
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     __A(				tr.AbstractTransform    ):




									def __init__(  self	,  _snake_case = " "       )					->   Dict:


													'''simple docstring'''




													__a    							=					sentence_delimiter




									def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Dict:


													'''simple docstring'''




													return list(_snake_case       )




									def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


													'''simple docstring'''




													__a    							=					[]
													for sent_idx, sentence in enumerate(_snake_case       ):
																	chars.extend(self.process_string(_snake_case       )       )
																	if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_snake_case       ) - 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 					__lowerCAmelCase    (    a__		,					a__						) ->						Tuple:
				return float((preds == labels).mean()						)


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Tuple:
				return {
				    "f1": float(fa_score(y_true=a__		,					y_pred=a__		,					average='''macro'''						)						),
				    "accuracy": simple_accuracy(a__		,					a__						),
				}


def 					__lowerCAmelCase    (    a__		,					a__		,					a__="exp"		,					a__=None		,					a__=False		,					a__=False		,					a__=None		,					a__=False		,					) ->						List[str]:
				# xtreme-s can only have one label
				__a    							=					[[label] for label in labels]
				__a    							=					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"`.'''						)

				__a    							=					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'''						)
				__a    							=					[[refs[i] for refs in labels] for i in range(a__						)]
				__a    							=					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 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Optional[int]:
				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(a__		,					a__						)['''wer''']

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

								def compute_score(a__		,					a__		,					a__="wer"						):
												__a    							=					0
												__a    							=					0
												for prediction, reference in zip(a__		,					a__						):
																if score_type == "wer":
																				__a    							=					compute_measures(a__		,					a__						)
																elif score_type == "cer":
																				__a    							=					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     __A(				datasets.Metric    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case=None	,  _snake_case=None       )					->   List[str]:


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

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A  :			int     				=				logging.get_logger(__name__)

A  :			Dict     				=				{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
					import detectrona







class     __A(				a    ):
				snake_case_		     =					'''layoutlmv2'''




				def __init__(  self	,  _snake_case=30_522	,  _snake_case=768	,  _snake_case=12	,  _snake_case=12	,  _snake_case=3_072	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case=0	,  _snake_case=1_024	,  _snake_case=128	,  _snake_case=32	,  _snake_case=True	,  _snake_case=256	,  _snake_case=64	,  _snake_case=True	,  _snake_case=[7, 7, 256]	,  _snake_case=128	,  _snake_case=128	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								super().__init__(
								    vocab_size=_snake_case	,  hidden_size=_snake_case	,  num_hidden_layers=_snake_case	,  num_attention_heads=_snake_case	,  intermediate_size=_snake_case	,  hidden_act=_snake_case	,  hidden_dropout_prob=_snake_case	,  attention_probs_dropout_prob=_snake_case	,  max_position_embeddings=_snake_case	,  type_vocab_size=_snake_case	,  initializer_range=_snake_case	,  layer_norm_eps=_snake_case	,  pad_token_id=_snake_case	,  **_snake_case	,  )
								__a    							=					max_ad_position_embeddings
								__a    							=					max_rel_pos
								__a    							=					rel_pos_bins
								__a    							=					fast_qkv
								__a    							=					max_rel_ad_pos
								__a    							=					rel_ad_pos_bins
								__a    							=					convert_sync_batchnorm
								__a    							=					image_feature_pool_shape
								__a    							=					coordinate_size
								__a    							=					shape_size
								__a    							=					has_relative_attention_bias
								__a    							=					has_spatial_attention_bias
								__a    							=					has_visual_segment_embedding
								__a    							=					(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
								    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
								    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
								    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
								    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
								    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
								    "MODEL.POST_NMS_TOPK_TEST": 1_000,
								    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
								    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
								    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
								    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
								    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
								    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
								    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
								    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
								    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
								    "MODEL.RESNETS.DEPTH": 101,
								    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
								    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.RESNETS.NUM_GROUPS": 32,
								    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
								    "MODEL.RESNETS.STRIDE_IN_1X1": False,
								}




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
												__a    							=					k.split('''.'''       )
												__a    							=					detectrona_config
												for attribute in attributes[:-1]:
																__a    							=					getattr(_snake_case	,  _snake_case       )
												setattr(_snake_case	,  attributes[-1]	,  _snake_case       )

								return detectrona_config