




'''simple docstring'''







from jiwer import compute_measures

import datasets


A     ='\\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'

A     ='\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (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 words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A     ='\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word 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    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION )
class 			_a  (	datasets.Metric ):
       def     A      (	self						:	Any				):



           '''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''',
               ]   ,				)







       def     A      (	self						:	List[Any]   ,				lowercase						:	List[str]=None   ,				lowercase						:	List[str]=None   ,				lowercase						:	Union[str, Any]=False				):



           '''simple docstring'''

           if concatenate_texts:
               return compute_measures(lowercase   ,				lowercase				)["wer"]
           else:
               UpperCAmelCase			       =		0
               UpperCAmelCase			       =		0
               for prediction, reference in zip(lowercase   ,				lowercase				):
                   UpperCAmelCase			       =		compute_measures(lowercase   ,				lowercase				)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


lowercase__		:Optional[int]   =    pytest.mark.integration







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												lowercase							=						Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A__) for x in np.arange(3_0).tolist()]})
												return dset




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												lowercase							=						dset.map(
												    lambda A__   ,A__: {"vecs": i * np.ones(5   ,dtype=np.floataa)}   ,with_indices=A__   ,keep_in_memory=A__)
												lowercase							=						dset.add_faiss_index('''vecs'''   ,batch_size=1_0_0   ,metric_type=faiss.METRIC_INNER_PRODUCT)
												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')
												dset.drop_index('''vecs''')




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs'''   ,batch_size=1_0_0   ,metric_type=faiss.METRIC_INNER_PRODUCT   ,)
												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')




							def  A__      (				self):
												import faiss

												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs'''   ,metric_type=faiss.METRIC_INNER_PRODUCT   ,)

												# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
												# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
												# see https://bugs.python.org/issue14243 and
												# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
												with tempfile.NamedTemporaryFile(delete=A__) as tmp_file:
																	dset.save_faiss_index('''vecs'''   ,tmp_file.name)
																	dset.load_faiss_index('''vecs2'''   ,tmp_file.name)
												os.unlink(tmp_file.name)

												lowercase	,						lowercase							=						dset.get_nearest_examples('''vecs2'''   ,np.ones(5   ,dtype=np.floataa))
												self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')




							def  A__      (				self):
												lowercase							=						self._create_dummy_dataset()
												dset.add_faiss_index_from_external_arrays(
												    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1   ,1)   ,index_name='''vecs''')
												dset.drop_index('''vecs''')
												self.assertRaises(A__   ,partial(dset.get_nearest_examples   ,'''vecs2'''   ,np.ones(5   ,dtype=np.floataa)))





							def  A__      (				self):
												from elasticsearch import Elasticsearch

												lowercase							=						self._create_dummy_dataset()
												with patch('''elasticsearch.Elasticsearch.search''') as mocked_search, patch(
												    '''elasticsearch.client.IndicesClient.create''') as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''') as mocked_bulk:
																	lowercase							=						{'''acknowledged''': True}
																	mocked_bulk.return_value([(True, None)] * 3_0)
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 2_9}]}}
																	lowercase							=						Elasticsearch()

																	dset.add_elasticsearch_index('''filename'''   ,es_client=A__)
																	lowercase	,						lowercase							=						dset.get_nearest_examples('''filename'''   ,'''my_name-train_29''')
																	self.assertEqual(examples['''filename'''][0]   ,'''my_name-train_29''')







@require_faiss
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT)

												# add vectors
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsNotNone(index.faiss_index)
												self.assertEqual(index.faiss_index.ntotal   ,5)
												index.add_vectors(np.zeros((5, 5)   ,dtype=np.floataa))
												self.assertEqual(index.faiss_index.ntotal   ,1_0)

												# single query
												lowercase							=						np.zeros(5   ,dtype=np.floataa)
												lowercase							=						1
												lowercase	,						lowercase							=						index.search(A__)
												self.assertRaises(A__   ,index.search   ,query.reshape(-1   ,1))
												self.assertGreater(scores[0]   ,0)
												self.assertEqual(indices[0]   ,1)

