





"""simple docstring"""
from __future__ import annotations

import sys
class lowerCamelCase__  :



   def __init__(       self      ,   snake_case      ,   snake_case	) ->    int:







      """simple docstring"""
      lowercase    :			str  =       letter
      lowercase    :			int  =       freq
      lowercase    :			dict[str, str]  =       {}





   def __repr__(       self	) ->    List[Any]:







      """simple docstring"""
      return f'''{self.letter}:{self.freq}'''




class lowerCamelCase__  :



   def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    int:







      """simple docstring"""
      lowercase    :			int  =       freq
      lowercase    :			Letter | TreeNode  =       left
      lowercase    :			Letter | TreeNode  =       right




def 			__snake_case						(      __A    )		->				list[Letter]:
   lowercase    :			dict[str, int]  =       {}
   with open(__A    ) as f:
      while True:
         lowercase    :			int  =       f.read(1    )
         if not c:
            break
         lowercase    :			Union[str, Any]  =       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 			__snake_case						(      __A    )		->				Letter | TreeNode:
   lowercase    :			list[Letter | TreeNode]  =       letters  # type: ignore
   while len(__A    ) > 1:
      lowercase    :			List[str]  =       response.pop(0    )
      lowercase    :			Optional[int]  =       response.pop(0    )
      lowercase    :			Optional[int]  =       left.freq + right.freq
      lowercase    :			str  =       TreeNode(__A ,__A ,__A    )
      response.append(__A    )
      response.sort(key=lambda __A    : x.freq    )
   return response[0]




def 			__snake_case						(      __A ,__A    )		->				list[Letter]:
   if isinstance(__A ,__A    ):
      lowercase    :			Optional[Any]  =       bitstring
      return [root]
   lowercase    :			TreeNode  =       root  # type: ignore
   lowercase    :			str  =       []
   letters += traverse_tree(treenode.left ,bitstring + """0"""    )
   letters += traverse_tree(treenode.right ,bitstring + """1"""    )
   return letters




def 			__snake_case						(      __A    )		->				None:
   lowercase    :			str  =       parse_file(__A    )
   lowercase    :			int  =       build_tree(__A    )
   lowercase    :			List[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:
         lowercase    :			Optional[int]  =       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 			__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


