


from __future__ import annotations





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			list[list[int]]:







			'''simple docstring'''
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       []
			_UpperCAmelCase			=       0
			_UpperCAmelCase			=       sum(_UpperCAmelCase   )
			create_state_space_tree(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   )
			return result





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       int							,       _UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       list[list[int]]							,       _UpperCAmelCase    :       int							,       )    ->			None:







			'''simple docstring'''



			if sum(_UpperCAmelCase   ) > max_sum or (remaining_nums_sum + sum(_UpperCAmelCase   )) < max_sum:
						return
			if sum(_UpperCAmelCase   ) == max_sum:
						result.append(_UpperCAmelCase   )
						return
			for index in range(_UpperCAmelCase							,       len(_UpperCAmelCase   )   ):
						create_state_space_tree(
						    _UpperCAmelCase							,       _UpperCAmelCase							,       index + 1							,       [*path, nums[index]]							,       _UpperCAmelCase							,       remaining_nums_sum - nums[index]							,       )
UpperCAmelCase__       =	[3, 34, 4, 12, 5, 2]
UpperCAmelCase__       =	9
UpperCAmelCase__       =	generate_sum_of_subsets_soln(nums, max_sum)

print(*result)





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   :



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



						"""simple docstring"""






						_UpperCAmelCase			=       device
						_UpperCAmelCase			=       CLIPTokenizerFast.from_pretrained(A)
						_UpperCAmelCase			=       [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]
						_UpperCAmelCase			=       [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]
						_UpperCAmelCase			=       torchvision.transforms.Normalize(self.image_mean			,		self.image_std)
						_UpperCAmelCase			=       torchvision.transforms.Resize(2_24)
						_UpperCAmelCase			=       torchvision.transforms.CenterCrop(2_24)



			def    _lowerCamelCase	(						self       :  str			,		A       :  Any)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.resize(A)
						_UpperCAmelCase			=       self.center_crop(A)
						_UpperCAmelCase			=       self.normalize(A)
						return images



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(text=A			,		**A)
						_UpperCAmelCase			=       self.preprocess_img(A)
						_UpperCAmelCase			=       {key: value.to(self.device) for (key, value) in encoding.items()}
						return encoding







class       __lowerCAmelCase   (      nn.Module						):



			def __init__(						self       :  List[Any]			,		A       :  Any=10			,		A       :  List[Any]=0.0_1			,		A       :  Optional[int]=None			,		A       :  int=None			,		A       :  Dict=None			,		A       :  Tuple=None			,		A       :  str=None			,		A       :  Dict=None			,		A       :  Union[str, Any]=False			,		A       :  Any=True			,		A       :  Any="image"			,		A       :  Tuple=True			,		A       :  List[Any]=False			,		A       :  int=False			,		A       :  int=False			,		)       ->   None:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       device if device else get_device()
						if vqgan:
									_UpperCAmelCase			=       vqgan
						else:
									_UpperCAmelCase			=       load_vqgan(self.device			,		conf_path=A			,		ckpt_path=A)
						self.vqgan.eval()
						if clip:
									_UpperCAmelCase			=       clip
						else:
									_UpperCAmelCase			=       CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
						self.clip.to(self.device)
						_UpperCAmelCase			=       ProcessorGradientFlow(device=self.device)

						_UpperCAmelCase			=       iterations
						_UpperCAmelCase			=       lr
						_UpperCAmelCase			=       log
						_UpperCAmelCase			=       make_grid
						_UpperCAmelCase			=       return_val
						_UpperCAmelCase			=       quantize
						_UpperCAmelCase			=       self.vqgan.decoder.z_shape



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  int=None			,		A       :  Union[str, Any]=None			,		A       :  Dict=5			,		A       :  Optional[Any]=True)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       []
						if output_path is None:
									_UpperCAmelCase			=       './animation.gif'
						if input_path is None:
									_UpperCAmelCase			=       self.save_path
						_UpperCAmelCase			=       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?)')
						_UpperCAmelCase			=       total_duration / len(A)
						_UpperCAmelCase			=       [frame_duration] * len(A)
						if extend_frames:
									_UpperCAmelCase			=       1.5
									_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]=None			,		A       :  Optional[int]=None)       ->   int:



						"""simple docstring"""






						if not (path or img):
									raise ValueError('Input either path or tensor')
						if img is not None:
									raise NotImplementedError
						_UpperCAmelCase			=       preprocess(Image.open(A)			,		target_image_size=2_56).to(self.device)
						_UpperCAmelCase			=       preprocess_vqgan(A)
						_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.encode(A)
						return z



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.latent.detach().requires_grad_()
						_UpperCAmelCase			=       base_latent + transform_vector
						if self.quantize:
									_UpperCAmelCase	,					*_UpperCAmelCase			=       self.vqgan.quantize(A)
						else:
									_UpperCAmelCase			=       trans_latent
						return self.vqgan.decode(A)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Dict			,		A       :  Dict			,		A       :  Optional[Any]=None)       ->   Any:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Dict			,		A       :  int			,		A       :  Tuple)       ->   str:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch.randn_like(self.latent			,		requires_grad=A			,		device=self.device)
						_UpperCAmelCase			=       torch.optim.Adam([vector]			,		lr=self.lr)

						for i in range(self.iterations):
									optim.zero_grad()
									_UpperCAmelCase			=       self._add_vector(A)
									_UpperCAmelCase			=       loop_post_process(A)
									_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  Dict			,		A       :  Any			,		A       :  Optional[int]			,		A       :  str)       ->   Any:



						"""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:
									_UpperCAmelCase			=       Image.open(A)
									_UpperCAmelCase			=       image.resize((2_56, 2_56))
									wandb.log('Original Image'			,		wandb.Image(A))



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  int)       ->   Dict:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any]			,		A       :  Union[str, Any]=None			,		A       :  int=None			,		A       :  Optional[Any]=True			,		A       :  Dict=False			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Any=None			,		)       ->   Dict:



						"""simple docstring"""






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

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

						_UpperCAmelCase			=       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"))
