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from functools import partial, wraps
from slm_lab.lib import logger, optimizer, util
import os
import pydash as ps
import torch
import torch.nn as nn

logger = logger.get_logger(__name__)

# register custom torch.optim
setattr(torch.optim, 'GlobalAdam', optimizer.GlobalAdam)
setattr(torch.optim, 'GlobalRMSprop', optimizer.GlobalRMSprop)
setattr(torch.optim, 'Lookahead', optimizer.Lookahead)
setattr(torch.optim, 'RAdam', optimizer.RAdam)

class NoOpLRScheduler:
    '''Symbolic LRScheduler class for API consistency'''

    def __init__(self, optim):
        self.optim = optim

    def step(self, epoch=None):

    def get_lr(self):
        if hasattr(self.optim, 'defaults'):
            return self.optim.defaults['lr']
        else:  # TODO retrieve lr more generally
            return self.optim.param_groups[0]['lr']

def build_fc_model(dims, activation=None):
    '''Build a full-connected model by interleaving nn.Linear and activation_fn'''
    assert len(dims) >= 2, 'dims need to at least contain input, output'
    # shift dims and make pairs of (in, out) dims per layer
    dim_pairs = list(zip(dims[:-1], dims[1:]))
    layers = []
    for in_d, out_d in dim_pairs:
        layers.append(nn.Linear(in_d, out_d))
        if activation is not None:
    model = nn.Sequential(*layers)
    return model

def get_nn_name(uncased_name):
    '''Helper to get the proper name in PyTorch nn given a case-insensitive name'''
    for nn_name in nn.__dict__:
        if uncased_name.lower() == nn_name.lower():
            return nn_name
    raise ValueError(f'Name {uncased_name} not found in {nn.__dict__}')

def get_activation_fn(activation):
    '''Helper to generate activation function layers for net'''
    ActivationClass = getattr(nn, get_nn_name(activation))
    return ActivationClass()

def get_loss_fn(cls, loss_spec):
    '''Helper to parse loss param and construct loss_fn for net'''
    LossClass = getattr(nn, get_nn_name(loss_spec['name']))
    loss_spec = ps.omit(loss_spec, 'name')
    loss_fn = LossClass(**loss_spec)
    return loss_fn

def get_lr_scheduler(optim, lr_scheduler_spec):
    '''Helper to parse lr_scheduler param and construct Pytorch optim.lr_scheduler'''
    if ps.is_empty(lr_scheduler_spec):
        lr_scheduler = NoOpLRScheduler(optim)
    elif lr_scheduler_spec['name'] == 'LinearToZero':
        LRSchedulerClass = getattr(torch.optim.lr_scheduler, 'LambdaLR')
        frame = float(lr_scheduler_spec['frame'])
        lr_scheduler = LRSchedulerClass(optim, lr_lambda=lambda x: 1 - x / frame)
        LRSchedulerClass = getattr(torch.optim.lr_scheduler, lr_scheduler_spec['name'])
        lr_scheduler_spec = ps.omit(lr_scheduler_spec, 'name')
        lr_scheduler = LRSchedulerClass(optim, **lr_scheduler_spec)
    return lr_scheduler

def get_optim(net, optim_spec):
    '''Helper to parse optim param and construct optim for net'''
    OptimClass = getattr(torch.optim, optim_spec['name'])
    optim_spec = ps.omit(optim_spec, 'name')
    if torch.is_tensor(net):  # for non-net tensor variable
        optim = OptimClass([net], **optim_spec)
        optim = OptimClass(net.parameters(), **optim_spec)
    return optim

def get_policy_out_dim(body):
    '''Helper method to construct the policy network out_dim for a body according to is_discrete, action_type'''
    action_dim = body.action_dim
    if body.is_discrete:
        if body.action_type == 'multi_discrete':
            assert ps.is_list(action_dim), action_dim
            policy_out_dim = action_dim
            assert ps.is_integer(action_dim), action_dim
            policy_out_dim = action_dim
        assert ps.is_integer(action_dim), action_dim
        if action_dim == 1:  # single action, use [loc, scale]
            policy_out_dim = 2
        else:  # multi-action, use [locs], [scales]
            policy_out_dim = [action_dim, action_dim]
    return policy_out_dim

def get_out_dim(body, add_critic=False):
    '''Construct the NetClass out_dim for a body according to is_discrete, action_type, and whether to add a critic unit'''
    policy_out_dim = get_policy_out_dim(body)
    if add_critic:
        if ps.is_list(policy_out_dim):
            out_dim = policy_out_dim + [1]
            out_dim = [policy_out_dim, 1]
        out_dim = policy_out_dim
    return out_dim

def init_layers(net, init_fn_name):
    '''Primary method to initialize the weights of the layers of a network'''
    if init_fn_name is None:

