"""
Note [ONNX operators that are added/updated from opset 8 to opset 9]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
New operators:
    Compress
    ConstantOfShape
    EyeLike
    MaxUnpool
    OneHot
    Sinh
    Cosh
    Asinh
    Acosh
    Atanh
    Shrink
    IsNaN
    Sign
    Erf
    Scatter
    Where
    NonZero
    TfIdfVectorizer
    MeanVarianceNormalization

Updated operators:
    BatchNormalization: removed spatial attribute.
    Greater, Less, Constant, MatMul, PRelu, Gemm, Flatten: more data types{integers} supported.
    Cast: more data types{string} supported.
    Upsample: moved scales from attribute to input.
    Scan
"""

import functools
import warnings

import torch
from torch._C import _onnx as _C_onnx
from torch.onnx import _type_utils, errors, symbolic_helper, symbolic_opset9 as opset9
from torch.onnx._internal import jit_utils, registration

_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=8)

block_listed_operators = (
    "nonzero",
    "where",
    "scatter",
    "scatter_add",
    "erf",
    "sign",
    "isnan",
    "gather",
    "arange",
    "masked_fill",
    "index_fill",
    "index_copy",
    "repeat_interleave",
    "any",
    "all",
)

for block_listed_op in block_listed_operators:
    _onnx_symbolic(f"aten::{block_listed_op}")(
        symbolic_helper._block_list_in_opset(block_listed_op)
    )


def _apply_params(*args, **kwargs):
    """Returns a decorator that calls the decorated (higher-order) function with the given parameters."""

    def _apply(fn):
        return fn(*args, **kwargs)

    return _apply


@_onnx_symbolic(
    "aten::upsample_nearest1d",
    decorate=[_apply_params("upsample_nearest1d", 3, "nearest")],
)
@_onnx_symbolic(
    "aten::upsample_nearest2d",
    decorate=[_apply_params("upsample_nearest2d", 4, "nearest")],
)
@_onnx_symbolic(
    "aten::upsample_nearest3d",
    decorate=[_apply_params("upsample_nearest3d", 5, "nearest")],
)
@_onnx_symbolic(
    "aten::upsample_linear1d",
    decorate=[_apply_params("upsample_linear1d", 3, "linear")],
)
@_onnx_symbolic(
    "aten::upsample_bilinear2d",
    decorate=[_apply_params("upsample_bilinear2d", 4, "linear")],
)
@_onnx_symbolic(
    "aten::upsample_trilinear3d",
    decorate=[_apply_params("upsample_trilinear3d", 5, "linear")],
)
def _interpolate(name, dim, interpolate_mode):
    def symbolic_fn(g, input, output_size, *args):
        scales, align_corners = symbolic_helper._get_interpolate_attributes(
            g, interpolate_mode, args
        )
        symbolic_helper._interpolate_warning(interpolate_mode)
        align_corners = symbolic_helper._maybe_get_scalar(align_corners)
        if align_corners:
            return symbolic_helper._unimplemented(name, "align_corners == True", input)
        output_size = symbolic_helper._maybe_get_const(output_size, "is")
        if symbolic_helper._is_value(output_size):
            return symbolic_helper._unimplemented(
                name, "torch._C.Value (output_size) indexing"
            )
        if scales is None:
            scales = [
                1.0
                if i < 2
                else float(output_size[-(dim - i)])
                / float(input.type().sizes()[-(dim - i)])
                for i in range(0, dim)
            ]
        return g.op("Upsample", input, mode_s=interpolate_mode, scales_f=scales)

    return symbolic_fn


@_onnx_symbolic("aten::__interpolate")
def __interpolate(
    g: jit_utils.GraphContext,
    input,
    size,
    scale_factor,
    mode,
    align_corners,
    recompute_scale_factor,
    antialias,
):
    align_corners = symbolic_helper._maybe_get_const(align_corners, "b")
    if not symbolic_helper._is_none(align_corners) and align_corners:
        return symbolic_helper._unimplemented("interpolate", "align_corners == True")

    if not symbolic_helper._is_none(scale_factor) and symbolic_helper._is_value(
        scale_factor
    ):
        return symbolic_helper._unimplemented(
            "interpolate", "dynamic scales in opset 8"
        )

    if not symbolic_helper._is_none(size) and symbolic_helper._is_value(size):
        return symbolic_helper._unimplemented("interpolate", "dynamic size in opset 8")

    scales, mode = symbolic_helper._interpolate_get_scales_and_mode(
        g, input, size, scale_factor, mode, align_corners
    )
    return g.op("Upsample", input, mode_s=mode, scales_f=scales)


