



"""simple docstring"""
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_UpperCAmelCase       =     logging.get_logger(__name__)

if is_tf_available():
   import tensorflow as tf
@dataclass
class    a		(					SCREAMING_SNAKE_CASE_    ):
  UpperCamelCase		:			Dict         =   'tf'
  UpperCamelCase		:			Optional[Any]         =   field(
      default=SCREAMING_SNAKE_CASE_		, metadata={'help': 'Name of TPU'}		, )

  UpperCamelCase		:			Optional[Any]         =   field(
      default=SCREAMING_SNAKE_CASE_		, metadata={'help': 'Zone of TPU'}		, )

  UpperCamelCase		:			List[str]         =   field(
      default=SCREAMING_SNAKE_CASE_		, metadata={'help': 'Name of Cloud TPU-enabled project'}		, )

  UpperCamelCase		:			Any         =   field(
      default=1.0		, metadata={'help': 'Power for the Polynomial decay LR scheduler.'}		, )

  UpperCamelCase		:			Tuple         =   field(default=SCREAMING_SNAKE_CASE_		, metadata={'help': 'Whether to activate the XLA compilation or not'}    )



  @cached_property
  def 		lowerCamelCase__    (  self	:   int						)       ->   Tuple["tf.distribute.Strategy", int]:


     '''simple docstring'''




     requires_backends(self    ,						["""tf"""]						)
     logger.info("""Tensorflow: setting up strategy"""						)

     SCREAMING_SNAKE_CASE_:	str            =tf.config.list_physical_devices("""GPU"""						)

     # Set to float16 at first
     if self.fpaa:
        tf.keras.mixed_precision.set_global_policy("""mixed_float16"""						)

     if self.no_cuda:
        SCREAMING_SNAKE_CASE_:	List[Any]            =tf.distribute.OneDeviceStrategy(device="""/cpu:0"""						)
     else:
        try:
           if self.tpu_name:
              SCREAMING_SNAKE_CASE_:	List[str]            =tf.distribute.cluster_resolver.TPUClusterResolver(
                  self.tpu_name    ,						zone=self.tpu_zone    ,						project=self.gcp_project						)
           else:
              SCREAMING_SNAKE_CASE_:	Optional[Any]            =tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
           if self.tpu_name:
              raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
           else:
              SCREAMING_SNAKE_CASE_:	Tuple            =None

        if tpu:
           # Set to bfloat16 in case of TPU
           if self.fpaa:
              tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""						)

           tf.config.experimental_connect_to_cluster(SCREAMING_SNAKE_CASE_						)
           tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_						)

           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_						)

        elif len(SCREAMING_SNAKE_CASE_						) == 0:
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =tf.distribute.OneDeviceStrategy(device="""/cpu:0"""						)
        elif len(SCREAMING_SNAKE_CASE_						) == 1:
           SCREAMING_SNAKE_CASE_:	List[str]            =tf.distribute.OneDeviceStrategy(device="""/gpu:0"""						)
        elif len(SCREAMING_SNAKE_CASE_						) > 1:
           # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
           SCREAMING_SNAKE_CASE_:	str            =tf.distribute.MirroredStrategy()
        else:
           raise ValueError("""Cannot find the proper strategy, please check your environment properties."""						)

     return strategy



  @property
  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   "tf.distribute.Strategy":


     '''simple docstring'''




     requires_backends(self    ,						["""tf"""]						)
     return self._setup_strategy



  @property
  def 		lowerCamelCase__    (  self	:   str						)       ->   int:


     '''simple docstring'''




     requires_backends(self    ,						["""tf"""]						)
     return self._setup_strategy.num_replicas_in_sync



  @property
  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   str:


     '''simple docstring'''




     return False  # TF Logging is handled by Keras not the Trainer



  @property
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   int:


     '''simple docstring'''




     if self.per_gpu_train_batch_size:
        logger.warning(
            """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
            """version. Using `--per_device_train_batch_size` is preferred."""						)
     SCREAMING_SNAKE_CASE_:	List[str]            =self.per_gpu_train_batch_size or self.per_device_train_batch_size
     return per_device_batch_size * self.n_replicas



  @property
  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   int:


     '''simple docstring'''




     if self.per_gpu_eval_batch_size:
        logger.warning(
            """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
            """version. Using `--per_device_eval_batch_size` is preferred."""						)
     SCREAMING_SNAKE_CASE_:	Tuple            =self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
     return per_device_batch_size * self.n_replicas





  @property
  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   int:


     '''simple docstring'''




     requires_backends(self    ,						["""tf"""]						)
     warnings.warn(
         """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""    ,						SCREAMING_SNAKE_CASE_    ,						)
     return self._setup_strategy.num_replicas_in_sync








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

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




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





  def __repr__(  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




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







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




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







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





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





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	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])



