"""Model-related utilities.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from . import backend as K
from .utils.generic_utils import has_arg
from .utils.generic_utils import to_list
from .engine.input_layer import Input
from .engine.input_layer import InputLayer
from .engine.training import Model
from .engine.sequential import Sequential
from .engine.saving import save_model
from .engine.saving import load_model
from .engine.saving import model_from_config
from .engine.saving import model_from_yaml
from .engine.saving import model_from_json

try:
    import h5py
except ImportError:
    h5py = None


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError('Expected `model` argument '
                         'to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError('Expected `model` argument '
                         'to be a functional `Model` instance, '
                         'got a `Sequential` instance instead:', model)

    layer_map = {}  # Cache for created layers.
    tensor_map = {}  # Map {reference_tensor: (corresponding_tensor, mask)}
    if input_tensors is None:
        # Create placeholders to build the model on top of.
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape,
                                 dtype=layer.dtype,
                                 sparse=layer.sparse,
                                 name=layer.name)
            input_tensors.append(input_tensor)
            # Cache newly created input layer.
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        # Make sure that all input tensors come from a Keras layer.
        # If tensor comes from an input layer: cache the input layer.
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x,
                                     name='input_wrapper_for_' + name)
                _input_tensors.append(input_tensor)
                # Cache newly created input layer.
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors

    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = (y, None)  # tensor, mask

    # Iterated over every node in the reference model, in depth order.
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            # Recover the corresponding layer.
            layer = node.outbound_layer

            # Get or create layer.
            if layer not in layer_map:
                # Clone layer.
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                # Reuse previously cloned layer.
                layer = layer_map[layer]
                # Don't call InputLayer multiple times.
                if isinstance(layer, InputLayer):
                    continue

            # Gather inputs to call the new layer.
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors

            # If all previous input tensors are available in tensor_map,
            # then call node.inbound_layer on them.
            computed_data = []  # List of tuples (input, mask).
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])

            if len(computed_data) == len(reference_input_tensors):
                # Call layer.
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(
                        layer(computed_tensor, **kwargs))
                    output_masks = to_list(
                        layer.compute_mask(computed_tensor,
                                           computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(
                        layer(computed_tensors, **kwargs))
                    output_masks = to_list(
                        layer.compute_mask(computed_tensors,
                                           computed_masks))
                # Update tensor_map.
                for x, y, mask in zip(reference_output_tensors,
                                      output_tensors,
                                      output_masks):
                    tensor_map[x] = (y, mask)

    # Check that we did compute the model outputs,
    # then instantiate a new model from inputs and outputs.
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


def _clone_sequential_model(model, input_tensors=None):
    """Clone a `Sequential` model instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Sequential`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Sequential` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Sequential):
        raise ValueError('Expected `model` argument '
                         'to be a `Sequential` model instance, '
                         'but got:', model)

    def clone(layer):
        return layer.__class__.from_config(layer.get_config())

    layers = [clone(layer) for layer in model.layers]
    if input_tensors is None:
        return Sequential(layers=layers, name=model.name)
    else:
        if len(to_list(input_tensors)) != 1:
            raise ValueError('To clone a `Sequential` model, we expect '
                             ' at most one tensor '
                             'as part of `input_tensors`.')
        x = to_list(input_tensors)[0]
        if K.is_keras_tensor(x):
            origin_layer = x._keras_history[0]
            if isinstance(origin_layer, InputLayer):
                return Sequential(layers=[origin_layer] + layers,
                                  name=model.name)
            else:
                raise ValueError('Cannot clone a `Sequential` model on top '
                                 'of a tensor that comes from a Keras layer '
                                 'other than an `InputLayer`. '
                                 'Use the functional API instead.')
        input_tensor = Input(tensor=x,
                             name='input_wrapper_for_' + str(x.name))
        input_layer = input_tensor._keras_history[0]
        return Sequential(layers=[input_layer] + layers, name=model.name)


