from __future__ import annotations

from dataclasses import dataclass
from typing import TYPE_CHECKING

import torchgen.api.ufunc as ufunc
from torchgen.api.translate import translate
from torchgen.api.types import (
    BaseCType,
    Binding,
    CType,
    Expr,
    NamedCType,
    opmath_t,
    scalar_t,
    StructuredImplSignature,
    VectorizedCType,
)
from torchgen.context import with_native_function
from torchgen.model import (
    Argument,
    BaseTy,
    BaseType,
    DispatchKey,
    NativeFunctionsGroup,
    ScalarType,
    UfuncKey,
)
from torchgen.utils import OrderedSet


if TYPE_CHECKING:
    from collections.abc import Sequence

    from torchgen.api.ufunc import UfunctorBindings


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
#                                  CUDA STUFF
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #

# NB: not bothering to generate dispatch stub forward declaration in header,
# we can just paste it whereever necessary

# TODO: use BackendIndex
# dispatch_key: DispatchKey  # only CPU/CUDA right now


# Represents functors for implementing CUDA ufuncs.
# Functors are templated by scalar_t because when USERS instantiate functors
# they are templated.  A functor looks something like this:
#
#   template <typename scalar_t>
#   struct CUDAFunctorOnSelf_add {
#     using opmath_t = at::opmath_type<scalar_t>;
#     opmath_t other_;
#     opmath_t alpha_;
#     CUDAFunctorOnSelf_add(opmath_t other, opmath_t alpha)
#         : other_(other), alpha_(alpha) {}
#     __device__ scalar_t operator()(scalar_t self) {
#       return ufunc::add(static_cast<opmath_t>(self), other_, alpha_);
#     }
#   };
#
@dataclass(frozen=True)
class UfunctorSignature:
    g: NativeFunctionsGroup
    scalar_tensor_idx: int | None
    name: str

    def arguments(self) -> UfunctorBindings:
        return ufunc.ufunctor_arguments(
            self.g, scalar_tensor_idx=self.scalar_tensor_idx, scalar_t=scalar_t
        )

    def fields(self) -> list[Binding]:
        # fields are renamed to have a trailing underscore, as is conventional
        return [b.rename(f"{b.name}_") for b in self.arguments().ctor]

    def returns_type(self) -> CType:
        # TODO: don't hardcode; return type will be inferred based on tags on
        # the native function
        return BaseCType(scalar_t)

    def decl_fields(self) -> str:
        return "\n".join(f"{f.type} {f.name};" for f in self.fields())

    def inline_defn_ctor(self) -> str:
        args_str = ", ".join(a.decl() for a in self.arguments().ctor)
        # NB: hypothetically could do this with translate but the
        # transition here is very regular
        init_str = ", ".join(f"{a.name}_({a.name})" for a in self.arguments().ctor)
        return f"{self.name}({args_str}) : {init_str} {{}}"

    def decl_apply(self) -> str:
        args_str = ", ".join(a.decl() for a in self.arguments().apply)
        return f"{self.returns_type().cpp_type()} operator()({args_str}) const"


@dataclass(frozen=True)
class UfuncSignature:
    g: NativeFunctionsGroup
    name: str
    compute_t: CType

    def arguments(self) -> list[Binding]:
        return ufunc.ufunc_arguments(self.g, compute_t=self.compute_t)

    def call(self, ctx: Sequence[Binding | Expr]) -> str:
        return f"{self.name}({', '.join(a.expr for a in translate(ctx, self.arguments()))})"


