from diffusers import CycleDiffusionPipeline, DDIMScheduler
import gradio as gr
import torch
from PIL import Image
import utils
import streamlit as st
import ptp_utils
import seq_aligner
import torch.nn.functional as nnf
from typing import Optional, Union, Tuple, List, Callable, Dict
import abc

LOW_RESOURCE = False
MAX_NUM_WORDS = 77

is_colab = utils.is_google_colab()


if True:
    model_id_or_path = "CompVis/stable-diffusion-v1-4"
    scheduler = DDIMScheduler.from_config(model_id_or_path,
                                          use_auth_token=st.secrets["USER_TOKEN"],
                                          subfolder="scheduler")
    pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path,
                                                  use_auth_token=st.secrets["USER_TOKEN"],
                                                  scheduler=scheduler)
    tokenizer = pipe.tokenizer

    if torch.cuda.is_available():
        pipe = pipe.to("cuda")

device_print = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
device = "cuda" if torch.cuda.is_available() else "cpu"


class LocalBlend:

    def __call__(self, x_t, attention_store):
        k = 1
        maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
        maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, 16, 16, MAX_NUM_WORDS) for item in maps]
        maps = torch.cat(maps, dim=1)
        maps = (maps * self.alpha_layers).sum(-1).mean(1)
        mask = nnf.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
        mask = nnf.interpolate(mask, size=(x_t.shape[2:]))
        mask = mask / mask.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
        mask = mask.gt(self.threshold)
        mask = (mask[:1] + mask[1:]).float()
        x_t = x_t[:1] + mask * (x_t - x_t[:1])
        return x_t

    def __init__(self, prompts: List[str], words: [List[List[str]]], threshold=.3):
        alpha_layers = torch.zeros(len(prompts),  1, 1, 1, 1, MAX_NUM_WORDS)
        for i, (prompt, words_) in enumerate(zip(prompts, words)):
            if type(words_) is str:
                words_ = [words_]
            for word in words_:
                ind = ptp_utils.get_word_inds(prompt, word, tokenizer)
                alpha_layers[i, :, :, :, :, ind] = 1
        self.alpha_layers = alpha_layers.to(device)
        self.threshold = threshold


class AttentionControl(abc.ABC):

    def step_callback(self, x_t):
        return x_t

    def between_steps(self):
        return

    @property
    def num_uncond_att_layers(self):
        return self.num_att_layers if LOW_RESOURCE else 0

    @abc.abstractmethod
    def forward(self, attn, is_cross: bool, place_in_unet: str):
        raise NotImplementedError

    def __call__(self, attn, is_cross: bool, place_in_unet: str):
        if self.cur_att_layer >= self.num_uncond_att_layers:
            if LOW_RESOURCE:
                attn = self.forward(attn, is_cross, place_in_unet)
            else:
                h = attn.shape[0]
                attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
        self.cur_att_layer += 1
        if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
            self.cur_att_layer = 0
            self.cur_step += 1
            self.between_steps()
        return attn

    def reset(self):
        self.cur_step = 0
        self.cur_att_layer = 0

    def __init__(self):
        self.cur_step = 0
        self.num_att_layers = -1
        self.cur_att_layer = 0


class EmptyControl(AttentionControl):

    def forward(self, attn, is_cross: bool, place_in_unet: str):
        return attn


class AttentionStore(AttentionControl):

    @staticmethod
    def get_empty_store():
        return {"down_cross": [], "mid_cross": [], "up_cross": [],
                "down_self": [],  "mid_self": [],  "up_self": []}

    def forward(self, attn, is_cross: bool, place_in_unet: str):
        key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
        if attn.shape[1] <= 32 ** 2:  # avoid memory overhead
            self.step_store[key].append(attn)
        return attn

    def between_steps(self):
        if len(self.attention_store) == 0:
            self.attention_store = self.step_store
        else:
            for key in self.attention_store:
                for i in range(len(self.attention_store[key])):
                    self.attention_store[key][i] += self.step_store[key][i]
        self.step_store = self.get_empty_store()

    def get_average_attention(self):
        average_attention = {key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store}
        return average_attention

    def reset(self):
        super(AttentionStore, self).reset()
        self.step_store = self.get_empty_store()
        self.attention_store = {}

    def __init__(self):
        super(AttentionStore, self).__init__()
        self.step_store = self.get_empty_store()
        self.attention_store = {}


class AttentionControlEdit(AttentionStore, abc.ABC):

    def step_callback(self, x_t):
        if self.local_blend is not None:
            x_t = self.local_blend(x_t, self.attention_store)
        return x_t

    def replace_self_attention(self, attn_base, att_replace):
        if att_replace.shape[2] <= 16 ** 2:
            return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
        else:
            return att_replace

