

from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
      import pyximport

      pyximport.install(setup_args={"include_dirs": np.get_include()})
      from . import algos_graphormer  # noqa E402
def   _lowerCAmelCase(						__A ,							__A = 512      ):
  UpperCAmelCase          =	x.shape[1] if len(x.shape      ) > 1 else 1
  UpperCAmelCase          =	1 + np.arange(0 ,							feature_num * offset ,							_UpperCAmelCase ,							dtype=np.intaa      )
  UpperCAmelCase          =	x + feature_offset
  return x
def   _lowerCAmelCase(						__A ,							__A=True      ):
  requires_backends(_UpperCAmelCase ,							["cython"]      )

  if keep_features and "edge_attr" in item.keys():  # edge_attr
    UpperCAmelCase          =	np.asarray(item["edge_attr"] ,							dtype=np.intaa      )
  else:
    UpperCAmelCase          =	np.ones((len(item["edge_index"][0]      ), 1) ,							dtype=np.intaa      )  # same embedding for all

  if keep_features and "node_feat" in item.keys():  # input_nodes
    UpperCAmelCase          =	np.asarray(item["node_feat"] ,							dtype=np.intaa      )
  else:
    UpperCAmelCase          =	np.ones((item["num_nodes"], 1) ,							dtype=np.intaa      )  # same embedding for all

  UpperCAmelCase          =	np.asarray(item["edge_index"] ,							dtype=np.intaa      )

  UpperCAmelCase          =	convert_to_single_emb(_UpperCAmelCase      ) + 1
  UpperCAmelCase          =	item['''num_nodes''']

  if len(edge_attr.shape      ) == 1:
    UpperCAmelCase          =	edge_attr[:, None]
  UpperCAmelCase          =	np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,							dtype=np.intaa      )
  UpperCAmelCase          =	convert_to_single_emb(_UpperCAmelCase      ) + 1

  # node adj matrix [num_nodes, num_nodes] bool
  UpperCAmelCase          =	np.zeros([num_nodes, num_nodes] ,							dtype=_UpperCAmelCase      )
  UpperCAmelCase          =	True

  UpperCAmelCase          =	algos_graphormer.floyd_warshall(_UpperCAmelCase      )
  UpperCAmelCase          =	np.amax(_UpperCAmelCase      )

  UpperCAmelCase          =	algos_graphormer.gen_edge_input(_UpperCAmelCase ,							_UpperCAmelCase ,							_UpperCAmelCase      )
  UpperCAmelCase          =	np.zeros([num_nodes + 1, num_nodes + 1] ,							dtype=np.single      )  # with graph token

  # combine
  UpperCAmelCase          =	input_nodes + 1  # we shift all indices by one for padding
  UpperCAmelCase          =	attn_bias
  UpperCAmelCase          =	attn_edge_type
  UpperCAmelCase          =	shortest_path_result.astype(np.intaa      ) + 1  # we shift all indices by one for padding
  UpperCAmelCase          =	np.sum(_UpperCAmelCase ,							axis=1      ).reshape(-1      ) + 1  # we shift all indices by one for padding
  UpperCAmelCase          =	item['''in_degree''']  # for undirected graph
  UpperCAmelCase          =	input_edges + 1  # we shift all indices by one for padding
  if "labels" not in item:
    UpperCAmelCase          =	item['''y''']

  return item






class     __magic_name__     :


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Optional[int]=2_0      ,    lowerCAmelCase__		:			Optional[Any]=False     )     ->   Optional[int]:





   if not is_cython_available():
     raise ImportError("Graphormer preprocessing needs Cython (pyximport)"     )

   UpperCAmelCase          =	spatial_pos_max
   UpperCAmelCase          =	on_the_fly_processing


 def __call__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			List[dict]     )     ->   Dict:





   if self.on_the_fly_processing:
     UpperCAmelCase          =	[preprocess_item(a_     ) for i in features]

   if not isinstance(features[0]      ,    a_     ):
     UpperCAmelCase          =	[vars(a_     ) for f in features]
   UpperCAmelCase          =	{}

   UpperCAmelCase          =	max(len(i["input_nodes"]     ) for i in features     )
   UpperCAmelCase          =	len(features[0]["input_nodes"][0]     )
   UpperCAmelCase          =	len(features[0]["attn_edge_type"][0][0]     )
   UpperCAmelCase          =	max(len(i["input_edges"][0][0]     ) for i in features     )
   UpperCAmelCase          =	len(features[0]["input_edges"][0][0][0]     )
   UpperCAmelCase          =	len(a_     )

   UpperCAmelCase          =	torch.zeros(a_      ,    max_node_num + 1      ,    max_node_num + 1      ,    dtype=torch.float     )
   UpperCAmelCase          =	torch.zeros(a_      ,    a_      ,    a_      ,    a_      ,    dtype=torch.long     )
   UpperCAmelCase          =	torch.zeros(a_      ,    a_      ,    a_      ,    dtype=torch.long     )
   UpperCAmelCase          =	torch.zeros(a_      ,    a_      ,    dtype=torch.long     )
   UpperCAmelCase          =	torch.zeros(a_      ,    a_      ,    a_      ,    dtype=torch.long     )
   UpperCAmelCase          =	torch.zeros(
       a_      ,    a_      ,    a_      ,    a_      ,    a_      ,    dtype=torch.long     )

   for ix, f in enumerate(a_     ):
     for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
       UpperCAmelCase          =	torch.tensor(f[k]     )

     if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]     ) > 0:
       UpperCAmelCase          =	float("-inf"     )

     UpperCAmelCase          =	f['''attn_bias''']
     UpperCAmelCase          =	f[
         '''attn_edge_type'''
     ]
     UpperCAmelCase          =	f['''spatial_pos''']
     UpperCAmelCase          =	f['''in_degree''']
     UpperCAmelCase          =	f['''input_nodes''']
     UpperCAmelCase          =	f['''input_edges''']

   UpperCAmelCase          =	batch['''in_degree''']

   UpperCAmelCase          =	features[0]['''labels''']
   if len(a_     ) == 1:  # one task
     if isinstance(sample[0]      ,    a_     ):  # regression
       UpperCAmelCase          =	torch.from_numpy(np.concatenate([i["labels"] for i in features]     )     )
     else:  # binary classification
       UpperCAmelCase          =	torch.from_numpy(np.concatenate([i["labels"] for i in features]     )     )
   else:  # multi task classification, left to float to keep the NaNs
     UpperCAmelCase          =	torch.from_numpy(np.stack([i["labels"] for i in features]      ,    axis=0     )     )

   return batch








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}'					)





