"""
    ########################################################################################################################################################
    # This algorithm is part of a code library for optimizing the Artificial Intelligence models of Sapiens Technology®, and its disclosure, distribution, #
    # or reverse engineering without the company's prior consent will be subject to legal proceedings and actions pursued by our legal department.         #
    ########################################################################################################################################################
"""
from sapiens_machine.optim.optimizer import Optimizer1State
from torch.optim import Optimizer
import torch
class LARS(Optimizer1State):
    def __init__(self, params, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
        if momentum == 0: raise NotImplementedError("LARS without momentum is not supported!")
        super().__init__("lars", params, lr, (momentum, dampening), 0.0, weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
class LARS8bit(Optimizer1State):
    def __init__(self, params, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, args=None, min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
        if momentum == 0: raise NotImplementedError("LARS without momentum is not supported!")
        super().__init__("lars", params, lr, (momentum, dampening), 0.0, weight_decay, 8, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
class LARS32bit(Optimizer1State):
    def __init__(self, params, lr, momentum=0, dampening=0, weight_decay=0, nesterov=False, args=None, min_8bit_size=4096, percentile_clipping=100, max_unorm=0.02):
        if momentum == 0: raise NotImplementedError("LARS without momentum is not supported!")
        super().__init__("lars", params, lr, (momentum, dampening), 0.0, weight_decay, 32, args, min_8bit_size, percentile_clipping, max_unorm=max_unorm, block_wise=False)
class PytorchLARS(Optimizer):
    def __init__(self, params, lr=0.01, momentum=0, dampening=0, weight_decay=0, nesterov=False, max_unorm=0.02):
        if lr < 0.0: raise ValueError(f"Invalid learning rate: {lr}")
        if momentum < 0.0: raise ValueError(f"Invalid momentum value: {momentum}")
        if weight_decay < 0.0: raise ValueError(f"Invalid weight_decay value: {weight_decay}")
        defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov, max_unorm=max_unorm)
        if nesterov and (momentum <= 0 or dampening != 0): raise ValueError("Nesterov momentum requires a momentum and zero dampening")
        super().__init__(params, defaults)
    def __setstate__(self, state):
        super().__setstate__(state)
        for group in self.param_groups: group.setdefault("nesterov", False)
    @torch.no_grad()
    def step(self, closure=None):
        loss = None
        if closure is not None:
            with torch.enable_grad(): loss = closure()
        for group in self.param_groups:
            params_with_grad = []
            d_p_list = []
            momentum_buffer_list = []
            weight_decay = group["weight_decay"]
            momentum = group["momentum"]
            dampening = group["dampening"]
            nesterov = group["nesterov"]
            max_unorm = group["max_unorm"]
            lr = group["lr"]
            for p in group["params"]:
                if p.grad is None: continue
                state = self.state[p]
                d_p = p.grad
                if weight_decay != 0: d_p = d_p.add(p, alpha=weight_decay)
                if momentum != 0:
                    buf = state.get("momentum_buffer", None)
                    if buf is None:
                        buf = torch.clone(d_p).detach()
                        state["momentum_buffer"] = buf
                    else: buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
                    if nesterov: update = d_p + buf * momentum
                    else: update = buf
                update_scale = 1.0
                if max_unorm > 0.0:
                    assert p.dtype == torch.float32
                    pnorm = torch.norm(p.detach())
                    unorm = torch.norm(update)
                    if unorm > max_unorm * pnorm: update_scale = max_unorm * pnorm / unorm
                p.add_(update, alpha=-lr * update_scale)
        return loss
"""
    ########################################################################################################################################################
    # This algorithm is part of a code library for optimizing the Artificial Intelligence models of Sapiens Technology®, and its disclosure, distribution, #
    # or reverse engineering without the company's prior consent will be subject to legal proceedings and actions pursued by our legal department.         #
    ########################################################################################################################################################
"""
