from __future__ import annotations

import abc
import copy
import functools
import html
from collections.abc import (
    Collection,
    Hashable,
    ItemsView,
    Iterable,
    Iterator,
    KeysView,
    Mapping,
    Sequence,
    Set,
    ValuesView,
)
from typing import Any

import tlz as toolz

import dask
from dask import config
from dask._task_spec import GraphNode
from dask.base import clone_key, flatten, is_dask_collection
from dask.core import keys_in_tasks, reverse_dict
from dask.tokenize import normalize_token, tokenize
from dask.typing import DaskCollection, Graph, Key
from dask.utils import ensure_dict, import_required, key_split
from dask.widgets import get_template


def compute_layer_dependencies(layers):
    """Returns the dependencies between layers"""

    def _find_layer_containing_key(key):
        for k, v in layers.items():
            if key in v:
                return k
        raise RuntimeError(f"{key!r} not found")

    all_keys = {key for layer in layers.values() for key in layer}
    ret = {k: set() for k in layers}
    for k, v in layers.items():
        for key in keys_in_tasks(all_keys - v.keys(), v.values()):
            ret[k].add(_find_layer_containing_key(key))
    return ret


class Layer(Graph):
    """High level graph layer

    This abstract class establish a protocol for high level graph layers.

    The main motivation of a layer is to represent a collection of tasks
    symbolically in order to speedup a series of operations significantly.
    Ideally, a layer should stay in this symbolic state until execution
    but in practice some operations will force the layer to generate all
    its internal tasks. We say that the layer has been materialized.

    Most of the default implementations in this class will materialize the
    layer. It is up to derived classes to implement non-materializing
    implementations.
    """

    annotations: Mapping[str, Any] | None
    collection_annotations: Mapping[str, Any] | None

    def __init__(
        self,
        annotations: Mapping[str, Any] | None = None,
        collection_annotations: Mapping[str, Any] | None = None,
    ):
        """Initialize Layer object.

        Parameters
        ----------
        annotations : Mapping[str, Any], optional
            By default, None.
            Annotations are metadata or soft constraints associated with tasks
            that dask schedulers may choose to respect:
            They signal intent without enforcing hard constraints.
            As such, they are primarily designed for use with the distributed
            scheduler. See the dask.annotate function for more information.
        collection_annotations : Mapping[str, Any], optional. By default, None.
            Experimental, intended to assist with visualizing the performance
            characteristics of Dask computations.
            These annotations are *not* passed to the distributed scheduler.
        """
        self.annotations = annotations or dask.get_annotations().copy() or None
        self.collection_annotations = collection_annotations or copy.copy(
            config.get("collection_annotations", None)
        )

    @functools.cached_property
    def has_legacy_tasks(self):
        """Check if the layer has legacy tasks

        Legacy tasks are those that are not in the form of a tuple
        (function, *args, **kwargs).
        """
        return any(not isinstance(v, GraphNode) for v in self.values())

    @abc.abstractmethod
    def is_materialized(self) -> bool:
        """Return whether the layer is materialized or not"""
        return True

    @abc.abstractmethod
    def get_output_keys(self) -> Set[Key]:
        """Return a set of all output keys

        Output keys are all keys in the layer that might be referenced by
        other layers.

        Classes overriding this implementation should not cause the layer
        to be materialized.

        Returns
        -------
        keys: Set
            All output keys
        """
        return self.keys()  # this implementation will materialize the graph

    def cull(
        self, keys: set[Key], all_hlg_keys: Collection[Key]
    ) -> tuple[Layer, Mapping[Key, set[Key]]]:
        """Remove unnecessary tasks from the layer

        In other words, return a new Layer with only the tasks required to
        calculate `keys` and a map of external key dependencies.

