# Copyright 2018 The TensorFlow 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.
# ==============================================================================


"""Utilities related to model visualization."""

import os
import sys

import tensorflow.compat.v2 as tf

from keras.src.utils import io_utils
from keras.src.utils import layer_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export

try:
    # pydot-ng is a fork of pydot that is better maintained.
    import pydot_ng as pydot
except ImportError:
    # pydotplus is an improved version of pydot
    try:
        import pydotplus as pydot
    except ImportError:
        # Fall back on pydot if necessary.
        try:
            import pydot
        except ImportError:
            pydot = None


def check_pydot():
    """Returns True if PyDot is available."""
    return pydot is not None


def check_graphviz():
    """Returns True if both PyDot and Graphviz are available."""
    if not check_pydot():
        return False
    try:
        # Attempt to create an image of a blank graph
        # to check the pydot/graphviz installation.
        pydot.Dot.create(pydot.Dot())
        return True
    except (OSError, pydot.InvocationException):
        return False


def is_wrapped_model(layer):
    from keras.src.engine import functional
    from keras.src.layers import Wrapper

    return isinstance(layer, Wrapper) and isinstance(
        layer.layer, functional.Functional
    )


def add_edge(dot, src, dst):
    if not dot.get_edge(src, dst):
        dot.add_edge(pydot.Edge(src, dst))


@keras_export("keras.utils.model_to_dot")
def model_to_dot(
    model,
    show_shapes=False,
    show_dtype=False,
    show_layer_names=True,
    rankdir="TB",
    expand_nested=False,
    dpi=96,
    subgraph=False,
    layer_range=None,
    show_layer_activations=False,
    show_trainable=False,
):
    """Convert a Keras model to dot format.

    Args:
      model: A Keras model instance.
      show_shapes: whether to display shape information.
      show_dtype: whether to display layer dtypes.
      show_layer_names: whether to display layer names.
      rankdir: `rankdir` argument passed to PyDot,
          a string specifying the format of the plot:
          'TB' creates a vertical plot;
          'LR' creates a horizontal plot.
      expand_nested: whether to expand nested models into clusters.
      dpi: Dots per inch.
      subgraph: whether to return a `pydot.Cluster` instance.
      layer_range: input of `list` containing two `str` items, which is the
          starting layer name and ending layer name (both inclusive) indicating
          the range of layers for which the `pydot.Dot` will be generated. It
          also accepts regex patterns instead of exact name. In such case, start
          predicate will be the first element it matches to `layer_range[0]`
          and the end predicate will be the last element it matches to
          `layer_range[1]`. By default `None` which considers all layers of
          model. Note that you must pass range such that the resultant subgraph
          must be complete.
      show_layer_activations: Display layer activations (only for layers that
          have an `activation` property).
      show_trainable: whether to display if a layer is trainable. Displays 'T'
          when the layer is trainable and 'NT' when it is not trainable.

    Returns:
      A `pydot.Dot` instance representing the Keras model or
      a `pydot.Cluster` instance representing nested model if
      `subgraph=True`.

    Raises:
      ValueError: if `model_to_dot` is called before the model is built.
      ImportError: if pydot is not available.
    """

    if not model.built:
        raise ValueError(
            "This model has not yet been built. "
            "Build the model first by calling `build()` or by calling "
            "the model on a batch of data."
        )

    from keras.src.engine import functional
    from keras.src.engine import sequential
    from keras.src.layers import Wrapper

    if not check_pydot():
        raise ImportError(
            "You must install pydot (`pip install pydot`) for "
            "model_to_dot to work."
        )

    if subgraph:
        dot = pydot.Cluster(style="dashed", graph_name=model.name)
        dot.set("label", model.name)
        dot.set("labeljust", "l")
    else:
        dot = pydot.Dot()
        dot.set("rankdir", rankdir)
        dot.set("concentrate", True)
        dot.set("dpi", dpi)
        dot.set_node_defaults(shape="record")

    if layer_range is not None:
        if len(layer_range) != 2:
            raise ValueError(
                "layer_range must be of shape (2,). Received: "
                f"layer_range = {layer_range} of length {len(layer_range)}"
            )
        if not isinstance(layer_range[0], str) or not isinstance(
            layer_range[1], str
        ):
            raise ValueError(
                "layer_range should contain string type only. "
                f"Received: {layer_range}"
            )
        layer_range = layer_utils.get_layer_index_bound_by_layer_name(
            model, layer_range
        )
        if layer_range[0] < 0 or layer_range[1] > len(model.layers):
            raise ValueError(
                "Both values in layer_range should be in range (0, "
                f"{len(model.layers)}. Received: {layer_range}"
            )

    sub_n_first_node = {}
    sub_n_last_node = {}
    sub_w_first_node = {}
    sub_w_last_node = {}

    layers = model.layers
    if not model._is_graph_network:
        node = pydot.Node(str(id(model)), label=model.name)
        dot.add_node(node)
        return dot
    elif isinstance(model, sequential.Sequential):
        if not model.built:
            model.build()
        layers = super(sequential.Sequential, model).layers

