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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation.  All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------

from logging import getLogger

from fusion_base import Fusion
from fusion_utils import FusionUtils
from onnx import NodeProto, TensorProto, helper
from onnx_model import OnnxModel

logger = getLogger(__name__)


class FusionEmbedLayerNoMask(Fusion):
    """
    Fuse embedding layer into one node (EmbedLayerNormalization).
    It supports the following model types: BERT, DistilBert, ALBert.
    """

    def __init__(self, model: OnnxModel, description: str = "no mask"):
        super().__init__(
            model,
            "EmbedLayerNormalization",
            ["LayerNormalization", "SkipLayerNormalization"],
            description,
        )
        self.utils = FusionUtils(model)
        self.shape_infer = None
        self.shape_infer_done = False

        # The following will be reset in each fuse call of FusionEmbedLayerNormalization
        self.attention = None
        self.embed_node = None

    def match_two_gather(self, add: NodeProto) -> None | tuple[NodeProto, NodeProto]:
        gather_0_path = self.model.match_parent_path(add, ["Gather"], [0])
        if gather_0_path is None:
            return None

        gather_1_path = self.model.match_parent_path(add, ["Gather"], [1])
        if gather_1_path is None:
            return None

        return gather_0_path[0], gather_1_path[0]

    def check_attention_subgraph(
        self,
        layernorm: NodeProto,
        input_name_to_nodes: dict[str, list[NodeProto]],
        is_distil_bert: bool,
    ) -> bool:
        """Check that LayerNormalization has a child of Attention node or subgraph like Attention.
        Args:
            layernorm (NodeProto): LayerNormalization node
            input_name_to_nodes (Dict[str, List[NodeProto]]): map from input name to nodes
            is_distil_bert (bool): whether it is DistilBert or not
        Returns:
            bool: whether there is Attention node or subgraph like Attention
        """
        self.attention = self.model.find_first_child_by_type(
            layernorm, "Attention", input_name_to_nodes, recursive=False
        )

        if self.attention is not None:
            return True

        if layernorm.output[0] not in input_name_to_nodes:
            return False
        children = input_name_to_nodes[layernorm.output[0]]
        children_types = sorted([child.op_type for child in children])

        # Try find MultiHeadAttention
        if children_types == ["MatMul", "MatMul", "MatMul", "SkipLayerNormalization"]:
            for node in children:
                if node.op_type == "SkipLayerNormalization":
                    path1 = self.model.match_parent_path(
                        node,
                        ["Add", "MatMul", "MultiHeadAttention", "MatMul"],
                        [None, None, 0, 0],
                    )
                    if path1 is not None and path1[-1].input[0] == layernorm.output[0]:
                        self.cross_attention = path1[2]
                        return True

        # In case user disables attention fusion, check whether subgraph looks like Attention.
        # For Albert, there is MatMul+Add after embedding layer before attention.
        if len(children) == 1 and children[0].op_type == "MatMul" and children[0].output[0] in input_name_to_nodes:
            grandchildren = input_name_to_nodes[children[0].output[0]]
            if (
                len(grandchildren) == 1
                and grandchildren[0].op_type == "Add"
                and grandchildren[0].output[0] in input_name_to_nodes
            ):
                nodes = input_name_to_nodes[grandchildren[0].output[0]]
                for node in nodes:
                    if node.op_type == "Attention":
                        self.attention = node
                        return True
                children_types = sorted([child.op_type for child in nodes])

        # Two Shape nodes might be merged by ORT
        if is_distil_bert:
            # SkipLayerNormailization might exist when model has been optimized by ORT first.
            if (
                children_types != ["MatMul", "MatMul", "MatMul", "Shape", "SkipLayerNormalization"]
                and children_types != ["Add", "MatMul", "MatMul", "MatMul", "Shape", "Shape"]
                and children_types != ["Add", "MatMul", "MatMul", "MatMul", "Shape"]
            ):
                logger.debug("No Attention like subgraph in children of LayerNormalization")
                return False
        else:
            if children_types != [
                "Add",
                "MatMul",
                "MatMul",
                "MatMul",
            ] and children_types != [
                "MatMul",
                "MatMul",
                "MatMul",
                "SkipLayerNormalization",
            ]:
                logger.debug("No Attention like subgraph in children of LayerNormalization")
                return False

