"""Utility routines."""

from __future__ import annotations

import itertools
import json
import re
import sys
import traceback
import warnings
from collections.abc import Mapping, MutableMapping
from copy import deepcopy
from itertools import groupby
from operator import itemgetter
from typing import TYPE_CHECKING, Any, Callable, Iterator, Literal, TypeVar, cast

import jsonschema
import narwhals.stable.v1 as nw
from narwhals.dependencies import get_polars, is_pandas_dataframe
from narwhals.typing import IntoDataFrame

from altair.utils.schemapi import SchemaBase, Undefined

if sys.version_info >= (3, 12):
    from typing import Protocol, runtime_checkable
else:
    from typing_extensions import Protocol, runtime_checkable
if sys.version_info >= (3, 10):
    from typing import ParamSpec
else:
    from typing_extensions import ParamSpec


if TYPE_CHECKING:
    import typing as t
    from types import ModuleType

    import pandas as pd
    from narwhals.typing import IntoExpr

    from altair.utils._dfi_types import DataFrame as DfiDataFrame
    from altair.vegalite.v5.schema._typing import StandardType_T as InferredVegaLiteType

V = TypeVar("V")
P = ParamSpec("P")
TIntoDataFrame = TypeVar("TIntoDataFrame", bound=IntoDataFrame)


@runtime_checkable
class DataFrameLike(Protocol):
    def __dataframe__(
        self, nan_as_null: bool = False, allow_copy: bool = True
    ) -> DfiDataFrame: ...


TYPECODE_MAP = {
    "ordinal": "O",
    "nominal": "N",
    "quantitative": "Q",
    "temporal": "T",
    "geojson": "G",
}

INV_TYPECODE_MAP = {v: k for k, v in TYPECODE_MAP.items()}


# aggregates from vega-lite version 4.6.0
AGGREGATES = [
    "argmax",
    "argmin",
    "average",
    "count",
    "distinct",
    "max",
    "mean",
    "median",
    "min",
    "missing",
    "product",
    "q1",
    "q3",
    "ci0",
    "ci1",
    "stderr",
    "stdev",
    "stdevp",
    "sum",
    "valid",
    "values",
    "variance",
    "variancep",
    "exponential",
    "exponentialb",
]

# window aggregates from vega-lite version 4.6.0
WINDOW_AGGREGATES = [
    "row_number",
    "rank",
    "dense_rank",
    "percent_rank",
    "cume_dist",
    "ntile",
    "lag",
    "lead",
    "first_value",
    "last_value",
    "nth_value",
]

# timeUnits from vega-lite version 4.17.0
TIMEUNITS = [
    "year",
    "quarter",
    "month",
    "week",
    "day",
    "dayofyear",
    "date",
    "hours",
    "minutes",
    "seconds",
    "milliseconds",
    "yearquarter",
    "yearquartermonth",
    "yearmonth",
    "yearmonthdate",
    "yearmonthdatehours",
    "yearmonthdatehoursminutes",
    "yearmonthdatehoursminutesseconds",
    "yearweek",
    "yearweekday",
    "yearweekdayhours",
    "yearweekdayhoursminutes",
    "yearweekdayhoursminutesseconds",
    "yeardayofyear",
    "quartermonth",
    "monthdate",
    "monthdatehours",
    "monthdatehoursminutes",
    "monthdatehoursminutesseconds",
    "weekday",
    "weeksdayhours",
    "weekdayhours",
    "weekdayhoursminutes",
    "weekdayhoursminutesseconds",
    "dayhours",
    "dayhoursminutes",
    "dayhoursminutesseconds",
    "hoursminutes",
    "hoursminutesseconds",
    "minutesseconds",
    "secondsmilliseconds",
    "utcyear",
    "utcquarter",
    "utcmonth",
    "utcweek",
    "utcday",
    "utcdayofyear",
    "utcdate",
    "utchours",
    "utcminutes",
    "utcseconds",
    "utcmilliseconds",
    "utcyearquarter",
    "utcyearquartermonth",
    "utcyearmonth",
    "utcyearmonthdate",
    "utcyearmonthdatehours",
    "utcyearmonthdatehoursminutes",
    "utcyearmonthdatehoursminutesseconds",
    "utcyearweek",
    "utcyearweekday",
    "utcyearweekdayhours",
    "utcyearweekdayhoursminutes",
    "utcyearweekdayhoursminutesseconds",
    "utcyeardayofyear",
    "utcquartermonth",
    "utcmonthdate",
    "utcmonthdatehours",
    "utcmonthdatehoursminutes",
    "utcmonthdatehoursminutesseconds",
    "utcweekday",
    "utcweekdayhours",
    "utcweekdayhoursminutes",
    "utcweekdayhoursminutesseconds",
    "utcdayhours",
    "utcdayhoursminutes",
    "utcdayhoursminutesseconds",
    "utchoursminutes",
    "utchoursminutesseconds",
    "utcminutesseconds",
    "utcsecondsmilliseconds",
]

