import sys
import torch
from contextlib import contextmanager
from torch.backends import ContextProp, PropModule, __allow_nonbracketed_mutation

def is_available():
    r"""Returns whether PyTorch is built with MKL-DNN support."""
    return torch._C.has_mkldnn

VERBOSE_OFF = 0
VERBOSE_ON = 1
VERBOSE_ON_CREATION = 2
class verbose(object):
    """
    On-demand oneDNN (former MKL-DNN) verbosing functionality
    To make it easier to debug performance issues, oneDNN can dump verbose
    messages containing information like kernel size, input data size and
    execution duration while executing the kernel. The verbosing functionality
    can be invoked via an environment variable named `DNNL_VERBOSE`. However,
    this methodology dumps messages in all steps. Those are a large amount of
    verbose messages. Moreover, for investigating the performance issues,
    generally taking verbose messages for one single iteration is enough.
    This on-demand verbosing functionality makes it possible to control scope
    for verbose message dumping. In the following example, verbose messages
    will be dumped out for the second inference only.

    .. highlight:: python
    .. code-block:: python

        import torch
        model(data)
        with torch.backends.mkldnn.verbose(torch.backends.mkldnn.VERBOSE_ON):
            model(data)

    Args:
        level: Verbose level
            - ``VERBOSE_OFF``: Disable verbosing
            - ``VERBOSE_ON``:  Enable verbosing
            - ``VERBOSE_ON_CREATION``: Enable verbosing, including oneDNN kernel creation
    """

    def __init__(self, level):
        self.level = level

    def __enter__(self):
        if self.level == VERBOSE_OFF:
            return
        st = torch._C._verbose.mkldnn_set_verbose(self.level)
        assert st, "Failed to set MKLDNN into verbose mode. Please consider to disable this verbose scope."
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        torch._C._verbose.mkldnn_set_verbose(VERBOSE_OFF)
        return False

def set_flags(_enabled):
    orig_flags = (torch._C._get_mkldnn_enabled(),)
    torch._C._set_mkldnn_enabled(_enabled)
    return orig_flags

@contextmanager
def flags(enabled=False):
    with __allow_nonbracketed_mutation():
        orig_flags = set_flags(enabled)
    try:
        yield
    finally:
        with __allow_nonbracketed_mutation():
            set_flags(orig_flags[0])

class MkldnnModule(PropModule):
    def __init__(self, m, name):
        super(MkldnnModule, self).__init__(m, name)

    enabled = ContextProp(torch._C._get_mkldnn_enabled, torch._C._set_mkldnn_enabled)

# Cool stuff from torch/backends/cudnn/__init__.py and
# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
sys.modules[__name__] = MkldnnModule(sys.modules[__name__], __name__)
