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

import logging
import os
import sys
from collections import deque
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
from onnx import (
    AttributeProto,
    ModelProto,
    NodeProto,
    TensorProto,
    external_data_helper,
    helper,
    numpy_helper,
    onnx_pb,
    save_model,
)
from shape_infer_helper import SymbolicShapeInferenceHelper

logger = logging.getLogger(__name__)


class OnnxModel:
    def __init__(self, model):
        self.initialize(model)

    def initialize(self, model):
        self.model = model
        self._node_name_suffix: Dict[str, int] = {}  # key is node name prefix, value is the last suffix generated
        self.shape_infer_helper = None
        self.all_graphs = None

    def infer_runtime_shape(self, dynamic_axis_mapping={}, update=False):
        if self.shape_infer_helper is None or update:
            self.shape_infer_helper = SymbolicShapeInferenceHelper(self.model)

        try:
            if self.shape_infer_helper.infer(dynamic_axis_mapping):
                return self.shape_infer_helper
        except:
            print("failed in shape inference", sys.exc_info()[0])

        return None

    def input_name_to_nodes(self):
        input_name_to_nodes = {}
        for node in self.nodes():
            for input_name in node.input:
                if input_name not in input_name_to_nodes:
                    input_name_to_nodes[input_name] = [node]
                else:
                    input_name_to_nodes[input_name].append(node)
        return input_name_to_nodes

    def output_name_to_node(self):
        output_name_to_node = {}
        for node in self.nodes():
            for output_name in node.output:
                output_name_to_node[output_name] = node
        return output_name_to_node

    def nodes(self):
        all_nodes = []
        for graph in self.graphs():
            for node in graph.node:
                all_nodes.append(node)
        return all_nodes

    def graph(self):
        return self.model.graph

    def graphs(self):
        if self.all_graphs is not None:
            return self.all_graphs
        self.all_graphs = []
        graph_queue = [self.model.graph]
        while graph_queue:
            graph = graph_queue.pop(0)
            self.all_graphs.append(graph)
            for node in graph.node:
                for attr in node.attribute:
                    if attr.type == AttributeProto.AttributeType.GRAPH:
                        assert isinstance(attr.g, onnx_pb.GraphProto)
                        graph_queue.append(attr.g)
                    if attr.type == AttributeProto.AttributeType.GRAPHS:
                        for g in attr.graphs:
                            assert isinstance(g, onnx_pb.GraphProto)
                            graph_queue.append(g)
        return self.all_graphs

    def get_graphs_input_names(self):
        input_names = []
        for graph in self.graphs():
            for input in graph.input:
                input_names.append(input.name)
        return input_names

    def get_graphs_output_names(self):
        output_names = []
        for graph in self.graphs():
            for output in graph.output:
                output_names.append(output.name)
        return output_names

    def get_graph_by_node(self, node):
        for graph in self.graphs():
            if node in graph.node:
                return graph
        return None

    def get_graph_by_name(self, graph_name):
        for graph in self.graphs():
            if graph_name == graph.name:
                return graph
        return None

    def get_topological_insert_id(self, graph, outputs):
        for idx, node in enumerate(graph.node):
            for input in node.input:
                if input in outputs:
                    return idx
        return len(graph.node)

    def remove_node(self, node):
        for graph in self.graphs():
            if node in graph.node:
                graph.node.remove(node)

    def remove_nodes(self, nodes_to_remove):
        for node in nodes_to_remove:
            self.remove_node(node)

    def add_node(self, node, graph_name=None):
        if graph_name is None or graph_name == self.model.graph.name:
            self.model.graph.node.extend([node])
        else:
            graph = self.get_graph_by_name(graph_name)
            insert_idx = self.get_topological_insert_id(graph, node.output)
            graph.node.insert(insert_idx, node)

    def add_nodes(self, nodes_to_add, node_name_to_graph_name=None):
        if node_name_to_graph_name is None:
            self.model.graph.node.extend(nodes_to_add)
        else:
            for node in nodes_to_add:
                graph_name = node_name_to_graph_name[node.name]
                self.add_node(node, graph_name)

    def add_initializer(self, tensor, graph_name=None):
        if graph_name is None or graph_name == self.model.graph.name:
            self.model.graph.initializer.extend([tensor])
        else:
            graph = self.get_graph_by_name(graph_name)
            graph.initializer.extend([tensor])

    def add_input(self, input, graph_name=None):
        if graph_name is None or graph_name == self.model.graph.name:
            self.model.graph.input.extend([input])
        else:
            graph = self.get_graph_by_name(graph_name)
            graph.input.extend([input])

