# mypy: allow-untyped-defs
import hashlib
import logging
from collections.abc import Sequence
from typing import cast

from torch._inductor.codegen.cuda.cutlass_python_evt import (
    CutlassEVTCodegen,
    MockCutlassHandler,
)
from torch._inductor.utils import Placeholder
from torch.utils._ordered_set import OrderedSet

from ...._dynamo.utils import counters
from ... import config
from ...codecache import code_hash, get_path
from ...ir import Buffer, ComputedBuffer, CUDATemplateBuffer, Pointwise
from ...scheduler import (
    BaseSchedulerNode,
    BaseScheduling,
    FusedSchedulerNode,
    SchedulerNode,
    WhyNoFuse,
)
from ...utils import get_fused_kernel_name, get_kernel_metadata, sympy_product
from ...virtualized import V
from ..common import BackendFeature, IndentedBuffer


log = logging.getLogger(__name__)


class WhyNoFuseNames(WhyNoFuse):
    def __init__(self, name1: str, name2: str) -> None:
        self.name1 = name1
        self.name2 = name2


class CUDACPPScheduling(BaseScheduling):
    """
    Partial Scheduling implementation for CUDA C++ Kernels.
    This class is intended to be used in combination with TritonScheduling,
    and delegated to by CUDACombinedScheduling.

    It handles fusion decisions and CUDA C++ specific template code generation.
    """

    @classmethod
    def get_backend_features(cls, device) -> OrderedSet[BackendFeature]:
        return OrderedSet()

    def group_fn(self, sizes):
        return tuple(V.graph.sizevars.simplify(sympy_product(s)) for s in sizes)

    @staticmethod
    def is_cuda_cpp_template(node: BaseSchedulerNode) -> bool:
        return isinstance(node, SchedulerNode) and isinstance(
            node.node, CUDATemplateBuffer
        )

    def is_cuda_cpp_fused_template(self, node: BaseSchedulerNode) -> bool:
        return isinstance(node, FusedSchedulerNode) and self.is_cuda_cpp_template(node)

    def can_fuse_vertical(
        self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
    ) -> bool:
        if self.is_cuda_cpp_template(node1) and isinstance(node2, BaseSchedulerNode):
            assert node1.node, "node1.node should not be None"
            return self._can_fuse_epilogue_impl(
                cast(CUDATemplateBuffer, node1.node),
                [],
                node2,  # type: ignore[arg-type]
            )
        elif self.is_cuda_cpp_fused_template(node1) and isinstance(
            node2, BaseSchedulerNode
        ):
            assert node1.node, "node1.node should not be None"
            assert node2.node, "node2.node should not be None"
            fnode1 = cast(FusedSchedulerNode, node1)
            return self._can_fuse_epilogue_impl(
                fnode1.get_template_node(),  # type: ignore[arg-type]
                self._unwrap_epilogue_nodes(fnode1),
                node2,  # type: ignore[arg-type]
            )

        return False

    def define_kernel(self, src_code: str, node_schedule) -> str:
        wrapper = V.graph.wrapper_code
        if src_code in wrapper.src_to_kernel:
            kernel_name = wrapper.src_to_kernel[src_code]
        else:
            fused_name = (
                get_fused_kernel_name(node_schedule, config.triton.descriptive_names)
                if config.triton.descriptive_names
                else ""
            )

            # use the original src_code as the key
            kernel_hash = hashlib.sha256(src_code.encode("utf-8")).hexdigest()[:8]
            if fused_name == "fused":
                # no EVT kernel, use the original kernel name
                kernel_name = f"cutlass_{kernel_hash}"
            else:
                kernel_name = f"cutlass_{fused_name}_{kernel_hash}"
            wrapper.src_to_kernel[src_code] = kernel_name
            src_code = src_code.replace(str(Placeholder.KERNEL_NAME), kernel_name)

            _, _, kernel_path = get_path(code_hash(src_code), "py")

            compile_wrapper = IndentedBuffer()
            compile_wrapper.writeline("async_compile.cuda(r'''")
            compile_wrapper.splice(src_code, strip=True)
            compile_wrapper.writeline(
                f"''', 'so', aot_compile={str(V.graph.aot_mode)})"
            )

            metadata_comment = f"# kernel path: {kernel_path}"
            origins, detailed_origins = get_kernel_metadata(node_schedule, wrapper)
            metadata_comment += "\n" + origins + "\n" + detailed_origins
            wrapper.define_kernel(
                kernel_name, compile_wrapper.getvalue(), metadata_comment
            )
        return kernel_name

    def codegen_template(
        self,
        template_node: BaseSchedulerNode,
        epilogue_nodes: Sequence[BaseSchedulerNode],
        prologue_nodes: Sequence[BaseSchedulerNode],
    ):
        """
        Codegen a CUDA template, possibly with fused epilogues
        """
        counters["inductor"]["cuda_epilogue_fusion_counter"] += len(epilogue_nodes)
        assert self.is_cuda_cpp_template(template_node), (
            "Template node passed to CUDAScheduler.codegen_template must be a SchedulerNode that wraps a CUDATemplateBuffer"
        )
        template_node = cast(SchedulerNode, template_node)
        _, (_numel, rnumel) = template_node.group
        assert rnumel == 1
        ctb: CUDATemplateBuffer = cast(CUDATemplateBuffer, template_node.node)
        epilogue_ir_nodes: list[Buffer] = [n.node for n in epilogue_nodes]  # type: ignore[misc]
        assert all(isinstance(n, ComputedBuffer) for n in epilogue_ir_nodes), (
            "Epilogue nodes must all be instances of ir.ComputedBuffer"
        )
        kernel, render = ctb.make_kernel_render(  # type: ignore[misc]
            ctb, epilogue_nodes=epilogue_nodes
        )
        with kernel:
            for node in [template_node, *epilogue_nodes]:
                node.mark_run()

