





"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[Any]         =["""image_processor""", """tokenizer"""]
							lowerCamelCase     :      Any         ="""CLIPImageProcessor"""
							lowerCamelCase     :      List[str]         =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				Any:
								a   :	Optional[Any]			=       None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."   ,      lowerCAmelCase__   ,      )
									a   :	int			=       kwargs.pop("feature_extractor"   )

								a   :	int			=       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
									raise ValueError("You need to specify an `image_processor`."   )
								if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`."   )

								super().__init__(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def __call__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				Union[str, Any]:

								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."   )

								if text is not None:
									a   :	Optional[Any]			=       self.tokenizer(lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   )

								if images is not None:
									a   :	str			=       self.image_processor(lowerCAmelCase__   ,      return_tensors=lowerCAmelCase__   ,      **lowerCAmelCase__   )

								if text is not None and images is not None:
									a   :	List[str]			=       image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCAmelCase__   )   ,      tensor_type=lowerCAmelCase__   )






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								return self.tokenizer.decode(*lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@property
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	List[str]			=       self.tokenizer.model_input_names
								a   :	List[Any]			=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









"""simple docstring"""




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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






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

	os.makedirs(F"""{class_data_dir}/images"""					, exist_ok=_lowercase      )
	if len(list(Path(F"""{class_data_dir}/images"""      ).iterdir()      )      ) >= num_class_images:
		return

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

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	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:
			a   :	Optional[Any]			=       class_images[count]
			count += 1
			try:
				a   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       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    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






	a   :	Optional[int]			=       argparse.ArgumentParser(""					, add_help=_lowercase      )
	parser.add_argument("--class_prompt"					, help="text prompt to retrieve images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--class_data_dir"					, help="path to save images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--num_class_images"					, help="number of images to download"					, default=200					, type=_lowercase      )
	return parser.parse_args()


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


