from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.tf_data_layer import TFDataLayer
from keras.src.random.seed_generator import SeedGenerator


@keras_export("keras.layers.RandomContrast")
class RandomContrast(TFDataLayer):
    """A preprocessing layer which randomly adjusts contrast during training.

    This layer will randomly adjust the contrast of an image or images
    by a random factor. Contrast is adjusted independently
    for each channel of each image during training.

    For each channel, this layer computes the mean of the image pixels in the
    channel and then adjusts each component `x` of each pixel to
    `(x - mean) * contrast_factor + mean`.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    in integer or floating point dtype.
    By default, the layer will output floats.

    **Note:** This layer is safe to use inside a `tf.data` pipeline
    (independently of which backend you're using).

    Input shape:
        3D (unbatched) or 4D (batched) tensor with shape:
        `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
        3D (unbatched) or 4D (batched) tensor with shape:
        `(..., height, width, channels)`, in `"channels_last"` format.

    Args:
        factor: a positive float represented as fraction of value, or a tuple of
            size 2 representing lower and upper bound.
            When represented as a single float, lower = upper.
            The contrast factor will be randomly picked between
            `[1.0 - lower, 1.0 + upper]`. For any pixel x in the channel,
            the output will be `(x - mean) * factor + mean`
            where `mean` is the mean value of the channel.
        seed: Integer. Used to create a random seed.
    """

    def __init__(self, factor, seed=None, **kwargs):
        super().__init__(**kwargs)
        self.factor = factor
        if isinstance(factor, (tuple, list)):
            self.lower = factor[0]
            self.upper = factor[1]
        else:
            self.lower = self.upper = factor
        if self.lower < 0.0 or self.upper < 0.0 or self.lower > 1.0:
            raise ValueError(
                "`factor` argument cannot have negative values or values "
                "greater than 1."
                f"Received: factor={factor}"
            )
        self.seed = seed
        self.generator = SeedGenerator(seed)

    def call(self, inputs, training=True):
        inputs = self.backend.cast(inputs, self.compute_dtype)
        if training:
            seed_generator = self._get_seed_generator(self.backend._backend)
            factor = self.backend.random.uniform(
                shape=(),
                minval=1.0 - self.lower,
                maxval=1.0 + self.upper,
                seed=seed_generator,
                dtype=self.compute_dtype,
            )

            outputs = self._adjust_constrast(inputs, factor)
            outputs = self.backend.numpy.clip(outputs, 0, 255)
            self.backend.numpy.reshape(outputs, self.backend.shape(inputs))
            return outputs
        else:
            return inputs

    def _adjust_constrast(self, inputs, contrast_factor):
        # reduce mean on height
        inp_mean = self.backend.numpy.mean(inputs, axis=-3, keepdims=True)
        # reduce mean on width
        inp_mean = self.backend.numpy.mean(inp_mean, axis=-2, keepdims=True)

        outputs = (inputs - inp_mean) * contrast_factor + inp_mean
        return outputs

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "factor": self.factor,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return {**base_config, **config}