import copy
import sys
import warnings

import numpy as np

from pandas._libs import lib
from pandas.compat import range, set_function_name, string_types
from pandas.util._decorators import cache_readonly

from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.dtypes.cast import astype_nansafe
from pandas.core.dtypes.common import (
    is_bool_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype,
    is_list_like, is_object_dtype, is_scalar)
from pandas.core.dtypes.dtypes import register_extension_dtype
from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
from pandas.core.dtypes.missing import isna, notna

from pandas.core import nanops
from pandas.core.arrays import ExtensionArray, ExtensionOpsMixin
from pandas.core.tools.numeric import to_numeric


class _IntegerDtype(ExtensionDtype):
    """
    An ExtensionDtype to hold a single size & kind of integer dtype.

    These specific implementations are subclasses of the non-public
    _IntegerDtype. For example we have Int8Dtype to represnt signed int 8s.

    The attributes name & type are set when these subclasses are created.
    """
    name = None
    base = None
    type = None
    na_value = np.nan

    def __repr__(self):
        sign = 'U' if self.is_unsigned_integer else ''
        return "{sign}Int{size}Dtype()".format(sign=sign,
                                               size=8 * self.itemsize)

    @cache_readonly
    def is_signed_integer(self):
        return self.kind == 'i'

    @cache_readonly
    def is_unsigned_integer(self):
        return self.kind == 'u'

    @property
    def _is_numeric(self):
        return True

    @cache_readonly
    def numpy_dtype(self):
        """ Return an instance of our numpy dtype """
        return np.dtype(self.type)

    @cache_readonly
    def kind(self):
        return self.numpy_dtype.kind

    @cache_readonly
    def itemsize(self):
        """ Return the number of bytes in this dtype """
        return self.numpy_dtype.itemsize

    @classmethod
    def construct_array_type(cls):
        """Return the array type associated with this dtype

        Returns
        -------
        type
        """
        return IntegerArray

    @classmethod
    def construct_from_string(cls, string):
        """
        Construction from a string, raise a TypeError if not
        possible
        """
        if string == cls.name:
            return cls()
        raise TypeError("Cannot construct a '{}' from "
                        "'{}'".format(cls, string))


def integer_array(values, dtype=None, copy=False):
    """
    Infer and return an integer array of the values.

    Parameters
    ----------
    values : 1D list-like
    dtype : dtype, optional
        dtype to coerce
    copy : boolean, default False

    Returns
    -------
    IntegerArray

    Raises
    ------
    TypeError if incompatible types
    """
    values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
    return IntegerArray(values, mask)


def safe_cast(values, dtype, copy):
    """
    Safely cast the values to the dtype if they
    are equivalent, meaning floats must be equivalent to the
    ints.

    """

    try:
        return values.astype(dtype, casting='safe', copy=copy)
    except TypeError:

        casted = values.astype(dtype, copy=copy)
        if (casted == values).all():
            return casted

        raise TypeError("cannot safely cast non-equivalent {} to {}".format(
            values.dtype, np.dtype(dtype)))


def coerce_to_array(values, dtype, mask=None, copy=False):
    """
    Coerce the input values array to numpy arrays with a mask

    Parameters
    ----------
    values : 1D list-like
    dtype : integer dtype
    mask : boolean 1D array, optional
    copy : boolean, default False
        if True, copy the input

    Returns
    -------
    tuple of (values, mask)
    """
    # if values is integer numpy array, preserve it's dtype
    if dtype is None and hasattr(values, 'dtype'):
        if is_integer_dtype(values.dtype):
            dtype = values.dtype

    if dtype is not None:
        if (isinstance(dtype, string_types) and
                (dtype.startswith("Int") or dtype.startswith("UInt"))):
            # Avoid DeprecationWarning from NumPy about np.dtype("Int64")
            # https://github.com/numpy/numpy/pull/7476
            dtype = dtype.lower()

        if not issubclass(type(dtype), _IntegerDtype):
            try:
                dtype = _dtypes[str(np.dtype(dtype))]
            except KeyError:
                raise ValueError("invalid dtype specified {}".format(dtype))

    if isinstance(values, IntegerArray):
        values, mask = values._data, values._mask
        if dtype is not None:
            values = values.astype(dtype.numpy_dtype, copy=False)

        if copy:
            values = values.copy()
            mask = mask.copy()
        return values, mask

