



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   :  List[str],   __lowercase   :  str = "cpu",   __lowercase   :  str = "openai/clip-vit-large-patch14"  ):
							lowercase__   					=			device
							lowercase__   					=			CLIPTokenizerFast.from_pretrained(__lowercase  )
							lowercase__   					=			[0.48145466, 0.4578275, 0.40821073]
							lowercase__   					=			[0.26862954, 0.26130258, 0.27577711]
							lowercase__   					=			torchvision.transforms.Normalize(self.image_mean,   self.image_std  )
							lowercase__   					=			torchvision.transforms.Resize(224  )
							lowercase__   					=			torchvision.transforms.CenterCrop(224  )




				def 							A__ (  self   :  Tuple,   __lowercase   :  Tuple  ):
							lowercase__   					=			self.resize(__lowercase  )
							lowercase__   					=			self.center_crop(__lowercase  )
							lowercase__   					=			self.normalize(__lowercase  )
							return images




				def __call__(  self   :  List[Any],   __lowercase   :  Tuple=None,   __lowercase   :  Optional[Any]=None,   **__lowercase   :  List[str]  ):
							lowercase__   					=			self.tokenizer(text=__lowercase,   **__lowercase  )
							lowercase__   					=			self.preprocess_img(__lowercase  )
							lowercase__   					=			{key: value.to(self.device  ) for (key, value) in encoding.items()}
							return encoding
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Optional[int],   __lowercase   :  str=10,   __lowercase   :  Optional[int]=0.01,   __lowercase   :  int=None,   __lowercase   :  int=None,   __lowercase   :  Optional[Any]=None,   __lowercase   :  List[str]=None,   __lowercase   :  int=None,   __lowercase   :  Optional[Any]=None,   __lowercase   :  List[str]=False,   __lowercase   :  str=True,   __lowercase   :  Union[str, Any]="image",   __lowercase   :  int=True,   __lowercase   :  str=False,   __lowercase   :  Union[str, Any]=False,   __lowercase   :  Dict=False,   ):
							super().__init__()
							lowercase__   					=			None
							lowercase__   					=			device if device else get_device()
							if vqgan:
										lowercase__   					=			vqgan
							else:
										lowercase__   					=			load_vqgan(self.device,   conf_path=__lowercase,   ckpt_path=__lowercase  )
							self.vqgan.eval()
							if clip:
										lowercase__   					=			clip
							else:
										lowercase__   					=			CLIPModel.from_pretrained("openai/clip-vit-base-patch32"  )
							self.clip.to(self.device  )
							lowercase__   					=			ProcessorGradientFlow(device=self.device  )

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




				def 							A__ (  self   :  List[str],   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Any=None,   __lowercase   :  Tuple=5,   __lowercase   :  int=True  ):
							lowercase__   					=			[]
							if output_path is None:
										lowercase__   					=			"./animation.gif"
							if input_path is None:
										lowercase__   					=			self.save_path
							lowercase__   					=			sorted(glob(input_path + "/*"  )  )
							if not len(__lowercase  ):
										raise ValueError(
										    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
										    " function?)"  )
							if len(__lowercase  ) == 1:
										print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"  )
							lowercase__   					=			total_duration / len(__lowercase  )
							lowercase__   					=			[frame_duration] * len(__lowercase  )
							if extend_frames:
										lowercase__   					=			1.5
										lowercase__   					=			3
							for file_name in paths:
										if file_name.endswith(".png"  ):
													images.append(imageio.imread(__lowercase  )  )
							imageio.mimsave(__lowercase,   __lowercase,   duration=__lowercase  )
							print(F'''gif saved to {output_path}'''  )




				def 							A__ (  self   :  Dict,   __lowercase   :  List[str]=None,   __lowercase   :  int=None  ):
							if not (path or img):
										raise ValueError("Input either path or tensor"  )
							if img is not None:
										raise NotImplementedError
							lowercase__   					=			preprocess(Image.open(__lowercase  ),   target_image_size=256  ).to(self.device  )
							lowercase__   					=			preprocess_vqgan(__lowercase  )
							lowercase__					,    *lowercase__   					=			self.vqgan.encode(__lowercase  )
							return z




				def 							A__ (  self   :  List[Any],   __lowercase   :  Any  ):
							lowercase__   					=			self.latent.detach().requires_grad_()
							lowercase__   					=			base_latent + transform_vector
							if self.quantize:
										lowercase__					,    *lowercase__   					=			self.vqgan.quantize(__lowercase  )
							else:
										lowercase__   					=			trans_latent
							return self.vqgan.decode(__lowercase  )




				def 							A__ (  self   :  Any,   __lowercase   :  List[str],   __lowercase   :  Union[str, Any],   __lowercase   :  Any=None  ):
							lowercase__   					=			self.clip_preprocessor(text=__lowercase,   images=__lowercase,   return_tensors="pt",   padding=__lowercase  )
							lowercase__   					=			self.clip(**__lowercase  )
							lowercase__   					=			clip_outputs.logits_per_image
							if weights is not None:
										lowercase__   					=			similarity_logits * weights
							return similarity_logits.sum()