# steps:
#   1. take the functional signature
#   2. use api.ufunc to convert it to template signature.  this establishes
#      the type of the template function
#   3. use api.ufunc (II) to generate a split struct / operator() signature.
#      this establish context in which we call the template signature
#
# StructuredImplSignature context
#   ~> functor constructor sig
#
# Functor constructor context
#   ~> functor fields sig
#
# Functor apply context (functor fields + functor apply sig)
#   ~> template sig
#


def eligible_for_binary_scalar_specialization(g: NativeFunctionsGroup) -> bool:
    num_tensors = sum(
        1 for a in g.functional.func.arguments.flat_non_out if a.type.is_tensor_like()
    )
    return num_tensors == 2


def compute_ufunc_cuda_functors(
    g: NativeFunctionsGroup,
) -> tuple[dict[ScalarType, dict[UfuncKey, UfunctorSignature]], str]:
    # First, build the functors.
    ufunctor_sigs: dict[ScalarType, dict[UfuncKey, UfunctorSignature]] = {}
    ufunctors: list[str] = []
    loops = g.out.ufunc_inner_loop
    scalar_tensor_idx_lookup = {
        UfuncKey.CUDAFunctorOnSelf: 1,
        UfuncKey.CUDAFunctorOnOther: 0,
        UfuncKey.CUDAFunctor: None,
    }
    if eligible_for_binary_scalar_specialization(g):
        keys = [
            UfuncKey.CUDAFunctorOnSelf,
            UfuncKey.CUDAFunctorOnOther,
            UfuncKey.CUDAFunctor,
        ]
    else:
        keys = [UfuncKey.CUDAFunctor]
        for k in [UfuncKey.CUDAFunctorOnSelf, UfuncKey.CUDAFunctorOnOther]:
            assert k not in loops, f"cannot use {k} on non-binary function"
    for k in keys:
        # If the key was directly defined, skip functor codegen; we assume the
        # user already done it for us
        if k in loops:
            ufunctor_sig = UfunctorSignature(
                g, scalar_tensor_idx=scalar_tensor_idx_lookup[k], name=loops[k].name
            )
            for dtype in loops[k].supported_dtypes:
                ufunctor_sigs.setdefault(dtype, {})[k] = ufunctor_sig
            continue

        # Note [ScalarOnly and Generic must match names for CUDA]
        # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        # Otherwise, look in ANY of the generic entries.  For simplicity of
        # codegen, both ScalarOnly and Generic are defined, the ufunc name
        # must match  (if they didn't match, we'd have to generate distinct
        # functors per dtype, which is awful, so we're not going to do it unless
        # someone really forces us to)
        ufunc_name = None
        supported_dtypes: OrderedSet[ScalarType] = OrderedSet()
        for lk in [UfuncKey.ScalarOnly, UfuncKey.Generic]:
            if lk not in loops:
                continue
            if ufunc_name is None:
                ufunc_name = loops[lk].name
            else:
                # See Note [ScalarOnly and Generic must match names for CUDA]
                assert ufunc_name == loops[lk].name, (
                    "ScalarOnly and Generic must have same ufunc name"
                )
            supported_dtypes |= loops[lk].supported_dtypes
        assert ufunc_name is not None

        name = f"{k}_{ufunc_name}"
        ufunctor_sig = UfunctorSignature(
            g, scalar_tensor_idx=scalar_tensor_idx_lookup[k], name=name
        )
        for dtype in supported_dtypes:
            ufunctor_sigs.setdefault(dtype, {})[k] = ufunctor_sig

        ufunc_sig = UfuncSignature(
            g, name=f"ufunc::{ufunc_name}", compute_t=BaseCType(opmath_t)
        )
        apply_ctx = ufunctor_sig.fields() + ufunctor_sig.arguments().apply
        ufunctors.append(
            f"""
template <typename scalar_t>
struct {ufunctor_sig.name} {{
  using opmath_t = at::opmath_type<scalar_t>;
  {ufunctor_sig.decl_fields()}
  {ufunctor_sig.inline_defn_ctor()}
  __device__ {ufunctor_sig.decl_apply()} {{
    return {ufunc_sig.call(apply_ctx)};
  }}
}};
"""
        )

    return ufunctor_sigs, "\n".join(ufunctors)


@dataclass(frozen=True)
class BinaryScalarSpecializationConfig:
    scalar_idx: int
    ctor_tensor: str
    ufunc_key: UfuncKey