        return True

    def match_position_embedding_distilbert(self, position_embedding_gather, input_ids, output_name_to_node):
        """  Match position embedding path from input_ids to Gather for DistilBert.
        Pattern is like the following:
                 (input_ids)
                      |
                     Shape
                       |   \
                       |    Gather (indices=1)
                       |       |
                       |      Cast (optional)
                       |       |
                       |      Range (start=0, end=*, delta=1)
                       |       |
                       |    Unsqueeze
                       |    /
                      Expand
                        |
                      Gather
        """
        # remove after tests pass
        path1 = self.model.match_parent_path(position_embedding_gather, ["Expand", "Shape"], [1, 1])
        if path1 is None:
            path1 = self.model.match_parent_path(
                position_embedding_gather,
                ["Expand", "Where", "Reshape", "Shape"],
                [1, 1, 2, 0],
            )
            if path1 is None:
                return False

        expand, shape = path1[0], path1[-1]
        if shape.input[0] != input_ids:
            return False

        _, path2, _ = self.model.match_parent_paths(
            expand,
            [
                (["Unsqueeze", "Range", "Cast", "Gather", "Shape"], [0, 0, 1, 0, 0]),
                (["Unsqueeze", "Range", "Gather", "Shape"], [0, 0, 1, 0]),
            ],
            output_name_to_node,
        )
        if path2 is None:
            return False

        range_node = path2[1]
        if not (
            self.utils.check_node_input_value(range_node, 0, 0) and self.utils.check_node_input_value(range_node, 2, 1)
        ):
            return False

        gather_node = path2[-2]
        if not (self.utils.check_node_input_value(gather_node, 1, 1)):
            return False

        shape_node = path2[-1]
        if shape_node.input[0] != input_ids:
            return False

        return True

    def match_position_embedding_roberta(self, position_embedding_gather, input_ids, output_name_to_node):
        """Match position embedding path from input_ids to Gather for Roberta.
        Roberta Embedding Layer Pattern (* is optional since it might be removed by ORT, ? is the padding word id):
          (input_ids) --> Equal(B=?) -- Not -- Cast(to=6) -- CumSum(axis=1) -- Mul -- Cast(to=7) -- Add(B=1) -- Cast(to=7)* --> Gather
                                                |                              ^
                                                V                              |
                                                +------------------------------+
        Roberta new pattern from transformers v4.9:
           (input_ids) --> Equal(B=?) -- Not -- Cast(to=6) -- CumSum(axis=1) -- Add(B=0) -- Mul -- Cast(to=7) -- Add(B=1) --> Gather
                                                |                                           ^
                                                V                                           |
                                                +-------------------------------------------+
        start_node = position_embedding_gather
        start_index = 1
        # match optional Cast node.
        parent = self.model.get_parent(start_node, start_index, output_name_to_node)
        if parent is None:
            return
        if parent.op_type == "Cast":
            if OnnxModel.get_node_attribute(parent, "to") != 7:
                return
            start_node = parent
            start_index = 0
        i, path, return_indices = self.model.match_parent_paths(
            start_node,
            [ (['Add', 'Cast', 'Mul', 'CumSum', 'Cast', 'Not', 'Equal'], [start_index, 0, 0, 0, 0, 0, 0]),
              (['Add', 'Cast', 'Mul', 'Add', 'CumSum', 'Cast', 'Not', 'Equal'], [start_index, 0, 0, 0, 0, 0, 0, 0])],
            output_name_to_node)
        if path is not None:
            # constant input of Add shall be 1.
            i, value = self.model.get_constant_input(path[0])
            if value != 1:
                return False
            _, self.padding_word_id = self.model.get_constant_input(path[-1])
            return input_ids == path[-1].input[0]
        """

        return False

    def match_position_embedding_bert(self, position_embedding_gather, input_ids, output_name_to_node):
        """  Match position embedding path from input_ids to Gather for BERT.
        BERT Embedding Layer Pattern:
                                    (input_ids)
                                   /         \
                                 /          Shape
                                /              |
                              /              Gather (indices=1)
                             /                  |
                            /                  Add (optional, B=0)
                           /                    |
                        Gather (segment_ids) Unsqueeze (axes=0)
                           \\        |           |
                            \\     Gather      Slice (data[1,512], starts=0, ends=*, axes=1, steps=1)
                              \\    /            |
                                Add          Gather
                                   \\       /
                                      Add
                                       |
                                LayerNormalization
        """
        path = self.model.match_parent_path(
            position_embedding_gather,
            ["Slice", "Unsqueeze"],
            [1, 2],
            output_name_to_node,
        )
        if path is None:
            return False

