'''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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			Any   ,		__snake_case							:			Dict				):
     _A    		=  1.5
     _A    		=  int(factor * num_class_images				)
     _A    		=  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    		=  client.query(text=__snake_case				)
          if len(__snake_case				) >= factor * num_class_images or num_images > 1e4:
               break
          else:
               _A    		=  int(factor * num_images				)
               _A    		=  ClipClient(
                   url='https://knn.laion.ai/knn-service'   ,		indice_name='laion_400m'   ,		num_images=__snake_case   ,		aesthetic_weight=0.1   ,		)

     _A    		=  0
     _A    		=  0
     _A    		=  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    		=  class_images[count]
               count += 1
               try:
                    _A    		=  requests.get(images['url']				)
                    if img.status_code == 2_0_0:
                         _A    		=  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		(      ):
     _A    		=  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_0_0   ,		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'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



