# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/_src/base.py
# class OptStep(NamedTuple):
# class KKTSolution(NamedTuple):
# class Solver(abc.ABC):
# class IterativeSolver(Solver):
# class StochasticSolver(IterativeSolver):
# class LinearOperator(object):
# class LineSearchStep(NamedTuple):
# class IterativeLineSearch(IterativeSolver):
#   def run(self,
#           init_params: Any,
#           *args,
#           **kwargs) -> OptStep:
#   def l2_optimality_error(self, params, *args, **kwargs):
#   def attribute_names(self):
#   def attribute_values(self):
#   def _get_loop_options(self):
#   def _cond_fun(self, inputs):
#   def _body_fun(self, inputs):
#   def _make_zero_step(self, init_params, state) -> OptStep:
#   def _run(self,
#            init_params: Any,
#            *args,
#            **kwargs) -> OptStep:
#   def run(self,
#           init_params: Any,
#           *args,
#           **kwargs) -> OptStep:
# def _where(cond, x, y):
#   def run_iterator(self,
#                    init_params: Any,
#                    iterator,
#                    *args,
#                    **kwargs) -> OptStep:
#   def __init__(self, A):
#   def shape(self):
#   def matvec(self, x):
#   def matvec_element(self, x, idx):
#   def rmatvec(self, x):
#   def rmatvec_element(self, x, idx):
#   def update_matvec(self, Ax, delta, idx):
#   def update_rmatvec(self, ATx, delta, idx):
#   def column_l2_norms(self, squared=False):
#   def tree_flatten(self):
#   def tree_unflatten(cls, aux_data, children):
#   def _make_zero_step(self, init_stepsize, state) -> LineSearchStep:
#   def run(self,
#           init_stepsize: float,
#           params: Any,
#           value: Optional[float] = None,
#           grad: Optional[Any] = None,
#           descent_direction: Optional[Any] = None,
#           *args,
#           **kwargs) -> LineSearchStep:
# 
# Path: jaxopt/_src/loop.py
# def _while_loop_scan(cond_fun, body_fun, init_val, max_iter):
#   def _iter(val):
#   def _fun(tup, it):
# def _while_loop_python(cond_fun, body_fun, init_val, maxiter):
# def _while_loop_lax(cond_fun, body_fun, init_val, maxiter):
#   def _cond_fun(_val):
#   def _body_fun(_val):
# def while_loop(cond_fun, body_fun, init_val, maxiter, unroll=False, jit=False):


from dataclasses import dataclass
from functools import partial
from typing import Any
from typing import Callable
from typing import NamedTuple
from typing import Optional
from jaxopt.tree_util import tree_add_scalar_mul, tree_l2_norm
from jaxopt.tree_util import tree_scalar_mul, tree_zeros_like
from jaxopt.tree_util import tree_add, tree_sub
from jaxopt._src import base
from jaxopt._src import loop
import jax
import jax.lax as lax
import jax.numpy as jnp

    return stepsize, next_params, f_next, violated

  init_val = stepsize, next_params, f_next, jnp.array(True)
  ret = loop.while_loop(cond_fun=lambda t: t[-1],  # check boolean violated
                        body_fun=body_fun,
                        init_val=init_val, maxiter=maxls,
                        unroll=unroll, jit=jit)
  return ret[:-1]   # remove boolean


class ArmijoState(NamedTuple):
  """Named tuple containing state information.

  Attributes:
    iter_num: iteration number
    error: residuals of current estimate
    value: current value of the loss
    stepsize: current stepsize
    velocity: momentum term
  """
  iter_num: int
  error: float
  value: float
  aux: Optional[Any]
  stepsize: float
  velocity: Optional[Any]


@dataclass(eq=False)

# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/tree_util.py

# 
# Path: jaxopt/_src/base.py
# class OptStep(NamedTuple):
# class KKTSolution(NamedTuple):
# class Solver(abc.ABC):
# class IterativeSolver(Solver):
# class StochasticSolver(IterativeSolver):
# class LinearOperator(object):
# class LineSearchStep(NamedTuple):
# class IterativeLineSearch(IterativeSolver):
#   def run(self,
#           init_params: Any,
#           *args,
#           **kwargs) -> OptStep:
#   def l2_optimality_error(self, params, *args, **kwargs):
#   def attribute_names(self):
#   def attribute_values(self):
#   def _get_loop_options(self):
#   def _cond_fun(self, inputs):
#   def _body_fun(self, inputs):
#   def _make_zero_step(self, init_params, state) -> OptStep:
#   def _run(self,
#            init_params: Any,
#            *args,
#            **kwargs) -> OptStep:
#   def run(self,
#           init_params: Any,
#           *args,
#           **kwargs) -> OptStep:
# def _where(cond, x, y):
#   def run_iterator(self,
#                    init_params: Any,
#                    iterator,
#                    *args,
#                    **kwargs) -> OptStep:
#   def __init__(self, A):
#   def shape(self):
#   def matvec(self, x):
#   def matvec_element(self, x, idx):
#   def rmatvec(self, x):
#   def rmatvec_element(self, x, idx):
#   def update_matvec(self, Ax, delta, idx):
#   def update_rmatvec(self, ATx, delta, idx):
#   def column_l2_norms(self, squared=False):
#   def tree_flatten(self):
#   def tree_unflatten(cls, aux_data, children):
#   def _make_zero_step(self, init_stepsize, state) -> LineSearchStep:
#   def run(self,
#           init_stepsize: float,
#           params: Any,
#           value: Optional[float] = None,
#           grad: Optional[Any] = None,
#           descent_direction: Optional[Any] = None,
#           *args,
#           **kwargs) -> LineSearchStep:
# 
# Path: jaxopt/_src/loop.py
# def _while_loop_scan(cond_fun, body_fun, init_val, max_iter):
#   def _iter(val):
#   def _fun(tup, it):
# def _while_loop_python(cond_fun, body_fun, init_val, maxiter):
# def _while_loop_lax(cond_fun, body_fun, init_val, maxiter):
#   def _cond_fun(_val):
#   def _body_fun(_val):
# def while_loop(cond_fun, body_fun, init_val, maxiter, unroll=False, jit=False):


# Path: jaxopt/_src/armijo_sgd.py
from dataclasses import dataclass
from functools import partial
from typing import Any
from typing import Callable
from typing import NamedTuple
from typing import Optional
from jaxopt.tree_util import tree_add_scalar_mul, tree_l2_norm
from jaxopt.tree_util import tree_scalar_mul, tree_zeros_like
from jaxopt.tree_util import tree_add, tree_sub
from jaxopt._src import base
from jaxopt._src import loop
import jax
import jax.lax as lax
import jax.numpy as jnp


    return stepsize, next_params, f_next, violated

  init_val = stepsize, next_params, f_next, jnp.array(True)
  ret = loop.while_loop(cond_fun=lambda t: t[-1],  # check boolean violated
                        body_fun=body_fun,
                        init_val=init_val, maxiter=maxls,
                        unroll=unroll, jit=jit)
  return ret[:-1]   # remove boolean


class ArmijoState(NamedTuple):
  """Named tuple containing state information.

  Attributes:
    iter_num: iteration number
    error: residuals of current estimate
    value: current value of the loss
    stepsize: current stepsize
    velocity: momentum term
  """
  iter_num: int
  error: float
  value: float
  aux: Optional[Any]
  stepsize: float
  velocity: Optional[Any]


@dataclass(eq=False)

class ArmijoSGD(base.StochasticSolver):