# Copyright 2024-2025 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe

from typing import Any, List

import executorch.backends.arm.tosa_quant_utils as tqutils
import executorch.backends.arm.tosa_utils as tutils
import torch

from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import (
    get_input_qparams,
)

from executorch.backends.arm.operators.node_visitor import (
    NodeVisitor,
    register_node_visitor,
)
from executorch.backends.arm.operators.operator_validation_utils import (
    validate_num_inputs,
    validate_same_dtype,
)
from executorch.backends.arm.tosa_mapping import TosaArg
from executorch.backends.arm.tosa_specification import TosaSpecification
from executorch.backends.arm.tosa_utils import reshape_for_broadcast


@register_node_visitor
class MulVisitor_080_BI(NodeVisitor):
    target = "aten.mul.Tensor"

    tosa_specs = [
        TosaSpecification.create_from_string("TOSA-0.80+BI"),
    ]

    def define_node(
        self,
        node: torch.fx.Node,
        tosa_graph: Any,
        inputs: List[TosaArg],
        output: TosaArg,
    ) -> None:

        import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore

        validate_num_inputs(self.target, inputs, 2)
        validate_same_dtype(self.target, [*inputs, output])

        if (
            inputs[0].dtype != ts.DType.INT8
            or inputs[1].dtype != ts.DType.INT8
            or output.dtype != ts.DType.INT8
        ):
            raise ValueError(
                f"Inputs and output for {self.target} need to be INT8, got "
                f"{inputs[0].dtype=}, {inputs[1].dtype=} and {output.dtype=}"
            )

        dim_order = (
            inputs[0].dim_order
            if len(inputs[0].shape) > len(inputs[1].shape)
            else inputs[1].dim_order
        )
        input_A = inputs[0]
        input_B = inputs[1]
        input_qparams = get_input_qparams(node)
        input_A_qargs = input_qparams[0]
        input_B_qargs = input_qparams[1]
        input_A.shape = tutils.tosa_shape(input_A.shape, input_A.dim_order)
        input_B.shape = tutils.tosa_shape(input_B.shape, input_B.dim_order)

        # Rescale inputs to INT32 with zp=0
        input_A_rescaled = tqutils.build_rescale_to_int32(
            tosa_graph,
            input_A,
            input_A_qargs.zp,
            [1.0],
        )
        input_B_rescaled = tqutils.build_rescale_to_int32(
            tosa_graph,
            input_B,
            input_B_qargs.zp,
            [1.0],
        )

        output_shape = tutils.tosa_shape(output.shape, output.dim_order)
        mul_output = tosa_graph.addIntermediate(output_shape, ts.DType.INT32)

        input1, input2 = tutils.reshape_for_broadcast(
            tosa_graph,
            [
                input_A_rescaled,
                input_B_rescaled,
            ],
            dim_order,
        )

        # Do the INT32 Mul
        attr = ts.TosaSerializerAttribute()
        attr.MulAttribute(shift=0)
        tosa_graph.addOperator(
            ts.TosaOp.Op().MUL,
            [input1.name, input2.name],
            [mul_output.name],
            attr,
        )
        output_scale = input_A_qargs.scale * input_B_qargs.scale
        tqutils.insert_rescale_op_to_int8(tosa_graph, mul_output, output_scale, node)


@register_node_visitor
class MulVisitor_080_MI(MulVisitor_080_BI):
    # inheriting 'target' from BI class

    tosa_specs = [
        TosaSpecification.create_from_string("TOSA-0.80+MI"),
    ]

    def define_node(
        self,
        node: torch.fx.Node,
        tosa_graph: Any,
        inputs: List[TosaArg],
        output: TosaArg,
    ) -> None:

        import tosa_tools.v0_80.serializer.tosa_serializer as ts  # type: ignore

        validate_num_inputs(self.target, inputs, 2)
        validate_same_dtype(self.target, [*inputs, output])

        if inputs[0].dtype == ts.DType.INT8:
            return super().define_node(node, tosa_graph, inputs, output)

        input1, input2 = reshape_for_broadcast(tosa_graph, inputs)

        attr = ts.TosaSerializerAttribute()
        attr.MulAttribute(shift=0)
        tosa_graph.addOperator(
            ts.TosaOp.Op().MUL, [input1.name, input2.name], [output.name], attr
        )


