





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

import datasets


snake_case_     :       int						=  "\\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_     :       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"

snake_case_     :       Optional[int]						=  "\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							:     Dict       ,     __magic_name__							:     int       ,     __magic_name__							:     Optional[int]       ,     __magic_name__							:     int = False       ,     __magic_name__							:     Tuple = False       ,     __magic_name__							:     Optional[Any] = False       ,     __magic_name__							:     List[Any] = False       ,     )					->       Dict:







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

													UpperCAmelCase_			:      List[str]     						=  TER(
													    normalized=A__       ,     no_punct=A__       ,     asian_support=A__       ,     case_sensitive=A__       ,     )
													UpperCAmelCase_			:      Optional[int]     						=  sb_ter.corpus_score(A__       ,     A__   )

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












'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint


@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       List[Any]:







													"""simple docstring"""
													# For consistency across different places the DisjunctiveConstraint is called,
													# dc.token_ids is a list of integers. It is also initialized only by integers.

													UpperCAmelCase_			:      List[str]     						=  [[1, 2, 4], [1, 2, 3, 4]]
													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )
													self.assertTrue(isinstance(dc.token_ids       ,     __magic_name__   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Dict:







													"""simple docstring"""
													# We can't have constraints that are complete subsets of another. This leads to a preverse
													# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
													# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
													# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
													# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
													UpperCAmelCase_			:      Tuple     						=  [[1, 2], [1, 2, 3, 4]]

													with self.assertRaises(__magic_name__   ):
																			DisjunctiveConstraint(__magic_name__   )  # fails here

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  [[1, 2, 3], [1, 2, 4]]

													UpperCAmelCase_			:      List[str]     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  dc.update(2   )
													UpperCAmelCase_			:      Optional[Any]     						=  stepped is True and completed is False and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(3   )
													UpperCAmelCase_			:      Dict     						=  stepped is True and completed is True and reset is False
													self.assertTrue(__magic_name__   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 3]   )



							def     UpperCAmelCase__	(							self							:     int   )					->       Dict:







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

													UpperCAmelCase_			:      Tuple     						=  DisjunctiveConstraint(__magic_name__   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(4   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.current_seq == [1, 2, 4]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

													dc.reset()

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      List[Any]     						=  dc.update(1   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 3   )
													self.assertTrue(dc.current_seq == [1]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  dc.update(2   )
													self.assertTrue(not dc.completed   )
													self.assertTrue(dc.remaining() == 2   )
													self.assertTrue(dc.current_seq == [1, 2]   )

													UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[int]     						=  dc.update(5   )
													self.assertTrue(dc.completed   )  # Completed!
													self.assertTrue(dc.remaining() == 0   )
													self.assertTrue(dc.current_seq == [1, 2, 5]   )





