
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 				a						(							snake_case__:					Dict						,   snake_case__:					str						,   snake_case__:					List[str]					):


				'''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=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:
								lowercase_       =					client.query(text=snake_case__					)
								if len(snake_case__					) >= 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=snake_case__						,   aesthetic_weight=0.1						,   )

				lowercase_       =					0
				lowercase_       =					0
				lowercase_       =					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:
												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 				a						(							):


				'''simple docstring'''
				lowercase_       =					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=200						,   type=snake_case__					)
				return parser.parse_args()


if __name__ == "__main__":
						__a  			=					parse_args()
						retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




