




'''simple docstring'''




def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      int       ,						):
 '''simple docstring'''





 UpperCAmelCase__										=    list(_UpperCAmelCase       )
 UpperCAmelCase__										=    list(_UpperCAmelCase       )
 UpperCAmelCase__										=    len(_UpperCAmelCase       )
 UpperCAmelCase__										=    len(_UpperCAmelCase       )

 UpperCAmelCase__										=    [
     [0 for _ in range(len_destination_seq + 1       )] for _ in range(len_source_seq + 1       )
 ]
 UpperCAmelCase__										=    [
     ['0' for _ in range(len_destination_seq + 1       )] for _ in range(len_source_seq + 1       )
 ]

 for i in range(1       ,						len_source_seq + 1       ):
  UpperCAmelCase__										=    i * delete_cost
  UpperCAmelCase__										=    F'''D{source_seq[i - 1]:c}'''

 for i in range(1       ,						len_destination_seq + 1       ):
  UpperCAmelCase__										=    i * insert_cost
  UpperCAmelCase__										=    F'''I{destination_seq[i - 1]:c}'''

 for i in range(1       ,						len_source_seq + 1       ):
  for j in range(1       ,						len_destination_seq + 1       ):
   if source_seq[i - 1] == destination_seq[j - 1]:
    UpperCAmelCase__										=    costs[i - 1][j - 1] + copy_cost
    UpperCAmelCase__										=    F'''C{source_seq[i - 1]:c}'''
   else:
    UpperCAmelCase__										=    costs[i - 1][j - 1] + replace_cost
    UpperCAmelCase__										=    F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]       )

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    UpperCAmelCase__										=    costs[i - 1][j] + delete_cost
    UpperCAmelCase__										=    F'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    UpperCAmelCase__										=    costs[i][j - 1] + insert_cost
    UpperCAmelCase__										=    F'''I{destination_seq[j - 1]:c}'''

 return costs, ops

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   UpperCAmelCase__										=    assemble_transformation(_UpperCAmelCase       ,						i - 1       ,						j - 1       )
   seq.append(ops[i][j]       )
   return seq
  elif ops[i][j][0] == "D":
   UpperCAmelCase__										=    assemble_transformation(_UpperCAmelCase       ,						i - 1       ,						_UpperCAmelCase       )
   seq.append(ops[i][j]       )
   return seq
  else:
   UpperCAmelCase__										=    assemble_transformation(_UpperCAmelCase       ,						_UpperCAmelCase       ,						j - 1       )
   seq.append(ops[i][j]       )
   return seq


if __name__ == "__main__":
     UpperCAmelCase_       				=      compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

     UpperCAmelCase_       				=      len(operations)
     UpperCAmelCase_       				=      len(operations[0])
     UpperCAmelCase_       				=      assemble_transformation(operations, m - 1, n - 1)

     UpperCAmelCase_       				=      list('Python')
     UpperCAmelCase_       				=      0
     UpperCAmelCase_       				=      0

     with open('min_cost.txt', 'w') as file:
          for op in sequence:
               print(''.join(string))

               if op[0] == "C":
                    file.write('%-16s' % 'Copy %c' % op[1])
                    file.write('\t\t\t' + ''.join(string))
                    file.write('\r\n')

                    cost -= 1
               elif op[0] == "R":
                    UpperCAmelCase_       				=      op[2]

                    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                    file.write('\t\t' + ''.join(string))
                    file.write('\r\n')

                    cost += 1
               elif op[0] == "D":
                    string.pop(i)

                    file.write('%-16s' % 'Delete %c' % op[1])
                    file.write('\t\t\t' + ''.join(string))
                    file.write('\r\n')

                    cost += 2
               else:
                    string.insert(i, op[1])

                    file.write('%-16s' % 'Insert %c' % op[1])
                    file.write('\t\t\t' + ''.join(string))
                    file.write('\r\n')

                    cost += 2

               i += 1

          print(''.join(string))
          print('Cost: ', cost)

          file.write('\r\nMinimum cost: ' + str(cost))






'''simple docstring'''




