





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      Any:






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




   @property
   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      1
       UpperCamelCase__							:				int          =      3
       UpperCamelCase__							:				Union[str, Any]          =      (32, 32)

       UpperCamelCase__							:				str          =      floats_tensor((batch_size, num_channels) + sizes,		rng=random.Random(0							)							).to(__magic_name__							)
       return image




   @property
   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Tuple          =      UNetaDConditionModel(
           block_out_channels=(32, 64),		layers_per_block=2,		sample_size=32,		in_channels=4,		out_channels=4,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),		cross_attention_dim=32,		)
       return model




   @property
   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				int          =      AutoencoderKL(
           block_out_channels=[32, 64],		in_channels=3,		out_channels=3,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],		latent_channels=4,		)
       return model




   @property
   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       torch.manual_seed(0							)
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTextConfig(
           bos_token_id=0,		eos_token_id=2,		hidden_size=32,		intermediate_size=37,		layer_norm_eps=1E-05,		num_attention_heads=4,		num_hidden_layers=5,		pad_token_id=1,		vocab_size=1000,		)
       return CLIPTextModel(__magic_name__							)




   @property
   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       def extract(*__magic_name__,		**__magic_name__							):






           class     lowercase__       :

              '''simple docstring'''


              def __init__(    self							) ->      List[str]:






                  """simple docstring"""
                  UpperCamelCase__							:				Optional[int]          =      torch.ones([0]							)




              def       UpperCamelCase__  (    self,		__magic_name__							) ->      Union[str, Any]:






                  """simple docstring"""
                  self.pixel_values.to(__magic_name__							)
                  return self




           return Out()

       return extract




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:				Any          =      self.dummy_cond_unet
       UpperCamelCase__							:				Optional[int]          =      DDIMScheduler(
           beta_start=0.0_0085,		beta_end=0.012,		beta_schedule='''scaled_linear''',		clip_sample=__magic_name__,		set_alpha_to_one=__magic_name__,		)

       UpperCamelCase__							:				List[Any]          =      self.dummy_vae
       UpperCamelCase__							:				str          =      self.dummy_text_encoder
       UpperCamelCase__							:				Dict          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__							:				Tuple          =      StableDiffusionPipeline(
           unet=__magic_name__,		scheduler=__magic_name__,		vae=__magic_name__,		text_encoder=__magic_name__,		tokenizer=__magic_name__,		safety_checker=__magic_name__,		feature_extractor=self.dummy_extractor,		)
       UpperCamelCase__							:				List[Any]          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				Any          =      '''A painting of a squirrel eating a burger'''

       UpperCamelCase__							:				Any          =      torch.Generator(device=__magic_name__							).manual_seed(0							)
       UpperCamelCase__							:				Any          =      sd_pipe([prompt],		generator=__magic_name__,		guidance_scale=6.0,		num_inference_steps=2,		output_type='''np'''							)
       UpperCamelCase__							:				Union[str, Any]          =      output.images

       UpperCamelCase__							:				Dict          =      torch.Generator(device=__magic_name__							).manual_seed(0							)
       UpperCamelCase__							:				Tuple          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=6.0,		num_inference_steps=2,		output_type='''np''',		return_dict=__magic_name__,		)[0]

       UpperCamelCase__							:				Any          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				List[Any]          =      image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__							:				Tuple          =      np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]							)

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




   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__							:				Union[str, Any]          =      self.dummy_cond_unet
       UpperCamelCase__							:				List[Any]          =      PNDMScheduler(skip_prk_steps=__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      self.dummy_vae
       UpperCamelCase__							:				int          =      self.dummy_text_encoder
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__							:				Union[str, Any]          =      StableDiffusionPipeline(
           unet=__magic_name__,		scheduler=__magic_name__,		vae=__magic_name__,		text_encoder=__magic_name__,		tokenizer=__magic_name__,		safety_checker=__magic_name__,		feature_extractor=self.dummy_extractor,		)
       UpperCamelCase__							:				Tuple          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				List[Any]          =      '''A painting of a squirrel eating a burger'''
       UpperCamelCase__							:				int          =      torch.Generator(device=__magic_name__							).manual_seed(0							)
       UpperCamelCase__							:				List[str]          =      sd_pipe([prompt],		generator=__magic_name__,		guidance_scale=6.0,		num_inference_steps=2,		output_type='''np'''							)

