from abc import ABCMeta
from random import Random

import modules.util.multi_gpu_util as multi
from modules.model.ChromaModel import ChromaModel, ChromaModelEmbedding
from modules.modelSetup.BaseModelSetup import BaseModelSetup
from modules.modelSetup.mixin.ModelSetupDebugMixin import ModelSetupDebugMixin
from modules.modelSetup.mixin.ModelSetupDiffusionLossMixin import ModelSetupDiffusionLossMixin
from modules.modelSetup.mixin.ModelSetupEmbeddingMixin import ModelSetupEmbeddingMixin
from modules.modelSetup.mixin.ModelSetupFlowMatchingMixin import ModelSetupFlowMatchingMixin
from modules.modelSetup.mixin.ModelSetupNoiseMixin import ModelSetupNoiseMixin
from modules.modelSetup.mixin.ModelSetupText2ImageMixin import ModelSetupText2ImageMixin
from modules.module.AdditionalEmbeddingWrapper import AdditionalEmbeddingWrapper
from modules.util.checkpointing_util import (
    enable_checkpointing_for_chroma_transformer,
    enable_checkpointing_for_t5_encoder_layers,
)
from modules.util.config.TrainConfig import TrainConfig
from modules.util.dtype_util import create_autocast_context, disable_fp16_autocast_context
from modules.util.enum.TrainingMethod import TrainingMethod
from modules.util.quantization_util import quantize_layers
from modules.util.torch_util import torch_gc
from modules.util.TrainProgress import TrainProgress

import torch
from torch import Tensor


#TODO share more code with Flux and other models
class BaseChromaSetup(
    BaseModelSetup,
    ModelSetupDiffusionLossMixin,
    ModelSetupDebugMixin,
    ModelSetupNoiseMixin,
    ModelSetupFlowMatchingMixin,
    ModelSetupEmbeddingMixin,
    ModelSetupText2ImageMixin,
    metaclass=ABCMeta
):
    LAYER_PRESETS = {
        "attn-mlp": ["attn", "ff.net"],
        "attn-only": ["attn"],
        "blocks": ["transformer_block"],
        "full": [],
    }

    def setup_optimizations(
            self,
            model: ChromaModel,
            config: TrainConfig,
    ):
        if config.gradient_checkpointing.enabled():
            model.transformer_offload_conductor = \
                enable_checkpointing_for_chroma_transformer(model.transformer, config)
            if model.text_encoder is not None:
                model.text_encoder_offload_conductor = \
                    enable_checkpointing_for_t5_encoder_layers(model.text_encoder, config)

        model.autocast_context, model.train_dtype = create_autocast_context(self.train_device, config.train_dtype, [
            config.weight_dtypes().transformer,
            config.weight_dtypes().text_encoder,
            config.weight_dtypes().vae,
            config.weight_dtypes().lora if config.training_method == TrainingMethod.LORA else None,
            config.weight_dtypes().embedding if config.train_any_embedding() else None,
        ], config.enable_autocast_cache)

        model.text_encoder_autocast_context, model.text_encoder_train_dtype = \
            disable_fp16_autocast_context(
                self.train_device,
                config.train_dtype,
                config.fallback_train_dtype,
                [
                    config.weight_dtypes().text_encoder,
                    config.weight_dtypes().lora if config.training_method == TrainingMethod.LORA else None,
                    config.weight_dtypes().embedding if config.train_any_embedding() else None,
                ],
                config.enable_autocast_cache,
            )

        quantize_layers(model.text_encoder, self.train_device, model.text_encoder_train_dtype, config)
        quantize_layers(model.vae, self.train_device, model.train_dtype, config)
        quantize_layers(model.transformer, self.train_device, model.train_dtype, config)

    def _setup_embeddings(
            self,
            model: ChromaModel,
            config: TrainConfig,
    ):
        additional_embeddings = []
        for embedding_config in config.all_embedding_configs():
            embedding_state = model.embedding_state_dicts.get(embedding_config.uuid, None)
            if embedding_state is None:
                with model.autocast_context:
                    embedding_state = self._create_new_embedding(
                        model,
                        embedding_config,
                        model.tokenizer,
                        model.text_encoder,
                        lambda text: model.encode_text(
                            text=text,
                            train_device=self.temp_device,
                        )[0][0][1:],
                    )
            else:
                embedding_state = embedding_state.get("t5_out", embedding_state.get("t5", None))

            if embedding_state is not None:
                embedding_state = embedding_state.to(
                    dtype=model.text_encoder.get_input_embeddings().weight.dtype,
                    device=self.train_device,
                ).detach()

            embedding = ChromaModelEmbedding(
                embedding_config.uuid,
                embedding_state,
                embedding_config.placeholder,
                embedding_config.is_output_embedding,
            )
            if embedding_config.uuid == config.embedding.uuid:
                model.embedding = embedding
            else:
                additional_embeddings.append(embedding)

        model.additional_embeddings = additional_embeddings

        if model.tokenizer is not None:
            self._add_embeddings_to_tokenizer(model.tokenizer, model.all_text_encoder_embeddings())

    def _setup_embedding_wrapper(
            self,
            model: ChromaModel,
            config: TrainConfig,
    ):
        if model.tokenizer is not None and model.text_encoder is not None:
            model.embedding_wrapper = AdditionalEmbeddingWrapper(
                tokenizer=model.tokenizer,
                orig_module=model.text_encoder.encoder.embed_tokens,
                embeddings=model.all_text_encoder_embeddings(),
            )

