
'''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 	__lowercase				:

			def __init__(self			,   A = "cpu"			,   A = "openai/clip-vit-large-patch14"      ):
								lowerCamelCase_					:      List[Any]     =		device
								lowerCamelCase_					:      Any     =		CLIPTokenizerFast.from_pretrained(A      )
								lowerCamelCase_					:      Optional[Any]     =		[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
								lowerCamelCase_					:      Tuple     =		[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
								lowerCamelCase_					:      str     =		torchvision.transforms.Normalize(self.image_mean			,   self.image_std      )
								lowerCamelCase_					:      Tuple     =		torchvision.transforms.Resize(2_2_4      )
								lowerCamelCase_					:      List[Any]     =		torchvision.transforms.CenterCrop(2_2_4      )

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Optional[int]     =		self.resize(A      )
								lowerCamelCase_					:      Optional[int]     =		self.center_crop(A      )
								lowerCamelCase_					:      Union[str, Any]     =		self.normalize(A      )
								return images






			def __call__(self			,   A=None			,   A=None			,   **A      ):
								lowerCamelCase_					:      Optional[int]     =		self.tokenizer(text=A			,   **A      )
								lowerCamelCase_					:      str     =		self.preprocess_img(A      )
								lowerCamelCase_					:      List[str]     =		{key: value.to(self.device      ) for (key, value) in encoding.items()}
								return encoding

class 	__lowercase				(  nn.Module    ):

			def __init__(self			,   A=1_0			,   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			,   ):
								super().__init__()
								lowerCamelCase_					:      str     =		None
								lowerCamelCase_					:      Optional[Any]     =		device if device else get_device()
								if vqgan:
													lowerCamelCase_					:      Optional[int]     =		vqgan
								else:
													lowerCamelCase_					:      List[Any]     =		load_vqgan(self.device			,   conf_path=A			,   ckpt_path=A      )
								self.vqgan.eval()
								if clip:
													lowerCamelCase_					:      List[str]     =		clip
								else:
													lowerCamelCase_					:      Tuple     =		CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''      )
								self.clip.to(self.device      )
								lowerCamelCase_					:      Optional[Any]     =		ProcessorGradientFlow(device=self.device      )

								lowerCamelCase_					:      str     =		iterations
								lowerCamelCase_					:      Optional[int]     =		lr
								lowerCamelCase_					:      Union[str, Any]     =		log
								lowerCamelCase_					:      Any     =		make_grid
								lowerCamelCase_					:      Optional[Any]     =		return_val
								lowerCamelCase_					:      Any     =		quantize
								lowerCamelCase_					:      Optional[int]     =		self.vqgan.decoder.z_shape

			def        UpperCAmelCase__ (self			,   A=None			,   A=None			,   A=5			,   A=True      ):
								lowerCamelCase_					:      List[Any]     =		[]
								if output_path is None:
													lowerCamelCase_					:      Optional[Any]     =		'''./animation.gif'''
								if input_path is None:
													lowerCamelCase_					:      List[Any]     =		self.save_path
								lowerCamelCase_					:      Optional[Any]     =		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_					:      List[Any]     =		[frame_duration] * len(A      )
								if extend_frames:
													lowerCamelCase_					:      str     =		1.5
													lowerCamelCase_					:      str     =		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      ):
								if not (path or img):
													raise ValueError('''Input either path or tensor'''      )
								if img is not None:
													raise NotImplementedError
								lowerCamelCase_					:      Any     =		preprocess(Image.open(A      )			,   target_image_size=2_5_6      ).to(self.device      )
								lowerCamelCase_					:      str     =		preprocess_vqgan(A      )
								lowerCamelCase_,      *lowerCamelCase_					:      List[Any]     =		self.vqgan.encode(A      )
								return z

