



'''simple docstring'''





from __future__ import annotations

from typing import Any






class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A = 0       )    ->     List[str]:


      """simple docstring"""



      _a      =  row, column
      _a      =  [[default_value for c in range(A       )] for r in range(A       )]



 def __str__(self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

      # Make string identifier
      _a      =  0
      for row_vector in self.array:
           for obj in row_vector:
                _a      =  max(A ,   len(str(A       )       )       )
      _a      =  f'''%{max_element_length}s'''

      # Make string and return
      def single_line(A       ) -> str:
           nonlocal string_format_identifier
           _a      =  '['
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector       )
           line += "]"
           return line

      s += "\n".join(single_line(A       ) for row_vector in self.array       )
      return s



 def __repr__(self       )    ->     Any:


      """simple docstring"""



      return str(self       )



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      if not (isinstance(A ,   (list, tuple)       ) and len(A       ) == 2):
           return False
      elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
      else:
           return True



 def __getitem__(self ,   A       )    ->     Dict:


      """simple docstring"""



      assert self.validate_indicies(A       )
      return self.array[loc[0]][loc[1]]



 def __setitem__(self ,   A ,   A       )    ->     List[str]:


      """simple docstring"""



      assert self.validate_indicies(A       )
      _a      =  value



 def __add__(self ,   A       )    ->     List[Any]:


      """simple docstring"""



      assert isinstance(A ,   A       )
      assert self.row == another.row and self.column == another.column

      # Add
      _a      =  Matrix(self.row ,   self.column       )
      for r in range(self.row       ):
           for c in range(self.column       ):
                _a      =  self[r, c] + another[r, c]
      return result



 def __neg__(self       )    ->     str:


      """simple docstring"""



      _a      =  Matrix(self.row ,   self.column       )
      for r in range(self.row       ):
           for c in range(self.column       ):
                _a      =  -self[r, c]
      return result



 def __sub__(self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      return self + (-another)



 def __mul__(self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      if isinstance(A ,   (int, float)       ):  # Scalar multiplication
           _a      =  Matrix(self.row ,   self.column       )
           for r in range(self.row       ):
                for c in range(self.column       ):
                     _a      =  self[r, c] * another
           return result
      elif isinstance(A ,   A       ):  # Matrix multiplication
           assert self.column == another.row
           _a      =  Matrix(self.row ,   another.column       )
           for r in range(self.row       ):
                for c in range(another.column       ):
                     for i in range(self.column       ):
                          result[r, c] += self[r, i] * another[i, c]
           return result
      else:
           _a      =  f'''Unsupported type given for another ({type(A       )})'''
           raise TypeError(A       )



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  Matrix(self.column ,   self.row       )
      for r in range(self.row       ):
           for c in range(self.column       ):
                _a      =  self[r, c]
      return result



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      assert isinstance(A ,   A       ) and isinstance(A ,   A       )
      assert self.row == self.column == u.row == v.row  # u, v should be column vector
      assert u.column == v.column == 1  # u, v should be column vector

      # Calculate
      _a      =  v.transpose()
      _a      =  (v_t * self * u)[0, 0] + 1
      if numerator_factor == 0:
           return None  # It's not invertable
      return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




  def lowerCAmelCase  ():



       """simple docstring"""



       _a      =  Matrix(3  ,	3  ,	0)
       for i in range(3):
            _a      =  1
       print(F'''a^(-1) is {ainv}''')
       # u, v
       _a      =  Matrix(3  ,	1  ,	0)
       _a      =  1, 2, -3
       _a      =  Matrix(3  ,	1  ,	0)
       _a      =  4, -2, 5
       print(F'''u is {u}''')
       print(F'''v is {v}''')
       print(F'''uv^T is {u * v.transpose()}''')
       # Sherman Morrison
       print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_)}''')




  def lowerCAmelCase  ():



       """simple docstring"""



       import doctest

       doctest.testmod()
  testa()






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)