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Dict       ):
 '''simple docstring'''





 if isinstance(SCREAMING_SNAKE_CASE__       ,						(list, tuple)       ):
  return honor_type(SCREAMING_SNAKE_CASE__       ,						(move_to_device(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ) for t in state)       )
 elif isinstance(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ):
  return type(SCREAMING_SNAKE_CASE__       )({k: move_to_device(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ) for k, v in state.items()}       )
 elif isinstance(SCREAMING_SNAKE_CASE__       ,						torch.Tensor       ):
  return state.to(SCREAMING_SNAKE_CASE__       )
 return state







class 						lowerCAmelCase_   ( torch.optim.Optimizer   ):







  '''simple docstring'''







  def __init__(						self    :	List[str]						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	Union[str, Any]=True						,		_UpperCAmelCase    :	List[str]=None ):






   """simple docstring"""






   UpperCAmelCase__										=    optimizer
   UpperCAmelCase__										=    scaler
   UpperCAmelCase__										=    AcceleratorState()
   UpperCAmelCase__										=    GradientState()
   UpperCAmelCase__										=    device_placement
   UpperCAmelCase__										=    False
   UpperCAmelCase__										=    None

   # Handle device placement
   if device_placement:
    UpperCAmelCase__										=    self.optimizer.state_dict()
    if self.accelerator_state.distributed_type == DistributedType.TPU:
     xm.send_cpu_data_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
    else:
     UpperCAmelCase__										=    move_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
    self.optimizer.load_state_dict(_UpperCAmelCase )


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   return self.optimizer.state


  @state.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    state


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   return self.optimizer.param_groups


  @param_groups.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    param_groups


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   return self.optimizer.defaults


  @defaults.setter
  def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    defaults


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any] ):






   """simple docstring"""






   self.optimizer.add_param_group(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
    xm.send_cpu_data_to_device(_UpperCAmelCase						,		self.accelerator_state.device )
   self.optimizer.load_state_dict(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   return self.optimizer.state_dict()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any]=None ):






   """simple docstring"""






   if self.gradient_state.sync_gradients:
    UpperCAmelCase__										=    """set_to_none""" in inspect.signature(self.optimizer.zero_grad ).parameters
    if accept_arg:
     if set_to_none is None:
      UpperCAmelCase__										=    False
     self.optimizer.zero_grad(set_to_none=_UpperCAmelCase )
    else:
     if set_to_none is not None:
      raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.""" )
     self.optimizer.zero_grad()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	List[str]=None ):






   """simple docstring"""






   if self.gradient_state.sync_gradients:
    if self.accelerator_state.distributed_type == DistributedType.TPU:
     UpperCAmelCase__										=    {"""closure""": closure} if closure is not None else {}
     xm.optimizer_step(self.optimizer						,		optimizer_args=_UpperCAmelCase )
    elif self.scaler is not None:
     UpperCAmelCase__										=    False
     if self._last_scale is None:
      # `get_scale` is an async operation requiring full synchronization
      # on CPU and GPUs before finishing. As a result, we store away
      # the prior one to reduce the call overhead
      UpperCAmelCase__										=    self.scaler.get_scale()
      UpperCAmelCase__										=    True
     self.scaler.step(self.optimizer						,		_UpperCAmelCase )
     self.scaler.update()
     UpperCAmelCase__										=    self.scaler.get_scale()
     if not new_scale:
      # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
      UpperCAmelCase__										=    scale_after < self._last_scale
     UpperCAmelCase__										=    scale_after

    else:
     self.optimizer.step(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   for param_group in self.optimizer.param_groups:
    UpperCAmelCase__										=    [parameters_map.get(_UpperCAmelCase						,		_UpperCAmelCase ) for p in param_group["""params"""]]


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   warnings.warn(
       """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
       """`optimizer.step_was_skipped` instead."""						,		_UpperCAmelCase						,		)
   return self._is_overflow


  @property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   return self._is_overflow


  def __getstate__(						self    :	Dict ):






   """simple docstring"""






   return self.__dict__.copy()



  def __setstate__(						self    :	Dict						,		_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






   self.__dict__.update(_UpperCAmelCase )
