






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








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])
