utils.py

from __future__ import annotations
import os
import cv2
import abc
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from diffusers.models.attention import Attention
from PIL import Image
import random
import matplotlib.pyplot as plt
import pdb
import math
from PIL import Image

def load_and_process_image(pil_image):
    transform = transforms.Compose(
        [
            transforms.Resize((1024, 1024)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ]
    )
    image = transform(pil_image)
    image = image.unsqueeze(0).half()
    return image


def auto_canny(image, sigma=0.66):
    v = np.median(image)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    edged = cv2.Canny(image, lower, upper)
    return edged



def get_views(height, width, window_size=32, stride=16, random_jitter=False):
        num_blocks_height = int((height - window_size) / stride - 1e-6) + 2 if height > window_size else 1
        num_blocks_width = int((width - window_size) / stride - 1e-6) + 2 if width > window_size else 1
        total_num_blocks = int(num_blocks_height * num_blocks_width)
        views = []
        for i in range(total_num_blocks):
            h_start = int((i // num_blocks_width) * stride)
            h_end = h_start + window_size
            w_start = int((i % num_blocks_width) * stride)
            w_end = w_start + window_size

            if h_end > height:
                h_start = int(h_start + height - h_end)
                h_end = int(height)
            if w_end > width:
                w_start = int(w_start + width - w_end)
                w_end = int(width)
            if h_start < 0:
                h_end = int(h_end - h_start)
                h_start = 0
            if w_start < 0:
                w_end = int(w_end - w_start)
                w_start = 0
            
            if random_jitter:
                jitter_range = (window_size - stride) // 4
                w_jitter = 0
                h_jitter = 0
                if (w_start != 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, jitter_range)
                elif (w_start == 0) and (w_end != width):
                    w_jitter = random.randint(-jitter_range, 0)
                elif (w_start != 0) and (w_end == width):
                    w_jitter = random.randint(0, jitter_range)
                if (h_start != 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, jitter_range)
                elif (h_start == 0) and (h_end != height):
                    h_jitter = random.randint(-jitter_range, 0)
                elif (h_start != 0) and (h_end == height):
                    h_jitter = random.randint(0, jitter_range)
                h_start += (h_jitter + jitter_range)
                h_end += (h_jitter + jitter_range)
                w_start += (w_jitter + jitter_range)
                w_end += (w_jitter + jitter_range)

            views.append((int(h_start), int(h_end), int(w_start), int(w_end)))
        return views



class P2PCrossAttnProcessor:
    def __init__(self, controller, place_in_unet):
        super().__init__()
        self.controller = controller
        self.place_in_unet = place_in_unet

    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None):
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        query = attn.to_q(hidden_states)

        is_cross = encoder_hidden_states is not None
        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)

        # one line change
        self.controller(attention_probs, is_cross, self.place_in_unet)

        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


def create_controller(
    prompts: List[str], cross_attention_kwargs: Dict, num_inference_steps: int, tokenizer, device, attn_res
) -> AttentionControl:
    edit_type = cross_attention_kwargs.get("edit_type", None)
    local_blend_words = cross_attention_kwargs.get("local_blend_words", None)
    equalizer_words = cross_attention_kwargs.get("equalizer_words", None)
    equalizer_strengths = cross_attention_kwargs.get("equalizer_strengths", None)
    n_cross_replace = cross_attention_kwargs.get("n_cross_replace", 0.4)
    n_self_replace = cross_attention_kwargs.get("n_self_replace", 0.4)

    if edit_type == 'visualize':
        return AttentionStore(device=device)

    # only replace
    if edit_type == "replace" and local_blend_words is None:
        return AttentionReplace(
            prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res
        )

    # replace + localblend
    if edit_type == "replace" and local_blend_words is not None:
        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
        return AttentionReplace(
            prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res
        )

    # only refine
    if edit_type == "refine" and local_blend_words is None:
        return AttentionRefine(
            prompts, num_inference_steps, n_cross_replace, n_self_replace, tokenizer=tokenizer, device=device, attn_res=attn_res
        )

    # refine + localblend
    if edit_type == "refine" and local_blend_words is not None:
        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
        return AttentionRefine(
            prompts, num_inference_steps, n_cross_replace, n_self_replace, lb, tokenizer=tokenizer, device=device, attn_res=attn_res
        )

