



import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase = "cpu"	,				__UpperCAmelCase = "openai/clip-vit-large-patch14"				):



    '''simple docstring'''
    __lowerCamelCase													=    device
    __lowerCamelCase													=    CLIPTokenizerFast.from_pretrained(__UpperCAmelCase				)
    __lowerCamelCase													=    [0.48_145_466, 0.4_578_275, 0.40_821_073]
    __lowerCamelCase													=    [0.26_862_954, 0.26_130_258, 0.27_577_711]
    __lowerCamelCase													=    torchvision.transforms.Normalize(self.image_mean	,				self.image_std				)
    __lowerCamelCase													=    torchvision.transforms.Resize(224				)
    __lowerCamelCase													=    torchvision.transforms.CenterCrop(224				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.resize(__UpperCAmelCase				)
    __lowerCamelCase													=    self.center_crop(__UpperCAmelCase				)
    __lowerCamelCase													=    self.normalize(__UpperCAmelCase				)
    return images




 def __call__(						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.tokenizer(text=__UpperCAmelCase	,				**__UpperCAmelCase				)
    __lowerCamelCase													=    self.preprocess_img(__UpperCAmelCase				)
    __lowerCamelCase													=    {key: value.to(self.device				) for (key, value) in encoding.items()}
    return encoding



class  __lowerCAmelCase     (	nn.Module   ):




 def __init__(						self	,				__UpperCAmelCase=10	,				__UpperCAmelCase=0.01	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase="image"	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=False	,				__UpperCAmelCase=False	,				):



    '''simple docstring'''
    super().__init__()
    __lowerCamelCase													=    None
    __lowerCamelCase													=    device if device else get_device()
    if vqgan:
       __lowerCamelCase													=    vqgan
    else:
       __lowerCamelCase													=    load_vqgan(self.device	,				conf_path=__UpperCAmelCase	,				ckpt_path=__UpperCAmelCase				)
    self.vqgan.eval()
    if clip:
       __lowerCamelCase													=    clip
    else:
       __lowerCamelCase													=    CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''				)
    self.clip.to(self.device				)
    __lowerCamelCase													=    ProcessorGradientFlow(device=self.device				)

    __lowerCamelCase													=    iterations
    __lowerCamelCase													=    lr
    __lowerCamelCase													=    log
    __lowerCamelCase													=    make_grid
    __lowerCamelCase													=    return_val
    __lowerCamelCase													=    quantize
    __lowerCamelCase													=    self.vqgan.decoder.z_shape




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=5	,				__UpperCAmelCase=True				):



    '''simple docstring'''
    __lowerCamelCase													=    []
    if output_path is None:
       __lowerCamelCase													=    '''./animation.gif'''
    if input_path is None:
       __lowerCamelCase													=    self.save_path
    __lowerCamelCase													=    sorted(glob(input_path + '''/*'''				)				)
    if not len(__UpperCAmelCase				):
       raise ValueError(
           '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
           ''' function?)'''				)
    if len(__UpperCAmelCase				) == 1:
       print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''				)
    __lowerCamelCase													=    total_duration / len(__UpperCAmelCase				)
    __lowerCamelCase													=    [frame_duration] * len(__UpperCAmelCase				)
    if extend_frames:
       __lowerCamelCase													=    1.5
       __lowerCamelCase													=    3
    for file_name in paths:
       if file_name.endswith('''.png'''				):
          images.append(imageio.imread(__UpperCAmelCase				)				)
    imageio.mimsave(__UpperCAmelCase	,				__UpperCAmelCase	,				duration=__UpperCAmelCase				)
    print(F"""gif saved to {output_path}"""				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    if not (path or img):
       raise ValueError('''Input either path or tensor'''				)
    if img is not None:
       raise NotImplementedError
    __lowerCamelCase													=    preprocess(Image.open(__UpperCAmelCase				)	,				target_image_size=256				).to(self.device				)
    __lowerCamelCase													=    preprocess_vqgan(__UpperCAmelCase				)
    __lowerCamelCase							,*__lowerCamelCase													=    self.vqgan.encode(__UpperCAmelCase				)
    return z




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.latent.detach().requires_grad_()
    __lowerCamelCase													=    base_latent + transform_vector
    if self.quantize:
       __lowerCamelCase							,*__lowerCamelCase													=    self.vqgan.quantize(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    trans_latent
    return self.vqgan.decode(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    __lowerCamelCase													=    self.clip_preprocessor(text=__UpperCAmelCase	,				images=__UpperCAmelCase	,				return_tensors='''pt'''	,				padding=__UpperCAmelCase				)
    __lowerCamelCase													=    self.clip(**__UpperCAmelCase				)
    __lowerCamelCase													=    clip_outputs.logits_per_image
    if weights is not None:
       __lowerCamelCase													=    similarity_logits * weights
    return similarity_logits.sum()




