import inspect
import os
import time
from typing import Any, Callable, Dict, List, Optional, Union, Tuple

import gc
import torch
import numpy as np
from glob import glob

from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.schedulers import (DPMSolverMultistepScheduler,
                                  EulerAncestralDiscreteScheduler,
                                  EulerDiscreteScheduler,
                                  KarrasDiffusionSchedulers)
from diffusers.models.embeddings import TimestepEmbedding, Timesteps
from diffusers.utils.torch_utils import randn_tensor
from diffusers.utils import logging
from PIL import Image
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
from .lyrasd_vae_model import LyraSdVaeModel
from .module.lyrasd_ip_adapter import LyraIPAdapter
from .lora_util import add_text_lora_layer, add_xltext_lora_layer, add_lora_to_opt_model, load_state_dict
from safetensors.torch import load_file
from .lyrasdxl_pipeline_base import LyraSDXLPipelineBase


def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
    """
    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
    """
    std_text = noise_pred_text.std(
        dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
    # rescale the results from guidance (fixes overexposure)
    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
    noise_cfg = guidance_rescale * noise_pred_rescaled + \
        (1 - guidance_rescale) * noise_cfg
    return noise_cfg


class LyraSdXLTxt2ImgPipeline(LyraSDXLPipelineBase, StableDiffusionXLPipeline):
    device = torch.device("cpu")
    dtype = torch.float32

    def __init__(self, device=torch.device("cuda"), dtype=torch.float16, vae_scale_factor=8, vae_scaling_factor=0.13025) -> None:
        self.register_to_config(force_zeros_for_empty_prompt=True)
        
        super().__init__(device, dtype, vae_scale_factor=vae_scale_factor, vae_scaling_factor=vae_scaling_factor)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        prompt_2: Optional[Union[str, List[str]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        denoising_end: Optional[float] = None,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        negative_prompt_2: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[Union[torch.Generator,
                                  List[torch.Generator]]] = None,
        latents: Optional[torch.FloatTensor] = None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[
            int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        target_size: Optional[Tuple[int, int]] = None,
        extra_tensor_dict: Optional[Dict[str, torch.FloatTensor]] = {},
        param_scale_dict: Optional[Dict[str, int]] = {},
        clip_skip: Optional[int] = None
    ):

        # 0. Default height and width to unet
        height = height or self.default_sample_size * self.vae_scale_factor
        width = width or self.default_sample_size * self.vae_scale_factor

        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            prompt_2,
            height,
            width,
            callback_steps,
            negative_prompt,
            negative_prompt_2,
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        )

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device

        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get(
                "scale", None) if cross_attention_kwargs is not None else None
        )
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self.encode_prompt(
            prompt=prompt,
            prompt_2=prompt_2,
            device=device,
            num_images_per_prompt=num_images_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=negative_prompt,
            negative_prompt_2=negative_prompt_2,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
            clip_skip=clip_skip
        )

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)

        timesteps = self.scheduler.timesteps

        # 5. Prepare latent variables
        num_channels_latents = self.unet_in_channels
        latents = self.prepare_latents(
            batch_size * num_images_per_prompt,
            num_channels_latents,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Prepare added time ids & embeddings
        add_text_embeds = pooled_prompt_embeds
        add_time_ids = list(
            original_size + crops_coords_top_left + target_size)
        add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)

        if do_classifier_free_guidance:
            prompt_embeds = torch.cat(
                [negative_prompt_embeds, prompt_embeds], dim=0)
            add_text_embeds = torch.cat(
                [negative_pooled_prompt_embeds, add_text_embeds], dim=0)
            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)

        prompt_embeds = prompt_embeds.to(device)
        add_text_embeds = add_text_embeds.to(device)
        add_time_ids = add_time_ids.to(device).repeat(
            batch_size * num_images_per_prompt, 1)

        # 8. Denoising loop
        num_warmup_steps = max(
            len(timesteps) - num_inference_steps * self.scheduler.order, 0)

        # 7.1 Apply denoising_end
        if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
            discrete_timestep_cutoff = int(
                round(
                    self.scheduler.config.num_train_timesteps
                    - (denoising_end * self.scheduler.config.num_train_timesteps)
                )
            )
            num_inference_steps = len(
                list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
            timesteps = timesteps[:num_inference_steps]

        aug_emb = self._get_aug_emb(
            add_time_ids, add_text_embeds, prompt_embeds.dtype)

        extra_tensor_dict2 = {}
        for name in extra_tensor_dict:
            if name in ["fp_hidden_states", "ip_hidden_states"]:
                v1, v2 = extra_tensor_dict[name][0], extra_tensor_dict[name][1]
                extra_tensor_dict2[name] = torch.cat(
                    [v1.repeat(num_images_per_prompt, 1, 1), v2.repeat(num_images_per_prompt, 1, 1)])
            else:
                extra_tensor_dict2[name] = extra_tensor_dict[name]

        # np.save("/workspace/prompt_embeds.npy", prompt_embeds.detach().cpu().numpy())
        # prompt_embeds = torch.from_numpy(np.load("/workspace/gt_prompt_embeds.npy")).cuda()
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat(
                    [latents] * 2) if do_classifier_free_guidance else latents

                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t)
                latent_model_input = latent_model_input.permute(
                    0, 2, 3, 1).contiguous()

                noise_pred = self.unet.forward(latent_model_input, prompt_embeds, t, aug_emb, None, None,
                                               None, None, None, extra_tensor_dict2, param_scale_dict).permute(0, 3, 1, 2).contiguous()

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * \
                        (noise_pred_text - noise_pred_uncond)

                if do_classifier_free_guidance and guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(
                        noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(
                    noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if output_type == "latent":
            return latents

        image = self.vae.decode(1 / self.vae.scaling_factor * latents)
        image = self.image_processor.postprocess(
            image, output_type=output_type)

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        return image