import math
from typing import TYPE_CHECKING, Any, Callable, Optional

import torch
import torch.optim
import logging
import os
import torch.distributed as dist

if TYPE_CHECKING:
    from torch.optim.optimizer import _params_t
else:
    _params_t = Any


class Prodigy(torch.optim.Optimizer):
    r"""
    Implements Adam with Prodigy step-sizes.
    Leave LR set to 1 unless you encounter instability.
   
    Arguments:
        params (iterable):
            Iterable of parameters to optimize or dicts defining parameter groups.
        lr (float):
            Learning rate adjustment parameter. Increases or decreases the Prodigy learning rate.
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        beta3 (float):
            coefficients for computing the Prodigy stepsize using running averages.
            If set to None, uses the value of square root of beta2 (default: None).
        eps (float):
            Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-8).
        weight_decay (float):
            Weight decay, i.e. a L2 penalty (default: 0).
        decouple (boolean):
            Use AdamW style decoupled weight decay
        use_bias_correction (boolean):
            Turn on Adam's bias correction. Off by default.
        safeguard_warmup (boolean):
            Remove lr from the denominator of D estimate to avoid issues during warm-up stage. Off by default.
        d0 (float):
            Initial D estimate for D-adaptation (default 1e-6). Rarely needs changing.
        d_coef (float):
            Coefficient in the expression for the estimate of d (default 1.0).
            Values such as 0.5 and 2.0 typically work as well. 
            Changing this parameter is the preferred way to tune the method.
        growth_rate (float):
            prevent the D estimate from growing faster than this multiplicative rate.
            Default is inf, for unrestricted. Values like 1.02 give a kind of learning
            rate warmup effect.
        fsdp_in_use (bool):
            If you're using sharded parameters, this should be set to True. The optimizer
            will attempt to auto-detect this, but if you're using an implementation other
            than PyTorch's builtin version, the auto-detection won't work.
    """
    def __init__(self, params, lr=1.0,
                 betas=(0.9, 0.999), beta3=None,
                 eps=1e-8, weight_decay=0, decouple=True, 
                 use_bias_correction=False, safeguard_warmup=False,
                 d0=1e-6, d_coef=1.0, growth_rate=float('inf'),
                 fsdp_in_use=False):
        if not 0.0 < d0:
            raise ValueError("Invalid d0 value: {}".format(d0))
        if not 0.0 < lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 < eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))

        if decouple and weight_decay > 0:
            print(f"Using decoupled weight decay")


        defaults = dict(lr=lr, betas=betas, beta3=beta3,
                        eps=eps, weight_decay=weight_decay,
                        d=d0, d0=d0, d_max=d0,
                        d_numerator=0.0, d_coef=d_coef,
                        k=0, growth_rate=growth_rate,
                        use_bias_correction=use_bias_correction,
                        decouple=decouple, safeguard_warmup=safeguard_warmup,
                        fsdp_in_use=fsdp_in_use)
        self.d0 = d0
        super().__init__(params, defaults)

    @property
    def supports_memory_efficient_fp16(self):
        return False

    @property
    def supports_flat_params(self):
        return True
    
    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        """""
        Iterate individually over all parameter groups, following the AdamW approach
        to support multiple parameter groups with distinct settings.
        """
        for group in self.param_groups:

            d_denom = 0.0

            use_bias_correction = group['use_bias_correction']
            beta1, beta2 = group['betas']
            beta3 = group['beta3']
            if beta3 is None:
                beta3 = math.sqrt(beta2)
            k = group['k']

            d = group['d']
            d_max = group['d_max']
            d_coef = group['d_coef']
            lr = group['lr']

            if use_bias_correction:
                bias_correction = ((1 - beta2**(k+1))**0.5) / (1 - beta1**(k+1))
            else:
                bias_correction = 1

            dlr = d * lr * bias_correction

            growth_rate = group['growth_rate']
            decouple = group['decouple']
            fsdp_in_use = group['fsdp_in_use']

            d_numerator = group['d_numerator']
            d_numerator *= beta3

            decay = group['weight_decay']
            k = group['k']
            eps = group['eps']
            group_lr = group['lr']
            d0 = group['d0']
            safeguard_warmup = group['safeguard_warmup']

            for p in group['params']:
                if p.grad is None:
                    continue
                if hasattr(p, "_fsdp_flattened"):
                    fsdp_in_use = True

                grad = p.grad.data

                # Apply weight decay (coupled variant)
                if decay != 0 and not decouple:
                    grad.add_(p.data, alpha=decay)

                state = self.state[p]

                # State initialization
                if 'step' not in state:
                    state['step'] = 0
                    state['s'] = torch.zeros_like(p.data).detach()
                    state['p0'] = p.detach().clone()
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data).detach()
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data).detach()

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                s = state['s']
                p0 = state['p0']

                if group_lr > 0.0:
                    # we use d / d0 instead of just d to avoid getting values that are too small
                    d_numerator += (d / d0) * dlr * torch.dot(grad.flatten(), (p0.data - p.data).flatten()).item()

                    # Adam EMA updates
                    exp_avg.mul_(beta1).add_(grad, alpha=d * (1 - beta1))
                    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=d * d * (1 - beta2))

                    if safeguard_warmup:
                        s.mul_(beta3).add_(grad, alpha=((d / d0) * d))
                    else:
                        s.mul_(beta3).add_(grad, alpha=((d / d0) * dlr))
                    d_denom += s.abs().sum().item()

            d_hat = d
            
            # if we have not done any progres, return
            # if we have any gradients available, will have d_denom > 0 (unless \|g\|=0)
            if d_denom == 0:
                continue  # skip this group instead of return loss

            if lr > 0.0:
                if fsdp_in_use:
                    dist_tensor = torch.zeros(2).to(next(group['params']).device) # adapting to the new approach
                    dist_tensor[0] = d_numerator
                    dist_tensor[1] = d_denom
                    dist.all_reduce(dist_tensor, op=dist.ReduceOp.SUM)
                    group_d_numerator = dist_tensor[0]
                    group_d_denom = dist_tensor[1]
                else:
                    group_d_numerator = d_numerator
                    group_d_denom = d_denom

                d_hat = d_coef * group_d_numerator / group_d_denom
                if d == group['d0']:
                    d = max(d, d_hat)
                d_max = max(d_max, d_hat)
                d = min(d_max, d * growth_rate)

            """
            Unified the second pass of 'for group in self.param_groups' with the first
            to ensure more consistent data for each group.
            """

            group['d_numerator'] = group_d_numerator
            group['d_denom'] = group_d_denom
            group['d'] = d
            group['d_max'] = d_max
            group['d_hat'] = d_hat

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data

                state = self.state[p]

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                state['step'] += 1

                denom = exp_avg_sq.sqrt().add_(d * eps)

                # Apply weight decay (decoupled variant)
                if decay != 0 and decouple:
                    p.data.add_(p.data, alpha=-decay * dlr)

                ### Take step
                p.data.addcdiv_(exp_avg, denom, value=-dlr)

            group['k'] = k + 1

        return loss