



"""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 SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase = "cpu"						,     _lowercase = "openai/clip-vit-large-patch14"							):





													'''simple docstring'''

													__a		:		Any									=       device
													__a		:		Tuple									=       CLIPTokenizerFast.from_pretrained(_lowercase							)
													__a		:		List[str]									=       [0.4814_5466, 0.457_8275, 0.4082_1073]
													__a		:		Optional[Any]									=       [0.2686_2954, 0.2613_0258, 0.2757_7711]
													__a		:		Optional[int]									=       torchvision.transforms.Normalize(self.image_mean						,     self.image_std							)
													__a		:		Tuple									=       torchvision.transforms.Resize(224							)
													__a		:		str									=       torchvision.transforms.CenterCrop(224							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       self.resize(_lowercase							)
													__a		:		List[str]									=       self.center_crop(_lowercase							)
													__a		:		Union[str, Any]									=       self.normalize(_lowercase							)
													return images





						def __call__(self						,     _lowercase=None						,     _lowercase=None						,     **_lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self.tokenizer(text=_lowercase						,     **_lowercase							)
													__a		:		Union[str, Any]									=       self.preprocess_img(_lowercase							)
													__a		:		Any									=       {key: value.to(self.device							) for (key, value) in encoding.items()}
													return encoding







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase=10						,     _lowercase=0.01						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=False						,     _lowercase=True						,     _lowercase="image"						,     _lowercase=True						,     _lowercase=False						,     _lowercase=False						,     _lowercase=False						,     ):





													'''simple docstring'''

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

													__a		:		Any									=       iterations
													__a		:		Optional[Any]									=       lr
													__a		:		Optional[Any]									=       log
													__a		:		Dict									=       make_grid
													__a		:		Dict									=       return_val
													__a		:		Tuple									=       quantize
													__a		:		Optional[Any]									=       self.vqgan.decoder.z_shape



						def     lowerCAmelCase__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase=5						,     _lowercase=True							):





													'''simple docstring'''

													__a		:		str									=       []
													if output_path is None:
																				__a		:		Optional[int]									=       """./animation.gif"""
													if input_path is None:
																				__a		:		List[str]									=       self.save_path
													__a		:		Tuple									=       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?)"""							)
													__a		:		Union[str, Any]									=       total_duration / len(_lowercase							)
													__a		:		Optional[Any]									=       [frame_duration] * len(_lowercase							)
													if extend_frames:
																				__a		:		Optional[int]									=       1.5
																				__a		:		str									=       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     lowerCAmelCase__(self						,     _lowercase=None						,     _lowercase=None							):





													'''simple docstring'''

													if not (path or img):
																				raise ValueError("""Input either path or tensor"""							)
													if img is not None:
																				raise NotImplementedError
													__a		:		Any									=       preprocess(Image.open(_lowercase							)						,     target_image_size=256							).to(self.device							)
													__a		:		str									=       preprocess_vqgan(_lowercase							)
													__a		:		List[str]									=       self.vqgan.encode(_lowercase							)
													return z



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[str]									=       self.latent.detach().requires_grad_()
													__a		:		Optional[Any]									=       base_latent + transform_vector
													if self.quantize:
																				__a		:		str									=       self.vqgan.quantize(_lowercase							)
													else:
																				__a		:		str									=       trans_latent
													return self.vqgan.decode(_lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase=None							):





													'''simple docstring'''

													__a		:		Tuple									=       self.clip_preprocessor(text=_lowercase						,     images=_lowercase						,     return_tensors="""pt"""						,     padding=_lowercase							)
													__a		:		Any									=       self.clip(**_lowercase							)
													__a		:		List[str]									=       clip_outputs.logits_per_image
													if weights is not None:
																				__a		:		Optional[int]									=       similarity_logits * weights
													return similarity_logits.sum()



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Any									=       self._get_clip_similarity(pos_prompts["""prompts"""]						,     _lowercase						,     weights=(1 / pos_prompts["""weights"""])							)
													if neg_prompts:
																				__a		:		Tuple									=       self._get_clip_similarity(neg_prompts["""prompts"""]						,     _lowercase						,     weights=neg_prompts["""weights"""]							)
													else:
																				__a		:		Any									=       torch.tensor([1]						,     device=self.device							)
													__a		:		Dict									=       -torch.log(_lowercase							) + torch.log(_lowercase							)
													return loss



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       torch.randn_like(self.latent						,     requires_grad=_lowercase						,     device=self.device							)
													__a		:		List[Any]									=       torch.optim.Adam([vector]						,     lr=self.lr							)

													for i in range(self.iterations							):
																				optim.zero_grad()
																				__a		:		int									=       self._add_vector(_lowercase							)
																				__a		:		Any									=       loop_post_process(_lowercase							)
																				__a		:		Dict									=       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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													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:
																				__a		:		Optional[int]									=       Image.open(_lowercase							)
																				__a		:		Union[str, Any]									=       image.resize((256, 256)							)
																				wandb.log("""Original Image"""						,     wandb.Image(_lowercase							)							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

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





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=True						,     _lowercase=False						,     _lowercase=True						,     _lowercase=True						,     _lowercase=None						,     ):





													'''simple docstring'''

													if image_path:
																				__a		:		int									=       self._get_latent(_lowercase							)
													else:
																				__a		:		Optional[Any]									=       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."
													__a		:		Union[str, Any]									=       self.process_prompts(_lowercase							)
													__a		:		Optional[Any]									=       self.process_prompts(_lowercase							)
													if save_final and save_path is None:
																				__a		:		Dict									=       os.path.join("""./outputs/"""						,     """_""".join(pos_prompts["""prompts"""]							)							)
													if not os.path.exists(_lowercase							):
																				os.makedirs(_lowercase							)
													else:
																				__a		:		Union[str, Any]									=       save_path + """_""" + get_timestamp()
																				os.makedirs(_lowercase							)
													__a		:		Tuple									=       save_path

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

													__a		:		Optional[int]									=       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'''							)							)




"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    help="The YAML config file corresponding to the original architecture.",
	)
	parser.add_argument(
	    "--num_in_channels",
	    default=None,
	    type=int,
	    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
	)
	parser.add_argument(
	    "--image_size",
	    default=512,
	    type=int,
	    help=(
	        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
	        " Base. Use 768 for Stable Diffusion v2."
	    ),
	)
	parser.add_argument(
	    "--extract_ema",
	    action="store_true",
	    help=(
	        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
	        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
	        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
	    ),
	)
	parser.add_argument(
	    "--upcast_attention",
	    action="store_true",
	    help=(
	        "Whether the attention computation should always be upcasted. This is necessary when running stable"
	        " diffusion 2.1."
	    ),
	)
	parser.add_argument(
	    "--from_safetensors",
	    action="store_true",
	    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
	)
	parser.add_argument(
	    "--to_safetensors",
	    action="store_true",
	    help="Whether to store pipeline in safetensors format or not.",
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

