

'''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 lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						List[Any]		,      lowerCAmelCase_	:						str = "cpu"		,      lowerCAmelCase_	:						str = "openai/clip-vit-large-patch14"			)      ->		None:





									'''simple docstring'''
									A__					:       Dict     =device
									A__					:       Optional[Any]     =CLIPTokenizerFast.from_pretrained(lowerCAmelCase_			)
									A__					:       List[Any]     =[0.48145466, 0.4578275, 0.40821073]
									A__					:       Optional[int]     =[0.26862954, 0.26130258, 0.27577711]
									A__					:       Union[str, Any]     =torchvision.transforms.Normalize(self.image_mean		,      self.image_std			)
									A__					:       Dict     =torchvision.transforms.Resize(2_24			)
									A__					:       List[Any]     =torchvision.transforms.CenterCrop(2_24			)


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str			)      ->		int:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.resize(lowerCAmelCase_			)
									A__					:       Union[str, Any]     =self.center_crop(lowerCAmelCase_			)
									A__					:       List[Any]     =self.normalize(lowerCAmelCase_			)
									return images



			def __call__(				self	:						Dict		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						List[Any]=None		,      **lowerCAmelCase_	:						List[str]			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =self.tokenizer(text=lowerCAmelCase_		,      **lowerCAmelCase_			)
									A__					:       Optional[int]     =self.preprocess_img(lowerCAmelCase_			)
									A__					:       List[str]     ={key: value.to(self.device			) for (key, value) in encoding.items()}
									return encoding



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






			def __init__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						int=10		,      lowerCAmelCase_	:						Dict=0.01		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Dict=False		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						Optional[Any]="image"		,      lowerCAmelCase_	:						Optional[Any]=True		,      lowerCAmelCase_	:						Tuple=False		,      lowerCAmelCase_	:						int=False		,      lowerCAmelCase_	:						List[str]=False		,      )      ->		None:





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

									A__					:       Union[str, Any]     =iterations
									A__					:       str     =lr
									A__					:       List[str]     =log
									A__					:       Any     =make_grid
									A__					:       Dict     =return_val
									A__					:       str     =quantize
									A__					:       str     =self.vqgan.decoder.z_shape


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						int=None		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[int]=5		,      lowerCAmelCase_	:						List[str]=True			)      ->		Any:





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


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						int=None			)      ->		List[Any]:





									'''simple docstring'''
									if not (path or img):
															raise ValueError("""Input either path or tensor"""			)
									if img is not None:
															raise NotImplementedError
									A__					:       Optional[Any]     =preprocess(Image.open(lowerCAmelCase_			)		,      target_image_size=2_56			).to(self.device			)
									A__					:       Tuple     =preprocess_vqgan(lowerCAmelCase_			)
									A__					:       Dict     =self.vqgan.encode(lowerCAmelCase_			)
									return z


			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Optional[int]			)      ->		int:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.latent.detach().requires_grad_()
									A__					:       List[str]     =base_latent + transform_vector
									if self.quantize:
															A__					:       List[Any]     =self.vqgan.quantize(lowerCAmelCase_			)
									else:
															A__					:       int     =trans_latent
									return self.vqgan.decode(lowerCAmelCase_			)


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						int=None			)      ->		Union[str, Any]:





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


			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						Any			)      ->		Tuple:





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


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[int]     =torch.randn_like(self.latent		,      requires_grad=lowerCAmelCase_		,      device=self.device			)
									A__					:       str     =torch.optim.Adam([vector]		,      lr=self.lr			)

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


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						Optional[int]		,      lowerCAmelCase_	:						Dict			)      ->		List[str]:





									'''simple docstring'''
									wandb.init(reinit=lowerCAmelCase_		,      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:
															A__					:       str     =Image.open(lowerCAmelCase_			)
															A__					:       Optional[Any]     =image.resize((2_56, 2_56)			)
															wandb.log("""Original Image"""		,      wandb.Image(lowerCAmelCase_			)			)


			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						Union[str, Any]			)      ->		Optional[int]:





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



			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      lowerCAmelCase_	:						Optional[int]=True		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						List[str]=True		,      lowerCAmelCase_	:						List[str]=None		,      )      ->		Optional[Any]:





									'''simple docstring'''
									if image_path:
															A__					:       Union[str, Any]     =self._get_latent(lowerCAmelCase_			)
									else:
															A__					:       str     =torch.randn(self.latent_dim		,      device=self.device			)
									if self.log:
															self._init_logging(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)

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

									A__					:       Dict     =self.vqgan.decode(self.latent			)[0]
									if show_intermediate:
															print("""Original Image"""			)
															show_pil(custom_to_pil(lowerCAmelCase_			)			)

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




'''simple docstring'''



import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp



@slow
@require_flax
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						List[str]			)      ->		List[str]:





									'''simple docstring'''
									return f"gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_			) for s in shape]			)}.npy"


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





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


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						str=0		,      lowerCAmelCase_	:						List[str]=(4, 4, 64, 64)		,      lowerCAmelCase_	:						Optional[Any]=False			)      ->		str:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Any     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return image


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						Optional[int]=False		,      lowerCAmelCase_	:						Dict="CompVis/stable-diffusion-v1-4"			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Any     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       int     ="""bf16""" if fpaa else None

									A__   ,						A__					:       Any     =FlaxUNetaDConditionModel.from_pretrained(
									    lowerCAmelCase_		,      subfolder="""unet"""		,      dtype=lowerCAmelCase_		,      revision=lowerCAmelCase_			)
									return model, params


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						int=0		,      lowerCAmelCase_	:						Optional[Any]=(4, 77, 7_68)		,      lowerCAmelCase_	:						List[str]=False			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Union[str, Any]     =jnp.bfloataa if fpaa else jnp.floataa
									A__					:       Optional[int]     =jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_		,      lowerCAmelCase_			)			)		,      dtype=lowerCAmelCase_			)
									return hidden_states


			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
			        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
			        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
			        [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						str			)      ->		int:





									'''simple docstring'''
									A__   ,						A__					:       Tuple     =self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,      fpaa=lowerCAmelCase_			)
									A__					:       List[Any]     =self.get_latents(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)
									A__					:       Optional[int]     =self.get_encoder_hidden_states(lowerCAmelCase_		,      fpaa=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[int]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       Tuple     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)



			@parameterized.expand(
			    [
			        # fmt: off
			        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
			        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
			        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
			        [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
			        # fmt: on
			    ]			)
			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[int]			)      ->		List[str]:





									'''simple docstring'''
									A__   ,						A__					:       List[str]     =self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,      fpaa=lowerCAmelCase_			)
									A__					:       Union[str, Any]     =self.get_latents(lowerCAmelCase_		,      shape=(4, 4, 96, 96)		,      fpaa=lowerCAmelCase_			)
									A__					:       Dict     =self.get_encoder_hidden_states(lowerCAmelCase_		,      shape=(4, 77, 10_24)		,      fpaa=lowerCAmelCase_			)

									A__					:       Optional[Any]     =model.apply(
									    {"""params""": params}		,      lowerCAmelCase_		,      jnp.array(lowerCAmelCase_		,      dtype=jnp.intaa			)		,      encoder_hidden_states=lowerCAmelCase_		,      ).sample

									assert sample.shape == latents.shape

									A__					:       Optional[Any]     =jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())			)		,      dtype=jnp.floataa			)
									A__					:       List[Any]     =jnp.array(lowerCAmelCase_		,      dtype=jnp.floataa			)

									# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
									assert jnp.allclose(lowerCAmelCase_		,      lowerCAmelCase_		,      atol=1e-2			)

