





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__UpperCAmelCase						=      '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

__UpperCAmelCase						=      '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


__UpperCAmelCase						=      '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,_KWARGS_DESCRIPTION      )
class 		__a							(	datasets.Metric      ):



       def  A							(     self	:   Any			):
           return datasets.MetricInfo(
               description=_DESCRIPTION					,			citation=_CITATION					,			inputs_description=_KWARGS_DESCRIPTION					,			features=datasets.Features(
                   {
                       """predictions""": datasets.Value("""string"""			),
                       """references""": datasets.Value("""string"""			),
                   }			)					,			homepage="""https://github.com/moussaKam/FrugalScore"""					,			)



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[str]			):
           if self.config_name == "default":
               lowerCAmelCase_					:	str						=   """moussaKam/frugalscore_tiny_bert-base_bert-score"""
           else:
               lowerCAmelCase_					:	List[str]						=   self.config_name
           lowerCAmelCase_					:	Dict						=   AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   AutoTokenizer.from_pretrained(UpperCAmelCase			)



       def  A							(     self	:   str					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   int=32					,			UpperCAmelCase	:   List[Any]=1_28					,			UpperCAmelCase	:   Tuple=None					,			):
           assert len(UpperCAmelCase			) == len(
               UpperCAmelCase			), "predictions and references should have the same number of sentences."
           if device is not None:
               assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
           else:
               lowerCAmelCase_					:	Optional[Any]						=   """gpu""" if torch.cuda.is_available() else """cpu"""
           lowerCAmelCase_					:	Dict						=   TrainingArguments(
               """trainer"""					,			fpaa=(device == """gpu""")					,			per_device_eval_batch_size=UpperCAmelCase					,			report_to="""all"""					,			no_cuda=(device == """cpu""")					,			log_level="""warning"""					,			)
           lowerCAmelCase_					:	Optional[Any]						=   {"""sentence1""": predictions, """sentence2""": references}
           lowerCAmelCase_					:	Dict						=   datasets.Dataset.from_dict(UpperCAmelCase			)

           def tokenize_function(UpperCAmelCase	:   Optional[int]			):
               return self.tokenizer(
                   data["""sentence1"""]					,			data["""sentence2"""]					,			max_length=UpperCAmelCase					,			truncation=UpperCAmelCase					,			padding=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[int]						=   raw_datasets.map(UpperCAmelCase					,			batched=UpperCAmelCase			)
           tokenized_datasets.remove_columns(["""sentence1""", """sentence2"""]			)
           lowerCAmelCase_					:	Optional[Any]						=   Trainer(self.model					,			UpperCAmelCase					,			tokenizer=self.tokenizer			)
           lowerCAmelCase_					:	int						=   trainer.predict(UpperCAmelCase			)
           return {"scores": list(predictions.predictions.squeeze(-1			)			)}









from __future__ import annotations

import sys

class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	str						=   letter
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	dict[str, str]						=   {}



       def __repr__(     self	:   Optional[int]			):
           return F'{self.letter}:{self.freq}'






class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   Letter | TreeNode					,			UpperCAmelCase	:   Letter | TreeNode			):
           lowerCAmelCase_					:	int						=   freq
           lowerCAmelCase_					:	Letter | TreeNode						=   left
           lowerCAmelCase_					:	Letter | TreeNode						=   right






def 			__UpperCamelCase				(			lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    lowerCAmelCase_					:	dict[str, int]						=   {}
    with open(lowercase__		) as f:
        while True:
            lowerCAmelCase_					:	int						=   f.read(1		)
            if not c:
                break
            lowerCAmelCase_					:	Optional[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 			__UpperCamelCase				(			lowercase__	:       list[Letter]		)	->      Letter | TreeNode:
    '''simple docstring'''





    lowerCAmelCase_					:	list[Letter | TreeNode]						=   letters  # type: ignore
    while len(lowercase__		) > 1:
        lowerCAmelCase_					:	str						=   response.pop(0		)
        lowerCAmelCase_					:	Any						=   response.pop(0		)
        lowerCAmelCase_					:	Union[str, Any]						=   left.freq + right.freq
        lowerCAmelCase_					:	Union[str, Any]						=   TreeNode(lowercase__		,    lowercase__		,    lowercase__		)
        response.append(lowercase__		)
        response.sort(key=lambda lowercase__		: x.freq		)
    return response[0]
def 			__UpperCamelCase				(			lowercase__	:       Letter | TreeNode		,    lowercase__	:       str		)	->      list[Letter]:
    '''simple docstring'''





    if isinstance(lowercase__		,    lowercase__		):
        lowerCAmelCase_					:	Dict						=   bitstring
        return [root]
    lowerCAmelCase_					:	TreeNode						=   root  # type: ignore
    lowerCAmelCase_					:	Union[str, Any]						=   []
    letters += traverse_tree(treenode.left		,    bitstring + """0"""		)
    letters += traverse_tree(treenode.right		,    bitstring + """1"""		)
    return letters
def 			__UpperCamelCase				(			lowercase__	:       str		)	->      None:
    '''simple docstring'''





    lowerCAmelCase_					:	Any						=   parse_file(lowercase__		)
    lowerCAmelCase_					:	int						=   build_tree(lowercase__		)
    lowerCAmelCase_					:	Tuple						=   {
        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:
            lowerCAmelCase_					:	List[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])