VALID_TYPECODES = list(itertools.chain(iter(TYPECODE_MAP), iter(INV_TYPECODE_MAP)))

SHORTHAND_UNITS = {
    "field": "(?P<field>.*)",
    "type": "(?P<type>{})".format("|".join(VALID_TYPECODES)),
    "agg_count": "(?P<aggregate>count)",
    "op_count": "(?P<op>count)",
    "aggregate": "(?P<aggregate>{})".format("|".join(AGGREGATES)),
    "window_op": "(?P<op>{})".format("|".join(AGGREGATES + WINDOW_AGGREGATES)),
    "timeUnit": "(?P<timeUnit>{})".format("|".join(TIMEUNITS)),
}

SHORTHAND_KEYS: frozenset[Literal["field", "aggregate", "type", "timeUnit"]] = (
    frozenset(("field", "aggregate", "type", "timeUnit"))
)


def infer_vegalite_type_for_pandas(
    data: object,
) -> InferredVegaLiteType | tuple[InferredVegaLiteType, list[Any]]:
    """
    From an array-like input, infer the correct vega typecode.

    ('ordinal', 'nominal', 'quantitative', or 'temporal').

    Parameters
    ----------
    data: object
    """
    # This is safe to import here, as this function is only called on pandas input.
    from pandas.api.types import infer_dtype

    typ = infer_dtype(data, skipna=False)

    if typ in {
        "floating",
        "mixed-integer-float",
        "integer",
        "mixed-integer",
        "complex",
    }:
        return "quantitative"
    elif typ == "categorical" and hasattr(data, "cat") and data.cat.ordered:
        return ("ordinal", data.cat.categories.tolist())
    elif typ in {"string", "bytes", "categorical", "boolean", "mixed", "unicode"}:
        return "nominal"
    elif typ in {
        "datetime",
        "datetime64",
        "timedelta",
        "timedelta64",
        "date",
        "time",
        "period",
    }:
        return "temporal"
    else:
        warnings.warn(
            f"I don't know how to infer vegalite type from '{typ}'.  "
            "Defaulting to nominal.",
            stacklevel=1,
        )
        return "nominal"


def merge_props_geom(feat: dict[str, Any]) -> dict[str, Any]:
    """
    Merge properties with geometry.

    * Overwrites 'type' and 'geometry' entries if existing.
    """
    geom = {k: feat[k] for k in ("type", "geometry")}
    try:
        feat["properties"].update(geom)
        props_geom = feat["properties"]
    except (AttributeError, KeyError):
        # AttributeError when 'properties' equals None
        # KeyError when 'properties' is non-existing
        props_geom = geom

    return props_geom


def sanitize_geo_interface(geo: t.MutableMapping[Any, Any]) -> dict[str, Any]:
    """
    Santize a geo_interface to prepare it for serialization.

