code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
         import torch_xla.core.xla_model as xm  # noqa: F401
  
         lowercase__			=	True
  except ImportError:
         lowercase__			=	False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  lowercase__			=	torch.distributed.is_available()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= importlib.util.find_spec(snake_case   ) is not None
     if package_exists:
        try:
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= importlib.metadata.metadata(snake_case   )
           return True
        except importlib.metadata.PackageNotFoundError:
           return False
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _torch_distributed_available
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     try:
        pass
     except ImportError:
        print(
            '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
            ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
            ''' Bindings for PyTorch*.'''   )
     return (
         importlib.util.find_spec('''torch_ccl'''   ) is not None
         or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''   ) is not None
     )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return importlib.metadata.version('''oneccl_bind_pt'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''transformer_engine'''   )
  
  
  @lru_cache
  def 				UpperCamelCase	(  snake_case=True   ):
  
  
  
  
     '''simple docstring'''
  
     if torch.cuda.is_available():
        return False
     if _tpu_available and check_device:
        try:
           # Will raise a RuntimeError if no XLA configuration is found
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= xm.xla_device()
           return True
        except RuntimeError:
           return False
     return _tpu_available
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''deepspeed'''   )
  
  
  def 				UpperCamelCase	(  snake_case=False   ):
  
  
  
  
     '''simple docstring'''
  
     if is_tpu_available():
        return not ignore_tpu
     if torch.cuda.is_available():
        return torch.cuda.is_bfaa_supported()
     if is_npu_available():
        return False
     return True
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= _is_package_available('''bitsandbytes'''   )
     if package_exists:
        SCREAMING_SNAKE_CASE__			:				Any       						= version.parse(importlib.metadata.version('''bitsandbytes'''   )   )
        return compare_versions(snake_case	,     '''>='''	,     '''0.39.0'''   )
     return False
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= _is_package_available('''bitsandbytes'''   )
     if package_exists:
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= version.parse(importlib.metadata.version('''bitsandbytes'''   )   )
        return compare_versions(snake_case	,     '''>='''	,     '''0.37.2'''   )
     return False
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''bitsandbytes'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''	,     '''False'''   )   ) == 1:
        SCREAMING_SNAKE_CASE__			:				Any       						= importlib.util.find_spec('''megatron'''   ) is not None
        if package_exists:
           try:
              SCREAMING_SNAKE_CASE__			:				Dict       						= parse(importlib.metadata.version('''megatron-lm'''   )   )
              return compare_versions(snake_case	,     '''>='''	,     '''2.2.0'''   )
           except Exception as e:
              warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""   )
              return False
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''safetensors'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''transformers'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''datasets'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''aim'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''tensorboard'''   ) or _is_package_available('''tensorboardX'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''wandb'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''comet_ml'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''boto3'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     if _is_package_available('''rich'''   ):
        if "ACCELERATE_DISABLE_RICH" in os.environ:
           warnings.warn(
               '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''   )
           return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''	,     snake_case   )
        return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''	,     snake_case   )
     return False
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''sagemaker'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''tqdm'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return _is_package_available('''mlflow'''   )
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     return is_torch_version('''>='''	,     '''1.12'''   ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  def 				UpperCamelCase	(  ):
  
  
  
  
     '''simple docstring'''
  
     def get_major_and_minor_from_version(snake_case   ):
        return str(version.parse(snake_case   ).major   ) + "." + str(version.parse(snake_case   ).minor   )
  
     SCREAMING_SNAKE_CASE__			:				str       						= importlib.metadata.version('''torch'''   )
     if importlib.util.find_spec('''intel_extension_for_pytorch'''   ) is None:
        return False
     SCREAMING_SNAKE_CASE__			:				List[str]       						= '''N/A'''
     try:
        SCREAMING_SNAKE_CASE__			:				int       						= importlib.metadata.version('''intel_extension_for_pytorch'''   )
     except importlib.metadata.PackageNotFoundError:
        return False
     SCREAMING_SNAKE_CASE__			:				Dict       						= get_major_and_minor_from_version(_torch_version   )
     SCREAMING_SNAKE_CASE__			:				int       						= get_major_and_minor_from_version(_ipex_version   )
     if torch_major_and_minor != ipex_major_and_minor:
        warnings.warn(
            f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
            f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""   )
        return False
     return True
  