        Examples
        --------
        >>> inc = lambda x: x + 1
        >>> add = lambda x, y: x + y
        >>> d = MaterializedLayer({'x': 1, 'y': (inc, 'x'), 'out': (add, 'x', 10)})
        >>> _, deps = d.cull({'out'}, d.keys())
        >>> deps
        {'out': {'x'}, 'x': set()}

        Returns
        -------
        layer: Layer
            Culled layer
        deps: Map
            Map of external key dependencies
        """

        if self.has_legacy_tasks:
            if len(keys) == len(self):
                # Nothing to cull if preserving all existing keys
                return (
                    self,
                    {k: self.get_dependencies(k, all_hlg_keys) for k in self.keys()},
                )
            ret_deps = {}
            seen = set()
            out = {}
            work = keys.copy()
            while work:
                k = work.pop()
                if k not in self:
                    continue
                out[k] = self[k]
                ret_deps[k] = self.get_dependencies(k, all_hlg_keys)
                for d in ret_deps[k]:
                    if d not in seen:
                        if d in self:
                            seen.add(d)
                            work.add(d)

            return MaterializedLayer(out, annotations=self.annotations), ret_deps
        else:
            from dask._task_spec import cull

            out = cull(dict(self), keys)
            return MaterializedLayer(out, annotations=self.annotations), {
                k: set(v.dependencies) for k, v in out.items()
            }

    def get_dependencies(self, key: Key, all_hlg_keys: Collection[Key]) -> set:
        """Get dependencies of `key` in the layer

        Parameters
        ----------
        key:
            The key to find dependencies of
        all_hlg_keys:
            All keys in the high level graph.

        Returns
        -------
        deps: set
            A set of dependencies
        """
        return keys_in_tasks(all_hlg_keys, [self[key]])

    def clone(
        self,
        keys: set,
        seed: Hashable,
        bind_to: Key | None = None,
    ) -> tuple[Layer, bool]:
        """Clone selected keys in the layer, as well as references to keys in other
        layers

        Parameters
        ----------
        keys
            Keys to be replaced. This never includes keys not listed by
            :meth:`get_output_keys`. It must also include any keys that are outside
            of this layer that may be referenced by it.
        seed
            Common hashable used to alter the keys; see :func:`dask.base.clone_key`
        bind_to
            Optional key to bind the leaf nodes to. A leaf node here is one that does
            not reference any replaced keys; in other words it's a node where the
            replacement graph traversal stops; it may still have dependencies on
            non-replaced nodes.
            A bound node will not be computed until after ``bind_to`` has been computed.

        Returns
        -------
        - New layer
        - True if the ``bind_to`` key was injected anywhere; False otherwise

        Notes
        -----
        This method should be overridden by subclasses to avoid materializing the layer.
        """
        from dask.graph_manipulation import chunks

        is_leaf: bool

        def clone_value(o):
            """Variant of distributed.utils_comm.subs_multiple, which allows injecting
            bind_to
            """
            nonlocal is_leaf

            typ = type(o)
            if typ is tuple and o and callable(o[0]):
                return (o[0],) + tuple(clone_value(i) for i in o[1:])
            elif typ is list:
                return [clone_value(i) for i in o]
            elif typ is dict:
                return {k: clone_value(v) for k, v in o.items()}
            else:
                try:
                    if o not in keys:
                        return o
                except TypeError:
                    return o
                is_leaf = False
                return clone_key(o, seed)

        dsk_new = {}
        bound = False

        for key, value in self.items():
            if key in keys:
                key = clone_key(key, seed)
                is_leaf = True
                value = clone_value(value)
                if bind_to is not None and is_leaf:
                    value = (chunks.bind, value, bind_to)
                    bound = True

            dsk_new[key] = value

        return MaterializedLayer(dsk_new), bound

    def __copy__(self):
        """Default shallow copy implementation"""
        obj = type(self).__new__(self.__class__)
        obj.__dict__.update(self.__dict__)
        return obj

    def _repr_html_(self, layer_index="", highlevelgraph_key="", dependencies=()):
        if highlevelgraph_key != "":
            shortname = key_split(highlevelgraph_key)
        elif hasattr(self, "name"):
            shortname = key_split(self.name)
        else:
            shortname = self.__class__.__name__

