


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)
UpperCAmelCase__     			=							'''\\n'''

UpperCAmelCase__     			=							'''\\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'''

UpperCAmelCase__     			=							'''\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'''

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

try:
		from jiwer import transforms as tr

		UpperCAmelCase__     			=							True
except ImportError:
		UpperCAmelCase__     			=							False

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







		class    lowerCamelCase__     ( tr.AbstractTransform):





							def __init__(self	,  UpperCAmelCase = " " )  ->     Union[str, Any]:
								_lowercase							   =sentence_delimiter





							def   __A      (self	,  UpperCAmelCase )  ->     Tuple:
								return list(lowercase_ )





							def   __A      (self	,  UpperCAmelCase )  ->     Any:
								_lowercase							   =[]
								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



		UpperCAmelCase__     			=							tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
elif _jiwer_available:
		UpperCAmelCase__     			=							tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)
else:
		UpperCAmelCase__     			=							None
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    int:







	"""simple docstring"""


	return float((preds == labels).mean()		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    Optional[Any]:







	"""simple docstring"""


	return {
	    "f1": float(fa_score(y_true=lowercase_  ,		y_pred=lowercase_  ,		average='''macro'''		)		),
	    "accuracy": simple_accuracy(lowercase_  ,		lowercase_		),
	}
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case="exp"  ,		__snake_case=None  ,		__snake_case=False  ,		__snake_case=False  ,		__snake_case=None  ,		__snake_case=False  ,		)  ->    Any:







	"""simple docstring"""


	_lowercase							   =[[label] for label in labels]
	_lowercase							   =list(lowercase_		)
	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"`.'''		)

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







	"""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:
		_lowercase							   =compute_measures(lowercase_  ,		lowercase_		)['''wer''']

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

		def compute_score(__snake_case  ,		__snake_case  ,		__snake_case="wer"		):
			_lowercase							   =0
			_lowercase							   =0
			for prediction, reference in zip(lowercase_  ,		lowercase_		):
				if score_type == "wer":
					_lowercase							   =compute_measures(lowercase_  ,		lowercase_		)
				elif score_type == "cer":
					_lowercase							   =compute_measures(
					    lowercase_  ,		lowercase_  ,		truth_transform=lowercase_  ,		hypothesis_transform=lowercase_		)
				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
				total += measures["substitutions"] + measures["deletions"] + measures["hits"]
			return incorrect / total

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







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





					def   __A      (self )  ->     Tuple:

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

						_lowercase							   ='''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	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None	,  UpperCAmelCase=None )  ->     Optional[Any]:
						_lowercase							   =bleu_kwargs if bleu_kwargs is not None else {}
						_lowercase							   =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":
							_lowercase							   =bleu_kwargs.pop('''smooth_method'''	,  '''exp''' )
							_lowercase							   =bleu_kwargs.pop('''smooth_value'''	,  lowercase_ )
							_lowercase							   =bleu_kwargs.pop('''force'''	,  lowercase_ )
							_lowercase							   =bleu_kwargs.pop('''lowercase'''	,  lowercase_ )
							_lowercase							   =bleu_kwargs.pop('''tokenize'''	,  lowercase_ )
							_lowercase							   =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"]:
							_lowercase							   =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}" )





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

