



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_      			=  "\\n"

UpperCamelCase_      			=  "\\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"

UpperCamelCase_      			=  "\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"

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

try:
							from jiwer import transforms as tr

							UpperCamelCase_      			=  True
except ImportError:
							UpperCamelCase_      			=  False

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







							class 				a_    (						tr.AbstractTransform							):

													def __init__(       self      :List[Any]  ,		_lowercase      :str = " ")						->   Tuple:
																			UpperCAmelCase_					=	sentence_delimiter

													def       __a			(       self      :Optional[int]  ,		_lowercase      :str)						->   Tuple:
																			return list(_lowercase)




													def       __a			(       self      :Any  ,		_lowercase      :List[str])						->   Optional[Any]:
																			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_      			=  tr.Compose(
							    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
							)
elif _jiwer_available:
							UpperCamelCase_      			=  tr.Compose(
							    [
							        tr.RemoveMultipleSpaces(),
							        tr.Strip(),
							        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
							        tr.ReduceToListOfListOfChars(),
							    ]
							)
else:
							UpperCamelCase_      			=  None







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase				,   __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				,   __UpperCAmelCase				,   __UpperCAmelCase="exp"				,   __UpperCAmelCase=None				,   __UpperCAmelCase=False				,   __UpperCAmelCase=False				,   __UpperCAmelCase=None				,   __UpperCAmelCase=False				,   )			->			Any:



						'''simple docstring'''






						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				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[Any]:



						'''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_					=	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				,   __UpperCAmelCase				,   __UpperCAmelCase="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 				a_    (						datasets.Metric							):

						def       __a			(       self      :Any)						->   str:
												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      :Optional[Any]  ,		_lowercase      :Tuple  ,		_lowercase      :List[Any]  ,		_lowercase      :Union[str, Any]=None  ,		_lowercase      :Optional[int]=None)						->   int:
												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}")










import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






						if number_of_simulations < 1:
												raise ValueError('''At least one simulation is necessary to estimate PI.'''      )

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	Point.random_unit_square()

												if random_point.is_in_unit_circle():
																		number_in_unit_circle += 1

						return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							UpperCamelCase_      			=  "Please enter the desired number of Monte Carlo simulations: "
							UpperCamelCase_      			=  estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")