        svg_repr = ""
        if (
            self.collection_annotations
            and self.collection_annotations.get("type") == "dask.array.core.Array"
        ):
            chunks = self.collection_annotations.get("chunks")
            if chunks:
                from dask.array.svg import svg

                svg_repr = svg(chunks)

        return get_template("highlevelgraph_layer.html.j2").render(
            materialized=self.is_materialized(),
            shortname=shortname,
            layer_index=layer_index,
            highlevelgraph_key=highlevelgraph_key,
            info=self.layer_info_dict(),
            dependencies=dependencies,
            svg_repr=svg_repr,
        )

    def layer_info_dict(self):
        info = {
            "layer_type": type(self).__name__,
            "is_materialized": self.is_materialized(),
            "number of outputs": f"{len(self.get_output_keys())}",
        }
        if self.annotations is not None:
            for key, val in self.annotations.items():
                info[key] = html.escape(str(val))
        if self.collection_annotations is not None:
            for key, val in self.collection_annotations.items():
                # Hide verbose chunk details from the HTML table
                if key != "chunks":
                    info[key] = html.escape(str(val))
        return info


class MaterializedLayer(Layer):
    """Fully materialized layer of `Layer`

    Parameters
    ----------
    mapping: Mapping
        The mapping between keys and tasks, typically a dask graph.
    """

    def __init__(self, mapping: Mapping, annotations=None, collection_annotations=None):
        super().__init__(
            annotations=annotations, collection_annotations=collection_annotations
        )
        if not isinstance(mapping, Mapping):
            raise TypeError(f"mapping must be a Mapping. Instead got {type(mapping)}")
        self.mapping = mapping

    def __contains__(self, k):
        return k in self.mapping

    def __getitem__(self, k):
        return self.mapping[k]

    def __iter__(self):
        return iter(self.mapping)

    def __len__(self):
        return len(self.mapping)

    def is_materialized(self):
        return True

    def get_output_keys(self):
        return self.keys()


class HighLevelGraph(Graph):
    """Task graph composed of layers of dependent subgraphs

    This object encodes a Dask task graph that is composed of layers of
    dependent subgraphs, such as commonly occurs when building task graphs
    using high level collections like Dask array, bag, or dataframe.

    Typically each high level array, bag, or dataframe operation takes the task
    graphs of the input collections, merges them, and then adds one or more new
    layers of tasks for the new operation.  These layers typically have at
    least as many tasks as there are partitions or chunks in the collection.
    The HighLevelGraph object stores the subgraphs for each operation
    separately in sub-graphs, and also stores the dependency structure between
    them.

    Parameters
    ----------
    layers : Mapping[str, Mapping]
        The subgraph layers, keyed by a unique name
    dependencies : Mapping[str, set[str]]
        The set of layers on which each layer depends
    key_dependencies : dict[Key, set], optional
        Mapping (some) keys in the high level graph to their dependencies. If
        a key is missing, its dependencies will be calculated on-the-fly.

    Examples
    --------
    Here is an idealized example that shows the internal state of a
    HighLevelGraph

    >>> import dask.dataframe as dd

    >>> df = dd.read_csv('myfile.*.csv')  # doctest: +SKIP
    >>> df = df + 100  # doctest: +SKIP
    >>> df = df[df.name == 'Alice']  # doctest: +SKIP

    >>> graph = df.__dask_graph__()  # doctest: +SKIP
    >>> graph.layers  # doctest: +SKIP
    {
     'read-csv': {('read-csv', 0): (pandas.read_csv, 'myfile.0.csv'),
                  ('read-csv', 1): (pandas.read_csv, 'myfile.1.csv'),
                  ('read-csv', 2): (pandas.read_csv, 'myfile.2.csv'),
                  ('read-csv', 3): (pandas.read_csv, 'myfile.3.csv')},
     'add': {('add', 0): (operator.add, ('read-csv', 0), 100),
             ('add', 1): (operator.add, ('read-csv', 1), 100),
             ('add', 2): (operator.add, ('read-csv', 2), 100),
             ('add', 3): (operator.add, ('read-csv', 3), 100)}
     'filter': {('filter', 0): (lambda part: part[part.name == 'Alice'], ('add', 0)),
                ('filter', 1): (lambda part: part[part.name == 'Alice'], ('add', 1)),
                ('filter', 2): (lambda part: part[part.name == 'Alice'], ('add', 2)),
                ('filter', 3): (lambda part: part[part.name == 'Alice'], ('add', 3))}
    }