            # typically there is a codegen pass which runs after mark_run
            # for this kernel we've already generated the C++ code, but we still
            # need to let the kernel know about loads/stores that occur in the fused
            # kernel for memory planning to properly optimize allocations
            ctb.emulate_store_fn()
            for node in epilogue_ir_nodes:
                with V.set_ops_handler(MockCutlassHandler(V.get_ops_handler())):
                    assert isinstance(
                        node, ComputedBuffer
                    )  # Not sure why we need to do this again
                    node.get_store_function()(CutlassEVTCodegen.get_index_vars(node))

        with V.set_kernel_handler(kernel):
            src_code = render()
            node_schedule = [template_node, *epilogue_nodes]
            kernel_name = self.define_kernel(src_code, node_schedule)

        # debug printing values of intermediate tensors
        _, call_args, arg_signatures, _ = kernel.args.python_argdefs()
        debug_printer_manager = V.graph.wrapper_code.debug_printer
        debug_printer_manager.set_printer_args(
            call_args, kernel_name, arg_signatures, kernel
        )
        with debug_printer_manager:
            self.codegen_comment(node_schedule, kernel_name)
            kernel.call_kernel(kernel_name, ctb)

        V.graph.removed_buffers |= kernel.removed_buffers
        self.free_buffers_in_scheduler()

    @staticmethod
    def _unwrap_epilogue_nodes(
        fused_node: FusedSchedulerNode,
    ) -> list[BaseSchedulerNode]:
        nodes = fused_node.get_nodes()
        template_node = fused_node.get_template_node()
        assert all(n.node is not None for n in nodes), (
            "All epilogue nodes should have an IRNode"
        )
        # pyrefly: ignore [redundant-cast]
        return cast(
            list[BaseSchedulerNode], [n for n in nodes if n.node is not template_node]
        )

    def _can_fuse_epilogue_impl(
        self,
        cuda_template_buffer: CUDATemplateBuffer,
        existing_epilogue_nodes: list[BaseSchedulerNode],
        node_to_fuse: BaseSchedulerNode,
    ) -> bool:
        """
        Check if the given node can be fused with the epilogue. At the moment, Kernels
        support fusion with Pointwise operations, wrapped in (named) ComputedBuffer nodes.

        Args:
            cuda_template_buffer : A CUDATemplateBuffer object representing the CUDA template and it's result buffer
            existing_epilogue_nodes : List[SchedulerNode]: The list of already fused epilogue nodes.
            node_to_fuse: The SchedulerNode node to be checked if it can be fused with the epilogue.
        Returns:
        - bool: True if the given node can be fused with the epilogue, False otherwise.

        """
        why = WhyNoFuseNames(cuda_template_buffer.get_name(), node_to_fuse.get_name())

        scheduler_nodes_to_fuse = node_to_fuse.get_nodes()

        assert isinstance(cuda_template_buffer, CUDATemplateBuffer)

        # Checks on constituent nodes
        for s_node in scheduler_nodes_to_fuse:
            node = s_node.node

            if not isinstance(node, ComputedBuffer):
                why(f"{node} is not a ComputedBuffer")
                return False
            elif not isinstance(node.data, Pointwise):
                why(f"{node} is not a Pointwise op")
                return False
            elif not node.get_computed_buffer_name():  # type: ignore[attr-defined]
                why(f"{node} does not have a computed buffer name")
                return False

            name = node.get_computed_buffer_name()  # type: ignore[attr-defined]
            # dtype can differ, and strides can differ as long as they are broadcastable
            if node.get_size() != cuda_template_buffer.get_size():
                why(
                    f"{name}'s size: {node.get_size()} differs from {cuda_template_buffer.get_name()}'s \
size: {cuda_template_buffer.get_size()}"
                )
                return False

        assert len(
            existing_epilogue_nodes
        ) or cuda_template_buffer.get_name() in OrderedSet(
            [rd.name for rd in node_to_fuse.read_writes.reads]
        ), "First epilogue node must read from cuda template buffer"

        if node_to_fuse.has_aliasing_or_mutation():
            why(f"{node_to_fuse.get_name()} has aliasing or mutation")
            return False
        elif node_to_fuse.is_reduction():
            why(
                f"{node_to_fuse.get_name()} is a reduction which is not yet supported by EVT"
            )
            return False
        elif (
            not config.cuda.cutlass_epilogue_fusion_enabled
            or not config.epilogue_fusion
        ):
            why("cutlass epilogue fusion is not enabled")
            return False
        elif not cuda_template_buffer.supports_epilogue_fusion:
            why("epilogue fusion is only supported for TMA-enabled gemm ops")
            return False

        try:
            from torch._inductor.codegen.cuda.cutlass_python_evt import (
                CutlassEVTCodegen,
            )

            CutlassEVTCodegen.ir_to_evt_python_code(
                cuda_template_buffer.get_name(),
                existing_epilogue_nodes + list(node_to_fuse.get_nodes()),
                OrderedSet(),
            )

        except NotImplementedError as e:
            not_implemented_op = str(e)
            if not_implemented_op.startswith("_op_"):
                not_implemented_op = not_implemented_op[4:]
                why(
                    f"Cannot fuse epilogue node {node_to_fuse} into {cuda_template_buffer.name}, \
likely due to unsupported operation: {not_implemented_op}"  # noqa: G004, B950
                )
                return False
            else:  # Likely due to unsupported dtype.
                why(
                    f"Cannot fuse epilogue node {node_to_fuse} into {cuda_template_buffer.name}. \
Reason: {not_implemented_op}"  # noqa: G004, B950
                )
                return False

        return True
