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_:      Optional[int]        =			'\\n'

A_:      Any        =			'\\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_:      Optional[Any]        =			'\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_:      Any        =			['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
A_:      Tuple        =			''

try:
    from jiwer import transforms as tr

    A_:      List[str]        =			True
except ImportError:
    A_:      Dict        =			False

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


    class _lowercase					(       tr.AbstractTransform						):





           """simple docstring"""




           def __init__(    self     ,   UpperCAmelCase = " "  ):







                  '''simple docstring'''






                  _lowercase				=	sentence_delimiter



           def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







                  '''simple docstring'''






                  return list(UpperCAmelCase  )



           def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







                  '''simple docstring'''






                  _lowercase				=	[]
                  for sent_idx, sentence in enumerate(UpperCAmelCase  ):
                         chars.extend(self.process_string(UpperCAmelCase  )  )
                         if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase  ) - 1:
                                chars.append(self.sentence_delimiter  )
                  return chars






    A_:      Any        =			tr.Compose(
        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
    )
elif _jiwer_available:
    A_:      List[str]        =			tr.Compose(
        [
            tr.RemoveMultipleSpaces(),
            tr.Strip(),
            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
            tr.ReduceToListOfListOfChars(),
        ]
    )
else:
    A_:      Optional[Any]        =			None


def 					__lowerCAmelCase			(  _A ,_A					):



       """simple docstring"""


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


def 					__lowerCAmelCase			(  _A ,_A					):



       """simple docstring"""


       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 ,):



       """simple docstring"""


       _lowercase				=	[[label] for label in labels]
       _lowercase				=	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\"`."""					)

       _lowercase				=	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"""					)
       _lowercase				=	[[refs[i] for refs in labels] for i in range(_A					)]
       _lowercase				=	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					):



       """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:
              _lowercase				=	compute_measures(_A ,_A					)["""wer"""]

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

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





       """simple docstring"""




       def     _UpperCAmelCase       (    self  ):







              '''simple docstring'''






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

              _lowercase				=	"""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(UpperCAmelCase  ), """references""": datasets.Value(UpperCAmelCase  )}  )     ,   codebase_urls=[]     ,   reference_urls=[]     ,   format="""numpy"""     ,   )



       def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None  ):







              '''simple docstring'''






              _lowercase				=	bleu_kwargs if bleu_kwargs is not None else {}
              _lowercase				=	wer_kwargs if wer_kwargs is not None else {}

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



import numpy as np


def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):



							"""simple docstring"""


							_lowercase				=	bin(int.from_bytes(text.encode(_A ,_A					) ,"""big"""					)					)[2:]
							return bits.zfill(8 * ((len(_A					) + 7) // 8)					)


def 					__lowerCAmelCase			(  _A ,_A="utf-8" ,_A="surrogatepass"					):



							"""simple docstring"""


							_lowercase				=	int(_A ,2					)
							return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""					).decode(_A ,_A					) or "\0"


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
														raise ValueError("""size of parity don't match with size of data"""					)

							_lowercase				=	[]
							_lowercase				=	[]
							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]

							# sorted information data for the size of the output data
							_lowercase				=	[]
							# data position template + parity
							_lowercase				=	[]
							# parity bit counter
							_lowercase				=	0
							# counter position of data bits
							_lowercase				=	0

							for x in range(1 ,size_par + len(_A					) + 1					):
														# Performs a template of bit positions - who should be given,
														# and who should be parity
														if qtd_bp < size_par:
																					if (np.log(_A					) / np.log(2					)).is_integer():
																												data_out_gab.append("""P"""					)
																												_lowercase				=	qtd_bp + 1
																					else:
																												data_out_gab.append("""D"""					)
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_ord.append(data[cont_data]					)
																					cont_data += 1
														else:
																					data_ord.append(_A					)

    # Calculates parity
							_lowercase				=	0  # parity bit counter
							for bp in range(1 ,size_par + 1					):
														# Bit counter one for a given parity
														_lowercase				=	0
														# counter to control the loop reading
														_lowercase				=	0
														for x in data_ord:
																					if x is not None:
																												try:
																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
																												except IndexError:
																																			_lowercase				=	"""0"""
																												if aux == "1" and x == "1":
																																			cont_bo += 1
																					cont_loop += 1
														parity.append(cont_bo % 2					)

														qtd_bp += 1

							# Mount the message
							_lowercase				=	0  # parity bit counter
							for x in range(0 ,size_par + len(_A					)					):
														if data_ord[x] is None:
																					data_out.append(str(parity[cont_bp]					)					)
																					cont_bp += 1
														else:
																					data_out.append(data_ord[x]					)

							return data_out


def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


							_lowercase				=	[]
							# Parity bit counter
							_lowercase				=	0
							# Counter p data bit reading
							_lowercase				=	0
							# list of parity received
							_lowercase				=	[]
							_lowercase				=	[]

							for x in range(1 ,len(_A					) + 1					):
														# Performs a template of bit positions - who should be given,
														#  and who should be parity
														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
																					data_out_gab.append("""P"""					)
																					_lowercase				=	qtd_bp + 1
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_output.append(data[cont_data]					)
														else:
																					parity_received.append(data[cont_data]					)
														cont_data += 1

							# -----------calculates the parity with the data
							_lowercase				=	[]
							_lowercase				=	[]
							_lowercase				=	[bin(_A					)[2:] for x in range(1 ,size_par + len(_A					) + 1					)]

							#  sorted information data for the size of the output data
							_lowercase				=	[]
							# Data position feedback + parity
							_lowercase				=	[]
							# Parity bit counter
							_lowercase				=	0
							# Counter p data bit reading
							_lowercase				=	0

							for x in range(1 ,size_par + len(_A					) + 1					):
														# Performs a template position of bits - who should be given,
														# and who should be parity
														if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
																					data_out_gab.append("""P"""					)
																					_lowercase				=	qtd_bp + 1
														else:
																					data_out_gab.append("""D"""					)

														# Sorts the data to the new output size
														if data_out_gab[-1] == "D":
																					data_ord.append(data_output[cont_data]					)
																					cont_data += 1
														else:
																					data_ord.append(_A					)

    # Calculates parity
							_lowercase				=	0  # parity bit counter
							for bp in range(1 ,size_par + 1					):
														# Bit counter one for a certain parity
														_lowercase				=	0
														# Counter to control loop reading
														_lowercase				=	0
														for x in data_ord:
																					if x is not None:
																												try:
																																			_lowercase				=	(bin_pos[cont_loop])[-1 * (bp)]
																												except IndexError:
																																			_lowercase				=	"""0"""
																												if aux == "1" and x == "1":
																																			cont_bo += 1
																					cont_loop += 1
														parity.append(str(cont_bo % 2					)					)

														qtd_bp += 1

							# Mount the message
							_lowercase				=	0  # Parity bit counter
							for x in range(0 ,size_par + len(_A					)					):
														if data_ord[x] is None:
																					data_out.append(str(parity[cont_bp]					)					)
																					cont_bp += 1
														else:
																					data_out.append(data_ord[x]					)

							_lowercase				=	parity_received == parity
							return data_output, ack