    >>> graph.dependencies  # doctest: +SKIP
    {
     'read-csv': set(),
     'add': {'read-csv'},
     'filter': {'add'}
    }

    See Also
    --------
    HighLevelGraph.from_collections :
        typically used by developers to make new HighLevelGraphs
    """

    layers: Mapping[str, Layer]
    dependencies: Mapping[str, set[str]]
    key_dependencies: dict[Key, set[Key]]
    _to_dict: dict
    _all_external_keys: set

    def __init__(
        self,
        layers: Mapping[str, Graph],
        dependencies: Mapping[str, set[str]],
        key_dependencies: dict[Key, set[Key]] | None = None,
    ):
        self.dependencies = dependencies
        self.key_dependencies = key_dependencies or {}
        # Makes sure that all layers are `Layer`
        self.layers = {
            k: v if isinstance(v, Layer) else MaterializedLayer(v)
            for k, v in layers.items()
        }

    @classmethod
    def _from_collection(cls, name, layer, collection):
        """`from_collections` optimized for a single collection"""
        if not is_dask_collection(collection):
            raise TypeError(type(collection))

        graph = collection.__dask_graph__()
        if isinstance(graph, HighLevelGraph):
            layers = ensure_dict(graph.layers, copy=True)
            layers[name] = layer
            deps = ensure_dict(graph.dependencies, copy=True)
            deps[name] = set(collection.__dask_layers__())
        else:
            key = _get_some_layer_name(collection)
            layers = {name: layer, key: graph}
            deps = {name: {key}, key: set()}

        return cls(layers, deps)

    @classmethod
    def from_collections(
        cls,
        name: str,
        layer: Graph,
        dependencies: Sequence[DaskCollection] = (),
    ) -> HighLevelGraph:
        """Construct a HighLevelGraph from a new layer and a set of collections

        This constructs a HighLevelGraph in the common case where we have a single
        new layer and a set of old collections on which we want to depend.

        This pulls out the ``__dask_layers__()`` method of the collections if
        they exist, and adds them to the dependencies for this new layer.  It
        also merges all of the layers from all of the dependent collections
        together into the new layers for this graph.

        Parameters
        ----------
        name : str
            The name of the new layer
        layer : Mapping
            The graph layer itself
        dependencies : List of Dask collections
            A list of other dask collections (like arrays or dataframes) that
            have graphs themselves

        Examples
        --------

        In typical usage we make a new task layer, and then pass that layer
        along with all dependent collections to this method.

        >>> def add(self, other):
        ...     name = 'add-' + tokenize(self, other)
        ...     layer = {(name, i): (add, input_key, other)
        ...              for i, input_key in enumerate(self.__dask_keys__())}
        ...     graph = HighLevelGraph.from_collections(name, layer, dependencies=[self])
        ...     return new_collection(name, graph)
        """
        if len(dependencies) == 1:
            return cls._from_collection(name, layer, dependencies[0])
        layers = {name: layer}
        name_dep: set[str] = set()
        deps: dict[str, set[str]] = {name: name_dep}
        for collection in toolz.unique(dependencies, key=id):
            if is_dask_collection(collection):
                graph = collection.__dask_graph__()
                if isinstance(graph, HighLevelGraph):
                    layers.update(graph.layers)
                    deps.update(graph.dependencies)
                    name_dep |= set(collection.__dask_layers__())
                else:
                    key = _get_some_layer_name(collection)
                    layers[key] = graph
                    name_dep.add(key)
                    deps[key] = set()
            else:
                raise TypeError(type(collection))

        return cls(layers, deps)

    def __getitem__(self, key: Key) -> Any:
        # Attempt O(1) direct access first, under the assumption that layer names match
        # either the keys (Scalar, Item, Delayed) or the first element of the key tuples
        # (Array, Bag, DataFrame, Series). This assumption is not always true.
        try:
            return self.layers[key][key]  # type: ignore[index]
        except KeyError:
            pass
        try:
            return self.layers[key[0]][key]  # type: ignore[index]
        except (KeyError, IndexError, TypeError):
            pass

