import copy
import inspect
from collections.abc import Callable

from modules.model.ZImageModel import ZImageModel
from modules.modelSampler.BaseModelSampler import BaseModelSampler, ModelSamplerOutput
from modules.util import factory
from modules.util.config.SampleConfig import SampleConfig
from modules.util.enum.AudioFormat import AudioFormat
from modules.util.enum.FileType import FileType
from modules.util.enum.ImageFormat import ImageFormat
from modules.util.enum.ModelType import ModelType
from modules.util.enum.NoiseScheduler import NoiseScheduler
from modules.util.enum.VideoFormat import VideoFormat
from modules.util.torch_util import torch_gc

import torch

from tqdm import tqdm


class ZImageSampler(BaseModelSampler):
    def __init__(
            self,
            train_device: torch.device,
            temp_device: torch.device,
            model: ZImageModel,
            model_type: ModelType,
    ):
        super().__init__(train_device, temp_device)

        self.model = model
        self.model_type = model_type
        self.pipeline = model.create_pipeline()

    @torch.no_grad()
    def __sample_base(
            self,
            prompt: str,
            negative_prompt: str,
            height: int,
            width: int,
            seed: int,
            random_seed: bool,
            diffusion_steps: int,
            cfg_scale: float,
            noise_scheduler: NoiseScheduler,
            on_update_progress: Callable[[int, int], None] = lambda _, __: None,
    ) -> ModelSamplerOutput:
        with self.model.autocast_context:
            generator = torch.Generator(device=self.train_device)
            if random_seed:
                generator.seed()
            else:
                generator.manual_seed(seed)

            noise_scheduler = copy.deepcopy(self.model.noise_scheduler)
            image_processor = self.pipeline.image_processor
            transformer = self.pipeline.transformer
            vae = self.pipeline.vae

            vae_scale_factor = 8
            num_latent_channels = transformer.in_channels
            #patch_size = 2

            # prepare prompt
            self.model.text_encoder_to(self.train_device)

            batch_size = 2 if cfg_scale > 1.0 else 1
            prompt_embedding = self.model.encode_text(
                text=[prompt, negative_prompt] if cfg_scale > 1.0 else prompt,
                batch_size=batch_size,
                train_device=self.train_device,
            )

            self.model.text_encoder_to(self.temp_device)
            torch_gc()

            # prepare latent image
            latent_image = torch.randn(
                size=(1, num_latent_channels, height // vae_scale_factor, width // vae_scale_factor),
                generator=generator,
                device=self.train_device,
                dtype=torch.float32,
            )

            # prepare timesteps
            noise_scheduler.set_timesteps(diffusion_steps, device=self.train_device)
            timesteps = noise_scheduler.timesteps

            # denoising loop
            extra_step_kwargs = {} #TODO remove
            if "generator" in set(inspect.signature(noise_scheduler.step).parameters.keys()):
                extra_step_kwargs["generator"] = generator

            self.model.transformer_to(self.train_device)
            for i, timestep in enumerate(tqdm(timesteps, desc="sampling")):
                latent_model_input = latent_image.unsqueeze(2).to(dtype=self.model.train_dtype.torch_dtype())
                latent_model_input = torch.cat([latent_model_input] * batch_size)
                latent_model_input_list = list(latent_model_input.unbind(dim=0))
                timestep_model_input = timestep.unsqueeze(0)
                assert timestep_model_input.ndim ==  1
                output_list = transformer(
                    latent_model_input_list,
                    (1000 - timestep_model_input) / 1000,
                    prompt_embedding,
                    return_dict=True
                ).sample

                noise_pred = - torch.stack(output_list, dim=0).squeeze(dim=2)

                if cfg_scale > 1.0:
                    noise_pred_positive, noise_pred_negative = noise_pred.chunk(2)
                    noise_pred = noise_pred_negative + cfg_scale * (noise_pred_positive - noise_pred_negative)

                latent_image = noise_scheduler.step(noise_pred, timestep, latent_image, return_dict=False, **extra_step_kwargs)[0]

                on_update_progress(i + 1, len(timesteps))

            self.model.transformer_to(self.temp_device)
            torch_gc()
            self.model.vae_to(self.train_device)

            latents = self.model.unscale_latents(latent_image)
            image = vae.decode(latents, return_dict=False)[0]

            image = image_processor.postprocess(image, output_type='pil')

            self.model.vae_to(self.temp_device)
            torch_gc()

            return ModelSamplerOutput(
                file_type=FileType.IMAGE,
                data=image[0],
            )

    def sample(
            self,
            sample_config: SampleConfig,
            destination: str,
            image_format: ImageFormat | None = None,
            video_format: VideoFormat | None = None,
            audio_format: AudioFormat | None = None,
            on_sample: Callable[[ModelSamplerOutput], None] = lambda _: None,
            on_update_progress: Callable[[int, int], None] = lambda _, __: None,
    ):
        sampler_output = self.__sample_base(
            prompt=sample_config.prompt,
            negative_prompt=sample_config.negative_prompt,
            height=self.quantize_resolution(sample_config.height, 64),
            width=self.quantize_resolution(sample_config.width, 64),
            seed=sample_config.seed,
            random_seed=sample_config.random_seed,
            diffusion_steps=sample_config.diffusion_steps,
            cfg_scale=sample_config.cfg_scale,
            noise_scheduler=sample_config.noise_scheduler,
            on_update_progress=on_update_progress,
        )

        self.save_sampler_output(
            sampler_output, destination,
            image_format, video_format, audio_format,
        )

        on_sample(sampler_output)

factory.register(BaseModelSampler, ZImageSampler, ModelType.Z_IMAGE)