        slice, unsqueeze = path
        slice_weight = self.model.get_constant_value(slice.input[0])
        if not (
            slice_weight is not None
            and len(slice_weight.shape) == 2
            and slice_weight.shape[0] == 1
            and self.utils.check_node_input_value(slice, 1, [0])
            and self.utils.check_node_input_value(slice, 3, [1])
            and (len(slice.input) == 4 or self.utils.check_node_input_value(slice, 4, [1]))
        ):
            return False

        opset_version = self.model.get_opset_version()
        if opset_version < 13:
            if not FusionUtils.check_node_attribute(unsqueeze, "axes", [0]):
                return False
        else:
            if not self.utils.check_node_input_value(unsqueeze, 1, [0]):
                return False

        node = self.model.get_parent(unsqueeze, 0, output_name_to_node)
        if node is None:
            return False
        if node.op_type == "Add":
            if not self.utils.check_node_input_value(node, 1, 0):
                return False
            gather = self.model.get_parent(node, 0, output_name_to_node)
        else:
            gather = node

        if gather is None or gather.op_type != "Gather":
            return False
        if not (self.utils.check_node_input_value(gather, 1, 1)):
            return False

        shape = self.model.get_parent(gather, 0, output_name_to_node)
        if shape is None or shape.op_type != "Shape":
            return False

        return input_ids == shape.input[0]

    def match_position_embedding(self, position_embedding_gather, input_ids, output_name_to_node):
        if self.match_position_embedding_bert(position_embedding_gather, input_ids, output_name_to_node):
            return True

        # TODO: Support roberta (position starts from 2 instead of 0) in EmbedLayerNormalization kernel
        #       related: https://github.com/huggingface/transformers/issues/10736
        # if self.match_position_embedding_roberta(position_embedding_gather, input_ids, output_name_to_node):
        #    return True

        if self.match_position_embedding_distilbert(position_embedding_gather, input_ids, output_name_to_node):
            return True

        return False

    def check_embedding(self, word_embedding_gather, segment_embedding_gather, position_embedding_gather):
        """Sanity check of embedding weights, and match hidden_size of weights and shape of inputs."""
        input_ids = word_embedding_gather.input[1]
        segment_ids = segment_embedding_gather.input[1] if segment_embedding_gather else None
        position_ids = position_embedding_gather.input[1]

        if not self.shape_infer_done:
            self.shape_infer = self.model.infer_runtime_shape(update=True)
            self.shape_infer_done = True

        if self.shape_infer is not None:
            input_ids_shape = self.shape_infer.get_edge_shape(input_ids)
            position_ids_shape = self.shape_infer.get_edge_shape(position_ids)
            assert input_ids_shape and position_ids_shape
            if not (
                len(input_ids_shape) == 2
                and len(position_ids_shape) == 2
                and input_ids_shape[1] == position_ids_shape[1]
            ):
                logger.info(
                    f"Cannot fuse EmbedLayerNormalization: input_ids and position_ids not matched in 2nd dimension: {input_ids_shape} vs {position_ids_shape}"
                )
                return False

            if segment_ids and not self.shape_infer.compare_shape(input_ids, segment_ids):
                logger.info(
                    f"Cannot fuse EmbedLayerNormalization: input_ids and segment_ids does not have same shape: {input_ids_shape} != {self.shape_infer.get_edge_shape(segment_ids)}"
                )
                return False

        word_embedding_table = self.model.get_constant_value(word_embedding_gather.input[0])
        if word_embedding_table is None or len(word_embedding_table.shape) != 2:
            logger.info("Cannot fuse EmbedLayerNormalization: word embedding table is not expected")
            return False

        position_embedding_table = self.model.get_constant_value(position_embedding_gather.input[0])
        if (
            position_embedding_table is None
            or len(position_embedding_table.shape) != 2
            or (word_embedding_table.shape[1] != position_embedding_table.shape[1])
        ):
            logger.info("Cannot fuse EmbedLayerNormalization: position embedding table is not expected")
            return False

        if segment_ids:
            segment_embedding_table = self.model.get_constant_value(segment_embedding_gather.input[0])
            if (
                segment_embedding_table is None
                or len(segment_embedding_table.shape) != 2
                or (word_embedding_table.shape[1] != segment_embedding_table.shape[1])
            ):
                logger.info("Cannot fuse EmbedLayerNormalization: segment embedding table is not expected")
                return False