        # Fall back to O(n) access
        for d in self.layers.values():
            try:
                return d[key]
            except KeyError:
                pass

        raise KeyError(key)

    def __len__(self) -> int:
        # NOTE: this will double-count keys that are duplicated between layers, so it's
        # possible that `len(hlg) > len(hlg.to_dict())`. However, duplicate keys should
        # not occur through normal use, and their existence would usually be a bug.
        # So we ignore this case in favor of better performance.
        # https://github.com/dask/dask/issues/7271
        return sum(len(layer) for layer in self.layers.values())

    def __iter__(self) -> Iterator[Key]:
        return iter(self.to_dict())

    def to_dict(self) -> dict[Key, Any]:
        """Efficiently convert to plain dict. This method is faster than dict(self)."""
        try:
            return self._to_dict
        except AttributeError:
            out = self._to_dict = ensure_dict(self)
            return out

    def keys(self) -> KeysView:
        """Get all keys of all the layers.

        This will in many cases materialize layers, which makes it a relatively
        expensive operation. See :meth:`get_all_external_keys` for a faster alternative.
        """
        return self.to_dict().keys()

    def get_all_external_keys(self) -> set[Key]:
        """Get all output keys of all layers

        This will in most cases _not_ materialize any layers, which makes
        it a relative cheap operation.

        Returns
        -------
        keys: set
            A set of all external keys
        """
        try:
            return self._all_external_keys
        except AttributeError:
            keys: set = set()
            for layer in self.layers.values():
                # Note: don't use `keys |= ...`, because the RHS is a
                # collections.abc.Set rather than a real set, and this will
                # cause a whole new set to be constructed.
                keys.update(layer.get_output_keys())
            self._all_external_keys = keys
            return keys

    def items(self) -> ItemsView[Key, Any]:
        return self.to_dict().items()

    def values(self) -> ValuesView[Any]:
        return self.to_dict().values()

    def get_all_dependencies(self) -> dict[Key, set[Key]]:
        """Get dependencies of all keys

        This will in most cases materialize all layers, which makes
        it an expensive operation.

        Returns
        -------
        map: Mapping
            A map that maps each key to its dependencies
        """
        all_keys = self.keys()
        missing_keys = all_keys - self.key_dependencies.keys()
        if missing_keys:
            for layer in self.layers.values():
                for k in missing_keys & layer.keys():
                    self.key_dependencies[k] = layer.get_dependencies(k, all_keys)
        return self.key_dependencies

    @property
    def dependents(self) -> dict[str, set[str]]:
        return reverse_dict(self.dependencies)

    def copy(self) -> HighLevelGraph:
        return HighLevelGraph(
            ensure_dict(self.layers, copy=True),
            ensure_dict(self.dependencies, copy=True),
            self.key_dependencies.copy(),
        )

    @classmethod
    def merge(cls, *graphs: Graph) -> HighLevelGraph:
        layers: dict[str, Graph] = {}
        dependencies: dict[str, set[str]] = {}
        for g in graphs:
            if isinstance(g, HighLevelGraph):
                layers.update(g.layers)
                dependencies.update(g.dependencies)
            elif isinstance(g, Mapping):
                layers[str(id(g))] = g
                dependencies[str(id(g))] = set()
            else:
                raise TypeError(g)
        return cls(layers, dependencies)

    def visualize(self, filename="dask-hlg.svg", format=None, **kwargs):
        """
        Visualize this dask high level graph.

        Requires ``graphviz`` to be installed.

