





'''simple docstring'''







import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


snake_case_   :				Tuple		=     '\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

snake_case_   :				Union[str, Any]		=     '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

snake_case_   :				Union[str, Any]		=     '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'


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

							def 			UpperCAmelCase__		(							self							:   Optional[int]      )    ->				Dict:







													"""simple docstring"""






													if version.parse(scb.__version__      ) < version.parse('''1.4.12'''      ):
																			raise ImportWarning(
																			    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
																			    '''You can install it with `pip install \"sacrebleu>=1.4.12\"`.'''      )
													return datasets.MetricInfo(
													    description=_DESCRIPTION				,   citation=_CITATION				,   homepage='''http://www.cs.umd.edu/~snover/tercom/'''				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
													        {
													            '''predictions''': datasets.Value('''string'''				,   id='''sequence'''      ),
													            '''references''': datasets.Sequence(datasets.Value('''string'''				,   id='''sequence'''      )				,   id='''references'''      ),
													        }      )				,   codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter''']				,   reference_urls=[
													        '''https://github.com/jhclark/tercom''',
													    ]				,   )



							def 			UpperCAmelCase__		(							self							:   Union[str, Any]				,   __magic_name__							:   List[str]				,   __magic_name__							:   List[str]				,   __magic_name__							:   bool = False				,   __magic_name__							:   bool = False				,   __magic_name__							:   bool = False				,   __magic_name__							:   bool = False				,   )    ->				Tuple:







													"""simple docstring"""
													UpperCAmelCase_	:					Optional[Any]     =     len(references[0]      )
													if any(len(UpperCamelCase__      ) != references_per_prediction for refs in references      ):
																			raise ValueError('''Sacrebleu requires the same number of references for each prediction'''      )
													UpperCAmelCase_	:					int     =     [[refs[i] for refs in references] for i in range(UpperCamelCase__      )]

													UpperCAmelCase_	:					str     =     TER(
													    normalized=UpperCamelCase__				,   no_punct=UpperCamelCase__				,   asian_support=UpperCamelCase__				,   case_sensitive=UpperCamelCase__				,   )
													UpperCAmelCase_	:					Any     =     sb_ter.corpus_score(UpperCamelCase__				,   UpperCamelCase__      )

													return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}







import re
import string

import numpy as np

import datasets


__lowerCAmelCase			:     Optional[int]           =   '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__lowerCAmelCase			:     Optional[int]           =   '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__lowerCAmelCase			:     Optional[int]           =   '\n'




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


  '''simple docstring'''







  def 							_lowercase							( self			: str    )			->	Optional[int]:







    """simple docstring"""






    return datasets.MetricInfo(
        description=_DESCRIPTION			,							citation=_CITATION			,							inputs_description=_KWARGS_DESCRIPTION			,							features=datasets.Features(
            {
                """predictions""": datasets.Value("""string"""			,							id="""sequence"""    ),
                """references""": datasets.Value("""string"""			,							id="""sequence"""    ),
            }    )			,							reference_urls=[]			,							)





  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: Optional[Any]=None			,							UpperCamelCase__			: Optional[Any]=False			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Tuple=False			,							)			->	Dict:







    """simple docstring"""






    if regexes_to_ignore is not None:
      for s in regexes_to_ignore:
        __magic_name__				     =       np.array([re.sub(UpperCamelCase__			,							""""""			,							UpperCamelCase__    ) for x in predictions]    )
        __magic_name__				     =       np.array([re.sub(UpperCamelCase__			,							""""""			,							UpperCamelCase__    ) for x in references]    )
    else:
      __magic_name__				     =       np.asarray(UpperCamelCase__    )
      __magic_name__				     =       np.asarray(UpperCamelCase__    )

    if ignore_case:
      __magic_name__				     =       np.char.lower(UpperCamelCase__    )
      __magic_name__				     =       np.char.lower(UpperCamelCase__    )

    if ignore_punctuation:
      __magic_name__				     =       string.punctuation.maketrans(""""""			,							""""""			,							string.punctuation    )
      __magic_name__				     =       np.char.translate(UpperCamelCase__			,							table=UpperCamelCase__    )
      __magic_name__				     =       np.char.translate(UpperCamelCase__			,							table=UpperCamelCase__    )

    if ignore_numbers:
      __magic_name__				     =       string.digits.maketrans(""""""			,							""""""			,							string.digits    )
      __magic_name__				     =       np.char.translate(UpperCamelCase__			,							table=UpperCamelCase__    )
      __magic_name__				     =       np.char.translate(UpperCamelCase__			,							table=UpperCamelCase__    )

    __magic_name__				     =       predictions == references

    return {"exact_match": np.mean(UpperCamelCase__    ) * 100}