    # only reweight
    if edit_type == "reweight" and local_blend_words is None:
        assert (
            equalizer_words is not None and equalizer_strengths is not None
        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
        assert len(equalizer_words) == len(
            equalizer_strengths
        ), "equalizer_words and equalizer_strengths must be of same length."
        equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
        return AttentionReweight(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            tokenizer=tokenizer,
            device=device,
            equalizer=equalizer,
            attn_res=attn_res,
        )

    # reweight and localblend
    if edit_type == "reweight" and local_blend_words:
        assert (
            equalizer_words is not None and equalizer_strengths is not None
        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
        assert len(equalizer_words) == len(
            equalizer_strengths
        ), "equalizer_words and equalizer_strengths must be of same length."
        equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
        lb = LocalBlend(prompts, local_blend_words, tokenizer=tokenizer, device=device, attn_res=attn_res)
        return AttentionReweight(
            prompts,
            num_inference_steps,
            n_cross_replace,
            n_self_replace,
            tokenizer=tokenizer,
            device=device,
            equalizer=equalizer,
            attn_res=attn_res,
            local_blend=lb,
        )

    raise ValueError(f"Edit type {edit_type} not recognized. Use one of: replace, refine, reweight.")


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 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:
            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, attn_res=None):
        self.cur_step = 0
        self.num_att_layers = -1
        self.cur_att_layer = 0
        self.attn_res = attn_res



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
            if self.device.type != 'cuda':
                attn = attn.cpu()
            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, attn_res=None, device='cuda'):
        super(AttentionStore, self).__init__(attn_res)
        self.step_store = self.get_empty_store()
        self.attention_store = {}
        self.device = device



class LocalBlend:
    def __call__(self, x_t, attention_store):
        # note that this code works on the latent level!
        k = 1
        # maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]  # These are the numbers because we want to take layers that are 256 x 256, I think this can be changed to something smarter...like, get all attentions where thesecond dim is self.attn_res[0] * self.attn_res[1] in up and down cross.
        maps = [m for m in attention_store["down_cross"] + attention_store["mid_cross"] +  attention_store["up_cross"] if m.shape[1] == self.attn_res[0] * self.attn_res[1]]
        maps = [item.reshape(self.alpha_layers.shape[0], -1, 1, self.attn_res[0], self.attn_res[1], self.max_num_words) for item in maps]
        maps = torch.cat(maps, dim=1)
        maps = (maps * self.alpha_layers).sum(-1).mean(1) # since alpha_layers is all 0s except where we edit, the product zeroes out all but what we change. Then, the sum adds the values of the original and what we edit. Then, we average across dim=1, which is the number of layers.
        mask = F.max_pool2d(maps, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
        mask = F.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:]
        mask = mask.to(torch.float16)

        x_t = x_t[:1] + mask * (x_t - x_t[:1]) # x_t[:1] is the original image. mask*(x_t - x_t[:1]) zeroes out the original image and removes the difference between the original and each image we are generating (mostly just one). Then, it applies the mask on the image. That is, it's only keeping the cells we want to generate.
        return x_t

    def __init__(
        self, prompts: List[str], words: [List[List[str]]], tokenizer, device, threshold=0.3, attn_res=None
    ):
        self.max_num_words = 77
        self.attn_res = attn_res

        alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
        for i, (prompt, words_) in enumerate(zip(prompts, words)):
            if isinstance(words_, str):
                words_ = [words_]
            for word in words_:
                ind = get_word_inds(prompt, word, tokenizer)
                alpha_layers[i, :, :, :, :, ind] = 1
        self.alpha_layers = alpha_layers.to(device) # a one-hot vector where the 1s are the words we modify (source and target)
        self.threshold = threshold


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] <= self.attn_res[0]**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_replace = 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_replace) * alpha_words
                    + (1 - alpha_words) * attn_replace
                )
                attn[1:] = attn_replace_new
            else:
                attn[1:] = self.replace_self_attention(attn_base, attn_replace)
            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],
        tokenizer,
        device,
        attn_res=None,
    ):
        super(AttentionControlEdit, self).__init__(attn_res=attn_res)
        # add tokenizer and device here