BinaryScalarSpecializationConfigs = [
    BinaryScalarSpecializationConfig(
        scalar_idx=0,
        ctor_tensor="self",
        ufunc_key=UfuncKey.CUDAFunctorOnOther,
    ),
    BinaryScalarSpecializationConfig(
        scalar_idx=1,
        ctor_tensor="other",
        ufunc_key=UfuncKey.CUDAFunctorOnSelf,
    ),
]


def compute_ufunc_cuda_dtype_body(
    g: NativeFunctionsGroup,
    dtype: ScalarType,
    inner_loops: dict[UfuncKey, UfunctorSignature],
    parent_ctx: Sequence[Binding],
) -> str:
    body = "using opmath_t = at::opmath_type<scalar_t>;"
    body += "if (false) {}\n"  # for ease of codegen
    for config in BinaryScalarSpecializationConfigs:
        if config.ufunc_key not in inner_loops:
            continue
        ufunctor_sig = inner_loops[config.ufunc_key]
        scalar_idx = config.scalar_idx + 1
        # Make a copy and at the same time widen the type (not permissible
        # without copy; we don't want to mutate the input argument anyway)
        ctx: list[Expr | Binding] = list(parent_ctx)
        ctx.append(
            Expr(
                expr=f"iter.scalar_value<opmath_t>({scalar_idx})",
                type=NamedCType(config.ctor_tensor, BaseCType(opmath_t)),
            )
        )
        ufunctor_ctor_exprs_str = ", ".join(
            a.expr for a in translate(ctx, ufunctor_sig.arguments().ctor)
        )

        # NB: ufunctor must be allocated before iter.remove_operand is called,
        # as it relies on iter
        body += f"""\
else if (iter.is_cpu_scalar({scalar_idx})) {{
  {ufunctor_sig.name}<scalar_t> ufunctor({ufunctor_ctor_exprs_str});
  iter.remove_operand({scalar_idx});
  gpu_kernel(iter, ufunctor);
}}"""

    ufunctor_sig = inner_loops[UfuncKey.CUDAFunctor]
    ufunctor_ctor_exprs_str = ", ".join(
        a.expr for a in translate(parent_ctx, ufunctor_sig.arguments().ctor)
    )
    body += f"""
else {{
  gpu_kernel(iter, {ufunctor_sig.name}<scalar_t>({ufunctor_ctor_exprs_str}));
}}
    """
    return body


@with_native_function
def compute_ufunc_cuda(g: NativeFunctionsGroup) -> str:
    # First, build the functors, indexing them by dtype
    ufunctor_sigs, ufunctors = compute_ufunc_cuda_functors(g)

    # Next, build the conditionals
    sig = StructuredImplSignature(g, ufunc.kernel_name(g, DispatchKey.CUDA))
    dtype_cases = []
    for dtype, inner_ufunc_sigs in ufunctor_sigs.items():
        dtype_cases.append(
            f"""
AT_DISPATCH_CASE(at::ScalarType::{dtype},
  [&]() {{
    {compute_ufunc_cuda_dtype_body(g, dtype, inner_ufunc_sigs, sig.arguments())}
  }}
)
"""
        )

    dtype_cases_str = "\n".join(dtype_cases)

    stub_sig = StubSignature(g)

    return f"""
{ufunctors}

{stub_sig.type_defn()};
{stub_sig.dispatch_decl()}

{stub_sig.kernel_defn()} {{
  AT_DISPATCH_SWITCH(iter.common_dtype(), "{sig.name}",
    {dtype_cases_str}
  );
}}
REGISTER_DISPATCH({stub_sig.name}, &{stub_sig.kernel_name})

{sig.defn()} {{
  {stub_sig.direct_call(sig.arguments())};
}}
"""


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
#
#                                   CPU STUFF
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #


@dataclass(frozen=True)
class StubSignature:
    g: NativeFunctionsGroup

    @property
    def name(self) -> str:
        return f"{str(self.g.functional.func.name.name)}_stub"

