from __future__ import annotations




def      lowerCAmelCase_				(  __A,						__A							)  ->       str:







    '''simple docstring'''


    UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						"", "", ""
    UpperCAmelCase__			=						[]

    for character in message_part:
        tmp.append(character_to_number[character]							)

    for each in tmp:
        one += each[0]
        two += each[1]
        three += each[2]

    return one + two + three




def      lowerCAmelCase_				(  __A,						__A							)  ->       tuple[str, str, str]:







    '''simple docstring'''


    UpperCAmelCase__	,   UpperCAmelCase__			=						"", ""
    UpperCAmelCase__			=						[]

    for character in message_part:
        this_part += character_to_number[character]

    for digit in this_part:
        tmp += digit
        if len(__A							) == len(__A							):
            result.append(__A							)
            UpperCAmelCase__			=						""

    return result[0], result[1], result[2]




def      lowerCAmelCase_				(  __A,						__A							)  ->       tuple[str, str, dict[str, str], dict[str, str]]:







    '''simple docstring'''


    UpperCAmelCase__			=						alphabet.replace(" ",						""							).upper()
    UpperCAmelCase__			=						message.replace(" ",						""							).upper()

    # Check length and characters
    if len(__A							) != 27:
        raise KeyError("Length of alphabet has to be 27."							)
    for each in message:
        if each not in alphabet:
            raise ValueError("Each message character has to be included in alphabet!"							)

    # Generate dictionares
    UpperCAmelCase__			=						(
        "111",
        "112",
        "113",
        "121",
        "122",
        "123",
        "131",
        "132",
        "133",
        "211",
        "212",
        "213",
        "221",
        "222",
        "223",
        "231",
        "232",
        "233",
        "311",
        "312",
        "313",
        "321",
        "322",
        "323",
        "331",
        "332",
        "333",
    )
    UpperCAmelCase__			=						{}
    UpperCAmelCase__			=						{}
    for letter, number in zip(__A,						__A							):
        UpperCAmelCase__			=						number
        UpperCAmelCase__			=						letter

    return message, alphabet, character_to_number, number_to_character




def      lowerCAmelCase_				(  __A,						__A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.",						__A = 5							)  ->       str:







    '''simple docstring'''


    UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						__prepare(
        __A,						__A							)
    UpperCAmelCase__	,   UpperCAmelCase__			=						"", ""

    for i in range(0,						len(__A							) + 1,						__A							):
        encrypted_numeric += __encrypt_part(
            message[i : i + period],						__A							)

    for i in range(0,						len(__A							),						3							):
        encrypted += number_to_character[encrypted_numeric[i : i + 3]]

    return encrypted




def      lowerCAmelCase_				(  __A,						__A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ.",						__A = 5							)  ->       str:







    '''simple docstring'''


    UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						__prepare(
        __A,						__A							)
    UpperCAmelCase__			=						[]
    UpperCAmelCase__			=						""

    for i in range(0,						len(__A							) + 1,						__A							):
        UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						__decrypt_part(message[i : i + period],						__A							)

        for j in range(0,						len(__A							)							):
            decrypted_numeric.append(a[j] + b[j] + c[j]							)

    for each in decrypted_numeric:
        decrypted += number_to_character[each]

    return decrypted


if __name__ == "__main__":
      UpperCamelCase__       							=      'DEFEND THE EAST WALL OF THE CASTLE.'
      UpperCamelCase__       							=      encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
      UpperCamelCase__       							=      decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
      print(f'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      lowerCAmelCase_				(  *__A							)  ->       Tuple:







      '''simple docstring'''


      UpperCAmelCase__			=						torch.cuda.device_count()
      print(f"""Device count: {num_processes}"""							)
      with patch_environment(
          world_size=__A,						master_addr="127.0.01",						master_port="29500",						mixed_precision=args[1].mixed_precision							):
            UpperCAmelCase__			=						PrepareForLaunch(__A,						distributed_type="MULTI_GPU"							)
            torch.multiprocessing.start_processes(__A,						args=__A,						nprocs=__A,						start_method="spawn"							)


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      UpperCamelCase__       							=      rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      UpperCamelCase__       							=      [
          'pip:./',
          'transformers',
          'datasets',
          'evaluate',
          'tqdm',
          'scipy',
          'scikit-learn',
          'tensorboard',
          'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
      ]
      UpperCamelCase__       							=      rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

      # Define train args/config, run train function
      UpperCamelCase__       							=      argparse.Namespace(cpu=False, mixed_precision='fp16')
      UpperCamelCase__       							=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



