






"""simple docstring"""







import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  1.5
					a__          =  int(factor * num_class_images)
					a__          =  ClipClient(
					    url='''https://knn.laion.ai/knn-service''' ,     indice_name='''laion_400m''' ,     num_images=lowerCamelCase_ ,     aesthetic_weight=0.1)

					os.makedirs(f'{class_data_dir}/images' ,     exist_ok=lowerCamelCase_)
					if len(list(Path(f'{class_data_dir}/images').iterdir())) >= num_class_images:
										return

					while True:
										a__          =  client.query(text=lowerCamelCase_)
										if len(lowerCamelCase_) >= factor * num_class_images or num_images > 1E4:
															break
										else:
															a__          =  int(factor * num_images)
															a__          =  ClipClient(
															    url='''https://knn.laion.ai/knn-service''' ,     indice_name='''laion_400m''' ,     num_images=lowerCamelCase_ ,     aesthetic_weight=0.1 ,     )

					a__          =  0
					a__          =  0
					a__          =  tqdm(desc='''downloading real regularization images''' ,     total=lowerCamelCase_)

					with open(f'{class_data_dir}/caption.txt' ,     '''w''') as fa, open(f'{class_data_dir}/urls.txt' ,     '''w''') as fa, open(
					    f'{class_data_dir}/images.txt' ,     '''w''') as fa:
										while total < num_class_images:
															a__          =  class_images[count]
															count += 1
															try:
																				a__          =  requests.get(images['''url'''])
																				if img.status_code == 200:
																									a__          =  Image.open(BytesIO(img.content))
																									with open(f'{class_data_dir}/images/{total}.jpg' ,     '''wb''') as f:
																														f.write(img.content)
																									fa.write(images['''caption'''] + '''\n''')
																									fa.write(images['''url'''] + '''\n''')
																									fa.write(f'{class_data_dir}/images/{total}.jpg' + '''\n''')
																									total += 1
																									pbar.update(1)
																				else:
																									continue
															except Exception:
																				continue
					return





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  argparse.ArgumentParser('''''' ,     add_help=lowerCamelCase_)
					parser.add_argument('''--class_prompt''' ,     help='''text prompt to retrieve images''' ,     required=lowerCamelCase_ ,     type=lowerCamelCase_)
					parser.add_argument('''--class_data_dir''' ,     help='''path to save images''' ,     required=lowerCamelCase_ ,     type=lowerCamelCase_)
					parser.add_argument('''--num_class_images''' ,     help='''number of images to download''' ,     default=200 ,     type=lowerCamelCase_)
					return parser.parse_args()


if __name__ == "__main__":
	__a       :	List[str]             = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


