


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     (				__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]     ):
							a__    =  1.5
							a__    =  int(factor * num_class_images     )
							a__    =  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__    =  client.query(text=__lowerCAmelCase     )
														if len(__lowerCAmelCase     ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					a__    =  int(factor * num_images     )
																					a__    =  ClipClient(
																					    url='https://knn.laion.ai/knn-service'      ,					indice_name='laion_400m'      ,					num_images=__lowerCAmelCase      ,					aesthetic_weight=0.1      ,					)

							a__    =  0
							a__    =  0
							a__    =  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__    =  class_images[count]
																					count += 1
																					try:
																												a__    =  requests.get(images['url']     )
																												if img.status_code == 2_0_0:
																																			a__    =  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     (				):
							a__    =  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_0_0      ,					type=__lowerCAmelCase     )
							return parser.parse_args()


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








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