       UpperCamelCase__							:				str          =      output.images

       UpperCamelCase__							:				str          =      torch.Generator(device=__magic_name__							).manual_seed(0							)
       UpperCamelCase__							:				List[str]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=6.0,		num_inference_steps=2,		output_type='''np''',		return_dict=__magic_name__,		)[0]

       UpperCamelCase__							:				Union[str, Any]          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Optional[int]          =      image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase__							:				Tuple          =      np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]							)

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




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      StableDiffusionPipeline.from_pretrained(
           '''hf-internal-testing/tiny-stable-diffusion-lms-pipe''',		safety_checker=__magic_name__							)
       assert isinstance(__magic_name__,		__magic_name__							)
       assert isinstance(pipe.scheduler,		__magic_name__							)
       assert pipe.safety_checker is None

       UpperCamelCase__							:				Tuple          =      pipe('''example prompt''',		num_inference_steps=2							).images[0]
       assert image is not None

       # check that there's no error when saving a pipeline with one of the models being None
       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:				List[str]          =      StableDiffusionPipeline.from_pretrained(__magic_name__							)

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       UpperCamelCase__							:				List[str]          =      pipe('''example prompt''',		num_inference_steps=2							).images[0]
       assert image is not None




   @unittest.skipIf(torch_device != '''cuda''',		'''This test requires a GPU'''							)
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.dummy_cond_unet
       UpperCamelCase__							:				Any          =      PNDMScheduler(skip_prk_steps=__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      self.dummy_vae
       UpperCamelCase__							:				str          =      self.dummy_text_encoder
       UpperCamelCase__							:				List[Any]          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

       # put models in fp16
       UpperCamelCase__							:				List[str]          =      unet.half()
       UpperCamelCase__							:				List[str]          =      vae.half()
       UpperCamelCase__							:				Optional[int]          =      bert.half()

       # make sure here that pndm scheduler skips prk
       UpperCamelCase__							:				Optional[int]          =      StableDiffusionPipeline(
           unet=__magic_name__,		scheduler=__magic_name__,		vae=__magic_name__,		text_encoder=__magic_name__,		tokenizer=__magic_name__,		safety_checker=__magic_name__,		feature_extractor=self.dummy_extractor,		)
       UpperCamelCase__							:				Optional[int]          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				List[Any]          =      '''A painting of a squirrel eating a burger'''
       UpperCamelCase__							:				Any          =      sd_pipe([prompt],		num_inference_steps=2,		output_type='''np'''							).images

       assert image.shape == (1, 64, 64, 3)






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

   '''simple docstring'''


   def       UpperCamelCase__  (    self							) ->      int:






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




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''',		safety_checker=__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
       UpperCamelCase__							:				Any          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				Any          =      (
           '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
           ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
           ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
           ''' children from bahnhof zoo, detailed '''
       )
       UpperCamelCase__							:				List[Any]          =      4003660346
       UpperCamelCase__							:				Dict          =      7

       # without safety guidance (sld_guidance_scale = 0)
       UpperCamelCase__							:				str          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=0,		)

       UpperCamelCase__							:				List[Any]          =      output.images
       UpperCamelCase__							:				Tuple          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Tuple          =      [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

       assert image.shape == (1, 512, 512, 3)

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

       # without safety guidance (strong configuration)
       UpperCamelCase__							:				List[str]          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=2000,		sld_warmup_steps=7,		sld_threshold=0.025,		sld_momentum_scale=0.5,		sld_mom_beta=0.7,		)

       UpperCamelCase__							:				str          =      output.images
       UpperCamelCase__							:				int          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Tuple          =      [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

       assert image.shape == (1, 512, 512, 3)

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




   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''',		safety_checker=__magic_name__							)
       UpperCamelCase__							:				List[str]          =      LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
       UpperCamelCase__							:				Dict          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				List[str]          =      '''padme amidala taking a bath artwork, safe for work, no nudity'''
       UpperCamelCase__							:				int          =      2734971755
       UpperCamelCase__							:				List[Any]          =      7