        self.tokenizer = tokenizer
        self.device = device

        self.batch_size = len(prompts)
        self.cross_replace_alpha = get_time_words_attention_alpha(
            prompts, num_steps, cross_replace_steps, self.tokenizer
        ).to(self.device)
        if isinstance(self_replace_steps, 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,
        tokenizer=None,
        device=None,
        attn_res=None,
    ):
        super(AttentionReplace, self).__init__(
            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
        )
        self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.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,
        tokenizer=None,
        device=None,
        attn_res=None
    ):
        super(AttentionRefine, self).__init__(
            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
        )
        self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)
        self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)
        self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])


class AttentionReweight(AttentionControlEdit):
    def replace_cross_attention(self, attn_base, att_replace):
        if self.prev_controller is not None:
            attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
        attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
        return attn_replace

    def __init__(
        self,
        prompts,
        num_steps: int,
        cross_replace_steps: float,
        self_replace_steps: float,
        equalizer,
        local_blend: Optional[LocalBlend] = None,
        controller: Optional[AttentionControlEdit] = None,
        tokenizer=None,
        device=None,
        attn_res=None,
    ):
        super(AttentionReweight, self).__init__(
            prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend, tokenizer, device, attn_res
        )
        self.equalizer = equalizer.to(self.device)
        self.prev_controller = controller


### util functions for all Edits
def update_alpha_time_word(
    alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None
):
    if isinstance(bounds, float):
        bounds = 0, bounds
    start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
    if word_inds is None:
        word_inds = torch.arange(alpha.shape[2])
    alpha[:start, prompt_ind, word_inds] = 0
    alpha[start:end, prompt_ind, word_inds] = 1
    alpha[end:, prompt_ind, word_inds] = 0
    return alpha


def get_time_words_attention_alpha(
    prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77
):
    if not isinstance(cross_replace_steps, dict):
        cross_replace_steps = {"default_": cross_replace_steps}
    if "default_" not in cross_replace_steps:
        cross_replace_steps["default_"] = (0.0, 1.0)
    alpha_time_words = torch.zeros(num_steps + 1, len(prompts) - 1, max_num_words)
    for i in range(len(prompts) - 1):
        alpha_time_words = update_alpha_time_word(alpha_time_words, cross_replace_steps["default_"], i)
    for key, item in cross_replace_steps.items():
        if key != "default_":
            inds = [get_word_inds(prompts[i], key, tokenizer) for i in range(1, len(prompts))]
            for i, ind in enumerate(inds):
                if len(ind) > 0:
                    alpha_time_words = update_alpha_time_word(alpha_time_words, item, i, ind)
    alpha_time_words = alpha_time_words.reshape(num_steps + 1, len(prompts) - 1, 1, 1, max_num_words)
    return alpha_time_words


### util functions for LocalBlend and ReplacementEdit
def get_word_inds(text: str, word_place: int, tokenizer):
    split_text = text.split(" ")
    if isinstance(word_place, str):
        word_place = [i for i, word in enumerate(split_text) if word_place == word]
    elif isinstance(word_place, int):
        word_place = [word_place]
    out = []
    if len(word_place) > 0:
        words_encode = [tokenizer.decode([item]).strip("#") for item in tokenizer.encode(text)][1:-1]
        cur_len, ptr = 0, 0

        for i in range(len(words_encode)):
            cur_len += len(words_encode[i])
            if ptr in word_place:
                out.append(i + 1)
            if cur_len >= len(split_text[ptr]):
                ptr += 1
                cur_len = 0
    return np.array(out)


### util functions for ReplacementEdit
def get_replacement_mapper_(x: str, y: str, tokenizer, max_len=77):
    words_x = x.split(" ")
    words_y = y.split(" ")
    if len(words_x) != len(words_y):
        raise ValueError(
            f"attention replacement edit can only be applied on prompts with the same length"
            f" but prompt A has {len(words_x)} words and prompt B has {len(words_y)} words."
        )
    inds_replace = [i for i in range(len(words_y)) if words_y[i] != words_x[i]]
    inds_source = [get_word_inds(x, i, tokenizer) for i in inds_replace]
    inds_target = [get_word_inds(y, i, tokenizer) for i in inds_replace]
    mapper = np.zeros((max_len, max_len))
    i = j = 0
    cur_inds = 0
    while i < max_len and j < max_len:
        if cur_inds < len(inds_source) and inds_source[cur_inds][0] == i:
            inds_source_, inds_target_ = inds_source[cur_inds], inds_target[cur_inds]
            if len(inds_source_) == len(inds_target_):
                mapper[inds_source_, inds_target_] = 1
            else:
                ratio = 1 / len(inds_target_)
                for i_t in inds_target_:
                    mapper[inds_source_, i_t] = ratio
            cur_inds += 1
            i += len(inds_source_)
            j += len(inds_target_)
        elif cur_inds < len(inds_source):
            mapper[i, j] = 1
            i += 1
            j += 1
        else:
            mapper[j, j] = 1
            i += 1
            j += 1