    @staticmethod
    def replace_node_input(node, old_input_name, new_input_name):
        assert isinstance(old_input_name, str) and isinstance(new_input_name, str)
        for j in range(len(node.input)):
            if node.input[j] == old_input_name:
                node.input[j] = new_input_name

    # This function is deprecated since we use onnxconverter-common
    def replace_input_of_all_nodes(self, old_input_name, new_input_name):
        for node in self.model.graph.node:
            OnnxModel.replace_node_input(node, old_input_name, new_input_name)

    @staticmethod
    def replace_node_output(node, old_output_name, new_output_name):
        assert isinstance(old_output_name, str) and isinstance(new_output_name, str)
        for j in range(len(node.output)):
            if node.output[j] == old_output_name:
                node.output[j] = new_output_name

    # This function is deprecated since we use onnxconverter-common
    def replace_output_of_all_nodes(self, old_output_name, new_output_name):
        for node in self.model.graph.node:
            OnnxModel.replace_node_output(node, old_output_name, new_output_name)

    def get_initializer(self, name):
        for graph in self.graphs():
            for tensor in graph.initializer:
                if tensor.name == name:
                    return tensor
        return None

    def get_nodes_by_op_type(self, op_type):
        nodes = []
        for node in self.nodes():
            if node.op_type == op_type:
                nodes.append(node)
        return nodes

    def get_children(self, node, input_name_to_nodes=None):
        if input_name_to_nodes is None:
            input_name_to_nodes = self.input_name_to_nodes()

        children = []
        for output in node.output:
            if output in input_name_to_nodes:
                for node in input_name_to_nodes[output]:
                    children.append(node)
        return children

    def get_parents(self, node, output_name_to_node=None):
        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        parents = []
        for input in node.input:
            if input in output_name_to_node:
                parents.append(output_name_to_node[input])
        return parents

    def get_parent(self, node, i, output_name_to_node=None):
        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        if len(node.input) <= i:
            return None

        input = node.input[i]
        if input not in output_name_to_node:
            return None

        return output_name_to_node[input]

    def match_first_parent(self, node, parent_op_type, output_name_to_node, exclude=[]):
        """
        Find parent node based on constraints on op_type.

        Args:
            node (str): current node name.
            parent_op_type (str): constraint of parent node op_type.
            output_name_to_node (dict): dictionary with output name as key, and node as value.
            exclude (list): list of nodes that are excluded (not allowed to match as parent).

        Returns:
            parent: The matched parent node. None if not found.
            index: The input index of matched parent node. None if not found.
        """
        for i, input in enumerate(node.input):
            if input in output_name_to_node:
                parent = output_name_to_node[input]
                if parent.op_type == parent_op_type and parent not in exclude:
                    return parent, i
                else:
                    logger.debug(f"To find first {parent_op_type}, current {parent.op_type}")
        return None, None

    def match_parent(
        self,
        node,
        parent_op_type,
        input_index=None,
        output_name_to_node=None,
        exclude=[],
        return_indice=None,
    ):
        """
        Find parent node based on constraints on op_type and index.
        When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index.

        Args:
            node (str): current node name.
            parent_op_type (str): constraint of parent node op_type.
            input_index (int or None): only check the parent given input index of current node.
            output_name_to_node (dict): dictionary with output name as key, and node as value.
            exclude (list): list of nodes that are excluded (not allowed to match as parent).
            return_indice (list): a list to append the input index when input_index is None.

