from __future__ import annotations

from functools import reduce as reduce
from typing import Any, Optional

from paddle.fluid.distribute_lookup_table import (
    find_distributed_lookup_table as find_distributed_lookup_table,
)
from paddle.fluid.framework import Program as Program
from paddle.fluid.framework import default_startup_program as default_startup_program

from ..fluid import framework as framework
from ..fluid import layers as layers
from ..fluid import unique_name as unique_name
from ..fluid.backward import append_backward as append_backward
from ..fluid.clip import GradientClipBase as GradientClipBase
from ..fluid.clip import GradientClipByNorm as GradientClipByNorm
from ..fluid.dygraph import no_grad as no_grad
from ..fluid.framework import Parameter as Parameter
from ..fluid.framework import program_guard as program_guard
from ..fluid.initializer import Constant as Constant
from ..fluid.layer_helper import LayerHelper as LayerHelper
from ..fluid.layers import ops as ops
from ..fluid.wrapped_decorator import (
    signature_safe_contextmanager as signature_safe_contextmanager,
)
from .lr import LRScheduler as LRScheduler

class Optimizer:
    regularization: Any = ...
    helper: Any = ...
    clear_gradients: Any = ...
    def __init__(
        self,
        learning_rate: Any,
        parameters: Optional[Any] = ...,
        weight_decay: Optional[Any] = ...,
        grad_clip: Optional[Any] = ...,
        name: Optional[Any] = ...,
    ): ...
    def state_dict(self): ...
    def set_state_dict(self, state_dict: Any) -> None: ...
    def get_opti_var_name_list(self): ...
    def set_lr(self, value: Any) -> None: ...
    def get_lr(self): ...
    def backward(
        self,
        loss: Any,
        startup_program: Optional[Any] = ...,
        parameters: Optional[Any] = ...,
        no_grad_set: Optional[Any] = ...,
        callbacks: Optional[Any] = ...,
    ): ...
    def apply_gradients(self, params_grads: Any): ...
    def append_regularization_ops(self, parameters_and_grads: Any, regularization: Optional[Any] = ...): ...
    def clear_grad(self, set_to_zero: bool = ...) -> None: ...
    def minimize(
        self,
        loss: Any,
        startup_program: Optional[Any] = ...,
        parameters: Optional[Any] = ...,
        no_grad_set: Optional[Any] = ...,
    ): ...
    def step(self): ...
