# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import cv2
from typing import Optional, Union, Tuple, List, Callable, Dict
from IPython.display import display
from tqdm import tqdm
import torch.nn.functional as F


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
    # font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
    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 view_images(images, num_rows=1, offset_ratio=0.02):
    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_)
    display(pil_img)


def diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource=False):
    if low_resource:
        noise_pred_uncond = model.unet(latents, t, encoder_hidden_states=context[0])["sample"]
        noise_prediction_text = model.unet(latents, t, encoder_hidden_states=context[1])["sample"]
    else:
        latents_input = torch.cat([latents] * 2)
        noise_pred = model.unet(latents_input, t, encoder_hidden_states=context)["sample"]
        noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
    noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
    latents = model.scheduler.step(noise_pred, t, latents)["prev_sample"]
    latents = controller.step_callback(latents)
    return latents


def latent2image(vae, latents):
    latents = 1 / 0.18215 * latents
    image = vae.decode(latents)['sample']
    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.cpu().permute(0, 2, 3, 1).numpy()
    image = (image * 255).astype(np.uint8)
    return image


def init_latent(latent, model, height, width, generator, batch_size):
    if latent is None:
        latent = torch.randn(
            (1, model.unet.in_channels, height // 8, width // 8),
            generator=generator,
        )
    latents = latent.expand(batch_size,  model.unet.in_channels, height // 8, width // 8).to(model.device)
    return latent, latents


@torch.no_grad()
def text2image_ldm(
    model,
    prompt:  List[str],
    controller,
    num_inference_steps: int = 50,
    guidance_scale: Optional[float] = 7.,
    generator: Optional[torch.Generator] = None,
    latent: Optional[torch.FloatTensor] = None,
):
    register_attention_control(model, controller)
    height = width = 256
    batch_size = len(prompt)
    
    uncond_input = model.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
    uncond_embeddings = model.bert(uncond_input.input_ids.to(model.device))[0]
    
    text_input = model.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
    text_embeddings = model.bert(text_input.input_ids.to(model.device))[0]
    latent, latents = init_latent(latent, model, height, width, generator, batch_size)
    context = torch.cat([uncond_embeddings, text_embeddings])
    
    model.scheduler.set_timesteps(num_inference_steps)
    for t in tqdm(model.scheduler.timesteps):
        latents = diffusion_step(model, controller, latents, context, t, guidance_scale)
    
    image = latent2image(model.vqvae, latents)
   
    return image, latent


@torch.no_grad()
def text2image_ldm_stable(
    model,
    prompt: List[str],
    controller,
    num_inference_steps: int = 50,
    guidance_scale: float = 7.5,
    generator: Optional[torch.Generator] = None,
    latent: Optional[torch.FloatTensor] = None,
    low_resource: bool = False,
):
    register_attention_control(model, controller)
    height = width = 512
    batch_size = len(prompt)

    text_input = model.tokenizer(
        prompt,
        padding="max_length",
        max_length=model.tokenizer.model_max_length,
        truncation=True,
        return_tensors="pt",
    )
    text_embeddings = model.text_encoder(text_input.input_ids.to(model.device))[0]
    max_length = text_input.input_ids.shape[-1]
    uncond_input = model.tokenizer(
        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
    )
    uncond_embeddings = model.text_encoder(uncond_input.input_ids.to(model.device))[0]
    
    context = [uncond_embeddings, text_embeddings]
    if not low_resource:
        context = torch.cat(context)
    latent, latents = init_latent(latent, model, height, width, generator, batch_size)
    
    # set timesteps
    extra_set_kwargs = {"offset": 1}
    model.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
    for t in tqdm(model.scheduler.timesteps):
        latents = diffusion_step(model, controller, latents, context, t, guidance_scale, low_resource)
    
    image = latent2image(model.vae, latents)
  
    return image, latent


def register_attention_control(model, controller):

    def ca_forward(self, place_in_unet):
        def forward(hidden_states, encoder_hidden_states=None, attention_mask=None):
            batch_size, sequence_length, _ = hidden_states.shape

            is_cross = encoder_hidden_states is not None
            encoder_hidden_states = encoder_hidden_states

            if self.group_norm is not None:
                hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

            query = self.to_q(hidden_states)
            # dim = query.shape[-1]
            query = self.reshape_heads_to_batch_dim(query)

            if self.added_kv_proj_dim is not None:
                key = self.to_k(hidden_states)
                value = self.to_v(hidden_states)
                encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states)
                encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states)

                key = self.reshape_heads_to_batch_dim(key)
                value = self.reshape_heads_to_batch_dim(value)
                encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj)
                encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj)

                key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)
                value = torch.concat([encoder_hidden_states_value_proj, value], dim=1)
            else:
                encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
                key = self.to_k(encoder_hidden_states)
                value = self.to_v(encoder_hidden_states)

                key = self.reshape_heads_to_batch_dim(key)
                value = self.reshape_heads_to_batch_dim(value)

            if attention_mask is not None:
                if attention_mask.shape[-1] != query.shape[1]:
                    target_length = query.shape[1]
                    attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
                    attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)

            assert self._slice_size is None or query.shape[0] // self._slice_size == 1

            if self.upcast_attention:
                query = query.float()
                key = key.float()

            attention_scores = torch.baddbmm(
                torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
                query,
                key.transpose(-1, -2),
                beta=0,
                alpha=self.scale,
            )

            if attention_mask is not None:
                attention_scores = attention_scores + attention_mask

            if self.upcast_softmax:
                attention_scores = attention_scores.float()

            attention_probs = attention_scores.softmax(dim=-1)

            # attn control
            attention_probs = controller(attention_probs, is_cross, place_in_unet)

            # cast back to the original dtype
            attention_probs = attention_probs.to(value.dtype)

            # compute attention output
            hidden_states = torch.bmm(attention_probs, value)

            # reshape hidden_states
            hidden_states = self.reshape_batch_dim_to_heads(hidden_states)

            # linear proj
            hidden_states = self.to_out[0](hidden_states)

            # dropout
            hidden_states = self.to_out[1](hidden_states)
            return hidden_states

        return forward

    class DummyController:

        def __call__(self, *args):
            return args[0]

        def __init__(self):
            self.num_att_layers = 0

    if controller is None:
        controller = DummyController()

    def register_recr(net_, count, place_in_unet):
        if net_.__class__.__name__ == 'CrossAttention':
            net_.forward = ca_forward(net_, place_in_unet)
            return count + 1
        elif hasattr(net_, 'children'):
            for net__ in net_.children():
                count = register_recr(net__, count, place_in_unet)
        return count

    cross_att_count = 0
    # sub_nets = model.unet.named_children()
    # we take unet as the input model
    sub_nets = model.named_children()
    for net in sub_nets:
        if "down" in net[0]:
            cross_att_count += register_recr(net[1], 0, "down")
        elif "up" in net[0]:
            cross_att_count += register_recr(net[1], 0, "up")
        elif "mid" in net[0]:
            cross_att_count += register_recr(net[1], 0, "mid")

    controller.num_att_layers = cross_att_count

    
def get_word_inds(text: str, word_place: int, tokenizer):
    split_text = text.split(" ")
    if type(word_place) is str:
        word_place = [i for i, word in enumerate(split_text) if word_place == word]
    elif type(word_place) is 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)


def update_alpha_time_word(alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int,
                           word_inds: Optional[torch.Tensor]=None):
    if type(bounds) is 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 type(cross_replace_steps) is not dict:
        cross_replace_steps = {"default_": cross_replace_steps}
    if "default_" not in cross_replace_steps:
        cross_replace_steps["default_"] = (0., 1.)
    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