from __future__ import annotations

from enum import Enum
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							24
   __lowerCAmelCase											=							21
   __lowerCAmelCase											=							18
   __lowerCAmelCase											=							15
   __lowerCAmelCase											=							12
   __lowerCAmelCase											=							9
   __lowerCAmelCase											=							6
   __lowerCAmelCase											=							3
   __lowerCAmelCase											=							2
   __lowerCAmelCase											=							1
   __lowerCAmelCase											=							-1
   __lowerCAmelCase											=							-2
   __lowerCAmelCase											=							-3
   __lowerCAmelCase											=							-6
   __lowerCAmelCase											=							-9
   __lowerCAmelCase											=							-12
   __lowerCAmelCase											=							-15
   __lowerCAmelCase											=							-18
   __lowerCAmelCase											=							-21
   __lowerCAmelCase											=							-24





class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							8
   __lowerCAmelCase											=							7
   __lowerCAmelCase											=							6
   __lowerCAmelCase											=							5
   __lowerCAmelCase											=							4
   __lowerCAmelCase											=							3
   __lowerCAmelCase											=							2
   __lowerCAmelCase											=							1





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				)		->  float:







 '''simple docstring'''
 if isinstance(UpperCamelCase_       ,				UpperCamelCase_					):
  UpperCamelCase      =			SIUnit[known_prefix.lower()]
 if isinstance(UpperCamelCase_       ,				UpperCamelCase_					):
  UpperCamelCase      =			SIUnit[unknown_prefix.lower()]
 UpperCamelCase      =			known_amount * (
     10 ** (known_prefix.value - unknown_prefix.value)
 )
 return unknown_amount





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				)		->  float:







 '''simple docstring'''
 if isinstance(UpperCamelCase_       ,				UpperCamelCase_					):
  UpperCamelCase      =			BinaryUnit[known_prefix.lower()]
 if isinstance(UpperCamelCase_       ,				UpperCamelCase_					):
  UpperCamelCase      =			BinaryUnit[unknown_prefix.lower()]
 UpperCamelCase      =			known_amount * (
     2 ** ((known_prefix.value - unknown_prefix.value) * 10)
 )
 return unknown_amount


if __name__ == "__main__":
     import doctest

     doctest.testmod()

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 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(UpperCamelCase_       ,				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
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			te.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				nn.LayerNorm					) and to_transformer_engine and _convert_ln:
   UpperCamelCase      =			te.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.Linear					) and not to_transformer_engine and _convert_linear:
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			nn.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

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

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





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 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(UpperCamelCase_       ,				(te.LayerNorm, te.Linear)					):
   return True
 return False