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[str]     =		self.latent.detach().requires_grad_()
								lowerCamelCase_					:      List[str]     =		base_latent + transform_vector
								if self.quantize:
													lowerCamelCase_,      *lowerCamelCase_					:      Dict     =		self.vqgan.quantize(A      )
								else:
													lowerCamelCase_					:      Optional[Any]     =		trans_latent
								return self.vqgan.decode(A      )

			def        UpperCAmelCase__ (self			,   A			,   A			,   A=None      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.clip_preprocessor(text=A			,   images=A			,   return_tensors='''pt'''			,   padding=A      )
								lowerCamelCase_					:      str     =		self.clip(**A      )
								lowerCamelCase_					:      Tuple     =		clip_outputs.logits_per_image
								if weights is not None:
													lowerCamelCase_					:      Optional[Any]     =		similarity_logits * weights
								return similarity_logits.sum()

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Dict     =		self._get_clip_similarity(pos_prompts['''prompts''']			,   A			,   weights=(1 / pos_prompts['''weights'''])      )
								if neg_prompts:
													lowerCamelCase_					:      str     =		self._get_clip_similarity(neg_prompts['''prompts''']			,   A			,   weights=neg_prompts['''weights''']      )
								else:
													lowerCamelCase_					:      Tuple     =		torch.tensor([1]			,   device=self.device      )
								lowerCamelCase_					:      Optional[Any]     =		-torch.log(A      ) + torch.log(A      )
								return loss

			def        UpperCAmelCase__ (self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      Tuple     =		torch.randn_like(self.latent			,   requires_grad=A			,   device=self.device      )
								lowerCamelCase_					:      int     =		torch.optim.Adam([vector]			,   lr=self.lr      )

								for i in range(self.iterations      ):
													optim.zero_grad()
													lowerCamelCase_					:      str     =		self._add_vector(A      )
													lowerCamelCase_					:      List[str]     =		loop_post_process(A      )
													lowerCamelCase_					:      List[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      ):
								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[Any]     =		Image.open(A      )
													lowerCamelCase_					:      Dict     =		image.resize((2_5_6, 2_5_6)      )
													wandb.log('''Original Image'''			,   wandb.Image(A      )      )

			def        UpperCAmelCase__ (self			,   A      ):
								if not prompts:
													return []
								lowerCamelCase_					:      Dict     =		[]
								lowerCamelCase_					:      Tuple     =		[]
								if isinstance(A			,   A      ):
													lowerCamelCase_					:      Dict     =		[prompt.strip() for prompt in prompts.split('''|'''      )]
								for prompt in prompts:
													if isinstance(A			,   (tuple, list)      ):
																		lowerCamelCase_					:      Optional[int]     =		prompt[0]
																		lowerCamelCase_					:      str     =		float(prompt[1]      )
													elif ":" in prompt:
																		lowerCamelCase_,      lowerCamelCase_					:      int     =		prompt.split(''':'''      )
																		lowerCamelCase_					:      Optional[Any]     =		float(A      )
													else:
																		lowerCamelCase_					:      Tuple     =		prompt
																		lowerCamelCase_					:      int     =		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			,   ):
								if image_path:
													lowerCamelCase_					:      Any     =		self._get_latent(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		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_					:      Optional[int]     =		self.process_prompts(A      )
								lowerCamelCase_					:      Optional[Any]     =		self.process_prompts(A      )
								if save_final and save_path is None:
													lowerCamelCase_					:      Any     =		os.path.join('''./outputs/'''			,   '''_'''.join(pos_prompts['''prompts''']      )      )
								if not os.path.exists(A      ):
													os.makedirs(A      )
								else:
													lowerCamelCase_					:      Any     =		save_path + '''_''' + get_timestamp()
													os.makedirs(A      )
								lowerCamelCase_					:      Tuple     =		save_path

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

								lowerCamelCase_					:      List[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'''


import os
from math import logaa




def lowercase_	(   _lowercase = "base_exp.txt"				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      float     =		0
					lowerCamelCase_					:      Dict     =		0
					for i, line in enumerate(open(os.path.join(os.path.dirname(_lowercase				)			,       _lowercase				)				)				):
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		list(map(_lowercase			,       line.split(''','''				)				)				)
										if x * logaa(_lowercase				) > largest:
															lowerCamelCase_					:      List[str]     =		x * logaa(_lowercase				)
															lowerCamelCase_					:      Union[str, Any]     =		i + 1
					return result


if __name__ == "__main__":
					print(solution())


