






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   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   1.5
				__magic_name__		:List[str]   =   int(factor * num_class_images			)
				__magic_name__		:Dict   =   ClipClient(
				    url='''https://knn.laion.ai/knn-service''',	indice_name='''laion_400m''',	num_images=snake_case,	aesthetic_weight=0.1			)

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

				while True:
								__magic_name__		:Union[str, Any]   =   client.query(text=snake_case			)
								if len(snake_case			) >= factor * num_class_images or num_images > 1E4:
												break
								else:
												__magic_name__		:str   =   int(factor * num_images			)
												__magic_name__		:int   =   ClipClient(
												    url='''https://knn.laion.ai/knn-service''',	indice_name='''laion_400m''',	num_images=snake_case,	aesthetic_weight=0.1,	)

				__magic_name__		:Optional[Any]   =   0
				__magic_name__		:Optional[int]   =   0
				__magic_name__		:Optional[int]   =   tqdm(desc='''downloading real regularization images''',	total=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:
												__magic_name__		:Tuple   =   class_images[count]
												count += 1
												try:
																__magic_name__		:Optional[Any]   =   requests.get(images['''url''']			)
																if img.status_code == 2_0_0:
																				__magic_name__		:Any   =   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   __lowercase       (			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   argparse.ArgumentParser('''''',	add_help=snake_case			)
				parser.add_argument('''--class_prompt''',	help='''text prompt to retrieve images''',	required=snake_case,	type=snake_case			)
				parser.add_argument('''--class_data_dir''',	help='''path to save images''',	required=snake_case,	type=snake_case			)
				parser.add_argument('''--num_class_images''',	help='''number of images to download''',	default=2_0_0,	type=snake_case			)
				return parser.parse_args()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[int]				=		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_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



