



# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_lowercase		:				Union[str, Any]      		=re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")

@total_ordering
@dataclass
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:str
						__lowerCAmelCase					:Optional[str]      =					None
						__lowerCAmelCase					:Optional[Union[str, int]]      =					None
						__lowerCAmelCase					:Optional[Union[str, int]]      =					None
						__lowerCAmelCase					:Optional[Union[str, int]]      =					None

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__	,						a__	,						a__       :						Dict							=  _str_to_version_tuple(self.version_str  )

						def __repr__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return self.major, self.minor, self.patch

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				str:
									"""simple docstring"""




									if isinstance(__lowercase ,       __lowercase  ):
												return Version(__lowercase  )
									elif isinstance(__lowercase ,       __lowercase  ):
												return other
									raise TypeError(F'''{other} (type {type(__lowercase  )}) cannot be compared to version.'''  )

						def __eq__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									try:
												a__       :						str							=  self._validate_operand(__lowercase  )
									except (TypeError, ValueError):
												return False
									else:
												return self.tuple == other.tuple

						def __lt__(       self ,       __lowercase  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						str							=  self._validate_operand(__lowercase  )
									return self.tuple < other.tuple

						def __hash__(       self  ) ->				List[Any]:
									"""simple docstring"""




									return hash(_version_tuple_to_str(self.tuple  )  )

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									a__       :						str							=  {f.name for f in dataclasses.fields(cls  )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									return self.version_str




def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any])							->	List[str]:







			"""simple docstring"""
			a__       :						str							=  _VERSION_REG.match(_lowercase)
			if not res:
						raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''')
			return tuple(int(_lowercase) for v in [res.group("""major"""), res.group("""minor"""), res.group("""patch""")])





def      lowerCAmelCase_					(  _lowercase    :       int)							->	Optional[Any]:







			"""simple docstring"""
			return ".".join(str(_lowercase) for v in version_tuple)









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

import datasets


_lowercase		:				Any      		="\\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"

_lowercase		:				str      		="\\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"

_lowercase		:				Optional[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        snake_case__    (datasets.Metric		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase = False ,       __lowercase = False ,       __lowercase = False ,       __lowercase = False ,       ) ->				Any:
									"""simple docstring"""




									a__       :						Any							=  len(references[0]  )
									if any(len(__lowercase  ) != references_per_prediction for refs in references  ):
												raise ValueError("""Sacrebleu requires the same number of references for each prediction"""  )
									a__       :						str							=  [[refs[i] for refs in references] for i in range(__lowercase  )]

									a__       :						int							=  TER(
									    normalized=__lowercase ,       no_punct=__lowercase ,       asian_support=__lowercase ,       case_sensitive=__lowercase ,       )
									a__       :						Optional[int]							=  sb_ter.corpus_score(__lowercase ,       __lowercase  )

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




