# coding: utf-8
"""Compatibility library."""

"""pandas"""
try:
    from pandas import Series, DataFrame, concat
    from pandas.api.types import is_sparse as is_dtype_sparse
    PANDAS_INSTALLED = True
except ImportError:
    PANDAS_INSTALLED = False

    class Series:
        """Dummy class for pandas.Series."""

        pass

    class DataFrame:
        """Dummy class for pandas.DataFrame."""

        pass

    concat = None
    is_dtype_sparse = None

"""matplotlib"""
try:
    import matplotlib
    MATPLOTLIB_INSTALLED = True
except ImportError:
    MATPLOTLIB_INSTALLED = False

"""graphviz"""
try:
    import graphviz
    GRAPHVIZ_INSTALLED = True
except ImportError:
    GRAPHVIZ_INSTALLED = False

"""datatable"""
try:
    import datatable
    if hasattr(datatable, "Frame"):
        DataTable = datatable.Frame
    else:
        DataTable = datatable.DataTable
    DATATABLE_INSTALLED = True
except ImportError:
    DATATABLE_INSTALLED = False

    class DataTable:
        """Dummy class for DataTable."""

        pass


"""sklearn"""
try:
    from sklearn.base import BaseEstimator
    from sklearn.base import RegressorMixin, ClassifierMixin
    from sklearn.preprocessing import LabelEncoder
    from sklearn.utils.class_weight import compute_sample_weight
    from sklearn.utils.multiclass import check_classification_targets
    from sklearn.utils.validation import assert_all_finite, check_X_y, check_array
    try:
        from sklearn.model_selection import StratifiedKFold, GroupKFold
        from sklearn.exceptions import NotFittedError
    except ImportError:
        from sklearn.cross_validation import StratifiedKFold, GroupKFold
        from sklearn.utils.validation import NotFittedError
    try:
        from sklearn.utils.validation import _check_sample_weight
    except ImportError:
        from sklearn.utils.validation import check_consistent_length

        # dummy function to support older version of scikit-learn
        def _check_sample_weight(sample_weight, X, dtype=None):
            check_consistent_length(sample_weight, X)
            return sample_weight

    SKLEARN_INSTALLED = True
    _LGBMModelBase = BaseEstimator
    _LGBMRegressorBase = RegressorMixin
    _LGBMClassifierBase = ClassifierMixin
    _LGBMLabelEncoder = LabelEncoder
    LGBMNotFittedError = NotFittedError
    _LGBMStratifiedKFold = StratifiedKFold
    _LGBMGroupKFold = GroupKFold
    _LGBMCheckXY = check_X_y
    _LGBMCheckArray = check_array
    _LGBMCheckSampleWeight = _check_sample_weight
    _LGBMAssertAllFinite = assert_all_finite
    _LGBMCheckClassificationTargets = check_classification_targets
    _LGBMComputeSampleWeight = compute_sample_weight
except ImportError:
    SKLEARN_INSTALLED = False
    _LGBMModelBase = object
    _LGBMClassifierBase = object
    _LGBMRegressorBase = object
    _LGBMLabelEncoder = None
    LGBMNotFittedError = ValueError
    _LGBMStratifiedKFold = None
    _LGBMGroupKFold = None
    _LGBMCheckXY = None
    _LGBMCheckArray = None
    _LGBMCheckSampleWeight = None
    _LGBMAssertAllFinite = None
    _LGBMCheckClassificationTargets = None
    _LGBMComputeSampleWeight = None

"""dask"""
try:
    from dask import delayed
    from dask.array import Array as dask_Array
    from dask.dataframe import _Frame as dask_Frame
    from dask.distributed import Client, default_client, get_worker, wait
    DASK_INSTALLED = True
except ImportError:
    DASK_INSTALLED = False
    delayed = None
    Client = object
    default_client = None
    get_worker = None
    wait = None

    class dask_Array:
        """Dummy class for dask.array.Array."""

        pass

    class dask_Frame:
        """Dummy class for ddask.dataframe._Frame."""

        pass
