





from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
_lowerCamelCase					:				Optional[int]  							=					'''\\n'''

_lowerCamelCase					:				List[str]  							=					'''\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'''

_lowerCamelCase					:				int  							=					'''\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'''

_lowerCamelCase					:				List[str]  							=					['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
_lowerCamelCase					:				Tuple  							=					''''''

try:
		from jiwer import transforms as tr

		_lowerCamelCase					:				Dict  							=					True
except ImportError:
		_lowerCamelCase					:				Union[str, Any]  							=					False

if _jiwer_available and version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):


		class 						lowerCamelCase   (tr.AbstractTransform			):







									"""simple docstring"""




									def __init__(					self	:     List[str], _UpperCAmelCase	:     Optional[int] = " "						)      -> List[Any]:





																"""simple docstring"""
																SCREAMING_SNAKE_CASE__   :			str      =						sentence_delimiter






									def 					A_	(					self	:     Tuple, _UpperCAmelCase	:     Optional[Any]						)      -> Tuple:





																"""simple docstring"""
																return list(__lowercase						)






									def 					A_	(					self	:     int, _UpperCAmelCase	:     List[str]						)      -> Any:





																"""simple docstring"""
																SCREAMING_SNAKE_CASE__   :			Optional[int]      =						[]
																for sent_idx, sentence in enumerate(__lowercase						):
																							chars.extend(self.process_string(__lowercase						)						)
																							if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowercase						) - 1:
																														chars.append(self.sentence_delimiter						)
																return chars


		_lowerCamelCase					:				Any  							=					tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
elif _jiwer_available:
		_lowerCamelCase					:				Any  							=					tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)
else:
		_lowerCamelCase					:				Tuple  							=					None




def _a					(  SCREAMING_SNAKE_CASE__			:  Any ,      SCREAMING_SNAKE_CASE__			:  int   )       ->				Optional[Any]:

							'''simple docstring'''






							return float((preds == labels).mean()   )




def _a					(  SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      SCREAMING_SNAKE_CASE__			:  Optional[Any]   )       ->				Any:

							'''simple docstring'''






							return {
							    "f1": float(fa_score(y_true=SCREAMING_SNAKE_CASE__ ,      y_pred=SCREAMING_SNAKE_CASE__ ,      average="macro"   )   ),
							    "accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   ),
							}




def _a					(  SCREAMING_SNAKE_CASE__			:  Tuple ,      SCREAMING_SNAKE_CASE__			:  Tuple ,      SCREAMING_SNAKE_CASE__			:  Any="exp" ,      SCREAMING_SNAKE_CASE__			:  List[Any]=None ,      SCREAMING_SNAKE_CASE__			:  Dict=False ,      SCREAMING_SNAKE_CASE__			:  List[Any]=False ,      SCREAMING_SNAKE_CASE__			:  Tuple=None ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any]=False ,      )       ->				int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						[[label] for label in labels]
							SCREAMING_SNAKE_CASE__   :			Any      =						list(SCREAMING_SNAKE_CASE__   )
							try:
														import sacrebleu as scb
							except ImportError:
														raise ValueError(
														    "sacrebleu has to be installed in order to apply the bleu metric for covost2."
														    "You can install it via `pip install sacrebleu`."   )

							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\"`."   )

							SCREAMING_SNAKE_CASE__   :			str      =						len(labels[0]   )
							if any(len(SCREAMING_SNAKE_CASE__   ) != references_per_prediction for refs in labels   ):
														raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
							SCREAMING_SNAKE_CASE__   :			Tuple      =						[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE__   )]
							SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						scb.corpus_bleu(
							    SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      smooth_method=SCREAMING_SNAKE_CASE__ ,      smooth_value=SCREAMING_SNAKE_CASE__ ,      force=SCREAMING_SNAKE_CASE__ ,      lowercase=SCREAMING_SNAKE_CASE__ ,      use_effective_order=SCREAMING_SNAKE_CASE__ ,      **({"tokenize": tokenize} if tokenize else {}) ,      )
							return {"bleu": output.score}




def _a					(  SCREAMING_SNAKE_CASE__			:  List[Any] ,      SCREAMING_SNAKE_CASE__			:  Tuple ,      SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any]   )       ->				Optional[int]:

							'''simple docstring'''






							try:
														from jiwer import compute_measures
							except ImportError:
														raise ValueError(
														    f'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
														    "You can install it via `pip install jiwer`."   )

							if concatenate_texts:
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						compute_measures(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )['wer']

														SCREAMING_SNAKE_CASE__   :			Tuple      =						compute_measures(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      truth_transform=SCREAMING_SNAKE_CASE__ ,      hypothesis_transform=SCREAMING_SNAKE_CASE__   )['wer']
														return {"wer": wer, "cer": cer}
							else:

														def compute_score(SCREAMING_SNAKE_CASE__			:  List[str] ,      SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  str="wer"   ):
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						0
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						0
																					for prediction, reference in zip(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   ):
																												if score_type == "wer":
																																			SCREAMING_SNAKE_CASE__   :			List[str]      =						compute_measures(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
																												elif score_type == "cer":
																																			SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						compute_measures(
																																			    SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      truth_transform=SCREAMING_SNAKE_CASE__ ,      hypothesis_transform=SCREAMING_SNAKE_CASE__   )
																												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																					return incorrect / total

														return {"wer": compute_score(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      "wer"   ), "cer": compute_score(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__ ,      "cer"   )}







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







							"""simple docstring"""




							def 					A_	(					self	:     Optional[int]						)      -> Optional[int]:





														"""simple docstring"""




														if self.config_name not in _CONFIG_NAMES:
																					raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''						)

														SCREAMING_SNAKE_CASE__   :			List[str]      =						'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

														return datasets.MetricInfo(
														    description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
														        {"predictions": datasets.Value(__lowercase						), "references": datasets.Value(__lowercase						)}						), codebase_urls=[], reference_urls=[], format="numpy", )






							def 					A_	(					self	:     List[str], _UpperCAmelCase	:     Optional[int], _UpperCAmelCase	:     Optional[Any], _UpperCAmelCase	:     Tuple=None, _UpperCAmelCase	:     Dict=None						)      -> Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						bleu_kwargs if bleu_kwargs is not None else {}
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						wer_kwargs if wer_kwargs is not None else {}

														if self.config_name == "fleurs-lang_id":
																					return {"accuracy": simple_accuracy(__lowercase, __lowercase						)}
														elif self.config_name == "minds14":
																					return fa_and_simple_accuracy(__lowercase, __lowercase						)
														elif self.config_name == "covost2":
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						bleu_kwargs.pop("smooth_method", "exp"						)
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						bleu_kwargs.pop("smooth_value", __lowercase						)
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						bleu_kwargs.pop("force", __lowercase						)
																					SCREAMING_SNAKE_CASE__   :			Optional[int]      =						bleu_kwargs.pop("lowercase", __lowercase						)
																					SCREAMING_SNAKE_CASE__   :			Dict      =						bleu_kwargs.pop("tokenize", __lowercase						)
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						bleu_kwargs.pop("use_effective_order", __lowercase						)
																					return bleu(
																					    preds=__lowercase, labels=__lowercase, smooth_method=__lowercase, smooth_value=__lowercase, force=__lowercase, lowercase=__lowercase, tokenize=__lowercase, use_effective_order=__lowercase, )
														elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						wer_kwargs.pop("concatenate_texts", __lowercase						)
																					return wer_and_cer(__lowercase, __lowercase, __lowercase, self.config_name						)
														else:
																					raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''						)












# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@classmethod
					def 	lowercase_							(				cls					):







						'''simple docstring'''




						return cls





					@slow
					@require_torch_gpu
					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.get_auto_remove_tmp_dir()
						lowerCamelCase_							:		Tuple							   =							f"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						lowerCamelCase_							:		List[Any]							   =							get_gpu_count()
						lowerCamelCase_							:		Union[str, Any]							   =							f"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
						lowerCamelCase_							:		int							   =							[sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase ,       env=self.get_env()					)

						lowerCamelCase_							:		Any							   =							os.path.join(__lowercase ,       'test_bleu.json'					)
						lowerCamelCase_							:		List[Any]							   =							load_json(__lowercase					)
						# print(metrics)
						self.assertGreaterEqual(metrics['bleu'] ,       25					)
