


"""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 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      List[Any]     ,   _snake_case						:      Union[str, Any]     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				1.5
	__snake_case	:						List[str]						=				int(factor * num_class_images     )
	__snake_case	:						Tuple						=				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:
		__snake_case	:						Dict						=				client.query(text=_snake_case     )
		if len(_snake_case     ) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			__snake_case	:						str						=				int(factor * num_images     )
			__snake_case	:						Union[str, Any]						=				ClipClient(
			    url='''https://knn.laion.ai/knn-service'''     ,   indice_name='''laion_400m'''     ,   num_images=_snake_case     ,   aesthetic_weight=0.1     ,   )

	__snake_case	:						Any						=				0
	__snake_case	:						List[str]						=				0
	__snake_case	:						Union[str, Any]						=				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:
			__snake_case	:						Tuple						=				class_images[count]
			count += 1
			try:
				__snake_case	:						List[str]						=				requests.get(images['''url''']     )
				if img.status_code == 200:
					__snake_case	:						Optional[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 		lowercase							(     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						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=200     ,   type=_snake_case     )
	return parser.parse_args()


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









"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


