





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							(  a   ,		a   ,		a    ) -> int:
					__A      :		Dict         =							1.5
					__A      :		Optional[Any]         =							int(factor * num_class_images    )
					__A      :		str         =							ClipClient(
					    url='https://knn.laion.ai/knn-service'   ,		indice_name='laion_400m'   ,		num_images=__snake_case   ,		aesthetic_weight=0.1    )

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

					while True:
										__A      :		Tuple         =							client.query(text=__snake_case    )
										if len(__snake_case    ) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															__A      :		Union[str, Any]         =							int(factor * num_images    )
															__A      :		Tuple         =							ClipClient(
															    url='https://knn.laion.ai/knn-service'   ,		indice_name='laion_400m'   ,		num_images=__snake_case   ,		aesthetic_weight=0.1   ,		)

					__A      :		Optional[Any]         =							0
					__A      :		List[str]         =							0
					__A      :		List[str]         =							tqdm(desc='downloading real regularization images'   ,		total=__snake_case    )

					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      :		List[Any]         =							class_images[count]
															count += 1
															try:
																				__A      :		int         =							requests.get(images['url']    )
																				if img.status_code == 2_00:
																									__A      :		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							(  ) -> Optional[int]:
					__A      :		List[Any]         =							argparse.ArgumentParser(''   ,		add_help=__snake_case    )
					parser.add_argument('--class_prompt'   ,		help='text prompt to retrieve images'   ,		required=__snake_case   ,		type=__snake_case    )
					parser.add_argument('--class_data_dir'   ,		help='path to save images'   ,		required=__snake_case   ,		type=__snake_case    )
					parser.add_argument('--num_class_images'   ,		help='number of images to download'   ,		default=2_00   ,		type=__snake_case    )
					return parser.parse_args()


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





'''simple docstring'''






import os
from pathlib import Path





def  _SCREAMING_SNAKE_CASE		(      ):
     from torch.utils.cpp_extension import load

     _A    		=  Path(__snake_case				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
     _A    		=  [
         root / filename
         for filename in [
             'vision.cpp',
             os.path.join('cpu'   ,		'ms_deform_attn_cpu.cpp'				),
             os.path.join('cuda'   ,		'ms_deform_attn_cuda.cu'				),
         ]
     ]

     load(
         'MultiScaleDeformableAttention'   ,		__snake_case   ,		with_cuda=__snake_case   ,		extra_include_paths=[str(__snake_case				)]   ,		extra_cflags=['-DWITH_CUDA=1']   ,		extra_cuda_cflags=[
             '-DCUDA_HAS_FP16=1',
             '-D__CUDA_NO_HALF_OPERATORS__',
             '-D__CUDA_NO_HALF_CONVERSIONS__',
             '-D__CUDA_NO_HALF2_OPERATORS__',
         ]   ,		)

     import MultiScaleDeformableAttention as MSDA

     return MSDA