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self._get_clip_similarity(pos_prompts['''prompts''']	,				__UpperCAmelCase	,				weights=(1 / pos_prompts['''weights'''])				)
    if neg_prompts:
       __lowerCamelCase													=    self._get_clip_similarity(neg_prompts['''prompts''']	,				__UpperCAmelCase	,				weights=neg_prompts['''weights''']				)
    else:
       __lowerCamelCase													=    torch.tensor([1]	,				device=self.device				)
    __lowerCamelCase													=    -torch.log(__UpperCAmelCase				) + torch.log(__UpperCAmelCase				)
    return loss




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    torch.randn_like(self.latent	,				requires_grad=__UpperCAmelCase	,				device=self.device				)
    __lowerCamelCase													=    torch.optim.Adam([vector]	,				lr=self.lr				)

    for i in range(self.iterations				):
       optim.zero_grad()
       __lowerCamelCase													=    self._add_vector(__UpperCAmelCase				)
       __lowerCamelCase													=    loop_post_process(__UpperCAmelCase				)
       __lowerCamelCase													=    self._get_CLIP_loss(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)
       print('''CLIP loss'''	,				__UpperCAmelCase				)
       if self.log:
          wandb.log({'''CLIP Loss''': clip_loss}				)
       clip_loss.backward(retain_graph=__UpperCAmelCase				)
       optim.step()
       if self.return_val == "image":
          yield custom_to_pil(transformed_img[0]				)
       else:
          yield vector




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    wandb.init(reinit=__UpperCAmelCase	,				project='''face-editor'''				)
    wandb.config.update({'''Positive Prompts''': positive_prompts}				)
    wandb.config.update({'''Negative Prompts''': negative_prompts}				)
    wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}				)
    if image_path:
       __lowerCamelCase													=    Image.open(__UpperCAmelCase				)
       __lowerCamelCase													=    image.resize((256, 256)				)
       wandb.log('''Original Image'''	,				wandb.Image(__UpperCAmelCase				)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if not prompts:
       return []
    __lowerCamelCase													=    []
    __lowerCamelCase													=    []
    if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
       __lowerCamelCase													=    [prompt.strip() for prompt in prompts.split('''|'''				)]
    for prompt in prompts:
       if isinstance(__UpperCAmelCase	,				(tuple, list)				):
          __lowerCamelCase													=    prompt[0]
          __lowerCamelCase													=    float(prompt[1]				)
       elif ":" in prompt:
          __lowerCamelCase							,__lowerCamelCase													=    prompt.split(''':'''				)
          __lowerCamelCase													=    float(__UpperCAmelCase				)
       else:
          __lowerCamelCase													=    prompt
          __lowerCamelCase													=    1.0
       processed_prompts.append(__UpperCAmelCase				)
       weights.append(__UpperCAmelCase				)
    return {
        "prompts": processed_prompts,
        "weights": torch.tensor(__UpperCAmelCase	,				device=self.device				),
    }




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=None	,				):



    '''simple docstring'''
    if image_path:
       __lowerCamelCase													=    self._get_latent(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    torch.randn(self.latent_dim	,				device=self.device				)
    if self.log:
       self._init_logging(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)

    assert pos_prompts, "You must provide at least one positive prompt."
    __lowerCamelCase													=    self.process_prompts(__UpperCAmelCase				)
    __lowerCamelCase													=    self.process_prompts(__UpperCAmelCase				)
    if save_final and save_path is None:
       __lowerCamelCase													=    os.path.join('''./outputs/'''	,				'''_'''.join(pos_prompts['''prompts''']				)				)
    if not os.path.exists(__UpperCAmelCase				):
       os.makedirs(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    save_path + '''_''' + get_timestamp()
       os.makedirs(__UpperCAmelCase				)
    __lowerCamelCase													=    save_path

    __lowerCamelCase													=    self.vqgan.decode(self.latent				)[0]
    if show_intermediate:
       print('''Original Image'''				)
       show_pil(custom_to_pil(__UpperCAmelCase				)				)

    __lowerCamelCase													=    loop_post_process(__UpperCAmelCase				)
    for iter, transformed_img in enumerate(self._optimize_CLIP(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)				):
       if show_intermediate:
          show_pil(__UpperCAmelCase				)
       if save_intermediate:
          transformed_img.save(os.path.join(self.save_path	,				F"""iter_{iter:03d}.png"""				)				)
       if self.log:
          wandb.log({'''Image''': wandb.Image(__UpperCAmelCase				)}				)
    if show_final:
       show_pil(__UpperCAmelCase				)
    if save_final:
       transformed_img.save(os.path.join(self.save_path	,				F"""iter_{iter:03d}_final.png"""				)				)











import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_			=					False



class  __lowerCAmelCase     (	unittest.TestCase   ):
 pass



@nightly
@require_torch_gpu
class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    generator.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''cyberpunk 2077'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''	,				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger '''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.text_to_image(
        prompt=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1

    __lowerCamelCase													=    pipe.image_variation(__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1