    # return torch.from_numpy(mapper).float()
    return torch.from_numpy(mapper).to(torch.float16)


def get_replacement_mapper(prompts, tokenizer, max_len=77):
    x_seq = prompts[0]
    mappers = []
    for i in range(1, len(prompts)):
        mapper = get_replacement_mapper_(x_seq, prompts[i], tokenizer, max_len)
        mappers.append(mapper)
    return torch.stack(mappers)


### util functions for ReweightEdit
def get_equalizer(
    text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer
):
    if isinstance(word_select, (int, str)):
        word_select = (word_select,)
    equalizer = torch.ones(len(values), 77)
    values = torch.tensor(values, dtype=torch.float32)
    for i, word in enumerate(word_select):
        inds = get_word_inds(text, word, tokenizer)
        equalizer[:, inds] = torch.FloatTensor(values[i])
    return equalizer


### util functions for RefinementEdit
class ScoreParams:
    def __init__(self, gap, match, mismatch):
        self.gap = gap
        self.match = match
        self.mismatch = mismatch

    def mis_match_char(self, x, y):
        if x != y:
            return self.mismatch
        else:
            return self.match


def get_matrix(size_x, size_y, gap):
    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
    matrix[0, 1:] = (np.arange(size_y) + 1) * gap
    matrix[1:, 0] = (np.arange(size_x) + 1) * gap
    return matrix


def get_traceback_matrix(size_x, size_y):
    matrix = np.zeros((size_x + 1, size_y + 1), dtype=np.int32)
    matrix[0, 1:] = 1
    matrix[1:, 0] = 2
    matrix[0, 0] = 4
    return matrix


def global_align(x, y, score):
    matrix = get_matrix(len(x), len(y), score.gap)
    trace_back = get_traceback_matrix(len(x), len(y))
    for i in range(1, len(x) + 1):
        for j in range(1, len(y) + 1):
            left = matrix[i, j - 1] + score.gap
            up = matrix[i - 1, j] + score.gap
            diag = matrix[i - 1, j - 1] + score.mis_match_char(x[i - 1], y[j - 1])
            matrix[i, j] = max(left, up, diag)
            if matrix[i, j] == left:
                trace_back[i, j] = 1
            elif matrix[i, j] == up:
                trace_back[i, j] = 2
            else:
                trace_back[i, j] = 3
    return matrix, trace_back


def get_aligned_sequences(x, y, trace_back):
    x_seq = []
    y_seq = []
    i = len(x)
    j = len(y)
    mapper_y_to_x = []
    while i > 0 or j > 0:
        if trace_back[i, j] == 3:
            x_seq.append(x[i - 1])
            y_seq.append(y[j - 1])
            i = i - 1
            j = j - 1
            mapper_y_to_x.append((j, i))
        elif trace_back[i][j] == 1:
            x_seq.append("-")
            y_seq.append(y[j - 1])
            j = j - 1
            mapper_y_to_x.append((j, -1))
        elif trace_back[i][j] == 2:
            x_seq.append(x[i - 1])
            y_seq.append("-")
            i = i - 1
        elif trace_back[i][j] == 4:
            break
    mapper_y_to_x.reverse()
    return x_seq, y_seq, torch.tensor(mapper_y_to_x, dtype=torch.int64)


def get_mapper(x: str, y: str, tokenizer, max_len=77):
    x_seq = tokenizer.encode(x)
    y_seq = tokenizer.encode(y)
    score = ScoreParams(0, 1, -1)
    matrix, trace_back = global_align(x_seq, y_seq, score)
    mapper_base = get_aligned_sequences(x_seq, y_seq, trace_back)[-1]
    alphas = torch.ones(max_len)
    alphas[: mapper_base.shape[0]] = mapper_base[:, 1].ne(-1).float()
    mapper = torch.zeros(max_len, dtype=torch.int64)
    mapper[: mapper_base.shape[0]] = mapper_base[:, 1]
    mapper[mapper_base.shape[0] :] = len(y_seq) + torch.arange(max_len - len(y_seq))
    return mapper, alphas


