



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE     =	{
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TvltModel""",
							    """TvltForPreTraining""",
							    """TvltForAudioVisualClassification""",
							    """TvltPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	["""TvltImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
							from .processing_tvlt import TvltProcessor
							from .feature_extraction_tvlt import TvltFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tvlt import (
														    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TvltForAudioVisualClassification,
														    TvltForPreTraining,
														    TvltModel,
														    TvltPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_tvlt import TvltImageProcessor


else:
							import sys

							__SCREAMING_SNAKE_CASE     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




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 						UpperCAmelCase__     (     tr.AbstractTransform  ):




							'''simple docstring'''






							def __init__(  self				:					Tuple			,			a_				:					str = " "					):
									'''simple docstring'''





									__UpperCAmelCase       :	str						=      sentence_delimiter




							def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					str					):
									'''simple docstring'''





									return list(a_					)




							def 					snake_case__	(  self				:					List[str]			,			a_				:					List[str]					):
									'''simple docstring'''





									__UpperCAmelCase       :	Optional[Any]						=      []
									for sent_idx, sentence in enumerate(a_					):
											chars.extend(self.process_string(a_					)					)
											if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(a_					) - 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 			a		( _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Union[str, Any]		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   int="exp"					,  _UpperCAmelCase				:   Optional[int]=None					,  _UpperCAmelCase				:   Dict=False					,  _UpperCAmelCase				:   List[Any]=False					,  _UpperCAmelCase				:   Optional[Any]=None					,  _UpperCAmelCase				:   str=False					,  ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      [[label] for label in labels]
		__UpperCAmelCase       :	Any						=      list(_UpperCAmelCase		)
		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"`.'''		)

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

def 			a		( _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   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:
				__UpperCAmelCase       :	int						=      compute_measures(_UpperCAmelCase					,  _UpperCAmelCase		)['''wer''']

				__UpperCAmelCase       :	List[Any]						=      compute_measures(_UpperCAmelCase					,  _UpperCAmelCase					,  truth_transform=_UpperCAmelCase					,  hypothesis_transform=_UpperCAmelCase		)['''wer''']
				return {"wer": wer, "cer": cer}
		else:

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

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

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




			'''simple docstring'''






			def 					snake_case__	(  self				:					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}'					)

					__UpperCAmelCase       :	Optional[int]						=      '''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(a_					), '''references''': datasets.Value(a_					)}					)			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy'''			,			)




			def 					snake_case__	(  self				:					Any			,			a_				:					Optional[int]			,			a_				:					str			,			a_				:					str=None			,			a_				:					str=None					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      bleu_kwargs if bleu_kwargs is not None else {}
					__UpperCAmelCase       :	Any						=      wer_kwargs if wer_kwargs is not None else {}

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





