import decimal
import numbers
import random
import sys

import numpy as np

from pandas.core.dtypes.base import ExtensionDtype

import pandas as pd
from pandas.api.extensions import no_default, register_extension_dtype
from pandas.core.arrays import ExtensionArray, ExtensionScalarOpsMixin


@register_extension_dtype
class DecimalDtype(ExtensionDtype):
    type = decimal.Decimal
    name = "decimal"
    na_value = decimal.Decimal("NaN")
    _metadata = ("context",)

    def __init__(self, context=None):
        self.context = context or decimal.getcontext()

    def __repr__(self) -> str:
        return f"DecimalDtype(context={self.context})"

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

        Returns
        -------
        type
        """
        return DecimalArray

    @classmethod
    def construct_from_string(cls, string):
        if string == cls.name:
            return cls()
        else:
            raise TypeError(f"Cannot construct a '{cls.__name__}' from '{string}'")

    @property
    def _is_numeric(self):
        return True


class DecimalArray(ExtensionArray, ExtensionScalarOpsMixin):
    __array_priority__ = 1000

    def __init__(self, values, dtype=None, copy=False, context=None):
        for val in values:
            if not isinstance(val, decimal.Decimal):
                raise TypeError("All values must be of type " + str(decimal.Decimal))
        values = np.asarray(values, dtype=object)

        self._data = values
        # Some aliases for common attribute names to ensure pandas supports
        # these
        self._items = self.data = self._data
        # those aliases are currently not working due to assumptions
        # in internal code (GH-20735)
        # self._values = self.values = self.data
        self._dtype = DecimalDtype(context)

    @property
    def dtype(self):
        return self._dtype

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

    @classmethod
    def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
        return cls._from_sequence([decimal.Decimal(x) for x in strings], dtype, copy)

    @classmethod
    def _from_factorized(cls, values, original):
        return cls(values)

    _HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)

    def to_numpy(self, dtype=None, copy=False, na_value=no_default, decimals=None):
        result = np.asarray(self, dtype=dtype)
        if decimals is not None:
            result = np.asarray([round(x, decimals) for x in result])
        return result

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        #
        if not all(
            isinstance(t, self._HANDLED_TYPES + (DecimalArray,)) for t in inputs
        ):
            return NotImplemented

        inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
        result = getattr(ufunc, method)(*inputs, **kwargs)

        def reconstruct(x):
            if isinstance(x, (decimal.Decimal, numbers.Number)):
                return x
            else:
                return DecimalArray._from_sequence(x)

        if isinstance(result, tuple):
            return tuple(reconstruct(x) for x in result)
        else:
            return reconstruct(result)

    def __getitem__(self, item):
        if isinstance(item, numbers.Integral):
            return self._data[item]
        else:
            # array, slice.
            item = pd.api.indexers.check_array_indexer(self, item)
            return type(self)(self._data[item])

    def take(self, indexer, allow_fill=False, fill_value=None):
        from pandas.api.extensions import take

        data = self._data
        if allow_fill and fill_value is None:
            fill_value = self.dtype.na_value

        result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
        return self._from_sequence(result)

    def copy(self):
        return type(self)(self._data.copy())

    def astype(self, dtype, copy=True):
        if isinstance(dtype, type(self.dtype)):
            return type(self)(self._data, context=dtype.context)
        return np.asarray(self, dtype=dtype)

    def __setitem__(self, key, value):
        if pd.api.types.is_list_like(value):
            if pd.api.types.is_scalar(key):
                raise ValueError("setting an array element with a sequence.")
            value = [decimal.Decimal(v) for v in value]
        else:
            value = decimal.Decimal(value)
        self._data[key] = value

    def __len__(self) -> int:
        return len(self._data)

    @property
    def nbytes(self) -> int:
        n = len(self)
        if n:
            return n * sys.getsizeof(self[0])
        return 0

    def isna(self):
        return np.array([x.is_nan() for x in self._data], dtype=bool)

    @property
    def _na_value(self):
        return decimal.Decimal("NaN")

    def _formatter(self, boxed=False):
        if boxed:
            return "Decimal: {0}".format
        return repr

    @classmethod
    def _concat_same_type(cls, to_concat):
        return cls(np.concatenate([x._data for x in to_concat]))

    def _reduce(self, name, skipna=True, **kwargs):

        if skipna:
            # If we don't have any NAs, we can ignore skipna
            if self.isna().any():
                other = self[~self.isna()]
                return other._reduce(name, **kwargs)

        if name == "sum" and len(self) == 0:
            # GH#29630 avoid returning int 0 or np.bool_(False) on old numpy
            return decimal.Decimal(0)

        try:
            op = getattr(self.data, name)
        except AttributeError:
            raise NotImplementedError(f"decimal does not support the {name} operation")
        return op(axis=0)


def to_decimal(values, context=None):
    return DecimalArray([decimal.Decimal(x) for x in values], context=context)


def make_data():
    return [decimal.Decimal(random.random()) for _ in range(100)]


DecimalArray._add_arithmetic_ops()
DecimalArray._add_comparison_ops()
