import random
from collections import OrderedDict
from torch.utils.data import DataLoader
from toolkit.prompt_utils import concat_prompt_embeds, split_prompt_embeds
from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
from toolkit.train_tools import get_torch_dtype, apply_snr_weight
import gc
import torch
from jobs.process import BaseSDTrainProcess


def flush():
    torch.cuda.empty_cache()
    gc.collect()


class ConceptReplacementConfig:
    def __init__(self, **kwargs):
        self.concept: str = kwargs.get('concept', '')
        self.replacement: str = kwargs.get('replacement', '')


class ConceptReplacer(BaseSDTrainProcess):

    def __init__(self, process_id: int, job, config: OrderedDict, **kwargs):
        super().__init__(process_id, job, config, **kwargs)
        replacement_list = self.config.get('replacements', [])
        self.replacement_list = [ConceptReplacementConfig(**x) for x in replacement_list]

    def before_model_load(self):
        pass

    def hook_before_train_loop(self):
        self.sd.vae.eval()
        self.sd.vae.to(self.device_torch)

        # textual inversion
        if self.embedding is not None:
            # set text encoder to train. Not sure if this is necessary but diffusers example did it
            self.sd.text_encoder.train()

    def hook_train_loop(self, batch):
        with torch.no_grad():
            dtype = get_torch_dtype(self.train_config.dtype)
            noisy_latents, noise, timesteps, conditioned_prompts, imgs = self.process_general_training_batch(batch)
            network_weight_list = batch.get_network_weight_list()

            # have a blank network so we can wrap it in a context and set multipliers without checking every time
            if self.network is not None:
                network = self.network
            else:
                network = BlankNetwork()

            batch_replacement_list = []
            # get a random replacement for each prompt
            for prompt in conditioned_prompts:
                replacement = random.choice(self.replacement_list)
                batch_replacement_list.append(replacement)

            # build out prompts
            concept_prompts = []
            replacement_prompts = []
            for idx, replacement in enumerate(batch_replacement_list):
                prompt = conditioned_prompts[idx]

                # insert shuffled concept at beginning and end of prompt
                shuffled_concept = [x.strip() for x in replacement.concept.split(',')]
                random.shuffle(shuffled_concept)
                shuffled_concept = ', '.join(shuffled_concept)
                concept_prompts.append(f"{shuffled_concept}, {prompt}, {shuffled_concept}")

                # insert replacement at beginning and end of prompt
                shuffled_replacement = [x.strip() for x in replacement.replacement.split(',')]
                random.shuffle(shuffled_replacement)
                shuffled_replacement = ', '.join(shuffled_replacement)
                replacement_prompts.append(f"{shuffled_replacement}, {prompt}, {shuffled_replacement}")

            # predict the replacement without network
            conditional_embeds = self.sd.encode_prompt(replacement_prompts).to(self.device_torch, dtype=dtype)

            replacement_pred = self.sd.predict_noise(
                latents=noisy_latents.to(self.device_torch, dtype=dtype),
                conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
                timestep=timesteps,
                guidance_scale=1.0,
            )

            del conditional_embeds
            replacement_pred = replacement_pred.detach()

        self.optimizer.zero_grad()
        flush()

        # text encoding
        grad_on_text_encoder = False
        if self.train_config.train_text_encoder:
            grad_on_text_encoder = True

        if self.embedding:
            grad_on_text_encoder = True

        # set the weights
        network.multiplier = network_weight_list

        # activate network if it exits
        with network:
            with torch.set_grad_enabled(grad_on_text_encoder):
                # embed the prompts
                conditional_embeds = self.sd.encode_prompt(concept_prompts).to(self.device_torch, dtype=dtype)
            if not grad_on_text_encoder:
                # detach the embeddings
                conditional_embeds = conditional_embeds.detach()
                self.optimizer.zero_grad()
                flush()

            noise_pred = self.sd.predict_noise(
                latents=noisy_latents.to(self.device_torch, dtype=dtype),
                conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
                timestep=timesteps,
                guidance_scale=1.0,
            )

            loss = torch.nn.functional.mse_loss(noise_pred.float(), replacement_pred.float(), reduction="none")
            loss = loss.mean([1, 2, 3])

            if self.train_config.min_snr_gamma is not None and self.train_config.min_snr_gamma > 0.000001:
                # add min_snr_gamma
                loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.min_snr_gamma)

            loss = loss.mean()

            # back propagate loss to free ram
            loss.backward()
            flush()

        # apply gradients
        self.optimizer.step()
        self.optimizer.zero_grad()
        self.lr_scheduler.step()

        if self.embedding is not None:
            # Let's make sure we don't update any embedding weights besides the newly added token
            self.embedding.restore_embeddings()

        loss_dict = OrderedDict(
            {'loss': loss.item()}
        )
        # reset network multiplier
        network.multiplier = 1.0

        return loss_dict