import functools
from collections import Sequence

import mmcv
import numpy as np
import torch


def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.
    """
    if isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError('type {} cannot be converted to tensor.'.format(
            type(data)))


def assert_tensor_type(func):

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        if not isinstance(args[0].data, torch.Tensor):
            raise AttributeError('{} has no attribute {} for type {}'.format(
                args[0].__class__.__name__, func.__name__, args[0].datatype))
        return func(*args, **kwargs)

    return wrapper


class DataContainer(object):

    def __init__(self, data, stack=False, padding_value=0):
        if isinstance(data, list):
            self._data = data
        else:
            self._data = to_tensor(data)
        self._stack = stack
        self._padding_value = padding_value

    def __repr__(self):
        return '{}({})'.format(self.__class__.__name__, repr(self.data))

    @property
    def data(self):
        return self._data

    @property
    def datatype(self):
        if isinstance(self.data, torch.Tensor):
            return self.data.type()
        else:
            return type(self.data)

    @property
    def stack(self):
        return self._stack

    @property
    def padding_value(self):
        return self._padding_value

    @assert_tensor_type
    def size(self, *args, **kwargs):
        return self.data.size(*args, **kwargs)

    @assert_tensor_type
    def dim(self):
        return self.data.dim()