    @abc.abstractmethod
    def replace_cross_attention(self, attn_base, att_replace):
        raise NotImplementedError

    def forward(self, attn, is_cross: bool, place_in_unet: str):
        super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
        if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
            h = attn.shape[0] // self.batch_size
            attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
            attn_base, attn_repalce = attn[0], attn[1:]
            if is_cross:
                alpha_words = self.cross_replace_alpha[self.cur_step]
                attn_replace_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (1 - alpha_words) * attn_repalce
                attn[1:] = attn_replace_new
            else:
                attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
            attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
        return attn

    def __init__(self, prompts, num_steps: int,
                 cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
                 self_replace_steps: Union[float, Tuple[float, float]],
                 local_blend: Optional[LocalBlend]):
        super(AttentionControlEdit, self).__init__()
        self.batch_size = len(prompts)
        self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps, tokenizer).to(device)
        if type(self_replace_steps) is float:
            self_replace_steps = 0, self_replace_steps
        self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
        self.local_blend = local_blend


class AttentionReplace(AttentionControlEdit):

    def replace_cross_attention(self, attn_base, att_replace):
        return torch.einsum('hpw,bwn->bhpn', attn_base, self.mapper)

    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
                 local_blend: Optional[LocalBlend] = None):
        super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
        self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)


class AttentionRefine(AttentionControlEdit):

    def replace_cross_attention(self, attn_base, att_replace):
        attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
        attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
        return attn_replace

    def __init__(self, prompts, num_steps: int, cross_replace_steps: float, self_replace_steps: float,
                 local_blend: Optional[LocalBlend] = None):
        super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend)
        self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
        self.mapper, alphas = self.mapper.to(device), alphas.to(device)
        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])


def get_equalizer(text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]]):
    if type(word_select) is int or type(word_select) is str:
        word_select = (word_select,)
    equalizer = torch.ones(len(values), 77)
    values = torch.tensor(values, dtype=torch.float32)
    for word in word_select:
        inds = ptp_utils.get_word_inds(text, word, tokenizer)
        equalizer[:, inds] = values
    return equalizer


def inference(source_prompt, target_prompt, source_guidance_scale=1, guidance_scale=5, num_inference_steps=100,
              width=512, height=512, seed=0, img=None, strength=0.7,
              cross_attention_control=None, cross_replace_steps=0.8, self_replace_steps=0.4):

    torch.manual_seed(seed)

    ratio = min(height / img.height, width / img.width)
    img = img.resize((int(img.width * ratio), int(img.height * ratio)))

    # create the CAC controller.
    if cross_attention_control == "replace":
        controller = AttentionReplace([source_prompt, target_prompt],
                                      num_inference_steps,
                                      cross_replace_steps=cross_replace_steps,
                                      self_replace_steps=self_replace_steps,
                                      )
        ptp_utils.register_attention_control(pipe, controller)
    elif cross_attention_control == "refine":
        controller = AttentionRefine([source_prompt, target_prompt],
                                     num_inference_steps,
                                     cross_replace_steps=cross_replace_steps,
                                     self_replace_steps=self_replace_steps,
                                     )
        ptp_utils.register_attention_control(pipe, controller)

    results = pipe(prompt=target_prompt,
                   source_prompt=source_prompt,
                   init_image=img,
                   num_inference_steps=num_inference_steps,
                   eta=0.1,
                   strength=strength,
                   guidance_scale=guidance_scale,
                   source_guidance_scale=source_guidance_scale,
                   )

    return replace_nsfw_images(results)


def replace_nsfw_images(results):
    for i in range(len(results.images)):
        if results.nsfw_content_detected[i]:
            results.images[i] = Image.open("nsfw.png")
    return results.images[0]


css = """.cycle-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.cycle-diffusion-div div h1{font-weight:900;margin-bottom:7px}.cycle-diffusion-div p{margin-bottom:10px;font-size:94%}.cycle-diffusion-div p a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
    gr.HTML(
        f"""
            <div class="cycle-diffusion-div">
              <div>
                <h1>CycleDiffusion with Stable Diffusion</h1>
              </div>
              <p>
                Demo for CycleDiffusion with Stable Diffusion. <br>
                CycleDiffusion (<a href="https://github.com/ChenWu98/cycle-diffusion">Github</a> | <a href="https://arxiv.org/abs/2210.05559">📄 Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br>
                It also supports Cross Attention Control (<a href="https://github.com/google/prompt-to-prompt">Github</a> | <a href="https://arxiv.org/abs/2208.01626">📄 Paper link</a>), which is a technique to transfer the attention map from the source prompt to the target prompt. <br>
              </p>
              <p>You can skip the queue in the colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>
               Running on <b>{device_print}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
              </p>
            </div>
        """
    )
    with gr.Row():