        # In normal case, word embedding table is the largest, and segment embedding table is the smallest, while position embedding table is in between.
        # TODO: use other information (like initializer names) to identify different embedding weights automatically.
        if word_embedding_table.shape[0] <= position_embedding_table.shape[0]:
            logger.warning(
                f"word_embedding_table ({word_embedding_gather.input[0]}) size {word_embedding_table.shape[0]} <= position_embedding_table ({position_embedding_gather.input[0]}) size {position_embedding_table.shape[0]}"
            )

        if segment_ids:
            if word_embedding_table.shape[0] <= segment_embedding_table.shape[0]:
                logger.warning(
                    f"word_embedding_table ({word_embedding_gather.input[0]}) size {word_embedding_table.shape[0]} <= segment_embedding_table ({segment_embedding_gather.input[0]}) size {segment_embedding_table.shape[0]}"
                )

            if position_embedding_table.shape[0] <= segment_embedding_table.shape[0]:
                logger.warning(
                    f"position_embedding_table ({position_embedding_gather.input[0]}) size {position_embedding_table.shape[0]} <= segment_embedding_table ({segment_embedding_gather.input[0]}) size {segment_embedding_table.shape[0]}"
                )

        return True

    def cast_to_int32(self, input_name: str) -> tuple[str, None | NodeProto]:
        """Cast a graph input or node input to int32.
        Args:
            input_name (str): name of graph input or node input
        Returns:
            A tuple of casted input name and the cast node.
            int32_output (str): If input is int32, it is the input name, Otherwise it is output name of Cast node.
            input_cast_node (Union[None, NodeProto]): Cast node. It could be None if input is int32.
        """
        input_cast_node = None
        graph_input = self.model.find_graph_input(input_name)
        if graph_input is not None:
            if graph_input.type.tensor_type.elem_type != TensorProto.INT32:
                int32_output, input_cast_node = self.utils.cast_input_to_int32(input_name)
            else:
                int32_output = input_name
        else:
            int32_output, input_cast_node = self.utils.cast_input_to_int32(input_name)

        return int32_output, input_cast_node

    def create_fused_node(
        self,
        input_ids: str,
        layernorm: NodeProto,
        word_embedding_gather: NodeProto,
        position_embedding_gather: NodeProto,
        segment_embedding_gather: None | NodeProto,
        position_ids: str | None = None,
        embedding_sum_output=False,
        embedding_sum_name=None,
    ):
        """Create an EmbedLayerNormalization node. Note that segment embedding is optional.
        Args:
            input_ids (str): input_ids for word embeddings
            layernorm (NodeProto): LayerNormalization or SkipLayerNormalization node.
            word_embedding_gather (NodeProto): the Gather node for word embedding
            position_embedding_gather (NodeProto): the Gather node for position embedding
            segment_embedding_gather (Union[None, NodeProto]): the Gather node for segment embedding, or None.
        Returns:
            NodeProto: the EmbedLayerNormalization node created.
        """
        nodes_to_add = []
        input_ids, _ = self.cast_to_int32(input_ids)

        node_name = self.model.create_node_name("EmbedLayerNormalization")

        if layernorm.op_type == "LayerNormalization":
            gamma = layernorm.input[1]
            beta = layernorm.input[2]
        else:  # SkipLayerNormalization
            gamma = layernorm.input[2]
            beta = layernorm.input[3]

        embed_node_inputs = None
        if segment_embedding_gather is not None:
            segment_ids, _ = self.cast_to_int32(segment_embedding_gather.input[1])

            embed_node_inputs = [
                input_ids,
                segment_ids,
                word_embedding_gather.input[0],
                position_embedding_gather.input[0],
                segment_embedding_gather.input[0],
                gamma,
                beta,
            ]
        else:  # no segment embedding
            embed_node_inputs = [
                input_ids,
                "",
                word_embedding_gather.input[0],
                position_embedding_gather.input[0],
                "",
                gamma,
                beta,
            ]

        if position_ids is not None:
            # Adding an empty input for mask before position_ids
            embed_node_inputs.append("")
            position_ids, _ = self.cast_to_int32(position_ids)
            embed_node_inputs.append(position_ids)

        embed_node_outputs = [node_name + "_output", node_name + "_dummy_mask_index"]
        if embedding_sum_output:
            name = embedding_sum_name if embedding_sum_name is not None else node_name + "_embedding_sum"
            embed_node_outputs.append(name)

        embed_node = helper.make_node(
            "EmbedLayerNormalization",
            embed_node_inputs,
            outputs=embed_node_outputs,
            name=node_name,
        )

        embed_node.domain = "com.microsoft"