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)

bc58d5e92c83b6fe6011be07b702c6cf9cd8728e
<mask token>


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError(
            'Expected `model` argument to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a functional `Model` instance, got a `Sequential` instance instead:'
            , model)
    layer_map = {}
    tensor_map = {}
    if input_tensors is None:
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape, dtype
                =layer.dtype, sparse=layer.sparse, name=layer.name)
            input_tensors.append(input_tensor)
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.
            input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x, name='input_wrapper_for_' + name
                    )
                _input_tensors.append(input_tensor)
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors
    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = y, None
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            layer = node.outbound_layer
            if layer not in layer_map:
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                layer = layer_map[layer]
                if isinstance(layer, InputLayer):
                    continue
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors
            computed_data = []
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])
            if len(computed_data) == len(reference_input_tensors):
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(layer(computed_tensor, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensor, computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(layer(computed_tensors, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensors, computed_masks))
                for x, y, mask in zip(reference_output_tensors,
                    output_tensors, output_masks):
                    tensor_map[x] = y, mask
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


<mask token>


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)

<mask token>


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError(
            'Expected `model` argument to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a functional `Model` instance, got a `Sequential` instance instead:'
            , model)
    layer_map = {}
    tensor_map = {}
    if input_tensors is None:
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape, dtype
                =layer.dtype, sparse=layer.sparse, name=layer.name)
            input_tensors.append(input_tensor)
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.
            input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x, name='input_wrapper_for_' + name
                    )
                _input_tensors.append(input_tensor)
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors
    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = y, None
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            layer = node.outbound_layer
            if layer not in layer_map:
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                layer = layer_map[layer]
                if isinstance(layer, InputLayer):
                    continue
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors
            computed_data = []
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])
            if len(computed_data) == len(reference_input_tensors):
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(layer(computed_tensor, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensor, computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(layer(computed_tensors, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensors, computed_masks))
                for x, y, mask in zip(reference_output_tensors,
                    output_tensors, output_masks):
                    tensor_map[x] = y, mask
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


def _clone_sequential_model(model, input_tensors=None):
    """Clone a `Sequential` model instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Sequential`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Sequential` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a `Sequential` model instance, but got:'
            , model)

    def clone(layer):
        return layer.__class__.from_config(layer.get_config())
    layers = [clone(layer) for layer in model.layers]
    if input_tensors is None:
        return Sequential(layers=layers, name=model.name)
    else:
        if len(to_list(input_tensors)) != 1:
            raise ValueError(
                'To clone a `Sequential` model, we expect  at most one tensor as part of `input_tensors`.'
                )
        x = to_list(input_tensors)[0]
        if K.is_keras_tensor(x):
            origin_layer = x._keras_history[0]
            if isinstance(origin_layer, InputLayer):
                return Sequential(layers=[origin_layer] + layers, name=
                    model.name)
            else:
                raise ValueError(
                    'Cannot clone a `Sequential` model on top of a tensor that comes from a Keras layer other than an `InputLayer`. Use the functional API instead.'
                    )
        input_tensor = Input(tensor=x, name='input_wrapper_for_' + str(x.name))
        input_layer = input_tensor._keras_history[0]
        return Sequential(layers=[input_layer] + layers, name=model.name)


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)

<mask token>
try:
    import h5py
except ImportError:
    h5py = None


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError(
            'Expected `model` argument to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a functional `Model` instance, got a `Sequential` instance instead:'
            , model)
    layer_map = {}
    tensor_map = {}
    if input_tensors is None:
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape, dtype
                =layer.dtype, sparse=layer.sparse, name=layer.name)
            input_tensors.append(input_tensor)
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.
            input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x, name='input_wrapper_for_' + name
                    )
                _input_tensors.append(input_tensor)
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors
    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = y, None
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            layer = node.outbound_layer
            if layer not in layer_map:
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                layer = layer_map[layer]
                if isinstance(layer, InputLayer):
                    continue
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors
            computed_data = []
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])
            if len(computed_data) == len(reference_input_tensors):
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(layer(computed_tensor, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensor, computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(layer(computed_tensors, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensors, computed_masks))
                for x, y, mask in zip(reference_output_tensors,
                    output_tensors, output_masks):
                    tensor_map[x] = y, mask
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


