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_    :






						'''simple docstring'''







						def __init__(	self      :			str	,	__lowerCamelCase      :			str = "cpu"	,	__lowerCamelCase      :			str = "openai/clip-vit-large-patch14" )      ->				None:
									A							:					int              =						device
									A							:					Optional[Any]              =						CLIPTokenizerFast.from_pretrained(__lowerCamelCase )
									A							:					Optional[int]              =						[0.48145466, 0.4578275, 0.40821073]
									A							:					Tuple              =						[0.26862954, 0.26130258, 0.27577711]
									A							:					List[Any]              =						torchvision.transforms.Normalize(self.image_mean	,	self.image_std )
									A							:					int              =						torchvision.transforms.Resize(2_24 )
									A							:					Optional[int]              =						torchvision.transforms.CenterCrop(2_24 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[int] )      ->				str:
									A							:					Tuple              =						self.resize(__lowerCamelCase )
									A							:					Dict              =						self.center_crop(__lowerCamelCase )
									A							:					Any              =						self.normalize(__lowerCamelCase )
									return images







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







class 	lowerCamelCase_    ( nn.Module						):






						'''simple docstring'''







						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	,	)      ->				None:
									super().__init__()
									A							:					Optional[Any]              =						None
									A							:					str              =						device if device else get_device()
									if vqgan:
												A							:					List[str]              =						vqgan
									else:
												A							:					Dict              =						load_vqgan(self.device	,	conf_path=__lowerCamelCase	,	ckpt_path=__lowerCamelCase )
									self.vqgan.eval()
									if clip:
												A							:					Optional[Any]              =						clip
									else:
												A							:					Dict              =						CLIPModel.from_pretrained("openai/clip-vit-base-patch32" )
									self.clip.to(self.device )
									A							:					Optional[Any]              =						ProcessorGradientFlow(device=self.device )

									A							:					Dict              =						iterations
									A							:					Tuple              =						lr
									A							:					Tuple              =						log
									A							:					Optional[int]              =						make_grid
									A							:					str              =						return_val
									A							:					List[Any]              =						quantize
									A							:					str              =						self.vqgan.decoder.z_shape







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[Any]=None	,	__lowerCamelCase      :			Dict=None	,	__lowerCamelCase      :			int=5	,	__lowerCamelCase      :			str=True )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						[]
									if output_path is None:
												A							:					List[Any]              =						"./animation.gif"
									if input_path is None:
												A							:					str              =						self.save_path
									A							:					Optional[int]              =						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?)" )
									A							:					Union[str, Any]              =						total_duration / len(__lowerCamelCase )
									A							:					Optional[Any]              =						[frame_duration] * len(__lowerCamelCase )
									if extend_frames:
												A							:					int              =						1.5
												A							:					int              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Dict=None )      ->				Union[str, Any]:
									if not (path or img):
												raise ValueError("Input either path or tensor" )
									if img is not None:
												raise NotImplementedError
									A							:					List[str]              =						preprocess(Image.open(__lowerCamelCase )	,	target_image_size=2_56 ).to(self.device )
									A							:					str              =						preprocess_vqgan(__lowerCamelCase )
									A							:					Optional[Any]              =						self.vqgan.encode(__lowerCamelCase )
									return z







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Optional[Any] )      ->				Union[str, Any]:
									A							:					int              =						self.latent.detach().requires_grad_()
									A							:					List[str]              =						base_latent + transform_vector
									if self.quantize:
												A							:					Union[str, Any]              =						self.vqgan.quantize(__lowerCamelCase )
									else:
												A							:					str              =						trans_latent
									return self.vqgan.decode(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				Dict:
									A							:					Union[str, Any]              =						self.clip_preprocessor(text=__lowerCamelCase	,	images=__lowerCamelCase	,	return_tensors="pt"	,	padding=__lowerCamelCase )
									A							:					int              =						self.clip(**__lowerCamelCase )
									A							:					Optional[int]              =						clip_outputs.logits_per_image
									if weights is not None:
												A							:					Any              =						similarity_logits * weights
									return similarity_logits.sum()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Any )      ->				str:
									A							:					Any              =						self._get_clip_similarity(pos_prompts["prompts"]	,	__lowerCamelCase	,	weights=(1 / pos_prompts["weights"]) )
									if neg_prompts:
												A							:					List[Any]              =						self._get_clip_similarity(neg_prompts["prompts"]	,	__lowerCamelCase	,	weights=neg_prompts["weights"] )
									else:
												A							:					Tuple              =						torch.tensor([1]	,	device=self.device )
									A							:					List[Any]              =						-torch.log(__lowerCamelCase ) + torch.log(__lowerCamelCase )
									return loss







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Dict )      ->				str:
									A							:					Any              =						torch.randn_like(self.latent	,	requires_grad=__lowerCamelCase	,	device=self.device )
									A							:					Optional[int]              =						torch.optim.Adam([vector]	,	lr=self.lr )

									for i in range(self.iterations ):
												optim.zero_grad()
												A							:					List[str]              =						self._add_vector(__lowerCamelCase )
												A							:					List[Any]              =						loop_post_process(__lowerCamelCase )
												A							:					Any              =						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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Optional[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:
												A							:					List[str]              =						Image.open(__lowerCamelCase )
												A							:					Optional[Any]              =						image.resize((2_56, 2_56) )
												wandb.log("Original Image"	,	wandb.Image(__lowerCamelCase ) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any] )      ->				List[Any]:
									if not prompts:
												return []
									A							:					Optional[Any]              =						[]
									A							:					int              =						[]
									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					int              =						[prompt.strip() for prompt in prompts.split("|" )]
									for prompt in prompts:
												if isinstance(__lowerCamelCase	,	(tuple, list) ):
															A							:					List[Any]              =						prompt[0]
															A							:					Optional[Any]              =						float(prompt[1] )
												elif ":" in prompt:
															A							:					Optional[Any]              =						prompt.split(":" )
															A							:					Union[str, Any]              =						float(__lowerCamelCase )
												else:
															A							:					Tuple              =						prompt
															A							:					Optional[int]              =						1.0
												processed_prompts.append(__lowerCamelCase )
												weights.append(__lowerCamelCase )
									return {
									    "prompts": processed_prompts,
									    "weights": torch.tensor(__lowerCamelCase	,	device=self.device ),
									}







						def 			SCREAMING_SNAKE_CASE__		(	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	,	)      ->				Optional[int]:
									if image_path:
												A							:					str              =						self._get_latent(__lowerCamelCase )
									else:
												A							:					List[Any]              =						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."
									A							:					List[str]              =						self.process_prompts(__lowerCamelCase )
									A							:					Dict              =						self.process_prompts(__lowerCamelCase )
									if save_final and save_path is None:
												A							:					Any              =						os.path.join("./outputs/"	,	"_".join(pos_prompts["prompts"] ) )
									if not os.path.exists(__lowerCamelCase ):
												os.makedirs(__lowerCamelCase )
									else:
												A							:					List[Any]              =						save_path + "_" + get_timestamp()
												os.makedirs(__lowerCamelCase )
									A							:					Dict              =						save_path

									A							:					List[str]              =						self.vqgan.decode(self.latent )[0]
									if show_intermediate:
												print("Original Image" )
												show_pil(custom_to_pil(__lowerCamelCase ) )

									A							:					Union[str, Any]              =						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""" ) )




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")