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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch.optim import Optimizer

from bitsandbytes.optim.optimizer import Optimizer1State


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,
        max_unorm=0.02,
    ):
        """
        Base LARS optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`):
                The learning rate.
            momentum (`float`, defaults to 0):
                The momentum value speeds up the optimizer by taking bigger steps.
            dampening (`float`, defaults to 0):
                The dampening value reduces the momentum of the optimizer.
            weight_decay (`float`, defaults to 1e-2):
                The weight decay value for the optimizer.
            nesterov (`bool`, defaults to `False`):
                Whether to use Nesterov momentum.
            optim_bits (`int`, defaults to 32):
                The number of bits of the optimizer state.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            max_unorm (`float`, defaults to 0.02):
                The maximum gradient norm.
        """
        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,
            max_unorm=max_unorm,
        )


class LARS8bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr,
        momentum=0,
        dampening=0,
        weight_decay=0,
        nesterov=False,
        args=None,
        min_8bit_size=4096,
        max_unorm=0.02,
    ):
        """
        8-bit LARS optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`):
                The learning rate.
            momentum (`float`, defaults to 0):
                The momentum value speeds up the optimizer by taking bigger steps.
            dampening (`float`, defaults to 0):
                The dampening value reduces the momentum of the optimizer.
            weight_decay (`float`, defaults to 1e-2):
                The weight decay value for the optimizer.
            nesterov (`bool`, defaults to `False`):
                Whether to use Nesterov momentum.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            max_unorm (`float`, defaults to 0.02):
                The maximum gradient norm.
        """
        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,
            max_unorm=max_unorm,
        )


class LARS32bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr,
        momentum=0,
        dampening=0,
        weight_decay=0,
        nesterov=False,
        args=None,
        min_8bit_size=4096,
        max_unorm=0.02,
    ):
        """
        32-bit LARS optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`):
                The learning rate.
            momentum (`float`, defaults to 0):
                The momentum value speeds up the optimizer by taking bigger steps.
            dampening (`float`, defaults to 0):
                The dampening value reduces the momentum of the optimizer.
            weight_decay (`float`, defaults to 1e-2):
                The weight decay value for the optimizer.
            nesterov (`bool`, defaults to `False`):
                Whether to use Nesterov momentum.
            args (`object`, defaults to `None`):
                An object with additional arguments.
            min_8bit_size (`int`, defaults to 4096):
                The minimum number of elements of the parameter tensors for 8-bit optimization.
            max_unorm (`float`, defaults to 0.02):
                The maximum gradient norm.
        """
        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,
            max_unorm=max_unorm,
        )


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):
        """Performs a single optimization step.
        Args:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            with torch.enable_grad():
                loss = closure()

        for group in self.param_groups:
            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

	# Copyright (c) Facebook, Inc. and its affiliates.
	#
	# This source code is licensed under the MIT license found in the
	# LICENSE file in the root directory of this source tree.
	import torch
	from torch.optim import Optimizer

	from bitsandbytes.optim.optimizer import Optimizer1State


	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,
	max_unorm=0.02,
	):
	"""
	Base LARS optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`):
	The learning rate.
	momentum (`float`, defaults to 0):
	The momentum value speeds up the optimizer by taking bigger steps.
	dampening (`float`, defaults to 0):
	The dampening value reduces the momentum of the optimizer.
	weight_decay (`float`, defaults to 1e-2):
	The weight decay value for the optimizer.
	nesterov (`bool`, defaults to `False`):
	Whether to use Nesterov momentum.
	optim_bits (`int`, defaults to 32):
	The number of bits of the optimizer state.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	max_unorm (`float`, defaults to 0.02):
	The maximum gradient norm.
	"""
	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,
	max_unorm=max_unorm,
	)


	class LARS8bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr,
	momentum=0,
	dampening=0,
	weight_decay=0,
	nesterov=False,
	args=None,
	min_8bit_size=4096,
	max_unorm=0.02,
	):
	"""
	8-bit LARS optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`):
	The learning rate.
	momentum (`float`, defaults to 0):
	The momentum value speeds up the optimizer by taking bigger steps.
	dampening (`float`, defaults to 0):
	The dampening value reduces the momentum of the optimizer.
	weight_decay (`float`, defaults to 1e-2):
	The weight decay value for the optimizer.
	nesterov (`bool`, defaults to `False`):
	Whether to use Nesterov momentum.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	max_unorm (`float`, defaults to 0.02):
	The maximum gradient norm.
	"""
	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,
	max_unorm=max_unorm,
	)


	class LARS32bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr,
	momentum=0,
	dampening=0,
	weight_decay=0,
	nesterov=False,
	args=None,
	min_8bit_size=4096,
	max_unorm=0.02,
	):
	"""
	32-bit LARS optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`):
	The learning rate.
	momentum (`float`, defaults to 0):
	The momentum value speeds up the optimizer by taking bigger steps.
	dampening (`float`, defaults to 0):
	The dampening value reduces the momentum of the optimizer.
	weight_decay (`float`, defaults to 1e-2):
	The weight decay value for the optimizer.
	nesterov (`bool`, defaults to `False`):
	Whether to use Nesterov momentum.
	args (`object`, defaults to `None`):
	An object with additional arguments.
	min_8bit_size (`int`, defaults to 4096):
	The minimum number of elements of the parameter tensors for 8-bit optimization.
	max_unorm (`float`, defaults to 0.02):
	The maximum gradient norm.
	"""
	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,
	max_unorm=max_unorm,
	)


	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):
	"""Performs a single optimization step.

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

	for group in self.param_groups:
	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