        Parameters
        ----------
        filename : str or None, optional
            The name of the file to write to disk. If the provided `filename`
            doesn't include an extension, '.png' will be used by default.
            If `filename` is None, no file will be written, and the graph is
            rendered in the Jupyter notebook only.
        format : {'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional
            Format in which to write output file. Default is 'svg'.
        color : {None, 'layer_type'}, optional (default: None)
            Options to color nodes.
            - None, no colors.
            - layer_type, color nodes based on the layer type.
        **kwargs
           Additional keyword arguments to forward to ``to_graphviz``.

        Examples
        --------
        >>> x.dask.visualize(filename='dask.svg')  # doctest: +SKIP
        >>> x.dask.visualize(filename='dask.svg', color='layer_type')  # doctest: +SKIP

        Returns
        -------
        result : IPython.display.Image, IPython.display.SVG, or None
            See dask.dot.dot_graph for more information.

        See Also
        --------
        dask.dot.dot_graph
        dask.base.visualize # low level variant
        """

        from dask.dot import graphviz_to_file

        g = to_graphviz(self, **kwargs)
        graphviz_to_file(g, filename, format)
        return g

    def _toposort_layers(self) -> list[str]:
        """Sort the layers in a high level graph topologically

        Parameters
        ----------
        hlg : HighLevelGraph
            The high level graph's layers to sort

        Returns
        -------
        sorted: list
            List of layer names sorted topologically
        """
        degree = {k: len(v) for k, v in self.dependencies.items()}
        reverse_deps: dict[str, list[str]] = {k: [] for k in self.dependencies}
        ready = []
        for k, v in self.dependencies.items():
            for dep in v:
                reverse_deps[dep].append(k)
            if not v:
                ready.append(k)
        ret = []
        while len(ready) > 0:
            layer = ready.pop()
            ret.append(layer)
            for rdep in reverse_deps[layer]:
                degree[rdep] -= 1
                if degree[rdep] == 0:
                    ready.append(rdep)
        return ret

    def cull(self, keys: Iterable[Key]) -> HighLevelGraph:
        """Return new HighLevelGraph with only the tasks required to calculate keys.

        In other words, remove unnecessary tasks from dask.

        Parameters
        ----------
        keys
            iterable of keys or nested list of keys such as the output of
            ``__dask_keys__()``

        Returns
        -------
        hlg: HighLevelGraph
            Culled high level graph
        """
        keys_set = set(flatten(keys))

        # Note: All Layer classes still in existence are of
        #       one of these types (or subclasses)
        #
        # - MaterializedLayer
        # - Blockwise
        # - ArrayOverlapLayer (which is basically as good as MaterializedLayer)
        if not any(layer.has_legacy_tasks for layer in self.layers.values()):
            all_ext_keys = set()
        else:
            # FIXME: Technically, we don't need to compute **all** keys but only
            # those of the current layer and all of its dependencies, i.e. if
            # there are legacy layers for IO followed by many blockwise layers,
            # we should still get by without this
            all_ext_keys = self.get_all_external_keys()

        ret_layers: dict = {}
        layer_dependencies = {}
        tok = tokenize(keys_set)
        for layer_name in reversed(self._toposort_layers()):
            new_layer_name = f"{layer_name}-{tok}"
            layer = self.layers[layer_name]
            if keys_set:
                culled_layer, culled_deps = layer.cull(keys_set, all_ext_keys)
                if not culled_deps:
                    continue

                # Update `keys` with all layer's external key dependencies,
                # which are all the layer's dependencies (`culled_deps`)
                # excluding the layer's output keys.
                for k, d in culled_deps.items():
                    keys_set |= d
                    keys_set.discard(k)
                layer = culled_layer
            # Save the culled layer and its key dependencies
            ret_layers[new_layer_name] = layer
            layer_dependencies[new_layer_name] = self.dependencies[layer_name]

        # Converting dict_keys to a real set lets Python optimise the set
        # intersection to iterate over the smaller of the two sets.
        ret_layers_keys = set(ret_layers.keys())
        ret_dependencies = {
            layer_name: layer_dependencies[layer_name] & ret_layers_keys
            for layer_name in ret_layers
        }

        return HighLevelGraph(ret_layers, ret_dependencies)

    def cull_layers(self, layers: Iterable[str]) -> HighLevelGraph:
        """Return a new HighLevelGraph with only the given layers and their
        dependencies. Internally, layers are not modified.

