



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							=       """\
"""

UpperCAmelCase							=       """\
    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							=       """
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							=       ["""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 			__snake_case   ( tr.AbstractTransform):







								'''simple docstring'''






								def __init__(				self	,	a_ = " "		):
												a__					   =		sentence_delimiter


								def _a   (				self	,	a_		):
												return list(a_		)







								def _a   (				self	,	a_		):
												a__					   =		[]
												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




	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_   (						__a  :					int			,		__a  :					str		):

				"""simple docstring"""
				return float((preds == labels).mean()		)

def       A_   (						__a  :					List[Any]			,		__a  :					Dict		):

				"""simple docstring"""
				return {
				    "f1": float(fa_score(y_true=__a			,		y_pred=__a			,		average="""macro"""		)		),
				    "accuracy": simple_accuracy(__a			,		__a		),
				}

def       A_   (						__a  :					Union[str, Any]			,		__a  :					Optional[int]			,		__a  :					Optional[Any]="exp"			,		__a  :					Optional[int]=None			,		__a  :					int=False			,		__a  :					List[Any]=False			,		__a  :					Union[str, Any]=None			,		__a  :					str=False			,		):

				"""simple docstring"""
				# 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       A_   (						__a  :					List[Any]			,		__a  :					Tuple			,		__a  :					Union[str, Any]			,		__a  :					Optional[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:
								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  :					str			,		__a  :					str			,		__a  :					int="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 			__snake_case   ( datasets.Metric):







							'''simple docstring'''






							def _a   (				self		):
											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(a_		), """references""": datasets.Value(a_		)}		)	,	codebase_urls=[]	,	reference_urls=[]	,	format="""numpy"""	,	)







							def _a   (				self	,	a_	,	a_	,	a_=None	,	a_=None		):
											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(a_	,	a_		)}
											elif self.config_name == "minds14":
															return fa_and_simple_accuracy(a_	,	a_		)
											elif self.config_name == "covost2":
															a__					   =		bleu_kwargs.pop("""smooth_method"""	,	"""exp"""		)
															a__					   =		bleu_kwargs.pop("""smooth_value"""	,	a_		)
															a__					   =		bleu_kwargs.pop("""force"""	,	a_		)
															a__					   =		bleu_kwargs.pop("""lowercase"""	,	a_		)
															a__					   =		bleu_kwargs.pop("""tokenize"""	,	a_		)
															a__					   =		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"]:
															a__					   =		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}'''		)





"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

     # creates points known as breakpoints to break the key_list_options at those
     # points and pivot each substring
     a											=sorted(set(self.__passcode		)		)
     a											=[]

     # algorithm for creating a new shuffled list, keys_l, out of key_list_options
     for i in key_list_options:
         temp_list.extend(__A		)

         # checking breakpoints at which to pivot temporary sublist and add it into
         # keys_l
         if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]		)
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
     return keys_l


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # decoding shift like Caesar cipher algorithm implementing negative shift or
     # reverse shift or left shift
     for i in encoded_message:
         a											=self.__key_list.index(__A		)
         decoded_message += self.__key_list[
             (position - self.__shift_key) % -len(self.__key_list		)
         ]

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=self.__key_list.index(__A		)
         encoded_message += self.__key_list[
             (position + self.__shift_key) % len(self.__key_list		)
         ]

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()