def get_refinement_mapper(prompts, tokenizer, max_len=77):
    x_seq = prompts[0]
    mappers, alphas = [], []
    for i in range(1, len(prompts)):
        mapper, alpha = get_mapper(x_seq, prompts[i], tokenizer, max_len)
        mappers.append(mapper)
        alphas.append(alpha)
    return torch.stack(mappers), torch.stack(alphas)





def aggregate_attention(prompts, attention_store: AttentionStore, height: int, width: int, from_where: List[str], is_cross: bool, select: int):
    out = []
    attention_maps = attention_store.get_average_attention()
    attention_map_height = height // 32
    attention_map_width = width // 32
    num_pixels = attention_map_height * attention_map_width
    for location in from_where:
        for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
            if item.shape[1] == num_pixels:
                cross_maps = item.reshape(len(prompts), -1, attention_map_width, attention_map_height, item.shape[-1])[select]
                out.append(cross_maps)
    out = torch.cat(out, dim=0)
    out = out.sum(0) / out.shape[0]
    return out.cpu()


def show_cross_attention(tokenizer, prompts, attention_store: AttentionStore, res: int, from_where: List[str], select: int = 0, t=0):
    tokens = tokenizer.encode(prompts[select])
    decoder = tokenizer.decode
    attention_maps = aggregate_attention(prompts=prompts, attention_store=attention_store, height=res, width=res, from_where=from_where, is_cross=True, select=select)
    images = []
    for i in range(len(tokens)):
        image = attention_maps[:, :, i]
        image = 255 * image / image.max()
        image = image.unsqueeze(-1).expand(*image.shape, 3)
        image = image.numpy().astype(np.uint8)
        image = np.array(Image.fromarray(image).resize((256, 256)))
        image = text_under_image(image, decoder(int(tokens[i])))
        images.append(image)

    view_images(np.stack(images, axis=0), t=t, from_where=from_where)
    

def show_self_attention_comp(attention_store: AttentionStore, res: int, from_where: List[str],
                        max_com=10, select: int = 0):
    attention_maps = aggregate_attention(attention_store, res, from_where, False, select).numpy().reshape((res ** 2, res ** 2))
    u, s, vh = np.linalg.svd(attention_maps - np.mean(attention_maps, axis=1, keepdims=True))
    images = []
    for i in range(max_com):
        image = vh[i].reshape(res, res)
        image = image - image.min()
        image = 255 * image / image.max()
        image = np.repeat(np.expand_dims(image, axis=2), 3, axis=2).astype(np.uint8)
        image = Image.fromarray(image).resize((256, 256))
        image = np.array(image)
        images.append(image)
    view_images(np.concatenate(images, axis=1),from_where=from_where)

def view_images(images, num_rows=1, offset_ratio=0.02, t=0, from_where= List[str]):
    if type(images) is list:
        num_empty = len(images) % num_rows
    elif images.ndim == 4:
        num_empty = images.shape[0] % num_rows
    else:
        images = [images]
        num_empty = 0

    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
    num_items = len(images)

    h, w, c = images[0].shape
    offset = int(h * offset_ratio)
    num_cols = num_items // num_rows
    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
    for i in range(num_rows):
        for j in range(num_cols):
            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
                i * num_cols + j]

    pil_img = Image.fromarray(image_)

    if len(from_where) > 1:
        from_where = '_'.join(from_where)

    save_path = f'./visualization/{from_where}'
    if not os.path.exists(save_path):
        os.mkdir(save_path)
    pil_img.save(f"{save_path}/{t}.png")


def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
    h, w, c = image.shape
    offset = int(h * .2)
    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
    font = cv2.FONT_HERSHEY_SIMPLEX
    img[:h] = image
    textsize = cv2.getTextSize(text, font, 1, 2)[0]
    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
    return img




def get_multidiffusion_prompts(tokenizer, prompts, threthod, attention_store:AttentionStore, height:int, width:int, from_where: List[str], scale_num=4, random_jitter=False):
    tokens = tokenizer.encode(prompts[0])
    decoder = tokenizer.decode

    # get cross_attention_maps
    attention_maps = aggregate_attention(prompts, attention_store, height, width, from_where, True, 0)

