



from collections import Counter

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

__UpperCAmelCase	       =	datasets.load_iris()

__UpperCAmelCase	       =	np.array(data['''data'''])
__UpperCAmelCase	       =	np.array(data['''target'''])
__UpperCAmelCase	       =	data['''target_names''']

__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase	       =	train_test_split(X, y)
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return np.linalg.norm(np.array(snake_case__     ) - np.array(snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[Any]	,					snake_case__	:   List[Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Any=5     )     ->					Any:
   UpperCamelCase   :  List[Any]						     =     zip(snake_case__	,					snake_case__     )
   # List of distances of all points from the point to be classified
   UpperCamelCase   :  Optional[Any]						     =     []
   for data_point in data:
      UpperCamelCase   :  Optional[Any]						     =     euclidean_distance(data_point[0]	,					snake_case__     )
      distances.append((distance, data_point[1])     )
   # Choosing 'k' points with the least distances.
   UpperCamelCase   :  Optional[int]						     =     [i[1] for i in sorted(snake_case__     )[:k]]
   # Most commonly occurring class among them
   # is the class into which the point is classified
   UpperCamelCase   :  List[Any]						     =     Counter(snake_case__     ).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]))








import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    parser.add_argument('''--user''', type=str, default='''ubuntu''')
    parser.add_argument('''--host''', type=str, default='''localhost''')
    parser.add_argument('''--key_path''', type=str, default=None)
    parser.add_argument('''--instance''', type=str, default='''V100:1''')
    parser.add_argument('''--provider''', type=str, default='''cheapest''')
    parser.add_argument('''--use_spot''', type=bool, default=False)
    parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
    __UpperCAmelCase       ,  __UpperCAmelCase	       =	parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
        )
    else:
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    __UpperCAmelCase	       =	args.example.rsplit('''/''', 1)[0]

    # Set up remote environment
    cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
    cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([F"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)



