
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       a		:







							def __init__(						self  :  int      ,				snake_case__  :  str = "cpu"      ,				snake_case__  :  str = "openai/clip-vit-large-patch14" ):
								"""simple docstring"""







								__lowerCAmelCase         =							device
								__lowerCAmelCase         =							CLIPTokenizerFast.from_pretrained(snake_case__ )
								__lowerCAmelCase         =							[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]
								__lowerCAmelCase         =							[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]
								__lowerCAmelCase         =							torchvision.transforms.Normalize(self.image_mean      ,				self.image_std )
								__lowerCAmelCase         =							torchvision.transforms.Resize(224 )
								__lowerCAmelCase         =							torchvision.transforms.CenterCrop(224 )







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.resize(snake_case__ )
								__lowerCAmelCase         =							self.center_crop(snake_case__ )
								__lowerCAmelCase         =							self.normalize(snake_case__ )
								return images







							def __call__(						self  :  Dict      ,				snake_case__  :  int=None      ,				snake_case__  :  int=None      ,				**snake_case__  :  Any ):
								"""simple docstring"""







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

class       a		(   nn.Module					):







							def __init__(						self  :  str      ,				snake_case__  :  Optional[int]=10      ,				snake_case__  :  Tuple=0.0_1      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  Dict=None      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  str=False      ,				snake_case__  :  Union[str, Any]=True      ,				snake_case__  :  Tuple="image"      ,				snake_case__  :  int=True      ,				snake_case__  :  str=False      ,				snake_case__  :  Any=False      ,				snake_case__  :  Dict=False      ,				):
								"""simple docstring"""







								super().__init__()
								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							device if device else get_device()
								if vqgan:
									__lowerCAmelCase         =							vqgan
								else:
									__lowerCAmelCase         =							load_vqgan(self.device      ,				conf_path=snake_case__      ,				ckpt_path=snake_case__ )
								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       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  List[Any]=None      ,				snake_case__  :  Tuple=None      ,				snake_case__  :  Optional[int]=5      ,				snake_case__  :  str=True ):
								"""simple docstring"""







								__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(snake_case__ ):
									raise ValueError(
									    "No images found in save path, aborting (did you pass save_intermediate=True to the generate"
									    " function?)" )
								if len(snake_case__ ) == 1:
									print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)" )
								__lowerCAmelCase         =							total_duration / len(snake_case__ )
								__lowerCAmelCase         =							[frame_duration] * len(snake_case__ )
								if extend_frames:
									__lowerCAmelCase         =							1.5
									__lowerCAmelCase         =							3
								for file_name in paths:
									if file_name.endswith(".png" ):
										images.append(imageio.imread(snake_case__ ) )
								imageio.mimsave(snake_case__      ,				snake_case__      ,				duration=snake_case__ )
								print(F"gif saved to {output_path}" )







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  str=None      ,				snake_case__  :  Tuple=None ):
								"""simple docstring"""







								if not (path or img):
									raise ValueError("Input either path or tensor" )
								if img is not None:
									raise NotImplementedError
								__lowerCAmelCase         =							preprocess(Image.open(snake_case__ )      ,				target_image_size=256 ).to(self.device )
								__lowerCAmelCase         =							preprocess_vqgan(snake_case__ )
								__lowerCAmelCase   ,						*__lowerCAmelCase         =							self.vqgan.encode(snake_case__ )
								return z







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Any ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[Any]      ,				snake_case__  :  List[str]      ,				snake_case__  :  Union[str, Any]=None ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  List[str]      ,				snake_case__  :  Tuple      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Optional[int] ):
								"""simple docstring"""







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

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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								wandb.init(reinit=snake_case__      ,				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(snake_case__ )
									__lowerCAmelCase         =							image.resize((256, 256) )
									wandb.log("Original Image"      ,				wandb.Image(snake_case__ ) )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  List[str] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  List[Any]=None      ,				snake_case__  :  Optional[int]=True      ,				snake_case__  :  Any=False      ,				snake_case__  :  Any=True      ,				snake_case__  :  Union[str, Any]=True      ,				snake_case__  :  Optional[Any]=None      ,				):
								"""simple docstring"""







								if image_path:
									__lowerCAmelCase         =							self._get_latent(snake_case__ )
								else:
									__lowerCAmelCase         =							torch.randn(self.latent_dim      ,				device=self.device )
								if self.log:
									self._init_logging(snake_case__      ,				snake_case__      ,				snake_case__ )

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

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

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



from __future__ import annotations

UpperCamelCase_   	=	[True] * 1_0_0_0_0_0_1
UpperCamelCase_   	=	2
while i * i <= 1_0_0_0_0_0_0:
					if seive[i]:
										for j in range(i * i, 1_0_0_0_0_0_1, i):
															UpperCamelCase_   	=	False
					i += 1






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return seive[n]






def 		_UpperCAmelCase   (	UpperCamelCase:     int  ):






	"""simple docstring"""


	return any(digit in "02468" for digit in str(UpperCamelCase  )  )






def 		_UpperCAmelCase   (	UpperCamelCase:     int = 1_0_0_0_0_0_0  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[2]  # result already includes the number 2.
	for num in range(3			,							limit + 1			,							2  ):
		if is_prime(UpperCamelCase  ) and not contains_an_even_digit(UpperCamelCase  ):
			__lowerCAmelCase         =							str(UpperCamelCase  )
			__lowerCAmelCase         =							[int(str_num[j:] + str_num[:j]  ) for j in range(len(UpperCamelCase  )  )]
			if all(is_prime(UpperCamelCase  ) for i in list_nums  ):
				result.append(UpperCamelCase  )
	return result






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	return len(find_circular_primes()  )


if __name__ == "__main__":
					print(f'''{len(find_circular_primes()) = }''')