        # Pass attribute "epsilon" from normalize node to EmbedLayerNormalization.
        for att in layernorm.attribute:
            if att.name == "epsilon":
                embed_node.attribute.extend([att])

        # Set default value to 1e-12 if no attribute is found.
        # OnnxRuntime 1.2.0 or older has no epsilon attribute. The optimized model can only work for 1.3.0 or later.
        if len(embed_node.attribute) == 0:
            embed_node.attribute.extend([helper.make_attribute("epsilon", 1.0e-12)])

        # Make sure new EmbedLayerNormalization node is the last one in self.nodes_to_add.
        nodes_to_add.append(embed_node)
        for node in nodes_to_add:
            self.node_name_to_graph_name[node.name] = self.this_graph_name
        self.nodes_to_add.extend(nodes_to_add)

        self.embed_node = embed_node
        return embed_node

    def finish_fusion(self, layernorm, embed_node):
        self.model.replace_input_of_all_nodes(layernorm.output[0], embed_node.output[0])
        # use prune graph to remove nodes that is not needed
        self.prune_graph = True

    def is_skip_layer_norm_with_sum_output(self, node):
        return (node.op_type == "SkipLayerNormalization") and len(node.output) > 3 and len(node.output[3]) > 0

    def fuse_gpt2(
        self, layernorm, add_before_layernorm, input_name_to_nodes, output_name_to_node, optional_segment_gather=None
    ):
        # graph checks
        # gpt2 has optional segment embedding, subgraph pattern is like
        #                      input_ids  position_ids
        #                         |        |
        #  token_ids           Gather    Gather
        #       |                   \   /
        #   Gather (optional)        Add _ _ _ _ _
        #                   \         |           |
        #                     LayerNormalization  |
        #                             |           |
        #                          Attention      |
        #                             |           |
        #                           Matmul        |
        #                             |          /
        #                            Add        /
        #                              \       /
        #                                 Add
        two_gather = self.match_two_gather(add_before_layernorm)
        if two_gather is None:
            return False

        word_embedding_gather, position_embedding_gather = two_gather
        input_ids = word_embedding_gather.input[1]
        position_ids = position_embedding_gather.input[1]

        if not self.check_attention_subgraph(layernorm, input_name_to_nodes, is_distil_bert=False):
            return False

        if not self.check_embedding(word_embedding_gather, None, position_embedding_gather):
            return False

        # If layernorm node is SkipLayerNormalization, we need look at its optional fourth output.
        # If the add_before_layernorm node is an Add node, then the add_output output is the first output of this node.
        # If the add_before_layernorm node is a SkipLayerNormalization node, then the add_output output
        # is the (optional) fourth index output of this node.
        # When add_before_layernorm is SkipLayerNormalization, add_before_layernorm and layernorm are same node.
        if layernorm.op_type == "SkipLayerNormalization":
            need_embedding_sum_output = self.is_skip_layer_norm_with_sum_output(layernorm)
            sum_output_index = 3
            node_with_sum_output = layernorm
            sum_output = layernorm.output[3] if need_embedding_sum_output else None
            is_sum_graph_output = (sum_output is not None) and (self.model.find_graph_output(sum_output) is not None)
        else:  # layernorm.op_type == "LayerNormalization"
            node_with_sum_output = add_before_layernorm
            sum_output_index = 0 if add_before_layernorm.op_type == "Add" else 3
            sum_output = (
                add_before_layernorm.output[sum_output_index]
                if len(add_before_layernorm.output) > sum_output_index
                else None
            )
            is_sum_graph_output = (sum_output is not None) and (self.model.find_graph_output(sum_output) is not None)
            is_sum_used_by_multiple_nodes = (
                sum_output and (sum_output in input_name_to_nodes) and len(input_name_to_nodes[sum_output]) > 1
            )
            need_embedding_sum_output = (sum_output is not None) and (
                add_before_layernorm.op_type != "Add" or is_sum_graph_output or is_sum_used_by_multiple_nodes
            )

