





"""simple docstring"""
def 			__snake_case						(      __A    )		->				Union[str, Any]:
   lowercase    :			Optional[int]  =       len(__A    )
   for i in range(length - 1    ):
      lowercase    :			Tuple  =       False
      for j in range(length - 1 - i    ):
         if collection[j] > collection[j + 1]:
            lowercase    :			Optional[Any]  =       True
            lowercase  ,					lowercase    :			Optional[Any]  =       collection[j + 1], collection[j]
      if not swapped:
         break  # Stop iteration if the collection is sorted.
   return collection


if __name__ == "__main__":
   import doctest
   import time

   doctest.testmod()

   lowerCAmelCase:	List[Any]							      =input("Enter numbers separated by a comma:").strip()
   lowerCAmelCase:	Optional[Any]							      =[int(item) for item in user_input.split(",")]
   lowerCAmelCase:	str							      =time.process_time()
   print(*bubble_sort(unsorted), sep=",")
   print(F'Processing time: {(time.process_time() - start)%1e9 + 7}')









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

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te




def 			__snake_case						(      __A ,__A=True ,__A=True ,__A=True    )		->				Union[str, Any]:
   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(__A ,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
         lowercase    :			Optional[int]  =       module.bias is not None
         lowercase    :			Union[str, Any]  =       te.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Dict  =       module.bias.data.clone()

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

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

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

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







def 			__snake_case						(      __A    )		->				Union[str, Any]:
   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(__A ,(te.LayerNorm, te.Linear)    ):
         return True
   return False


