





'''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()







'''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])

