import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# from utils import d_model

'''
optimizer = NoamOpt(d_model, 1, 4000, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
model_size: 256
factor: 1
warmup: 4000
optimizer: torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9)
'''
class NoamOpt:
    "Optim wrapper that implements rate."
    def __init__(self, model_size, factor, warmup, optimizer):
        self.optimizer = optimizer
        self._step = 0
        self.warmup = warmup
        self.factor = factor
        self.model_size = model_size
        self._rate = 0
        
    def step(self):
        "Update parameters and rate"
        self._step += 1
        rate = self.rate()
        # print("This is the rate:", rate)
        for p in self.optimizer.param_groups:
            p['lr'] = rate
        self._rate = rate
        self.optimizer.step()
        
    def rate(self, step = None):
        "Implement `lrate` above"
        if step is None:
            step = self._step
        return self.factor * (self.model_size ** (-0.5) * min(step ** (-0.5), step * self.warmup ** (-1.5)))
        
def get_std_opt(model):
    return NoamOpt(729, 2, 4000, torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))