"""
timedelta support tools
"""

import numpy as np
import pandas as pd
import pandas._libs.tslib as tslib

from pandas.core.dtypes.common import (
    _ensure_object,
    is_integer_dtype,
    is_timedelta64_dtype,
    is_list_like)
from pandas.core.dtypes.generic import ABCSeries, ABCIndexClass


def to_timedelta(arg, unit='ns', box=True, errors='raise'):
    """
    Convert argument to timedelta

    Parameters
    ----------
    arg : string, timedelta, list, tuple, 1-d array, or Series
    unit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, which is an
        integer/float number
    box : boolean, default True
        - If True returns a Timedelta/TimedeltaIndex of the results
        - if False returns a np.timedelta64 or ndarray of values of dtype
          timedelta64[ns]
    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
        - If 'raise', then invalid parsing will raise an exception
        - If 'coerce', then invalid parsing will be set as NaT
        - If 'ignore', then invalid parsing will return the input

    Returns
    -------
    ret : timedelta64/arrays of timedelta64 if parsing succeeded

    Examples
    --------

    Parsing a single string to a Timedelta:

    >>> pd.to_timedelta('1 days 06:05:01.00003')
    Timedelta('1 days 06:05:01.000030')
    >>> pd.to_timedelta('15.5us')
    Timedelta('0 days 00:00:00.000015')

    Parsing a list or array of strings:

    >>> pd.to_timedelta(['1 days 06:05:01.00003', '15.5us', 'nan'])
    TimedeltaIndex(['1 days 06:05:01.000030', '0 days 00:00:00.000015', NaT],
                   dtype='timedelta64[ns]', freq=None)

    Converting numbers by specifying the `unit` keyword argument:

    >>> pd.to_timedelta(np.arange(5), unit='s')
    TimedeltaIndex(['00:00:00', '00:00:01', '00:00:02',
                    '00:00:03', '00:00:04'],
                   dtype='timedelta64[ns]', freq=None)
    >>> pd.to_timedelta(np.arange(5), unit='d')
    TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                   dtype='timedelta64[ns]', freq=None)

    See also
    --------
    pandas.DataFrame.astype : Cast argument to a specified dtype.
    pandas.to_datetime : Convert argument to datetime.
    """
    unit = _validate_timedelta_unit(unit)

    if errors not in ('ignore', 'raise', 'coerce'):
        raise ValueError("errors must be one of 'ignore', "
                         "'raise', or 'coerce'}")

    if arg is None:
        return arg
    elif isinstance(arg, ABCSeries):
        from pandas import Series
        values = _convert_listlike(arg._values, unit=unit,
                                   box=False, errors=errors)
        return Series(values, index=arg.index, name=arg.name)
    elif isinstance(arg, ABCIndexClass):
        return _convert_listlike(arg, unit=unit, box=box,
                                 errors=errors, name=arg.name)
    elif is_list_like(arg) and getattr(arg, 'ndim', 1) == 0:
        # extract array scalar and process below
        arg = arg.item()
    elif is_list_like(arg) and getattr(arg, 'ndim', 1) == 1:
        return _convert_listlike(arg, unit=unit, box=box, errors=errors)
    elif getattr(arg, 'ndim', 1) > 1:
        raise TypeError('arg must be a string, timedelta, list, tuple, '
                        '1-d array, or Series')

    # ...so it must be a scalar value. Return scalar.
    return _coerce_scalar_to_timedelta_type(arg, unit=unit,
                                            box=box, errors=errors)


_unit_map = {
    'Y': 'Y',
    'y': 'Y',
    'W': 'W',
    'w': 'W',
    'D': 'D',
    'd': 'D',
    'days': 'D',
    'Days': 'D',
    'day': 'D',
    'Day': 'D',
    'M': 'M',
    'H': 'h',
    'h': 'h',
    'm': 'm',
    'T': 'm',
    'S': 's',
    's': 's',
    'L': 'ms',
    'MS': 'ms',
    'ms': 'ms',
    'US': 'us',
    'us': 'us',
    'NS': 'ns',
    'ns': 'ns',
}


def _validate_timedelta_unit(arg):
    """ provide validation / translation for timedelta short units """
    try:
        return _unit_map[arg]
    except:
        if arg is None:
            return 'ns'
        raise ValueError("invalid timedelta unit {arg} provided"
                         .format(arg=arg))


def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'):
    """Convert string 'r' to a timedelta object."""

    try:
        result = tslib.convert_to_timedelta64(r, unit)
    except ValueError:
        if errors == 'raise':
            raise
        elif errors == 'ignore':
            return r

        # coerce
        result = pd.NaT

    if box:
        result = tslib.Timedelta(result)
    return result


def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
    """Convert a list of objects to a timedelta index object."""

    if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
        arg = np.array(list(arg), dtype='O')

    # these are shortcut-able
    if is_timedelta64_dtype(arg):
        value = arg.astype('timedelta64[ns]')
    elif is_integer_dtype(arg):
        value = arg.astype('timedelta64[{unit}]'.format(unit=unit)).astype(
            'timedelta64[ns]', copy=False)
    else:
        try:
            value = tslib.array_to_timedelta64(_ensure_object(arg),
                                               unit=unit, errors=errors)
            value = value.astype('timedelta64[ns]', copy=False)
        except ValueError:
            if errors == 'ignore':
                return arg
            else:
                # This else-block accounts for the cases when errors='raise'
                # and errors='coerce'. If errors == 'raise', these errors
                # should be raised. If errors == 'coerce', we shouldn't
                # expect any errors to be raised, since all parsing errors
                # cause coercion to pd.NaT. However, if an error / bug is
                # introduced that causes an Exception to be raised, we would
                # like to surface it.
                raise

    if box:
        from pandas import TimedeltaIndex
        value = TimedeltaIndex(value, unit='ns', name=name)
    return value