import torch
import warnings
import inspect
from sys import maxsize as maxsize
from typing import Set

import torch.onnx
# This import monkey-patches graph manipulation methods on Graph, used for the
# ONNX symbolics
import torch.onnx.utils

from functools import wraps
from torch._C import OptionalType


# Note [Edit Symbolic Files]
# EDITING THIS FILE AND SYMBOLIC_OPSET<VERSION> FILES? READ THIS FIRST!
#
# - These files is ONLY for ATen operators (e.g., operators that show up in the
#   trace as aten::blah).  If you need to special case a primitive operator,
#   look at _run_symbolic_function
# - Parameter ordering does NOT necessarily match what is in VariableType.cpp;
#   tensors are always first, then non-tensor arguments.
# - Parameter names must *exactly* match the names in VariableType.cpp, because
#   dispatch is done with keyword arguments.
# - Looking for inplace ops?  They're detected by the trailing underscore, and
#   transparently dispatched to their non inplace versions in
#   'run_symbolic_function'.   See Note [Export inplace]
#
# ----------------------------------------------------------------------------------
# A note on Tensor types
# ----------------------------------------------------------------------------------
#
# In general, we should avoid depending on the type of Tensor Values contained
# within the trace graph. However, this is sometimes unavoidable (due to ONNX
# spec requirements, etc). The TensorType object has accessors for these properties
# that return the property if it is statically known and return nullopt otherwise.
#
# In general, we should prefer to rely on the least specific information possible.
# For example, not relying on tensor properties at all is better than relying
# on the number of dimensions which is better than relying on
# concrete shapes. Doing so will make the export symbolics
# more robust to different graphs.

# ---------------------------------------------------------------------------------
# Helper functions
# ---------------------------------------------------------------------------------

# Save some builtins as locals, because we'll shadow them below
_sum = sum


def _parse_arg(value, desc, arg_name=None, node_name=None):
    if desc == 'none':
        return value
    if desc == 'v' or not _is_value(value):
        return value
    if value.node().mustBeNone():
        return None
    if value.node().kind() == 'onnx::Constant':
        tval = value.node()['value']
        if desc == 'i':
            return int(tval)
        elif desc == 'f':
            return float(tval)
        elif desc == 'b':
            return bool(tval)
        elif desc == 's':
            return str(tval)
        elif desc == 't':
            return tval
        elif desc == 'is':
            return [int(v) for v in tval]
        elif desc == 'fs':
            return [float(v) for v in tval]
        else:
            raise RuntimeError("ONNX symbolic doesn't know to interpret Constant node")
    elif value.node().kind() == 'prim::ListConstruct':
        if desc == 'is':
            for v in value.node().inputs():
                if v.node().kind() != 'onnx::Constant':
                    raise RuntimeError("Failed to export an ONNX attribute '" + v.node().kind() +
                                       "', since it's not constant, please try to make "
                                       "things (e.g., kernel size) static if possible")
            return [int(v.node()['value']) for v in value.node().inputs()]
        else:
            raise RuntimeError("ONNX symbolic doesn't know to interpret ListConstruct node")

    if arg_name is None or node_name is None:
        raise RuntimeError("Expected node type 'onnx::Constant', got '{}'.".format(value.node().kind()))
    else:
        raise RuntimeError("Expected node type 'onnx::Constant' "
                           "for argument '{}' of node '{}', got '{}'.".format(arg_name, node_name, value.node().kind()))


def _maybe_get_const(value, desc):
    if _is_value(value) and value.node().kind() == 'onnx::Constant':
        return _parse_arg(value, desc)
    return value


def _maybe_get_scalar(value):
    value_t = _maybe_get_const(value, 't')
    if isinstance(value_t, torch.Tensor) and value_t.shape == ():
        return value_t
    return value


def _get_const(value, desc, arg_name):
    if _is_value(value) and value.node().kind() not in ('onnx::Constant', 'prim::Constant'):
        raise RuntimeError("ONNX symbolic expected a constant value of the {} argument, got `{}`".format(arg_name, value))
    return _parse_arg(value, desc)


def _unpack_list(list_value):
    list_node = list_value.node()
    assert list_node.kind() == "prim::ListConstruct"
    return list(list_node.inputs())


