# Copyright 2022 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
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# ==============================================================================
"""Unit Normalization layer."""


import tensorflow.compat.v2 as tf

from keras.engine import base_layer
from keras.utils import tf_utils

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


@keras_export("keras.layers.UnitNormalization", v1=[])
class UnitNormalization(base_layer.Layer):
    """Unit normalization layer.

    Normalize a batch of inputs so that each input in the batch has a L2 norm
    equal to 1 (across the axes specified in `axis`).

    Example:

    >>> data = tf.constant(np.arange(6).reshape(2, 3), dtype=tf.float32)
    >>> normalized_data = tf.keras.layers.UnitNormalization()(data)
    >>> print(tf.reduce_sum(normalized_data[0, :] ** 2).numpy())
    1.0

    Args:
      axis: Integer or list/tuple. The axis or axes to normalize across.
        Typically this is the features axis or axes. The left-out axes are
        typically the batch axis or axes. Defaults to `-1`, the last dimension
        in the input.
    """

    def __init__(self, axis=-1, **kwargs):
        super().__init__(**kwargs)
        if isinstance(axis, (list, tuple)):
            self.axis = list(axis)
        elif isinstance(axis, int):
            self.axis = axis
        else:
            raise TypeError(
                "Invalid value for `axis` argument: "
                "expected an int or a list/tuple of ints. "
                f"Received: axis={axis}"
            )
        self.supports_masking = True

    def build(self, input_shape):
        self.axis = tf_utils.validate_axis(self.axis, input_shape)

    def call(self, inputs):
        inputs = tf.cast(inputs, self.compute_dtype)
        return tf.linalg.l2_normalize(inputs, axis=self.axis)

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = super().get_config()
        config.update({"axis": self.axis})
        return config