# This file is adapted from https://github.com/tensorflow/tensorflow/blob/master
# /tensorflow/python/framework/graph_util_impl.py
#
# Copyright 2015 The TensorFlow Authors, 2019 Analytics Zoo Authors.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helpers to manipulate a tensor graph in python.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import re
import six

from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export

_VARIABLE_OPS = {
    "Assign",
    "AssignAdd",
    "AssignSub",
    "Queue",
    "ScatterAdd",
    "ScatterSub",
    "ScatterUpdate",
    "TruncatedNormal",
    "Variable",
    "VariableV2",
}


def _is_variable_op(op):
    """Returns true if 'op' refers to a Variable node."""
    return op in _VARIABLE_OPS


@deprecation.deprecated(
    date=None,
    instructions="Use `tf.compat.v1.graph_util.must_run_on_cpu`")
@tf_export(v1=["graph_util.must_run_on_cpu"])
def must_run_on_cpu(node, pin_variables_on_cpu=False):
    """Returns True if the given node_def must run on CPU, otherwise False.
    Args:
      node: The node to be assigned to a device. Could be either an ops.Operation
        or NodeDef.
      pin_variables_on_cpu: If True, this function will return False if node_def
        represents a variable-related op.
    Returns:
      True if the given node must run on CPU, otherwise False.
    """

    if isinstance(node, ops.Operation):
        node_def = node.node_def
    else:
        assert isinstance(node, node_def_pb2.NodeDef)
        node_def = node

    # If the op is a variable-related op, should we pin it on CPU?
    if pin_variables_on_cpu and _is_variable_op(node_def.op):
        return True

    # Constant operations producing a string or int32 must run on CPU.
    if node_def.op == "Const":
        # Get the value of the 'dtype' attr
        dtype = node_def.attr["dtype"].type
        if dtype == dtypes.string or dtype == dtypes.int32:
            return True

    if node_def.op in ["DynamicStitch", "ParallelDynamicStitch"]:
        dtype = node_def.attr["T"].type
        if dtype == dtypes.int32:
            # DynamicStitch on GPU only works for int32 values.
            return True

    if node_def.op in ["Cast"]:
        dtype = node_def.attr["SrcT"].type
        if dtype == dtypes.int32:
            # Cast on GPU does not works for int32 values.
            return True
    return False


################################################################################
#
# device functions for use in with g.device(...)
#
################################################################################


def _node_name(n):
    if n.startswith("^"):
        return n[1:]
    else:
        return n.split(":")[0]


def _extract_graph_summary(graph_def):
    """Extracts useful information from the graph and returns them."""
    name_to_input_name = {}  # Keyed by the dest node name.
    name_to_node = {}  # Keyed by node name.

    # Keeps track of node sequences. It is important to still output the
    # operations in the original order.
    name_to_seq_num = {}  # Keyed by node name.
    seq = 0
    for node in graph_def.node:
        n = _node_name(node.name)
        name_to_node[n] = node
        name_to_input_name[n] = [_node_name(x) for x in node.input]
        if "_class" in node.attr:
            # Prevent colocated nodes being lost
            for v in node.attr["_class"].list.s:
                v_str = v.decode("utf-8")
                if v_str.startswith("loc:@"):
                    colocated_node = v_str[5:]
                    name_to_input_name[n].append(colocated_node)
        name_to_seq_num[n] = seq
        seq += 1
    return name_to_input_name, name_to_node, name_to_seq_num


def _assert_nodes_are_present(name_to_node, nodes):
    """Assert that nodes are present in the graph."""
    for d in nodes:
        assert d in name_to_node, "%s is not in graph" % d


def _bfs_for_reachable_nodes(target_nodes, name_to_input_name):
    """Breadth first search for reachable nodes from target nodes."""
    nodes_to_keep = set()
    # Breadth first search to find all the nodes that we should keep.
    next_to_visit = target_nodes[:]
    while next_to_visit:
        node = next_to_visit[0]
        del next_to_visit[0]
        if node in nodes_to_keep:
            # Already visited this node.
            continue
        nodes_to_keep.add(node)
        if node in name_to_input_name:
            next_to_visit += name_to_input_name[node]
    return nodes_to_keep


