_tree(self, abstract: bool = False) -> strReturns a string representation of the tree of tokenizer elements
contained in self.
abstract: bool: Whether to print the name of the
abstract base class or the concrete class for each
_TokenizerElement instance.
tokenizer_element_tree_concrete
Property wrapper for tokenizer_element_tree so that it
can be used in properties_to_serialize.
def tokenizer_element_dict(self) -> dictNested dictionary of the internal TokenizerElements.
name: strSerializes MazeTokenizer into a key for encoding in zanj
def summary(self) -> dict[str, str]Single-level dictionary of the internal
TokenizerElements.
def has_element(
self,
*elements: Sequence[type[maze_dataset.tokenization.maze_tokenizer._TokenizerElement] | maze_dataset.tokenization.maze_tokenizer._TokenizerElement]
) -> boolReturns True if the MazeTokenizerModular instance
contains ALL of the items specified in elements.
Querying with a partial subset of _TokenizerElement
fields is not currently supported. To do such a query, assemble multiple
calls to has_elements.
elements: Singleton or iterable of
_TokenizerElement instances or classes. If an instance is
provided, then comparison is done via instance equality. If a class is
provided, then comparison isdone via isinstance. I.e., any
instance of that class is accepted.def is_valid(self)Returns True if self is a valid tokenizer.
Evaluates the validity of all of self.tokenizer_elements
according to each one’s method.
def is_legacy_equivalent(self) -> boolReturns if self has identical stringification behavior
as any legacy MazeTokenizer.
def is_tested_tokenizer(self, do_assert: bool = False) -> boolReturns if the tokenizer is returned by
all_tokenizers.get_all_tokenizers, the set of tested and
reliable tokenizers.
Since evaluating all_tokenizers.get_all_tokenizers is
expensive, instead checks for membership of self’s hash in
get_all_tokenizer_hashes().
if do_assert is True, raises an
AssertionError if the tokenizer is not tested.
def is_AOTP(self) -> booldef is_UT(self) -> booldef from_legacy(
cls,
legacy_maze_tokenizer: maze_dataset.tokenization.maze_tokenizer.MazeTokenizer | maze_dataset.tokenization.maze_tokenizer.TokenizationMode
) -> maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModularMaps a legacy MazeTokenizer or
TokenizationMode to its equivalent
MazeTokenizerModular instance.
def from_tokens(
cls,
tokens: str | list[str]
) -> maze_dataset.tokenization.maze_tokenizer.MazeTokenizerModularInfers most MazeTokenizerModular parameters from a full
sequence of tokens.
token_arr: list[str] | Nonemap from index to token
tokenizer_map: dict[str, int]map from token to index
vocab_size: intNumber of tokens in the static vocab
n_tokens: intpadding_token_index: intdef to_tokens(self, maze: maze_dataset.maze.lattice_maze.LatticeMaze) -> list[str]Converts maze into a list of tokens.
def coords_to_strings(
self,
coords: list[tuple[int, int] | jaxtyping.Int8[ndarray, 'row_col']]
) -> list[str]def strings_to_coords(
text: str,
when_noncoord: Literal['except', 'skip', 'include'] = 'skip'
) -> list[str | tuple[int, int]]def encode(text: str | list[str]) -> list[int]encode a string or list of strings into a list of tokens
def decode(token_ids: Sequence[int], joined_tokens: bool = False) -> list[str] | strdecode a list of tokens into a string or list of strings
def serialize(self) -> dict[str, typing.Any]returns the class as a dict, implemented by using
@serializable_dataclass decorator
def load(cls, data: Union[dict[str, Any], ~T]) -> Type[~T]takes in an appropriately structured dict and returns an instance of
the class, implemented by using @serializable_dataclass
decorator
def validate_fields_types(
self: muutils.json_serialize.serializable_dataclass.SerializableDataclass,
on_typecheck_error: muutils.errormode.ErrorMode = ErrorMode.Except
) -> boolvalidate the types of all the fields on a
SerializableDataclass. calls
SerializableDataclass__validate_field_type for each
field
def set_tokenizer_hashes_path(path: pathlib.Path)set path to tokenizer hashes, and reload the hashes if needed
the hashes are expected to be stored in and read from
_TOKENIZER_HASHES_PATH, which by default is
Path(__file__).parent / "MazeTokenizerModular_hashes.npz"
or in this file’s directory.