    values = np.array(values, copy=copy)
    if is_object_dtype(values):
        inferred_type = lib.infer_dtype(values, skipna=True)
        if inferred_type == 'empty':
            values = np.empty(len(values))
            values.fill(np.nan)
        elif inferred_type not in ['floating', 'integer',
                                   'mixed-integer', 'mixed-integer-float']:
            raise TypeError("{} cannot be converted to an IntegerDtype".format(
                values.dtype))

    elif not (is_integer_dtype(values) or is_float_dtype(values)):
        raise TypeError("{} cannot be converted to an IntegerDtype".format(
            values.dtype))

    if mask is None:
        mask = isna(values)
    else:
        assert len(mask) == len(values)

    if not values.ndim == 1:
        raise TypeError("values must be a 1D list-like")
    if not mask.ndim == 1:
        raise TypeError("mask must be a 1D list-like")

    # infer dtype if needed
    if dtype is None:
        dtype = np.dtype('int64')
    else:
        dtype = dtype.type

    # if we are float, let's make sure that we can
    # safely cast

    # we copy as need to coerce here
    if mask.any():
        values = values.copy()
        values[mask] = 1
        values = safe_cast(values, dtype, copy=False)
    else:
        values = safe_cast(values, dtype, copy=False)

    return values, mask


class IntegerArray(ExtensionArray, ExtensionOpsMixin):
    """
    Array of integer (optional missing) values.

    .. versionadded:: 0.24.0

    .. warning::

       IntegerArray is currently experimental, and its API or internal
       implementation may change without warning.

    We represent an IntegerArray with 2 numpy arrays:

    - data: contains a numpy integer array of the appropriate dtype
    - mask: a boolean array holding a mask on the data, True is missing

    To construct an IntegerArray from generic array-like input, use
    :func:`pandas.array` with one of the integer dtypes (see examples).

    See :ref:`integer_na` for more.

    Parameters
    ----------
    values : numpy.ndarray
        A 1-d integer-dtype array.
    mask : numpy.ndarray
        A 1-d boolean-dtype array indicating missing values.
    copy : bool, default False
        Whether to copy the `values` and `mask`.

    Returns
    -------
    IntegerArray

    Examples
    --------
    Create an IntegerArray with :func:`pandas.array`.

    >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
    >>> int_array
    <IntegerArray>
    [1, NaN, 3]
    Length: 3, dtype: Int32

    String aliases for the dtypes are also available. They are capitalized.

    >>> pd.array([1, None, 3], dtype='Int32')
    <IntegerArray>
    [1, NaN, 3]
    Length: 3, dtype: Int32

    >>> pd.array([1, None, 3], dtype='UInt16')
    <IntegerArray>
    [1, NaN, 3]
    Length: 3, dtype: UInt16
    """

    @cache_readonly
    def dtype(self):
        return _dtypes[str(self._data.dtype)]

    def __init__(self, values, mask, copy=False):
        if not (isinstance(values, np.ndarray)
                and is_integer_dtype(values.dtype)):
            raise TypeError("values should be integer numpy array. Use "
                            "the 'integer_array' function instead")
        if not (isinstance(mask, np.ndarray) and is_bool_dtype(mask.dtype)):
            raise TypeError("mask should be boolean numpy array. Use "
                            "the 'integer_array' function instead")

        if copy:
            values = values.copy()
            mask = mask.copy()

        self._data = values
        self._mask = mask

    @classmethod
    def _from_sequence(cls, scalars, dtype=None, copy=False):
        return integer_array(scalars, dtype=dtype, copy=copy)

    @classmethod
    def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
        scalars = to_numeric(strings, errors="raise")
        return cls._from_sequence(scalars, dtype, copy)

    @classmethod
    def _from_factorized(cls, values, original):
        return integer_array(values, dtype=original.dtype)

    def _formatter(self, boxed=False):
        def fmt(x):
            if isna(x):
                return 'NaN'
            return str(x)
        return fmt

    def __getitem__(self, item):
        if is_integer(item):
            if self._mask[item]:
                return self.dtype.na_value
            return self._data[item]
        return type(self)(self._data[item], self._mask[item])

    def _coerce_to_ndarray(self):
        """
        coerce to an ndarary of object dtype
        """

        # TODO(jreback) make this better
        data = self._data.astype(object)
        data[self._mask] = self._na_value
        return data

    __array_priority__ = 1000  # higher than ndarray so ops dispatch to us

    def __array__(self, dtype=None):
        """
        the array interface, return my values
        We return an object array here to preserve our scalar values
        """
        return self._coerce_to_ndarray()