



'''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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						List[str]			     = 1.5
       _UpperCAmelCase  :						List[Any]			     = int(factor * num_class_images		)
       _UpperCAmelCase  :						Dict			     = ClipClient(
           url="""https://knn.laion.ai/knn-service"""       ,   indice_name="""laion_400m"""       ,   num_images=_lowercase       ,   aesthetic_weight=0.1		)

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

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

       _UpperCAmelCase  :						int			     = 0
       _UpperCAmelCase  :						str			     = 0
       _UpperCAmelCase  :						Union[str, Any]			     = tqdm(desc="""downloading real regularization images"""       ,   total=_lowercase		)

       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  :						Tuple			     = class_images[count]
                     count += 1
                     try:
                            _UpperCAmelCase  :						Optional[Any]			     = requests.get(images["""url"""]		)
                            if img.status_code == 200:
                                   _UpperCAmelCase  :						Dict			     = 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     snake_case_					(     )->		str:





       '''simple docstring'''


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


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




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