        This is a variant of :meth:`HighLevelGraph.cull` which is much faster and does
        not risk creating a collision between two layers with the same name and
        different content when two culled graphs are merged later on.

        Returns
        -------
        hlg: HighLevelGraph
            Culled high level graph
        """
        to_visit = set(layers)
        ret_layers = {}
        ret_dependencies = {}
        while to_visit:
            k = to_visit.pop()
            ret_layers[k] = self.layers[k]
            ret_dependencies[k] = self.dependencies[k]
            to_visit |= ret_dependencies[k] - ret_dependencies.keys()

        return HighLevelGraph(ret_layers, ret_dependencies)

    def validate(self) -> None:
        # Check dependencies
        for layer_name, deps in self.dependencies.items():
            if layer_name not in self.layers:
                raise ValueError(f"dependencies[{layer_name!r}] not found in layers")
            for dep in deps:
                if dep not in self.dependencies:
                    raise ValueError(f"{dep!r} not found in dependencies")

        for layer in self.layers.values():
            assert hasattr(layer, "annotations")

        # Re-calculate all layer dependencies
        dependencies = compute_layer_dependencies(self.layers)

        # Check keys
        dep_key1 = self.dependencies.keys()
        dep_key2 = dependencies.keys()
        if dep_key1 != dep_key2:
            raise ValueError(
                f"incorrect dependencies keys {set(dep_key1)!r} "
                f"expected {set(dep_key2)!r}"
            )

        # Check values
        for k in dep_key1:
            if self.dependencies[k] != dependencies[k]:
                raise ValueError(
                    f"incorrect HLG dependencies[{k!r}]: {self.dependencies[k]!r} "
                    f"expected {dependencies[k]!r} from task dependencies"
                )

    def __repr__(self) -> str:
        representation = f"{type(self).__name__} with {len(self.layers)} layers.\n"
        representation += f"<{self.__class__.__module__}.{self.__class__.__name__} object at {hex(id(self))}>\n"
        for i, layerkey in enumerate(self._toposort_layers()):
            representation += f" {i}. {layerkey}\n"
        return representation

    def _repr_html_(self) -> str:
        return get_template("highlevelgraph.html.j2").render(
            type=type(self).__name__,
            layers=self.layers,
            toposort=self._toposort_layers(),
            layer_dependencies=self.dependencies,
            n_outputs=len(self.get_all_external_keys()),
        )


def to_graphviz(
    hg,
    data_attributes=None,
    function_attributes=None,
    rankdir="BT",
    graph_attr=None,
    node_attr=None,
    edge_attr=None,
    **kwargs,
):
    from dask.dot import label, name

    graphviz = import_required(
        "graphviz",
        "Drawing dask graphs with the graphviz visualization engine requires the `graphviz` "
        "python library and the `graphviz` system library.\n\n"
        "Please either conda or pip install as follows:\n\n"
        "  conda install python-graphviz     # either conda install\n"
        "  python -m pip install graphviz    # or pip install and follow installation instructions",
    )

    data_attributes = data_attributes or {}
    function_attributes = function_attributes or {}
    graph_attr = graph_attr or {}
    node_attr = node_attr or {}
    edge_attr = edge_attr or {}

    graph_attr["rankdir"] = rankdir
    node_attr["shape"] = "box"
    node_attr["fontname"] = "helvetica"

    graph_attr.update(kwargs)
    g = graphviz.Digraph(
        graph_attr=graph_attr, node_attr=node_attr, edge_attr=edge_attr
    )

    n_tasks = {}
    for layer in hg.dependencies:
        n_tasks[layer] = len(hg.layers[layer])

    min_tasks = min(n_tasks.values())
    max_tasks = max(n_tasks.values())