        Returns:
            parent: The matched parent node.
        """
        assert node is not None
        assert input_index is None or input_index >= 0

        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        if input_index is None:
            parent, index = self.match_first_parent(node, parent_op_type, output_name_to_node, exclude)
            if return_indice is not None:
                return_indice.append(index)
            return parent

        if input_index >= len(node.input):
            logger.debug(f"input_index {input_index} >= node inputs {len(node.input)}")
            return None

        parent = self.get_parent(node, input_index, output_name_to_node)
        if parent is not None and parent.op_type == parent_op_type and parent not in exclude:
            return parent

        if parent is not None:
            logger.debug(f"Expect {parent_op_type}, Got {parent.op_type}")

        return None

    def match_parent_paths(self, node, paths, output_name_to_node):
        for i, path in enumerate(paths):
            assert isinstance(path, List) or isinstance(path, Tuple)
            return_indice = []
            matched = self.match_parent_path(node, path[0], path[1], output_name_to_node, return_indice)
            if matched:
                return i, matched, return_indice
        return -1, None, None

    def match_parent_path(
        self,
        node,
        parent_op_types,
        parent_input_index,
        output_name_to_node=None,
        return_indice=None,
    ):
        """
        Find a sequence of input edges based on constraints on parent op_type and index.
        When input_index is None, we will find the first parent node based on constraints, and return_indice will be appended the corresponding input index.

        Args:
            node (str): current node name.
            parent_op_types (str): constraint of parent node op_type of each input edge.
            parent_input_index (list): constraint of input index of each input edge. None means no constraint.
            output_name_to_node (dict): dictionary with output name as key, and node as value.
            return_indice (list): a list to append the input index when there is no constraint on input index of an edge.

        Returns:
            parents: a list of matched parent node.
        """
        assert len(parent_input_index) == len(parent_op_types)

        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        current_node = node
        matched_parents = []
        for i, op_type in enumerate(parent_op_types):
            matched_parent = self.match_parent(
                current_node,
                op_type,
                parent_input_index[i],
                output_name_to_node,
                exclude=[],
                return_indice=return_indice,
            )
            if matched_parent is None:
                logger.debug(
                    f"Failed to match index={i} parent_input_index={parent_input_index[i]} op_type={op_type}",
                    stack_info=True,
                )
                return None

            matched_parents.append(matched_parent)
            current_node = matched_parent

        return matched_parents

    def find_first_child_by_type(self, node, child_type, input_name_to_nodes=None, recursive=True):
        children = self.get_children(node, input_name_to_nodes)
        dq = deque(children)
        while len(dq) > 0:
            current_node = dq.pop()
            if current_node.op_type == child_type:
                return current_node

            if recursive:
                children = self.get_children(current_node, input_name_to_nodes)
                for child in children:
                    dq.appendleft(child)

        return None

    def find_first_parent_by_type(self, node, parent_type, output_name_to_node=None, recursive=True):
        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        parents = self.get_parents(node, output_name_to_node)
        dq = deque(parents)
        while len(dq) > 0:
            current_node = dq.pop()
            if current_node.op_type == parent_type:
                return current_node

            if recursive:
                parents = self.get_parents(current_node, output_name_to_node)
                for parent in parents:
                    dq.appendleft(parent)

        return None

    def get_constant_value(self, output_name):
        for node in self.get_nodes_by_op_type("Constant"):
            if node.output[0] == output_name:
                for att in node.attribute:
                    if att.name == "value":
                        return numpy_helper.to_array(att.t)

        # Fall back to intializer since constant folding might have been
        # applied.
        initializer = self.get_initializer(output_name)
        if initializer is not None:
            return numpy_helper.to_array(initializer)

        return None

    def get_constant_input(self, node):
        for i, input in enumerate(node.input):
            value = self.get_constant_value(input)
            if value is not None:
                return i, value

        return None, None

    def find_constant_input(self, node, expected_value, delta=0.000001):
        i, value = self.get_constant_input(node)
        if value is not None and value.size == 1 and abs(value - expected_value) < delta:
            return i

        return -1

    def is_constant_with_specified_dimension(self, output_name, dimensions, description):
        value = self.get_constant_value(output_name)
        if value is None:
            logger.debug(f"{description} {output_name} is not initializer.")
            return False

        if len(value.shape) != dimensions:
            logger.debug(f"{description} {output_name} shall have {dimensions} dimensions. Got shape {value.shape}")
            return False

        return True

    def has_constant_input(self, node, expected_value, delta=0.000001):
        return self.find_constant_input(node, expected_value, delta) >= 0

    def get_children_subgraph_nodes(self, root_node, stop_nodes, input_name_to_nodes=None):
        if input_name_to_nodes is None:
            input_name_to_nodes = self.input_name_to_nodes()

        children = input_name_to_nodes[root_node.output[0]]

        unique_nodes = []

        dq = deque(children)
        while len(dq) > 0:
            current_node = dq.pop()
            if current_node in stop_nodes:
                continue