def _clone_sequential_model(model, input_tensors=None):
    """Clone a `Sequential` model instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Sequential`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Sequential` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a `Sequential` model instance, but got:'
            , model)

    def clone(layer):
        return layer.__class__.from_config(layer.get_config())
    layers = [clone(layer) for layer in model.layers]
    if input_tensors is None:
        return Sequential(layers=layers, name=model.name)
    else:
        if len(to_list(input_tensors)) != 1:
            raise ValueError(
                'To clone a `Sequential` model, we expect  at most one tensor as part of `input_tensors`.'
                )
        x = to_list(input_tensors)[0]
        if K.is_keras_tensor(x):
            origin_layer = x._keras_history[0]
            if isinstance(origin_layer, InputLayer):
                return Sequential(layers=[origin_layer] + layers, name=
                    model.name)
            else:
                raise ValueError(
                    'Cannot clone a `Sequential` model on top of a tensor that comes from a Keras layer other than an `InputLayer`. Use the functional API instead.'
                    )
        input_tensor = Input(tensor=x, name='input_wrapper_for_' + str(x.name))
        input_layer = input_tensor._keras_history[0]
        return Sequential(layers=[input_layer] + layers, name=model.name)


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)

<mask token>
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from . import backend as K
from .utils.generic_utils import has_arg
from .utils.generic_utils import to_list
from .engine.input_layer import Input
from .engine.input_layer import InputLayer
from .engine.training import Model
from .engine.sequential import Sequential
from .engine.saving import save_model
from .engine.saving import load_model
from .engine.saving import model_from_config
from .engine.saving import model_from_yaml
from .engine.saving import model_from_json
try:
    import h5py
except ImportError:
    h5py = None


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError(
            'Expected `model` argument to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a functional `Model` instance, got a `Sequential` instance instead:'
            , model)
    layer_map = {}
    tensor_map = {}
    if input_tensors is None:
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape, dtype
                =layer.dtype, sparse=layer.sparse, name=layer.name)
            input_tensors.append(input_tensor)
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.
            input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x, name='input_wrapper_for_' + name
                    )
                _input_tensors.append(input_tensor)
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors
    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = y, None
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            layer = node.outbound_layer
            if layer not in layer_map:
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                layer = layer_map[layer]
                if isinstance(layer, InputLayer):
                    continue
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors
            computed_data = []
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])
            if len(computed_data) == len(reference_input_tensors):
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(layer(computed_tensor, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensor, computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(layer(computed_tensors, **kwargs))
                    output_masks = to_list(layer.compute_mask(
                        computed_tensors, computed_masks))
                for x, y, mask in zip(reference_output_tensors,
                    output_tensors, output_masks):
                    tensor_map[x] = y, mask
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


def _clone_sequential_model(model, input_tensors=None):
    """Clone a `Sequential` model instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Sequential`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Sequential` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Sequential):
        raise ValueError(
            'Expected `model` argument to be a `Sequential` model instance, but got:'
            , model)

    def clone(layer):
        return layer.__class__.from_config(layer.get_config())
    layers = [clone(layer) for layer in model.layers]
    if input_tensors is None:
        return Sequential(layers=layers, name=model.name)
    else:
        if len(to_list(input_tensors)) != 1:
            raise ValueError(
                'To clone a `Sequential` model, we expect  at most one tensor as part of `input_tensors`.'
                )
        x = to_list(input_tensors)[0]
        if K.is_keras_tensor(x):
            origin_layer = x._keras_history[0]
            if isinstance(origin_layer, InputLayer):
                return Sequential(layers=[origin_layer] + layers, name=
                    model.name)
            else:
                raise ValueError(
                    'Cannot clone a `Sequential` model on top of a tensor that comes from a Keras layer other than an `InputLayer`. Use the functional API instead.'
                    )
        input_tensor = Input(tensor=x, name='input_wrapper_for_' + str(x.name))
        input_layer = input_tensor._keras_history[0]
        return Sequential(layers=[input_layer] + layers, name=model.name)


