import math
import warnings

import torch
import torch.backends.cudnn as cudnn

from torch._six import PY2, PY37
from ..nn.modules.utils import _single, _pair, _triple, _quadruple, \
    _list_with_default

from collections import OrderedDict


_builtin_table = None

_modules_containing_builtins = (torch, torch._C._nn)

_builtin_ops = [
    # Pairs of (function, op_name)
    (_list_with_default, "aten::list_with_default"),
    (_pair, "aten::_pair"),
    (_quadruple, "aten::_quadruple"),
    (_single, "aten::_single"),
    (_triple, "aten::_triple"),
    (OrderedDict, "aten::dict"),
    (dict, "aten::dict"),
    (cudnn.is_acceptable, "aten::cudnn_is_acceptable"),
    (math.ceil, "aten::ceil"),
    (math.copysign, "aten::copysign"),
    (math.erf, "aten::erf"),
    (math.erfc, "aten::erfc"),
    (math.exp, "aten::exp"),
    (math.expm1, "aten::expm1"),
    (math.fabs, "aten::fabs"),
    (math.floor, "aten::floor"),
    (math.gamma, "aten::gamma"),
    (math.lgamma, "aten::lgamma"),
    (math.log, "aten::log"),
    (math.log10, "aten::log10"),
    (math.log1p, "aten::log1p"),
    (math.pow, "aten::pow"),
    (math.sqrt, "aten::sqrt"),
    (math.isnan, "aten::isnan"),
    (math.asinh, "aten::asinh"),
    (math.atanh, "aten::atanh"),
    (math.cosh, "aten::cosh"),
    (math.sinh, "aten::sinh"),
    (math.tanh, "aten::tanh"),
    (math.acos, "aten::acos"),
    (math.asin, "aten::asin"),
    (math.atan, "aten::atan"),
    (math.atan2, "aten::atan2"),
    (math.cos, "aten::cos"),
    (math.sin, "aten::sin"),
    (math.tan, "aten::tan"),
    (math.asinh, "aten::asinh"),
    (math.atanh, "aten::atanh"),
    (math.acosh, "aten::acosh"),
    (math.sinh, "aten::sinh"),
    (math.cosh, "aten::cosh"),
    (math.tanh, "aten::tanh"),
    (math.fmod, "aten::fmod"),
    (math.modf, "aten::modf"),
    (math.factorial, "aten::factorial"),
    (math.frexp, "aten::frexp"),
    (math.isnan, "aten::isnan"),
    (math.isinf, "aten::isinf"),
    (math.degrees, "aten::degrees"),
    (math.radians, "aten::radians"),
    (math.ldexp, "aten::ldexp"),
    (torch.autograd.grad, "aten::grad"),
    (torch.autograd.backward, "aten::backward"),
    (torch._C._infer_size, "aten::_infer_size"),
    (torch.nn.functional._no_grad_embedding_renorm_, "aten::_no_grad_embedding_renorm_"),
    (torch.nn.functional.assert_int_or_pair, "aten::_assert_int_or_pair"),
    (torch.nn.functional.interpolate, "aten::__interpolate"),
    (torch.nn.functional.upsample_bilinear, "aten::__upsample_bilinear"),
    (torch.nn.functional.upsample_nearest, "aten::__upsample_nearest"),
    (torch.nn.functional.upsample, "aten::__upsample"),
    (torch.nn.init._no_grad_fill_, "aten::_no_grad_fill_"),
    (torch.nn.init._no_grad_normal_, "aten::_no_grad_normal_"),
    (torch.nn.init._no_grad_uniform_, "aten::_no_grad_uniform_"),
    (torch.nn.init._no_grad_zero_, "aten::_no_grad_zero_"),
    (torch._C._get_tracing_state, "aten::_get_tracing_state"),
    (warnings.warn, "aten::warn"),
    (torch._VF.stft, "aten::stft"),
    (torch._VF.cdist, "aten::cdist")
]

# ops in torch.functional are bound to torch 
# in these cases, we want to resolve the function to their python implementation 
# instead looking up a builtin "aten::" schema

def _gen_torch_functional_registered_ops():
    # eventually ops should encompass all of torch/functional.py, (torch.functional.__all__) 
    # but we are currently only able to compile some of the functions. additionally, 
    # some functions directly map to their aten:: implementations. 
    # TODO: add support for more ops
    ops = ["stft", "lu", "lu_unpack", "cdist"]
    return set(getattr(torch.functional, name) for name in ops)

_functional_registered_ops = _gen_torch_functional_registered_ops()

def _is_special_functional_bound_op(fn):
    return fn in _functional_registered_ops

# lazily built to ensure the correct initialization order
def _get_builtin_table():
    global _builtin_table
    if _builtin_table is not None:
        return _builtin_table
    _builtin_table = {}

    def register_all(mod):
        for name in dir(mod):
            v = getattr(mod, name)
            if callable(v) and not _is_special_functional_bound_op(v):
                _builtin_ops.append((v, "aten::" + name))
    for mod in _modules_containing_builtins:
        register_all(mod)

    if not PY2:
        _builtin_ops.append((math.gcd, "aten::gcd"))
        _builtin_ops.append((math.isfinite, "aten::isfinite"))
    if PY37:
        _builtin_ops.append((math.remainder, "aten::mathremainder"))

    import torch.distributed.autograd as dist_autograd
    if dist_autograd.is_available():
        _builtin_ops.append((dist_autograd.get_gradients, "aten::get_gradients"))

    # populate the _builtin_table from _builtin_ops
    for builtin, aten_op in _builtin_ops:
        _builtin_table[id(builtin)] = aten_op

    return _builtin_table


def _register_builtin(fn, op):
    _get_builtin_table()[id(fn)] = op


def _find_builtin(fn):
    return _get_builtin_table().get(id(fn))
