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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 Lion(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        is_paged=False,
    ):
        """
        Base Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
            is_paged=is_paged,
        )


class Lion8bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
        is_paged=False,
    ):
        """
        8-bit Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            8,
            args,
            min_8bit_size,
            is_paged=is_paged,
        )


class Lion32bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
        is_paged=False,
    ):
        """
        32-bit Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
            is_paged (`bool`, defaults to `False`):
                Whether the optimizer is a paged optimizer or not.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            32,
            args,
            min_8bit_size,
            is_paged=is_paged,
        )


class PagedLion(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
    ):
        """
        Paged Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
            is_paged=True,
        )


class PagedLion8bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
    ):
        """
        Paged 8-bit Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            8,
            args,
            min_8bit_size,
            is_paged=True,
        )


class PagedLion32bit(Optimizer1State):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.99),
        weight_decay=0,
        args=None,
        min_8bit_size=4096,
    ):
        """
        Paged 32-bit Lion optimizer.
        Arguments:
            params (`torch.tensor`):
                The input parameters to optimize.
            lr (`float`, defaults to 1e-4):
                The learning rate.
            betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
                The beta values are the decay rates of the first and second-order moment of the optimizer.
            weight_decay (`float`, defaults to 0):
                The weight decay value for the optimizer.
            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.
        """
        super().__init__(
            "lion",
            params,
            lr,
            betas,
            0.0,
            weight_decay,
            32,
            args,
            min_8bit_size,
            is_paged=True,
        )

	# 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 Lion(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	is_paged=False,
	):
	"""
	Base Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	optim_bits,
	args,
	min_8bit_size,
	is_paged=is_paged,
	)


	class Lion8bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	args=None,
	min_8bit_size=4096,
	is_paged=False,
	):
	"""
	8-bit Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	8,
	args,
	min_8bit_size,
	is_paged=is_paged,
	)


	class Lion32bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	args=None,
	min_8bit_size=4096,
	is_paged=False,
	):
	"""
	32-bit Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	is_paged (`bool`, defaults to `False`):
	Whether the optimizer is a paged optimizer or not.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	32,
	args,
	min_8bit_size,
	is_paged=is_paged,
	)


	class PagedLion(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	optim_bits=32,
	args=None,
	min_8bit_size=4096,
	):
	"""
	Paged Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	optim_bits,
	args,
	min_8bit_size,
	is_paged=True,
	)


	class PagedLion8bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	args=None,
	min_8bit_size=4096,
	):
	"""
	Paged 8-bit Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	8,
	args,
	min_8bit_size,
	is_paged=True,
	)


	class PagedLion32bit(Optimizer1State):
	def __init__(
	self,
	params,
	lr=1e-4,
	betas=(0.9, 0.99),
	weight_decay=0,
	args=None,
	min_8bit_size=4096,
	):
	"""
	Paged 32-bit Lion optimizer.

	Arguments:
	params (`torch.tensor`):
	The input parameters to optimize.
	lr (`float`, defaults to 1e-4):
	The learning rate.
	betas (`tuple(float, float)`, defaults to (0.9, 0.999)):
	The beta values are the decay rates of the first and second-order moment of the optimizer.
	weight_decay (`float`, defaults to 0):
	The weight decay value for the optimizer.
	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.
	"""
	super().__init__(
	"lion",
	params,
	lr,
	betas,
	0.0,
	weight_decay,
	32,
	args,
	min_8bit_size,
	is_paged=True,
	)