




'''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     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Dict ,    UpperCamelCase__    :					Tuple				):




     """simple docstring"""


     a_     :							Any     						=  1.5
     a_     :							Any     						=  int(factor * num_class_images				)
     a_     :							Dict     						=  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:
          a_     :							Optional[int]     						=  client.query(text=UpperCamelCase__				)
          if len(UpperCamelCase__				) >= factor * num_class_images or num_images > 1E4:
               break
          else:
               a_     :							Any     						=  int(factor * num_images				)
               a_     :							List[Any]     						=  ClipClient(
                   url="""https://knn.laion.ai/knn-service""" ,    indice_name="""laion_400m""" ,    num_images=UpperCamelCase__ ,    aesthetic_weight=0.1 ,    )

     a_     :							Optional[Any]     						=  0
     a_     :							int     						=  0
     a_     :							Union[str, Any]     						=  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:
               a_     :							Dict     						=  class_images[count]
               count += 1
               try:
                    a_     :							List[str]     						=  requests.get(images["""url"""]				)
                    if img.status_code == 200:
                         a_     :							int     						=  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     (      ):




     """simple docstring"""


     a_     :							str     						=  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_      :  Tuple				     =  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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

 lowerCAmelCase_      :  List[Any]				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
