
"""simple docstring"""

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)
_A            = '\\n'

_A            = '\\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'

_A            = '\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'

_A            = ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_A            = ''

try:
					from jiwer import transforms as tr

					_A            = True
except ImportError:
					_A            = False

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






					class        _lowercase   (				tr.AbstractTransform		):




						def __init__( self		, UpperCAmelCase_ = " "					)		->							Union[str, Any]:
								lowerCamelCase     :			Optional[Any]		=    sentence_delimiter




						def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							int:
								return list(UpperCAmelCase_					)




						def 			_UpperCamelCase    ( self		, UpperCAmelCase_					)		->							int:
								lowerCamelCase     :			Union[str, Any]		=    []
								for sent_idx, sentence in enumerate(UpperCAmelCase_					):
										chars.extend(self.process_string(UpperCAmelCase_					)					)
										if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase_					) - 1:
												chars.append(self.sentence_delimiter					)
								return chars


					_A            = tr.Compose(
					    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
					)
elif _jiwer_available:
					_A            = tr.Compose(
					    [
					        tr.RemoveMultipleSpaces(),
					        tr.Strip(),
					        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
					        tr.ReduceToListOfListOfChars(),
					    ]
					)
else:
					_A            = None
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		return float((preds == labels).mean()			)
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		return {
		    "f1": float(fa_score(y_true=a_,						y_pred=a_,						average='macro'			)			),
		    "accuracy": simple_accuracy(a_,						a_			),
		}
def 		UpperCAmelCase					(		a_,						a_,						a_="exp",						a_=None,						a_=False,						a_=False,						a_=None,						a_=False,						):





		'''simple docstring'''


		lowerCamelCase     :			Dict		=    [[label] for label in labels]
		lowerCamelCase     :			Dict		=    list(a_			)
		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"`.'			)

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





		'''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:
				lowerCamelCase     :			Union[str, Any]		=    compute_measures(a_,						a_			)['wer']

				lowerCamelCase     :			List[str]		=    compute_measures(a_,						a_,						truth_transform=a_,						hypothesis_transform=a_			)['wer']
				return {"wer": wer, "cer": cer}
		else:

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

				return {"wer": compute_score(a_,						a_,						'wer'			), "cer": compute_score(a_,						a_,						'cer'			)}






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




	def 			_UpperCamelCase    ( self					)		->							int:
			if self.config_name not in _CONFIG_NAMES:
					raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

			lowerCamelCase     :			Any		=    '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(UpperCAmelCase_					), 'references': datasets.Value(UpperCAmelCase_					)}					)		, codebase_urls=[]		, reference_urls=[]		, format='numpy'		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_=None		, UpperCAmelCase_=None					)		->							str:
			lowerCamelCase     :			List[Any]		=    bleu_kwargs if bleu_kwargs is not None else {}
			lowerCamelCase     :			Tuple		=    wer_kwargs if wer_kwargs is not None else {}

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












'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        SCREAMING_SNAKE_CASE(				__A   ,      __A   ,      __A     ):

       @register_to_config
       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = False					,		)    ->					Optional[int]:

           """simple docstring"""



           super().__init__()

           __lowercase							=						nn.Linear(lowerCamelCase__					,		lowerCamelCase__					,		bias=lowerCamelCase__     )

           __lowercase							=						nn.Embedding(lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						False

           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )

           __lowercase							=						TaConfig(
               d_model=lowerCamelCase__					,		num_heads=lowerCamelCase__					,		d_kv=lowerCamelCase__					,		d_ff=lowerCamelCase__					,		feed_forward_proj=lowerCamelCase__					,		dropout_rate=lowerCamelCase__					,		is_decoder=lowerCamelCase__					,		is_encoder_decoder=lowerCamelCase__					,		)
           __lowercase							=						nn.ModuleList()
           for lyr_num in range(lowerCamelCase__     ):
               __lowercase							=						TaBlock(lowerCamelCase__     )
               self.encoders.append(lowerCamelCase__     )

           __lowercase							=						TaLayerNorm(lowerCamelCase__     )
           __lowercase							=						nn.Dropout(p=lowerCamelCase__     )




       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					str:

           """simple docstring"""



           __lowercase							=						self.input_proj(lowerCamelCase__     )

           # terminal relative positional encodings
           __lowercase							=						encoder_inputs.shape[1]
           __lowercase							=						torch.arange(lowerCamelCase__					,		device=encoder_inputs.device     )

           __lowercase							=						encoder_inputs_mask.sum(-1     )
           __lowercase							=						torch.roll(input_positions.unsqueeze(0     )					,		tuple(seq_lens.tolist()     )					,		dims=0     )
           x += self.position_encoding(lowerCamelCase__     )

           __lowercase							=						self.dropout_pre(lowerCamelCase__     )

           # inverted the attention mask
           __lowercase							=						encoder_inputs.size()
           __lowercase							=						self.get_extended_attention_mask(lowerCamelCase__					,		lowerCamelCase__     )

           for lyr in self.encoders:
               __lowercase							=						lyr(lowerCamelCase__					,		lowerCamelCase__     )[0]
           __lowercase							=						self.layer_norm(lowerCamelCase__     )

           return self.dropout_post(lowerCamelCase__     ), encoder_inputs_mask