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)

"""Model-related utilities.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from . import backend as K
from .utils.generic_utils import has_arg
from .utils.generic_utils import to_list
from .engine.input_layer import Input
from .engine.input_layer import InputLayer
from .engine.training import Model
from .engine.sequential import Sequential
from .engine.saving import save_model
from .engine.saving import load_model
from .engine.saving import model_from_config
from .engine.saving import model_from_yaml
from .engine.saving import model_from_json

try:
    import h5py
except ImportError:
    h5py = None


def _clone_functional_model(model, input_tensors=None):
    """Clone a functional `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Model):
        raise ValueError('Expected `model` argument '
                         'to be a `Model` instance, got ', model)
    if isinstance(model, Sequential):
        raise ValueError('Expected `model` argument '
                         'to be a functional `Model` instance, '
                         'got a `Sequential` instance instead:', model)

    layer_map = {}  # Cache for created layers.
    tensor_map = {}  # Map {reference_tensor: (corresponding_tensor, mask)}
    if input_tensors is None:
        # Create placeholders to build the model on top of.
        input_layers = []
        input_tensors = []
        for layer in model.input_layers:
            input_tensor = Input(batch_shape=layer.batch_input_shape,
                                 dtype=layer.dtype,
                                 sparse=layer.sparse,
                                 name=layer.name)
            input_tensors.append(input_tensor)
            # Cache newly created input layer.
            newly_created_input_layer = input_tensor._keras_history[0]
            layer_map[layer] = newly_created_input_layer
        for original_input_layer, cloned_input_layer in zip(model.input_layers, input_layers):
            layer_map[original_input_layer] = cloned_input_layer
    else:
        # Make sure that all input tensors come from a Keras layer.
        # If tensor comes from an input layer: cache the input layer.
        input_tensors = to_list(input_tensors)
        _input_tensors = []
        for i, x in enumerate(input_tensors):
            if not K.is_keras_tensor(x):
                name = model.input_layers[i].name
                input_tensor = Input(tensor=x,
                                     name='input_wrapper_for_' + name)
                _input_tensors.append(input_tensor)
                # Cache newly created input layer.
                original_input_layer = x._keras_history[0]
                newly_created_input_layer = input_tensor._keras_history[0]
                layer_map[original_input_layer] = newly_created_input_layer
            else:
                _input_tensors.append(x)
        input_tensors = _input_tensors

    for x, y in zip(model.inputs, input_tensors):
        tensor_map[x] = (y, None)  # tensor, mask

    # Iterated over every node in the reference model, in depth order.
    depth_keys = list(model._nodes_by_depth.keys())
    depth_keys.sort(reverse=True)
    for depth in depth_keys:
        nodes = model._nodes_by_depth[depth]
        for node in nodes:
            # Recover the corresponding layer.
            layer = node.outbound_layer

            # Get or create layer.
            if layer not in layer_map:
                # Clone layer.
                new_layer = layer.__class__.from_config(layer.get_config())
                layer_map[layer] = new_layer
                layer = new_layer
            else:
                # Reuse previously cloned layer.
                layer = layer_map[layer]
                # Don't call InputLayer multiple times.
                if isinstance(layer, InputLayer):
                    continue

            # Gather inputs to call the new layer.
            reference_input_tensors = node.input_tensors
            reference_output_tensors = node.output_tensors

