





'''simple docstring'''







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       _lowercase    :







     def __init__(			self     , A__ = "cpu"     , A__ = "openai/clip-vit-large-patch14"     )			->						Optional[Any]:
      snake_case					=      device
      snake_case					=      CLIPTokenizerFast.from_pretrained(snake_case__     )
      snake_case					=      [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
      snake_case					=      [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
      snake_case					=      torchvision.transforms.Normalize(self.image_mean     , self.image_std     )
      snake_case					=      torchvision.transforms.Resize(2_24     )
      snake_case					=      torchvision.transforms.CenterCrop(2_24     )







     def      UpperCamelCase      (			self     , A__     )			->						List[Any]:
      snake_case					=      self.resize(snake_case__     )
      snake_case					=      self.center_crop(snake_case__     )
      snake_case					=      self.normalize(snake_case__     )
      return images







     def __call__(			self     , A__=None     , A__=None     , **A__     )			->						int:
      snake_case					=      self.tokenizer(text=snake_case__     , **snake_case__     )
      snake_case					=      self.preprocess_img(snake_case__     )
      snake_case					=      {key: value.to(self.device     ) for (key, value) in encoding.items()}
      return encoding




class       _lowercase    (							nn.Module       ):







     def __init__(			self     , A__=10     , A__=0.0_1     , A__=None     , A__=None     , A__=None     , A__=None     , A__=None     , A__=None     , A__=False     , A__=True     , A__="image"     , A__=True     , A__=False     , A__=False     , A__=False     , )			->						Union[str, Any]:
      super().__init__()
      snake_case					=      None
      snake_case					=      device if device else get_device()
      if vqgan:
       snake_case					=      vqgan
      else:
       snake_case					=      load_vqgan(self.device     , conf_path=snake_case__     , ckpt_path=snake_case__     )
      self.vqgan.eval()
      if clip:
       snake_case					=      clip
      else:
       snake_case					=      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''     )
      self.clip.to(self.device     )
      snake_case					=      ProcessorGradientFlow(device=self.device     )

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







     def      UpperCamelCase      (			self     , A__=None     , A__=None     , A__=5     , A__=True     )			->						Tuple:
      snake_case					=      []
      if output_path is None:
       snake_case					=      './animation.gif'
      if input_path is None:
       snake_case					=      self.save_path
      snake_case					=      sorted(glob(input_path + '''/*'''     )     )
      if not len(snake_case__     ):
       raise ValueError(
           '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
           ''' function?)'''     )
      if len(snake_case__     ) == 1:
       print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''     )
      snake_case					=      total_duration / len(snake_case__     )
      snake_case					=      [frame_duration] * len(snake_case__     )
      if extend_frames:
       snake_case					=      1.5
       snake_case					=      3
      for file_name in paths:
       if file_name.endswith('''.png'''     ):
        images.append(imageio.imread(snake_case__     )     )
      imageio.mimsave(snake_case__     , snake_case__     , duration=snake_case__     )
      print(F"""gif saved to {output_path}"""     )







     def      UpperCamelCase      (			self     , A__=None     , A__=None     )			->						Any:

      if not (path or img):
       raise ValueError('''Input either path or tensor'''     )
      if img is not None:
       raise NotImplementedError
      snake_case					=      preprocess(Image.open(snake_case__     )     , target_image_size=2_56     ).to(self.device     )
      snake_case					=      preprocess_vqgan(snake_case__     )
      snake_case					=      self.vqgan.encode(snake_case__     )
      return z







     def      UpperCamelCase      (			self     , A__     )			->						Dict:
      snake_case					=      self.latent.detach().requires_grad_()
      snake_case					=      base_latent + transform_vector
      if self.quantize:
       snake_case					=      self.vqgan.quantize(snake_case__     )
      else:
       snake_case					=      trans_latent
      return self.vqgan.decode(snake_case__     )







     def      UpperCamelCase      (			self     , A__     , A__     , A__=None     )			->						List[str]:
      snake_case					=      self.clip_preprocessor(text=snake_case__     , images=snake_case__     , return_tensors='''pt'''     , padding=snake_case__     )
      snake_case					=      self.clip(**snake_case__     )
      snake_case					=      clip_outputs.logits_per_image
      if weights is not None:
       snake_case					=      similarity_logits * weights
      return similarity_logits.sum()







     def      UpperCamelCase      (			self     , A__     , A__     , A__     )			->						List[str]:
      snake_case					=      self._get_clip_similarity(pos_prompts['''prompts''']     , snake_case__     , weights=(1 / pos_prompts['''weights'''])     )
      if neg_prompts:
       snake_case					=      self._get_clip_similarity(neg_prompts['''prompts''']     , snake_case__     , weights=neg_prompts['''weights''']     )
      else:
       snake_case					=      torch.tensor([1]     , device=self.device     )
      snake_case					=      -torch.log(snake_case__     ) + torch.log(snake_case__     )
      return loss







     def      UpperCamelCase      (			self     , A__     , A__     , A__     )			->						List[Any]:
      snake_case					=      torch.randn_like(self.latent     , requires_grad=snake_case__     , device=self.device     )
      snake_case					=      torch.optim.Adam([vector]     , lr=self.lr     )

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







     def      UpperCamelCase      (			self     , A__     , A__     , A__     )			->						Optional[int]:
      wandb.init(reinit=snake_case__     , 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:
       snake_case					=      Image.open(snake_case__     )
       snake_case					=      image.resize((2_56, 2_56)     )
       wandb.log('''Original Image'''     , wandb.Image(snake_case__     )     )







     def      UpperCamelCase      (			self     , A__     )			->						int:

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







     def      UpperCamelCase      (			self     , A__     , A__=None     , A__=None     , A__=True     , A__=False     , A__=True     , A__=True     , A__=None     , )			->						Union[str, Any]:

      if image_path:
       snake_case					=      self._get_latent(snake_case__     )
      else:
       snake_case					=      torch.randn(self.latent_dim     , device=self.device     )
      if self.log:
       self._init_logging(snake_case__     , snake_case__     , snake_case__     )

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

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

      snake_case					=      loop_post_process(snake_case__     )
      for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case__     , snake_case__     , snake_case__     )     ):
       if show_intermediate:
        show_pil(snake_case__     )
       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(snake_case__     )}     )
      if show_final:
       show_pil(snake_case__     )
      if save_final:
       transformed_img.save(os.path.join(self.save_path     , F"""iter_{iter:03d}_final.png"""     )     )





'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase     :  str											=    False
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
            # remove text_unet
            pipe.remove_unused_weights()
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Any       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				List[Any]       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				Any       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=2   ,						output_type='numpy' ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(snake_case__ )
                   _lowerCAmelCase     :				Any       =							VersatileDiffusionTextToImagePipeline.from_pretrained(snake_case__ )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							generator.manual_seed(0 )
            _lowerCAmelCase     :				Tuple       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						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 a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							VersatileDiffusionTextToImagePipeline.from_pretrained(
                'shi-labs/versatile-diffusion'   ,						torch_dtype=torch.floataa )
            pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Dict       =							'A painting of a squirrel eating a burger '
            _lowerCAmelCase     :				Any       =							torch.manual_seed(0 )
            _lowerCAmelCase     :				int       =							pipe(
                prompt=snake_case__   ,						generator=snake_case__   ,						guidance_scale=7.5   ,						num_inference_steps=50   ,						output_type='numpy' ).images

            _lowerCAmelCase     :				Optional[int]       =							image[0, 253:256, 253:256, -1]

            assert image.shape == (1, 512, 512, 3)
            _lowerCAmelCase     :				Dict       =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

            assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2

