import torch, math
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import numpy as np
from typing import Union, List, Optional, Tuple

from ..diffusion import FlowMatchScheduler
from ..core import ModelConfig, gradient_checkpoint_forward
from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput

from transformers import AutoTokenizer
from ..models.z_image_text_encoder import ZImageTextEncoder
from ..models.z_image_dit import ZImageDiT
from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder


class ZImagePipeline(BasePipeline):

    def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
        super().__init__(
            device=device, torch_dtype=torch_dtype,
            height_division_factor=16, width_division_factor=16,
        )
        self.scheduler = FlowMatchScheduler("Z-Image")
        self.text_encoder: ZImageTextEncoder = None
        self.dit: ZImageDiT = None
        self.vae_encoder: FluxVAEEncoder = None
        self.vae_decoder: FluxVAEDecoder = None
        self.tokenizer: AutoTokenizer = None
        self.in_iteration_models = ("dit",)
        self.units = [
            ZImageUnit_ShapeChecker(),
            ZImageUnit_PromptEmbedder(),
            ZImageUnit_NoiseInitializer(),
            ZImageUnit_InputImageEmbedder(),
        ]
        self.model_fn = model_fn_z_image
    
    
    @staticmethod
    def from_pretrained(
        torch_dtype: torch.dtype = torch.bfloat16,
        device: Union[str, torch.device] = "cuda",
        model_configs: list[ModelConfig] = [],
        tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
        vram_limit: float = None,
    ):
        # Initialize pipeline
        pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
        model_pool = pipe.download_and_load_models(model_configs, vram_limit)
        
        # Fetch models
        pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
        pipe.dit = model_pool.fetch_model("z_image_dit")
        pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
        pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
        if tokenizer_config is not None:
            tokenizer_config.download_if_necessary()
            pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
        
        # VRAM Management
        pipe.vram_management_enabled = pipe.check_vram_management_state()
        return pipe
    
    
    @torch.no_grad()
    def __call__(
        self,
        # Prompt
        prompt: str,
        negative_prompt: str = "",
        cfg_scale: float = 1.0,
        # Image
        input_image: Image.Image = None,
        denoising_strength: float = 1.0,
        # Shape
        height: int = 1024,
        width: int = 1024,
        # Randomness
        seed: int = None,
        rand_device: str = "cpu",
        # Steps
        num_inference_steps: int = 8,
        # Progress bar
        progress_bar_cmd = tqdm,
    ):
        # Scheduler
        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
        
        # Parameters
        inputs_posi = {
            "prompt": prompt,
        }
        inputs_nega = {
            "negative_prompt": negative_prompt,
        }
        inputs_shared = {
            "cfg_scale": cfg_scale,
            "input_image": input_image, "denoising_strength": denoising_strength,
            "height": height, "width": width,
            "seed": seed, "rand_device": rand_device,
            "num_inference_steps": num_inference_steps,
        }
        for unit in self.units:
            inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)

        # Denoise
        self.load_models_to_device(self.in_iteration_models)
        models = {name: getattr(self, name) for name in self.in_iteration_models}
        for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
            timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
            noise_pred = self.cfg_guided_model_fn(
                self.model_fn, cfg_scale,
                inputs_shared, inputs_posi, inputs_nega,
                **models, timestep=timestep, progress_id=progress_id
            )
            inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
        
        # Decode
        self.load_models_to_device(['vae'])
        image = self.vae_decoder(inputs_shared["latents"])
        image = self.vae_output_to_image(image)
        self.load_models_to_device([])

        return image


class ZImageUnit_ShapeChecker(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("height", "width"),
            output_params=("height", "width"),
        )

    def process(self, pipe: ZImagePipeline, height, width):
        height, width = pipe.check_resize_height_width(height, width)
        return {"height": height, "width": width}


class ZImageUnit_PromptEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            seperate_cfg=True,
            input_params_posi={"prompt": "prompt"},
            input_params_nega={"prompt": "negative_prompt"},
            output_params=("prompt_embeds",),
            onload_model_names=("text_encoder",)
        )
    
    def encode_prompt(
        self,
        pipe,
        prompt: Union[str, List[str]],
        device: Optional[torch.device] = None,
        max_sequence_length: int = 512,
    ) -> List[torch.FloatTensor]:
        if isinstance(prompt, str):
            prompt = [prompt]

        for i, prompt_item in enumerate(prompt):
            messages = [
                {"role": "user", "content": prompt_item},
            ]
            prompt_item = pipe.tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True,
                enable_thinking=True,
            )
            prompt[i] = prompt_item

        text_inputs = pipe.tokenizer(
            prompt,
            padding="max_length",
            max_length=max_sequence_length,
            truncation=True,
            return_tensors="pt",
        )

        text_input_ids = text_inputs.input_ids.to(device)
        prompt_masks = text_inputs.attention_mask.to(device).bool()

        prompt_embeds = pipe.text_encoder(
            input_ids=text_input_ids,
            attention_mask=prompt_masks,
            output_hidden_states=True,
        ).hidden_states[-2]

        embeddings_list = []

        for i in range(len(prompt_embeds)):
            embeddings_list.append(prompt_embeds[i][prompt_masks[i]])

        return embeddings_list

    def process(self, pipe: ZImagePipeline, prompt):
        pipe.load_models_to_device(self.onload_model_names)
        prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
        return {"prompt_embeds": prompt_embeds}


class ZImageUnit_NoiseInitializer(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("height", "width", "seed", "rand_device"),
            output_params=("noise",),
        )

    def process(self, pipe: ZImagePipeline, height, width, seed, rand_device):
        noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
        return {"noise": noise}


class ZImageUnit_InputImageEmbedder(PipelineUnit):
    def __init__(self):
        super().__init__(
            input_params=("input_image", "noise"),
            output_params=("latents", "input_latents"),
            onload_model_names=("vae_encoder",)
        )

    def process(self, pipe: ZImagePipeline, input_image, noise):
        if input_image is None:
            return {"latents": noise, "input_latents": None}
        pipe.load_models_to_device(['vae'])
        image = pipe.preprocess_image(input_image)
        input_latents = pipe.vae_encoder(image)
        if pipe.scheduler.training:
            return {"latents": noise, "input_latents": input_latents}
        else:
            latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
            return {"latents": latents, "input_latents": input_latents}


def model_fn_z_image(
    dit: ZImageDiT,
    latents=None,
    timestep=None,
    prompt_embeds=None,
    use_gradient_checkpointing=False,
    use_gradient_checkpointing_offload=False,
    **kwargs,
):
    latents = [rearrange(latents, "B C H W -> C B H W")]
    timestep = (1000 - timestep) / 1000
    model_output = dit(
        latents,
        timestep,
        prompt_embeds,
        use_gradient_checkpointing=use_gradient_checkpointing,
        use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
    )[0][0]
    model_output = -model_output
    model_output = rearrange(model_output, "C B H W -> B C H W")
    return model_output