r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers to
collate samples fetched from dataset into Tensor(s).

These **needs** to be in global scope since Py2 doesn't support serializing
static methods.

`default_collate` and `default_convert` are exposed to users via 'dataloader.py'.
"""

import torch
import re
import collections
from torch._six import string_classes

np_str_obj_array_pattern = re.compile(r'[SaUO]')


def default_convert(data):
    r"""
        Function that converts each NumPy array element into a :class:`torch.Tensor`. If the input is a `Sequence`,
        `Collection`, or `Mapping`, it tries to convert each element inside to a :class:`torch.Tensor`.
        If the input is not an NumPy array, it is left unchanged.
        This is used as the default function for collation when both `batch_sampler` and
        `batch_size` are NOT defined in :class:`~torch.utils.data.DataLoader`.

        The general input type to output type mapping is similar to that
        of :func:`~torch.utils.data.default_collate`. See the description there for more details.

        Args:
            data: a single data point to be converted

        Examples:
            >>> # Example with `int`
            >>> default_convert(0)
            0
            >>> # Example with NumPy array
            >>> default_convert(np.array([0, 1]))
            tensor([0, 1])
            >>> # Example with NamedTuple
            >>> Point = namedtuple('Point', ['x', 'y'])
            >>> default_convert(Point(0, 0))
            Point(x=0, y=0)
            >>> default_convert(Point(np.array(0), np.array(0)))
            Point(x=tensor(0), y=tensor(0))
            >>> # Example with List
            >>> default_convert([np.array([0, 1]), np.array([2, 3])])
            [tensor([0, 1]), tensor([2, 3])]
    """
    elem_type = type(data)
    if isinstance(data, torch.Tensor):
        return data
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        # array of string classes and object
        if elem_type.__name__ == 'ndarray' \
                and np_str_obj_array_pattern.search(data.dtype.str) is not None:
            return data
        return torch.as_tensor(data)
    elif isinstance(data, collections.abc.Mapping):
        try:
            return elem_type({key: default_convert(data[key]) for key in data})
        except TypeError:
            # The mapping type may not support `__init__(iterable)`.
            return {key: default_convert(data[key]) for key in data}
    elif isinstance(data, tuple) and hasattr(data, '_fields'):  # namedtuple
        return elem_type(*(default_convert(d) for d in data))
    elif isinstance(data, tuple):
        return [default_convert(d) for d in data]  # Backwards compatibility.
    elif isinstance(data, collections.abc.Sequence) and not isinstance(data, string_classes):
        try:
            return elem_type([default_convert(d) for d in data])
        except TypeError:
            # The sequence type may not support `__init__(iterable)` (e.g., `range`).
            return [default_convert(d) for d in data]
    else:
        return data


default_collate_err_msg_format = (
    "default_collate: batch must contain tensors, numpy arrays, numbers, "
    "dicts or lists; found {}")


def default_collate(batch):
    r"""
        Function that takes in a batch of data and puts the elements within the batch
        into a tensor with an additional outer dimension - batch size. The exact output type can be
        a :class:`torch.Tensor`, a `Sequence` of :class:`torch.Tensor`, a
        Collection of :class:`torch.Tensor`, or left unchanged, depending on the input type.
        This is used as the default function for collation when
        `batch_size` or `batch_sampler` is defined in :class:`~torch.utils.data.DataLoader`.

        Here is the general input type (based on the type of the element within the batch) to output type mapping:

            * :class:`torch.Tensor` -> :class:`torch.Tensor` (with an added outer dimension batch size)
            * NumPy Arrays -> :class:`torch.Tensor`
            * `float` -> :class:`torch.Tensor`
            * `int` -> :class:`torch.Tensor`
            * `str` -> `str` (unchanged)
            * `bytes` -> `bytes` (unchanged)
            * `Mapping[K, V_i]` -> `Mapping[K, default_collate([V_1, V_2, ...])]`
            * `NamedTuple[V1_i, V2_i, ...]` -> `NamedTuple[default_collate([V1_1, V1_2, ...]),
              default_collate([V2_1, V2_2, ...]), ...]`
            * `Sequence[V1_i, V2_i, ...]` -> `Sequence[default_collate([V1_1, V1_2, ...]),
              default_collate([V2_1, V2_2, ...]), ...]`

        Args:
            batch: a single batch to be collated

        Examples:
            >>> # Example with a batch of `int`s:
            >>> default_collate([0, 1, 2, 3])
            tensor([0, 1, 2, 3])
            >>> # Example with a batch of `str`s:
            >>> default_collate(['a', 'b', 'c'])
            ['a', 'b', 'c']
            >>> # Example with `Map` inside the batch:
            >>> default_collate([{'A': 0, 'B': 1}, {'A': 100, 'B': 100}])
            {'A': tensor([  0, 100]), 'B': tensor([  1, 100])}
            >>> # Example with `NamedTuple` inside the batch:
            >>> Point = namedtuple('Point', ['x', 'y'])
            >>> default_collate([Point(0, 0), Point(1, 1)])
            Point(x=tensor([0, 1]), y=tensor([0, 1]))
            >>> # Example with `Tuple` inside the batch:
            >>> default_collate([(0, 1), (2, 3)])
            [tensor([0, 2]), tensor([1, 3])]
            >>> # Example with `List` inside the batch:
            >>> default_collate([[0, 1], [2, 3]])
            [tensor([0, 2]), tensor([1, 3])]
    """
    elem = batch[0]
    elem_type = type(elem)
    if isinstance(elem, torch.Tensor):
        out = None
        if torch.utils.data.get_worker_info() is not None:
            # If we're in a background process, concatenate directly into a
            # shared memory tensor to avoid an extra copy
            numel = sum(x.numel() for x in batch)
            storage = elem.storage()._new_shared(numel, device=elem.device)
            out = elem.new(storage).resize_(len(batch), *list(elem.size()))
        return torch.stack(batch, 0, out=out)
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        if elem_type.__name__ == 'ndarray' or elem_type.__name__ == 'memmap':
            # array of string classes and object
            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
                raise TypeError(default_collate_err_msg_format.format(elem.dtype))

            return default_collate([torch.as_tensor(b) for b in batch])
        elif elem.shape == ():  # scalars
            return torch.as_tensor(batch)
    elif isinstance(elem, float):
        return torch.tensor(batch, dtype=torch.float64)
    elif isinstance(elem, int):
        return torch.tensor(batch)
    elif isinstance(elem, string_classes):
        return batch
    elif isinstance(elem, collections.abc.Mapping):
        try:
            return elem_type({key: default_collate([d[key] for d in batch]) for key in elem})
        except TypeError:
            # The mapping type may not support `__init__(iterable)`.
            return {key: default_collate([d[key] for d in batch]) for key in elem}
    elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
        return elem_type(*(default_collate(samples) for samples in zip(*batch)))
    elif isinstance(elem, collections.abc.Sequence):
        # check to make sure that the elements in batch have consistent size
        it = iter(batch)
        elem_size = len(next(it))
        if not all(len(elem) == elem_size for elem in it):
            raise RuntimeError('each element in list of batch should be of equal size')
        transposed = list(zip(*batch))  # It may be accessed twice, so we use a list.

        if isinstance(elem, tuple):
            return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
        else:
            try:
                return elem_type([default_collate(samples) for samples in transposed])
            except TypeError:
                # The sequence type may not support `__init__(iterable)` (e.g., `range`).
                return [default_collate(samples) for samples in transposed]

    raise TypeError(default_collate_err_msg_format.format(elem_type))