r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.

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

import torch
import random
import os
from collections import namedtuple
# from torch._six import queue
import queue
# from torch.multiprocessing import Queue as queue
from torch._utils import ExceptionWrapper
from torch.utils.data._utils import (
    signal_handling,
    MP_STATUS_CHECK_INTERVAL,
    IS_WINDOWS,
)

from .my_random_resize_crop import MyRandomResizedCrop

__all__ = ["worker_loop"]

if IS_WINDOWS:
    import ctypes
    from ctypes.wintypes import DWORD, BOOL, HANDLE

    # On Windows, the parent ID of the worker process remains unchanged when the manager process
    # is gone, and the only way to check it through OS is to let the worker have a process handle
    # of the manager and ask if the process status has changed.
    class ManagerWatchdog(object):
        def __init__(self):
            self.manager_pid = os.getppid()

            self.kernel32 = ctypes.WinDLL("kernel32", use_last_error=True)
            self.kernel32.OpenProcess.argtypes = (DWORD, BOOL, DWORD)
            self.kernel32.OpenProcess.restype = HANDLE
            self.kernel32.WaitForSingleObject.argtypes = (HANDLE, DWORD)
            self.kernel32.WaitForSingleObject.restype = DWORD

            # Value obtained from https://msdn.microsoft.com/en-us/library/ms684880.aspx
            SYNCHRONIZE = 0x00100000
            self.manager_handle = self.kernel32.OpenProcess(
                SYNCHRONIZE, 0, self.manager_pid
            )

            if not self.manager_handle:
                raise ctypes.WinError(ctypes.get_last_error())

            self.manager_dead = False

        def is_alive(self):
            if not self.manager_dead:
                # Value obtained from https://msdn.microsoft.com/en-us/library/windows/desktop/ms687032.aspx
                self.manager_dead = (
                    self.kernel32.WaitForSingleObject(self.manager_handle, 0) == 0
                )
            return not self.manager_dead


else:

    class ManagerWatchdog(object):
        def __init__(self):
            self.manager_pid = os.getppid()
            self.manager_dead = False

        def is_alive(self):
            if not self.manager_dead:
                self.manager_dead = os.getppid() != self.manager_pid
            return not self.manager_dead


_worker_info = None


class WorkerInfo(object):
    __initialized = False

    def __init__(self, **kwargs):
        for k, v in kwargs.items():
            setattr(self, k, v)
        self.__initialized = True

    def __setattr__(self, key, val):
        if self.__initialized:
            raise RuntimeError(
                "Cannot assign attributes to {} objects".format(self.__class__.__name__)
            )
        return super(WorkerInfo, self).__setattr__(key, val)


def get_worker_info():
    r"""Returns the information about the current
    :class:`~torch.utils.data.DataLoader` iterator worker process.

    When called in a worker, this returns an object guaranteed to have the
    following attributes:

    * :attr:`id`: the current worker id.
    * :attr:`num_workers`: the total number of workers.
    * :attr:`seed`: the random seed set for the current worker. This value is
      determined by main process RNG and the worker id. See
      :class:`~torch.utils.data.DataLoader`'s documentation for more details.
    * :attr:`dataset`: the copy of the dataset object in **this** process. Note
      that this will be a different object in a different process than the one
      in the main process.

    When called in the main process, this returns ``None``.

    .. note::
       When used in a :attr:`worker_init_fn` passed over to
       :class:`~torch.utils.data.DataLoader`, this method can be useful to
       set up each worker process differently, for instance, using ``worker_id``
       to configure the ``dataset`` object to only read a specific fraction of a
       sharded dataset, or use ``seed`` to seed other libraries used in dataset
       code (e.g., NumPy).
    """
    return _worker_info


r"""Dummy class used to signal the end of an IterableDataset"""
_IterableDatasetStopIteration = namedtuple(
    "_IterableDatasetStopIteration", ["worker_id"]
)


def worker_loop(
    dataset_kind,
    dataset,
    index_queue,
    data_queue,
    done_event,
    auto_collation,
    collate_fn,
    drop_last,
    seed,
    init_fn,
    worker_id,
    num_workers,
):
    # See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
    # logic of this function.

    try:
        # Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
        # module's handlers are executed after Python returns from C low-level
        # handlers, likely when the same fatal signal had already happened
        # again.
        # https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
        signal_handling._set_worker_signal_handlers()

        torch.set_num_threads(1)
        random.seed(seed)
        torch.manual_seed(seed)

        global _worker_info
        _worker_info = WorkerInfo(
            id=worker_id, num_workers=num_workers, seed=seed, dataset=dataset
        )

        from torch.utils.data import _DatasetKind

        init_exception = None

        try:
            if init_fn is not None:
                init_fn(worker_id)

            fetcher = _DatasetKind.create_fetcher(
                dataset_kind, dataset, auto_collation, collate_fn, drop_last
            )
        except Exception:
            init_exception = ExceptionWrapper(
                where="in DataLoader worker process {}".format(worker_id)
            )

        # When using Iterable mode, some worker can exit earlier than others due
        # to the IterableDataset behaving differently for different workers.
        # When such things happen, an `_IterableDatasetStopIteration` object is
        # sent over to the main process with the ID of this worker, so that the
        # main process won't send more tasks to this worker, and will send
        # `None` to this worker to properly exit it.
        #
        # Note that we cannot set `done_event` from a worker as it is shared
        # among all processes. Instead, we set the `iteration_end` flag to
        # signify that the iterator is exhausted. When either `done_event` or
        # `iteration_end` is set, we skip all processing step and just wait for
        # `None`.
        iteration_end = False

        watchdog = ManagerWatchdog()

        while watchdog.is_alive():
            try:
                r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
            except queue.Empty:
                continue
            if r is None:
                # Received the final signal
                assert done_event.is_set() or iteration_end
                break
            elif done_event.is_set() or iteration_end:
                # `done_event` is set. But I haven't received the final signal
                # (None) yet. I will keep continuing until get it, and skip the
                # processing steps.
                continue
            idx, index = r
            """ Added """
            MyRandomResizedCrop.sample_image_size(idx)
            """ Added """
            if init_exception is not None:
                data = init_exception
                init_exception = None
            else:
                try:
                    data = fetcher.fetch(index)
                except Exception as e:
                    if (
                        isinstance(e, StopIteration)
                        and dataset_kind == _DatasetKind.Iterable
                    ):
                        data = _IterableDatasetStopIteration(worker_id)
                        # Set `iteration_end`
                        #   (1) to save future `next(...)` calls, and
                        #   (2) to avoid sending multiple `_IterableDatasetStopIteration`s.
                        iteration_end = True
                    else:
                        # It is important that we don't store exc_info in a variable.
                        # `ExceptionWrapper` does the correct thing.
                        # See NOTE [ Python Traceback Reference Cycle Problem ]
                        data = ExceptionWrapper(
                            where="in DataLoader worker process {}".format(worker_id)
                        )
            data_queue.put((idx, data))
            del data, idx, index, r  # save memory
    except KeyboardInterrupt:
        # Main process will raise KeyboardInterrupt anyways.
        pass
    if done_event.is_set():
        data_queue.cancel_join_thread()
        data_queue.close()