    * Make a copy
    * Convert type array or _Array to list
    * Convert tuples to lists (using json.loads/dumps)
    * Merge properties with geometry
    """
    geo = deepcopy(geo)

    # convert type _Array or array to list
    for key in geo:
        if str(type(geo[key]).__name__).startswith(("_Array", "array")):
            geo[key] = geo[key].tolist()

    # convert (nested) tuples to lists
    geo_dct: dict = json.loads(json.dumps(geo))

    # sanitize features
    if geo_dct["type"] == "FeatureCollection":
        geo_dct = geo_dct["features"]
        if len(geo_dct) > 0:
            for idx, feat in enumerate(geo_dct):
                geo_dct[idx] = merge_props_geom(feat)
    elif geo_dct["type"] == "Feature":
        geo_dct = merge_props_geom(geo_dct)
    else:
        geo_dct = {"type": "Feature", "geometry": geo_dct}

    return geo_dct


def numpy_is_subtype(dtype: Any, subtype: Any) -> bool:
    # This is only called on `numpy` inputs, so it's safe to import it here.
    import numpy as np

    try:
        return np.issubdtype(dtype, subtype)
    except (NotImplementedError, TypeError):
        return False


def sanitize_pandas_dataframe(df: pd.DataFrame) -> pd.DataFrame:  # noqa: C901
    """
    Sanitize a DataFrame to prepare it for serialization.

    * Make a copy
    * Convert RangeIndex columns to strings
    * Raise ValueError if column names are not strings
    * Raise ValueError if it has a hierarchical index.
    * Convert categoricals to strings.
    * Convert np.bool_ dtypes to Python bool objects
    * Convert np.int dtypes to Python int objects
    * Convert floats to objects and replace NaNs/infs with None.
    * Convert DateTime dtypes into appropriate string representations
    * Convert Nullable integers to objects and replace NaN with None
    * Convert Nullable boolean to objects and replace NaN with None
    * convert dedicated string column to objects and replace NaN with None
    * Raise a ValueError for TimeDelta dtypes
    """
    # This is safe to import here, as this function is only called on pandas input.
    # NumPy is a required dependency of pandas so is also safe to import.
    import numpy as np
    import pandas as pd

    df = df.copy()

    if isinstance(df.columns, pd.RangeIndex):
        df.columns = df.columns.astype(str)

    for col_name in df.columns:
        if not isinstance(col_name, str):
            msg = (
                f"Dataframe contains invalid column name: {col_name!r}. "
                "Column names must be strings"
            )
            raise ValueError(msg)

    if isinstance(df.index, pd.MultiIndex):
        msg = "Hierarchical indices not supported"
        raise ValueError(msg)
    if isinstance(df.columns, pd.MultiIndex):
        msg = "Hierarchical indices not supported"
        raise ValueError(msg)

    def to_list_if_array(val):
        if isinstance(val, np.ndarray):
            return val.tolist()
        else:
            return val

