




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


UpperCAmelCase_		:					Any          =  pytest.mark.integration




@require_faiss
class 		__A   (      snake_case_      ):

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(__snake_case			) for x in np.arange(30			).tolist()]}			)
										return dset

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						int										=self._create_dummy_dataset()
										__magic_name__						:						int										=dset.map(
										    lambda __snake_case     ,			__snake_case			: {"vecs": i * np.ones(5     ,			dtype=np.floataa			)}     ,			with_indices=__snake_case     ,			keep_in_memory=__snake_case			)
										__magic_name__						:						Tuple										=dset.add_faiss_index("""vecs"""     ,			batch_size=1_00     ,			metric_type=faiss.METRIC_INNER_PRODUCT			)
										__magic_name__					,			__magic_name__						:						Optional[Any]										=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 :Union[str, Any]			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						Tuple										=self._create_dummy_dataset()
										dset.add_faiss_index_from_external_arrays(
										    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1     ,			1			)     ,			index_name="""vecs"""     ,			batch_size=1_00     ,			metric_type=faiss.METRIC_INNER_PRODUCT     ,			)
										__magic_name__					,			__magic_name__						:						Dict										=dset.get_nearest_examples("""vecs"""     ,			np.ones(5     ,			dtype=np.floataa			)			)
										self.assertEqual(examples["""filename"""][0]     ,			"""my_name-train_29"""			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						Tuple										=self._create_dummy_dataset()
										dset.add_faiss_index_from_external_arrays(
										    external_arrays=np.ones((30, 5)			) * np.arange(30			).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=__snake_case			) as tmp_file:
														dset.save_faiss_index("""vecs"""     ,			tmp_file.name			)
														dset.load_faiss_index("""vecs2"""     ,			tmp_file.name			)
										os.unlink(tmp_file.name			)

										__magic_name__					,			__magic_name__						:						Any										=dset.get_nearest_examples("""vecs2"""     ,			np.ones(5     ,			dtype=np.floataa			)			)
										self.assertEqual(examples["""filename"""][0]     ,			"""my_name-train_29"""			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self._create_dummy_dataset()
										dset.add_faiss_index_from_external_arrays(
										    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1     ,			1			)     ,			index_name="""vecs"""			)
										dset.drop_index("""vecs"""			)
										self.assertRaises(__snake_case     ,			partial(dset.get_nearest_examples     ,			"""vecs2"""     ,			np.ones(5     ,			dtype=np.floataa			)			)			)


						def      A__    (		self :Tuple			):
										'''simple docstring'''

										from elasticsearch import Elasticsearch

										__magic_name__						:						Tuple										=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:
														__magic_name__						:						int										={"""acknowledged""": True}
														mocked_bulk.return_value([(True, None)] * 30			)
														__magic_name__						:						Optional[int]										={"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
														__magic_name__						:						str										=Elasticsearch()

														dset.add_elasticsearch_index("""filename"""     ,			es_client=__snake_case			)
														__magic_name__					,			__magic_name__						:						Dict										=dset.get_nearest_examples("""filename"""     ,			"""my_name-train_29"""			)
														self.assertEqual(examples["""filename"""][0]     ,			"""my_name-train_29"""			)




@require_faiss
class 		__A   (      snake_case_      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						int										=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     ,			10			)

										# single query
										__magic_name__						:						int										=np.zeros(5     ,			dtype=np.floataa			)
										__magic_name__						:						Union[str, Any]										=1
										__magic_name__					,			__magic_name__						:						int										=index.search(__snake_case			)
										self.assertRaises(__snake_case     ,			index.search     ,			query.reshape(-1     ,			1			)			)
										self.assertGreater(scores[0]     ,			0			)
										self.assertEqual(indices[0]     ,			1			)

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

						def      A__    (		self :str			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						List[str]										=FaissIndex(string_factory="""Flat"""			)
										index.add_vectors(np.eye(5     ,			dtype=np.floataa			)			)
										self.assertIsInstance(index.faiss_index     ,			faiss.IndexFlat			)
										__magic_name__						:						Tuple										=FaissIndex(string_factory="""LSH"""			)
										index.add_vectors(np.eye(5     ,			dtype=np.floataa			)			)
										self.assertIsInstance(index.faiss_index     ,			faiss.IndexLSH			)
										with self.assertRaises(__snake_case			):
														__magic_name__						:						Dict										=FaissIndex(string_factory="""Flat"""     ,			custom_index=faiss.IndexFlat(5			)			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						str										=faiss.IndexFlat(5			)
										__magic_name__						:						Union[str, Any]										=FaissIndex(custom_index=__snake_case			)
										index.add_vectors(np.eye(5     ,			dtype=np.floataa			)			)
										self.assertIsInstance(index.faiss_index     ,			faiss.IndexFlat			)


						def      A__    (		self :str			):
										'''simple docstring'''

										import faiss

										__magic_name__						:						List[Any]										=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=__snake_case			) as tmp_file:
														index.save(tmp_file.name			)
														__magic_name__						:						int										=FaissIndex.load(tmp_file.name			)
										os.unlink(tmp_file.name			)

										__magic_name__						:						Optional[int]										=np.zeros(5     ,			dtype=np.floataa			)
										__magic_name__						:						Dict										=1
										__magic_name__					,			__magic_name__						:						List[str]										=index.search(__snake_case			)
										self.assertGreater(scores[0]     ,			0			)
										self.assertEqual(indices[0]     ,			1			)