            if current_node not in unique_nodes:
                unique_nodes.append(current_node)

                for output in current_node.output:
                    if output in input_name_to_nodes:
                        children = input_name_to_nodes[output]
                        for child in children:
                            dq.appendleft(child)

        return unique_nodes

    def tensor_shape_to_list(self, tensor_type):
        """Convert tensor shape to list"""
        shape_list = []
        for d in tensor_type.shape.dim:
            if d.HasField("dim_value"):
                shape_list.append(d.dim_value)  # known dimension
            elif d.HasField("dim_param"):
                shape_list.append(d.dim_param)  # unknown dimension with symbolic name
            else:
                shape_list.append("?")  # shall not happen
        return shape_list

    def get_dtype(self, input_or_output: str):
        """Try get data type given a name (could be initializer, graph input or output)."""
        tensor_type_map = {obj.name: obj.type for obj in self.model.graph.value_info}

        if input_or_output in tensor_type_map:
            return tensor_type_map[input_or_output].tensor_type.elem_type

        graph_input = self.find_graph_input(input_or_output)
        if graph_input:
            return graph_input.type.tensor_type.elem_type

        graph_output = self.find_graph_output(input_or_output)
        if graph_output:
            return graph_output.type.tensor_type.elem_type

        return None

    @staticmethod
    def get_node_attribute(node: NodeProto, attribute_name: str):
        for attr in node.attribute:
            if attr.name == attribute_name:
                value = helper.get_attribute_value(attr)
                return value
        return None

    def convert_model_float32_to_float16(self, cast_input_output=True):
        logger.warning(
            "The function convert_model_float32_to_float16 is deprecated. Use convert_float_to_float16 instead!"
        )
        self.convert_float_to_float16(use_symbolic_shape_infer=True, keep_io_types=cast_input_output)

    def convert_float_to_float16(self, use_symbolic_shape_infer=True, **kwargs):
        """Convert a model to half (default) or mixed precision.
           To use mixed precision, user need specify which graph inputs, outputs, operator type or list of nodes shall keep in float32.
           By default, we use symbolic shape inference to get shape and type information. If not, ONNX shape inference will be used.
           Note that symbolic/ONNX shape inference might fail, and the conversion might not proceed without shape and type information.

        Args:
            use_symbolic_shape_infer (bool, optional): use symbolic shape inference instead of onnx shape inference. Defaults to True.
            keep_io_types (Union[bool, List[str]], optional): It could be boolean or a list of float32 input/output names.
                                                              If True, model inputs/outputs should be left as float32. Defaults to False.
            op_block_list (List[str], optional): List of operator types to leave as float32.
                                                 Defaults to None, which will use `float16.DEFAULT_OP_BLOCK_LIST` as default.
            node_block_list (List[str], optional): List of node names to leave as float32. Defaults to None.
            force_fp16_initializers(bool): force converting all float initializers to float16.
                                           Default to false, which will convert only the one needed to avoid precision loss.
            min_positive_val (float, optional): minimal positive value. Defaults to 1e-7.
            max_finite_val (float, optional): maximal finite value. Defaults to 1e4.
        """
        if "keep_io_types" not in kwargs:
            kwargs["keep_io_types"] = True

        def float_to_float16_func():
            # TODO: import from onnxconverter_common when it is stable
            # try:
            #    import onnxconverter_common as oc
            #    from packaging.version import Version
            #    if Version(oc.__version__) > Version("1.9.0"):
            #        from onnxconverter_common.float16 import convert_float_to_float16
            #        return convert_float_to_float16
            # except ImportError:
            #    pass

            from float16 import convert_float_to_float16

            return convert_float_to_float16

        convert_float_to_float16 = float_to_float16_func()

        model = self.model
        if use_symbolic_shape_infer:
            # Use symbolic shape inference since custom operators (like Gelu, SkipLayerNormalization etc) are not recognized by onnx shape inference.
            shape_infer_helper = SymbolicShapeInferenceHelper(model)
            model = shape_infer_helper.infer_shapes(model, auto_merge=True, guess_output_rank=False)

        parameters = {"disable_shape_infer": use_symbolic_shape_infer}
        parameters.update(
            {
                key: kwargs[key]
                for key in [
                    "keep_io_types",
                    "min_positive_val",
                    "max_finite_val",
                    "op_block_list",
                    "node_block_list",
                    "force_fp16_initializers",
                ]
                if key in kwargs
            }
        )

        fp16_model = convert_float_to_float16(model, **parameters)
        self.initialize(fp16_model)