    # get nonlinearity
    nonlinearity = get_nn_name(net.hid_layers_activation).lower()
    if nonlinearity == 'leakyrelu':
        nonlinearity = 'leaky_relu'  # guard name

    # get init_fn and add arguments depending on nonlinearity
    init_fn = getattr(nn.init, init_fn_name)
    if 'kaiming' in init_fn_name:  # has 'nonlinearity' as arg
        assert nonlinearity in ['relu', 'leaky_relu'], f'Kaiming initialization not supported for {nonlinearity}'
        init_fn = partial(init_fn, nonlinearity=nonlinearity)
    elif 'orthogonal' in init_fn_name or 'xavier' in init_fn_name:  # has 'gain' as arg
        gain = nn.init.calculate_gain(nonlinearity)
        init_fn = partial(init_fn, gain=gain)

    # finally, apply init_params to each layer in its modules
    net.apply(partial(init_params, init_fn=init_fn))

def init_params(module, init_fn):
    '''Initialize module's weights using init_fn, and biases to 0.0'''
    bias_init = 0.0
    classname = util.get_class_name(module)
    if 'Net' in classname:  # skip if it's a net, not pytorch layer
    elif classname == 'BatchNorm2d':
        pass  # can't init BatchNorm2d
    elif any(k in classname for k in ('Conv', 'Linear')):
        nn.init.constant_(module.bias, bias_init)
    elif 'GRU' in classname:
        for name, param in module.named_parameters():
            if 'weight' in name:
            elif 'bias' in name:
                nn.init.constant_(param, bias_init)

# params methods

def save(net, model_path):
    '''Save model weights to path''', util.smart_path(model_path))

def save_algorithm(algorithm, ckpt=None):
    '''Save all the nets for an algorithm'''
    agent = algorithm.agent
    net_names = algorithm.net_names
    model_prepath = agent.spec['meta']['model_prepath']
    if ckpt is not None:
        model_prepath += f'_ckpt-{ckpt}'
    for net_name in net_names:
        net = getattr(algorithm, net_name)
        model_path = f'{model_prepath}_{net_name}'
        save(net, model_path)
        optim_name = net_name.replace('net', 'optim')
        optim = getattr(algorithm, optim_name, None)
        if optim is not None:  # only trainable net has optim
            optim_path = f'{model_prepath}_{net_name}'
            save(optim, optim_path)
    logger.debug(f'Saved algorithm {util.get_class_name(algorithm)} nets {net_names} to {model_prepath}_*.pt')

def load(net, model_path):
    '''Save model weights from a path into a net module'''
    device = None if torch.cuda.is_available() else 'cpu'
    net.load_state_dict(torch.load(util.smart_path(model_path), map_location=device))

def load_algorithm(algorithm):
    '''Save all the nets for an algorithm'''
    agent = algorithm.agent
    net_names = algorithm.net_names
    model_prepath = agent.spec['meta']['model_prepath']
    if util.get_lab_mode() == 'enjoy':
        model_prepath += '_ckpt-best''Loading algorithm {util.get_class_name(algorithm)} nets {net_names} from {model_prepath}_*.pt')
    for net_name in net_names:
        net = getattr(algorithm, net_name)
        model_path = f'{model_prepath}_{net_name}'
        load(net, model_path)
        optim_name = net_name.replace('net', 'optim')
        optim = getattr(algorithm, optim_name, None)
        if optim is not None:  # only trainable net has optim
            optim_path = f'{model_prepath}_{net_name}'
            load(optim, optim_path)

def copy(src_net, tar_net):
    '''Copy model weights from src to target'''

def polyak_update(src_net, tar_net, old_ratio=0.5):
    Polyak weight update to update a target tar_net, retain old weights by its ratio, i.e.
    target <- old_ratio * source + (1 - old_ratio) * target
    for src_param, tar_param in zip(src_net.parameters(), tar_net.parameters()): * + (1.0 - old_ratio) *

def to_check_train_step():
    '''Condition for running assert_trained'''
    return os.environ.get('PY_ENV') == 'test' or util.get_lab_mode() == 'dev'

def dev_check_train_step(fn):
    Decorator to check if net.train_step actually updates the network weights properly
    Triggers only if to_check_train_step is True (dev/test mode)

    def train_step(self, ...):
    def check_fn(*args, **kwargs):
        if not to_check_train_step():
            return fn(*args, **kwargs)

        net = args[0]  # first arg self
        # get pre-update parameters to compare
        pre_params = [param.clone() for param in net.parameters()]