@deprecation.deprecated(
    date=None,
    instructions="Use `tf.compat.v1.graph_util.extract_sub_graph`")
@tf_export(v1=["graph_util.extract_sub_graph"])
def extract_sub_graph(graph_def, dest_nodes):
    """Extract the subgraph that can reach any of the nodes in 'dest_nodes'.
    Args:
      graph_def: A graph_pb2.GraphDef proto.
      dest_nodes: A list of strings specifying the destination node names.
    Returns:
      The GraphDef of the sub-graph.
    Raises:
      TypeError: If 'graph_def' is not a graph_pb2.GraphDef proto.
    """

    if not isinstance(graph_def, graph_pb2.GraphDef):
        raise TypeError("graph_def must be a graph_pb2.GraphDef proto.")

    if isinstance(dest_nodes, six.string_types):
        raise TypeError("dest_nodes must be a list.")

    name_to_input_name, name_to_node, name_to_seq_num = _extract_graph_summary(
        graph_def)
    _assert_nodes_are_present(name_to_node, dest_nodes)

    nodes_to_keep = _bfs_for_reachable_nodes(dest_nodes, name_to_input_name)

    nodes_to_keep_list = sorted(
        list(nodes_to_keep), key=lambda n: name_to_seq_num[n])
    # Now construct the output GraphDef
    out = graph_pb2.GraphDef()
    for n in nodes_to_keep_list:
        out.node.extend([copy.deepcopy(name_to_node[n])])
    out.library.CopyFrom(graph_def.library)
    out.versions.CopyFrom(graph_def.versions)

    return out


@deprecation.deprecated(
    date=None,
    instructions="Use `tf.compat.v1.graph_util.tensor_shape_from_node_def_name`"
)
@tf_export(v1=["graph_util.tensor_shape_from_node_def_name"])
def tensor_shape_from_node_def_name(graph, input_name):
    """Convenience function to get a shape from a NodeDef's input string."""
    # To get a tensor, the name must be in the form <input>:<port>, for example
    # 'Mul:0'. The GraphDef input strings don't always have the port specified
    # though, so if there isn't a colon we need to add a default ':0' to the end.
    if ":" not in input_name:
        canonical_name = input_name + ":0"
    else:
        canonical_name = input_name
    tensor = graph.get_tensor_by_name(canonical_name)
    shape = tensor.get_shape()
    return shape


@deprecation.deprecated(
    date=None,
    instructions="Use `tf.compat.v1.graph_util.convert_variables_to_constants`")
@tf_export(v1=["graph_util.convert_variables_to_constants"])
def convert_variables_to_constants(sess,
                                   input_graph_def,
                                   output_node_names,
                                   variable_names_whitelist=None,
                                   variable_names_blacklist=None):
    """Replaces all the variables in a graph with constants of the same values.
    If you have a trained graph containing Variable ops, it can be convenient to
    convert them all to Const ops holding the same values. This makes it possible
    to describe the network fully with a single GraphDef file, and allows the
    removal of a lot of ops related to loading and saving the variables.
    Args:
      sess: Active TensorFlow session containing the variables.
      input_graph_def: GraphDef object holding the network.
      output_node_names: List of name strings for the result nodes of the graph.
      variable_names_whitelist: The set of variable names to convert (by default,
                                all variables are converted).
      variable_names_blacklist: The set of variable names to omit converting
                                to constants.
    Returns:
      GraphDef containing a simplified version of the original.
    """

    def trace_back_find_variable(origin_name, name_to_nodes):

        nodes_in_path = set()
        control_ops = ["Enter", "Exit", "NextIteration", "Switch"]

        current_name = origin_name
        while name_to_nodes[current_name].op != "VarHandleOp":
            nodes_in_path.add(current_name)
            current_node = name_to_nodes[current_name]
            op_name = current_node.op
            if op_name in control_ops or op_name == "Identity":
                curr_input_name = _node_name(current_node.input[0])
            else:
                raise ValueError("Op type %s should not be in the path " +
                                 "between ReadVariableOp and VarHandleOp" % current_node.op)
            current_name = curr_input_name

        return current_name, nodes_in_path

    def create_const_op(node_name, dtype, data, data_shape=None):
        """Creates a Const op."""
        output_node = node_def_pb2.NodeDef()
        output_node.op = "Const"
        output_node.name = node_name
        output_node.attr["dtype"].CopyFrom(dtype)
        output_node.attr["value"].CopyFrom(
            attr_value_pb2.AttrValue(
                tensor=tensor_util.make_tensor_proto(
                    data, dtype=dtype.type, shape=data_shape)))
        return output_node