        # make the fused node
        embed_node = self.create_fused_node(
            input_ids,
            layernorm,
            word_embedding_gather,
            position_embedding_gather,
            optional_segment_gather,
            position_ids,
            embedding_sum_output=need_embedding_sum_output,
            embedding_sum_name=sum_output if is_sum_graph_output else None,
        )

        if need_embedding_sum_output:
            node_with_sum_output.output[sum_output_index] = "_no_use__to_be_removed_"
            if not is_sum_graph_output:
                self.model.replace_input_of_all_nodes(sum_output, embed_node.output[2])

        self.finish_fusion(layernorm, embed_node)
        return True

    def fuse_distilbert(self, layernorm, add_before_layernorm, input_name_to_nodes, output_name_to_node):
        """Fuse embedding layer for DistilBert
        Args:
            layernorm (NodeProto): node of LayerNormalization or SkipLayerNormalization
            add_before_layernorm (NodeProto): the Add node before LayerNormalization, or the SkipLayerNormalization itself
            input_name_to_nodes (Dict[str, List[NodeProto]]): map from input name to nodes
            output_name_to_node (Dict[str, List[NodeProto]]): map from output name to nodes
        """

        # DistilBert has no segment embedding, subgraph pattern is like
        #       input_ids
        #        |      \
        #        |     (position_embedding_subgraph)
        #        |        |
        #     Gather    Gather
        #          \   /
        #           Add
        #            |
        #    LayerNormalization
        two_gather = self.match_two_gather(add_before_layernorm)
        if two_gather is None:
            return False

        word_embedding_gather, position_embedding_gather = two_gather
        input_ids = word_embedding_gather.input[1]

        if not self.check_attention_subgraph(layernorm, input_name_to_nodes, is_distil_bert=True):
            return False

        if not self.match_position_embedding(position_embedding_gather, input_ids, output_name_to_node):
            return False

        if not self.check_embedding(word_embedding_gather, None, position_embedding_gather):
            return False

        embed_node = self.create_fused_node(
            input_ids, layernorm, word_embedding_gather, position_embedding_gather, None
        )
        self.finish_fusion(layernorm, embed_node)
        return True

    def fuse_bert(self, layernorm, add_before_layernorm, input_name_to_nodes, output_name_to_node):
        """Fuse embedding layer for Bert
        Args:
            layernorm (NodeProto): node of LayerNormalization or SkipLayerNormalization
            add_before_layernorm (NodeProto): the Add node before LayerNormalization, or the SkipLayerNormalization itself
            input_name_to_nodes (Dict[str, List[NodeProto]]): map from input name to nodes
            output_name_to_node (Dict[str, List[NodeProto]]): map from output name to nodes
        """

        add_2_gather = self.model.match_parent_path(add_before_layernorm, ["Add"], [0])
        if add_2_gather is None:
            return False

        two_gather = self.match_two_gather(add_2_gather[0])
        if two_gather is None:
            return False

        word_embedding_gather, segment_embedding_gather = two_gather

        input_ids = word_embedding_gather.input[1]

        if not self.check_attention_subgraph(layernorm, input_name_to_nodes, is_distil_bert=False):
            return False

        position_embedding_path = self.model.match_parent_path(add_before_layernorm, ["Gather"], [1])
        if position_embedding_path is None:
            return False

        position_embedding_gather = position_embedding_path[0]
        if not self.match_position_embedding(position_embedding_gather, input_ids, output_name_to_node):
            if not self.match_position_embedding(segment_embedding_gather, input_ids, output_name_to_node):
                return False
            # position and segment are switched
            temp = segment_embedding_gather
            segment_embedding_gather = position_embedding_gather
            position_embedding_gather = temp

        if not self.check_embedding(word_embedding_gather, segment_embedding_gather, position_embedding_gather):
            return False

        embed_node = self.create_fused_node(
            input_ids,
            layernorm,
            word_embedding_gather,
            position_embedding_gather,
            segment_embedding_gather,
        )
        self.finish_fusion(layernorm, embed_node)
        return True