    # Create graph nodes.
    for i, layer in enumerate(layers):
        if (layer_range) and (i < layer_range[0] or i >= layer_range[1]):
            continue

        layer_id = str(id(layer))

        # Append a wrapped layer's label to node's label, if it exists.
        layer_name = layer.name
        class_name = layer.__class__.__name__

        if isinstance(layer, Wrapper):
            if expand_nested and isinstance(layer.layer, functional.Functional):
                submodel_wrapper = model_to_dot(
                    layer.layer,
                    show_shapes,
                    show_dtype,
                    show_layer_names,
                    rankdir,
                    expand_nested,
                    subgraph=True,
                    show_layer_activations=show_layer_activations,
                    show_trainable=show_trainable,
                )
                # sub_w : submodel_wrapper
                sub_w_nodes = submodel_wrapper.get_nodes()
                sub_w_first_node[layer.layer.name] = sub_w_nodes[0]
                sub_w_last_node[layer.layer.name] = sub_w_nodes[-1]
                dot.add_subgraph(submodel_wrapper)
            else:
                layer_name = f"{layer_name}({layer.layer.name})"
                child_class_name = layer.layer.__class__.__name__
                class_name = f"{class_name}({child_class_name})"

        if expand_nested and isinstance(layer, functional.Functional):
            submodel_not_wrapper = model_to_dot(
                layer,
                show_shapes,
                show_dtype,
                show_layer_names,
                rankdir,
                expand_nested,
                subgraph=True,
                show_layer_activations=show_layer_activations,
                show_trainable=show_trainable,
            )
            # sub_n : submodel_not_wrapper
            sub_n_nodes = submodel_not_wrapper.get_nodes()
            sub_n_first_node[layer.name] = sub_n_nodes[0]
            sub_n_last_node[layer.name] = sub_n_nodes[-1]
            dot.add_subgraph(submodel_not_wrapper)

        # Create node's label.
        label = class_name

        # Rebuild the label as a table including the layer's activation.
        if (
            show_layer_activations
            and hasattr(layer, "activation")
            and layer.activation is not None
        ):
            if hasattr(layer.activation, "name"):
                activation_name = layer.activation.name
            elif hasattr(layer.activation, "__name__"):
                activation_name = layer.activation.__name__
            else:
                activation_name = str(layer.activation)
            label = "{%s|%s}" % (label, activation_name)

        # Rebuild the label as a table including the layer's name.
        if show_layer_names:
            label = f"{layer_name}|{label}"

        # Rebuild the label as a table including the layer's dtype.
        if show_dtype:

            def format_dtype(dtype):
                if dtype is None:
                    return "?"
                else:
                    return str(dtype)

            label = f"{label}|{format_dtype(layer.dtype)}"

        # Rebuild the label as a table including input/output shapes.
        if show_shapes:

            def format_shape(shape):
                return (
                    str(shape)
                    .replace(str(None), "None")
                    .replace("{", r"\{")
                    .replace("}", r"\}")
                )

            try:
                outputlabels = format_shape(layer.output_shape)
            except AttributeError:
                outputlabels = "?"
            if hasattr(layer, "input_shape"):
                inputlabels = format_shape(layer.input_shape)
            elif hasattr(layer, "input_shapes"):
                inputlabels = ", ".join(
                    [format_shape(ishape) for ishape in layer.input_shapes]
                )
            else:
                inputlabels = "?"
            label = "{%s}|{input:|output:}|{{%s}|{%s}}" % (
                label,
                inputlabels,
                outputlabels,
            )

        # Rebuild the label as a table including trainable status
        if show_trainable:
            label = f"{'T' if layer.trainable else 'NT'}|{label}"

        if not expand_nested or not isinstance(layer, functional.Functional):
            node = pydot.Node(layer_id, label=label)
            dot.add_node(node)

    # Connect nodes with edges.
    for i, layer in enumerate(layers):
        if (layer_range) and (i <= layer_range[0] or i >= layer_range[1]):
            continue
        layer_id = str(id(layer))
        for i, node in enumerate(layer._inbound_nodes):
            node_key = layer.name + "_ib-" + str(i)
            if node_key in model._network_nodes:
                for inbound_layer in tf.nest.flatten(node.inbound_layers):
                    inbound_layer_id = str(id(inbound_layer))
                    if not expand_nested:
                        assert dot.get_node(inbound_layer_id)
                        assert dot.get_node(layer_id)
                        add_edge(dot, inbound_layer_id, layer_id)
                    else:
                        # if inbound_layer is not Model or wrapped Model
                        if not isinstance(
                            inbound_layer, functional.Functional
                        ) and not is_wrapped_model(inbound_layer):
                            # if current layer is not Model or wrapped Model
                            if not isinstance(
                                layer, functional.Functional
                            ) and not is_wrapped_model(layer):
                                assert dot.get_node(inbound_layer_id)
                                assert dot.get_node(layer_id)
                                add_edge(dot, inbound_layer_id, layer_id)
                            # if current layer is Model
                            elif isinstance(layer, functional.Functional):
                                add_edge(
                                    dot,
                                    inbound_layer_id,
                                    sub_n_first_node[layer.name].get_name(),
                                )
                            # if current layer is wrapped Model
                            elif is_wrapped_model(layer):
                                add_edge(dot, inbound_layer_id, layer_id)
                                name = sub_w_first_node[
                                    layer.layer.name
                                ].get_name()
                                add_edge(dot, layer_id, name)
                        # if inbound_layer is Model
                        elif isinstance(inbound_layer, functional.Functional):
                            name = sub_n_last_node[
                                inbound_layer.name
                            ].get_name()
                            if isinstance(layer, functional.Functional):
                                output_name = sub_n_first_node[
                                    layer.name
                                ].get_name()
                                add_edge(dot, name, output_name)
                            else:
                                add_edge(dot, name, layer_id)
                        # if inbound_layer is wrapped Model
                        elif is_wrapped_model(inbound_layer):
                            inbound_layer_name = inbound_layer.layer.name
                            add_edge(
                                dot,
                                sub_w_last_node[inbound_layer_name].get_name(),
                                layer_id,
                            )
    return dot