    for dtype_item in df.dtypes.items():
        # We know that the column names are strings from the isinstance check
        # further above but mypy thinks it is of type Hashable and therefore does not
        # let us assign it to the col_name variable which is already of type str.
        col_name = cast(str, dtype_item[0])
        dtype = dtype_item[1]
        dtype_name = str(dtype)
        if dtype_name == "category":
            # Work around bug in to_json for categorical types in older versions
            # of pandas as they do not properly convert NaN values to null in to_json.
            # We can probably remove this part once we require pandas >= 1.0
            col = df[col_name].astype(object)
            df[col_name] = col.where(col.notnull(), None)
        elif dtype_name == "string":
            # dedicated string datatype (since 1.0)
            # https://pandas.pydata.org/pandas-docs/version/1.0.0/whatsnew/v1.0.0.html#dedicated-string-data-type
            col = df[col_name].astype(object)
            df[col_name] = col.where(col.notnull(), None)
        elif dtype_name == "bool":
            # convert numpy bools to objects; np.bool is not JSON serializable
            df[col_name] = df[col_name].astype(object)
        elif dtype_name == "boolean":
            # dedicated boolean datatype (since 1.0)
            # https://pandas.io/docs/user_guide/boolean.html
            col = df[col_name].astype(object)
            df[col_name] = col.where(col.notnull(), None)
        elif dtype_name.startswith(("datetime", "timestamp")):
            # Convert datetimes to strings. This needs to be a full ISO string
            # with time, which is why we cannot use ``col.astype(str)``.
            # This is because Javascript parses date-only times in UTC, but
            # parses full ISO-8601 dates as local time, and dates in Vega and
            # Vega-Lite are displayed in local time by default.
            # (see https://github.com/vega/altair/issues/1027)
            df[col_name] = (
                df[col_name].apply(lambda x: x.isoformat()).replace("NaT", "")
            )
        elif dtype_name.startswith("timedelta"):
            msg = (
                f'Field "{col_name}" has type "{dtype}" which is '
                "not supported by Altair. Please convert to "
                "either a timestamp or a numerical value."
                ""
            )
            raise ValueError(msg)
        elif dtype_name.startswith("geometry"):
            # geopandas >=0.6.1 uses the dtype geometry. Continue here
            # otherwise it will give an error on np.issubdtype(dtype, np.integer)
            continue
        elif (
            dtype_name
            in {
                "Int8",
                "Int16",
                "Int32",
                "Int64",
                "UInt8",
                "UInt16",
                "UInt32",
                "UInt64",
                "Float32",
                "Float64",
            }
        ):  # nullable integer datatypes (since 24.0) and nullable float datatypes (since 1.2.0)
            # https://pandas.pydata.org/pandas-docs/version/0.25/whatsnew/v0.24.0.html#optional-integer-na-support
            col = df[col_name].astype(object)
            df[col_name] = col.where(col.notnull(), None)
        elif numpy_is_subtype(dtype, np.integer):
            # convert integers to objects; np.int is not JSON serializable
            df[col_name] = df[col_name].astype(object)
        elif numpy_is_subtype(dtype, np.floating):
            # For floats, convert to Python float: np.float is not JSON serializable
            # Also convert NaN/inf values to null, as they are not JSON serializable
            col = df[col_name]
            bad_values = col.isnull() | np.isinf(col)
            df[col_name] = col.astype(object).where(~bad_values, None)
        elif dtype == object:  # noqa: E721
            # Convert numpy arrays saved as objects to lists
            # Arrays are not JSON serializable
            col = df[col_name].astype(object).apply(to_list_if_array)
            df[col_name] = col.where(col.notnull(), None)
    return df


def sanitize_narwhals_dataframe(
    data: nw.DataFrame[TIntoDataFrame],
) -> nw.DataFrame[TIntoDataFrame]:
    """Sanitize narwhals.DataFrame for JSON serialization."""
    schema = data.schema
    columns: list[IntoExpr] = []
    # See https://github.com/vega/altair/issues/1027 for why this is necessary.
    local_iso_fmt_string = "%Y-%m-%dT%H:%M:%S"
    for name, dtype in schema.items():
        if dtype == nw.Date and nw.get_native_namespace(data) is get_polars():
            # Polars doesn't allow formatting `Date` with time directives.
            # The date -> datetime cast is extremely fast compared with `to_string`
            columns.append(
                nw.col(name).cast(nw.Datetime).dt.to_string(local_iso_fmt_string)
            )
        elif dtype == nw.Date:
            columns.append(nw.col(name).dt.to_string(local_iso_fmt_string))
        elif dtype == nw.Datetime:
            columns.append(nw.col(name).dt.to_string(f"{local_iso_fmt_string}%.f"))
        elif dtype == nw.Duration:
            msg = (
                f'Field "{name}" has type "{dtype}" which is '
                "not supported by Altair. Please convert to "
                "either a timestamp or a numerical value."
                ""
            )
            raise ValueError(msg)
        else:
            columns.append(name)
    return data.select(columns)


def to_eager_narwhals_dataframe(data: IntoDataFrame) -> nw.DataFrame[Any]:
    """
    Wrap `data` in `narwhals.DataFrame`.