        if model.embedding_wrapper is not None:
            model.embedding_wrapper.hook_to_module()

    def _setup_embeddings_requires_grad(
            self,
            model: ChromaModel,
            config: TrainConfig,
    ):
        if model.text_encoder is not None:
            for embedding, embedding_config in zip(model.all_text_encoder_embeddings(),
                                                   config.all_embedding_configs(), strict=True):
                train_embedding = \
                    embedding_config.train \
                    and config.text_encoder.train_embedding \
                    and not self.stop_embedding_training_elapsed(embedding_config, model.train_progress)
                embedding.requires_grad_(train_embedding)

    def predict(
            self,
            model: ChromaModel,
            batch: dict,
            config: TrainConfig,
            train_progress: TrainProgress,
            *,
            deterministic: bool = False,
    ) -> dict:
        with model.autocast_context:
            batch_seed = 0 if deterministic else train_progress.global_step * multi.world_size() + multi.rank()
            generator = torch.Generator(device=config.train_device)
            generator.manual_seed(batch_seed)
            rand = Random(batch_seed)

            vae_scaling_factor = model.vae.config['scaling_factor']
            vae_shift_factor = model.vae.config['shift_factor']

            text_encoder_output, text_attention_mask = model.encode_text(
                train_device=self.train_device,
                batch_size=batch['latent_image'].shape[0],
                rand=rand,
                tokens=batch.get("tokens"),
                tokens_mask=batch.get("tokens_mask"),
                text_encoder_layer_skip=config.text_encoder_layer_skip,
                text_encoder_output=batch['text_encoder_hidden_state'] \
                    if 'text_encoder_hidden_state' in batch and not config.train_text_encoder_or_embedding() else None,
                text_encoder_dropout_probability=config.text_encoder.dropout_probability if not deterministic else None,
            )

            latent_image = batch['latent_image']
            scaled_latent_image = (latent_image - vae_shift_factor) * vae_scaling_factor

            latent_noise = self._create_noise(scaled_latent_image, config, generator)

            timestep = self._get_timestep_discrete(
                model.noise_scheduler.config['num_train_timesteps'],
                deterministic,
                generator,
                scaled_latent_image.shape[0],
                config,
            )

            scaled_noisy_latent_image, sigma = self._add_noise_discrete(
                scaled_latent_image,
                latent_noise,
                timestep,
                model.noise_scheduler.timesteps,
            )

            latent_input = scaled_noisy_latent_image

            text_ids = torch.zeros(
                size=(text_encoder_output.shape[1], 3),
                device=self.train_device,
            )

            image_ids = model.prepare_latent_image_ids(
                latent_input.shape[2],
                latent_input.shape[3],
                self.train_device,
                model.train_dtype.torch_dtype()
            )

            packed_latent_input = model.pack_latents(latent_input)
            image_seq_len = packed_latent_input.shape[1]
            image_attention_mask = torch.full((packed_latent_input.shape[0], image_seq_len), True, dtype=torch.bool, device=text_attention_mask.device)
            attention_mask = torch.cat([text_attention_mask, image_attention_mask], dim=1) if not torch.all(text_attention_mask) else None

            packed_predicted_flow = model.transformer(
                hidden_states=packed_latent_input.to(dtype=model.train_dtype.torch_dtype()),
                timestep=timestep / 1000,
                encoder_hidden_states=text_encoder_output.to(dtype=model.train_dtype.torch_dtype()),
                txt_ids=text_ids.to(dtype=model.train_dtype.torch_dtype()),
                img_ids=image_ids.to(dtype=model.train_dtype.torch_dtype()),
                attention_mask=attention_mask,
                joint_attention_kwargs=None,
                return_dict=True
            ).sample

            predicted_flow = model.unpack_latents(
                packed_predicted_flow,
                latent_input.shape[2],
                latent_input.shape[3],
            )

            flow = latent_noise - scaled_latent_image
            model_output_data = {
                'loss_type': 'target',
                'timestep': timestep,
                'predicted': predicted_flow,
                'target': flow,
            }

            if config.debug_mode:
                with torch.no_grad():
                    predicted_scaled_latent_image = scaled_noisy_latent_image - predicted_flow * sigma
                    self._save_tokens("7-prompt", batch['tokens'], model.tokenizer, config, train_progress)
                    self._save_latent("1-noise", latent_noise, config, train_progress)
                    self._save_latent("2-noisy_image", scaled_noisy_latent_image, config, train_progress)
                    self._save_latent("3-predicted_flow", predicted_flow, config, train_progress)
                    self._save_latent("4-flow", flow, config, train_progress)
                    self._save_latent("5-predicted_image", predicted_scaled_latent_image, config, train_progress)
                    self._save_latent("6-image", scaled_latent_image, config, train_progress)

        return model_output_data

    def calculate_loss(
            self,
            model: ChromaModel,
            batch: dict,
            data: dict,
            config: TrainConfig,
    ) -> Tensor:
        return self._flow_matching_losses(
            batch=batch,
            data=data,
            config=config,
            train_device=self.train_device,
            sigmas=model.noise_scheduler.sigmas,
        ).mean()


    def prepare_text_caching(self, model: ChromaModel, config: TrainConfig):
        model.to(self.temp_device)

        if not config.train_text_encoder_or_embedding():
            model.text_encoder_to(self.train_device)

        model.eval()
        torch_gc()