@register_node_visitor
class MulVisitor_INT(NodeVisitor):
    target = "aten.mul.Tensor"

    tosa_specs = [
        TosaSpecification.create_from_string("TOSA-1.0+INT"),
    ]

    def define_node(
        self,
        node: torch.fx.Node,
        tosa_graph: Any,
        inputs: List[TosaArg],
        output: TosaArg,
    ) -> None:

        import serializer.tosa_serializer as ts  # type: ignore

        validate_num_inputs(self.target, inputs, 2)
        validate_same_dtype(self.target, [*inputs, output])

        if (
            inputs[0].dtype != ts.DType.INT8
            or inputs[1].dtype != ts.DType.INT8
            or output.dtype != ts.DType.INT8
        ):
            raise ValueError(
                f"Inputs and output for {self.target} need to be INT8, got "
                f"{inputs[0].dtype=}, {inputs[1].dtype=} and {output.dtype=}"
            )

        input_A = inputs[0]
        input_B = inputs[1]
        input_qparams = get_input_qparams(node)
        input_A_qargs = input_qparams[0]
        input_B_qargs = input_qparams[1]
        input_A.shape = tutils.tosa_shape(input_A.shape, input_A.dim_order)
        input_B.shape = tutils.tosa_shape(input_B.shape, input_B.dim_order)

        # Rescale inputs to INT32 with zp=0
        input_A_rescaled = tqutils.build_rescale_to_int32(
            tosa_graph,
            input_A,
            input_A_qargs.zp,
            [1.0],
            tosa_spec=self.tosa_spec,
        )
        input_B_rescaled = tqutils.build_rescale_to_int32(
            tosa_graph,
            input_B,
            input_B_qargs.zp,
            [1.0],
            tosa_spec=self.tosa_spec,
        )

        output_shape = tutils.tosa_shape(output.shape, output.dim_order)
        mul_output = tosa_graph.addIntermediate(output_shape, ts.DType.INT32)

        # Do the INT32 Mul
        tosa_graph.addConst([1], ts.DType.INT8, 0, name=f"{node.name}_shift")
        tosa_graph.addOperator(
            ts.TosaOp.Op().MUL,
            [input_A_rescaled.name, input_B_rescaled.name, f"{node.name}_shift"],
            [mul_output.name],
        )
        output_scale = input_A_qargs.scale * input_B_qargs.scale
        tqutils.insert_rescale_op_to_int8(
            tosa_graph, mul_output, output_scale, node, self.tosa_spec
        )


@register_node_visitor
class MulVisitor_FP(MulVisitor_INT):
    # inheriting 'target' from INT class

    tosa_specs = [TosaSpecification.create_from_string("TOSA-1.0+FP")]

    def define_node(
        self,
        node: torch.fx.Node,
        tosa_graph: Any,
        inputs: List[TosaArg],
        output: TosaArg,
    ) -> None:

        import serializer.tosa_serializer as ts  # type: ignore

        validate_num_inputs(self.target, inputs, 2)
        validate_same_dtype(self.target, [*inputs, output])

        if inputs[0].dtype == ts.DType.INT8:
            return super().define_node(node, tosa_graph, inputs, output)

        input1, input2 = inputs

        tosa_graph.addConst([1], ts.DType.INT8, 0, name=f"{node.name}_shift")
        tosa_graph.addOperator(
            ts.TosaOp.Op().MUL,
            [input1.name, input2.name, f"{node.name}_shift"],
            [output.name],
        )