



'''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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Dict     =					1.5
			A_     :						Union[str, Any]     =					int(factor * num_class_images   )
			A_     :						List[Any]     =					ClipClient(
			    url="""https://knn.laion.ai/knn-service"""						,				indice_name="""laion_400m"""						,				num_images=lowerCamelCase__						,				aesthetic_weight=0.1   )

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

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

			A_     :						Tuple     =					0
			A_     :						List[Any]     =					0
			A_     :						Dict     =					tqdm(desc="""downloading real regularization images"""						,				total=lowerCamelCase__   )

			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_     :						Union[str, Any]     =					class_images[count]
									count += 1
									try:
												A_     :						Optional[int]     =					requests.get(images["""url"""]   )
												if img.status_code == 2_00:
															A_     :						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      a      (			):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					argparse.ArgumentParser(""""""						,				add_help=lowerCamelCase__   )
			parser.add_argument("""--class_prompt"""						,				help="""text prompt to retrieve images"""						,				required=lowerCamelCase__						,				type=lowerCamelCase__   )
			parser.add_argument("""--class_data_dir"""						,				help="""path to save images"""						,				required=lowerCamelCase__						,				type=lowerCamelCase__   )
			parser.add_argument("""--num_class_images"""						,				help="""number of images to download"""						,				default=2_00						,				type=lowerCamelCase__   )
			return parser.parse_args()


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




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)