# Check if list_value is output from prim::ListConstruct
# This is usually called before _unpack_list to ensure the list can be unpacked.
def _is_packed_list(list_value):
    return _is_value(list_value) and list_value.node().kind() == "prim::ListConstruct"


def parse_args(*arg_descriptors):
    def decorator(fn):
        fn._arg_descriptors = arg_descriptors

        def wrapper(g, *args, **kwargs):
            # some args may be optional, so the length may be smaller
            assert len(arg_descriptors) >= len(args)
            try:
                sig = inspect.signature(fn)
                arg_names = list(sig.parameters.keys())[1:]
                fn_name = fn.__name__
            except Exception:
                arg_names = [None] * len(args)  # type: ignore
                fn_name = None  # type: ignore
            args = [_parse_arg(arg, arg_desc, arg_name, fn_name)  # type: ignore
                    for arg, arg_desc, arg_name in zip(args, arg_descriptors, arg_names)]  # type: ignore
            # only support _outputs in kwargs
            assert len(kwargs) <= 1
            if len(kwargs) == 1:
                assert '_outputs' in kwargs
            return fn(g, *args, **kwargs)
        # In Python 2 functools.wraps chokes on partially applied functions, so we need this as a workaround
        try:
            wrapper = wraps(fn)(wrapper)
        except Exception:
            pass
        return wrapper
    return decorator


def _scalar(x):
    """Convert a scalar tensor into a Python value."""
    assert x.numel() == 1
    return x.item()


def _if_scalar_type_as(g, self, tensor):
    """
    Convert self into the same type of tensor, as necessary.

    We only support implicit casting for scalars, so we never
    actually need to insert an ONNX cast operator here; just
    fix up the scalar.
    """
    if isinstance(self, torch._C.Value):
        return self

    scalar_type = tensor.type().scalarType()
    if scalar_type:
        ty = scalar_type.lower()
        return getattr(self, ty)()

    return self


def _is_none(x):
    return x.node().mustBeNone()

def _is_value(x):
    return isinstance(x, torch._C.Value)

def _is_tensor(x):
    return x.type().isSubtypeOf(torch._C.TensorType.get())

def _is_tensor_list(x):
    return isinstance(x.type(), torch._C.ListType) and isinstance(x.type().getElementType(), torch._C.TensorType)

def _get_tensor_rank(x):
    if not _is_tensor(x) or x.type() is None:
        return None
    return x.type().dim()

def _get_tensor_sizes(x, allow_nonstatic=True):
    if not _is_tensor(x) or x.type() is None:
        return None
    if allow_nonstatic:
        # Each individual symbol is returned as None.
        # e.g. [1, 'a', 'b'] -> [1, None, None]
        return x.type().varyingSizes()
    # returns None, if exists any symbol in sizes.
    # e.g. [1, 'a', 'b'] -> None
    return x.type().sizes()

def _get_tensor_dim_size(x, dim):
    try:
        sizes = _get_tensor_sizes(x)
        return sizes[dim]
    except Exception:
        pass
    return None

def _unimplemented(op, msg):
    warnings.warn("ONNX export failed on " + op + " because " + msg + " not supported")


def _onnx_unsupported(op_name):
    raise RuntimeError('Unsupported: ONNX export of operator {}. '
                       'Please feel free to request support or submit a pull request on PyTorch GitHub.'.format(op_name))


def _onnx_opset_unsupported(op_name, current_opset, supported_opset):
    raise RuntimeError('Unsupported: ONNX export of {} in '
                       'opset {}. Please try opset version {}.'.format(op_name, current_opset, supported_opset))

def _onnx_opset_unsupported_detailed(op_name, current_opset, supported_opset, reason):
    raise RuntimeError('Unsupported: ONNX export of {} in '
                       'opset {}. {}. Please try opset version {}.'.format(op_name, current_opset, reason, supported_opset))


def _block_list_in_opset(name):
    def symbolic_fn(*args, **kwargs):
        raise RuntimeError("ONNX export failed on {}, which is not implemented for opset {}. "
                           "Try exporting with other opset versions."
                           .format(name, _export_onnx_opset_version))
    return symbolic_fn


def _try_get_scalar_type(*args):
    for arg in args:
        try:
            return arg.type().scalarType()
        except RuntimeError:
            pass
    return None


