import copy
import functools
import os
from random import randint
import blobfile as bf
import torch as th
import torch.distributed as dist
from torch.nn.parallel.distributed import DistributedDataParallel as DDP
from torch.optim import AdamW
from torch.utils.tensorboard import SummaryWriter
import torchvision
from typing import Dict
import matplotlib.pyplot as plt

from diff_scm.utils import dist_util, logger
from diff_scm.utils.fp16_util import MixedPrecisionTrainer
from diff_scm.models.nn import update_ema
from diff_scm.models.resample import LossAwareSampler, UniformSampler


# For ImageNet experiments, this was a good default value.
# We found that the lg_loss_scale quickly climbed to
# 20-21 within the first ~1K steps of training.
INITIAL_LOG_LOSS_SCALE = 20.0


class TrainLoop:
    def __init__(
            self,
            *,
            model,
            diffusion,
            data,
            data_val,
            batch_size,
            microbatch,
            lr,
            ema_rate,
            log_interval,
            save_interval,
            resume_checkpoint,
            use_fp16=False,
            fp16_scale_growth=1e-3,
            schedule_sampler=None,
            weight_decay=0.0,
            lr_anneal_steps=0,
            main_data_indentifier: str = "image",
            cond_dropout_rate: float = 0.0,
            conditioning_variable: str = "y",
            iterations: int = 70e3,
    ):
        self.model = model
        self.diffusion = diffusion
        self.data = data
        self.data_val = data_val
        self.batch_size = batch_size
        self.microbatch = microbatch if microbatch > 0 else batch_size
        self.lr = lr
        self.ema_rate = (
            [ema_rate]
            if isinstance(ema_rate, float)
            else [float(x) for x in ema_rate.split(",")]
        )
        self.log_interval = log_interval
        self.save_interval = save_interval
        self.resume_checkpoint = resume_checkpoint
        self.use_fp16 = use_fp16
        self.fp16_scale_growth = fp16_scale_growth
        self.schedule_sampler = schedule_sampler or UniformSampler(diffusion)
        self.weight_decay = weight_decay
        self.lr_anneal_steps = lr_anneal_steps
        self.main_data_indentifier = main_data_indentifier
        self.conditioning_variable = conditioning_variable
        self.cond_dropout_rate = cond_dropout_rate
        self.iterations = iterations

        # self.writer = SummaryWriter(logger.get_current() / 'tensorboard')
        self.step = 0
        self.resume_step = 0
        self.global_batch = self.batch_size * dist.get_world_size()

        self.sync_cuda = th.cuda.is_available()

        self._load_and_sync_parameters()
        self.mp_trainer = MixedPrecisionTrainer(
            model=self.model,
            use_fp16=self.use_fp16,
            fp16_scale_growth=fp16_scale_growth,
        )

        self.opt = AdamW(
            self.mp_trainer.master_params, lr=self.lr, weight_decay=self.weight_decay
        )
        if self.resume_step:
            self._load_optimizer_state()
            # Model was resumed, either due to a restart or a checkpoint
            # being specified at the command line.
            self.ema_params = [
                self._load_ema_parameters(rate) for rate in self.ema_rate
            ]
        else:
            self.ema_params = [
                copy.deepcopy(self.mp_trainer.master_params)
                for _ in range(len(self.ema_rate))
            ]

        if th.cuda.is_available():
            self.use_ddp = True
            self.ddp_model = DDP(
                self.model,
                device_ids=[dist_util.dev()],
                output_device=dist_util.dev(),
                broadcast_buffers=False,
                bucket_cap_mb=128,
                find_unused_parameters=True,
            )
        else:
            if dist.get_world_size() > 1:
                logger.warn(
                    "Distributed training requires CUDA. "
                    "Gradients will not be synchronized properly!"
                )
            self.use_ddp = False
            self.ddp_model = self.model

    def _load_and_sync_parameters(self):
        resume_checkpoint = find_resume_checkpoint() or self.resume_checkpoint

