

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				bool:





    '''simple docstring'''






    A__   				=       len(SCREAMING_SNAKE_CASE__				) + 1
    A__   				=       len(SCREAMING_SNAKE_CASE__				) + 1

    # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
    # length i of input_string matches with prefix string of length j of
    # given pattern.
    # "dp" stands for dynamic programming.
    A__   				=       [[0 for i in range(SCREAMING_SNAKE_CASE__				)] for j in range(SCREAMING_SNAKE_CASE__				)]

    # since string of zero length match pattern of zero length
    A__   				=       1

    # since pattern of zero length will never match with string of non-zero length
    for i in range(1   ,      SCREAMING_SNAKE_CASE__				):
        A__   				=       0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1   ,      SCREAMING_SNAKE_CASE__				):
        A__   				=       dp[0][j - 2] if pattern[j - 1] == '*' else 0

    # now using bottom-up approach to find for all remaining lengths
    for i in range(1   ,      SCREAMING_SNAKE_CASE__				):
        for j in range(1   ,      SCREAMING_SNAKE_CASE__				):
            if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                A__   				=       dp[i - 1][j - 1]

            elif pattern[j - 1] == "*":
                if dp[i][j - 2] == 1:
                    A__   				=       1
                elif pattern[j - 2] in (input_string[i - 1], "."):
                    A__   				=       dp[i - 1][j]
                else:
                    A__   				=       0
            else:
                A__   				=       0

    return bool(dp[-1][-1]				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # inputing the strings
     # input_string = input("input a string :")
     # pattern = input("input a pattern :")

     lowercase_			=  "aab"
     lowercase_			=  "c*a*b"

     # using function to check whether given string matches the given pattern
     if match_pattern(input_string, pattern):
          print(f"""{input_string} matches the given pattern {pattern}""")
     else:
          print(f"""{input_string} does not match with the given pattern {pattern}""")




import comet  # From: unbabel-comet
import torch

import datasets


lowercase_			=  datasets.logging.get_logger(__name__)

lowercase_			=  "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase_			=  "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase_			=  "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"


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







    """simple docstring"""





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





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='https://unbabel.github.io/COMET/html/index.html',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'sources': datasets.Value('string',id='sequence'		),
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/Unbabel/COMET'],reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ],)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   str:





        '''simple docstring'''







        if self.config_name == "default":
            A__   				=       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'		)		)
        else:
            A__   				=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	List[Any],lowercase_			:	Tuple=None,lowercase_			:	List[str]=False		)->   int:





        '''simple docstring'''







        if gpus is None:
            A__   				=       1 if torch.cuda.is_available() else 0
        A__   				=       {'src': sources, 'mt': predictions, 'ref': references}
        A__   				=       [dict(zip(lowercase_,lowercase_		)		) for t in zip(*data.values()		)]
        A__				,						A__   				=       self.scorer.predict(lowercase_,gpus=lowercase_,progress_bar=lowercase_		)
        return {"mean_score": mean_score, "scores": scores}

