


from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

UpperCAmelCase_      : Optional[int]  					=       datasets.load_iris()

UpperCAmelCase_      : int  					=       np.array(data['data'])
UpperCAmelCase_      : Optional[int]  					=       np.array(data['target'])
UpperCAmelCase_      : Tuple  					=       data['target_names']

UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_      : Tuple  					=       train_test_split(X, y)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			str				)	->		str:

       """simple docstring"""

       return np.linalg.norm(np.array(__A				) - np.array(__A				)				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			Optional[Any]  ,    __A :			Union[str, Any]  ,    __A :			Tuple  ,    __A :			Any=5				)	->		str:

       """simple docstring"""

       a_					:		str							=  zip(__A  ,    __A				)
       # List of distances of all points from the point to be classified
       a_					:		Tuple							=  []
       for data_point in data:
              a_					:		int							=  euclidean_distance(data_point[0]  ,    __A				)
              distances.append((distance, data_point[1])				)
       # Choosing 'k' points with the least distances.
       a_					:		Tuple							=  [i[1] for i in sorted(__A				)[:k]]
       # Most commonly occurring class among them
       # is the class into which the point is classified
       a_					:		Tuple							=  Counter(__A				).most_common(1				)[0][0]
       return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))










"""simple docstring"""






import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCAmelCase_   : Dict   			= logging.getLogger(__name__)

if __name__ == "__main__":
  UpperCAmelCase_   : List[str]   			= argparse.ArgumentParser(
      description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
  )
  parser.add_argument(
      """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
  )
  parser.add_argument(
      """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
  )
  parser.add_argument("""--vocab_size""", default=30522, type=int)
  UpperCAmelCase_   : Optional[Any]   			= parser.parse_args()

  logger.info(f'''Loading data from {args.data_file}''')
  with open(args.data_file, """rb""") as fp:
    UpperCAmelCase_   : Union[str, Any]   			= pickle.load(fp)

  logger.info("""Counting occurrences for MLM.""")
  UpperCAmelCase_   : Any   			= Counter()
  for tk_ids in data:
    counter.update(tk_ids)
  UpperCAmelCase_   : List[Any]   			= [0] * args.vocab_size
  for k, v in counter.items():
    UpperCAmelCase_   : Dict   			= v

  logger.info(f'''Dump to {args.token_counts_dump}''')
  with open(args.token_counts_dump, """wb""") as handle:
    pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