    cache = {}

    color = kwargs.get("color")
    if color == "layer_type":
        layer_colors = {
            "DataFrameIOLayer": ["#CCC7F9", False],  # purple
            "ArrayOverlayLayer": ["#FFD9F2", False],  # pink
            "BroadcastJoinLayer": ["#D9F2FF", False],  # blue
            "Blockwise": ["#D9FFE6", False],  # green
            "BlockwiseLayer": ["#D9FFE6", False],  # green
            "MaterializedLayer": ["#DBDEE5", False],  # gray
        }

    for layer in hg.dependencies:
        layer_name = name(layer)
        attrs = data_attributes.get(layer, {})

        node_label = label(layer, cache=cache)
        node_size = (
            20
            if max_tasks == min_tasks
            else int(20 + ((n_tasks[layer] - min_tasks) / (max_tasks - min_tasks)) * 20)
        )

        layer_type = str(type(hg.layers[layer]).__name__)
        node_tooltips = (
            f"A {layer_type.replace('Layer', '')} Layer with {n_tasks[layer]} Tasks.\n"
        )

        layer_ca = hg.layers[layer].collection_annotations
        if layer_ca:
            if layer_ca.get("type") == "dask.array.core.Array":
                node_tooltips += (
                    f"Array Shape: {layer_ca.get('shape')}\n"
                    f"Data Type: {layer_ca.get('dtype')}\n"
                    f"Chunk Size: {layer_ca.get('chunksize')}\n"
                    f"Chunk Type: {layer_ca.get('chunk_type')}\n"
                )

            if layer_ca.get("type") == "dask.dataframe.core.DataFrame":
                dftype = {"pandas.core.frame.DataFrame": "pandas"}
                cols = layer_ca.get("columns")

                node_tooltips += (
                    f"Number of Partitions: {layer_ca.get('npartitions')}\n"
                    f"DataFrame Type: {dftype.get(layer_ca.get('dataframe_type'))}\n"
                    f"{len(cols)} DataFrame Columns: {str(cols) if len(str(cols)) <= 40 else '[...]'}\n"
                )

        attrs.setdefault("label", str(node_label))
        attrs.setdefault("fontsize", str(node_size))
        attrs.setdefault("tooltip", str(node_tooltips))

        if color == "layer_type":
            node_color = layer_colors.get(layer_type)[0]
            layer_colors.get(layer_type)[1] = True

            attrs.setdefault("fillcolor", str(node_color))
            attrs.setdefault("style", "filled")

        g.node(layer_name, **attrs)

    for layer, deps in hg.dependencies.items():
        layer_name = name(layer)
        for dep in deps:
            dep_name = name(dep)
            g.edge(dep_name, layer_name)

    if color == "layer_type":
        legend_title = "Key"

        legend_label = (
            '<<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0" CELLPADDING="5">'
            "<TR><TD><B>Legend: Layer types</B></TD></TR>"
        )

        for layer_type, color in layer_colors.items():
            if color[1]:
                legend_label += f'<TR><TD BGCOLOR="{color[0]}">{layer_type}</TD></TR>'

        legend_label += "</TABLE>>"

        attrs = data_attributes.get(legend_title, {})
        attrs.setdefault("label", str(legend_label))
        attrs.setdefault("fontsize", "20")
        attrs.setdefault("margin", "0")

        g.node(legend_title, **attrs)

    return g


def _get_some_layer_name(collection) -> str:
    """Somehow get a unique name for a Layer from a non-HighLevelGraph dask mapping"""
    try:
        (name,) = collection.__dask_layers__()
        return name
    except (AttributeError, ValueError):
        # collection does not define the optional __dask_layers__ method
        # or it spuriously returns more than one layer
        return str(id(collection))


@normalize_token.register(HighLevelGraph)
def register_highlevelgraph(hlg):
    # Note: Layer keys are not necessarily identifying HLGs uniquely
    # see https://github.com/dask/dask/issues/9888
    return normalize_token(list(hlg.layers.keys()))
