import math

import torch

from geoopt.tensor import ManifoldParameter, ManifoldTensor
from geoopt.samplers.base import Sampler

__all__ = ["SGRHMC"]


class SGRHMC(Sampler):
    r"""
    Stochastic Gradient Riemannian Hamiltonian Monte-Carlo.

    Parameters
    ----------
    params : iterable
        iterables of tensors for which to perform sampling
    epsilon : float
        step size
    n_steps : int
        number of leapfrog steps
    alpha : float
        :math:`(1 - alpha)` -- momentum term
    """

    def __init__(self, params, epsilon=1e-3, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0

        for group in self.param_groups:
            for p in group["params"]:
                state = self.state[p]

                if "v" not in state:
                    state["v"] = torch.zeros_like(p)

                epsilon = group["epsilon"]
                v = state["v"]
                v.normal_().mul_(epsilon)

                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float("nan")
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group["params"]:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold

                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp

                        epsilon, alpha = group["epsilon"], group["alpha"]

                        v = self.state[p]["v"]

                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)

                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(
                            math.sqrt(2 * alpha * epsilon) * n
                        )
                        p.grad.zero_()

                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()

        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group["params"]:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue

            manifold = p.manifold
            p.copy_(manifold.projx(p))
            # proj here is ok
            state = self.state[p]
            if not state:
                continue
            state["v"].copy_(manifold.proju(p, state["v"]))

67d4e49628ad87a28d077bd5ab68fc64441ead43
<mask token>


class SGRHMC(Sampler):
    <mask token>

    def __init__(self, params, epsilon=0.001, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                if 'v' not in state:
                    state['v'] = torch.zeros_like(p)
                epsilon = group['epsilon']
                v = state['v']
                v.normal_().mul_(epsilon)
                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float('nan')
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group['params']:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold
                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp
                        epsilon, alpha = group['epsilon'], group['alpha']
                        v = self.state[p]['v']
                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)
                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(math.
                            sqrt(2 * alpha * epsilon) * n)
                        p.grad.zero_()
                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()
        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group['params']:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue
            manifold = p.manifold
            p.copy_(manifold.projx(p))
            state = self.state[p]
            if not state:
                continue
            state['v'].copy_(manifold.proju(p, state['v']))

<mask token>


class SGRHMC(Sampler):
    """
    Stochastic Gradient Riemannian Hamiltonian Monte-Carlo.

    Parameters
    ----------
    params : iterable
        iterables of tensors for which to perform sampling
    epsilon : float
        step size
    n_steps : int
        number of leapfrog steps
    alpha : float
        :math:`(1 - alpha)` -- momentum term
    """

    def __init__(self, params, epsilon=0.001, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                if 'v' not in state:
                    state['v'] = torch.zeros_like(p)
                epsilon = group['epsilon']
                v = state['v']
                v.normal_().mul_(epsilon)
                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float('nan')
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group['params']:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold
                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp
                        epsilon, alpha = group['epsilon'], group['alpha']
                        v = self.state[p]['v']
                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)
                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(math.
                            sqrt(2 * alpha * epsilon) * n)
                        p.grad.zero_()
                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()
        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group['params']:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue
            manifold = p.manifold
            p.copy_(manifold.projx(p))
            state = self.state[p]
            if not state:
                continue
            state['v'].copy_(manifold.proju(p, state['v']))

<mask token>
__all__ = ['SGRHMC']


class SGRHMC(Sampler):
    """
    Stochastic Gradient Riemannian Hamiltonian Monte-Carlo.

    Parameters
    ----------
    params : iterable
        iterables of tensors for which to perform sampling
    epsilon : float
        step size
    n_steps : int
        number of leapfrog steps
    alpha : float
        :math:`(1 - alpha)` -- momentum term
    """

    def __init__(self, params, epsilon=0.001, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                if 'v' not in state:
                    state['v'] = torch.zeros_like(p)
                epsilon = group['epsilon']
                v = state['v']
                v.normal_().mul_(epsilon)
                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float('nan')
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group['params']:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold
                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp
                        epsilon, alpha = group['epsilon'], group['alpha']
                        v = self.state[p]['v']
                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)
                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(math.
                            sqrt(2 * alpha * epsilon) * n)
                        p.grad.zero_()
                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()
        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group['params']:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue
            manifold = p.manifold
            p.copy_(manifold.projx(p))
            state = self.state[p]
            if not state:
                continue
            state['v'].copy_(manifold.proju(p, state['v']))

import math
import torch
from geoopt.tensor import ManifoldParameter, ManifoldTensor
from geoopt.samplers.base import Sampler
__all__ = ['SGRHMC']


class SGRHMC(Sampler):
    """
    Stochastic Gradient Riemannian Hamiltonian Monte-Carlo.

