code: 
  
  from __future__ import annotations
  
  import sys
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Tuple				,			__A	:    str				,			__A	:    int       ):
  		__A   : str								= letter
  		__A   : int								= freq
  		__A   : dict[str, str]								= {}
  
  
  
  
  	def __repr__(    self	:    Tuple       ):
  		return F"""{self.letter}:{self.freq}"""
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    int				,			__A	:    Letter | TreeNode				,			__A	:    Letter | TreeNode       ):
  		__A   : int								= freq
  		__A   : Letter | TreeNode								= left
  		__A   : Letter | TreeNode								= right
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      str					)				->							list[Letter]:
  	__A   : dict[str, int]								= {}
  	with open(a__					) as f:
  		while True:
  			__A   : Optional[Any]								= f.read(1					)
  			if not c:
  				break
  			__A   : List[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        __SCREAMING_SNAKE_CASE    (      a__						:      list[Letter]					)				->							Letter | TreeNode:
  	__A   : list[Letter | TreeNode]								= letters  # type: ignore
  	while len(a__					) > 1:
  		__A   : Tuple								= response.pop(0					)
  		__A   : Optional[int]								= response.pop(0					)
  		__A   : Dict								= left.freq + right.freq
  		__A   : Any								= TreeNode(a__ ,a__ ,a__					)
  		response.append(a__					)
  		response.sort(key=lambda a__					: x.freq					)
  	return response[0]
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Letter | TreeNode ,a__						:      str					)				->							list[Letter]:
  	if isinstance(a__ ,a__					):
  		__A   : Tuple								= bitstring
  		return [root]
  	__A   : TreeNode								= root  # type: ignore
  	__A   : int								= []
  	letters += traverse_tree(treenode.left ,bitstring + """0"""					)
  	letters += traverse_tree(treenode.right ,bitstring + """1"""					)
  	return letters
  def        __SCREAMING_SNAKE_CASE    (      a__						:      str					)				->							None:
  	__A   : Union[str, Any]								= parse_file(a__					)
  	__A   : Any								= build_tree(a__					)
  	__A   : int								= {
  	    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:
  			__A   : str								= 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])
  
  
  
  
  
  
style_context: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  