from keras import backend
from keras import constraints
from keras import initializers
from keras import ops
from keras import regularizers
from keras.api_export import keras_export
from keras.backend import standardize_dtype
from keras.layers.input_spec import InputSpec
from keras.layers.layer import Layer


@keras_export("keras.layers.BatchNormalization")
class BatchNormalization(Layer):
    """Layer that normalizes its inputs.

    Batch normalization applies a transformation that maintains the mean output
    close to 0 and the output standard deviation close to 1.

    Importantly, batch normalization works differently during training and
    during inference.

    **During training** (i.e. when using `fit()` or when calling the layer/model
    with the argument `training=True`), the layer normalizes its output using
    the mean and standard deviation of the current batch of inputs. That is to
    say, for each channel being normalized, the layer returns
    `gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta`, where:

    - `epsilon` is small constant (configurable as part of the constructor
    arguments)
    - `gamma` is a learned scaling factor (initialized as 1), which
    can be disabled by passing `scale=False` to the constructor.
    - `beta` is a learned offset factor (initialized as 0), which
    can be disabled by passing `center=False` to the constructor.

    **During inference** (i.e. when using `evaluate()` or `predict()` or when
    calling the layer/model with the argument `training=False` (which is the
    default), the layer normalizes its output using a moving average of the
    mean and standard deviation of the batches it has seen during training. That
    is to say, it returns
    `gamma * (batch - self.moving_mean) / sqrt(self.moving_var+epsilon) + beta`.

    `self.moving_mean` and `self.moving_var` are non-trainable variables that
    are updated each time the layer in called in training mode, as such:

    - `moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum)`
    - `moving_var = moving_var * momentum + var(batch) * (1 - momentum)`

    As such, the layer will only normalize its inputs during inference
    *after having been trained on data that has similar statistics as the
    inference data*.

    Args:
        axis: Integer, the axis that should be normalized
            (typically the features axis). For instance, after a `Conv2D` layer
            with `data_format="channels_first"`, use `axis=1`.
        momentum: Momentum for the moving average.
        epsilon: Small float added to variance to avoid dividing by zero.
        center: If `True`, add offset of `beta` to normalized tensor.
            If `False`, `beta` is ignored.
        scale: If `True`, multiply by `gamma`. If `False`, `gamma` is not used.
            When the next layer is linear this can be disabled
            since the scaling will be done by the next layer.
        beta_initializer: Initializer for the beta weight.
        gamma_initializer: Initializer for the gamma weight.
        moving_mean_initializer: Initializer for the moving mean.
        moving_variance_initializer: Initializer for the moving variance.
        beta_regularizer: Optional regularizer for the beta weight.
        gamma_regularizer: Optional regularizer for the gamma weight.
        beta_constraint: Optional constraint for the beta weight.
        gamma_constraint: Optional constraint for the gamma weight.
        synchronized: Only applicable with the TensorFlow backend.
            If `True`, synchronizes the global batch statistics (mean and
            variance) for the layer across all devices at each training step
            in a distributed training strategy.
            If `False`, each replica uses its own local batch statistics.
        **kwargs: Base layer keyword arguments (e.g. `name` and `dtype`).

    Call arguments:
        inputs: Input tensor (of any rank).
        training: Python boolean indicating whether the layer should behave in
            training mode or in inference mode.
            - `training=True`: The layer will normalize its inputs using
            the mean and variance of the current batch of inputs.
            - `training=False`: The layer will normalize its inputs using
            the mean and variance of its moving statistics, learned during
            training.
        mask: Binary tensor of shape broadcastable to `inputs` tensor, with
            `True` values indicating the positions for which mean and variance
            should be computed. Masked elements of the current inputs are not
            taken into account for mean and variance computation during
            training. Any prior unmasked element values will be taken into
            account until their momentum expires.

    Reference:

    - [Ioffe and Szegedy, 2015](https://arxiv.org/abs/1502.03167).

    **About setting `layer.trainable = False` on a `BatchNormalization` layer:**

    The meaning of setting `layer.trainable = False` is to freeze the layer,
    i.e. its internal state will not change during training:
    its trainable weights will not be updated
    during `fit()` or `train_on_batch()`, and its state updates will not be run.

    Usually, this does not necessarily mean that the layer is run in inference
    mode (which is normally controlled by the `training` argument that can
    be passed when calling a layer). "Frozen state" and "inference mode"
    are two separate concepts.

    However, in the case of the `BatchNormalization` layer, **setting
    `trainable = False` on the layer means that the layer will be
    subsequently run in inference mode** (meaning that it will use
    the moving mean and the moving variance to normalize the current batch,
    rather than using the mean and variance of the current batch).

    Note that:

    - Setting `trainable` on an model containing other layers will recursively
        set the `trainable` value of all inner layers.
    - If the value of the `trainable` attribute is changed after calling
        `compile()` on a model, the new value doesn't take effect for this model
        until `compile()` is called again.
    """

    def __init__(
        self,
        axis=-1,
        momentum=0.99,
        epsilon=1e-3,
        center=True,
        scale=True,
        beta_initializer="zeros",
        gamma_initializer="ones",
        moving_mean_initializer="zeros",
        moving_variance_initializer="ones",
        beta_regularizer=None,
        gamma_regularizer=None,
        beta_constraint=None,
        gamma_constraint=None,
        synchronized=False,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.axis = int(axis)

        if synchronized and backend.backend() != "tensorflow":
            raise ValueError(
                "Argument synchronized=True is only supported "
                "with the TensorFlow backend."
            )
        self.synchronized = synchronized