@require_faiss
def 		lowerCAmelCase_ (						lowerCamelCase ):
				import faiss

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

				__magic_name__						:						Dict										="""index.faiss"""
				__magic_name__						:						List[Any]										=F"mock://{index_name}"
				index.save(lowerCamelCase						,  storage_options=mockfs.storage_options )
				__magic_name__						:						str										=FaissIndex.load(lowerCamelCase						,  storage_options=mockfs.storage_options )

				__magic_name__						:						Union[str, Any]										=np.zeros(5						,  dtype=np.floataa )
				__magic_name__						:						Optional[int]										=1
				__magic_name__					,			__magic_name__						:						List[str]										=index.search(lowerCamelCase )
				assert scores[0] > 0
				assert indices[0] == 1




@require_elasticsearch
class 		__A   (      snake_case_      ):

						def      A__    (		self :str			):
										'''simple docstring'''

										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:
														__magic_name__						:						Union[str, Any]										=Elasticsearch()
														__magic_name__						:						Optional[Any]										={"""acknowledged""": True}
														__magic_name__						:						Union[str, Any]										=ElasticSearchIndex(es_client=__snake_case			)
														mocked_bulk.return_value([(True, None)] * 3			)
														index.add_documents(["""foo""", """bar""", """foobar"""]			)

														# single query
														__magic_name__						:						Optional[Any]										="""foo"""
														__magic_name__						:						str										={"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
														__magic_name__					,			__magic_name__						:						Union[str, Any]										=index.search(__snake_case			)
														self.assertEqual(scores[0]     ,			1			)
														self.assertEqual(indices[0]     ,			0			)

														# single query with timeout
														__magic_name__						:						str										="""foo"""
														__magic_name__						:						List[Any]										={"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
														__magic_name__					,			__magic_name__						:						List[Any]										=index.search(__snake_case     ,			request_timeout=30			)
														self.assertEqual(scores[0]     ,			1			)
														self.assertEqual(indices[0]     ,			0			)

														# batched queries
														__magic_name__						:						Dict										=["""foo""", """bar""", """foobar"""]
														__magic_name__						:						str										={"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
														__magic_name__					,			__magic_name__						:						Optional[Any]										=index.search_batch(__snake_case			)
														__magic_name__						:						Dict										=[scores[0] for scores in total_scores]
														__magic_name__						:						Union[str, Any]										=[indices[0] for indices in total_indices]
														self.assertGreater(np.min(__snake_case			)     ,			0			)
														self.assertListEqual([1, 1, 1]     ,			__snake_case			)

														# batched queries with timeout
														__magic_name__						:						Tuple										=["""foo""", """bar""", """foobar"""]
														__magic_name__						:						str										={"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
														__magic_name__					,			__magic_name__						:						Tuple										=index.search_batch(__snake_case     ,			request_timeout=30			)
														__magic_name__						:						List[Any]										=[scores[0] for scores in total_scores]
														__magic_name__						:						List[Any]										=[indices[0] for indices in total_indices]
														self.assertGreater(np.min(__snake_case			)     ,			0			)
														self.assertListEqual([1, 1, 1]     ,			__snake_case			)











'''simple docstring'''






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


A__							:  Dict    =''''''


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




      class 					UpperCAmelCase	(     tr.AbstractTransform  ):




            def __init__(     self     :					Any     ,     __snake_case     :					str = " "    )							-> Any:
                  _lowerCAmelCase					=				sentence_delimiter




            def 							lowercase__       (     self     :					str     ,     __snake_case     :					str    )							-> Optional[int]:
                  return list(__snake_case    )




            def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					List[str]    )							-> Union[str, Any]:
                  _lowerCAmelCase					=				[]
                  for sent_idx, sentence in enumerate(__snake_case    ):
                        chars.extend(self.process_string(__snake_case    )    )
                        if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__snake_case    ) - 1:
                              chars.append(self.sentence_delimiter    )
                  return chars




      A__							:  Optional[Any]    =tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      A__							:  Tuple    =tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


A__							:  List[str]    ='''\
@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.}
}
'''

A__							:  Optional[int]    ='''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (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 characters,
N is the number of characters in the reference (N=S+D+C).

CER\'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
performance of the ASR system with a CER of 0 being a perfect score.
'''

A__							:  List[str]    ='''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''




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




      def 							lowercase__       (     self     :					Any    )							-> Dict:
            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 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					List[str]     ,     __snake_case     :					Tuple=False    )							-> List[Any]:
            if concatenate_texts:
                  return jiwer.compute_measures(
                      __snake_case     ,     __snake_case     ,     truth_transform=__snake_case     ,     hypothesis_transform=__snake_case     ,     )["wer"]

            _lowerCAmelCase					=				0
            _lowerCAmelCase					=				0
            for prediction, reference in zip(__snake_case     ,     __snake_case    ):
                  _lowerCAmelCase					=				jiwer.compute_measures(
                      __snake_case     ,     __snake_case     ,     truth_transform=__snake_case     ,     hypothesis_transform=__snake_case     ,     )
                  incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                  total += measures["substitutions"] + measures["deletions"] + measures["hits"]

            return incorrect / total






