



from jiwer import compute_measures

import datasets


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

UpperCAmelCase_		 =					"""\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
"""

UpperCAmelCase_		 =					"""
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> wer = datasets.load_metric(\"wer\")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""


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



 """simple docstring"""

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[Any]:
    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''',
        ]			,						)

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					Tuple=None			,						__lowerCAmelCase					:					List[str]=False      )   ->	Union[str, Any]:
    if concatenate_texts:
       return compute_measures(lowerCamelCase__			,						lowerCamelCase__      )["wer"]
    else:
       _A      =       0
       _A      =       0
       for prediction, reference in zip(lowerCamelCase__			,						lowerCamelCase__      ):
          _A      =       compute_measures(lowerCamelCase__			,						lowerCamelCase__      )
          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	(      SCREAMING_SNAKE_CASE      ):
	return input_array.reshape((input_array.size, 1)      )



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

	return covariance_sum / features.shape[1]



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

	return covariance_sum / features.shape[1]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):

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

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	assert classes > dimensions

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

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



def 			_A	(      ):
	# Create dummy dataset with 2 classes and 3 features
	UpperCAmelCase__:	List[str]									= np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]      )
	UpperCAmelCase__:	Optional[int]									= np.array([0, 0, 0, 1, 1]      )
	UpperCAmelCase__:	List[Any]									= 2
	UpperCAmelCase__:	Union[str, Any]									= 2

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



def 			_A	(      ):
	UpperCAmelCase__:	Optional[Any]									= np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]      )
	UpperCAmelCase__:	Optional[int]									= 2
	UpperCAmelCase__:	Union[str, 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(SCREAMING_SNAKE_CASE      ) as error_info:
		UpperCAmelCase__:	Any									= principal_component_analysis(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
		if not np.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
			raise AssertionError
		assert error_info.type is AssertionError


if __name__ == "__main__":
					import doctest

					doctest.testmod()