






"""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			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	1.5
      UpperCAmelCase          =	int(factor * num_class_images							)
      UpperCAmelCase          =	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:
            UpperCAmelCase          =	client.query(text=_snake_case							)
            if len(_snake_case							) >= 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=_snake_case				,	aesthetic_weight=0.1				,	)

      UpperCAmelCase          =	0
      UpperCAmelCase          =	0
      UpperCAmelCase          =	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:
                  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			(		):







      """simple docstring"""






      UpperCAmelCase          =	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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase					      =  {"""configuration_glpn""": ["""GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GLPNConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""GLPNFeatureExtractor"""]
  _UpperCamelCase					      =  ["""GLPNImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """GLPN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GLPNForDepthEstimation""",
      """GLPNLayer""",
      """GLPNModel""",
      """GLPNPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

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



