


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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     1.5
   UpperCAmelCase							=     int(factor * num_class_images   )
   UpperCAmelCase							=     ClipClient(
       url="""https://knn.laion.ai/knn-service"""  ,						indice_name="""laion_400m"""  ,						num_images=lowerCAmelCase_  ,						aesthetic_weight=0.1   )

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

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

   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   UpperCAmelCase							=     tqdm(desc="""downloading real regularization images"""  ,						total=lowerCAmelCase_   )

   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							=     class_images[count]
         count += 1
         try:
            UpperCAmelCase							=     requests.get(images["""url"""]   )
            if img.status_code == 2_0_0:
               UpperCAmelCase							=     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   _UpperCamelCase				(						)						->Union[str, Any]:
   UpperCAmelCase							=     argparse.ArgumentParser(""""""  ,						add_help=lowerCAmelCase_   )
   parser.add_argument("""--class_prompt"""  ,						help="""text prompt to retrieve images"""  ,						required=lowerCAmelCase_  ,						type=lowerCAmelCase_   )
   parser.add_argument("""--class_data_dir"""  ,						help="""path to save images"""  ,						required=lowerCAmelCase_  ,						type=lowerCAmelCase_   )
   parser.add_argument("""--num_class_images"""  ,						help="""number of images to download"""  ,						default=2_0_0  ,						type=lowerCAmelCase_   )
   return parser.parse_args()


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







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu






@require_cpu
class   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     accelerator.prepare(__lowerCamelCase       )
         try:
            pickle.loads(pickle.dumps(__lowerCamelCase       )       )
         except Exception as e:
            self.fail(F"""Accelerated optimizer pickling failed with {e}"""       )
         AcceleratorState._reset_state()