  
  @lru_cache
  def 				UpperCamelCase	(  snake_case=False   ):
  
  
  
  
     '''simple docstring'''
  
     if importlib.util.find_spec('''torch'''   ) is None or importlib.util.find_spec('''torch_npu'''   ) is None:
        return False
  
     import torch
     import torch_npu  # noqa: F401
  
     if check_device:
        try:
           # Will raise a RuntimeError if no NPU is found
           SCREAMING_SNAKE_CASE__			:				int       						= torch.npu.device_count()
           return torch.npu.is_available()
        except RuntimeError:
           return False
     return hasattr(snake_case	,     '''npu'''   ) and torch.npu.is_available()
  
  
  
  
  
  
  
  @lru_cache
  def 				UpperCamelCase	(  snake_case=False   ):
  
  
  
  
     '''simple docstring'''
  
     if not parse_flag_from_env('''ACCELERATE_USE_XPU'''	,     default=snake_case   ):
        return False
     if is_ipex_available():
        import torch
  
        if is_torch_version('''<='''	,     '''1.12'''   ):
           return False
     else:
        return False
  
     import intel_extension_for_pytorch  # noqa: F401
  
     if check_device:
        try:
           # Will raise a RuntimeError if no XPU  is found
           SCREAMING_SNAKE_CASE__			:				str       						= torch.xpu.device_count()
           return torch.xpu.is_available()
        except RuntimeError:
           return False
     return hasattr(snake_case	,     '''xpu'''   ) and torch.xpu.is_available()
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  
  
  
  
  
  class 						lowercase     :
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				str       						= letter
           SCREAMING_SNAKE_CASE__			:				int       						= freq
           SCREAMING_SNAKE_CASE__			:				dict[str, str]       						= {}
  
  
  
  
  
  
        def __repr__(self					)       ->   str:
           return f"""{self.letter}:{self.freq}"""
  
  
  
  
  
  
  class 						lowercase     :
  
  
  
  
  
  
        def __init__(self ,  a__ ,  a__ ,  a__					)       ->   Union[str, Any]:
           SCREAMING_SNAKE_CASE__			:				int       						= freq
           SCREAMING_SNAKE_CASE__			:				Letter | TreeNode       						= left
           SCREAMING_SNAKE_CASE__			:				Letter | TreeNode       						= right
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				dict[str, int]       						= {}
     with open(snake_case   ) as f:
        while True:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= f.read(1   )
           if not c:
              break
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= chars[c] + 1 if c in chars else 1
     return sorted((Letter(snake_case	,     snake_case   ) for c, f in chars.items())	,     key=lambda snake_case   : x.freq   )
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				list[Letter | TreeNode]       						= letters  # type: ignore
     while len(snake_case   ) > 1:
        SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= response.pop(0   )
        SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= response.pop(0   )
        SCREAMING_SNAKE_CASE__			:				Dict       						= left.freq + right.freq
        SCREAMING_SNAKE_CASE__			:				Any       						= TreeNode(snake_case	,     snake_case	,     snake_case   )
        response.append(snake_case   )
        response.sort(key=lambda snake_case   : x.freq   )
     return response[0]
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     if isinstance(snake_case	,     snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= bitstring
        return [root]
     SCREAMING_SNAKE_CASE__			:				TreeNode       						= root  # type: ignore
     SCREAMING_SNAKE_CASE__			:				int       						= []
     letters += traverse_tree(treenode.left	,     bitstring + '''0'''   )
     letters += traverse_tree(treenode.right	,     bitstring + '''1'''   )
     return letters
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= parse_file(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= build_tree(snake_case   )
     SCREAMING_SNAKE_CASE__			:				List[str]       						= {
         k: v for letter in traverse_tree(snake_case	,     ''''''   ) for k, v in letter.bitstring.items()
     }
     print(f"""Huffman Coding  of {file_path}: """   )
     with open(snake_case   ) as f:
        while True:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= 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])
  
  