        # Convert_float_to_float16 might add Cast(to=10) --> Cast(to=1) when two consequent nodes are computed in FP32.
        # Below are post-processing that removes those Cast nodes.
        # Remove first Cast nodes in path like  --> Cast --> Cast -->
        nodes_to_remove = []
        for node in self.nodes():
            if node.op_type == "Cast":
                parent = self.get_parent(node, 0)
                if parent and parent.op_type == "Cast":
                    if self.get_children(parent) == 1:  # cannot be removed if its output is used by multiple nodes
                        self.replace_input_of_all_nodes(parent.output[0], parent.input[0])
                        nodes_to_remove.append(parent)

        # Remove the second cast node.
        for node in self.nodes():
            if (
                node.op_type == "Cast"
                and OnnxModel.get_node_attribute(node, "to") == int(TensorProto.FLOAT)
                and self.get_dtype(node.input[0]) == int(TensorProto.FLOAT)
            ):

                if self.find_graph_output(node.output[0]):
                    self.replace_output_of_all_nodes(node.input[0], node.output[0])
                else:
                    self.replace_input_of_all_nodes(node.output[0], node.input[0])
                nodes_to_remove.append(node)

        self.remove_nodes(nodes_to_remove)

        if nodes_to_remove:
            self.prune_graph()
            print(f"removed {len(nodes_to_remove)} Cast nodes from float16 model")

    def create_node_name(self, op_type, name_prefix=None):
        """Create a unique node name that starts with a prefix (default is operator type).
           The name will not be duplicated with any name that generated or existed in current graphs.
        Args:
            op_type (str): operator type
            name_prefix (str, optional): prefix of node name. Defaults to None.

        Returns:
            str: node name
        """

        if name_prefix:
            prefix = name_prefix if name_prefix.endswith("_") else (name_prefix + "_")
        else:
            prefix = op_type + "_"

        suffix: int = 0
        if prefix in self._node_name_suffix:
            suffix = self._node_name_suffix[prefix] + 1
        else:
            # Check existed node name only once for a prefix as we assume create_node_name is called for every new node in fusion.
            for node in self.nodes():
                if node.name and node.name.startswith(prefix):
                    try:
                        index = int(node.name[len(prefix) :])
                        suffix = max(index + 1, suffix)
                    except ValueError:
                        continue

        # Record the generated suffix so that we can avoid generating duplicated name.
        self._node_name_suffix[prefix] = suffix

        return prefix + str(suffix)

    def find_graph_input(self, input_name):
        for input in self.model.graph.input:
            if input.name == input_name:
                return input
        return None

    def find_graph_output(self, output_name):
        for output in self.model.graph.output:
            if output.name == output_name:
                return output
        return None

    def get_parent_subgraph_nodes(self, node, stop_nodes, output_name_to_node=None):
        if output_name_to_node is None:
            output_name_to_node = self.output_name_to_node()

        unique_nodes = []

        parents = self.get_parents(node, output_name_to_node)
        dq = deque(parents)
        while len(dq) > 0:
            current_node = dq.pop()
            if current_node in stop_nodes:
                continue

            if current_node not in unique_nodes:
                unique_nodes.append(current_node)

                for input in current_node.input:
                    if input in output_name_to_node:
                        dq.appendleft(output_name_to_node[input])

        return unique_nodes

    def get_graph_inputs(self, current_node, recursive=False):
        """
        Find graph inputs that linked to current node.
        """
        graph_inputs = []
        for input in current_node.input:
            if self.find_graph_input(input) and input not in graph_inputs:
                graph_inputs.append(input)

        if recursive:
            parent_nodes = self.get_parent_subgraph_nodes(current_node, [])
            for node in parent_nodes:
                for input in node.input:
                    if self.find_graph_input(input) and input not in graph_inputs:
                        graph_inputs.append(input)
        return graph_inputs

    @staticmethod
    def input_index(node_output, child_node):
        index = 0
        for input in child_node.input:
            if input == node_output:
                return index
            index += 1
        return -1

    def remove_unused_constant(self):
        input_name_to_nodes = self.input_name_to_nodes()

        # remove unused constant
        unused_nodes = []
        nodes = self.nodes()
        for node in nodes:
            if node.op_type == "Constant" and node.output[0] not in input_name_to_nodes:
                unused_nodes.append(node)

        self.remove_nodes(unused_nodes)

        if len(unused_nodes) > 0:
            logger.debug(f"Removed unused constant nodes: {len(unused_nodes)}")

    def prune_graph(self, outputs=None):
        """
        Prune graph to keep only required outputs. It removes unnecessary inputs and nodes.
        Nodes are not linked (directly or indirectly) to any required output will be removed.