    @property
    def kernel_name(self) -> str:
        return f"{str(self.g.functional.func.name.name)}_kernel"

    @property
    def type_name(self) -> str:
        return f"{str(self.g.functional.func.name.name)}_fn"

    def arguments(self) -> list[Binding]:
        return ufunc.stub_arguments(self.g)

    def type(self) -> str:
        cpp_args = self.arguments()
        return f"void(*)(TensorIteratorBase&, {', '.join(a.type for a in cpp_args)})"

    def dispatch_decl(self) -> str:
        return f"DECLARE_DISPATCH({self.type_name}, {self.name})"

    def dispatch_defn(self) -> str:
        return f"DEFINE_DISPATCH({self.name})"

    def kernel_defn(self) -> str:
        return f"void {self.kernel_name}(TensorIteratorBase& iter, {', '.join(a.defn() for a in self.arguments())})"

    def type_defn(self) -> str:
        return f"using {self.type_name} = {self.type()}"

    # must be called from context where this is TensorIteratorBase*
    def call(self, ctx: Sequence[Binding]) -> str:
        return f"{self.name}(device_type(), *this, {', '.join(a.expr for a in translate(ctx, self.arguments()))})"

    # used in CUDA to skip the unnecessary dynamic dispatch
    def direct_call(self, ctx: Sequence[Binding]) -> str:
        return f"{self.kernel_name}(*this, {', '.join(a.expr for a in translate(ctx, self.arguments()))})"


@with_native_function
def compute_ufunc_cpu(g: NativeFunctionsGroup) -> str:
    stub_sig = StubSignature(g)
    sig = StructuredImplSignature(g, ufunc.kernel_name(g, DispatchKey.CPU))

    return f"""
{stub_sig.type_defn()};
{stub_sig.dispatch_decl()}
{stub_sig.dispatch_defn()};

{sig.defn()} {{
  {stub_sig.call(sig.arguments())};
}}
"""


def compute_ufunc_cpu_dtype_body(
    g: NativeFunctionsGroup,
    dtype: ScalarType,
    inner_loops: dict[UfuncKey, UfuncSignature],
    parent_ctx: Sequence[Binding],
) -> str:
    assert UfuncKey.CPUScalar in inner_loops, f"{dtype}, {inner_loops.keys()}"
    assert inner_loops.keys() <= {UfuncKey.CPUScalar, UfuncKey.CPUVector}
    scalar_loop = inner_loops[UfuncKey.CPUScalar]
    vec_loop = None
    if UfuncKey.CPUVector in inner_loops:
        vec_loop = inner_loops[UfuncKey.CPUVector]

    # NB: We DON'T use translate here, because translate is
    # incapable of CSE'ing the scalar accesses in case it is also
    # used by Vectorized; also, the unpacking here is very simple
    # and only affects Scalar; everything else is implicitly captured
    # by the lambda

    # Setup scalar in scope
    body = []
    ctx = []
    for b in parent_ctx:
        if isinstance(b.argument, Argument) and b.argument.type != BaseType(
            BaseTy.Scalar
        ):
            continue
        body.append(f"auto _s_{b.name} = {b.name}.to<scalar_t>();")
        ctx.append(Expr(f"_s_{b.name}", NamedCType(b.nctype.name, BaseCType(scalar_t))))
    if vec_loop is not None:
        for b in parent_ctx:
            if isinstance(b.argument, Argument) and b.argument.type != BaseType(
                BaseTy.Scalar
            ):
                continue
            body.append(
                f"auto _v_{b.name} = at::vec::Vectorized<scalar_t>(_s_{b.name});"
            )
            ctx.append(
                Expr(
                    f"_v_{b.name}",
                    NamedCType(b.nctype.name, VectorizedCType(BaseCType(scalar_t))),
                )
            )

