

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 (     A__      :  Tuple ,  A__      :  List[str] ,  A__      :  Any	)       -> Any:





      """simple docstring"""



      _lowercase      			=1.5
      _lowercase      			=int(factor * num_class_images	)
      _lowercase      			=ClipClient(
          url='https://knn.laion.ai/knn-service' ,  indice_name='laion_400m' ,  num_images=A__ ,  aesthetic_weight=0.1	)

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

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

      _lowercase      			=0
      _lowercase      			=0
      _lowercase      			=tqdm(desc='downloading real regularization images' ,  total=A__	)

      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:
                  _lowercase      			=class_images[count]
                  count += 1
                  try:
                        _lowercase      			=requests.get(images['url']	)
                        if img.status_code == 200:
                              _lowercase      			=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 (     )       -> Tuple:





      """simple docstring"""



      _lowercase      			=argparse.ArgumentParser('' ,  add_help=A__	)
      parser.add_argument('--class_prompt' ,  help='text prompt to retrieve images' ,  required=A__ ,  type=A__	)
      parser.add_argument('--class_data_dir' ,  help='path to save images' ,  required=A__ ,  type=A__	)
      parser.add_argument('--num_class_images' ,  help='number of images to download' ,  default=200 ,  type=A__	)
      return parser.parse_args()


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





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


