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
     snake_case__				= 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')
     snake_case__			,    snake_case__				= 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')
          snake_case__				= rh.cluster(
              name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
          )
     else:
          snake_case__				= rh.cluster(
              name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     snake_case__				= 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)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()