        if resume_checkpoint:
            self.resume_step = parse_resume_step_from_filename(resume_checkpoint)
            if dist.get_rank() == 0:
                logger.log(f"loading model from checkpoint: {resume_checkpoint}...")
                self.model.load_state_dict(
                    dist_util.load_state_dict(
                        resume_checkpoint, map_location=dist_util.dev()
                    )
                )

        dist_util.sync_params(self.model.parameters())

    def _load_ema_parameters(self, rate):
        ema_params = copy.deepcopy(self.mp_trainer.master_params)

        main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
        ema_checkpoint = find_ema_checkpoint(main_checkpoint, self.resume_step, rate)
        if ema_checkpoint:
            if dist.get_rank() == 0:
                logger.log(f"loading EMA from checkpoint: {ema_checkpoint}...")
                state_dict = dist_util.load_state_dict(
                    ema_checkpoint, map_location=dist_util.dev()
                )
                ema_params = self.mp_trainer.state_dict_to_master_params(state_dict)

        dist_util.sync_params(ema_params)
        return ema_params

    def _load_optimizer_state(self):
        main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
        opt_checkpoint = bf.join(
            bf.dirname(main_checkpoint), f"opt{self.resume_step:06}.pt"
        )
        if bf.exists(opt_checkpoint):
            logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
            state_dict = dist_util.load_state_dict(
                opt_checkpoint, map_location=dist_util.dev()
            )
            self.opt.load_state_dict(state_dict)

    def run_loop(self):
        while (
                not self.lr_anneal_steps
                or self.step + self.resume_step < self.iterations
        ):
            data_dict = next(self.data)
            self.run_step(data_dict)
            if self.step % self.save_interval == 0:
                self.save()
            if self.step % self.log_interval == 0:
                for val_step in range(self.log_interval):
                    val_data_dict = next(self.data_val)
                    self.forward_backward(val_data_dict, phase="val")
                logger.dumpkvs()
            self.step += 1
        # Save the last checkpoint if it wasn't already saved.
        if (self.step - 1) % self.save_interval != 0:
            self.save()

    def run_step(self, data_dict):
        self.forward_backward(data_dict, phase="train")
        took_step = self.mp_trainer.optimize(self.opt)
        if took_step:
            self._update_ema()
        self._anneal_lr()
        self.log_step()

    def conditioning_dropout(self, model_conditionals: Dict):
        '''         
        By setting the self.conditioning_variable to self.num_classes,
            we are telling the Embedding layer in the model to use non-class informative embedding (padding idx default to 0).
        '''

        idx2drop = int(model_conditionals["y"].shape[0]*self.cond_dropout_rate)
        model_conditionals["y"][th.randint(model_conditionals["y"].shape[0],(idx2drop,))] = self.model.num_classes
        
        return model_conditionals
        


    def forward_backward(self, data_dict, phase: str = "train"):
        
        # self.main_data_indentifier = "image"
        batch = data_dict.pop(self.main_data_indentifier)
        model_conditionals = data_dict

        assert phase in ["train", "val"]
        if phase == "train":
            self.ddp_model.train()
            if self.cond_dropout_rate != 0:
                model_conditionals = self.conditioning_dropout(model_conditionals)
        else:
            self.ddp_model.eval()
        
        self.mp_trainer.zero_grad()
        for i in range(0, batch.shape[0], self.microbatch):
            micro = batch[i: i + self.microbatch].to(dist_util.dev())
            micro_cond = {
                k: v[i: i + self.microbatch].to(dist_util.dev())
                for k, v in model_conditionals.items()
            }
            last_batch = (i + self.microbatch) >= batch.shape[0]
            t, weights = self.schedule_sampler.sample(micro.shape[0], dist_util.dev())

            compute_losses = functools.partial(
                self.diffusion.training_losses,
                self.ddp_model,
                micro,
                t,
                model_kwargs=micro_cond
            )

            if last_batch or not self.use_ddp:
                losses = compute_losses()
            else:
                with self.ddp_model.no_sync():
                    losses = compute_losses()

            if isinstance(self.schedule_sampler, LossAwareSampler):
                self.schedule_sampler.update_with_local_losses(
                    t, losses["loss"].detach()
                )