However, this might not always work, so we provide a way to change this.
def get_all_tokenizer_hashes() -> jaxtyping.Int64[ndarray, 'n_tokenizers']docs for maze-dataset
v1.1.0
generate and save the hashes of all supported tokenizers
calls
maze_dataset.tokenization.all_tokenizers.save_hashes()
Usage:
To save to the default location (inside package,
maze_dataset/tokenization/MazeTokenizerModular_hashes.npy):
python -m maze_dataset.tokenization.save_hashesto save to a custom location:
python -m maze_dataset.tokenization.save_hashes /path/to/save/to.npyto check hashes shipped with the package:
python -m maze_dataset.tokenization.save_hashes --checkmaze_dataset.tokenization.save_hashesgenerate and save the hashes of all supported tokenizers
calls
<a href="all_tokenizers.html#save_hashes">maze_dataset.tokenization.all_tokenizers.save_hashes()</a>
Usage:
To save to the default location (inside package,
maze_dataset/tokenization/MazeTokenizerModular_hashes.npy):
python -m <a href="">maze_dataset.tokenization.save_hashes</a>to save to a custom location:
python -m <a href="">maze_dataset.tokenization.save_hashes</a> /path/to/save/to.npyto check hashes shipped with the package:
python -m <a href="">maze_dataset.tokenization.save_hashes</a> --checkdocs for maze-dataset
v1.1.0
misc utilities for the maze_dataset package
bool_array_from_stringcorner_first_ndindexmanhattan_distancelattice_max_degreeslattice_connection_arrayadj_list_to_nested_setFiniteValuedall_instancesmaze_dataset.utilsmisc utilities for the maze_dataset package
def bool_array_from_string(
string: str,
shape: list[int],
true_symbol: str = 'T'
) -> jaxtyping.Bool[ndarray, '*shape']Transform a string into an ndarray of bools.
string: str The string representation of the array shape: list[int] The shape of the resulting array true_symbol: The character to parse as True. Whitespace will be removed. All other characters will be parsed as False.
np.ndarray A ndarray with dtype bool of shape shape
bool_array_from_string( … “TT TF”, shape=[2,2] … ) array([[ True, True], [ True, False]])
def corner_first_ndindex(n: int, ndim: int = 2) -> list[tuple]returns an array of indices, sorted by distance from the corner
this gives the property that np.ndindex((n,n)) is equal
to the first n^2 elements of np.ndindex((n+1, n+1))
>>> corner_first_ndindex(1)
[(0, 0)]
>>> corner_first_ndindex(2)
[(0, 0), (0, 1), (1, 0), (1, 1)]
>>> corner_first_ndindex(3)
[(0, 0), (0, 1), (1, 0), (1, 1), (0, 2), (2, 0), (1, 2), (2, 1), (2, 2)]def manhattan_distance(
edges: jaxtyping.Int[ndarray, 'edges coord=2 row_col=2'] | jaxtyping.Int[ndarray, 'coord=2 row_col=2']
) -> jaxtyping.Int[ndarray, 'edges'] | jaxtyping.Int[ndarray, '']Returns the Manhattan distance between two coords.
def lattice_max_degrees(n: int) -> jaxtyping.Int8[ndarray, 'row col']Returns an array with the maximum possible degree for each coord.
def lattice_connection_array(
n: int
) -> jaxtyping.Int8[ndarray, 'edges=2*n*(n-1) leading_trailing_coord=2 row_col=2']Returns a 3D NumPy array containing all the edges in a 2D square lattice of size n x n. Thanks Claude.
n: The size of the square lattice.np.ndarray: A 3D NumPy array of shape containing the coordinates of the edges in the 2D square lattice. In each pair, the coord with the smaller sum always comes first.
def adj_list_to_nested_set(adj_list: list) -> setUsed for comparison of adj_lists
Adj_list looks like [[[0, 1], [1, 1]], [[0, 0], [0, 1]], …] We don’t care about order of coordinate pairs within the adj_list or coordinates within each coordinate pair.
