


"""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 lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: List[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=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:
  __lowercase									=	client.query(text=UpperCamelCase__ )
  if len(UpperCamelCase__ ) >= 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=UpperCamelCase__				,   aesthetic_weight=0.1				,   )

 __lowercase									=	0
 __lowercase									=	0
 __lowercase									=	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:
   __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 lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	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__":
       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_tf_available, is_torch_available


UpperCAmelCase__  							={
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecAudioForAudioFrameClassification",
           "Data2VecAudioForCTC",
           "Data2VecAudioForSequenceClassification",
           "Data2VecAudioForXVector",
           "Data2VecAudioModel",
           "Data2VecAudioPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecTextForCausalLM",
           "Data2VecTextForMaskedLM",
           "Data2VecTextForMultipleChoice",
           "Data2VecTextForQuestionAnswering",
           "Data2VecTextForSequenceClassification",
           "Data2VecTextForTokenClassification",
           "Data2VecTextModel",
           "Data2VecTextPreTrainedModel",
       ]
       UpperCAmelCase__  							=[
           "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Data2VecVisionForImageClassification",
           "Data2VecVisionForMaskedImageModeling",
           "Data2VecVisionForSemanticSegmentation",
           "Data2VecVisionModel",
           "Data2VecVisionPreTrainedModel",
       ]

if is_tf_available():
       UpperCAmelCase__  							=[
           "TFData2VecVisionForImageClassification",
           "TFData2VecVisionForSemanticSegmentation",
           "TFData2VecVisionModel",
           "TFData2VecVisionPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
       from .configuration_dataavec_text import (
           DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DataaVecTextConfig,
           DataaVecTextOnnxConfig,
       )
       from .configuration_dataavec_vision import (
           DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DataaVecVisionConfig,
           DataaVecVisionOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_dataavec_audio import (
                  DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecAudioForAudioFrameClassification,
                  DataaVecAudioForCTC,
                  DataaVecAudioForSequenceClassification,
                  DataaVecAudioForXVector,
                  DataaVecAudioModel,
                  DataaVecAudioPreTrainedModel,
              )
              from .modeling_dataavec_text import (
                  DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecTextForCausalLM,
                  DataaVecTextForMaskedLM,
                  DataaVecTextForMultipleChoice,
                  DataaVecTextForQuestionAnswering,
                  DataaVecTextForSequenceClassification,
                  DataaVecTextForTokenClassification,
                  DataaVecTextModel,
                  DataaVecTextPreTrainedModel,
              )
              from .modeling_dataavec_vision import (
                  DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DataaVecVisionForImageClassification,
                  DataaVecVisionForMaskedImageModeling,
                  DataaVecVisionForSemanticSegmentation,
                  DataaVecVisionModel,
                  DataaVecVisionPreTrainedModel,
              )
       if is_tf_available():
              from .modeling_tf_dataavec_vision import (
                  TFDataaVecVisionForImageClassification,
                  TFDataaVecVisionForSemanticSegmentation,
                  TFDataaVecVisionModel,
                  TFDataaVecVisionPreTrainedModel,
              )

else:
       import sys

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