code: 
  
  
  import json
  import os
  import shutil
  import sys
  import tempfile
  import unittest
  from unittest import TestCase
  from unittest.mock import patch
  
  import faiss
  import numpy as np
  from datasets import Dataset
  
  from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
  from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
  from transformers.integrations import is_ray_available
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
  from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
  from transformers.testing_utils import require_ray
  
  
  sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
  
  if is_torch_available():
  	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
  else:
  	A_    :					int             =None
  
  if is_ray_available():
  	import ray  # noqa: E402 # isort:skip
  	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
  else:
  	A_    :					List[Any]             =None
  	A_    :					int             =None
  	A_    :					Optional[int]             =None
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  												a_					=		unittest.skip("""test requires Datasets, Faiss, psutil"""      )(UpperCAmelCase__      )
  						return test_case
  
  
  
  
  
  @require_distributed_retrieval
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		tempfile.mkdtemp()
  													a_					=		8
  
  													# DPR tok
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    """dpr_tokenizer"""		)
  													os.makedirs(_UpperCAmelCase			,    exist_ok=_UpperCAmelCase		)
  													a_					=		os.path.join(_UpperCAmelCase			,    DPR_VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  													# BART tok
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    """bart_tokenizer"""		)
  													os.makedirs(_UpperCAmelCase			,    exist_ok=_UpperCAmelCase		)
  													a_					=		os.path.join(_UpperCAmelCase			,    BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(_UpperCAmelCase			,    BART_VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,    """dpr_tokenizer"""		)		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,    """bart_tokenizer"""		)		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													shutil.rmtree(self.tmpdirname		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		Dataset.from_dict(
  													    {
  													        """id""": ["""0""", """1"""],
  													        """text""": ["""foo""", """bar"""],
  													        """title""": ["""Foo""", """Bar"""],
  													        """embeddings""": [np.ones(self.retrieval_vector_size		), 2 * np.ones(self.retrieval_vector_size		)],
  													    }		)
  													dataset.add_faiss_index("""embeddings"""			,    string_factory="""Flat"""			,    metric_type=faiss.METRIC_INNER_PRODUCT		)
  													return dataset
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=12_345		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_dummy_dataset()
  													a_					=		RagConfig(
  													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    )
  													with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
  																			a_					=		dataset
  																			a_					=		RagPyTorchDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    )
  																			if init_retrieval:
  																									retriever.init_retrieval(_UpperCAmelCase		)
  													return retriever
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													ray.init(local_mode=_UpperCAmelCase		)
  													a_					=		RagConfig(
  													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    )
  													a_					=		ray.remote(_UpperCAmelCase		)
  													a_					=		[remote_cls.remote() for _ in range(1		)]
  													with patch("""transformers.models.rag.retrieval_rag.load_dataset"""		) as mock_load_dataset:
  																			a_					=		self.get_dummy_dataset()
  																			a_					=		RagRayDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    )
  																			if init_retrieval:
  																									retriever.init_retrieval()
  													return retriever
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase=12_345		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_dummy_dataset()
  													a_					=		RagConfig(
  													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    index_name="""custom"""			,    )
  													if from_disk:
  																			a_					=		os.path.join(self.tmpdirname			,    """dataset"""		)
  																			a_					=		os.path.join(self.tmpdirname			,    """index.faiss"""		)
  																			dataset.get_index("""embeddings"""		).save(os.path.join(self.tmpdirname			,    """index.faiss"""		)		)
  																			dataset.drop_index("""embeddings"""		)
  																			dataset.save_to_disk(os.path.join(self.tmpdirname			,    """dataset"""		)		)
  																			del dataset
  																			a_					=		RagPyTorchDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    )
  													else:
  																			a_					=		RagPyTorchDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    index=CustomHFIndex(config.retrieval_vector_size			,    _UpperCAmelCase		)			,    )
  													if init_retrieval:
  																			retriever.init_retrieval(_UpperCAmelCase		)
  													return retriever
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													ray.init(local_mode=_UpperCAmelCase		)
  													a_					=		self.get_dummy_dataset()
  													a_					=		RagConfig(
  													    retrieval_vector_size=self.retrieval_vector_size			,    question_encoder=DPRConfig().to_dict()			,    generator=BartConfig().to_dict()			,    index_name="""custom"""			,    )
  													a_					=		ray.remote(_UpperCAmelCase		)
  													a_					=		[remote_cls.remote() for _ in range(1		)]
  													if from_disk:
  																			a_					=		os.path.join(self.tmpdirname			,    """dataset"""		)
  																			a_					=		os.path.join(self.tmpdirname			,    """index.faiss"""		)
  																			dataset.get_index("""embeddings"""		).save(os.path.join(self.tmpdirname			,    """index.faiss"""		)		)
  																			dataset.drop_index("""embeddings"""		)
  																			dataset.save_to_disk(os.path.join(self.tmpdirname			,    """dataset"""		)		)
  																			del dataset
  																			a_					=		RagRayDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    index=CustomHFIndex.load_from_disk(
  																			        vector_size=config.retrieval_vector_size			,    dataset_path=config.passages_path			,    index_path=config.index_path			,    )			,    )
  													else:
  																			a_					=		RagRayDistributedRetriever(
  																			    _UpperCAmelCase			,    question_encoder_tokenizer=self.get_dpr_tokenizer()			,    generator_tokenizer=self.get_bart_tokenizer()			,    retrieval_workers=_UpperCAmelCase			,    index=CustomHFIndex(config.retrieval_vector_size			,    _UpperCAmelCase		)			,    )
  													if init_retrieval:
  																			retriever.init_retrieval()
  													return retriever
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		retriever.retrieve(_UpperCAmelCase			,    n_docs=_UpperCAmelCase		)
  													self.assertEqual(retrieved_doc_embeds.shape			,    (2, n_docs, self.retrieval_vector_size)		)
  													self.assertEqual(len(_UpperCAmelCase		)			,    2		)
  													self.assertEqual(sorted(doc_dicts[0]		)			,    ["""embeddings""", """id""", """text""", """title"""]		)
  													self.assertEqual(len(doc_dicts[0]["""id"""]		)			,    _UpperCAmelCase		)
  													self.assertEqual(doc_dicts[0]["""id"""][0]			,    """1"""		)  # max inner product is reached with second doc
  													self.assertEqual(doc_dicts[1]["""id"""][0]			,    """0"""		)  # max inner product is reached with first doc
  													self.assertListEqual(doc_ids.tolist()			,    [[1], [0]]		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  
  													self.distributed_retriever_check(
  													    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  
  													self.distributed_retriever_check(
  													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  
  													self.distributed_retriever_check(
  													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )
  
  
  							@require_ray
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  
  													self.distributed_retriever_check(
  													    self.get_dummy_ray_distributed_retriever(init_retrieval=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													ray.shutdown()
  
  
  							@require_ray
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  													with self.assertRaises(_UpperCAmelCase		):
  																			self.distributed_retriever_check(
  																			    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase			,    )
  													ray.shutdown()
  
  
  
  
  
  
  							@require_ray
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		1
  													a_					=		np.array(
  													    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]			,    dtype=np.floataa		)
  
  													self.distributed_retriever_check(
  													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_UpperCAmelCase			,    from_disk=_UpperCAmelCase		)			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													ray.shutdown()
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)