'''simple docstring'''

from __future__ import annotations

import sys




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :str       =letter
        _UpperCamelCase  :int       =freq
        _UpperCamelCase  :dict[str, str]       ={}




   def __repr__(    self     )				->     str:
        """simple docstring"""





        return f'''{self.letter}:{self.freq}'''




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =freq
        _UpperCamelCase  :Letter | TreeNode       =left
        _UpperCamelCase  :Letter | TreeNode       =right




def  _lowerCAmelCase				(				__a      )  ->      list[Letter]:
     '''simple docstring'''







     _UpperCamelCase  :dict[str, int]       ={}
     with open(__a      ) as f:
          while True:
               _UpperCamelCase  :Union[str, Any]       =f.read(1      )
               if not c:
                    break
               _UpperCamelCase  :str       =chars[c] + 1 if c in chars else 1
     return sorted((Letter(__a			,      __a      ) for c, f in chars.items())			,      key=lambda __a      : x.freq      )



def  _lowerCAmelCase				(				__a      )  ->      Letter | TreeNode:
     '''simple docstring'''







     _UpperCamelCase  :list[Letter | TreeNode]       =letters  # type: ignore
     while len(__a      ) > 1:
          _UpperCamelCase  :List[Any]       =response.pop(0      )
          _UpperCamelCase  :Any       =response.pop(0      )
          _UpperCamelCase  :Optional[Any]       =left.freq + right.freq
          _UpperCamelCase  :Dict       =TreeNode(__a			,      __a			,      __a      )
          response.append(__a      )
          response.sort(key=lambda __a      : x.freq      )
     return response[0]



def  _lowerCAmelCase				(				__a			,      __a      )  ->      list[Letter]:
     '''simple docstring'''







     if isinstance(__a			,      __a      ):
          _UpperCamelCase  :Tuple       =bitstring
          return [root]
     _UpperCamelCase  :TreeNode       =root  # type: ignore
     _UpperCamelCase  :int       =[]
     letters += traverse_tree(treenode.left			,      bitstring + """0"""      )
     letters += traverse_tree(treenode.right			,      bitstring + """1"""      )
     return letters



def  _lowerCAmelCase				(				__a      )  ->      None:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =parse_file(__a      )
     _UpperCamelCase  :Dict       =build_tree(__a      )
     _UpperCamelCase  :Union[str, Any]       ={
         k: v for letter in traverse_tree(__a			,      """"""      ) for k, v in letter.bitstring.items()
     }
     print(F'''Huffman Coding  of {file_path}: '''      )
     with open(__a      ) as f:
          while True:
               _UpperCamelCase  :Union[str, Any]       =f.read(1      )
               if not c:
                    break
               print(letters[c]			,      end=""" """      )
     print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])







"""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

