
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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
     '''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=lowerCAmelCase__				,    aesthetic_weight=0.1	)

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

     while True:
          lowercase					=		client.query(text=lowerCAmelCase__	)
          if len(lowerCAmelCase__	) >= 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=lowerCAmelCase__				,    aesthetic_weight=0.1				,    )

     lowercase					=		0
     lowercase					=		0
     lowercase					=		tqdm(desc='''downloading real regularization images'''				,    total=lowerCAmelCase__	)

     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  UpperCamelCase					(							):
     '''simple docstring'''
     lowercase					=		argparse.ArgumentParser(''''''				,    add_help=lowerCAmelCase__	)
     parser.add_argument('''--class_prompt'''				,    help='''text prompt to retrieve images'''				,    required=lowerCAmelCase__				,    type=lowerCAmelCase__	)
     parser.add_argument('''--class_data_dir'''				,    help='''path to save images'''				,    required=lowerCAmelCase__				,    type=lowerCAmelCase__	)
     parser.add_argument('''--num_class_images'''				,    help='''number of images to download'''				,    default=200				,    type=lowerCAmelCase__	)
     return parser.parse_args()


if __name__ == "__main__":
       lowercase__	:Optional[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_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

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




