



"""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				,       lowerCamelCase__ = "cpu"				,       lowerCamelCase__ = "openai/clip-vit-large-patch14"   ):
									_lowerCamelCase        =				device
									_lowerCamelCase        =				CLIPTokenizerFast.from_pretrained(lowerCamelCase__   )
									_lowerCamelCase        =				[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]
									_lowerCamelCase        =				[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]
									_lowerCamelCase        =				torchvision.transforms.Normalize(self.image_mean				,       self.image_std   )
									_lowerCamelCase        =				torchvision.transforms.Resize(2_2_4   )
									_lowerCamelCase        =				torchvision.transforms.CenterCrop(2_2_4   )

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.resize(lowerCamelCase__   )
									_lowerCamelCase        =				self.center_crop(lowerCamelCase__   )
									_lowerCamelCase        =				self.normalize(lowerCamelCase__   )
									return images





					def __call__(    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       **lowerCamelCase__   ):
									_lowerCamelCase        =				self.tokenizer(text=lowerCamelCase__				,       **lowerCamelCase__   )
									_lowerCamelCase        =				self.preprocess_img(lowerCamelCase__   )
									_lowerCamelCase        =				{key: value.to(self.device   ) for (key, value) in encoding.items()}
									return encoding


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__=1_0				,       lowerCamelCase__=0.0_1				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__="image"				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       lowerCamelCase__=False				,       ):
									super().__init__()
									_lowerCamelCase        =				None
									_lowerCamelCase        =				device if device else get_device()
									if vqgan:
													_lowerCamelCase        =				vqgan
									else:
													_lowerCamelCase        =				load_vqgan(self.device				,       conf_path=lowerCamelCase__				,       ckpt_path=lowerCamelCase__   )
									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 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=5				,       lowerCamelCase__=True   ):
									_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(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?)'''   )
									_lowerCamelCase        =				total_duration / len(lowerCamelCase__   )
									_lowerCamelCase        =				[frame_duration] * len(lowerCamelCase__   )
									if extend_frames:
													_lowerCamelCase        =				1.5
													_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None   ):
									if not (path or img):
													raise ValueError('''Input either path or tensor'''   )
									if img is not None:
													raise NotImplementedError
									_lowerCamelCase        =				preprocess(Image.open(lowerCamelCase__   )				,       target_image_size=2_5_6   ).to(self.device   )
									_lowerCamelCase        =				preprocess_vqgan(lowerCamelCase__   )
									_lowerCamelCase  ,     *_lowerCamelCase        =				self.vqgan.encode(lowerCamelCase__   )
									return z

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.latent.detach().requires_grad_()
									_lowerCamelCase        =				base_latent + transform_vector
									if self.quantize:
													_lowerCamelCase  ,     *_lowerCamelCase        =				self.vqgan.quantize(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				trans_latent
									return self.vqgan.decode(lowerCamelCase__   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=None   ):
									_lowerCamelCase        =				self.clip_preprocessor(text=lowerCamelCase__				,       images=lowerCamelCase__				,       return_tensors='''pt'''				,       padding=lowerCamelCase__   )
									_lowerCamelCase        =				self.clip(**lowerCamelCase__   )
									_lowerCamelCase        =				clip_outputs.logits_per_image
									if weights is not None:
													_lowerCamelCase        =				similarity_logits * weights
									return similarity_logits.sum()

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self._get_clip_similarity(pos_prompts['''prompts''']				,       lowerCamelCase__				,       weights=(1 / pos_prompts['''weights'''])   )
									if neg_prompts:
													_lowerCamelCase        =				self._get_clip_similarity(neg_prompts['''prompts''']				,       lowerCamelCase__				,       weights=neg_prompts['''weights''']   )
									else:
													_lowerCamelCase        =				torch.tensor([1]				,       device=self.device   )
									_lowerCamelCase        =				-torch.log(lowerCamelCase__   ) + torch.log(lowerCamelCase__   )
									return loss

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				torch.randn_like(self.latent				,       requires_grad=lowerCamelCase__				,       device=self.device   )
									_lowerCamelCase        =				torch.optim.Adam([vector]				,       lr=self.lr   )

									for i in range(self.iterations   ):
													optim.zero_grad()
													_lowerCamelCase        =				self._add_vector(lowerCamelCase__   )
													_lowerCamelCase        =				loop_post_process(lowerCamelCase__   )
													_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									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:
													_lowerCamelCase        =				Image.open(lowerCamelCase__   )
													_lowerCamelCase        =				image.resize((2_5_6, 2_5_6)   )
													wandb.log('''Original Image'''				,       wandb.Image(lowerCamelCase__   )   )

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





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=None				,       ):
									if image_path:
													_lowerCamelCase        =				self._get_latent(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				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."
									_lowerCamelCase        =				self.process_prompts(lowerCamelCase__   )
									_lowerCamelCase        =				self.process_prompts(lowerCamelCase__   )
									if save_final and save_path is None:
													_lowerCamelCase        =				os.path.join('''./outputs/'''				,       '''_'''.join(pos_prompts['''prompts''']   )   )
									if not os.path.exists(lowerCamelCase__   ):
													os.makedirs(lowerCamelCase__   )
									else:
													_lowerCamelCase        =				save_path + '''_''' + get_timestamp()
													os.makedirs(lowerCamelCase__   )
									_lowerCamelCase        =				save_path

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

									_lowerCamelCase        =				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"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def        lowerCAmelCase_(   lowercase_     :							List[Any]							)	->       Optional[Any]:
				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase_							)




def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       List[str]:
				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				_lowerCamelCase        =				terminalreporter.config.getoption('''--make-reports'''							)
				if make_reports:
								pytest_terminal_summary_main(lowercase_						,   id=lowercase_							)
