


"""simple docstring"""






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

import datasets


_lowerCamelCase     =  '\\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'

_lowerCamelCase     =  '\\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'

_lowerCamelCase     =  '\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 			lowerCamelCase_		(			datasets.Metric   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = False       ,		):
								SCREAMING_SNAKE_CASE__     							=				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"     )
								SCREAMING_SNAKE_CASE__     							=				[[refs[i] for refs in references] for i in range(UpperCAmelCase__     )]

								SCREAMING_SNAKE_CASE__     							=				TER(
								    normalized=UpperCAmelCase__       ,		no_punct=UpperCAmelCase__       ,		asian_support=UpperCAmelCase__       ,		case_sensitive=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				sb_ter.corpus_score(UpperCAmelCase__       ,		UpperCAmelCase__     )

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




"""simple docstring"""






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase     =  logging.get_logger(__name__)


@dataclass
class 			lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()   )}   )
				_lowerCAmelCase      :      str      							=					field(
				    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
				_lowerCAmelCase      :      int      							=					field(
				    default=128       ,  metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }       ,  )
				_lowerCAmelCase      :      bool      							=					field(
				    default=lowercase       ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.task_name.lower()


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Tuple      							=					"train"
				_lowerCAmelCase      :      Union[str, Any]      							=					"dev"
				_lowerCAmelCase      :      List[Any]      							=					"test"


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      GlueDataTrainingArguments
				_lowerCAmelCase      :      str
				_lowerCAmelCase      :      List[InputFeatures]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = Split.train       ,		UpperCAmelCase__ = None       ,		):
								warnings.warn(
								    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
								    "library. You can have a look at this example script for pointers: "
								    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"       ,		UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				args
								SCREAMING_SNAKE_CASE__     							=				glue_processors[args.task_name]()
								SCREAMING_SNAKE_CASE__     							=				glue_output_modes[args.task_name]
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												try:
																SCREAMING_SNAKE_CASE__     							=				Split[mode]
												except KeyError:
																raise KeyError("mode is not a valid split name"     )
        # Load data features from cache or dataset file
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir       ,		f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,		)
								SCREAMING_SNAKE_CASE__     							=				self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
												# HACK(label indices are swapped in RoBERTa pretrained model)
												SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE__     							=				label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE__     							=				cached_features_file + ".lock"
								with FileLock(UpperCAmelCase__     ):
												if os.path.exists(UpperCAmelCase__     ) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE__     							=				time.time()
																SCREAMING_SNAKE_CASE__     							=				torch.load(UpperCAmelCase__     )
																logger.info(
																    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,		time.time() - start     )
												else:
																logger.info(f'''Creating features from dataset file at {args.data_dir}'''     )

																if mode == Split.dev:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_dev_examples(args.data_dir     )
																elif mode == Split.test:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_test_examples(args.data_dir     )
																else:
																				SCREAMING_SNAKE_CASE__     							=				self.processor.get_train_examples(args.data_dir     )
																if limit_length is not None:
																				SCREAMING_SNAKE_CASE__     							=				examples[:limit_length]
																SCREAMING_SNAKE_CASE__     							=				glue_convert_examples_to_features(
																    UpperCAmelCase__       ,		UpperCAmelCase__       ,		max_length=args.max_seq_length       ,		label_list=UpperCAmelCase__       ,		output_mode=self.output_mode       ,		)
																SCREAMING_SNAKE_CASE__     							=				time.time()
																torch.save(self.features       ,		UpperCAmelCase__     )
																# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																logger.info(
																    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''     )



				def __len__(						self     ):
								return len(self.features     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								return self.features[i]






				def   lowerCAmelCase__		(						self     ):
								return self.label_list
