






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_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  int					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :int          =	1.5
	UpperCamelCase     :str          =	int(factor * num_class_images					)
	UpperCamelCase     :int          =	ClipClient(
	    url="""https://knn.laion.ai/knn-service"""	,			indice_name="""laion_400m"""	,			num_images=__magic_name__	,			aesthetic_weight=0.1					)

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

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

	UpperCamelCase     :Optional[Any]          =	0
	UpperCamelCase     :Any          =	0
	UpperCamelCase     :List[Any]          =	tqdm(desc="""downloading real regularization images"""	,			total=__magic_name__					)

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






	"""simple docstring"""



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


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








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def __A    (      )->  Tuple:





						"""simple docstring"""



						raise RuntimeError('CUDA out of memory.'	)





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			nn.Linear(3       ,    4    )
							_UpperCAmelCase									=			nn.BatchNormad(4    )
							_UpperCAmelCase									=			nn.Linear(4       ,    5    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase    )    )    )





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()

							mock_training_loop_function()
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]

							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase    ):
													nonlocal batch_sizes
													batch_sizes.append(UpperCAmelCase    )
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							_UpperCAmelCase					, _UpperCAmelCase									=			mock_training_loop_function('hello'    )
							self.assertListEqual(UpperCAmelCase       ,    [128, 64, 32, 16, 8]    )
							self.assertListEqual([bs, arga]       ,    [8, 'hello']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=0    )
							def mock_training_loop_function(UpperCAmelCase    ):
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													if batch_size > 0:
																			raise_fake_out_of_memory()
													pass

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('No executable batch size found, reached zero.'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=128    )
							def mock_training_loop_function(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													if batch_size != 8:
																			raise raise_fake_out_of_memory()

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function(128       ,    'hello'       ,    'world'    )
													self.assertIn('Batch size was passed into `f`'       ,    cm.exception.args[0]    )
													self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'       ,    cm.exception.args[0]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							@find_executable_batch_size(starting_batch_size=16    )
							def mock_training_loop_function(UpperCAmelCase    ):
													raise ValueError('Oops, we had an error!'    )

							with self.assertRaises(UpperCAmelCase    ) as cm:
													mock_training_loop_function()
													self.assertIn('Oops, we had an error!'       ,    cm.exception.args[0]    )

	@require_cuda
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.cuda.memory_allocated()
							_UpperCAmelCase									=			ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
							_UpperCAmelCase									=			release_memory(UpperCAmelCase    )
							self.assertEqual(torch.cuda.memory_allocated()       ,    UpperCAmelCase    )
