


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       a_ (       SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						str            =1.5
       _lowerCamelCase			:						Optional[Any]            =int(factor * num_class_images	)
       _lowerCamelCase			:						Union[str, Any]            =ClipClient(
           url='https://knn.laion.ai/knn-service'						,     indice_name='laion_400m'						,     num_images=SCREAMING_SNAKE_CASE__						,     aesthetic_weight=0.1	)

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

       while True:
              _lowerCamelCase			:						Tuple            =client.query(text=SCREAMING_SNAKE_CASE__	)
              if len(SCREAMING_SNAKE_CASE__	) >= factor * num_class_images or num_images > 1e4:
                     break
              else:
                     _lowerCamelCase			:						Dict            =int(factor * num_images	)
                     _lowerCamelCase			:						Tuple            =ClipClient(
                         url='https://knn.laion.ai/knn-service'						,     indice_name='laion_400m'						,     num_images=SCREAMING_SNAKE_CASE__						,     aesthetic_weight=0.1						,     )

       _lowerCamelCase			:						int            =0
       _lowerCamelCase			:						Optional[Any]            =0
       _lowerCamelCase			:						Dict            =tqdm(desc='downloading real regularization images'						,     total=SCREAMING_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:
                     _lowerCamelCase			:						Union[str, Any]            =class_images[count]
                     count += 1
                     try:
                            _lowerCamelCase			:						Dict            =requests.get(images['url']	)
                            if img.status_code == 200:
                                   _lowerCamelCase			:						Tuple            =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       a_ (       ):
       '''simple docstring'''





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


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






import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


