import numpy as np


class LambdaWarmUpCosineScheduler:
    """
    note: use with a base_lr of 1.0
    """
    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
        self.lr_warm_up_steps = warm_up_steps
        self.lr_start = lr_start
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.lr_max_decay_steps = max_decay_steps
        self.last_lr = 0.
        self.verbosity_interval = verbosity_interval

    def schedule(self, n, **kwargs):
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
        if n < self.lr_warm_up_steps:
            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
            self.last_lr = lr
            return lr
        else:
            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
            t = min(t, 1.0)
            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
                    1 + np.cos(t * np.pi))
            self.last_lr = lr
            return lr

    def __call__(self, n, **kwargs):
        return self.schedule(n,**kwargs)


class LambdaWarmUpCosineScheduler2:
    """
    supports repeated iterations, configurable via lists
    note: use with a base_lr of 1.0.
    """
    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
        self.lr_warm_up_steps = warm_up_steps
        self.f_start = f_start
        self.f_min = f_min
        self.f_max = f_max
        self.cycle_lengths = cycle_lengths
        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
        self.last_f = 0.
        self.verbosity_interval = verbosity_interval

    def find_in_interval(self, n):
        interval = 0
        for cl in self.cum_cycles[1:]:
            if n <= cl:
                return interval
            interval += 1

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                                                       f"current cycle {cycle}")
        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
            t = min(t, 1.0)
            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
                    1 + np.cos(t * np.pi))
            self.last_f = f
            return f

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0 and n % self.verbosity_interval == 0: 
            print(f"current step: {n}, recent lr-multiplier: {self.last_f}, current cycle {cycle}")

        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
            self.last_f = f
            return f

class EveryDreamScheduler:
    """
    f_min: minimum lr multiplier
    f_max: maximum lr multiplier
    f_start: lr multiplier at the beginning of the warm-up phase
    warm_up_steps: number of steps in the warm-up phase
    steps_to_min: number of steps to reach f_min multiplier
    """
    def __init__(self, f_min=0.5, f_max=1.0, f_start=0.5, warm_up_steps=500, steps_to_min=5000, verbosity_interval=100) -> None:
        self.f_min = f_min
        self.f_max = f_max
        self.f_start = f_start
        self.warm_up_steps = warm_up_steps
        self.steps_to_min = steps_to_min
        self.last_f = 0.0
        self.verbosity_interval = verbosity_interval
    
    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)
    
    def schedule(self, n, **kawrgs):
        if self.verbosity_interval > 0 and n % self.verbosity_interval == 0: 
            print(f"current step: {n}, recent lr-multiplier: {self.last_f:0.3f}")

        if n < self.warm_up_steps:
            self.last_f = self.f_start
        elif n < self.steps_to_min:
            self.last_f = self.f_min + (self.f_max - self.f_min) * (self.steps_to_min - n) / (self.steps_to_min)
        else:
            self.last_f = self.f_min

        return self.last_f