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

									"""simple docstring"""


									lowerCamelCase					:			Dict									= device
									lowerCamelCase					:			Any									= CLIPTokenizerFast.from_pretrained(A      )
									lowerCamelCase					:			Union[str, Any]									= [0.4814_5466, 0.457_8275, 0.4082_1073]
									lowerCamelCase					:			int									= [0.2686_2954, 0.2613_0258, 0.2757_7711]
									lowerCamelCase					:			Optional[int]									= torchvision.transforms.Normalize(self.image_mean,    self.image_std      )
									lowerCamelCase					:			str									= torchvision.transforms.Resize(224      )
									lowerCamelCase					:			Union[str, Any]									= torchvision.transforms.CenterCrop(224      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.resize(A      )
									lowerCamelCase					:			List[str]									= self.center_crop(A      )
									lowerCamelCase					:			List[str]									= self.normalize(A      )
									return images




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

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.tokenizer(text=A,    **A      )
									lowerCamelCase					:			List[str]									= self.preprocess_img(A      )
									lowerCamelCase					:			Any									= {key: value.to(self.device      ) for (key, value) in encoding.items()}
									return encoding





class     __snake_case		(   nn.Module):
					def __init__(     self,    A=10,    A=0.01,    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,    ):

									"""simple docstring"""


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

									lowerCamelCase					:			Any									= iterations
									lowerCamelCase					:			List[str]									= lr
									lowerCamelCase					:			int									= log
									lowerCamelCase					:			Optional[int]									= make_grid
									lowerCamelCase					:			Optional[Any]									= return_val
									lowerCamelCase					:			str									= quantize
									lowerCamelCase					:			int									= self.vqgan.decoder.z_shape
					def     UpperCAmelCase_							(     self,    A=None,    A=None,    A=5,    A=True      ):

									"""simple docstring"""


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

									"""simple docstring"""


									if not (path or img):
													raise ValueError('Input either path or tensor'      )
									if img is not None:
													raise NotImplementedError
									lowerCamelCase					:			Tuple									= preprocess(Image.open(A      ),    target_image_size=256      ).to(self.device      )
									lowerCamelCase					:			int									= preprocess_vqgan(A      )
									lowerCamelCase					:			Optional[int]									= self.vqgan.encode(A      )
									return z
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.latent.detach().requires_grad_()
									lowerCamelCase					:			int									= base_latent + transform_vector
									if self.quantize:
													lowerCamelCase					:			int									= self.vqgan.quantize(A      )
									else:
													lowerCamelCase					:			int									= trans_latent
									return self.vqgan.decode(A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A=None      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.clip_preprocessor(text=A,    images=A,    return_tensors='pt',    padding=A      )
									lowerCamelCase					:			Dict									= self.clip(**A      )
									lowerCamelCase					:			Optional[int]									= clip_outputs.logits_per_image
									if weights is not None:
													lowerCamelCase					:			int									= similarity_logits * weights
									return similarity_logits.sum()
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self._get_clip_similarity(pos_prompts['prompts'],    A,    weights=(1 / pos_prompts['weights'])      )
									if neg_prompts:
													lowerCamelCase					:			List[Any]									= self._get_clip_similarity(neg_prompts['prompts'],    A,    weights=neg_prompts['weights']      )
									else:
													lowerCamelCase					:			Optional[Any]									= torch.tensor([1],    device=self.device      )
									lowerCamelCase					:			int									= -torch.log(A      ) + torch.log(A      )
									return loss
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= torch.randn_like(self.latent,    requires_grad=A,    device=self.device      )
									lowerCamelCase					:			Tuple									= torch.optim.Adam([vector],    lr=self.lr      )

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

									"""simple docstring"""


									wandb.init(reinit=A,    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					:			List[str]									= Image.open(A      )
													lowerCamelCase					:			str									= image.resize((256, 256)      )
													wandb.log('Original Image',    wandb.Image(A      )      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


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




					def     UpperCAmelCase_							(     self,    A,    A=None,    A=None,    A=True,    A=False,    A=True,    A=True,    A=None,    ):

									"""simple docstring"""


									if image_path:
													lowerCamelCase					:			Dict									= self._get_latent(A      )
									else:
													lowerCamelCase					:			List[str]									= torch.randn(self.latent_dim,    device=self.device      )
									if self.log:
													self._init_logging(A,    A,    A      )

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

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

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


'''simple docstring'''






from manim import *




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

									lowerCamelCase					:			List[str]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			List[Any]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			str									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Any									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Dict									= VGroup(A,    A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			str									= Text('CPU',    font_size=24      )
									lowerCamelCase					:			int									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									cpu.move_to([-2.5, -0.5, 0]      )
									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(1      )]
									lowerCamelCase					:			Union[str, Any]									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Optional[Any]									= Text('GPU',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									gpu.align_to(A,    A      )
									gpu.set_x(gpu.get_x() - 1      )

									self.add(A      )

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(6      )]
									lowerCamelCase					:			Optional[Any]									= VGroup(*A      ).arrange(A,    buff=0      )

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

									self.play(
									    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    )

									lowerCamelCase					:			str									= MarkupText(
									    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',    font_size=24,    )

									lowerCamelCase					:			Any									= Square(side_length=2.2      )
									key.move_to([-5, 2, 0]      )

									lowerCamelCase					:			Tuple									= MarkupText(
									    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',    font_size=18,    )

									key_text.move_to([-5, 2.4, 0]      )

									step_a.move_to([2, 2, 0]      )
									self.play(Write(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

									lowerCamelCase					:			str									= []
									lowerCamelCase					:			Optional[int]									= []
									lowerCamelCase					:			Optional[Any]									= []
									for i, rect in enumerate(A      ):
													lowerCamelCase					:			List[str]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0.0      ).set_fill(A,    opacity=0.7      )
													cpu_target.move_to(A      )
													cpu_target.generate_target()
													lowerCamelCase					:			int									= 0.46 / 4
													lowerCamelCase					:			Optional[int]									= 0.46 / 3

													if i == 0:
																	cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),    buff=0.02,    direction=A      )
																	cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
													elif i == 3:
																	cpu_target.target.next_to(cpu_targs[0].target,    direction=A,    buff=0.0      )
													else:
																	cpu_target.target.next_to(cpu_targs[i - 1].target,    direction=A,    buff=0.0      )
													cpu_targs.append(A      )

													first_animations.append(rect.animate(run_time=0.5      ).set_stroke(A      )      )
													second_animations.append(MoveToTarget(A,    run_time=1.5      )      )

									self.play(*A      )
									self.play(*A      )

									self.wait()