    If `data` is not supported by Narwhals, but it is convertible
    to a PyArrow table, then first convert to a PyArrow Table,
    and then wrap in `narwhals.DataFrame`.
    """
    data_nw = nw.from_native(data, eager_or_interchange_only=True)
    if nw.get_level(data_nw) == "interchange":
        # If Narwhals' support for `data`'s class is only metadata-level, then we
        # use the interchange protocol to convert to a PyArrow Table.
        from altair.utils.data import arrow_table_from_dfi_dataframe

        pa_table = arrow_table_from_dfi_dataframe(data)  # type: ignore[arg-type]
        data_nw = nw.from_native(pa_table, eager_only=True)
    return data_nw


def parse_shorthand(  # noqa: C901
    shorthand: dict[str, Any] | str,
    data: pd.DataFrame | DataFrameLike | None = None,
    parse_aggregates: bool = True,
    parse_window_ops: bool = False,
    parse_timeunits: bool = True,
    parse_types: bool = True,
) -> dict[str, Any]:
    """
    General tool to parse shorthand values.

    These are of the form:

    - "col_name"
    - "col_name:O"
    - "average(col_name)"
    - "average(col_name):O"

    Optionally, a dataframe may be supplied, from which the type
    will be inferred if not specified in the shorthand.

    Parameters
    ----------
    shorthand : dict or string
        The shorthand representation to be parsed
    data : DataFrame, optional
        If specified and of type DataFrame, then use these values to infer the
        column type if not provided by the shorthand.
    parse_aggregates : boolean
        If True (default), then parse aggregate functions within the shorthand.
    parse_window_ops : boolean
        If True then parse window operations within the shorthand (default:False)
    parse_timeunits : boolean
        If True (default), then parse timeUnits from within the shorthand
    parse_types : boolean
        If True (default), then parse typecodes within the shorthand

    Returns
    -------
    attrs : dict
        a dictionary of attributes extracted from the shorthand

    Examples
    --------
    >>> import pandas as pd
    >>> data = pd.DataFrame({"foo": ["A", "B", "A", "B"], "bar": [1, 2, 3, 4]})

    >>> parse_shorthand("name") == {"field": "name"}
    True

    >>> parse_shorthand("name:Q") == {"field": "name", "type": "quantitative"}
    True

    >>> parse_shorthand("average(col)") == {"aggregate": "average", "field": "col"}
    True

    >>> parse_shorthand("foo:O") == {"field": "foo", "type": "ordinal"}
    True

    >>> parse_shorthand("min(foo):Q") == {
    ...     "aggregate": "min",
    ...     "field": "foo",
    ...     "type": "quantitative",
    ... }
    True

    >>> parse_shorthand("month(col)") == {
    ...     "field": "col",
    ...     "timeUnit": "month",
    ...     "type": "temporal",
    ... }
    True

    >>> parse_shorthand("year(col):O") == {
    ...     "field": "col",
    ...     "timeUnit": "year",
    ...     "type": "ordinal",
    ... }
    True

    >>> parse_shorthand("foo", data) == {"field": "foo", "type": "nominal"}
    True

    >>> parse_shorthand("bar", data) == {"field": "bar", "type": "quantitative"}
    True

    >>> parse_shorthand("bar:O", data) == {"field": "bar", "type": "ordinal"}
    True

    >>> parse_shorthand("sum(bar)", data) == {
    ...     "aggregate": "sum",
    ...     "field": "bar",
    ...     "type": "quantitative",
    ... }
    True

