# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""A Python wrapper around Vega-Lite."""

import json
from typing import TYPE_CHECKING, Any, Dict, Optional, cast

from typing_extensions import Final

import streamlit.elements.legacy_data_frame as data_frame
import streamlit.elements.lib.dicttools as dicttools
from streamlit.logger import get_logger
from streamlit.proto.VegaLiteChart_pb2 import VegaLiteChart as VegaLiteChartProto
from streamlit.runtime.metrics_util import gather_metrics

if TYPE_CHECKING:
    from streamlit.delta_generator import DeltaGenerator
    from streamlit.elements.arrow import Data

LOGGER: Final = get_logger(__name__)


class LegacyVegaLiteMixin:
    @gather_metrics("_legacy_vega_lite_chart")
    def _legacy_vega_lite_chart(
        self,
        data: "Data" = None,
        spec: Optional[Dict[str, Any]] = None,
        use_container_width: bool = False,
        **kwargs: Any,
    ) -> "DeltaGenerator":
        """Display a chart using the Vega-Lite library.

        Parameters
        ----------
        data : pandas.DataFrame, pandas.Styler, numpy.ndarray, Iterable, dict,
            or None
            Either the data to be plotted or a Vega-Lite spec containing the
            data (which more closely follows the Vega-Lite API).

        spec : dict or None
            The Vega-Lite spec for the chart. If the spec was already passed in
            the previous argument, this must be set to None. See
            https://vega.github.io/vega-lite/docs/ for more info.

        use_container_width : bool
            If True, set the chart width to the column width. This takes
            precedence over Vega-Lite's native `width` value.

        **kwargs : any
            Same as spec, but as keywords.

        Example
        -------

        >>> import pandas as pd
        >>> import numpy as np
        >>>
        >>> df = pd.DataFrame(
        ...     np.random.randn(200, 3),
        ...     columns=['a', 'b', 'c'])
        >>>
        >>> st._legacy_vega_lite_chart(df, {
        ...     'mark': {'type': 'circle', 'tooltip': True},
        ...     'encoding': {
        ...         'x': {'field': 'a', 'type': 'quantitative'},
        ...         'y': {'field': 'b', 'type': 'quantitative'},
        ...         'size': {'field': 'c', 'type': 'quantitative'},
        ...         'color': {'field': 'c', 'type': 'quantitative'},
        ...     },
        ... })

        .. output::
           https://static.streamlit.io/0.25.0-2JkNY/index.html?id=8jmmXR8iKoZGV4kXaKGYV5
           height: 200px

        Examples of Vega-Lite usage without Streamlit can be found at
        https://vega.github.io/vega-lite/examples/. Most of those can be easily
        translated to the syntax shown above.

        """
        vega_lite_chart_proto = VegaLiteChartProto()
        marshall(
            vega_lite_chart_proto,
            data,
            spec,
            use_container_width=use_container_width,
            **kwargs,
        )
        return self.dg._enqueue("vega_lite_chart", vega_lite_chart_proto)

    @property
    def dg(self) -> "DeltaGenerator":
        """Get our DeltaGenerator."""
        return cast("DeltaGenerator", self)


def marshall(
    proto: VegaLiteChartProto,
    data: "Data" = None,
    spec: Optional[Dict[str, Any]] = None,
    use_container_width: bool = False,
    **kwargs: Any,
) -> None:
    """Construct a Vega-Lite chart object.

    See DeltaGenerator._legacy_vega_lite_chart for docs.
    """
    # Support passing data inside spec['datasets'] and spec['data'].
    # (The data gets pulled out of the spec dict later on.)
    if isinstance(data, dict) and spec is None:
        spec = data
        data = None

    # Support passing no spec arg, but filling it with kwargs.
    # Example:
    #   marshall(proto, baz='boz')
    if spec is None:
        spec = dict()
    else:
        # Clone the spec dict, since we may be mutating it.
        spec = dict(spec)

    # Support passing in kwargs. Example:
    #   marshall(proto, {foo: 'bar'}, baz='boz')
    if len(kwargs):
        # Merge spec with unflattened kwargs, where kwargs take precedence.
        # This only works for string keys, but kwarg keys are strings anyways.
        spec = dict(spec, **dicttools.unflatten(kwargs, _CHANNELS))

    if len(spec) == 0:
        raise ValueError("Vega-Lite charts require a non-empty spec dict.")

    if "autosize" not in spec:
        spec["autosize"] = {"type": "fit", "contains": "padding"}

    # Pull data out of spec dict when it's in a 'dataset' key:
    #   marshall(proto, {datasets: {foo: df1, bar: df2}, ...})
    if "datasets" in spec:
        for k, v in spec["datasets"].items():
            dataset = proto.datasets.add()
            dataset.name = str(k)
            dataset.has_name = True
            data_frame.marshall_data_frame(v, dataset.data)
        del spec["datasets"]

    # Pull data out of spec dict when it's in a top-level 'data' key:
    #   marshall(proto, {data: df})
    #   marshall(proto, {data: {values: df, ...}})
    #   marshall(proto, {data: {url: 'url'}})
    #   marshall(proto, {data: {name: 'foo'}})
    if "data" in spec:
        data_spec = spec["data"]

        if isinstance(data_spec, dict):
            if "values" in data_spec:
                data = data_spec["values"]
                del spec["data"]
        else:
            data = data_spec
            del spec["data"]

    proto.spec = json.dumps(spec)
    proto.use_container_width = use_container_width

    if data is not None:
        data_frame.marshall_data_frame(data, proto.data)


# See https://vega.github.io/vega-lite/docs/encoding.html
_CHANNELS: Final = {
    "x",
    "y",
    "x2",
    "y2",
    "xError",
    "yError2",
    "xError",
    "yError2",
    "longitude",
    "latitude",
    "color",
    "opacity",
    "fillOpacity",
    "strokeOpacity",
    "strokeWidth",
    "size",
    "shape",
    "text",
    "tooltip",
    "href",
    "key",
    "order",
    "detail",
    "facet",
    "row",
    "column",
}
