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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.
from bitsandbytes.optim.optimizer import Optimizer1State


class SGD(Optimizer1State):
    def __init__(
        self,
        params,
        lr,
        momentum=0,
        dampening=0,
        weight_decay=0,
        nesterov=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
    ):
        """
        Base SGD 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 0.0):
                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.
        """
        if momentum == 0:
            raise NotImplementedError("SGD without momentum is not supported!")
        super().__init__(
            "momentum",
            params,
            lr,
            (momentum, dampening),
            0.0,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
        )


class SGD8bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr,
        momentum=0,
        dampening=0,
        weight_decay=0,
        nesterov=False,
        args=None,
        min_8bit_size=4096,
    ):
        """
        8-bit SGD 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 0.0):
                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.
        """
        if momentum == 0:
            raise NotImplementedError("SGD without momentum is not supported!")
        super().__init__(
            "momentum",
            params,
            lr,
            (momentum, dampening),
            0.0,
            weight_decay,
            8,
            args,
            min_8bit_size,
        )


class SGD32bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr,
        momentum=0,
        dampening=0,
        weight_decay=0,
        nesterov=False,
        args=None,
        min_8bit_size=4096,
    ):
        """
        32-bit SGD 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 0.0):
                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.
        """
        if momentum == 0:
            raise NotImplementedError("SGD without momentum is not supported!")
        super().__init__(
            "momentum",
            params,
            lr,
            (momentum, dampening),
            0.0,
            weight_decay,
            32,
            args,
            min_8bit_size,
        )

	# 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.
	from bitsandbytes.optim.optimizer import Optimizer1State


	class SGD(Optimizer1State):
	def __init__(
	self,
	params,
	lr,
	momentum=0,
	dampening=0,
	weight_decay=0,
	nesterov=False,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	):
	"""
	Base SGD 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 0.0):
	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.
	"""
	if momentum == 0:
	raise NotImplementedError("SGD without momentum is not supported!")
	super().__init__(
	"momentum",
	params,
	lr,
	(momentum, dampening),
	0.0,
	weight_decay,
	optim_bits,
	args,
	min_8bit_size,
	)


	class SGD8bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr,
	momentum=0,
	dampening=0,
	weight_decay=0,
	nesterov=False,
	args=None,
	min_8bit_size=4096,
	):
	"""
	8-bit SGD 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 0.0):
	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.
	"""
	if momentum == 0:
	raise NotImplementedError("SGD without momentum is not supported!")
	super().__init__(
	"momentum",
	params,
	lr,
	(momentum, dampening),
	0.0,
	weight_decay,
	8,
	args,
	min_8bit_size,
	)


	class SGD32bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr,
	momentum=0,
	dampening=0,
	weight_decay=0,
	nesterov=False,
	args=None,
	min_8bit_size=4096,
	):
	"""
	32-bit SGD 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 0.0):
	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.
	"""
	if momentum == 0:
	raise NotImplementedError("SGD without momentum is not supported!")
	super().__init__(
	"momentum",
	params,
	lr,
	(momentum, dampening),
	0.0,
	weight_decay,
	32,
	args,
	min_8bit_size,
	)