



from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

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


_snake_case	=      ""


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



						class 						UpperCAmelCase_					( tr.AbstractTransform):







													def __init__(     self,    __a = " "):

																				'''simple docstring'''

																				_lowerCAmelCase  :							Union[str, Any]				     =					sentence_delimiter







													def      snake_case__   (     self,    __a):

																				'''simple docstring'''

																				return list(__a)







													def      snake_case__   (     self,    __a):

																				'''simple docstring'''

																				_lowerCAmelCase  :							int				     =					[]
																				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



						_snake_case	=      tr.Compose(
						    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
						)
else:
						_snake_case	=      tr.Compose(
						    [
						        tr.RemoveMultipleSpaces(),
						        tr.Strip(),
						        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
						        tr.ReduceToListOfListOfChars(),
						    ]
						)


_snake_case	=      "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

_snake_case	=      "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

_snake_case	=      "\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,      _KWARGS_DESCRIPTION)
class 						UpperCAmelCase_					( datasets.Metric):







							def      snake_case__   (     self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
														        {
														            "predictions": datasets.Value("string",    id="sequence"),
														            "references": datasets.Value("string",    id="sequence"),
														        }),    codebase_urls=["https://github.com/jitsi/jiwer/"],    reference_urls=[
														        "https://en.wikipedia.org/wiki/Word_error_rate",
														        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
														    ],    )







							def      snake_case__   (     self,    __a,    __a,    __a=False):

														'''simple docstring'''

														if concatenate_texts:
																					return jiwer.compute_measures(
																					    __a,    __a,    truth_transform=__a,    hypothesis_transform=__a,    )["wer"]

														_lowerCAmelCase  :							Optional[int]				     =					0
														_lowerCAmelCase  :							Tuple				     =					0
														for prediction, reference in zip(__a,    __a):
																					_lowerCAmelCase  :							Any				     =					jiwer.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





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return input_array.reshape((input_array.size, 1)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							Tuple				     =					features[:, labels == i]
														_lowerCAmelCase  :							Dict				     =					data.mean(1						)
														# Centralize the data of class i
														_lowerCAmelCase  :							Union[str, Any]				     =					data - column_reshape(_lowerCamelCase						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += np.dot(_lowerCamelCase       ,				centered_data.T						)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							int				     =					np.dot(_lowerCamelCase       ,				centered_data.T						)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					features.mean(1						)
							_lowerCAmelCase  :							List[str]				     =					np.nan
							for i in range(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					features[:, labels == i]
														_lowerCAmelCase  :							Optional[Any]				     =					data.shape[1]
														_lowerCAmelCase  :							Optional[Any]				     =					data.mean(1						)
														if i > 0:
																					# If covariance_sum is not None
																					covariance_sum += device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)
														else:
																					# If covariance_sum is np.nan (i.e. first loop)
																					_lowerCAmelCase  :							Optional[Any]				     =					device_data * np.dot(
																					    column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)       ,				(column_reshape(_lowerCamelCase						) - column_reshape(_lowerCamelCase						)).T       ,				)

							return covariance_sum / features.shape[1]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if features.any():
														_lowerCAmelCase  :							List[Any]				     =					features.mean(1						)
														# Center the dataset
														_lowerCAmelCase  :							List[Any]				     =					features - np.reshape(_lowerCamelCase       ,				(data_mean.size, 1)						)
														_lowerCAmelCase  :							Optional[Any]				     =					np.dot(_lowerCamelCase       ,				centered_data.T						) / features.shape[1]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[Any]				     =					np.linalg.eigh(_lowerCamelCase						)
														# Take all the columns in the reverse order (-1), and then takes only the first
														_lowerCAmelCase  :							Union[str, Any]				     =					eigenvectors[:, ::-1][:, 0:dimensions]
														# Project the database on the new space
														_lowerCAmelCase  :							List[Any]				     =					np.dot(filtered_eigenvectors.T       ,				_lowerCamelCase						)
														logging.info("Principal Component Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							assert classes > dimensions

							# Check if features have been already loaded
							if features.any:
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					eigh(
														    covariance_between_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				covariance_within_classes(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)       ,				)
														_lowerCAmelCase  :							List[str]				     =					eigenvectors[:, ::-1][:, :dimensions]
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					np.linalg.svd(_lowerCamelCase						)
														_lowerCAmelCase  :							Optional[Any]				     =					svd_matrix[:, 0:dimensions]
														_lowerCAmelCase  :							str				     =					np.dot(filtered_svd_matrix.T       ,				_lowerCamelCase						)
														logging.info("Linear Discriminant Analysis computed"						)

														return projected_data
							else:
														logging.basicConfig(level=logging.ERROR       ,				format="%(message)s"       ,				force=_lowerCamelCase						)
														logging.error("Dataset empty"						)
														raise AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]						)
							_lowerCAmelCase  :							List[Any]				     =					np.array([0, 0, 0, 1, 1]						)
							_lowerCAmelCase  :							List[Any]				     =					2
							_lowerCAmelCase  :							Union[str, Any]				     =					2

							# Assert that the function raises an AssertionError if dimensions > classes
							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Union[str, Any]				     =					linear_discriminant_analysis(
														    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														if isinstance(_lowerCamelCase       ,				np.ndarray						):
																					raise AssertionError(
																					    "Did not raise AssertionError for dimensions > classes"						)
														assert error_info.type is AssertionError






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						)
							_lowerCAmelCase  :							List[str]				     =					2
							_lowerCAmelCase  :							List[Any]				     =					np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]]						)

							with pytest.raises(_lowerCamelCase						) as error_info:
														_lowerCAmelCase  :							Tuple				     =					principal_component_analysis(_lowerCamelCase       ,				_lowerCamelCase						)
														if not np.allclose(_lowerCamelCase       ,				_lowerCamelCase						):
																					raise AssertionError
														assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()
