'''simple docstring'''




from __future__ import annotations





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', '', ''
      _lowerCAmelCase		   =   []

      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 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							dict[str, str]    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''
      _lowerCAmelCase		   =   []

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

      for digit in this_part:
            tmp += digit
            if len(_lowerCamelCase    ) == len(_lowerCamelCase    ):
                  result.append(_lowerCamelCase    )
                  _lowerCAmelCase		   =   ''

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





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      # Validate message and alphabet, set to upper and remove spaces
      _lowerCAmelCase		   =   alphabet.replace(' '  ,  ''    ).upper()
      _lowerCAmelCase		   =   message.replace(' '  ,  ''    ).upper()

      # Check length and characters
      if len(_lowerCamelCase    ) != 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
      _lowerCAmelCase		   =   (
          '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',
      )
      _lowerCAmelCase		   =   {}
      _lowerCAmelCase		   =   {}
      for letter, number in zip(_lowerCamelCase  ,  _lowerCamelCase    ):
            _lowerCAmelCase		   =   number
            _lowerCAmelCase		   =   letter

      return message, alphabet, character_to_number, number_to_character





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   '', ''

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

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

      return encrypted





def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."  ,  _lowerCamelCase			:							int = 5    ):
      _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase		   =   __prepare(
          _lowerCamelCase  ,  _lowerCamelCase    )
      _lowerCAmelCase		   =   []
      _lowerCAmelCase		   =   ''

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

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

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

      return decrypted


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






'''simple docstring'''




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def 			A_   ( *_lowerCamelCase			:							Union[str, Any]    ):
      _lowerCAmelCase		   =   torch.cuda.device_count()
      print(F"Device count: {num_processes}"    )
      with patch_environment(
          world_size=_lowerCamelCase  ,  master_addr='127.0.01'  ,  master_port='29500'  ,  mixed_precision=args[1].mixed_precision    ):
            _lowerCAmelCase		   =   PrepareForLaunch(_lowerCamelCase  ,  distributed_type='MULTI_GPU'    )
            torch.multiprocessing.start_processes(_lowerCamelCase  ,  args=_lowerCamelCase  ,  nprocs=_lowerCamelCase  ,  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
 snake_case  =			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
 snake_case  =			[
     '''pip:./''',
     '''transformers''',
     '''datasets''',
     '''evaluate''',
     '''tqdm''',
     '''scipy''',
     '''scikit-learn''',
     '''tensorboard''',
     '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
 ]
 snake_case  =			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

 # Define train args/config, run train function
 snake_case  =			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
 snake_case  =			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 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'])