        self.momentum = float(momentum)
        self.epsilon = float(epsilon)
        self.center = center
        self.scale = scale
        self.beta_initializer = initializers.get(beta_initializer)
        self.gamma_initializer = initializers.get(gamma_initializer)
        self.moving_mean_initializer = initializers.get(moving_mean_initializer)
        self.moving_variance_initializer = initializers.get(
            moving_variance_initializer
        )
        self.beta_regularizer = regularizers.get(beta_regularizer)
        self.gamma_regularizer = regularizers.get(gamma_regularizer)
        self.beta_constraint = constraints.get(beta_constraint)
        self.gamma_constraint = constraints.get(gamma_constraint)
        self.supports_masking = True

    def build(self, input_shape):
        shape = (input_shape[self.axis],)
        if self.scale:
            self.gamma = self.add_weight(
                shape=shape,
                name="gamma",
                initializer=self.gamma_initializer,
                regularizer=self.gamma_regularizer,
                constraint=self.gamma_constraint,
                trainable=True,
            )
        if self.center:
            self.beta = self.add_weight(
                shape=shape,
                name="beta",
                initializer=self.beta_initializer,
                regularizer=self.beta_regularizer,
                constraint=self.beta_constraint,
                trainable=True,
            )
        self.moving_mean = self.add_weight(
            shape=shape,
            name="moving_mean",
            initializer=self.moving_mean_initializer,
            trainable=False,
        )
        self.moving_variance = self.add_weight(
            shape=shape,
            name="moving_variance",
            initializer=self.moving_variance_initializer,
            trainable=False,
        )
        self.input_spec = InputSpec(
            ndim=len(input_shape), axes={self.axis: input_shape[self.axis]}
        )
        reduction_axes = list(range(len(input_shape)))
        del reduction_axes[self.axis]
        self._reduction_axes = reduction_axes
        self.built = True

    def compute_output_shape(self, input_shape):
        return input_shape

    def call(self, inputs, training=None, mask=None):
        input_dtype = standardize_dtype(inputs.dtype)
        if input_dtype in ("float16", "bfloat16"):
            # BN is prone to overflowing for float16/bfloat16 inputs, so we opt
            # out BN for mixed precision.
            inputs = ops.cast(inputs, "float32")

        if training and self.trainable:
            mean, variance = self._moments(
                inputs,
                mask,
            )
            moving_mean = ops.cast(self.moving_mean, inputs.dtype)
            moving_variance = ops.cast(self.moving_variance, inputs.dtype)
            self.moving_mean.assign(
                ops.cast(
                    moving_mean * self.momentum + mean * (1.0 - self.momentum),
                    inputs.dtype,
                )
            )
            self.moving_variance.assign(
                ops.cast(
                    moving_variance * self.momentum
                    + variance * (1.0 - self.momentum),
                    inputs.dtype,
                )
            )
        else:
            moving_mean = ops.cast(self.moving_mean, inputs.dtype)
            moving_variance = ops.cast(self.moving_variance, inputs.dtype)
            mean = moving_mean
            variance = moving_variance

        if self.scale:
            gamma = ops.cast(self.gamma, inputs.dtype)
        else:
            gamma = None

        if self.center:
            beta = ops.cast(self.beta, inputs.dtype)
        else:
            beta = None

        outputs = ops.batch_normalization(
            x=inputs,
            mean=mean,
            variance=variance,
            axis=self.axis,
            offset=beta,
            scale=gamma,
            epsilon=self.epsilon,
        )
        if input_dtype in ("float16", "bfloat16"):
            outputs = ops.cast(outputs, input_dtype)
        return outputs

    def get_config(self):
        base_config = super().get_config()
        config = {
            "axis": self.axis,
            "momentum": self.momentum,
            "epsilon": self.epsilon,
            "center": self.center,
            "scale": self.scale,
            "beta_initializer": initializers.serialize(self.beta_initializer),
            "gamma_initializer": initializers.serialize(self.gamma_initializer),
            "moving_mean_initializer": initializers.serialize(
                self.moving_mean_initializer
            ),
            "moving_variance_initializer": initializers.serialize(
                self.moving_variance_initializer
            ),
            "beta_regularizer": regularizers.serialize(self.beta_regularizer),
            "gamma_regularizer": regularizers.serialize(self.gamma_regularizer),
            "beta_constraint": constraints.serialize(self.beta_constraint),
            "gamma_constraint": constraints.serialize(self.gamma_constraint),
            "synchronized": self.synchronized,
        }
        return {**base_config, **config}

    def _moments(self, inputs, mask):
        if mask is None:
            return ops.moments(
                inputs,
                axes=self._reduction_axes,
                synchronized=self.synchronized,
            )

        mask_weights = ops.cast(
            mask,
            inputs.dtype,
        )
        mask_weights_broadcasted = ops.expand_dims(
            mask_weights,
            axis=-1,
        )
        weighted_inputs = mask_weights_broadcasted * inputs

        weighted_input_sum = ops.sum(
            weighted_inputs,
            self._reduction_axes,
            keepdims=True,
        )
        sum_of_weights = ops.sum(
            mask_weights_broadcasted,
            self._reduction_axes,
            keepdims=True,
        )
        mean = weighted_input_sum / (sum_of_weights + backend.config.epsilon())

        difference = weighted_inputs - mean
        squared_difference = ops.square(difference)
        weighted_distsq = ops.sum(
            mask_weights_broadcasted * squared_difference,
            self._reduction_axes,
            keepdims=True,
        )
        variance = weighted_distsq / (sum_of_weights + backend.config.epsilon())

        return ops.squeeze(mean), ops.squeeze(variance)
