
# modified from https://github.com/HarryVolek/PyTorch_Speaker_Verification

import os
import yaml
import torch.distributed as dist
import logging
import math

from torch.optim.lr_scheduler import LambdaLR

logger = logging.getLogger(__name__)

class ConstantLRSchedule(LambdaLR):
    """ Constant learning rate schedule.
    """
    def __init__(self, optimizer, last_epoch=-1):
        super(ConstantLRSchedule, self).__init__(optimizer, lambda _: 1.0, last_epoch=last_epoch)


class WarmupConstantSchedule(LambdaLR):
    """ Linear warmup and then constant.
        Linearly increases learning rate schedule from 0 to 1 over `warmup_steps` training steps.
        Keeps learning rate schedule equal to 1. after warmup_steps.
    """
    def __init__(self, optimizer, warmup_steps, last_epoch=-1):
        self.warmup_steps = warmup_steps
        super(WarmupConstantSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)

    def lr_lambda(self, step):
        if step < self.warmup_steps:
            return float(step) / float(max(1.0, self.warmup_steps))
        return 1.


class WarmupLinearSchedule(LambdaLR):
    """ Linear warmup and then linear decay.
        Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
        Linearly decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps.
    """
    def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1):
        self.warmup_steps = warmup_steps
        self.t_total = t_total
        super(WarmupLinearSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)

    def lr_lambda(self, step):
        if step < self.warmup_steps:
            return float(step) / float(max(1, self.warmup_steps))
        return max(0.0, float(self.t_total - step) / float(max(1.0, self.t_total - self.warmup_steps)))


class WarmupCosineSchedule(LambdaLR):
    """ Linear warmup and then cosine decay.
        Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
        Decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps following a cosine curve.
        If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
    """
    def __init__(self, optimizer, warmup_steps, t_total, cycles=.5, last_epoch=-1):
        self.warmup_steps = warmup_steps
        self.t_total = t_total
        self.cycles = cycles
        super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)

    def lr_lambda(self, step):
        if step < self.warmup_steps:
            return float(step) / float(max(1.0, self.warmup_steps))
        # progress after warmup
        progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
        return max(0.0, 0.5 * (1. + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))
    
def get_rank():
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    return dist.get_rank()

def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()

def is_main_process():
    return get_rank() == 0

def format_step(step):
    if isinstance(step, str):
        return step
    s = ""
    if len(step) > 0:
        s += "Training Epoch: {} ".format(step[0])
    if len(step) > 1:
        s += "Training Iteration: {} ".format(step[1])
    if len(step) > 2:
        s += "Validation Iteration: {} ".format(step[2])
    return s

def load_hparam_str(hp_str):
    path = 'temp-restore.yaml'
    with open(path, 'w') as f:
        f.write(hp_str)
    ret = HParam(path)
    os.remove(path)
    return ret


def load_hparam(filename):
    stream = open(filename, 'r')
    docs = yaml.load_all(stream, Loader=yaml.Loader)
    hparam_dict = dict()
    for doc in docs:
        for k, v in doc.items():
            hparam_dict[k] = v
    return hparam_dict


def merge_dict(user, default):
    if isinstance(user, dict) and isinstance(default, dict):
        for k, v in default.items():
            if k not in user:
                user[k] = v
            else:
                user[k] = merge_dict(user[k], v)
    return user


class Dotdict(dict):
    """
    a dictionary that supports dot notation 
    as well as dictionary access notation 
    usage: d = DotDict() or d = DotDict({'val1':'first'})
    set attributes: d.val2 = 'second' or d['val2'] = 'second'
    get attributes: d.val2 or d['val2']
    """
    __getattr__ = dict.__getitem__
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    def __init__(self, dct=None):
        dct = dict() if not dct else dct
        for key, value in dct.items():
            if hasattr(value, 'keys'):
                value = Dotdict(value)
            self[key] = value


class HParam(Dotdict):

    def __init__(self, file):
        super(Dotdict, self).__init__()
        hp_dict = load_hparam(file)
        hp_dotdict = Dotdict(hp_dict)
        for k, v in hp_dotdict.items():
            setattr(self, k, v)
            
    __getattr__ = Dotdict.__getitem__
    __setattr__ = Dotdict.__setitem__
    __delattr__ = Dotdict.__delitem__