												# batched queries
												lowercase							=						np.eye(5   ,dtype=np.floataa)[::-1]
												lowercase	,						lowercase							=						index.search_batch(A__)
												self.assertRaises(A__   ,index.search_batch   ,queries[0])
												lowercase							=						[scores[0] for scores in total_scores]
												lowercase							=						[indices[0] for indices in total_indices]
												self.assertGreater(np.min(A__)   ,0)
												self.assertListEqual([4, 3, 2, 1, 0]   ,A__)




							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(string_factory='''Flat''')
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexFlat)
												lowercase							=						FaissIndex(string_factory='''LSH''')
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexLSH)
												with self.assertRaises(A__):
																	lowercase							=						FaissIndex(string_factory='''Flat'''   ,custom_index=faiss.IndexFlat(5))




							def  A__      (				self):
												import faiss

												lowercase							=						faiss.IndexFlat(5)
												lowercase							=						FaissIndex(custom_index=A__)
												index.add_vectors(np.eye(5   ,dtype=np.floataa))
												self.assertIsInstance(index.faiss_index   ,faiss.IndexFlat)





							def  A__      (				self):
												import faiss

												lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT)
												index.add_vectors(np.eye(5   ,dtype=np.floataa))

												# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
												# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
												# see https://bugs.python.org/issue14243 and
												# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
												with tempfile.NamedTemporaryFile(delete=A__) as tmp_file:
																	index.save(tmp_file.name)
																	lowercase							=						FaissIndex.load(tmp_file.name)
												os.unlink(tmp_file.name)

												lowercase							=						np.zeros(5   ,dtype=np.floataa)
												lowercase							=						1
												lowercase	,						lowercase							=						index.search(A__)
												self.assertGreater(scores[0]   ,0)
												self.assertEqual(indices[0]   ,1)



@require_faiss
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					import faiss

					lowercase							=						FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT		)
					index.add_vectors(np.eye(5 ,			dtype=np.floataa		)		)

					lowercase							=						'''index.faiss'''
					lowercase							=						f'mock://{index_name}'
					index.save(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)
					lowercase							=						FaissIndex.load(lowerCAmelCase__ ,			storage_options=mockfs.storage_options		)

					lowercase							=						np.zeros(5 ,			dtype=np.floataa		)
					lowercase							=						1
					lowercase	,						lowercase							=						index.search(lowerCAmelCase__		)
					assert scores[0] > 0
					assert indices[0] == 1







@require_elasticsearch
class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def  A__      (				self):
												from elasticsearch import Elasticsearch

												with patch('''elasticsearch.Elasticsearch.search''') as mocked_search, patch(
												    '''elasticsearch.client.IndicesClient.create''') as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''') as mocked_bulk:
																	lowercase							=						Elasticsearch()
																	lowercase							=						{'''acknowledged''': True}
																	lowercase							=						ElasticSearchIndex(es_client=A__)
																	mocked_bulk.return_value([(True, None)] * 3)
																	index.add_documents(['''foo''', '''bar''', '''foobar'''])

																	# single query
																	lowercase							=						'''foo'''
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
																	lowercase	,						lowercase							=						index.search(A__)
																	self.assertEqual(scores[0]   ,1)
																	self.assertEqual(indices[0]   ,0)

																	# single query with timeout
																	lowercase							=						'''foo'''
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
																	lowercase	,						lowercase							=						index.search(A__   ,request_timeout=3_0)
																	self.assertEqual(scores[0]   ,1)
																	self.assertEqual(indices[0]   ,0)

																	# batched queries
																	lowercase							=						['''foo''', '''bar''', '''foobar''']
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
																	lowercase	,						lowercase							=						index.search_batch(A__)
																	lowercase							=						[scores[0] for scores in total_scores]
																	lowercase							=						[indices[0] for indices in total_indices]
																	self.assertGreater(np.min(A__)   ,0)
																	self.assertListEqual([1, 1, 1]   ,A__)

																	# batched queries with timeout
																	lowercase							=						['''foo''', '''bar''', '''foobar''']
																	lowercase							=						{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
																	lowercase	,						lowercase							=						index.search_batch(A__   ,request_timeout=3_0)
																	lowercase							=						[scores[0] for scores in total_scores]
																	lowercase							=						[indices[0] for indices in total_indices]
																	self.assertGreater(np.min(A__)   ,0)
																	self.assertListEqual([1, 1, 1]   ,A__)