    >>> parse_shorthand("count()", data) == {
    ...     "aggregate": "count",
    ...     "type": "quantitative",
    ... }
    True
    """
    from altair.utils.data import is_data_type

    if not shorthand:
        return {}

    patterns = []

    if parse_aggregates:
        patterns.extend([r"{agg_count}\(\)"])
        patterns.extend([r"{aggregate}\({field}\)"])
    if parse_window_ops:
        patterns.extend([r"{op_count}\(\)"])
        patterns.extend([r"{window_op}\({field}\)"])
    if parse_timeunits:
        patterns.extend([r"{timeUnit}\({field}\)"])

    patterns.extend([r"{field}"])

    if parse_types:
        patterns = list(itertools.chain(*((p + ":{type}", p) for p in patterns)))

    regexps = (
        re.compile(r"\A" + p.format(**SHORTHAND_UNITS) + r"\Z", re.DOTALL)
        for p in patterns
    )

    # find matches depending on valid fields passed
    if isinstance(shorthand, dict):
        attrs = shorthand
    else:
        attrs = next(
            exp.match(shorthand).groupdict()  # type: ignore[union-attr]
            for exp in regexps
            if exp.match(shorthand) is not None
        )

    # Handle short form of the type expression
    if "type" in attrs:
        attrs["type"] = INV_TYPECODE_MAP.get(attrs["type"], attrs["type"])

    # counts are quantitative by default
    if attrs == {"aggregate": "count"}:
        attrs["type"] = "quantitative"

    # times are temporal by default
    if "timeUnit" in attrs and "type" not in attrs:
        attrs["type"] = "temporal"

    # if data is specified and type is not, infer type from data
    if "type" not in attrs and is_data_type(data):
        unescaped_field = attrs["field"].replace("\\", "")
        data_nw = nw.from_native(data, eager_or_interchange_only=True)
        schema = data_nw.schema
        if unescaped_field in schema:
            column = data_nw[unescaped_field]
            if schema[unescaped_field] in {
                nw.Object,
                nw.Unknown,
            } and is_pandas_dataframe(nw.to_native(data_nw)):
                attrs["type"] = infer_vegalite_type_for_pandas(nw.to_native(column))
            else:
                attrs["type"] = infer_vegalite_type_for_narwhals(column)
            if isinstance(attrs["type"], tuple):
                attrs["sort"] = attrs["type"][1]
                attrs["type"] = attrs["type"][0]

    # If an unescaped colon is still present, it's often due to an incorrect data type specification
    # but could also be due to using a column name with ":" in it.
    if (
        "field" in attrs
        and ":" in attrs["field"]
        and attrs["field"][attrs["field"].rfind(":") - 1] != "\\"
    ):
        raise ValueError(
            '"{}" '.format(attrs["field"].split(":")[-1])
            + "is not one of the valid encoding data types: {}.".format(
                ", ".join(TYPECODE_MAP.values())
            )
            + "\nFor more details, see https://altair-viz.github.io/user_guide/encodings/index.html#encoding-data-types. "
            + "If you are trying to use a column name that contains a colon, "
            + 'prefix it with a backslash; for example "column\\:name" instead of "column:name".'
        )
    return attrs


def infer_vegalite_type_for_narwhals(
    column: nw.Series,
) -> InferredVegaLiteType | tuple[InferredVegaLiteType, list]:
    dtype = column.dtype
    if (
        nw.is_ordered_categorical(column)
        and not (categories := column.cat.get_categories()).is_empty()
    ):
        return "ordinal", categories.to_list()
    if dtype in {nw.String, nw.Categorical, nw.Boolean}:
        return "nominal"
    elif dtype.is_numeric():
        return "quantitative"
    elif dtype in {nw.Datetime, nw.Date}:
        return "temporal"
    else:
        msg = f"Unexpected DtypeKind: {dtype}"
        raise ValueError(msg)


def use_signature(obj: Callable[P, Any]):  # -> Callable[..., Callable[P, V]]:
    """Apply call signature and documentation of `obj` to the decorated method."""

    def decorate(func: Callable[..., V]) -> Callable[P, V]:
        # call-signature of func is exposed via __wrapped__.
        # we want it to mimic obj.__init__

