import numpy as np
import tensorflow as tf
from tensorflow import keras


class WarmUpCosine(keras.optimizers.schedules.LearningRateSchedule):
    """A LearningRateSchedule that uses a warmup cosine decay schedule."""

    def __init__(self, lr_start, lr_max, warmup_steps, total_steps):
        """
        Args:
            lr_start: The initial learning rate
            lr_max: The maximum learning rate to which lr should increase to in
                the warmup steps
            warmup_steps: The number of steps for which the model warms up
            total_steps: The total number of steps for the model training
        """
        super().__init__()
        self.lr_start = lr_start
        self.lr_max = lr_max
        self.warmup_steps = warmup_steps
        self.total_steps = total_steps
        self.pi = tf.constant(np.pi)

    def __call__(self, step):
        # Check whether the total number of steps is larger than the warmup
        # steps. If not, then throw a value error.
        if self.total_steps < self.warmup_steps:
            raise ValueError(
                f"Total number of steps {self.total_steps} must be"
                + f"larger or equal to warmup steps {self.warmup_steps}."
            )

        # `cos_annealed_lr` is a graph that increases to 1 from the initial
        # step to the warmup step. After that this graph decays to -1 at the
        # final step mark.
        cos_annealed_lr = tf.cos(
            self.pi
            * (tf.cast(step, tf.float32) - self.warmup_steps)
            / tf.cast(self.total_steps - self.warmup_steps, tf.float32)
        )

        # Shift the mean of the `cos_annealed_lr` graph to 1. Now the grpah goes
        # from 0 to 2. Normalize the graph with 0.5 so that now it goes from 0
        # to 1. With the normalized graph we scale it with `lr_max` such that
        # it goes from 0 to `lr_max`
        learning_rate = 0.5 * self.lr_max * (1 + cos_annealed_lr)

        # Check whether warmup_steps is more than 0.
        if self.warmup_steps > 0:
            # Check whether lr_max is larger that lr_start. If not, throw a value
            # error.
            if self.lr_max < self.lr_start:
                raise ValueError(
                    f"lr_start {self.lr_start} must be smaller or"
                    + f"equal to lr_max {self.lr_max}."
                )

            # Calculate the slope with which the learning rate should increase
            # in the warumup schedule. The formula for slope is m = ((b-a)/steps)
            slope = (self.lr_max - self.lr_start) / self.warmup_steps

            # With the formula for a straight line (y = mx+c) build the warmup
            # schedule
            warmup_rate = slope * tf.cast(step, tf.float32) + self.lr_start

            # When the current step is lesser that warmup steps, get the line
            # graph. When the current step is greater than the warmup steps, get
            # the scaled cos graph.
            learning_rate = tf.where(
                step < self.warmup_steps, warmup_rate, learning_rate
            )

        # When the current step is more that the total steps, return 0 else return
        # the calculated graph.
        return tf.where(
            step > self.total_steps, 0.0, learning_rate, name="learning_rate"
        )