




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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       lowerCamelCase   :      Optional[Any]			 =					1.5
       lowerCamelCase   :      Optional[int]			 =					int(factor * num_class_images    )
       lowerCamelCase   :      str			 =					ClipClient(
           url="https://knn.laion.ai/knn-service" ,indice_name="laion_400m" ,num_images=_SCREAMING_SNAKE_CASE ,aesthetic_weight=0.1    )

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

       while True:
              lowerCamelCase   :      List[Any]			 =					client.query(text=_SCREAMING_SNAKE_CASE    )
              if len(_SCREAMING_SNAKE_CASE    ) >= factor * num_class_images or num_images > 1e4:
                     break
              else:
                     lowerCamelCase   :      Any			 =					int(factor * num_images    )
                     lowerCamelCase   :      int			 =					ClipClient(
                         url="https://knn.laion.ai/knn-service" ,indice_name="laion_400m" ,num_images=_SCREAMING_SNAKE_CASE ,aesthetic_weight=0.1 ,)

       lowerCamelCase   :      Any			 =					0
       lowerCamelCase   :      str			 =					0
       lowerCamelCase   :      Dict			 =					tqdm(desc="downloading real regularization images" ,total=_SCREAMING_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:
                     lowerCamelCase   :      str			 =					class_images[count]
                     count += 1
                     try:
                            lowerCamelCase   :      Union[str, Any]			 =					requests.get(images["url"]    )
                            if img.status_code == 200:
                                   lowerCamelCase   :      Dict			 =					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      (      )	-> List[Any]:
       lowerCamelCase   :      Dict			 =					argparse.ArgumentParser("" ,add_help=_SCREAMING_SNAKE_CASE    )
       parser.add_argument("--class_prompt" ,help="text prompt to retrieve images" ,required=_SCREAMING_SNAKE_CASE ,type=_SCREAMING_SNAKE_CASE    )
       parser.add_argument("--class_data_dir" ,help="path to save images" ,required=_SCREAMING_SNAKE_CASE ,type=_SCREAMING_SNAKE_CASE    )
       parser.add_argument("--num_class_images" ,help="number of images to download" ,default=200 ,type=_SCREAMING_SNAKE_CASE    )
       return parser.parse_args()


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[str]  =   parse_args()
       retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)












import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       # Initialise PyTorch model
       lowerCamelCase   :      Any			 =					TaConfig.from_json_file(_SCREAMING_SNAKE_CASE    )
       print(f'''Building PyTorch model from configuration: {config}'''    )
       lowerCamelCase   :      str			 =					TaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
       model.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      str  =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       SCREAMING_SNAKE_CASE__       :      str  =   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






