
"""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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							1.5
      UpperCAmelCase_    				=							int(factor * num_class_images   )
      UpperCAmelCase_    				=							ClipClient(
          url="https://knn.laion.ai/knn-service"			,    indice_name="laion_400m"			,    num_images=__UpperCamelCase			,    aesthetic_weight=0.1   )

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

      while True:
            UpperCAmelCase_    				=							client.query(text=__UpperCamelCase   )
            if len(__UpperCamelCase   ) >= 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=__UpperCamelCase			,    aesthetic_weight=0.1			,    )

      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							tqdm(desc="downloading real regularization images"			,    total=__UpperCamelCase   )

      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 == 200:
                              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      a__			(   ):
      UpperCAmelCase_    				=							argparse.ArgumentParser(""			,    add_help=__UpperCamelCase   )
      parser.add_argument("--class_prompt"			,    help="text prompt to retrieve images"			,    required=__UpperCamelCase			,    type=__UpperCamelCase   )
      parser.add_argument("--class_data_dir"			,    help="path to save images"			,    required=__UpperCamelCase			,    type=__UpperCamelCase   )
      parser.add_argument("--num_class_images"			,    help="number of images to download"			,    default=200			,    type=__UpperCamelCase   )
      return parser.parse_args()


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









"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      =		{
    """configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Swinv2ForImageClassification""",
      """Swinv2ForMaskedImageModeling""",
      """Swinv2Model""",
      """Swinv2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