       UpperCamelCase__							:				str          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=0,		)

       UpperCamelCase__							:				Any          =      output.images
       UpperCamelCase__							:				Any          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Optional[int]          =      [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

       assert image.shape == (1, 512, 512, 3)

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

       UpperCamelCase__							:				Dict          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				str          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=2000,		sld_warmup_steps=7,		sld_threshold=0.025,		sld_momentum_scale=0.5,		sld_mom_beta=0.7,		)

       UpperCamelCase__							:				Any          =      output.images
       UpperCamelCase__							:				Optional[Any]          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Union[str, Any]          =      [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

       assert image.shape == (1, 512, 512, 3)

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




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							)
       UpperCamelCase__							:				Dict          =      sd_pipe.to(__magic_name__							)
       sd_pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      (
           '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
           ''' leyendecker'''
       )
       UpperCamelCase__							:				Any          =      1044355234
       UpperCamelCase__							:				Optional[Any]          =      12

       UpperCamelCase__							:				List[Any]          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=0,		)

       UpperCamelCase__							:				Any          =      output.images
       UpperCamelCase__							:				Union[str, Any]          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				List[Any]          =      np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]							)

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-7

       UpperCamelCase__							:				List[str]          =      torch.manual_seed(__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      sd_pipe(
           [prompt],		generator=__magic_name__,		guidance_scale=__magic_name__,		num_inference_steps=50,		output_type='''np''',		width=512,		height=512,		sld_guidance_scale=2000,		sld_warmup_steps=7,		sld_threshold=0.025,		sld_momentum_scale=0.5,		sld_mom_beta=0.7,		)

       UpperCamelCase__							:				int          =      output.images
       UpperCamelCase__							:				Dict          =      image[0, -3:, -3:, -1]
       UpperCamelCase__							:				Dict          =      np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]							)
       assert image.shape == (1, 512, 512, 3)

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








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__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "cpu",		__magic_name__ = "openai/clip-vit-large-patch14"							) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      device
       UpperCamelCase__							:				Union[str, Any]          =      CLIPTokenizerFast.from_pretrained(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      [0.4814_5466, 0.457_8275, 0.4082_1073]
       UpperCamelCase__							:				Union[str, Any]          =      [0.2686_2954, 0.2613_0258, 0.2757_7711]
       UpperCamelCase__							:				Dict          =      torchvision.transforms.Normalize(self.image_mean,		self.image_std							)
       UpperCamelCase__							:				List[str]          =      torchvision.transforms.Resize(224							)
       UpperCamelCase__							:				Union[str, Any]          =      torchvision.transforms.CenterCrop(224							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.resize(__magic_name__							)
       UpperCamelCase__							:				Dict          =      self.center_crop(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      self.normalize(__magic_name__							)
       return images




   def __call__(    self,		__magic_name__=None,		__magic_name__=None,		**__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.tokenizer(text=__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      self.preprocess_img(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      {key: value.to(self.device							) for (key, value) in encoding.items()}
       return encoding






class     lowercase__       (	nn.Module				):

   '''simple docstring'''


   def __init__(    self,		__magic_name__=10,		__magic_name__=0.01,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=None,		__magic_name__=False,		__magic_name__=True,		__magic_name__="image",		__magic_name__=True,		__magic_name__=False,		__magic_name__=False,		__magic_name__=False,		) ->      None:






       """simple docstring"""
       super().__init__()
       UpperCamelCase__							:				Dict          =      None
       UpperCamelCase__							:				Tuple          =      device if device else get_device()
       if vqgan:
           UpperCamelCase__							:				Union[str, Any]          =      vqgan
       else:
           UpperCamelCase__							:				Any          =      load_vqgan(self.device,		conf_path=__magic_name__,		ckpt_path=__magic_name__							)
       self.vqgan.eval()
       if clip:
           UpperCamelCase__							:				Optional[Any]          =      clip
       else:
           UpperCamelCase__							:				Any          =      CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''							)
       self.clip.to(self.device							)
       UpperCamelCase__							:				str          =      ProcessorGradientFlow(device=self.device							)

