import torch._C


class _OpNamespace(object):
    """
    An op namespace to dynamically bind Operators into Python.

    Say a user has created a custom Operator called "my_namespace::my_op". To
    call this op, the user will write torch.ops.my_namespace.my_op(...).
    At startup, this operation will not yet be bound into Python. Instead, the
    following sequence of magic tricks will occur:
    1. `torch.ops.my_namespace` will invoke the `__getattr__` magic method
       on the `torch.ops` object, which will create a new `_OpNamespace`
       object called `my_namespace` and set it as an attribute on the `ops`
       object.
    2. `torch.ops.my_namespace.my_op` will then invoke `__getattr__` on
       the `my_namespace` object, which will retrieve the operation via
       `torch.get_operation`, a function bound from C++, and then in a similar
       fashion bind this new object onto the `my_namespace` object.
    3. `torch.ops.my_namespace.my_op(...)` then calls this new operation
        and subsequent accesses will incur no further lookup (the namespace and
        operation will already exist).
    """
    def __init__(self, name):
        self.name = name

    def __getattr__(self, op_name):
        # Get the op `my_namespace::my_op` if available. This will also check
        # for overloads and raise an exception if there are more than one.
        op = torch._C._jit_get_operation('{}::{}'.format(self.name, op_name))
        setattr(self, op_name, op)
        return op


class _Ops(object):
    def __getattr__(self, name):
        # Here we are creating `torch.ops.my_namespace`
        namespace = _OpNamespace(name)
        setattr(self, name, namespace)
        return namespace


# The ops "namespace"
ops = _Ops()