

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_									=   pytest.mark.integration





@require_faiss
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        _lowercase	       =Dataset.from_dict({'filename': ['my_name-train' + '_' + str(lowerCAmelCase					) for x in np.arange(30					).tolist()]}					)
        return dset




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






        import faiss

        _lowercase	       =self._create_dummy_dataset()
        _lowercase	       =dset.map(
            lambda lowerCAmelCase							,   lowerCAmelCase					: {"vecs": i * np.ones(5							,   dtype=np.floataa					)}							,   with_indices=lowerCAmelCase							,   keep_in_memory=lowerCAmelCase					)
        _lowercase	       =dset.add_faiss_index('vecs'							,   batch_size=100							,   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					)  -> List[Any]:
        '''simple docstring'''






        import faiss

        _lowercase	       =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=100							,   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					)  -> List[Any]:
        '''simple docstring'''






        import faiss

        _lowercase	       =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=lowerCAmelCase					) 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					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =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(lowerCAmelCase							,   partial(dset.get_nearest_examples							,   'vecs2'							,   np.ones(5							,   dtype=np.floataa					)					)					)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        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)] * 30					)
              _lowercase	       ={'hits': {'hits': [{'_score': 1, '_id': 29}]}}
              _lowercase	       =Elasticsearch()

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





@require_faiss
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




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






        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							,   10					)

        # single query
        _lowercase	       =np.zeros(5							,   dtype=np.floataa					)
        _lowercase	       =1
        _lowercase       ,       _lowercase	       =index.search(lowerCAmelCase					)
        self.assertRaises(lowerCAmelCase							,   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(lowerCAmelCase					)
        self.assertRaises(lowerCAmelCase							,   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(lowerCAmelCase					)							,   0					)
        self.assertListEqual([4, 3, 2, 1, 0]							,   lowerCAmelCase					)




  def   A__     (				self					)  -> Any:
        '''simple docstring'''






        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(lowerCAmelCase					):
              _lowercase	       =FaissIndex(string_factory='Flat'							,   custom_index=faiss.IndexFlat(5					)					)




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






        import faiss

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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        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=lowerCAmelCase					) 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(lowerCAmelCase					)
        self.assertGreater(scores[0]							,   0					)
        self.assertEqual(indices[0]							,   1					)
@require_faiss
def      a      (			A__	:   str     )    ->      List[str]:





      """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(A__      ,   storage_options=mockfs.storage_options     )
      _lowercase	       =FaissIndex.load(A__      ,   storage_options=mockfs.storage_options     )

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







@require_elasticsearch
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self					)  -> Optional[Any]:
        '''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:
              _lowercase	       =Elasticsearch()
              _lowercase	       ={'acknowledged': True}
              _lowercase	       =ElasticSearchIndex(es_client=lowerCAmelCase					)
              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(lowerCAmelCase					)
              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(lowerCAmelCase							,   request_timeout=30					)
              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(lowerCAmelCase					)
              _lowercase	       =[scores[0] for scores in total_scores]
              _lowercase	       =[indices[0] for indices in total_indices]
              self.assertGreater(np.min(lowerCAmelCase					)							,   0					)
              self.assertListEqual([1, 1, 1]							,   lowerCAmelCase					)

              # batched queries with timeout
              _lowercase	       =['foo', 'bar', 'foobar']
              _lowercase	       ={'hits': {'hits': [{'_score': 1, '_id': 1}]}}
              _lowercase       ,       _lowercase	       =index.search_batch(lowerCAmelCase							,   request_timeout=30					)
              _lowercase	       =[scores[0] for scores in total_scores]
              _lowercase	       =[indices[0] for indices in total_indices]
              self.assertGreater(np.min(lowerCAmelCase					)							,   0					)
              self.assertListEqual([1, 1, 1]							,   lowerCAmelCase					)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_									=   {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLNetForMultipleChoice',
      'XLNetForQuestionAnswering',
      'XLNetForQuestionAnsweringSimple',
      'XLNetForSequenceClassification',
      'XLNetForTokenClassification',
      'XLNetLMHeadModel',
      'XLNetModel',
      'XLNetPreTrainedModel',
      'load_tf_weights_in_xlnet',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLNetForMultipleChoice',
      'TFXLNetForQuestionAnsweringSimple',
      'TFXLNetForSequenceClassification',
      'TFXLNetForTokenClassification',
      'TFXLNetLMHeadModel',
      'TFXLNetMainLayer',
      'TFXLNetModel',
      'TFXLNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


