

from typing import Dict

from .base import GenericTensor, Pipeline


class SCREAMING_SNAKE_CASE  (		UpperCAmelCase_							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				if tokenize_kwargs is None:
					snake_case_      		= {}

				if truncation is not None:
					if "truncation" in tokenize_kwargs:
						raise ValueError(
						    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'							)
					snake_case_      		= truncation

				snake_case_      		= tokenize_kwargs

				snake_case_      		= {}
				if return_tensors is not None:
					snake_case_      		= return_tensors

				return preprocess_params, {}, postprocess_params


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.framework
				snake_case_      		= self.tokenizer(_snake_case ,				return_tensors=_snake_case ,				**_snake_case							)
				return model_inputs


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.model(**_snake_case							)
				return model_outputs


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=False							):

				"""simple docstring"""


				if return_tensors:
					return model_outputs[0]
				if self.framework == "pt":
					return model_outputs[0].tolist()
				elif self.framework == "tf":
					return model_outputs[0].numpy().tolist()







			def __call__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				return super().__call__(*_snake_case ,				**_snake_case							)




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(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= 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:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= 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:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= 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'''
	snake_case_      		= 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											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