def _select_helper(g, self, dim, index, apply_reshape=True):
    index_const = _maybe_get_scalar(index)
    index_dim = _get_tensor_rank(index)
    if not _is_value(index_const):
        # Index is a constant scalar. Make it a size 1 constant tensor.
        index = g.op("Constant", value_t=torch.LongTensor([index_const]))
    elif index_dim is not None and apply_reshape:
        if index_dim == 0:
            # Index is a scalar. Reshape it to a size 1 tensor.
            index = g.op("Reshape", index, g.op("Constant", value_t=torch.LongTensor([1])))

    index_scalar_type = index.type().scalarType()
    if index_scalar_type is None or index_scalar_type not in ['Long', 'Int']:
        index = g.op("Cast", index, to_i=cast_pytorch_to_onnx["Long"])
    return g.op("Gather", self, index, axis_i=dim)


def _slice_helper(g, input, axes, starts, ends, steps=None, dynamic_slice=False):
    if _export_onnx_opset_version <= 9:
        from torch.onnx.symbolic_opset9 import _slice as _slice9
        return _slice9(g, input, axes, starts, ends)
    else:
        from torch.onnx.symbolic_opset10 import _slice as _slice10
        return _slice10(g, input, axes, starts, ends, steps, dynamic_slice)

def _hardtanh_helper(g, input, min_val, max_val):
    if _export_onnx_opset_version <= 10:
        from torch.onnx.symbolic_opset9 import hardtanh
        return hardtanh(g, input, min_val, max_val)
    else:
        from torch.onnx.symbolic_opset11 import hardtanh  # type: ignore[no-redef]
        return hardtanh(g, input, min_val, max_val)

def _is_fp(value):
    if value:
        if isinstance(value, torch.Tensor):
            type = value.dtype
            return (type == 'torch.float32') or (type == 'torch.float64') or (type == 'torch.float16')
        else:
            type = value.type().scalarType()
            if type is None:
                warnings.warn("Type cannot be inferred, which might cause exported graph to produce incorrect results.")
            return (type == 'Float') or (type == 'Double') or (type == 'Half')
    return False

def _generate_wrapped_number(g, scalar):
    """
    Create a wrapped number based on https://github.com/pytorch/pytorch/issues/9515
    A Tensor is a considered a "wrapped number" if it is
    auto-wrapped from a C++ or Python number type. Integer types are
    wrapped as 0-dim int64 tensors and floating-point types are
    wrapped as 0-dim double tensors.

    The input to this function is constant value. If the data type 
    is a floating point type, it is converted to a 0-dim double
    tensor, else it is converted to a 0-dim tensor of its original type
    """
    assert not isinstance(scalar, torch.Tensor)
    if isinstance(scalar, float):
        return g.op("Constant", value_t=torch.tensor(scalar, dtype=torch.double))
    return g.op("Constant", value_t=torch.tensor(scalar))

def _sort_helper(g, input, dim, decending=True, out=None):
    if out is not None:
        _unimplemented("Sort", "Out parameter is not supported")
    shape_ = g.op("Shape", input)
    dim_size_ = g.op("Gather", shape_, g.op("Constant", value_t=torch.tensor([dim], dtype=torch.int64)))
    if _export_onnx_opset_version <= 10:
        if not decending:
            _unimplemented("Sort", "Ascending is not supported")
        return g.op("TopK", input, dim_size_, axis_i=dim, outputs=2)
    else:
        return g.op("TopK", input, dim_size_, axis_i=dim, largest_i=decending, outputs=2)


def _topk_helper(g, input, k, dim, largest=True, sorted=False, out=None):
    if out is not None:
        _unimplemented("TopK", "Out parameter is not supported")
    if not _is_value(k):
        k = g.op("Constant", value_t=torch.tensor([k], dtype=torch.int64))
    else:
        k = g.op("Reshape", k, g.op("Constant", value_t=torch.tensor([1])))
    if _export_onnx_opset_version <= 10:
        if not largest:
            _unimplemented("TopK", "Ascending is not supported")
        return g.op("TopK", input, k, axis_i=dim, outputs=2)
    else:
        return g.op("TopK", input, k, axis_i=dim, largest_i=largest, sorted_i=sorted, outputs=2)


def _interpolate_warning(interpolate_mode):
    onnx_op = "onnx:Resize" if _export_onnx_opset_version >= 10 else "onnx:Upsample"