






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)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

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