       UpperCamelCase__							:				Union[str, Any]          =      iterations
       UpperCamelCase__							:				Tuple          =      lr
       UpperCamelCase__							:				Optional[int]          =      log
       UpperCamelCase__							:				List[Any]          =      make_grid
       UpperCamelCase__							:				Optional[Any]          =      return_val
       UpperCamelCase__							:				str          =      quantize
       UpperCamelCase__							:				int          =      self.vqgan.decoder.z_shape




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None,		__magic_name__=5,		__magic_name__=True							) ->      Optional[Any]:






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




   def       UpperCamelCase__  (    self,		__magic_name__=None,		__magic_name__=None							) ->      Any:






       """simple docstring"""
       if not (path or img):
           raise ValueError('''Input either path or tensor'''							)
       if img is not None:
           raise NotImplementedError
       UpperCamelCase__							:				List[Any]          =      preprocess(Image.open(__magic_name__							),		target_image_size=256							).to(self.device							)
       UpperCamelCase__							:				str          =      preprocess_vqgan(__magic_name__							)
       UpperCamelCase__   ,*UpperCamelCase__							:				Union[str, Any]          =      self.vqgan.encode(__magic_name__							)
       return z




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.latent.detach().requires_grad_()
       UpperCamelCase__							:				Any          =      base_latent + transform_vector
       if self.quantize:
           UpperCamelCase__   ,*UpperCamelCase__							:				int          =      self.vqgan.quantize(__magic_name__							)
       else:
           UpperCamelCase__							:				Optional[int]          =      trans_latent
       return self.vqgan.decode(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__=None							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.clip_preprocessor(text=__magic_name__,		images=__magic_name__,		return_tensors='''pt''',		padding=__magic_name__							)
       UpperCamelCase__							:				Optional[int]          =      self.clip(**__magic_name__							)
       UpperCamelCase__							:				Tuple          =      clip_outputs.logits_per_image
       if weights is not None:
           UpperCamelCase__							:				List[Any]          =      similarity_logits * weights
       return similarity_logits.sum()




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self._get_clip_similarity(pos_prompts['''prompts'''],		__magic_name__,		weights=(1 / pos_prompts['''weights'''])							)
       if neg_prompts:
           UpperCamelCase__							:				Tuple          =      self._get_clip_similarity(neg_prompts['''prompts'''],		__magic_name__,		weights=neg_prompts['''weights''']							)
       else:
           UpperCamelCase__							:				Optional[int]          =      torch.tensor([1],		device=self.device							)
       UpperCamelCase__							:				Tuple          =      -torch.log(__magic_name__							) + torch.log(__magic_name__							)
       return loss




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      torch.randn_like(self.latent,		requires_grad=__magic_name__,		device=self.device							)
       UpperCamelCase__							:				Optional[int]          =      torch.optim.Adam([vector],		lr=self.lr							)

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       wandb.init(reinit=__magic_name__,		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:
           UpperCamelCase__							:				List[str]          =      Image.open(__magic_name__							)
           UpperCamelCase__							:				List[Any]          =      image.resize((256, 256)							)
           wandb.log('''Original Image''',		wandb.Image(__magic_name__							)							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None,		__magic_name__=None,		__magic_name__=True,		__magic_name__=False,		__magic_name__=True,		__magic_name__=True,		__magic_name__=None,		) ->      str:






       """simple docstring"""
       if image_path:
           UpperCamelCase__							:				Union[str, Any]          =      self._get_latent(__magic_name__							)
       else:
           UpperCamelCase__							:				Dict          =      torch.randn(self.latent_dim,		device=self.device							)
       if self.log:
           self._init_logging(__magic_name__,		__magic_name__,		__magic_name__							)

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

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

       UpperCamelCase__							:				Optional[Any]          =      loop_post_process(__magic_name__							)
       for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__,		__magic_name__,		__magic_name__							)							):
           if show_intermediate:
               show_pil(__magic_name__							)
           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(__magic_name__							)}							)
       if show_final:
           show_pil(__magic_name__							)
       if save_final:
           transformed_img.save(os.path.join(self.save_path,		f"iter_{iter:03d}_final.png"							)							)