        # error: Accessing "__init__" on an instance is unsound,
        # since instance.__init__ could be from an incompatible subclass  [misc]
        wrapped = (
            obj.__init__ if (isinstance(obj, type) and issubclass(obj, object)) else obj  # type: ignore [misc]
        )
        func.__wrapped__ = wrapped  # type: ignore[attr-defined]
        func._uses_signature = obj  # type: ignore[attr-defined]

        # Supplement the docstring of func with information from obj
        if doc_in := obj.__doc__:
            doc_lines = doc_in.splitlines(keepends=True)[1:]
            # Patch in a reference to the class this docstring is copied from,
            # to generate a hyperlink.
            line_1 = f"{func.__doc__ or f'Refer to :class:`{obj.__name__}`'}\n"
            func.__doc__ = "".join((line_1, *doc_lines))
            return func
        else:
            msg = f"Found no doc for {obj!r}"
            raise AttributeError(msg)

    return decorate


def update_nested(
    original: t.MutableMapping[Any, Any],
    update: t.Mapping[Any, Any],
    copy: bool = False,
) -> t.MutableMapping[Any, Any]:
    """
    Update nested dictionaries.

    Parameters
    ----------
    original : MutableMapping
        the original (nested) dictionary, which will be updated in-place
    update : Mapping
        the nested dictionary of updates
    copy : bool, default False
        if True, then copy the original dictionary rather than modifying it

    Returns
    -------
    original : MutableMapping
        a reference to the (modified) original dict

    Examples
    --------
    >>> original = {"x": {"b": 2, "c": 4}}
    >>> update = {"x": {"b": 5, "d": 6}, "y": 40}
    >>> update_nested(original, update)  # doctest: +SKIP
    {'x': {'b': 5, 'c': 4, 'd': 6}, 'y': 40}
    >>> original  # doctest: +SKIP
    {'x': {'b': 5, 'c': 4, 'd': 6}, 'y': 40}
    """
    if copy:
        original = deepcopy(original)
    for key, val in update.items():
        if isinstance(val, Mapping):
            orig_val = original.get(key, {})
            if isinstance(orig_val, MutableMapping):
                original[key] = update_nested(orig_val, val)
            else:
                original[key] = val
        else:
            original[key] = val
    return original


def display_traceback(in_ipython: bool = True):
    exc_info = sys.exc_info()

    if in_ipython:
        from IPython.core.getipython import get_ipython

        ip = get_ipython()
    else:
        ip = None

    if ip is not None:
        ip.showtraceback(exc_info)
    else:
        traceback.print_exception(*exc_info)


_ChannelType = Literal["field", "datum", "value"]
_CHANNEL_CACHE: _ChannelCache
"""Singleton `_ChannelCache` instance.

Initialized on first use.
"""


class _ChannelCache:
    channel_to_name: dict[type[SchemaBase], str]
    name_to_channel: dict[str, dict[_ChannelType, type[SchemaBase]]]

    @classmethod
    def from_channels(cls, channels: ModuleType, /) -> _ChannelCache:
        # - This branch is only kept for tests that depend on mocking `channels`.
        # - No longer needs to pass around `channels` reference and rebuild every call.
        c_to_n = {
            c: c._encoding_name
            for c in channels.__dict__.values()
            if isinstance(c, type)
            and issubclass(c, SchemaBase)
            and hasattr(c, "_encoding_name")
        }
        self = cls.__new__(cls)
        self.channel_to_name = c_to_n
        self.name_to_channel = _invert_group_channels(c_to_n)
        return self

