
"""simple docstring"""




import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


A__   :					Dict							=   'base_with_context'




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Any       =     nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']						)						)
  _lowercase:			List[Any]       =     nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']						) ,				requires_grad=_UpperCamelCase						)
  for lyr_num, lyr in enumerate(model.encoders						):
    _lowercase:			Any       =     weights[f'''layers_{lyr_num}''']
    _lowercase:			str       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

    _lowercase:			Optional[int]       =     ly_weight['''attention''']
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			Any       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

    _lowercase:			str       =     nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

    _lowercase:			Tuple       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

  _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)
  return model




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			int       =     nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T						)						)

  _lowercase:			Union[str, Any]       =     nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']						) ,				requires_grad=_UpperCamelCase						)

  for lyr_num, lyr in enumerate(model.encoders						):
    _lowercase:			Optional[Any]       =     weights[f'''layers_{lyr_num}''']
    _lowercase:			Tuple       =     ly_weight['''attention''']

    _lowercase:			Dict       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)
    _lowercase:			List[Any]       =     nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

  _lowercase:			int       =     nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)

  return model




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Tuple       =     nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T						)						)
  _lowercase:			Dict       =     nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T						)						)

  _lowercase:			Tuple       =     nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']						) ,				requires_grad=_UpperCamelCase						)

  _lowercase:			Optional[int]       =     nn.Parameter(
      torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T						)						)

  for lyr_num, lyr in enumerate(model.decoders						):
    _lowercase:			Optional[int]       =     weights[f'''layers_{lyr_num}''']
    _lowercase:			List[Any]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']						)						)

    _lowercase:			List[str]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T						)						)

    _lowercase:			Optional[int]       =     ly_weight['''self_attention''']
    _lowercase:			Optional[Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			str       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			List[str]       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

    _lowercase:			List[Any]       =     ly_weight['''MultiHeadDotProductAttention_0''']
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			str       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
    _lowercase:			List[str]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']						)						)

    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
    _lowercase:			List[str]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
    _lowercase:			Tuple       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

  _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']						)						)

  _lowercase:			List[Any]       =     nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T						)						)

  return model




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     checkpoints.load_tax_checkpoint(args.checkpoint_path						)
  _lowercase:			Any       =     jnp.tree_util.tree_map(onp.array ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     [
      '''from __gin__ import dynamic_registration''',
      '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
      '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
      '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
  ]

  _lowercase:			Union[str, Any]       =     os.path.join(args.checkpoint_path ,				'''..''' ,				'''config.gin'''						)
  _lowercase:			Union[str, Any]       =     inference.parse_training_gin_file(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     inference.InferenceModel(args.checkpoint_path ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' ,				variance_type='''fixed_large'''						)

  _lowercase:			Dict       =     SpectrogramNotesEncoder(
      max_length=synth_model.sequence_length['''inputs'''] ,				vocab_size=synth_model.model.module.config.vocab_size ,				d_model=synth_model.model.module.config.emb_dim ,				dropout_rate=synth_model.model.module.config.dropout_rate ,				num_layers=synth_model.model.module.config.num_encoder_layers ,				num_heads=synth_model.model.module.config.num_heads ,				d_kv=synth_model.model.module.config.head_dim ,				d_ff=synth_model.model.module.config.mlp_dim ,				feed_forward_proj='''gated-gelu''' ,				)

  _lowercase:			Optional[int]       =     SpectrogramContEncoder(
      input_dims=synth_model.audio_codec.n_dims ,				targets_context_length=synth_model.sequence_length['''targets_context'''] ,				d_model=synth_model.model.module.config.emb_dim ,				dropout_rate=synth_model.model.module.config.dropout_rate ,				num_layers=synth_model.model.module.config.num_encoder_layers ,				num_heads=synth_model.model.module.config.num_heads ,				d_kv=synth_model.model.module.config.head_dim ,				d_ff=synth_model.model.module.config.mlp_dim ,				feed_forward_proj='''gated-gelu''' ,				)

  _lowercase:			Any       =     TaFilmDecoder(
      input_dims=synth_model.audio_codec.n_dims ,				targets_length=synth_model.sequence_length['''targets_context'''] ,				max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time ,				d_model=synth_model.model.module.config.emb_dim ,				num_layers=synth_model.model.module.config.num_decoder_layers ,				num_heads=synth_model.model.module.config.num_heads ,				d_kv=synth_model.model.module.config.head_dim ,				d_ff=synth_model.model.module.config.mlp_dim ,				dropout_rate=synth_model.model.module.config.dropout_rate ,				)

  _lowercase:			str       =     load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     load_decoder(ta_checkpoint['''target''']['''decoder'''] ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''						)

  _lowercase:			Any       =     SpectrogramDiffusionPipeline(
      notes_encoder=_UpperCamelCase ,				continuous_encoder=_UpperCamelCase ,				decoder=_UpperCamelCase ,				scheduler=_UpperCamelCase ,				melgan=_UpperCamelCase ,				)
  if args.save:
    pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
  A__   :					List[str]							=   argparse.ArgumentParser()

  parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
  parser.add_argument(
      '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
  )
  parser.add_argument(
      '--checkpoint_path',
      default=f"""{MODEL}/checkpoint_500000""",
      type=str,
      required=False,
      help='Path to the original jax model checkpoint.',
  )
  A__   :					Optional[Any]							=   parser.parse_args()

  main(args)


