


"""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      (   __snake_case							:      Optional[Any]						, __snake_case							:      Any						, __snake_case							:      List[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					Dict   =		1.5
   _lowerCamelCase	:					List[Any]   =		int(factor * num_class_images   )
   _lowerCamelCase	:					List[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:
      _lowerCamelCase	:					Optional[int]   =		client.query(text=__snake_case   )
      if len(__snake_case   ) >= factor * num_class_images or num_images > 1E4:
         break
      else:
         _lowerCamelCase	:					Any   =		int(factor * num_images   )
         _lowerCamelCase	:					str   =		ClipClient(
             url="""https://knn.laion.ai/knn-service"""						, indice_name="""laion_400m"""						, num_images=__snake_case						, aesthetic_weight=0.1						, )

   _lowerCamelCase	:					Dict   =		0
   _lowerCamelCase	:					List[Any]   =		0
   _lowerCamelCase	:					Tuple   =		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:
         _lowerCamelCase	:					Dict   =		class_images[count]
         count += 1
         try:
            _lowerCamelCase	:					Optional[Any]   =		requests.get(images["""url"""]   )
            if img.status_code == 200:
               _lowerCamelCase	:					Optional[int]   =		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      (   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		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=200						, type=__snake_case   )
   return parser.parse_args()


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





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

