




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__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  str = "cpu"      ,   __lowerCamelCase       :  str = "openai/clip-vit-large-patch14"							)      ->  None:
							a					=    device
							a					=    CLIPTokenizerFast.from_pretrained(__lowerCamelCase							)
							a					=    [0.48_145_466, 0.4_578_275, 0.40_821_073]
							a					=    [0.26_862_954, 0.26_130_258, 0.27_577_711]
							a					=    torchvision.transforms.Normalize(self.image_mean      ,   self.image_std							)
							a					=    torchvision.transforms.Resize(2_24							)
							a					=    torchvision.transforms.CenterCrop(2_24							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple							)      ->  List[Any]:
							a					=    self.resize(__lowerCamelCase							)
							a					=    self.center_crop(__lowerCamelCase							)
							a					=    self.normalize(__lowerCamelCase							)
							return images





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




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



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

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





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





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  Union[str, Any]:
							a					=    self.latent.detach().requires_grad_()
							a					=    base_latent + transform_vector
							if self.quantize:
										a					,	*a					=    self.vqgan.quantize(__lowerCamelCase							)
							else:
										a					=    trans_latent
							return self.vqgan.decode(__lowerCamelCase							)





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





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





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  List[str]:
							a					=    torch.randn_like(self.latent      ,   requires_grad=__lowerCamelCase      ,   device=self.device							)
							a					=    torch.optim.Adam([vector]      ,   lr=self.lr							)

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





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





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

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

							a					=    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"""							)							)












from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__lowerCAmelCase	:       List[Any]     =	logging.get_logger(__name__)


__lowerCAmelCase	:       List[Any]     =	TypeVar('DatasetType', Dataset, IterableDataset)




def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[List[float]] = None,						A	:      Optional[int] = None,						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      Literal["first_exhausted", "all_exhausted"] = "first_exhausted",						):






			'''simple docstring'''







			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""     )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )
			else:
						return _interleave_iterable_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )






def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      int = 0,						):






			'''simple docstring'''







			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."     )
			for i, dataset in enumerate(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(DatasetDict, IterableDatasetDict)     ):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."     )
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""     )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(A,						info=A,						split=A,						axis=A     )
			else:
						return _concatenate_iterable_datasets(A,						info=A,						split=A,						axis=A     )






