






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_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  int					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :int          =	1.5
	UpperCamelCase     :str          =	int(factor * num_class_images					)
	UpperCamelCase     :int          =	ClipClient(
	    url="""https://knn.laion.ai/knn-service"""	,			indice_name="""laion_400m"""	,			num_images=__magic_name__	,			aesthetic_weight=0.1					)

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

	while True:
		UpperCamelCase     :Optional[int]          =	client.query(text=__magic_name__					)
		if len(__magic_name__					) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			UpperCamelCase     :str          =	int(factor * num_images					)
			UpperCamelCase     :Any          =	ClipClient(
			    url="""https://knn.laion.ai/knn-service"""	,			indice_name="""laion_400m"""	,			num_images=__magic_name__	,			aesthetic_weight=0.1	,			)

	UpperCamelCase     :Optional[Any]          =	0
	UpperCamelCase     :Any          =	0
	UpperCamelCase     :List[Any]          =	tqdm(desc="""downloading real regularization images"""	,			total=__magic_name__					)

	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:
			UpperCamelCase     :str          =	class_images[count]
			count += 1
			try:
				UpperCamelCase     :Optional[int]          =	requests.get(images["""url"""]					)
				if img.status_code == 200:
					UpperCamelCase     :List[Any]          =	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_							(							)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	argparse.ArgumentParser(""""""	,			add_help=__magic_name__					)
	parser.add_argument("""--class_prompt"""	,			help="""text prompt to retrieve images"""	,			required=__magic_name__	,			type=__magic_name__					)
	parser.add_argument("""--class_data_dir"""	,			help="""path to save images"""	,			required=__magic_name__	,			type=__magic_name__					)
	parser.add_argument("""--num_class_images"""	,			help="""number of images to download"""	,			default=200	,			type=__magic_name__					)
	return parser.parse_args()


if __name__ == "__main__":
	UpperCAmelCase_ :						Tuple      =      parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
	from .camera import create_pan_cameras
	from .pipeline_shap_e import ShapEPipeline
	from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
	from .renderer import (
	    BoundingBoxVolume,
	    ImportanceRaySampler,
	    MLPNeRFModelOutput,
	    MLPNeRSTFModel,
	    ShapEParamsProjModel,
	    ShapERenderer,
	    StratifiedRaySampler,
	    VoidNeRFModel,
	)