    # Setup lambda signature
    # NB: simplified version of ufunctor_arguments
    scalar_bindings = []
    vec_bindings = []
    for a in g.functional.func.arguments.flat_non_out:
        if not a.type.is_tensor_like():
            continue
        assert a.type == BaseType(BaseTy.Tensor)
        scalar_bindings.append(
            Binding(
                name=a.name,
                nctype=NamedCType(a.name, BaseCType(scalar_t)),
                argument=a,
            )
        )
        if vec_loop is not None:
            vec_bindings.append(
                Binding(
                    name=a.name,
                    nctype=NamedCType(a.name, VectorizedCType(BaseCType(scalar_t))),
                    argument=a,
                )
            )

    def with_ctx(b: Sequence[Binding]) -> list[Expr | Binding]:
        r: list[Expr | Binding] = []
        r.extend(ctx)
        r.extend(b)
        return r

    body_str = "\n".join(body)
    if vec_loop is not None:
        return f"""
{body_str}
cpu_kernel_vec(iter,
  [=]({", ".join(b.decl() for b in scalar_bindings)}) {{ return {scalar_loop.call(with_ctx(scalar_bindings))}; }},
  [=]({", ".join(b.decl() for b in vec_bindings)}) {{ return {vec_loop.call(with_ctx(vec_bindings))}; }}
);
"""
    else:
        return f"""
{body_str}
cpu_kernel(iter,
  [=]({", ".join(b.decl() for b in scalar_bindings)}) {{ return {scalar_loop.call(with_ctx(scalar_bindings))}; }}
);
"""


@with_native_function
def compute_ufunc_cpu_kernel(g: NativeFunctionsGroup) -> str:
    stub_sig = StubSignature(g)

    # Reindex the ufunc by dtypes; processing generic/scalaronly as well
    loops = g.out.ufunc_inner_loop
    ufunc_sigs: dict[ScalarType, dict[UfuncKey, UfuncSignature]] = {}
    for k in [UfuncKey.CPUScalar, UfuncKey.CPUVector]:
        lks = []
        # ORDER MATTERS: this specifies overriding precedence
        if k in loops:  # should happen rarely
            lks.append(k)
        if UfuncKey.ScalarOnly in loops and k is UfuncKey.CPUScalar:
            lks.append(UfuncKey.ScalarOnly)
        if UfuncKey.Generic in loops:
            lks.append(UfuncKey.Generic)
        # TODO: don't hardcode ufunc:: namespace here, should be centralized smh
        for lk in lks:
            for dtype in loops[lk].supported_dtypes:
                compute_t: CType
                if k is UfuncKey.CPUScalar:
                    compute_t = BaseCType(scalar_t)
                elif k is UfuncKey.CPUVector:
                    compute_t = VectorizedCType(BaseCType(scalar_t))
                else:
                    raise AssertionError
                inner_ufunc_sigs = ufunc_sigs.setdefault(dtype, {})
                if k not in inner_ufunc_sigs:
                    inner_ufunc_sigs[k] = UfuncSignature(
                        g, name=f"ufunc::{loops[lk].name}", compute_t=compute_t
                    )

    # Build the conditionals
    dtype_cases = []
    for dtype, inner_ufunc_sigs in ufunc_sigs.items():
        dtype_cases.append(
            f"""
AT_DISPATCH_CASE(at::ScalarType::{dtype},
  [&]() {{
    {compute_ufunc_cpu_dtype_body(g, dtype, inner_ufunc_sigs, stub_sig.arguments())}
  }}
)
"""
        )

    dtype_cases_str = "\n".join(dtype_cases)
    return f"""
namespace {{

{stub_sig.kernel_defn()} {{
  AT_DISPATCH_SWITCH(iter.common_dtype(), "{stub_sig.name}",
    {dtype_cases_str}
  );
}}

}} // anonymous namespace

{stub_sig.type_defn()};
{stub_sig.dispatch_decl()}
REGISTER_DISPATCH({stub_sig.name}, &{stub_sig.kernel_name})
"""
