



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__     =	"""\
"""

lowerCamelCase__     =	"""\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

lowerCamelCase__     =	"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.
        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',
        'tokenize', 'use_effective_order'.
    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.
        Keywords include 'concatenate_texts'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'
    \"f1\": F1 score - for 'minds14'
    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'
Examples:

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'wer': 0.56, 'cer': 0.27}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'bleu': 31.65}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'accuracy': 0.6}

    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {'f1': 0.58, 'accuracy': 0.6}
"""

lowerCamelCase__     =	["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
lowerCamelCase__     =	""""""

try:
						from jiwer import transforms as tr

						lowerCamelCase__     =	True
except ImportError:
						lowerCamelCase__     =	False

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






						class 				SCREAMING_SNAKE_CASE     (							tr.AbstractTransform   ):





													def __init__(				self						:						int ,			__lowercase						:						str = " "		):

																	'''simple docstring'''
																	__a					       =						sentence_delimiter





													def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						str		):

																	'''simple docstring'''
																	return list(__lowercase		)







													def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						List[str]		):

																	'''simple docstring'''
																	__a					       =						[]
																	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__     =	tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
elif _jiwer_available:
						lowerCamelCase__     =	tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)
else:
						lowerCamelCase__     =	None

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     List[Any]      ):
				"""simple docstring"""







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

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Any			,       _SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     List[Any]="exp"			,       _SCREAMING_SNAKE_CASE    :     Dict=None			,       _SCREAMING_SNAKE_CASE    :     Tuple=False			,       _SCREAMING_SNAKE_CASE    :     str=False			,       _SCREAMING_SNAKE_CASE    :     Optional[int]=None			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]=False			,       ):
				"""simple docstring"""







				__a					       =						[[label] for label in labels]
				__a					       =						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\"`."""      )

				__a					       =						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"""      )
				__a					       =						[[refs[i] for refs in labels] for i in range(_SCREAMING_SNAKE_CASE      )]
				__a					       =						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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Union[str, Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     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:
								__a					       =						compute_measures(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )["""wer"""]

								__a					       =						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    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[int]="wer"      ):
												__a					       =						0
												__a					       =						0
												for prediction, reference in zip(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      ):
																if score_type == "wer":
																				__a					       =						compute_measures(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
																elif score_type == "cer":
																				__a					       =						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 				SCREAMING_SNAKE_CASE     (							datasets.Metric   ):





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

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

											__a					       =						"""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   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						int ,			__lowercase						:						List[Any] ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						int=None		):

											'''simple docstring'''
											__a					       =						bleu_kwargs if bleu_kwargs is not None else {}
											__a					       =						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":
															__a					       =						bleu_kwargs.pop("""smooth_method""" ,			"""exp"""		)
															__a					       =						bleu_kwargs.pop("""smooth_value""" ,			__lowercase		)
															__a					       =						bleu_kwargs.pop("""force""" ,			__lowercase		)
															__a					       =						bleu_kwargs.pop("""lowercase""" ,			__lowercase		)
															__a					       =						bleu_kwargs.pop("""tokenize""" ,			__lowercase		)
															__a					       =						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"]:
															__a					       =						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 logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







				__a					       =						filter(lambda _SCREAMING_SNAKE_CASE      : p.requires_grad			,       model.parameters()      )
				__a					       =						sum([np.prod(p.size()      ) for p in model_parameters]      )
				return params


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

											'''simple docstring'''
											__a					       =						{F"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
											pl_module.logger.log_metrics(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
											__a					       =						trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
											# Log results
											__a					       =						Path(pl_module.hparams.output_dir		)
											if type_path == "test":
															__a					       =						od / """test_results.txt"""
															__a					       =						od / """test_generations.txt"""
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__a					       =						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a					       =						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

											if "preds" in metrics:
															__a					       =						"""\n""".join(metrics["""preds"""]		)
															generations_file.open("""w+"""		).write(__lowercase		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# mp stands for million parameters
											trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											return self._write_logs(__lowercase ,			__lowercase ,			"""test"""		)







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''simple docstring'''
											save_json(pl_module.metrics ,			pl_module.metrics_save_path		)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")



def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{val_avg_loss:.4f}-{step_count}"""
				else:
								raise NotImplementedError(
								    f"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
								    """ this function."""      )

				__a					       =						ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE			,       filename=_SCREAMING_SNAKE_CASE			,       monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       save_top_k=_SCREAMING_SNAKE_CASE			,       )
				return checkpoint_callback

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				return EarlyStopping(
				    monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       patience=_SCREAMING_SNAKE_CASE			,       verbose=_SCREAMING_SNAKE_CASE			,       )
