code: 
  
  
  
  
  
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  lowerCamelCase										=  '''\
  @inproceedings{ramshaw-marcus-1995-text,
      title = "Text Chunking using Transformation-Based Learning",
      author = "Ramshaw, Lance  and
        Marcus, Mitch",
      booktitle = "Third Workshop on Very Large Corpora",
      year = "1995",
      url = "https://www.aclweb.org/anthology/W95-0107",
  }
  @misc{seqeval,
    title={{seqeval}: A Python framework for sequence labeling evaluation},
    url={https://github.com/chakki-works/seqeval},
    note={Software available from https://github.com/chakki-works/seqeval},
    author={Hiroki Nakayama},
    year={2018},
  }
  '''
  
  lowerCamelCase										=  '''\
  seqeval is a Python framework for sequence labeling evaluation.
  seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
  
  This is well-tested by using the Perl script conlleval, which can be used for
  measuring the performance of a system that has processed the CoNLL-2000 shared task data.
  
  seqeval supports following formats:
  IOB1
  IOB2
  IOE1
  IOE2
  IOBES
  
  See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
  '''
  
  lowerCamelCase										=  '''
  Produces labelling scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
      references: List of List of reference labels (Ground truth (correct) target values)
      suffix: True if the IOB prefix is after type, False otherwise. default: False
      scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
          default: None
      mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
          If you want to only count exact matches, pass mode="strict". default: None.
      sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
      zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
          "warn". "warn" acts as 0, but the warning is raised.
  
  Returns:
      \'scores\': dict. Summary of the scores for overall and per type
          Overall:
              \'accuracy\': accuracy,
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure,
          Per type:
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure
  Examples:
  
      >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> seqeval = datasets.load_metric("seqeval")
      >>> results = seqeval.compute(predictions=predictions, references=references)
      >>> print(list(results.keys()))
      [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
      >>> print(results["overall_f1"])
      0.5
      >>> print(results["PER"]["f1"])
      1.0
  '''
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,			_KWARGS_DESCRIPTION      )
  class 					snake_case_							(       datasets.Metric      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         return datasets.MetricInfo(
             description=_DESCRIPTION   ,       citation=_CITATION   ,       homepage='https://github.com/chakki-works/seqeval'   ,       inputs_description=_KWARGS_DESCRIPTION   ,       features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('string'   ,       id='label'	)   ,       id='sequence'	),
                     'references': datasets.Sequence(datasets.Value('string'   ,       id='label'	)   ,       id='sequence'	),
                 }	)   ,       codebase_urls=['https://github.com/chakki-works/seqeval']   ,       reference_urls=['https://github.com/chakki-works/seqeval']   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	Any   ,       snake_case__:	Optional[Any]   ,       snake_case__:	bool = False   ,       snake_case__:	Optional[str] = None   ,       snake_case__:	Optional[str] = None   ,       snake_case__:	Optional[List[int]] = None   ,       snake_case__:	Union[str, int] = "warn"   ,       )  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         if scheme is not None:
             try:
                 _lowercase     =	importlib.import_module('seqeval.scheme'	)
                 _lowercase     =	getattr(snake_case__   ,       snake_case__	)
             except AttributeError:
                 raise ValueError(f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''	)
         _lowercase     =	classification_report(
             y_true=snake_case__   ,       y_pred=snake_case__   ,       suffix=snake_case__   ,       output_dict=snake_case__   ,       scheme=snake_case__   ,       mode=snake_case__   ,       sample_weight=snake_case__   ,       zero_division=snake_case__   ,       )
         report.pop('macro avg'	)
         report.pop('weighted avg'	)
         _lowercase     =	report.pop('micro avg'	)
  
         _lowercase     =	{
             type_name: {
                 'precision': score['precision'],
                 'recall': score['recall'],
                 'f1': score['f1-score'],
                 'number': score['support'],
             }
             for type_name, score in report.items()
         }
         _lowercase     =	overall_score['precision']
         _lowercase     =	overall_score['recall']
         _lowercase     =	overall_score['f1-score']
         _lowercase     =	accuracy_score(y_true=snake_case__   ,       y_pred=snake_case__	)
  
         return scores
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Any   ,       snake_case__:	str   ,       snake_case__:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	letter
         _lowercase     =	freq
         _lowercase     =	{}
  
     def __repr__(    self:	Optional[int]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return f'''{self.letter}:{self.freq}'''
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	Letter | TreeNode   ,       snake_case__:	Letter | TreeNode	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	freq
         _lowercase     =	left
         _lowercase     =	right
  
  
  
  
  
  
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	{}
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              _lowercase     =	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        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	letters  # type: ignore
      while len(_lowercase      ) > 1:
          _lowercase     =	response.pop(0      )
          _lowercase     =	response.pop(0      )
          _lowercase     =	left.freq + right.freq
          _lowercase     =	TreeNode(_lowercase,      _lowercase,      _lowercase      )
          response.append(_lowercase      )
          response.sort(key=lambda _lowercase      : x.freq      )
      return response[0]
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if isinstance(_lowercase,      _lowercase      ):
          _lowercase     =	bitstring
          return [root]
      _lowercase     =	root  # type: ignore
      _lowercase     =	[]
      letters += traverse_tree(treenode.left,      bitstring + '0'      )
      letters += traverse_tree(treenode.right,      bitstring + '1'      )
      return letters
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	parse_file(_lowercase      )
      _lowercase     =	build_tree(_lowercase      )
      _lowercase     =	{
          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:
              _lowercase     =	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])
  