# NOTE: We should create a wrapper for this kind of operation, after resolving the shape/type propagation
#       issue for "cast" operators. Some symbolic functions depend on shape information of input tensor, which
#       is lost after casting.
def _try_cast_integer_to_float(g: jit_utils.GraphContext, *args):
    floating_scalar_types = {
        _type_utils.JitScalarType.HALF,
        _type_utils.JitScalarType.FLOAT,
        _type_utils.JitScalarType.DOUBLE,
    }
    old_type = None
    # Cast the input tensor to Float if its scalarType is known and is not floating number.
    # If casting is performed, return the old scalarType, otherwise return None.
    arg0_type = _type_utils.JitScalarType.from_value(
        args[0], _type_utils.JitScalarType.UNDEFINED
    )
    if arg0_type != _type_utils.JitScalarType.UNDEFINED:
        old_type = arg0_type
        if old_type not in floating_scalar_types:
            old_type = old_type.scalar_name()
            args = tuple(
                g.op("Cast", arg, to_i=_C_onnx.TensorProtoDataType.FLOAT)
                for arg in args
            )
        else:
            return (None,) + args
    else:
        warnings.warn(
            "Only floating datatype is supported for these operators: "
            "{Greater, Less, MatMul, PRelu, Gemm, Flatten}. This might cause "
            "the onnx model to be incorrect, if inputs have integer datatypes."
        )
    return (old_type,) + args


def _cast_to_type(g: jit_utils.GraphContext, input, to_type):
    if to_type is None:
        return input
    return getattr(opset9, f"_cast_{to_type}")(g, input, False)


def _comparison_operator(g: jit_utils.GraphContext, input, other, op_name):
    other = symbolic_helper._maybe_get_scalar(other)
    other = symbolic_helper._if_scalar_type_as(other, input)
    _, input, other = _try_cast_integer_to_float(g, input, other)
    return g.op(op_name, input, other)


# NOTE: For symbolics {gt, lt, bmm, matmul, prelu, mm, addmm, view, flatten},
#       integer input type not supported in opset8. Cast to float if possible.
@_onnx_symbolic("aten::gt")
def gt(g: jit_utils.GraphContext, input, other):
    return _comparison_operator(g, input, other, "Greater")


@_onnx_symbolic("aten::lt")
def lt(g: jit_utils.GraphContext, input, other):
    return _comparison_operator(g, input, other, "Less")


@_onnx_symbolic("aten::bmm")
def bmm(g: jit_utils.GraphContext, self, other):
    if symbolic_helper._try_get_scalar_type(self):
        old_type, self, other = _try_cast_integer_to_float(g, self, other)
        return _cast_to_type(g, g.op("MatMul", self, other), old_type)
    else:
        return g.op("MatMul", self, other)


@_onnx_symbolic("aten::matmul")
def matmul(g: jit_utils.GraphContext, self, other):
    return bmm(g, self, other)


@_onnx_symbolic("aten::prelu")
def prelu(g: jit_utils.GraphContext, self, weight):
    self_rank = symbolic_helper._get_tensor_rank(self)
    weight_sizes = symbolic_helper._get_tensor_sizes(weight)
    if self_rank is not None and self_rank > 2:
        weight = g.op("Unsqueeze", weight, axes_i=list(range(1, self_rank - 1)))
    elif self_rank == 0 and weight_sizes == [1]:
        # self and weight are both scalar but weight has rank == 1, squeeze weight.
        weight = symbolic_helper._squeeze_helper(g, weight, [0])
    if symbolic_helper._try_get_scalar_type(self):
        old_type, self, weight = _try_cast_integer_to_float(g, self, weight)
        return _cast_to_type(g, g.op("PRelu", self, weight), old_type)
    else:
        return g.op("PRelu", self, weight)


