class Dense:
    """Just your regular densely-connected NN layer.

    `Dense` implements the operation:
    `output = activation(dot(input, kernel) + bias)`
    where `activation` is the element-wise activation function
    passed as the `activation` argument, `kernel` is a weights matrix
    created by the layer, and `bias` is a bias vector created by the layer
    (only applicable if `use_bias` is `True`).

    Note: if the input to the layer has a rank greater than 2, then
    it is flattened prior to the initial dot product with `kernel`.

    # Example

    ```python
        # as first layer in a sequential model:
        model = Sequential()
        model.add(Dense(32, input_shape=(16,)))
        # now the model will take as input arrays of shape (*, 16)
        # and output arrays of shape (*, 32)

        # after the first layer, you don't need to specify
        # the size of the input anymore:
        model.add(Dense(32))
    ```

    # Arguments
        units: Positive integer, dimensionality of the output space.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to
            the `kernel` weights matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).

    # Input shape
        nD tensor with shape: `(batch_size, ..., input_dim)`.
        The most common situation would be
        a 2D input with shape `(batch_size, input_dim)`.

    # Output shape
        nD tensor with shape: `(batch_size, ..., units)`.
        For instance, for a 2D input with shape `(batch_size, input_dim)`,
        the output would have shape `(batch_size, units)`.
    """

    def __init__(self, units,
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        ...


def to_categorical(y, num_classes=None, dtype='float32'):
    """Converts a class vector (integers) to binary class matrix.

    E.g. for use with categorical_crossentropy.

    # Arguments
        y: class vector to be converted into a matrix
            (integers from 0 to num_classes).
        num_classes: total number of classes.
        dtype: The data type expected by the input, as a string
            (`float32`, `float64`, `int32`...)

    # Returns
        A binary matrix representation of the input. The classes axis
        is placed last.

    # Example

    ```python
    # Consider an array of 5 labels out of a set of 3 classes {0, 1, 2}:
    > labels
    array([0, 2, 1, 2, 0])
    # `to_categorical` converts this into a matrix with as many
    # columns as there are classes. The number of rows
    # stays the same.
    > to_categorical(labels)
    array([[ 1.,  0.,  0.],
           [ 0.,  0.,  1.],
           [ 0.,  1.,  0.],
           [ 0.,  0.,  1.],
           [ 1.,  0.,  0.]], dtype=float32)
    ```
    """


class ImageDataGenerator:
    """Generate batches of tensor image data with real-time data augmentation.

    The data will be looped over (in batches).

    # Arguments
        featurewise_center: Boolean.
            Set input mean to 0 over the dataset, feature-wise.
        samplewise_center: Boolean. Set each sample mean to 0.
        featurewise_std_normalization: Boolean.
            Divide inputs by std of the dataset, feature-wise.
        samplewise_std_normalization: Boolean. Divide each input by its std.
        zca_whitening: Boolean. Apply ZCA whitening.
        zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.
        rotation_range: Int. Degree range for random rotations.
        width_shift_range: Float, 1-D array-like or int
            - float: fraction of total width, if < 1, or pixels if >= 1.
            - 1-D array-like: random elements from the array.
            - int: integer number of pixels from interval
                `(-width_shift_range, +width_shift_range)`
            - With `width_shift_range=2` possible values
                are integers `[-1, 0, +1]`,
                same as with `width_shift_range=[-1, 0, +1]`,
                while with `width_shift_range=1.0` possible values are floats
                in the interval `[-1.0, +1.0)`.
        height_shift_range: Float, 1-D array-like or int
            - float: fraction of total height, if < 1, or pixels if >= 1.
            - 1-D array-like: random elements from the array.
            - int: integer number of pixels from interval
                `(-height_shift_range, +height_shift_range)`
            - With `height_shift_range=2` possible values
                are integers `[-1, 0, +1]`,
                same as with `height_shift_range=[-1, 0, +1]`,
                while with `height_shift_range=1.0` possible values are floats
                in the interval `[-1.0, +1.0)`.
        brightness_range: Tuple or list of two floats. Range for picking
            a brightness shift value from.
        shear_range: Float. Shear Intensity
            (Shear angle in counter-clockwise direction in degrees)
        zoom_range: Float or [lower, upper]. Range for random zoom.
            If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.
        channel_shift_range: Float. Range for random channel shifts.
        fill_mode: One of {"constant", "nearest", "reflect" or "wrap"}.
            Default is 'nearest'.
            Points outside the boundaries of the input are filled
            according to the given mode:
            - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)
            - 'nearest':  aaaaaaaa|abcd|dddddddd
            - 'reflect':  abcddcba|abcd|dcbaabcd
            - 'wrap':  abcdabcd|abcd|abcdabcd
        cval: Float or Int.
            Value used for points outside the boundaries
            when `fill_mode = "constant"`.
        horizontal_flip: Boolean. Randomly flip inputs horizontally.
        vertical_flip: Boolean. Randomly flip inputs vertically.
        rescale: rescaling factor. Defaults to None.
            If None or 0, no rescaling is applied,
            otherwise we multiply the data by the value provided
            (after applying all other transformations).
        preprocessing_function: function that will be applied on each input.
            The function will run after the image is resized and augmented.
            The function should take one argument:
            one image (NumPy tensor with rank 3),
            and should output a NumPy tensor with the same shape.
        data_format: Image data format,
            either "channels_first" or "channels_last".
            "channels_last" mode means that the images should have shape
            `(samples, height, width, channels)`,
            "channels_first" mode means that the images should have shape
            `(samples, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        validation_split: Float. Fraction of images reserved for validation
            (strictly between 0 and 1).
        interpolation_order: int, order to use for
            the spline interpolation. Higher is slower.
        dtype: Dtype to use for the generated arrays.