FiniteValued = ~FiniteValuedFiniteValuedThe details of this type are not possible to fully define via the
Python 3.10 typing library. This custom generic type is a generic domain
of many types which have a finite, discrete, and well-defined range
space. FiniteValued defines the domain of supported types
for the all_instances function, since that function relies
heavily on static typing. These types may be nested in an arbitrarily
deep tree via Container Types and Superclass Types (see below). The
leaves of the tree must always be Primitive Types.
FiniteValued Subtypes*: Indicates that this subtype is not yet supported by
all_instances
FiniteValued
(Unbounded) TypesThese are NOT valid subtypes, and are listed for illustrative
purposes only. This list is not comprehensive. While the finite and
discrete nature of digital computers means that the cardinality of these
types is technically finite, they are considered unbounded types in this
context. - No Container subtype may contain any of these unbounded
subtypes. - int - float - str -
list - set: Set types without a
FiniteValued argument are unbounded - tuple:
Tuple types without a fixed length are unbounded
Primitive types are non-nested types which resolve directly to a
concrete range of values - bool: has 2 possible values -
*enum.Enum: The range of a concrete Enum
subclass is its set of enum members - typing.Literal: Every
type constructed using Literal has a finite set of possible
literal values in its definition. This is the preferred way to include
limited ranges of non-FiniteValued types such as
int or str in a FiniteValued
hierarchy.
Container types are types which contain zero or more fields of
FiniteValued type. The range of a container type is the
cartesian product of their field types, except for
set[FiniteValued]. - tuple[FiniteValued]:
Tuples of fixed length whose elements are each
FiniteValued. - IsDataclass: Concrete
dataclasses whose fields are FiniteValued. - Standard
concrete class: Regular classes could be supported just like dataclasses
if all their data members are FiniteValued-typed. -
set[FiniteValued]: Sets of fixed length of a
FiniteValued type.
Superclass types don’t directly contain data members like container
types. Their range is the union of the ranges of their subtypes. -
Abstract dataclasses: Abstract dataclasses whose subclasses are all
FiniteValued superclass or container types -
IsDataclass: Concrete dataclasses which also have their
own subclasses. - Standard abstract classes: Abstract dataclasses
whose subclasses are all FiniteValued superclass or
container types - UnionType: Any union of
FiniteValued types, e.g., bool | Literal[2, 3]
def all_instances(
type_: ~FiniteValued,
validation_funcs: dict[~FiniteValued, typing.Callable[[~FiniteValued], bool]] | None = None
) -> Generator[~FiniteValued, NoneType, NoneType]Returns all possible values of an instance of type_ if
finite instances exist. Uses type hinting to construct the possible
values. All nested elements of type_ must themselves be
typed. Do not use with types whose members contain circular references.
Function is susceptible to infinite recursion if type_ is a
dataclass whose member tree includes another instance of
type_.
type_: FiniteValued A finite-valued type. See docstring
on FiniteValued for full details.validation_funcs: dict[FiniteValued, Callable[[FiniteValued], bool]] | None
A mapping of types to auxiliary functions to validate instances of that
type. This optional argument can provide an additional, more precise
layer of validation for the instances generated beyond what type hinting
alone can provide. See validation_funcs Details section
below. (default: None)type_ ValuesSee docstring on FiniteValued for full details.
type_ may be: - FiniteValued - A
finite-valued, fixed-length Generic tuple type. E.g.,
tuple[bool], tuple[bool, MyEnum] are OK.
tuple[bool, ...] is NOT supported, since the length of the
tuple is not fixed. - Nested versions of any of the types in this list -
A UnionType of any of the types in this list
validation_funcs
Detailsvalidation_funcs is applied after all instances have
been generated according to type hints.type_ is in validation_funcs, then the
list of instances is filtered by
validation_funcs[type_](instance).validation_funcs is passed down for all recursive calls
of all_instances.validation_funcs supports subclass checking.type_ is not found in validation_funcs,
then the search is performed iteratively in mro order.type_ is found while searching in
mro order, that validation function is applied and the list is
returned.type_ is found, then no filter is
applied.