import math





def  lowercase(  UpperCamelCase_					)		->  str:







 '''simple docstring'''
 if number == 0:
  UpperCamelCase      =			"""Zero"""
  return words
 else:
  UpperCamelCase      =			math.logaa(UpperCamelCase_					)
  UpperCamelCase      =			digits + 1
  UpperCamelCase      =			{}
  UpperCamelCase      =			""""""
  UpperCamelCase      =			"""One"""
  UpperCamelCase      =			"""Two"""
  UpperCamelCase      =			"""Three"""
  UpperCamelCase      =			"""Four"""
  UpperCamelCase      =			"""Five"""
  UpperCamelCase      =			"""Six"""
  UpperCamelCase      =			"""Seven"""
  UpperCamelCase      =			"""Eight"""
  UpperCamelCase      =			"""Nine"""

  UpperCamelCase      =			{}
  UpperCamelCase      =			""""""
  UpperCamelCase      =			"""Twenty"""
  UpperCamelCase      =			"""Thirty"""
  UpperCamelCase      =			"""Forty"""
  UpperCamelCase      =			"""Fifty"""
  UpperCamelCase      =			"""Sixty"""
  UpperCamelCase      =			"""Seventy"""
  UpperCamelCase      =			"""Eighty"""
  UpperCamelCase      =			"""Ninety"""

  UpperCamelCase      =			{}
  UpperCamelCase      =			"""Ten"""
  UpperCamelCase      =			"""Eleven"""
  UpperCamelCase      =			"""Twelve"""
  UpperCamelCase      =			"""Thirteen"""
  UpperCamelCase      =			"""Fourteen"""
  UpperCamelCase      =			"""Fifteen"""
  UpperCamelCase      =			"""Sixteen"""
  UpperCamelCase      =			"""Seventeen"""
  UpperCamelCase      =			"""Eighteen"""
  UpperCamelCase      =			"""Nineteen"""

  UpperCamelCase      =			{}
  UpperCamelCase      =			"""Hundred,"""
  UpperCamelCase      =			"""Thousand,"""
  UpperCamelCase      =			"""Lakh,"""
  UpperCamelCase      =			"""Crore,"""

  UpperCamelCase      =			number
  UpperCamelCase      =			""""""
  UpperCamelCase      =			0
  UpperCamelCase      =			int(UpperCamelCase_					)
  while counter < digits:
   UpperCamelCase      =			temp_num % 10
   if counter % 2 == 0:
    UpperCamelCase      =			""""""
    if counter in placevalue and current != 0:
     UpperCamelCase      =			placevalue[counter]
    if counter == 2:
     UpperCamelCase      =			singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 100) // 10) == 1:
      UpperCamelCase      =			teens[current] + addition + words
      UpperCamelCase      =			temp_num // 10
      counter += 1
     else:
      UpperCamelCase      =			singles[current] + addition + words

    else:
     UpperCamelCase      =			doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      UpperCamelCase      =			teens[number % 10] + words
     else:
      UpperCamelCase      =			""""""
      if counter in placevalue:
       UpperCamelCase      =			placevalue[counter]
      UpperCamelCase      =			doubles[current] + addition + words
    else:
     UpperCamelCase      =			""""""
     if counter in placevalue:
      if current == 0 and ((temp_num % 100) // 10) == 0:
       UpperCamelCase      =			""""""
      else:
       UpperCamelCase      =			placevalue[counter]
     if ((temp_num % 100) // 10) == 1:
      UpperCamelCase      =			teens[current] + addition + words
      UpperCamelCase      =			temp_num // 10
      counter += 1
     else:
      UpperCamelCase      =			singles[current] + addition + words
   counter += 1
   UpperCamelCase      =			temp_num // 10
 return words


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