    # Examples

    Example of using `.flow(x, y)`:
    ```python
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    y_train = np_utils.to_categorical(y_train, num_classes)
    y_test = np_utils.to_categorical(y_test, num_classes)
    datagen = ImageDataGenerator(
        featurewise_center=True,
        featurewise_std_normalization=True,
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True)
    # compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(x_train)
    # fits the model on batches with real-time data augmentation:
    model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
                        steps_per_epoch=len(x_train) / 32, epochs=epochs)
    # here's a more "manual" example
    for e in range(epochs):
        print('Epoch', e)
        batches = 0
        for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):
            model.fit(x_batch, y_batch)
            batches += 1
            if batches >= len(x_train) / 32:
                # we need to break the loop by hand because
                # the generator loops indefinitely
                break
    ```
    Example of using `.flow_from_directory(directory)`:
    ```python
    train_datagen = ImageDataGenerator(
            rescale=1./255,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True)
    test_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_directory(
            'data/train',
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')
    validation_generator = test_datagen.flow_from_directory(
            'data/validation',
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')
    model.fit_generator(
            train_generator,
            steps_per_epoch=2000,
            epochs=50,
            validation_data=validation_generator,
            validation_steps=800)
    ```
    Example of transforming images and masks together.
    ```python
    # we create two instances with the same arguments
    data_gen_args = dict(featurewise_center=True,
                         featurewise_std_normalization=True,
                         rotation_range=90,
                         width_shift_range=0.1,
                         height_shift_range=0.1,
                         zoom_range=0.2)
    image_datagen = ImageDataGenerator(**data_gen_args)
    mask_datagen = ImageDataGenerator(**data_gen_args)
    # Provide the same seed and keyword arguments to the fit and flow methods
    seed = 1
    image_datagen.fit(images, augment=True, seed=seed)
    mask_datagen.fit(masks, augment=True, seed=seed)
    image_generator = image_datagen.flow_from_directory(
        'data/images',
        class_mode=None,
        seed=seed)
    mask_generator = mask_datagen.flow_from_directory(
        'data/masks',
        class_mode=None,
        seed=seed)
    # combine generators into one which yields image and masks
    train_generator = zip(image_generator, mask_generator)
    model.fit_generator(
        train_generator,
        steps_per_epoch=2000,
        epochs=50)
    ```
    Example of using ```.flow_from_dataframe(dataframe, directory,
                                            x_col, y_col)```:
    ```python
    train_df = pandas.read_csv("./train.csv")
    valid_df = pandas.read_csv("./valid.csv")
    train_datagen = ImageDataGenerator(
            rescale=1./255,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True)
    test_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_dataframe(
            dataframe=train_df,
            directory='data/train',
            x_col="filename",
            y_col="class",
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')
    validation_generator = test_datagen.flow_from_dataframe(
            dataframe=valid_df,
            directory='data/validation',
            x_col="filename",
            y_col="class",
            target_size=(150, 150),
            batch_size=32,
            class_mode='binary')
    model.fit_generator(
            train_generator,
            steps_per_epoch=2000,
            epochs=50,
            validation_data=validation_generator,
            validation_steps=800)
    ```
    """

    def __init__(self,
                 featurewise_center=False,
                 samplewise_center=False,
                 featurewise_std_normalization=False,
                 samplewise_std_normalization=False,
                 zca_whitening=False,
                 zca_epsilon=1e-6,
                 rotation_range=0,
                 width_shift_range=0.,
                 height_shift_range=0.,
                 brightness_range=None,
                 shear_range=0.,
                 zoom_range=0.,
                 channel_shift_range=0.,
                 fill_mode='nearest',
                 cval=0.,
                 horizontal_flip=False,
                 vertical_flip=False,
                 rescale=None,
                 preprocessing_function=None,
                 data_format='channels_last',
                 validation_split=0.0,
                 interpolation_order=1,
                 dtype='float32'):
        ...

    def flow(self,
             x,
             y=None,
             batch_size=32,
             shuffle=True,
             sample_weight=None,
             seed=None,
             save_to_dir=None,
             save_prefix='',
             save_format='png',
             subset=None):
        """Takes data & label arrays, generates batches of augmented data.

