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import numpy
import pandas
from numba import prange, types
from numba.targets.registry import cpu_target

from sdc.hiframes.pd_series_ext import SeriesType
from sdc.utilities.utils import sdc_overload_method

from sdc.utilities.sdc_typing_utils import TypeChecker


@sdc_overload_method(SeriesType, 'map')
def hpat_pandas_series_map(self, arg, na_action=None):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.map

    Limitations
    -----------
    - Series data types String is currently unsupported by Intel Scalable Dataframe Compiler.
    - ``arg`` as Series is currently unsupported by Intel Scalable Dataframe Compiler.
    - ``arg`` as function should return scalar. Other types \
        are currently unsupported by Intel Scalable Dataframe Compiler.
    - ``na_action`` is currently unsupported by Intel Scalable Dataframe Compiler.

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_map.py
       :language: python
       :lines: 36-
       :caption: `map()` accepts a function.
       :name: ex_series_map

    .. command-output:: python ./series/series_map.py
       :cwd: ../../../examples

    .. seealso::

        :ref:`Series.map <pandas.Series.apply>`
            For applying more complex functions on a Series.
        :ref:`DataFrame.apply <pandas.DataFrame.apply>`
            Apply a function row-/column-wise.
        :ref:`DataFrame.applymap <pandas.DataFrame.applymap>`
            Apply a function elementwise on a whole DataFrame.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************

    .. only:: developer
        Test: python -m sdc.runtests sdc.tests.test_series -k map
    """

    ty_checker = TypeChecker("Method map().")
    ty_checker.check(self, SeriesType)

    if isinstance(arg, types.Callable):
        sig = arg.get_call_type(cpu_target.typing_context, [self.dtype], {})
        output_type = sig.return_type

        def impl(self, arg, na_action=None):
            input_arr = self._data
            length = len(input_arr)

            output_arr = numpy.empty(length, dtype=output_type)

            for i in prange(length):
                output_arr[i] = arg(input_arr[i])

            return pandas.Series(output_arr, index=self._index, name=self._name)

        return impl

    if isinstance(arg, types.DictType):
        output_type = self.dtype

        def impl(self, arg, na_action=None):
            input_arr = self._data
            length = len(input_arr)

            output_arr = numpy.empty(length, dtype=output_type)

            for i in prange(length):
                output_arr[i] = arg.get(input_arr[i], numpy.nan)

            return pandas.Series(output_arr, index=self._index, name=self._name)

        return impl

numpy.empty