@_onnx_symbolic("aten::mm")
def mm(g: jit_utils.GraphContext, self, other):
    # Create a dummy C tensor. Only needed for API purposes, the value is
    # since beta = 0
    scalar_type = symbolic_helper._try_get_scalar_type(self, other)
    if scalar_type is None:
        raise errors.SymbolicValueError(
            "mm can only operate on tensors with known types", self
        )
    zero_constant = g.op(
        "Constant",
        value_t=torch.tensor([0], dtype=scalar_type.dtype()),
    )

    if symbolic_helper._try_get_scalar_type(self):
        old_type, self, other, zero_constant = _try_cast_integer_to_float(
            g, self, other, zero_constant
        )
        return _cast_to_type(
            g,
            g.op("Gemm", self, other, zero_constant, beta_f=0.0, alpha_f=1.0),
            old_type,
        )
    return g.op("Gemm", self, other, zero_constant, beta_f=0.0, alpha_f=1.0)


@_onnx_symbolic("aten::addmm")
@symbolic_helper.parse_args("v", "v", "v", "t", "t")
def addmm(g: jit_utils.GraphContext, self, mat1, mat2, beta, alpha):
    if symbolic_helper._try_get_scalar_type(self):
        old_type, self, mat1, mat2 = _try_cast_integer_to_float(g, self, mat1, mat2)
        return _cast_to_type(
            g,
            g.op(
                "Gemm",
                mat1,
                mat2,
                self,
                beta_f=symbolic_helper._scalar(beta),
                alpha_f=symbolic_helper._scalar(alpha),
            ),
            old_type,
        )
    else:
        return g.op(
            "Gemm",
            mat1,
            mat2,
            self,
            beta_f=symbolic_helper._scalar(beta),
            alpha_f=symbolic_helper._scalar(alpha),
        )


@_onnx_symbolic("aten::flatten")
def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
    start_dim_i = symbolic_helper._get_const(start_dim, "i", "start_dim")
    end_dim_i = symbolic_helper._get_const(end_dim, "i", "end_dim")

    dim = input.type().dim()
    if end_dim_i < 0:
        end_dim_i = dim + end_dim_i
    # use ONNX's Flatten operator for cases where the output shape is 2D
    if start_dim_i == 1 and end_dim_i == dim - 1:
        if symbolic_helper._try_get_scalar_type(input):
            old_type, input = _try_cast_integer_to_float(g, input)
            return _cast_to_type(
                g, g.op("Flatten", input, axis_i=start_dim_i), old_type
            )
        else:
            return g.op("Flatten", input, axis_i=start_dim_i)
    if start_dim_i == 0 and end_dim_i == dim - 2:
        if symbolic_helper._try_get_scalar_type(input):
            old_type, input = _try_cast_integer_to_float(g, input)
            return _cast_to_type(
                g, g.op("Flatten", input, axis_i=end_dim_i + 1), old_type
            )
        else:
            return g.op("Flatten", input, axis_i=end_dim_i + 1)

    return opset9.flatten(g, input, start_dim, end_dim)


def _constant_fill(g: jit_utils.GraphContext, sizes, dtype: int, const_value):
    if dtype is None:
        scalar_type = _type_utils.JitScalarType.FLOAT
    else:
        scalar_type = _type_utils.JitScalarType(dtype)
    if not scalar_type.dtype().is_floating_point:
        result = g.op(
            "ConstantFill",
            sizes,
            dtype_i=_type_utils.JitScalarType.FLOAT.onnx_type(),
            input_as_shape_i=1,
            value_f=const_value,
        )
        return g.op("Cast", result, to_i=scalar_type.onnx_type())
    else:
        return g.op(