






"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True     ,  _lowerCamelCase=True							):
 if not is_fpa_available():
  raise ImportError('Using `convert_model` requires transformer_engine to be installed.'							)
 for name, module in model.named_children():
  if isinstance(_lowerCamelCase     ,  nn.Linear							) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape							):
    return
   lowerCamelCase__	:     List[Any]								=		module.bias is not None
   lowerCamelCase__	:     Tuple								=		te.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Optional[Any]								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     List[str]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  nn.LayerNorm							) and to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     Tuple								=		te.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Tuple								=		module.weight.data.clone()
   lowerCamelCase__	:     Optional[Any]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.Linear							) and not to_transformer_engine and _convert_linear:
   lowerCamelCase__	:     Optional[Any]								=		module.bias is not None
   lowerCamelCase__	:     Union[str, Any]								=		nn.Linear(
       module.in_features     ,  module.out_features     ,  bias=_lowerCamelCase     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     str								=		module.weight.data.clone()
   if has_bias:
    lowerCamelCase__	:     Optional[int]								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  elif isinstance(_lowerCamelCase     ,  te.LayerNorm							) and not to_transformer_engine and _convert_ln:
   lowerCamelCase__	:     str								=		nn.LayerNorm(module.normalized_shape[0]     ,  eps=module.eps     ,  params_dtype=module.weight.dtype							)
   lowerCamelCase__	:     Dict								=		module.weight.data.clone()
   lowerCamelCase__	:     Dict								=		module.bias.data.clone()

   setattr(_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							)
  else:
   convert_model(
       _lowerCamelCase     ,  to_transformer_engine=_lowerCamelCase     ,  _convert_linear=_convert_linear     ,  _convert_ln=_convert_ln     ,  )






def 			lowerCamelCase_    (		_lowerCamelCase							):
 if not is_fpa_available():
  raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'							)
 for m in model.modules():
  if isinstance(_lowerCamelCase     ,  (te.LayerNorm, te.Linear)							):
   return True
 return False









"""simple docstring"""
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  ):
 lowerCamelCase__	:     Any								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     int								=		len(_lowerCamelCase							)
 lowerCamelCase__	:     Optional[int]								=		len(_lowerCamelCase							)

 lowerCamelCase__	:     Tuple								=		[
     [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]
 lowerCamelCase__	:     Dict								=		[
     ['0' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]

 for i in range(1     ,  len_source_seq + 1							):
  lowerCamelCase__	:     Optional[int]								=		i * delete_cost
  lowerCamelCase__	:     List[str]								=		f'''D{source_seq[i - 1]:c}'''

 for i in range(1     ,  len_destination_seq + 1							):
  lowerCamelCase__	:     int								=		i * insert_cost
  lowerCamelCase__	:     str								=		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]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j - 1] + copy_cost
    lowerCamelCase__	:     str								=		f'''C{source_seq[i - 1]:c}'''
   else:
    lowerCamelCase__	:     Tuple								=		costs[i - 1][j - 1] + replace_cost
    lowerCamelCase__	:     List[str]								=		f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j] + delete_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    lowerCamelCase__	:     Optional[Any]								=		costs[i][j - 1] + insert_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''I{destination_seq[j - 1]:c}'''

 return costs, ops






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   lowerCamelCase__	:     List[str]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq
  elif ops[i][j][0] == "D":
   lowerCamelCase__	:     Union[str, Any]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  _lowerCamelCase							)
   seq.append(ops[i][j]							)
   return seq
  else:
   lowerCamelCase__	:     Tuple								=		assemble_transformation(_lowerCamelCase     ,  _lowerCamelCase     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq


if __name__ == "__main__":
      A_,			A_     :					List[str]							=     compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

      A_     :					str							=     len(operations)
      A_     :					Dict							=     len(operations[0])
      A_     :					Any							=     assemble_transformation(operations, m - 1, n - 1)

      A_     :					int							=     list("Python")
      A_     :					Optional[Any]							=     0
      A_     :					Tuple							=     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":
                        A_     :					str							=     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))