    Parameters
    ----------
    params : iterable
        iterables of tensors for which to perform sampling
    epsilon : float
        step size
    n_steps : int
        number of leapfrog steps
    alpha : float
        :math:`(1 - alpha)` -- momentum term
    """

    def __init__(self, params, epsilon=0.001, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0
        for group in self.param_groups:
            for p in group['params']:
                state = self.state[p]
                if 'v' not in state:
                    state['v'] = torch.zeros_like(p)
                epsilon = group['epsilon']
                v = state['v']
                v.normal_().mul_(epsilon)
                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float('nan')
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group['params']:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold
                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp
                        epsilon, alpha = group['epsilon'], group['alpha']
                        v = self.state[p]['v']
                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)
                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(math.
                            sqrt(2 * alpha * epsilon) * n)
                        p.grad.zero_()
                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()
        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group['params']:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue
            manifold = p.manifold
            p.copy_(manifold.projx(p))
            state = self.state[p]
            if not state:
                continue
            state['v'].copy_(manifold.proju(p, state['v']))

import math

import torch

from geoopt.tensor import ManifoldParameter, ManifoldTensor
from geoopt.samplers.base import Sampler

__all__ = ["SGRHMC"]


class SGRHMC(Sampler):
    r"""
    Stochastic Gradient Riemannian Hamiltonian Monte-Carlo.

    Parameters
    ----------
    params : iterable
        iterables of tensors for which to perform sampling
    epsilon : float
        step size
    n_steps : int
        number of leapfrog steps
    alpha : float
        :math:`(1 - alpha)` -- momentum term
    """

    def __init__(self, params, epsilon=1e-3, n_steps=1, alpha=0.1):
        defaults = dict(epsilon=epsilon, alpha=alpha)
        super().__init__(params, defaults)
        self.n_steps = n_steps

    def step(self, closure):
        H_old = 0.0
        H_new = 0.0

        for group in self.param_groups:
            for p in group["params"]:
                state = self.state[p]

                if "v" not in state:
                    state["v"] = torch.zeros_like(p)

                epsilon = group["epsilon"]
                v = state["v"]
                v.normal_().mul_(epsilon)

                r = v / epsilon
                H_old += 0.5 * (r * r).sum().item()
        logp = float("nan")
        for _ in range(self.n_steps + 1):
            logp = closure()
            logp.backward()
            logp = logp.item()
            with torch.no_grad():
                for group in self.param_groups:
                    for p in group["params"]:
                        if isinstance(p, (ManifoldParameter, ManifoldTensor)):
                            manifold = p.manifold
                        else:
                            manifold = self._default_manifold

                        egrad2rgrad = manifold.egrad2rgrad
                        retr_transp = manifold.retr_transp

                        epsilon, alpha = group["epsilon"], group["alpha"]

                        v = self.state[p]["v"]

                        p_, v_ = retr_transp(p, v, v)
                        p.copy_(p_)
                        v.copy_(v_)

                        n = egrad2rgrad(p, torch.randn_like(v))
                        v.mul_(1 - alpha).add_(epsilon * p.grad).add_(
                            math.sqrt(2 * alpha * epsilon) * n
                        )
                        p.grad.zero_()

                        r = v / epsilon
                        H_new += 0.5 * (r * r).sum().item()

        if not self.burnin:
            self.steps += 1
            self.log_probs.append(logp)

    @torch.no_grad()
    def stabilize_group(self, group):
        for p in group["params"]:
            if not isinstance(p, (ManifoldParameter, ManifoldTensor)):
                continue

            manifold = p.manifold
            p.copy_(manifold.projx(p))
            # proj here is ok
            state = self.state[p]
            if not state:
                continue
            state["v"].copy_(manifold.proju(p, state["v"]))