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  int,   __lowercase   :  List[str],   __lowercase   :  int  ):
							lowercase__   					=			self._get_clip_similarity(pos_prompts["prompts"],   __lowercase,   weights=(1 / pos_prompts["weights"])  )
							if neg_prompts:
										lowercase__   					=			self._get_clip_similarity(neg_prompts["prompts"],   __lowercase,   weights=neg_prompts["weights"]  )
							else:
										lowercase__   					=			torch.tensor([1],   device=self.device  )
							lowercase__   					=			-torch.log(__lowercase  ) + torch.log(__lowercase  )
							return loss




				def 							A__ (  self   :  Dict,   __lowercase   :  Optional[int],   __lowercase   :  Any,   __lowercase   :  Union[str, Any]  ):
							lowercase__   					=			torch.randn_like(self.latent,   requires_grad=__lowercase,   device=self.device  )
							lowercase__   					=			torch.optim.Adam([vector],   lr=self.lr  )

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




				def 							A__ (  self   :  int,   __lowercase   :  Dict,   __lowercase   :  Optional[Any],   __lowercase   :  List[str]  ):
							wandb.init(reinit=__lowercase,   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:
										lowercase__   					=			Image.open(__lowercase  )
										lowercase__   					=			image.resize((256, 256)  )
										wandb.log("Original Image",   wandb.Image(__lowercase  )  )




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




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Tuple=None,   __lowercase   :  List[str]=None,   __lowercase   :  Tuple=True,   __lowercase   :  List[str]=False,   __lowercase   :  str=True,   __lowercase   :  Dict=True,   __lowercase   :  Optional[int]=None,   ):
							if image_path:
										lowercase__   					=			self._get_latent(__lowercase  )
							else:
										lowercase__   					=			torch.randn(self.latent_dim,   device=self.device  )
							if self.log:
										self._init_logging(__lowercase,   __lowercase,   __lowercase  )

							assert pos_prompts, "You must provide at least one positive prompt."
							lowercase__   					=			self.process_prompts(__lowercase  )
							lowercase__   					=			self.process_prompts(__lowercase  )
							if save_final and save_path is None:
										lowercase__   					=			os.path.join("./outputs/",   "_".join(pos_prompts["prompts"]  )  )
							if not os.path.exists(__lowercase  ):
										os.makedirs(__lowercase  )
							else:
										lowercase__   					=			save_path + "_" + get_timestamp()
										os.makedirs(__lowercase  )
							lowercase__   					=			save_path

							lowercase__   					=			self.vqgan.decode(self.latent  )[0]
							if show_intermediate:
										print("Original Image"  )
										show_pil(custom_to_pil(__lowercase  )  )

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









from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase_						=       2_9979_2458

# Symbols
lowercase_			,      lowercase_			,      lowercase_			,      lowercase_						=       symbols("""ct x y z""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if velocity > c:
						raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"					)
			elif velocity < 1:
						# Usually the speed should be much higher than 1 (c order of magnitude)
						raise ValueError("Speed must be greater than or equal to 1!"					)

			return velocity / c



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_					) ** 2					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return np.array(
			    [
			        [gamma(SCREAMING_SNAKE_CASE_					), -gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [-gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), gamma(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [0, 0, 1, 0],
			        [0, 0, 0, 1],
			    ]					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = None					):
			# Ensure event is not empty
			if event is None:
						lowercase__   					=			np.array([ct, x, y, z]					)  # Symbolic four vector
			else:
						event[0] *= c  # x0 is ct (speed of light * time)

			return transformation_matrix(SCREAMING_SNAKE_CASE_					) @ event


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Example of symbolic vector:
							lowercase_						=       transform(2997_9245)
							print("""Example of four vector: """)
							print(F'ct\' = {four_vector[0]}')
							print(F'x\' = {four_vector[1]}')
							print(F'y\' = {four_vector[2]}')
							print(F'z\' = {four_vector[3]}')

							# Substitute symbols with numerical values
							lowercase_						=       {ct: c, x: 1, y: 1, z: 1}
							lowercase_						=       [four_vector[i].subs(sub_dict) for i in range(4)]

							print(F'\n{numerical_vector}')




