






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowerCamelCase__							=     """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

lowerCamelCase__							=     """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowerCamelCase__							=     """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""






def   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   Dict  ):




     '''simple docstring'''






     return float((preds == labels).mean()  )






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Dict          =						simple_accuracy(snake_case__	,snake_case__  )
     __snake_case			:Union[str, Any]          =						float(fa_score(y_true=snake_case__	,y_pred=snake_case__  )  )
     return {
         "accuracy": acc,
         "f1": fa,
     }






def   UpperCamelCase  (       snake_case__     :   Any	,snake_case__     :   Tuple  ):




     '''simple docstring'''






     __snake_case			:Any          =						float(pearsonr(snake_case__	,snake_case__  )[0]  )
     __snake_case			:List[str]          =						float(spearmanr(snake_case__	,snake_case__  )[0]  )
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,    _KWARGS_DESCRIPTION)
class 			snake_case__   (							datasets.Metric):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            if self.config_name not in [
                "sst2",
                "mnli",
                "mnli_mismatched",
                "mnli_matched",
                "cola",
                "stsb",
                "mrpc",
                "qqp",
                "qnli",
                "rte",
                "wnli",
                "hans",
            ]:
                 raise KeyError(
                     """You should supply a configuration name selected in """
                     """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                     """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""    )
            return datasets.MetricInfo(
                description=_DESCRIPTION			,						citation=_CITATION			,						inputs_description=_KWARGS_DESCRIPTION			,						features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""    ),
                        """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""    ),
                    }    )			,						codebase_urls=[]			,						reference_urls=[]			,						format="""numpy"""			,						)



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      Dict:






            '''simple docstring'''





            if self.config_name == "cola":
                 return {"matthews_correlation": matthews_corrcoef(a__			,						a__    )}
            elif self.config_name == "stsb":
                 return pearson_and_spearman(a__			,						a__    )
            elif self.config_name in ["mrpc", "qqp"]:
                 return acc_and_fa(a__			,						a__    )
            elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                 return {"accuracy": simple_accuracy(a__			,						a__    )}
            else:
                 raise KeyError(
                     """You should supply a configuration name selected in """
                     """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                     """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""    )










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   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Dict	,snake_case__     :   List[str]  ):




     '''simple docstring'''






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

     __snake_case			:Dict          =						0
     __snake_case			:Tuple          =						0
     __snake_case			:Dict          =						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			:List[Any]          =						class_images[count]
               count += 1
               try:
                    __snake_case			:str          =						requests.get(images["""url"""]  )
                    if img.status_code == 200:
                         __snake_case			: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   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						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__":
     lowerCamelCase__							=     parse_args()
     retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


