



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   (						A__						,  A__						,  A__	):
      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   _lowerCAmelCase   (						):
      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__":
       a__      :				int   	=			parse_args()
       retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