    def fuse(self, node, input_name_to_nodes, output_name_to_node):
        first_add_path = self.model.match_parent_path(node, ["Add"], [0])
        if node.op_type == "LayerNormalization":
            if first_add_path is None:
                return
            add_before_layernorm = first_add_path[0]
            optional_segment_gather = None
        else:  # SkipLayerNormalization
            gather_0_path = self.model.match_parent_path(node, ["Gather"], [0])
            gather_1_path = self.model.match_parent_path(node, ["Gather"], [1])
            if gather_0_path is None and gather_1_path is not None:
                if first_add_path is None:
                    return
                add_before_layernorm = first_add_path[0]
                optional_segment_gather = gather_1_path[0]
            elif gather_0_path is not None and gather_1_path is None:
                first_add_path = self.model.match_parent_path(node, ["Add"], [1])
                if first_add_path is None:
                    return
                add_before_layernorm = first_add_path[0]
                optional_segment_gather = gather_0_path[0]
            else:
                add_before_layernorm = node  # Add is fused into SkipLayerNormalization
                optional_segment_gather = None

        if self.fuse_gpt2(
            node, add_before_layernorm, input_name_to_nodes, output_name_to_node, optional_segment_gather
        ):
            return

        if self.fuse_distilbert(node, add_before_layernorm, input_name_to_nodes, output_name_to_node):
            return

        if self.fuse_bert(node, add_before_layernorm, input_name_to_nodes, output_name_to_node):
            return


class FusionEmbedLayerNormalization(FusionEmbedLayerNoMask):
    def __init__(self, model: OnnxModel, use_mask_index=False):
        super().__init__(model, "with mask")
        self.use_mask_index = use_mask_index

    def replace_mask(self, mask_int32, attention_nodes):
        # Inputs of EmbedLayerNorm: input_ids, segment_ids (optional), word_embedding, position_embedding,
        #           segment_embedding (optional), gamma, beta, mask (optional), position_ids (optional)
        embed_node = self.embed_node
        if len(embed_node.input) == 7:
            embed_node.input.append(mask_int32)
            logger.debug("append mask to %s", embed_node.name)
        elif len(embed_node.input) > 7 and not embed_node.input[7]:
            embed_node.input[7] = mask_int32
            logger.debug("replace mask in %s", embed_node.name)
        else:
            logger.debug("skip mask in %s", embed_node.name)
            return

        for attention_node in attention_nodes:
            logger.debug("update mask_index in %s", attention_node.name)
            if attention_node.op_type == "Attention":
                attention_node.input[3] = embed_node.output[1]
            elif attention_node.op_type == "MultiHeadAttention":
                attention_node.input[4] = embed_node.output[1]

    def fuse(self, node, input_name_to_nodes, output_name_to_node):
        # Reset attention and embed_node so that we know fusion is successful when they are not None.
        self.attention = None
        self.cross_attention = None
        self.embed_node = None
        super().fuse(node, input_name_to_nodes, output_name_to_node)

        if self.embed_node is None:
            return

        if not self.use_mask_index:
            logger.debug("--use_mask_index is not set: EmbedLayerNormalization will not have mask")
            self.increase_counter("EmbedLayerNormalization(no mask)")
            return

        if self.attention is None and self.cross_attention is None:
            logger.debug("EmbedLayerNormalization will not have mask since attention node is not found")
            self.increase_counter("EmbedLayerNormalization(no mask)")
            return

        if self.attention:
            mask_int32 = self.attention.input[3]
        else:
            mask_int32 = self.cross_attention.input[4]

        children_nodes = input_name_to_nodes[mask_int32]
        if self.model.find_graph_input(mask_int32):
            attention_nodes = [node for node in children_nodes if node.op_type in ["Attention", "MultiHeadAttention"]]
            self.replace_mask(mask_int32, attention_nodes)
            self.increase_counter("EmbedLayerNormalization(with mask)")
            return

        if mask_int32 not in output_name_to_node:
            logger.debug("EmbedLayerNormalization will not have mask since %s is not a node output", mask_int32)
            self.increase_counter("EmbedLayerNormalization(no mask)")
            return

        node = output_name_to_node[mask_int32]
        if node.op_type in ["ReduceSum", "Cast"]:
            attention_nodes = [node for node in children_nodes if node.op_type in ["Attention", "MultiHeadAttention"]]
            if node.op_type == "ReduceSum":
                mask_int32 = node.input[0]
                if len(children_nodes) == len(attention_nodes):
                    self.nodes_to_remove.append(node)
            self.replace_mask(mask_int32, attention_nodes)
            self.increase_counter("EmbedLayerNormalization(with mask)")