@keras_export("keras.utils.plot_model")
def plot_model(
    model,
    to_file="model.png",
    show_shapes=False,
    show_dtype=False,
    show_layer_names=True,
    rankdir="TB",
    expand_nested=False,
    dpi=96,
    layer_range=None,
    show_layer_activations=False,
    show_trainable=False,
):
    """Converts a Keras model to dot format and save to a file.

    Example:

    ```python
    input = tf.keras.Input(shape=(100,), dtype='int32', name='input')
    x = tf.keras.layers.Embedding(
        output_dim=512, input_dim=10000, input_length=100)(input)
    x = tf.keras.layers.LSTM(32)(x)
    x = tf.keras.layers.Dense(64, activation='relu')(x)
    x = tf.keras.layers.Dense(64, activation='relu')(x)
    x = tf.keras.layers.Dense(64, activation='relu')(x)
    output = tf.keras.layers.Dense(1, activation='sigmoid', name='output')(x)
    model = tf.keras.Model(inputs=[input], outputs=[output])
    dot_img_file = '/tmp/model_1.png'
    tf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)
    ```

    Args:
      model: A Keras model instance
      to_file: File name of the plot image.
      show_shapes: whether to display shape information.
      show_dtype: whether to display layer dtypes.
      show_layer_names: whether to display layer names.
      rankdir: `rankdir` argument passed to PyDot,
          a string specifying the format of the plot: 'TB' creates a vertical
            plot; 'LR' creates a horizontal plot.
      expand_nested: Whether to expand nested models into clusters.
      dpi: Dots per inch.
      layer_range: input of `list` containing two `str` items, which is the
        starting layer name and ending layer name (both inclusive) indicating
        the range of layers for which the plot will be generated. It also
        accepts regex patterns instead of exact name. In such case, start
        predicate will be the first element it matches to `layer_range[0]` and
        the end predicate will be the last element it matches to
        `layer_range[1]`. By default `None` which considers all layers of model.
        Note that you must pass range such that the resultant subgraph must be
        complete.
      show_layer_activations: Display layer activations (only for layers that
        have an `activation` property).
      show_trainable: whether to display if a layer is trainable. Displays 'T'
        when the layer is trainable and 'NT' when it is not trainable.

    Raises:
      ImportError: if graphviz or pydot are not available.
      ValueError: if `plot_model` is called before the model is built.

    Returns:
      A Jupyter notebook Image object if Jupyter is installed.
      This enables in-line display of the model plots in notebooks.
    """

    if not model.built:
        raise ValueError(
            "This model has not yet been built. "
            "Build the model first by calling `build()` or by calling "
            "the model on a batch of data."
        )

    if not check_graphviz():
        message = (
            "You must install pydot (`pip install pydot`) "
            "and install graphviz "
            "(see instructions at https://graphviz.gitlab.io/download/) "
            "for plot_model to work."
        )
        if "IPython.core.magics.namespace" in sys.modules:
            # We don't raise an exception here in order to avoid crashing
            # notebook tests where graphviz is not available.
            io_utils.print_msg(message)
            return
        else:
            raise ImportError(message)

    dot = model_to_dot(
        model,
        show_shapes=show_shapes,
        show_dtype=show_dtype,
        show_layer_names=show_layer_names,
        rankdir=rankdir,
        expand_nested=expand_nested,
        dpi=dpi,
        layer_range=layer_range,
        show_layer_activations=show_layer_activations,
        show_trainable=show_trainable,
    )
    to_file = io_utils.path_to_string(to_file)
    if dot is None:
        return
    _, extension = os.path.splitext(to_file)
    if not extension:
        extension = "png"
    else:
        extension = extension[1:]
    # Save image to disk.
    dot.write(to_file, format=extension)
    # Return the image as a Jupyter Image object, to be displayed in-line.
    # Note that we cannot easily detect whether the code is running in a
    # notebook, and thus we always return the Image if Jupyter is available.
    if extension != "pdf":
        try:
            from IPython import display

            return display.Image(filename=to_file)
        except ImportError:
            pass