    @classmethod
    def from_cache(cls) -> _ChannelCache:
        global _CHANNEL_CACHE
        try:
            cached = _CHANNEL_CACHE
        except NameError:
            cached = cls.__new__(cls)
            cached.channel_to_name = _init_channel_to_name()
            cached.name_to_channel = _invert_group_channels(cached.channel_to_name)
            _CHANNEL_CACHE = cached
        return _CHANNEL_CACHE

    def get_encoding(self, tp: type[Any], /) -> str:
        if encoding := self.channel_to_name.get(tp):
            return encoding
        msg = f"positional of type {type(tp).__name__!r}"
        raise NotImplementedError(msg)

    def _wrap_in_channel(self, obj: Any, encoding: str, /):
        if isinstance(obj, SchemaBase):
            return obj
        elif isinstance(obj, str):
            obj = {"shorthand": obj}
        elif isinstance(obj, (list, tuple)):
            return [self._wrap_in_channel(el, encoding) for el in obj]
        if channel := self.name_to_channel.get(encoding):
            tp = channel["value" if "value" in obj else "field"]
            try:
                # Don't force validation here; some objects won't be valid until
                # they're created in the context of a chart.
                return tp.from_dict(obj, validate=False)
            except jsonschema.ValidationError:
                # our attempts at finding the correct class have failed
                return obj
        else:
            warnings.warn(f"Unrecognized encoding channel {encoding!r}", stacklevel=1)
            return obj

    def infer_encoding_types(self, kwargs: dict[str, Any], /):
        return {
            encoding: self._wrap_in_channel(obj, encoding)
            for encoding, obj in kwargs.items()
            if obj is not Undefined
        }


def _init_channel_to_name():
    """
    Construct a dictionary of channel type to encoding name.

    Note
    ----
    The return type is not expressible using annotations, but is used
    internally by `mypy`/`pyright` and avoids the need for type ignores.

    Returns
    -------
        mapping: dict[type[`<subclass of FieldChannelMixin and SchemaBase>`] | type[`<subclass of ValueChannelMixin and SchemaBase>`] | type[`<subclass of DatumChannelMixin and SchemaBase>`], str]
    """
    from altair.vegalite.v5.schema import channels as ch

    mixins = ch.FieldChannelMixin, ch.ValueChannelMixin, ch.DatumChannelMixin

    return {
        c: c._encoding_name
        for c in ch.__dict__.values()
        if isinstance(c, type) and issubclass(c, mixins) and issubclass(c, SchemaBase)
    }


def _invert_group_channels(
    m: dict[type[SchemaBase], str], /
) -> dict[str, dict[_ChannelType, type[SchemaBase]]]:
    """Grouped inverted index for `_ChannelCache.channel_to_name`."""

    def _reduce(it: Iterator[tuple[type[Any], str]]) -> Any:
        """
        Returns a 1-2 item dict, per channel.

        Never includes `datum`, as it is never utilized in `wrap_in_channel`.
        """
        item: dict[Any, type[SchemaBase]] = {}
        for tp, _ in it:
            name = tp.__name__
            if name.endswith("Datum"):
                continue
            elif name.endswith("Value"):
                sub_key = "value"
            else:
                sub_key = "field"
            item[sub_key] = tp
        return item

    grouper = groupby(m.items(), itemgetter(1))
    return {k: _reduce(chans) for k, chans in grouper}


def infer_encoding_types(
    args: tuple[Any, ...], kwargs: dict[str, Any], channels: ModuleType | None = None
):
    """
    Infer typed keyword arguments for args and kwargs.

    Parameters
    ----------
    args : Sequence
        Sequence of function args
    kwargs : MutableMapping
        Dict of function kwargs
    channels : ModuleType
        The module containing all altair encoding channel classes.

    Returns
    -------
    kwargs : dict
        All args and kwargs in a single dict, with keys and types
        based on the channels mapping.
    """
    cache = (
        _ChannelCache.from_channels(channels)
        if channels
        else _ChannelCache.from_cache()
    )
    # First use the mapping to convert args to kwargs based on their types.
    for arg in args:
        el = next(iter(arg), None) if isinstance(arg, (list, tuple)) else arg
        encoding = cache.get_encoding(type(el))
        if encoding not in kwargs:
            kwargs[encoding] = arg
        else:
            msg = f"encoding {encoding!r} specified twice."
            raise ValueError(msg)

    return cache.infer_encoding_types(kwargs)