            # If all previous input tensors are available in tensor_map,
            # then call node.inbound_layer on them.
            computed_data = []  # List of tuples (input, mask).
            for x in reference_input_tensors:
                if x in tensor_map:
                    computed_data.append(tensor_map[x])

            if len(computed_data) == len(reference_input_tensors):
                # Call layer.
                if node.arguments:
                    kwargs = node.arguments
                else:
                    kwargs = {}
                if len(computed_data) == 1:
                    computed_tensor, computed_mask = computed_data[0]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_mask
                    output_tensors = to_list(
                        layer(computed_tensor, **kwargs))
                    output_masks = to_list(
                        layer.compute_mask(computed_tensor,
                                           computed_mask))
                    computed_tensors = [computed_tensor]
                    computed_masks = [computed_mask]
                else:
                    computed_tensors = [x[0] for x in computed_data]
                    computed_masks = [x[1] for x in computed_data]
                    if has_arg(layer.call, 'mask'):
                        if 'mask' not in kwargs:
                            kwargs['mask'] = computed_masks
                    output_tensors = to_list(
                        layer(computed_tensors, **kwargs))
                    output_masks = to_list(
                        layer.compute_mask(computed_tensors,
                                           computed_masks))
                # Update tensor_map.
                for x, y, mask in zip(reference_output_tensors,
                                      output_tensors,
                                      output_masks):
                    tensor_map[x] = (y, mask)

    # Check that we did compute the model outputs,
    # then instantiate a new model from inputs and outputs.
    output_tensors = []
    for x in model.outputs:
        assert x in tensor_map, 'Could not compute output ' + str(x)
        tensor, _ = tensor_map[x]
        output_tensors.append(tensor)
    return Model(input_tensors, output_tensors, name=model.name)


def _clone_sequential_model(model, input_tensors=None):
    """Clone a `Sequential` model instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Sequential`.
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Sequential` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if not isinstance(model, Sequential):
        raise ValueError('Expected `model` argument '
                         'to be a `Sequential` model instance, '
                         'but got:', model)

    def clone(layer):
        return layer.__class__.from_config(layer.get_config())

    layers = [clone(layer) for layer in model.layers]
    if input_tensors is None:
        return Sequential(layers=layers, name=model.name)
    else:
        if len(to_list(input_tensors)) != 1:
            raise ValueError('To clone a `Sequential` model, we expect '
                             ' at most one tensor '
                             'as part of `input_tensors`.')
        x = to_list(input_tensors)[0]
        if K.is_keras_tensor(x):
            origin_layer = x._keras_history[0]
            if isinstance(origin_layer, InputLayer):
                return Sequential(layers=[origin_layer] + layers,
                                  name=model.name)
            else:
                raise ValueError('Cannot clone a `Sequential` model on top '
                                 'of a tensor that comes from a Keras layer '
                                 'other than an `InputLayer`. '
                                 'Use the functional API instead.')
        input_tensor = Input(tensor=x,
                             name='input_wrapper_for_' + str(x.name))
        input_layer = input_tensor._keras_history[0]
        return Sequential(layers=[input_layer] + layers, name=model.name)


def clone_model(model, input_tensors=None):
    """Clone any `Model` instance.

    Model cloning is similar to calling a model on new inputs,
    except that it creates new layers (and thus new weights) instead
    of sharing the weights of the existing layers.

    # Arguments
        model: Instance of `Model`
            (could be a functional model or a Sequential model).
        input_tensors: optional list of input tensors
            to build the model upon. If not provided,
            placeholders will be created.

    # Returns
        An instance of `Model` reproducing the behavior
        of the original model, on top of new inputs tensors,
        using newly instantiated weights.

    # Raises
        ValueError: in case of invalid `model` argument value.
    """
    if isinstance(model, Sequential):
        return _clone_sequential_model(model, input_tensors=input_tensors)
    else:
        return _clone_functional_model(model, input_tensors=input_tensors)
