# Copyright 2015 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.
# ==============================================================================
"""Rectified Linear Unit activation layer."""


from tf_keras.src import backend
from tf_keras.src.engine.base_layer import Layer
from tf_keras.src.utils import tf_utils

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


@keras_export("keras.layers.ReLU")
class ReLU(Layer):
    """Rectified Linear Unit activation function.

    With default values, it returns element-wise `max(x, 0)`.

    Otherwise, it follows:

    ```
        f(x) = max_value if x >= max_value
        f(x) = x if threshold <= x < max_value
        f(x) = negative_slope * (x - threshold) otherwise
    ```

    Usage:

    >>> layer = tf.keras.layers.ReLU()
    >>> output = layer([-3.0, -1.0, 0.0, 2.0])
    >>> list(output.numpy())
    [0.0, 0.0, 0.0, 2.0]
    >>> layer = tf.keras.layers.ReLU(max_value=1.0)
    >>> output = layer([-3.0, -1.0, 0.0, 2.0])
    >>> list(output.numpy())
    [0.0, 0.0, 0.0, 1.0]
    >>> layer = tf.keras.layers.ReLU(negative_slope=1.0)
    >>> output = layer([-3.0, -1.0, 0.0, 2.0])
    >>> list(output.numpy())
    [-3.0, -1.0, 0.0, 2.0]
    >>> layer = tf.keras.layers.ReLU(threshold=1.5)
    >>> output = layer([-3.0, -1.0, 1.0, 2.0])
    >>> list(output.numpy())
    [0.0, 0.0, 0.0, 2.0]

    Input shape:
        Arbitrary. Use the keyword argument `input_shape`
        (tuple of integers, does not include the batch axis)
        when using this layer as the first layer in a model.

    Output shape:
        Same shape as the input.

    Args:
        max_value: Float >= 0. Maximum activation value. None means unlimited.
            Defaults to `None`.
        negative_slope: Float >= 0. Negative slope coefficient.
            Defaults to `0.`.
        threshold: Float >= 0. Threshold value for thresholded activation.
            Defaults to `0.`.
    """

    def __init__(
        self, max_value=None, negative_slope=0.0, threshold=0.0, **kwargs
    ):
        super().__init__(**kwargs)
        if max_value is not None and max_value < 0.0:
            raise ValueError(
                "max_value of a ReLU layer cannot be a negative "
                f"value. Received: {max_value}"
            )
        if negative_slope is None or negative_slope < 0.0:
            raise ValueError(
                "negative_slope of a ReLU layer cannot be a negative "
                f"value. Received: {negative_slope}"
            )
        if threshold is None or threshold < 0.0:
            raise ValueError(
                "threshold of a ReLU layer cannot be a negative "
                f"value. Received: {threshold}"
            )

        self.supports_masking = True
        if max_value is not None:
            max_value = backend.cast_to_floatx(max_value)
        self.max_value = max_value
        self.negative_slope = backend.cast_to_floatx(negative_slope)
        self.threshold = backend.cast_to_floatx(threshold)

    def call(self, inputs):
        # alpha is used for leaky relu slope in activations instead of
        # negative_slope.
        return backend.relu(
            inputs,
            alpha=self.negative_slope,
            max_value=self.max_value,
            threshold=self.threshold,
        )

    def get_config(self):
        config = {
            "max_value": self.max_value,
            "negative_slope": self.negative_slope,
            "threshold": self.threshold,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))

    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
        return input_shape