






"""simple docstring"""



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
						from scipy.stats import pearsonr, spearmanr
						from sklearn.metrics import fa_score, matthews_corrcoef


A__       :				Dict 					=					(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    List[Any]  )				->	Tuple:
							warnings.warn(UpperCamelCase__				,						UpperCamelCase__  )
							requires_backends(UpperCamelCase__				,						"sklearn"  )
							return (preds == labels).mean()

def  _snake_case				(     lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    str  )				->	List[Any]:
							warnings.warn(UpperCamelCase__				,						UpperCamelCase__  )
							requires_backends(UpperCamelCase__				,						"sklearn"  )
							lowerCamelCase_       :							List[Any]									=simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )
							lowerCamelCase_       :							str									=fa_score(y_true=UpperCamelCase__				,						y_pred=UpperCamelCase__  )
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}

def  _snake_case				(     lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    int  )				->	Any:
							warnings.warn(UpperCamelCase__				,						UpperCamelCase__  )
							requires_backends(UpperCamelCase__				,						"sklearn"  )
							lowerCamelCase_       :							str									=pearsonr(UpperCamelCase__				,						UpperCamelCase__  )[0]
							lowerCamelCase_       :							Optional[Any]									=spearmanr(UpperCamelCase__				,						UpperCamelCase__  )[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}

def  _snake_case				(     lowerCamelCase__	:    List[Any]				,						lowerCamelCase__	:    Optional[Any]				,						lowerCamelCase__	:    Any  )				->	List[Any]:
							warnings.warn(UpperCamelCase__				,						UpperCamelCase__  )
							requires_backends(UpperCamelCase__				,						"sklearn"  )
							assert len(UpperCamelCase__  ) == len(UpperCamelCase__  ), F"""Predictions and labels have mismatched lengths {len(UpperCamelCase__  )} and {len(UpperCamelCase__  )}"""
							if task_name == "cola":
														return {"mcc": matthews_corrcoef(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "sst-2":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "mrpc":
														return acc_and_fa(UpperCamelCase__				,						UpperCamelCase__  )
							elif task_name == "sts-b":
														return pearson_and_spearman(UpperCamelCase__				,						UpperCamelCase__  )
							elif task_name == "qqp":
														return acc_and_fa(UpperCamelCase__				,						UpperCamelCase__  )
							elif task_name == "mnli":
														return {"mnli/acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "mnli-mm":
														return {"mnli-mm/acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "qnli":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "rte":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "wnli":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							elif task_name == "hans":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							else:
														raise KeyError(UpperCamelCase__  )

def  _snake_case				(     lowerCamelCase__	:    Tuple				,						lowerCamelCase__	:    Tuple				,						lowerCamelCase__	:    List[str]  )				->	Optional[Any]:
							warnings.warn(UpperCamelCase__				,						UpperCamelCase__  )
							requires_backends(UpperCamelCase__				,						"sklearn"  )
							if len(UpperCamelCase__  ) != len(UpperCamelCase__  ):
														raise ValueError(F"""Predictions and labels have mismatched lengths {len(UpperCamelCase__  )} and {len(UpperCamelCase__  )}"""  )
							if task_name == "xnli":
														return {"acc": simple_accuracy(UpperCamelCase__				,						UpperCamelCase__  )}
							else:
														raise KeyError(UpperCamelCase__  )






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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''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=UpperCamelCase__   ,							aesthetic_weight=0.1      )

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

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= 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=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							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 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(''   ,							add_help=UpperCamelCase__      )
							parser.add_argument('--class_prompt'   ,							help='text prompt to retrieve images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--class_data_dir'   ,							help='path to save images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--num_class_images'   ,							help='number of images to download'   ,							default=200   ,							type=UpperCamelCase__      )
							return parser.parse_args()


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




