






"""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       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			str						,						lowerCAmelCase				:			str = "cpu"						,						lowerCAmelCase				:			str = "openai/clip-vit-large-patch14"					):
										lowerCAmelCase  =				device
										lowerCAmelCase  =				CLIPTokenizerFast.from_pretrained(__lowerCamelCase					)
										lowerCAmelCase  =				[0.4814_5466, 0.457_8275, 0.4082_1073]
										lowerCAmelCase  =				[0.2686_2954, 0.2613_0258, 0.2757_7711]
										lowerCAmelCase  =				torchvision.transforms.Normalize(self.image_mean						,						self.image_std					)
										lowerCAmelCase  =				torchvision.transforms.Resize(224					)
										lowerCAmelCase  =				torchvision.transforms.CenterCrop(224					)


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]					):
										lowerCAmelCase  =				self.resize(__lowerCamelCase					)
										lowerCAmelCase  =				self.center_crop(__lowerCamelCase					)
										lowerCAmelCase  =				self.normalize(__lowerCamelCase					)
										return images




						def __call__(							self				:			List[str]						,						lowerCAmelCase				:			int=None						,						lowerCAmelCase				:			int=None						,						**lowerCAmelCase				:			Optional[int]					):
										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       SCREAMING_SNAKE_CASE__					(					nn.Module					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			List[str]=10						,						lowerCAmelCase				:			Tuple=0.01						,						lowerCAmelCase				:			Dict=None						,						lowerCAmelCase				:			List[Any]=None						,						lowerCAmelCase				:			str=None						,						lowerCAmelCase				:			str=None						,						lowerCAmelCase				:			List[str]=None						,						lowerCAmelCase				:			int=None						,						lowerCAmelCase				:			Dict=False						,						lowerCAmelCase				:			Optional[Any]=True						,						lowerCAmelCase				:			str="image"						,						lowerCAmelCase				:			Optional[Any]=True						,						lowerCAmelCase				:			List[str]=False						,						lowerCAmelCase				:			str=False						,						lowerCAmelCase				:			List[str]=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   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[Any]=None						,						lowerCAmelCase				:			Dict=None						,						lowerCAmelCase				:			int=5						,						lowerCAmelCase				:			str=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   __lowercase (							self				:			int						,						lowerCAmelCase				:			List[str]=None						,						lowerCAmelCase				:			Dict=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=256					).to(self.device					)
										lowerCAmelCase  =				preprocess_vqgan(__lowerCamelCase					)
										lowerCAmelCase  =				self.vqgan.encode(__lowerCamelCase					)
										return z


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				self.latent.detach().requires_grad_()
										lowerCAmelCase  =				base_latent + transform_vector
										if self.quantize:
														lowerCAmelCase  =				self.vqgan.quantize(__lowerCamelCase					)
										else:
														lowerCAmelCase  =				trans_latent
										return self.vqgan.decode(__lowerCamelCase					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[Any]=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   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Any					):
										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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Dict					):
										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   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Union[str, Any]					):
										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((256, 256)					)
														wandb.log("""Original Image"""						,						wandb.Image(__lowerCamelCase					)					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[Any]					):
										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  =				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   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			List[Any]=None						,						lowerCAmelCase				:			Optional[Any]=None						,						lowerCAmelCase				:			Union[str, Any]=True						,						lowerCAmelCase				:			List[Any]=False						,						lowerCAmelCase				:			int=True						,						lowerCAmelCase				:			List[str]=True						,						lowerCAmelCase				:			Optional[int]=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"""







from __future__ import annotations





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowerCAmelCase ,						lowerCAmelCase  =				array[indexa], array[indexa]





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								for i in range(snake_case__      ,       low + middle ):
												comp_and_swap(snake_case__      ,       snake_case__      ,       i + middle      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )
								bitonic_merge(snake_case__      ,       low + middle      ,       snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : list[int]      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  None:


				'''simple docstring'''






				if length > 1:
								lowerCAmelCase  =				int(length / 2 )
								bitonic_sort(snake_case__      ,       snake_case__      ,       snake_case__      ,       1 )
								bitonic_sort(snake_case__      ,       low + middle      ,       snake_case__      ,       0 )
								bitonic_merge(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		a      =							input('Enter numbers separated by a comma:\n').strip()
		a      =							[int(item.strip()) for item in user_input.split(',')]

		bitonic_sort(unsorted, 0, len(unsorted), 1)
		print('\nSorted array in ascending order is: ', end='')
		print(*unsorted, sep=', ')

		bitonic_merge(unsorted, 0, len(unsorted), 0)
		print('Sorted array in descending order is: ', end='')
		print(*unsorted, sep=', ')