        Args:
            outputs (list): a list of graph outputs to retain. If it is None, all graph outputs will be kept.
        """
        if len(self.graphs()) > 1:
            logger.debug(f"Skip prune_graph since graph has subgraph")
            return

        if outputs is None:
            outputs = [output.name for output in self.model.graph.output]

        output_name_to_node = self.output_name_to_node()
        all_nodes = []
        for output in outputs:
            if output in output_name_to_node:
                last_node = output_name_to_node[output]
                if last_node in all_nodes:
                    continue
                nodes = self.get_parent_subgraph_nodes(last_node, [])
                all_nodes.append(last_node)
                all_nodes.extend(nodes)

        nodes_to_remove = []
        for node in self.model.graph.node:
            if node not in all_nodes:
                nodes_to_remove.append(node)

        self.remove_nodes(nodes_to_remove)

        # remove outputs not in list
        output_to_remove = []
        for output in self.model.graph.output:
            if output.name not in outputs:
                output_to_remove.append(output)
        for output in output_to_remove:
            self.model.graph.output.remove(output)

        # remove inputs not used by any node.
        input_name_to_nodes = self.input_name_to_nodes()
        input_to_remove = []
        for input in self.model.graph.input:
            if input.name not in input_name_to_nodes:
                input_to_remove.append(input)
        for input in input_to_remove:
            self.model.graph.input.remove(input)

        if input_to_remove or output_to_remove or nodes_to_remove:
            logger.info(
                "Graph pruned: {} inputs, {} outputs and {} nodes are removed".format(
                    len(input_to_remove), len(output_to_remove), len(nodes_to_remove)
                )
            )

        self.update_graph()

    def update_graph(self, verbose=False):
        graph = self.model.graph

        remaining_input_names = []
        for node in graph.node:
            if node.op_type in ["Loop", "Scan", "If"]:
                # TODO: handle inner graph
                logger.debug(f"Skip update_graph since graph has operator: {node.op_type}")
                return
            if node.op_type != "Constant":
                for input_name in node.input:
                    if input_name not in remaining_input_names:
                        remaining_input_names.append(input_name)
        if verbose:
            logger.debug(f"remaining input names: {remaining_input_names}")

        # remove graph input that is not used
        inputs_to_remove = []
        for input in graph.input:
            if input.name not in remaining_input_names:
                inputs_to_remove.append(input)
        for input in inputs_to_remove:
            graph.input.remove(input)

        names_to_remove = [input.name for input in inputs_to_remove]
        logger.debug(f"remove {len(inputs_to_remove)} unused inputs: {names_to_remove}")

        # remove weights that are not used
        weights_to_remove = []
        weights_to_keep = []
        for initializer in graph.initializer:
            if initializer.name not in remaining_input_names and not self.find_graph_output(initializer.name):
                weights_to_remove.append(initializer)
            else:
                weights_to_keep.append(initializer.name)
        for initializer in weights_to_remove:
            graph.initializer.remove(initializer)

        names_to_remove = [initializer.name for initializer in weights_to_remove]
        logger.debug(f"remove {len(weights_to_remove)} unused initializers: {names_to_remove}")
        if verbose:
            logger.debug(f"remaining initializers:{weights_to_keep}")

        self.remove_unused_constant()

    def is_safe_to_fuse_nodes(self, nodes_to_remove, keep_outputs, input_name_to_nodes, output_name_to_node):
        for node_to_remove in nodes_to_remove:
            for output_to_remove in node_to_remove.output:
                if output_to_remove in keep_outputs:
                    continue

                if output_to_remove in input_name_to_nodes:
                    for impacted_node in input_name_to_nodes[output_to_remove]:
                        if impacted_node not in nodes_to_remove:
                            logger.debug(
                                f"it is not safe to remove nodes since output {output_to_remove} is used by {impacted_node}"
                            )
                            return False
        return True