        # Arguments
            x: Input data. Numpy array of rank 4 or a tuple.
                If tuple, the first element
                should contain the images and the second element
                another numpy array or a list of numpy arrays
                that gets passed to the output
                without any modifications.
                Can be used to feed the model miscellaneous data
                along with the images.
                In case of grayscale data, the channels axis of the image array
                should have value 1, in case
                of RGB data, it should have value 3, and in case
                of RGBA data, it should have value 4.
            y: Labels.
            batch_size: Int (default: 32).
            shuffle: Boolean (default: True).
            sample_weight: Sample weights.
            seed: Int (default: None).
            save_to_dir: None or str (default: None).
                This allows you to optionally specify a directory
                to which to save the augmented pictures being generated
                (useful for visualizing what you are doing).
            save_prefix: Str (default: `''`).
                Prefix to use for filenames of saved pictures
                (only relevant if `save_to_dir` is set).
            save_format: one of "png", "jpeg"
                (only relevant if `save_to_dir` is set). Default: "png".
            subset: Subset of data (`"training"` or `"validation"`) if
                `validation_split` is set in `ImageDataGenerator`.

        # Returns
            An `Iterator` yielding tuples of `(x, y)`
                where `x` is a numpy array of image data
                (in the case of a single image input) or a list
                of numpy arrays (in the case with
                additional inputs) and `y` is a numpy array
                of corresponding labels. If 'sample_weight' is not None,
                the yielded tuples are of the form `(x, y, sample_weight)`.
                If `y` is None, only the numpy array `x` is returned.
        """
        ...

    def flow_from_directory(self,
                            directory,
                            target_size=(256, 256),
                            color_mode='rgb',
                            classes=None,
                            class_mode='categorical',
                            batch_size=32,
                            shuffle=True,
                            seed=None,
                            save_to_dir=None,
                            save_prefix='',
                            save_format='png',
                            follow_links=False,
                            subset=None,
                            interpolation='nearest'):
        """Takes the path to a directory & generates batches of augmented data.

        # Arguments
            directory: string, path to the target directory.
                It should contain one subdirectory per class.
                Any PNG, JPG, BMP, PPM or TIF images
                inside each of the subdirectories directory tree
                will be included in the generator.
                See [this script](
                https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)
                for more details.
            target_size: Tuple of integers `(height, width)`,
                default: `(256, 256)`.
                The dimensions to which all images found will be resized.
            color_mode: One of "grayscale", "rgb", "rgba". Default: "rgb".
                Whether the images will be converted to
                have 1, 3, or 4 channels.
            classes: Optional list of class subdirectories
                (e.g. `['dogs', 'cats']`). Default: None.
                If not provided, the list of classes will be automatically
                inferred from the subdirectory names/structure
                under `directory`, where each subdirectory will
                be treated as a different class
                (and the order of the classes, which will map to the label
                indices, will be alphanumeric).
                The dictionary containing the mapping from class names to class
                indices can be obtained via the attribute `class_indices`.
            class_mode: One of "categorical", "binary", "sparse",
                "input", or None. Default: "categorical".
                Determines the type of label arrays that are returned:
                - "categorical" will be 2D one-hot encoded labels,
                - "binary" will be 1D binary labels,
                    "sparse" will be 1D integer labels,
                - "input" will be images identical
                    to input images (mainly used to work with autoencoders).
                - If None, no labels are returned
                  (the generator will only yield batches of image data,
                  which is useful to use with `model.predict_generator()`).
                  Please note that in case of class_mode None,
                  the data still needs to reside in a subdirectory
                  of `directory` for it to work correctly.
            batch_size: Size of the batches of data (default: 32).
            shuffle: Whether to shuffle the data (default: True)
                If set to False, sorts the data in alphanumeric order.
            seed: Optional random seed for shuffling and transformations.
            save_to_dir: None or str (default: None).
                This allows you to optionally specify
                a directory to which to save
                the augmented pictures being generated
                (useful for visualizing what you are doing).
            save_prefix: Str. Prefix to use for filenames of saved pictures
                (only relevant if `save_to_dir` is set).
            save_format: One of "png", "jpeg"
                (only relevant if `save_to_dir` is set). Default: "png".
            follow_links: Whether to follow symlinks inside
                class subdirectories (default: False).
            subset: Subset of data (`"training"` or `"validation"`) if
                `validation_split` is set in `ImageDataGenerator`.
            interpolation: Interpolation method used to
                resample the image if the
                target size is different from that of the loaded image.
                Supported methods are `"nearest"`, `"bilinear"`,
                and `"bicubic"`.
                If PIL version 1.1.3 or newer is installed, `"lanczos"` is also
                supported. If PIL version 3.4.0 or newer is installed,
                `"box"` and `"hamming"` are also supported.
                By default, `"nearest"` is used.

        # Returns
            A `DirectoryIterator` yielding tuples of `(x, y)`
                where `x` is a numpy array containing a batch
                of images with shape `(batch_size, *target_size, channels)`
                and `y` is a numpy array of corresponding labels.
        """
        ...