        with gr.Column(scale=55):
            with gr.Group():

                img = gr.Image(label="Input image", height=512, tool="editor", type="pil")

                image_out = gr.Image(label="Output image", height=512)
                # gallery = gr.Gallery(
                #     label="Generated images", show_label=False, elem_id="gallery"
                # ).style(grid=[1], height="auto")

        with gr.Column(scale=45):
            with gr.Tab("Edit options"):
                with gr.Group():
                    with gr.Row():
                        source_prompt = gr.Textbox(label="Source prompt", placeholder="Source prompt describes the input image")
                        source_guidance_scale = gr.Slider(label="Source guidance scale", value=1, minimum=1, maximum=10)
                    with gr.Row():
                        target_prompt = gr.Textbox(label="Target prompt", placeholder="Target prompt describes the output image")
                        guidance_scale = gr.Slider(label="Target guidance scale", value=5, minimum=1, maximum=10)
                    with gr.Row():
                        strength = gr.Slider(label="Strength", value=0.7, minimum=0.5, maximum=1, step=0.01)

                    with gr.Row():
                        generate = gr.Button(value="Edit")
            with gr.Tab("Basic options"):
                with gr.Group():
                    with gr.Row():
                        num_inference_steps = gr.Slider(label="Number of inference steps", value=100, minimum=25, maximum=500, step=1)
                        width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
                        height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)

                    with gr.Row():
                        seed = gr.Slider(0, 2147483647, label='Seed', value=0, step=1)

            with gr.Tab("CAC options"):
                with gr.Group():
                    with gr.Row():
                        cross_attention_control = gr.Radio(label="CAC type", choices=["None", "Replace", "Refine"], value="None")
                    with gr.Row():
                        # If not "None", the following two parameters will be used.
                        cross_replace_steps = gr.Slider(label="Cross replace steps", value=0.8, minimum=0.0, maximum=1, step=0.01)
                        self_replace_steps = gr.Slider(label="Self replace steps", value=0.4, minimum=0.0, maximum=1, step=0.01)

    inputs = [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps,
              width, height, seed, img, strength,
              cross_attention_control, cross_replace_steps, self_replace_steps]
    generate.click(inference, inputs=inputs, outputs=image_out)

    ex = gr.Examples(
        [
            ["An astronaut riding a horse", "An astronaut riding an elephant", 1, 2, 100, "images/astronaut_horse.png", 0.8, "None", 0, 0],
            ["An astronaut riding a horse", "An astronaut riding a elephant", 1, 2, 100, "images/astronaut_horse.png", 0.9, "Replace", 0.15, 0.10],
            ["A black colored car.", "A blue colored car.", 1, 2, 100, "images/black_car.png", 0.85, "None", 0, 0],
            ["A black colored car.", "A blue colored car.", 1, 5, 100, "images/black_car.png", 0.95, "Replace", 0.8, 0.4],
            ["A black colored car.", "A red colored car.", 1, 5, 100, "images/black_car.png", 1, "Replace", 0.8, 0.4],
            ["An aerial view of autumn scene.", "An aerial view of winter scene.", 1, 5, 100, "images/mausoleum.png", 0.9, "None", 0.0, 0.0],
            ["An aerial view of autumn scene.", "An aerial view of winter scene.", 1, 5, 100, "images/mausoleum.png", 1, "Replace", 0.8, 0.4],
            ["A green apple and a black backpack on the floor.", "A red apple and a black backpack on the floor.", 1, 7, 100, "images/apple_bag.png", 0.9, "None", 0.0, 0.0],
            ["A green apple and a black backpack on the floor.", "A red apple and a black backpack on the floor.", 1, 7, 100, "images/apple_bag.png", 0.9, "Replace", 0.8, 0.4],
        ],
        [source_prompt, target_prompt, source_guidance_scale, guidance_scale, num_inference_steps,
         img, strength,
         cross_attention_control, cross_replace_steps, self_replace_steps],
        image_out, inference, cache_examples=False)

    gr.Markdown('''
      Space built with Diffusers 🧨 by HuggingFace 🤗.
      [![Twitter Follow](https://img.shields.io/twitter/follow/ChenHenryWu?style=social)](https://twitter.com/ChenHenryWu) 
      ![visitors](https://visitor-badge.glitch.me/badge?page_id=ChenWu98.CycleDiffusion)
    ''')

if not is_colab:
    demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab)