

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      ):







							def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			str = " "   ):

												'''simple docstring'''





												__magic_name__				=     sentence_delimiter







							def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			str   ):

												'''simple docstring'''





												return list(__UpperCamelCase   )







							def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			List[str]   ):

												'''simple docstring'''





												__magic_name__				=     []
												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__							=				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        _lowercase  (					a_					:       Union[str, Any]       ,a_					:       List[str]					)					->						Any:




					'''simple docstring'''







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




def        _lowercase  (					a_					:       int       ,a_					:       List[Any]					)					->						List[Any]:




					'''simple docstring'''







					return {
					    "f1": float(fa_score(y_true=a_       ,y_pred=a_       ,average='macro'					)					),
					    "accuracy": simple_accuracy(a_       ,a_					),
					}




def        _lowercase  (					a_					:       str       ,a_					:       List[Any]       ,a_					:       List[str]="exp"       ,a_					:       List[str]=None       ,a_					:       List[Any]=False       ,a_					:       List[Any]=False       ,a_					:       Tuple=None       ,a_					:       List[str]=False       ,)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     [[label] for label in labels]
					__magic_name__				=     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"`.'					)

					__magic_name__				=     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'					)
					__magic_name__				=     [[refs[i] for refs in labels] for i in range(a_					)]
					__magic_name__				=     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        _lowercase  (					a_					:       Tuple       ,a_					:       Optional[Any]       ,a_					:       Optional[int]       ,a_					:       List[Any]					)					->						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:
										__magic_name__				=     compute_measures(a_       ,a_					)['wer']

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





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

						__magic_name__				=     '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 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]	,			__UpperCamelCase:			Tuple	,			__UpperCamelCase:			Union[str, Any]	,			__UpperCamelCase:			Union[str, Any]=None	,			__UpperCamelCase:			str=None   ):

						'''simple docstring'''





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







from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0   ):
											raise ValueError('RHS must be > 0'   )

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

						# Number of decision variables x1, x2, x3...
						__magic_name__				=     n_vars

						# Number of artificial variables to be minimised
						__magic_name__				=     len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

						# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
						__magic_name__				=     (self.n_art_vars > 0) + 1

						# Number of slack variables added to make inequalities into equalities
						__magic_name__				=     self.n_rows - self.n_stages

						# Objectives for each stage
						__magic_name__				=     ['max']

						# In two stage simplex, first minimise then maximise
						if self.n_art_vars:
											self.objectives.append('min'   )

						__magic_name__				=     ['']

						# Index of current pivot row and column
						__magic_name__				=     None
						__magic_name__				=     None

						# Does objective row only contain (non)-negative values?
						__magic_name__				=     False







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''simple docstring'''





						if len(__UpperCamelCase   ) != 3:
											raise ValueError('Must provide n_vars, n_slack, and n_art_vars'   )

						if not all(x >= 0 and isinstance(__UpperCamelCase	,			__UpperCamelCase   ) for x in args   ):
											raise ValueError('All arguments must be non-negative integers'   )

						# decision | slack | artificial
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						for i in range(3   ):
											for j in range(args[i]   ):
																titles.append(string_starts[i] + str(j + 1   )   )
						titles.append('RHS'   )
						return titles







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     np.argmax(sign * tableau[0, : self.n_vars]   )

						# Choice is only valid if below 0 for maximise, and above for minimise
						if sign * self.tableau[0, col_idx] <= 0:
											__magic_name__				=     True
											return 0, 0

						# Pivot row is chosen as having the lowest quotient when elements of
						# the pivot column divide the right-hand side

						# Slice excluding the objective rows
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

						# Elements of pivot column within slice
						__magic_name__				=     tableau[s, col_idx]

						# Array filled with nans
						__magic_name__				=     np.full(self.n_rows - self.n_stages	,			np.nan   )

						# If element in pivot column is greater than zeron_stages, return
						# quotient or nan otherwise
						__magic_name__				=     np.divide(__UpperCamelCase	,			__UpperCamelCase	,			out=__UpperCamelCase	,			where=divisor > 0   )

						# Arg of minimum quotient excluding the nan values. n_stages is added
						# to compensate for earlier exclusion of objective columns
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     piv_row[col_idx]

						# Entry becomes 1
						piv_row *= 1 / piv_val

						# Variable in pivot column becomes basic, ie the only non-zero entry
						for idx, coeff in enumerate(tableau[:, col_idx]   ):
											tableau[idx] += -coeff * piv_row
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

						# Slice containing ids for artificial columns
						__magic_name__				=     slice(-self.n_art_vars - 1	,			-1   )

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

						# Delete the objective row of the first stage
						__magic_name__				=     np.delete(__UpperCamelCase	,			0	,			axis=0   )

						__magic_name__				=     1
						self.n_rows -= 1
						__magic_name__				=     0
						__magic_name__				=     False
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     self.generate_col_titles(
																    self.n_vars	,			self.n_slack	,			self.n_art_vars   )

																# Find the values of each variable at optimal solution
																return self.interpret_tableau(self.tableau	,			self.col_titles   )

											__magic_name__,						__magic_name__				=     self.find_pivot(self.tableau   )

											# If there are no more negative values in objective row
											if self.stop_iter:
																# Delete artificial variable columns and rows. Update attributes
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





						__magic_name__				=     {'P': abs(tableau[0, -1]   )}

						for i in range(self.n_vars   ):
											# Gives ids of nonzero entries in the ith column
											__magic_name__				=     np.nonzero(tableau[:, i]   )
											__magic_name__				=     len(nonzero[0]   )

											# First entry in the nonzero ids
											__magic_name__				=     nonzero[0][0]
											__magic_name__				=     tableau[nonzero_rowidx, i]

											# If there is only one nonzero value in column, which is one
											if n_nonzero == nonzero_val == 1:
																__magic_name__				=     tableau[nonzero_rowidx, -1]
																__magic_name__				=     rhs_val

        # Check for basic variables
						for title in col_titles:
											# Don't add RHS or slack variables to output dict
											if title[0] not in "R-s-a":
																output_dict.setdefault(__UpperCamelCase	,			0   )
						return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