    # This graph only includes the nodes needed to evaluate the output nodes, and
    # removes unneeded nodes like those involved in saving and assignment.
    inference_graph = extract_sub_graph(input_graph_def, output_node_names)

    # Identify the ops in the graph.
    map_name_to_node = {
        node.name: node for node in inference_graph.node
    }

    # Get list of variables.
    variable_names = []
    variable_dict_names = []
    resource_identity_types = {}
    read_variable_op_types = {}
    for node in inference_graph.node:
        if node.op in ["Variable", "VariableV2", "VarHandleOp"]:
            variable_name = node.name
            if ((variable_names_whitelist is not None
                 and variable_name not in variable_names_whitelist)
                or (variable_names_blacklist is not None
                    and variable_name in variable_names_blacklist)):
                continue
            variable_dict_names.append(variable_name)
            if node.op == "VarHandleOp":
                variable_names.append(variable_name + "/Read/ReadVariableOp:0")
            else:
                variable_names.append(variable_name + ":0")
        elif node.op in ["ReadVariableOp", "ResourceGather", "VariableShape"]:
            # There can be one or more Identity or control flow ops in between the ReadVariableOp
            # and VarHandleOp.  Store them with the associated dtypes.
            source_op_name, nodes_in_path = trace_back_find_variable(_node_name(node.input[0]),
                                                                     map_name_to_node)
            dtype = map_name_to_node[source_op_name].attr["dtype"]
            for node_name in nodes_in_path:
                resource_identity_types[node_name] = dtype
            read_variable_op_types[node.name] = dtype

    # Gets map of variables and the associated data.
    if variable_names:
        returned_variables = sess.run(variable_names)
    else:
        returned_variables = []
    variables_data_map = dict(zip(variable_dict_names, returned_variables))
    logging.info("Froze %d variables.", len(returned_variables))

    # Reconstruct the graph with constants in place of variables.
    output_graph_def = graph_pb2.GraphDef()
    how_many_converted = 0
    for input_node in inference_graph.node:
        output_node = node_def_pb2.NodeDef()
        if input_node.name in variables_data_map:
            data = variables_data_map[input_node.name]
            output_node = create_const_op(input_node.name, input_node.attr["dtype"],
                                          data, data.shape)
            how_many_converted += 1
        elif input_node.name in resource_identity_types:
            # Converts the Identities of type RESOURCE_DT to the appropriate type
            # based on the input they are referencing.
            output_node.CopyFrom(input_node)
            output_node.attr["T"].CopyFrom(resource_identity_types[input_node.name])
        elif input_node.op == "ReadVariableOp":
            # The first branch converts all VarHandleOps of ResourceVariables to
            # constants, so we need to convert the associated ReadVariableOps to
            # Identity ops.
            output_node.op = "Identity"
            output_node.name = input_node.name
            output_node.input.extend([input_node.input[0]])
            output_node.attr["T"].CopyFrom(input_node.attr["dtype"])
            if "_class" in input_node.attr:
                output_node.attr["_class"].CopyFrom(input_node.attr["_class"])
        elif input_node.op == "ResourceGather":
            # The first branch converts all VarHandleOps of ResourceGather to
            # constants, so we need to convert the associated ResourceGather to Gather
            # ops with a Const axis feeding into it.
            if input_node.attr["batch_dims"].i != 0:
                raise ValueError("batch_dims != 0 is not supported by freeze_graph.")
            axis_data = input_node.attr["batch_dims"].i
            axis_node_name = input_node.name + "/axis"
            axis_dtype = input_node.attr["Tindices"]
            output_axis_node = create_const_op(axis_node_name, axis_dtype, axis_data)
            output_graph_def.node.extend([output_axis_node])

