





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:				Optional[int]  			=    logging.get_logger(__name__)

a		:				Any  			=    {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[str]         ="""openai-gpt"""
							lowerCamelCase     :      Tuple         ={
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}






							def __init__(     self   ,      lowerCAmelCase__=4_0478   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=768   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__="cls_index"   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=0.1   ,      **lowerCAmelCase__   ,      )			->				List[Any]:
								a   :	Any			=       vocab_size
								a   :	Union[str, Any]			=       n_positions
								a   :	Optional[Any]			=       n_embd
								a   :	Dict			=       n_layer
								a   :	Dict			=       n_head
								a   :	List[str]			=       afn
								a   :	str			=       resid_pdrop
								a   :	Any			=       embd_pdrop
								a   :	List[str]			=       attn_pdrop
								a   :	Dict			=       layer_norm_epsilon
								a   :	List[str]			=       initializer_range
								a   :	Union[str, Any]			=       summary_type
								a   :	Union[str, Any]			=       summary_use_proj
								a   :	Any			=       summary_activation
								a   :	Any			=       summary_first_dropout
								a   :	Union[str, Any]			=       summary_proj_to_labels
								super().__init__(**lowerCAmelCase__   )









"""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)