    # view cross_attention_maps
    images = []
    for i in range(len(tokens)):
        image = attention_maps[:, :, i]
        image = 255 * image / image.max()
        image = image.unsqueeze(-1).expand(*image.shape, 3).numpy().astype(np.uint8)
        image = np.array(Image.fromarray(image).resize((256, 256)))
        image = text_under_image(image, decoder(int(tokens[i])))
        images.append(image)

    # get high attention regions
    masks = []

    for i in range(len(tokens)):
        attention_map = attention_maps[:, :, i]
        attention_map = attention_map.to(torch.float32)
        words = decoder(int(tokens[i]))
        mask = torch.where(attention_map > attention_map.mean(), 1, 0).numpy().astype(np.uint8)
        mask = mask * 255
        # process mask
        kernel = np.ones((3, 3), np.uint8)
        eroded_mask = cv2.erode(mask, kernel, iterations=mask.shape[0]//16)
        dilated_mask = cv2.dilate(eroded_mask, kernel, iterations=mask.shape[0]//16)
        masks.append(dilated_mask)

    # dict for prompts and views
    prompt_dict = {}
    view_dict = {}

    ori_w, ori_h = mask.shape
    window_size = max(ori_h, ori_w)
    for scale in range(2, scale_num+1):
        # current height and width
        cur_w = ori_w * scale
        cur_h = ori_h * scale
        views = get_views(height=cur_h, width=cur_w, window_size=window_size, stride=window_size/2, random_jitter=random_jitter)

        words_in_patch = []
        for i, mask in enumerate(masks):
            # skip endoftext and beginof text masks
            if i == 0 or i == len(masks) - 1:
                continue
            # upscale masks
            mask = cv2.resize(mask, (cur_w, cur_h), interpolation=cv2.INTER_NEAREST)
            if random_jitter:
                jitter_range = int((ori_h - ori_h/2) // 4)
                mask = np.pad(mask, ((jitter_range, jitter_range), (jitter_range, jitter_range)), 'constant', constant_values=(0, 0))
            
            word_in_patch =[]
            word = decoder(int(tokens[i]))
            for i, view in enumerate(views):
                h_start, h_end, w_start, w_end = view
                view_mask = mask[h_start:h_end, w_start:w_end]
                if (view_mask/255).sum() / (ori_h * ori_w) >= threthod:
                    word_in_patch.append(word)   # word in patch
                else:
                    word_in_patch.append('')     # word not in patch
            words_in_patch.append(word_in_patch)

        # get prompts for each view
        result = []
        prompts_for_each_views = [' '.join(strings) for strings in zip(*words_in_patch)]
        for prompt in prompts_for_each_views:
            prompt = prompt.split()
            result.append(" ".join(prompt))
        # save prompts and views in each scale
        prompt_dict[scale] = result
        view_dict[scale] = views
    
    return prompt_dict, view_dict





class ScaledAttnProcessor:
    r"""
    Default processor for performing attention-related computations.
    """

    def __init__(self, processor, test_res, train_res):
        self.processor = processor
        self.test_res = test_res
        self.train_res = train_res

    def __call__(
            self,
            attn,
            hidden_states,
            encoder_hidden_states=None,
            attention_mask=None,
            temb=None,
    ):
        input_ndim = hidden_states.ndim
        # print(f"cross attention: {not encoder_hidden_states is None}")
        # if encoder_hidden_states is None:
        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            sequence_length = height * width
        else:
            batch_size, sequence_length, _ = hidden_states.shape

        test_train_ratio = (self.test_res ** 2.0) / (self.train_res ** 2.0)
        # test_train_ratio = float(self.test_res / self.train_res)
        # print(f"test_train_ratio: {test_train_ratio}")
        train_sequence_length = sequence_length / test_train_ratio
        scale_factor = math.log(sequence_length, train_sequence_length) ** 0.5
        # else:
        #     scale_factor = 1
        # print(f"scale factor: {scale_factor}")

        original_scale = attn.scale
        attn.scale = attn.scale * scale_factor
        hidden_states = self.processor(attn, hidden_states, encoder_hidden_states, attention_mask, temb, scale = attn.scale )
        # hidden_states = super(ScaledAttnProcessor, self).__call__(
        #     attn, hidden_states, encoder_hidden_states, attention_mask, temb)
        attn.scale = original_scale
        return hidden_states