            loss = (losses["loss"] * weights).mean()
            log_loss_dict(
                self.diffusion, t, {phase + '_' + k: v * weights for k, v in losses.items()}
            )
            if phase == "train":
                self.mp_trainer.backward(loss)

    def _update_ema(self):
        for rate, params in zip(self.ema_rate, self.ema_params):
            update_ema(params, self.mp_trainer.master_params, rate=rate)

    def _anneal_lr(self):
        if not self.lr_anneal_steps:
            return
        frac_done = (self.step + self.resume_step) / self.lr_anneal_steps
        lr = self.lr * (1 - frac_done)
        for param_group in self.opt.param_groups:
            param_group["lr"] = lr

    def log_step(self):
        logger.logkv("step", self.step + self.resume_step)
        logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)

    def save(self):
        def save_checkpoint(rate, params):
            state_dict = self.mp_trainer.master_params_to_state_dict(params)
            if dist.get_rank() == 0:
                logger.log(f"saving model {rate}...")
                if not rate:
                    filename = f"model{(self.step + self.resume_step):06d}.pt"
                else:
                    filename = f"ema_{rate}_{(self.step + self.resume_step):06d}.pt"
                with bf.BlobFile(bf.join(get_blob_logdir(), filename), "wb") as f:
                    th.save(state_dict, f)

        save_checkpoint(0, self.mp_trainer.master_params)
        for rate, params in zip(self.ema_rate, self.ema_params):
            save_checkpoint(rate, params)

        if dist.get_rank() == 0:
            with bf.BlobFile(
                    bf.join(get_blob_logdir(), f"opt{(self.step + self.resume_step):06d}.pt"),
                    "wb",
            ) as f:
                th.save(self.opt.state_dict(), f)

        dist.barrier()



def parse_resume_step_from_filename(filename):
    """
    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
    checkpoint's number of steps.
    """
    split = filename.split("model")
    if len(split) < 2:
        return 0
    split1 = split[-1].split(".")[0]
    try:
        return int(split1)
    except ValueError:
        return 0


def get_blob_logdir():
    # You can change this to be a separate path to save checkpoints to
    # a blobstore or some external drive.
    return logger.get_dir()


def find_resume_checkpoint():
    # On your infrastructure, you may want to override this to automatically
    # discover the latest checkpoint on your blob storage, etc.
    return None


def matplotlib_imshow(img: th.tensor):
    img = torchvision.utils.make_grid(img.detach().cpu())
    img = img / 2 + 0.5  # unnormalize
    npimg = img.numpy()
    plt.imshow(npimg, cmap="Greys")


def find_ema_checkpoint(main_checkpoint, step, rate):
    if main_checkpoint is None:
        return None
    filename = f"ema_{rate}_{(step):06d}.pt"
    path = bf.join(bf.dirname(main_checkpoint), filename)
    if bf.exists(path):
        return path
    return None


def log_loss_dict(diffusion, ts, losses):
    for key, values in losses.items():
        values_mean = values.mean()
        values_cpu = values_mean.to('cpu')
        values_item = values_cpu.item()
        logger.logkv_mean(key, values_item)
        # Log the quantiles (four quartiles, in particular).
        '''for sub_t, sub_loss in zip(ts.cpu().numpy(), values.detach().cpu().numpy()):
            quartile = int(4 * sub_t / diffusion.num_timesteps)
            logger.logkv_mean(f"{key}_q{quartile}", sub_loss)'''


def get_random_vector_excluding(vector_to_exclude: th.tensor, nb_classes: int = 10):
    def get_random_number_excluding(exclude):
        randInt = randint(0, nb_classes)
        return get_random_number_excluding(exclude) if randInt == exclude else randInt

    new_random_vector = th.tensor([get_random_number_excluding(number) for number in vector_to_exclude]).to()
    new_random_vector.to(vector_to_exclude.device)

    assert (new_random_vector != vector_to_exclude).all().numpy(), "tensors should be different"
    assert (new_random_vector.size() == vector_to_exclude.size()), "vectors should have the same size"

    return new_random_vector