    @staticmethod
    def graph_topological_sort(graph):
        deps_count = [0] * len(graph.node)  # dependency count of each node
        deps_to_nodes = {}  # input to node indice
        sorted_nodes = []  # initialize sorted_nodes
        for node_idx, node in enumerate(graph.node):
            # CANNOT use len(node.input) directly because input can be optional
            deps_count[node_idx] = sum(1 for _ in node.input if _)
            if deps_count[node_idx] == 0:  # Constant doesn't depend on any inputs
                sorted_nodes.append(graph.node[node_idx])
                continue

            for input_name in node.input:
                if input_name not in deps_to_nodes:
                    deps_to_nodes[input_name] = [node_idx]
                else:
                    deps_to_nodes[input_name].append(node_idx)

        # Note: this logic only applies to top level graph since a sub graph could use intializer from parent graph
        initializer_names = [init.name for init in graph.initializer]
        graph_input_names = [input.name for input in graph.input]
        input_names = initializer_names + graph_input_names
        input_names.sort()
        prev_input_name = None
        for input_name in input_names:
            if prev_input_name == input_name:
                continue

            prev_input_name = input_name
            if input_name in deps_to_nodes:
                for node_idx in deps_to_nodes[input_name]:
                    deps_count[node_idx] = deps_count[node_idx] - 1
                    if deps_count[node_idx] == 0:
                        sorted_nodes.append(graph.node[node_idx])

        start = 0
        end = len(sorted_nodes)

        while start < end:
            for output in sorted_nodes[start].output:
                if output in deps_to_nodes:
                    for node_idx in deps_to_nodes[output]:
                        deps_count[node_idx] = deps_count[node_idx] - 1
                        if deps_count[node_idx] == 0:
                            sorted_nodes.append(graph.node[node_idx])
                            end = end + 1
            start = start + 1

        assert end == len(graph.node), "Graph is not a DAG"
        graph.ClearField("node")
        graph.node.extend(sorted_nodes)

    def topological_sort(self):
        # TODO: support graph_topological_sort() in subgraphs
        # for graph in self.graphs():
        #    self.graph_topological_sort(graph)
        OnnxModel.graph_topological_sort(self.model.graph)

    def save_model_to_file(self, output_path, use_external_data_format=False, all_tensors_to_one_file=True):
        logger.info(f"Sort graphs in topological order")
        self.topological_sort()

        Path(output_path).parent.mkdir(parents=True, exist_ok=True)

        if output_path.endswith(".json"):  # Output text for testing small model.
            assert isinstance(self.model, ModelProto)
            with open(output_path, "w") as out:
                out.write(str(self.model))
        else:
            # Save model to external data, which is needed for model size > 2GB
            if use_external_data_format:
                output_dir = Path(output_path).parent
                output_dir.mkdir(parents=True, exist_ok=True)
                location = Path(output_path).name + ".data" if all_tensors_to_one_file else None

                # Show warnings of potential confliction of existing external data file.
                if all_tensors_to_one_file:
                    if os.path.exists(location):
                        logger.warning(
                            f"External data file ({location}) existed. Please remove the file and try again."
                        )
                else:
                    if os.listdir(output_dir):
                        logger.warning(
                            f"Output directory ({output_dir}) for external data is not empty. Please try again with a new directory."
                        )

                external_data_helper.convert_model_to_external_data(
                    self.model,
                    all_tensors_to_one_file=all_tensors_to_one_file,
                    location=location,
                )
            save_model(self.model, output_path)

        logger.info(f"Model saved to {output_path}")

    def get_graph_inputs_excluding_initializers(self):
        """
        Returns real graph inputs (excluding initializers from older onnx model).
        """
        graph_inputs = []
        for input in self.model.graph.input:
            if self.get_initializer(input.name) is None:
                graph_inputs.append(input)
        return graph_inputs

    def get_opset_version(self):
        """Get opset version of onnx domain

        Raises:
            RuntimeError: ONNX model has no opset for default domain.

        Returns:
            int: opset version of onnx domain.
        """
        for opset in self.model.opset_import:
            if opset.domain in ["", "ai.onnx"]:
                return opset.version
        raise RuntimeError("ONNX model has no opset for default domain")
