# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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.

import math
import numbers

import pandas as pd
from sklearn.base import BaseEstimator

from ...tensor import tensor as astensor
from ...dataframe import DataFrame, Series
from ...dataframe.core import DATAFRAME_TYPE, SERIES_TYPE


def convert_to_tensor_or_dataframe(item):
    if isinstance(item, (DATAFRAME_TYPE, pd.DataFrame)):
        item = DataFrame(item)
    elif isinstance(item, (SERIES_TYPE, pd.Series)):
        item = Series(item)
    else:
        item = astensor(item)
    return item


def concat_chunks(chunks):
    tileable = chunks[0].op.create_tileable_from_chunks(chunks)
    return tileable.op.concat_tileable_chunks(tileable).chunks[0]


def copy_learned_attributes(from_estimator: BaseEstimator,
                            to_estimator: BaseEstimator):
    attrs = {k: v for k, v in vars(from_estimator).items() if k.endswith('_')}
    for k, v in attrs.items():
        setattr(to_estimator, k, v)


def is_scalar_nan(x):
    """Tests if x is NaN.

    This function is meant to overcome the issue that np.isnan does not allow
    non-numerical types as input, and that np.nan is not float('nan').

    Parameters
    ----------
    x : any type

    Returns
    -------
    boolean

    Examples
    --------
    >>> is_scalar_nan(np.nan)
    True
    >>> is_scalar_nan(float("nan"))
    True
    >>> is_scalar_nan(None)
    False
    >>> is_scalar_nan("")
    False
    >>> is_scalar_nan([np.nan])
    False
    """
    return isinstance(x, numbers.Real) and math.isnan(x)