            output_node.op = "GatherV2"
            output_node.name = input_node.name
            output_node.input.extend(
                [input_node.input[0], input_node.input[1], axis_node_name])
            output_node.attr["Tparams"].CopyFrom(input_node.attr["dtype"])
            output_node.attr["Tindices"].CopyFrom(input_node.attr["Tindices"])
            output_node.attr["Taxis"].CopyFrom(axis_dtype)
            if "_class" in input_node.attr:
                output_node.attr["_class"].CopyFrom(input_node.attr["_class"])
        elif input_node.op == "VariableShape":
            output_node.op = "Shape"
            output_node.name = input_node.name
            output_node.input.extend([input_node.input[0]])
            output_node.attr["T"].CopyFrom(read_variable_op_types[input_node.name])
            output_node.attr["out_type"].CopyFrom(input_node.attr["out_type"])
        else:
            output_node.CopyFrom(input_node)
        output_graph_def.node.extend([output_node])

    output_graph_def.library.CopyFrom(inference_graph.library)
    logging.info("Converted %d variables to const ops.", how_many_converted)
    return output_graph_def


@deprecation.deprecated(
    date=None,
    instructions="Use `tf.compat.v1.graph_util.remove_training_nodes`")
@tf_export(v1=["graph_util.remove_training_nodes"])
def remove_training_nodes(input_graph, protected_nodes=None):
    """Prunes out nodes that aren't needed for inference.
    There are nodes like Identity and CheckNumerics that are only useful
    during training, and can be removed in graphs that will be used for
    nothing but inference. Here we identify and remove them, returning an
    equivalent graph. To be specific, CheckNumerics nodes are always removed, and
    Identity nodes that aren't involved in control edges are spliced out so that
    their input and outputs are directly connected.
    Args:
      input_graph: Model to analyze and prune.
      protected_nodes: An optional list of names of nodes to be kept
        unconditionally. This is for example useful to preserve Identity output
        nodes.
    Returns:
      A list of nodes with the unnecessary ones removed.
    """
    if not protected_nodes:
        protected_nodes = []

    types_to_remove = {"CheckNumerics": True}

    input_nodes = input_graph.node
    names_to_remove = {}
    for node in input_nodes:
        if node.op in types_to_remove and node.name not in protected_nodes:
            names_to_remove[node.name] = True

    nodes_after_removal = []
    for node in input_nodes:
        if node.name in names_to_remove:
            continue
        new_node = node_def_pb2.NodeDef()
        new_node.CopyFrom(node)
        input_before_removal = node.input
        del new_node.input[:]
        for full_input_name in input_before_removal:
            input_name = re.sub(r"^\^", "", full_input_name)
            if input_name in names_to_remove:
                continue
            new_node.input.append(full_input_name)
        nodes_after_removal.append(new_node)

    types_to_splice = {"Identity": True}
    control_input_names = set()
    node_names_with_control_input = set()
    for node in nodes_after_removal:
        for node_input in node.input:
            if "^" in node_input:
                control_input_names.add(node_input.replace("^", ""))
                node_names_with_control_input.add(node.name)

    names_to_splice = {}
    for node in nodes_after_removal:
        if node.op in types_to_splice and node.name not in protected_nodes:
            # We don't want to remove nodes that have control edge inputs, because
            # they might be involved in subtle dependency issues that removing them
            # will jeopardize.
            if node.name not in node_names_with_control_input:
                names_to_splice[node.name] = node.input[0]

    # We also don't want to remove nodes which are used as control edge inputs.
    names_to_splice = {name: value for name, value in names_to_splice.items()
                       if name not in control_input_names}

    nodes_after_splicing = []
    for node in nodes_after_removal:
        if node.name in names_to_splice:
            continue
        new_node = node_def_pb2.NodeDef()
        new_node.CopyFrom(node)
        input_before_removal = node.input
        del new_node.input[:]
        for full_input_name in input_before_removal:
            input_name = re.sub(r"^\^", "", full_input_name)
            while input_name in names_to_splice:
                full_input_name = names_to_splice[input_name]
                input_name = re.sub(r"^\^", "", full_input_name)
            new_node.input.append(full_input_name)
        nodes_after_splicing.append(new_node)

    output_graph = graph_pb2.GraphDef()
    output_graph.node.extend(nodes_after_splicing)
    return output_graph