"""simple docstring"""




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


A__   :					Any							=   pytest.mark.integration


@require_faiss
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




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





         """simple docstring"""




         import faiss

         _lowercase:			Dataset       =     self._create_dummy_dataset()
         _lowercase:			List[str]       =     dset.map(
             lambda A_ ,				A_       : {"vecs": i * np.ones(5 ,				dtype=np.floataa       )} ,				with_indices=A_ ,				keep_in_memory=A_       )
         _lowercase:			Dict       =     dset.add_faiss_index('''vecs''' ,				batch_size=100 ,				metric_type=faiss.METRIC_INNER_PRODUCT       )
         _lowercase ,				_lowercase:			Union[str, 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 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         import faiss

         _lowercase:			Dataset       =     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:			Dict       =     dset.get_nearest_examples('''vecs''' ,				np.ones(5 ,				dtype=np.floataa       )       )
         self.assertEqual(examples['''filename'''][0] ,				'''my_name-train_29'''       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         import faiss

         _lowercase:			Dataset       =     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=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:			Dict       =     dset.get_nearest_examples('''vecs2''' ,				np.ones(5 ,				dtype=np.floataa       )       )
         self.assertEqual(examples['''filename'''][0] ,				'''my_name-train_29'''       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




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



       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         from elasticsearch import Elasticsearch

         _lowercase:			Dataset       =     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:			List[Any]       =     {'''acknowledged''': True}
           mocked_bulk.return_value([(True, None)] * 30       )
           _lowercase:			List[str]       =     {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
           _lowercase:			int       =     Elasticsearch()

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


@require_faiss
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         import faiss

         _lowercase:			str       =     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:			List[Any]       =     np.zeros(5 ,				dtype=np.floataa       )
         _lowercase:			int       =     1
         _lowercase ,				_lowercase:			Optional[Any]       =     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:			Tuple       =     np.eye(5 ,				dtype=np.floataa       )[::-1]
         _lowercase ,				_lowercase:			str       =     index.search_batch(A_       )
         self.assertRaises(A_ ,				index.search_batch ,				queries[0]       )
         _lowercase:			Tuple       =     [scores[0] for scores in total_scores]
         _lowercase:			Union[str, Any]       =     [indices[0] for indices in total_indices]
         self.assertGreater(np.min(A_       ) ,				0       )
         self.assertListEqual([4, 3, 2, 1, 0] ,				A_       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         import faiss

         _lowercase:			Union[str, Any]       =     FaissIndex(string_factory='''Flat'''       )
         index.add_vectors(np.eye(5 ,				dtype=np.floataa       )       )
         self.assertIsInstance(index.faiss_index ,				faiss.IndexFlat       )
         _lowercase:			Dict       =     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:			List[str]       =     FaissIndex(string_factory='''Flat''' ,				custom_index=faiss.IndexFlat(5       )       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         import faiss

         _lowercase:			Any       =     faiss.IndexFlat(5       )
         _lowercase:			List[Any]       =     FaissIndex(custom_index=A_       )
         index.add_vectors(np.eye(5 ,				dtype=np.floataa       )       )
         self.assertIsInstance(index.faiss_index ,				faiss.IndexFlat       )



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         import faiss

         _lowercase:			Tuple       =     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:			Optional[Any]       =     FaissIndex.load(tmp_file.name       )
         os.unlink(tmp_file.name       )

         _lowercase:			Optional[Any]       =     np.zeros(5 ,				dtype=np.floataa       )
         _lowercase:			Union[str, Any]       =     1
         _lowercase ,				_lowercase:			Tuple       =     index.search(A_       )
         self.assertGreater(scores[0] ,				0       )
         self.assertEqual(indices[0] ,				1       )




@require_faiss
def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  import faiss

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

  _lowercase:			Tuple       =     '''index.faiss'''
  _lowercase:			str       =     f'''mock://{index_name}'''
  index.save(_UpperCamelCase ,				storage_options=mockfs.storage_options						)
  _lowercase:			List[Any]       =     FaissIndex.load(_UpperCamelCase ,				storage_options=mockfs.storage_options						)

  _lowercase:			Union[str, Any]       =     np.zeros(5 ,				dtype=np.floataa						)
  _lowercase:			Dict       =     1
  _lowercase ,				_lowercase:			str       =     index.search(_UpperCamelCase						)
  assert scores[0] > 0
  assert indices[0] == 1







@require_elasticsearch
class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self       )     ->	int:





         """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:			int       =     Elasticsearch()
           _lowercase:			Tuple       =     {'''acknowledged''': True}
           _lowercase:			Tuple       =     ElasticSearchIndex(es_client=A_       )
           mocked_bulk.return_value([(True, None)] * 3       )
           index.add_documents(['''foo''', '''bar''', '''foobar''']       )

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

           # single query with timeout
           _lowercase:			Optional[int]       =     '''foo'''
           _lowercase:			Union[str, Any]       =     {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
           _lowercase ,				_lowercase:			List[Any]       =     index.search(A_ ,				request_timeout=30       )
           self.assertEqual(scores[0] ,				1       )
           self.assertEqual(indices[0] ,				0       )

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

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