        # run train_step, get loss
        loss = fn(*args, **kwargs)
        assert not torch.isnan(loss).any(), loss

        # get post-update parameters to compare
        post_params = [param.clone() for param in net.parameters()]
        if loss == 0.0:
            # if loss is 0, there should be no updates
            # TODO if without momentum, parameters should not change too
            for p_name, param in net.named_parameters():
                assert param.grad.norm() == 0
            # check parameter updates
                assert not all(torch.equal(w1, w2) for w1, w2 in zip(pre_params, post_params)), f'Model parameter is not updated in train_step(), check if your tensor is detached from graph. Loss: {loss:g}'
            except Exception as e:
                if os.environ.get('PY_ENV') == 'test':
                    # raise error if in unit test

            # check grad norms
            min_norm, max_norm = 0.0, 1e5
            for p_name, param in net.named_parameters():
                    grad_norm = param.grad.norm()
                    assert min_norm < grad_norm < max_norm, f'Gradient norm for {p_name} is {grad_norm:g}, fails the extreme value check {min_norm} < grad_norm < {max_norm}. Loss: {loss:g}. Check your network and loss computation.'
                except Exception as e:
        logger.debug('Passed network parameter update check.')
        # store grad norms for debugging
        return loss
    return check_fn

def get_grad_norms(algorithm):
    '''Gather all the net's grad norms of an algorithm for debugging'''
    grad_norms = []
    for net_name in algorithm.net_names:
        net = getattr(algorithm, net_name)
        if net.grad_norms is not None:
    return grad_norms

def init_global_nets(algorithm):
    Initialize global_nets for Hogwild using an identical instance of an algorithm from an isolated Session
    in spec.meta.distributed, specify either:
    - 'shared': global network parameter is shared all the time. In this mode, algorithm local network will be replaced directly by global_net via overriding by identify attribute name
    - 'synced': global network parameter is periodically synced to local network after each gradient push. In this mode, algorithm will keep a separate reference to `global_{net}` for each of its network
    dist_mode = algorithm.agent.spec['meta']['distributed']
    assert dist_mode in ('shared', 'synced'), f'Unrecognized distributed mode'
    global_nets = {}
    for net_name in algorithm.net_names:
        optim_name = net_name.replace('net', 'optim')
        if not hasattr(algorithm, optim_name):  # only for trainable network, i.e. has an optim
        g_net = getattr(algorithm, net_name)
        g_net.share_memory()  # make net global
        if dist_mode == 'shared':  # use the same name to override the local net
            global_nets[net_name] = g_net
        else:  # keep a separate reference for syncing
            global_nets[f'global_{net_name}'] = g_net
        # if optim is Global, set to override the local optim and its scheduler
        optim = getattr(algorithm, optim_name)
        if hasattr(optim, 'share_memory'):
            optim.share_memory()  # make optim global
            global_nets[optim_name] = optim
            if hasattr(optim, 'optimizer'):  # for Lookahead with an inner optimizer
                global_nets[f'{optim_name}_optimizer'] = optim.optimizer
            lr_scheduler_name = net_name.replace('net', 'lr_scheduler')
            lr_scheduler = getattr(algorithm, lr_scheduler_name)
            global_nets[lr_scheduler_name] = lr_scheduler'Initialized global_nets attr {list(global_nets.keys())} for Hogwild')
    return global_nets

def set_global_nets(algorithm, global_nets):
    '''For Hogwild, set attr built in init_global_nets above. Use in algorithm init.'''
    # set attr first so algorithm always has self.global_{net} to pass into train_step
    for net_name in algorithm.net_names:
        setattr(algorithm, f'global_{net_name}', None)
    # set attr created in init_global_nets
    if global_nets is not None:
        # handle inner-optimizer recovery
        inner_opt_keys = [k for k in global_nets if k.endswith('_optimizer')]
        for inner_opt_key in inner_opt_keys:
            opt = global_nets[inner_opt_key.replace('_optimizer', '')]  # optimizer which has a inner optimizer
            setattr(opt, 'optimizer', global_nets.pop(inner_opt_key))
        # set global nets and optims
        util.set_attr(algorithm, global_nets)'Set global_nets attr {list(global_nets.keys())} for Hogwild')

def push_global_grads(net, global_net):
    '''Push gradients to global_net, call inside train_step between loss.backward() and optim.step()'''
    for param, global_param in zip(net.parameters(), global_net.parameters()):
        if global_param.grad is not None:
            return  # quick skip
        global_param._grad = param.grad