"""simple docstring"""







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_    :    Optional[int]											=						"""\
"""

UpperCamelCase_    :    str											=						"""\
    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.
"""

UpperCamelCase_    :    Optional[Any]											=						"""
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}
"""

UpperCamelCase_    :    Any											=						["""fleurs-asr""", """mls""", """voxpopuli""", """babel""", """covost2""", """fleurs-lang_id""", """minds14"""]
UpperCamelCase_    :    int											=						""""""

try:
						from jiwer import transforms as tr

						UpperCamelCase_    :    List[Any]											=						True
except ImportError:
						UpperCamelCase_    :    int											=						False

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


						class      _lowercase						(				tr.AbstractTransform					):

										def __init__(       self    :      List[Any]    ,      _lowercase    :      str = " "       ):


																'''simple docstring'''
																UpperCAmelCase__   		=						sentence_delimiter

										def     A	(       self    :      Optional[Any]    ,      _lowercase    :      str       ):


																'''simple docstring'''
																return list(_lowercase       )

										def     A	(       self    :      Tuple    ,      _lowercase    :      List[str]       ):


																'''simple docstring'''
																UpperCAmelCase__   		=						[]
																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_    :    Dict											=						tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
elif _jiwer_available:
						UpperCamelCase_    :    Union[str, Any]											=						tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)
else:
						UpperCamelCase_    :    Optional[Any]											=						None


def 					a__ (						UpperCAmelCase__    :     Optional[int]						,     UpperCAmelCase__    :     Optional[int]     )->		Dict:
						return float((preds == labels).mean()     )


def 					a__ (						UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Optional[int]     )->		List[str]:
						return {
						    "f1": float(fa_score(y_true=UpperCAmelCase__						,     y_pred=UpperCAmelCase__						,     average="""macro"""     )     ),
						    "accuracy": simple_accuracy(UpperCAmelCase__						,     UpperCAmelCase__     ),
						}


def 					a__ (						UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     List[Any]						,     UpperCAmelCase__    :     Dict="exp"						,     UpperCAmelCase__    :     str=None						,     UpperCAmelCase__    :     List[str]=False						,     UpperCAmelCase__    :     str=False						,     UpperCAmelCase__    :     Tuple=None						,     UpperCAmelCase__    :     Optional[Any]=False						,     )->		Optional[Any]:
						# xtreme-s can only have one label
						UpperCAmelCase__   		=						[[label] for label in labels]
						UpperCAmelCase__   		=						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__   		=						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__   		=						[[refs[i] for refs in labels] for i in range(UpperCAmelCase__     )]
						UpperCAmelCase__   		=						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__    :     Dict						,     UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     Tuple     )->		Any:
						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__   		=						compute_measures(UpperCAmelCase__						,     UpperCAmelCase__     )["""wer"""]

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

												def compute_score(UpperCAmelCase__    :     Optional[int]						,     UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     str="wer"     ):
																		UpperCAmelCase__   		=						0
																		UpperCAmelCase__   		=						0
																		for prediction, reference in zip(UpperCAmelCase__						,     UpperCAmelCase__     ):
																								if score_type == "wer":
																														UpperCAmelCase__   		=						compute_measures(UpperCAmelCase__						,     UpperCAmelCase__     )
																								elif score_type == "cer":
																														UpperCAmelCase__   		=						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      _lowercase						(				datasets.Metric					):

				def     A	(       self    :      List[Any]       ):


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

										UpperCAmelCase__   		=						"""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    :      str    ,      _lowercase    :      List[Any]    ,      _lowercase    :      Any    ,      _lowercase    :      Union[str, Any]=None    ,      _lowercase    :      Union[str, Any]=None       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						bleu_kwargs if bleu_kwargs is not None else {}
										UpperCAmelCase__   		=						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":
																UpperCAmelCase__   		=						bleu_kwargs.pop("""smooth_method"""    ,      """exp"""       )
																UpperCAmelCase__   		=						bleu_kwargs.pop("""smooth_value"""    ,      _lowercase       )
																UpperCAmelCase__   		=						bleu_kwargs.pop("""force"""    ,      _lowercase       )
																UpperCAmelCase__   		=						bleu_kwargs.pop("""lowercase"""    ,      _lowercase       )
																UpperCAmelCase__   		=						bleu_kwargs.pop("""tokenize"""    ,      _lowercase       )
																UpperCAmelCase__   		=						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"]:
																UpperCAmelCase__   		=						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}'       )


"""simple docstring"""







class      _lowercase						:

				def __init__(       self    :      Tuple    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						data
										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						None


def 					a__ (						UpperCAmelCase__    :     BinaryTreeNode | None						,     UpperCAmelCase__    :     int     )->		BinaryTreeNode | None:
						if node is None:
												UpperCAmelCase__   		=						BinaryTreeNode(UpperCAmelCase__     )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												UpperCAmelCase__   		=						insert(node.left_child						,     UpperCAmelCase__     )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												UpperCAmelCase__   		=						insert(node.right_child						,     UpperCAmelCase__     )
						return node


def 					a__ (						UpperCAmelCase__    :     None | BinaryTreeNode     )->		list[int]:  # if node is None,return
						if node:
												UpperCAmelCase__   		=						inorder(node.left_child     )
												UpperCAmelCase__   		=						[*inorder_array, node.data]
												UpperCAmelCase__   		=						inorder_array + inorder(node.right_child     )
						else:
												UpperCAmelCase__   		=						[]
						return inorder_array


def 					a__ (						)->		BinaryTreeNode | None:
						UpperCAmelCase__   		=						insert(UpperCAmelCase__						,     1_5     )
						insert(UpperCAmelCase__						,     1_0     )
						insert(UpperCAmelCase__						,     2_5     )
						insert(UpperCAmelCase__						,     6     )
						insert(UpperCAmelCase__						,     1_4     )
						insert(UpperCAmelCase__						,     2_0     )
						insert(UpperCAmelCase__						,     6_0     )
						return root


def 					a__ (						)->		None:
						# main function
						UpperCAmelCase__   		=						make_tree()
						print("""Printing values of binary search tree in Inorder Traversal."""     )
						inorder(UpperCAmelCase__     )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

