code: 
  '''simple docstring'''
  
  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:
  					UpperCamelCase_     			=       None
  
  if is_ray_available():
  					import ray  # noqa: E402 # isort:skip
  					from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
  else:
  					UpperCamelCase_     			=       None
  					UpperCamelCase_     			=       None
  					UpperCamelCase_     			=       None
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]		)    ->						Optional[Any]:
  		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  				_lowerCAmelCase       :  Union[str, Any]   				=					unittest.skip("""test requires Datasets, Faiss, psutil"""		)(_lowerCamelCase		)
  		return test_case
  
  
  
  @require_distributed_retrieval
  class   a_  (_a    ):
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Tuple   				=					tempfile.mkdtemp()
  									_lowerCAmelCase       :  int   				=					8
  
  									# DPR tok
  									_lowerCAmelCase       :  Optional[Any]   				=					[
  									    """[UNK]""",
  									    """[CLS]""",
  									    """[SEP]""",
  									    """[PAD]""",
  									    """[MASK]""",
  									    """want""",
  									    """##want""",
  									    """##ed""",
  									    """wa""",
  									    """un""",
  									    """runn""",
  									    """##ing""",
  									    """,""",
  									    """low""",
  									    """lowest""",
  									]
  									_lowerCAmelCase       :  List[str]   				=					os.path.join(self.tmpdirname   ,   """dpr_tokenizer"""  )
  									os.makedirs(snake_case_   ,   exist_ok=snake_case_  )
  									_lowerCAmelCase       :  Dict   				=					os.path.join(snake_case_   ,   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
  									_lowerCAmelCase       :  str   				=					[
  									    """l""",
  									    """o""",
  									    """w""",
  									    """e""",
  									    """r""",
  									    """s""",
  									    """t""",
  									    """i""",
  									    """d""",
  									    """n""",
  									    """\u0120""",
  									    """\u0120l""",
  									    """\u0120n""",
  									    """\u0120lo""",
  									    """\u0120low""",
  									    """er""",
  									    """\u0120lowest""",
  									    """\u0120newer""",
  									    """\u0120wider""",
  									    """<unk>""",
  									]
  									_lowerCAmelCase       :  Union[str, Any]   				=					dict(zip(snake_case_   ,   range(len(snake_case_  )  )  )  )
  									_lowerCAmelCase       :  List[Any]   				=					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  									_lowerCAmelCase       :  Optional[Any]   				=					{"""unk_token""": """<unk>"""}
  
  									_lowerCAmelCase       :  Tuple   				=					os.path.join(self.tmpdirname   ,   """bart_tokenizer"""  )
  									os.makedirs(snake_case_   ,   exist_ok=snake_case_  )
  									_lowerCAmelCase       :  str   				=					os.path.join(snake_case_   ,   BART_VOCAB_FILES_NAMES["""vocab_file"""]  )
  									_lowerCAmelCase       :  Any   				=					os.path.join(snake_case_   ,   BART_VOCAB_FILES_NAMES["""merges_file"""]  )
  									with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""  ) as fp:
  											fp.write(json.dumps(snake_case_  ) + """\n"""  )
  									with open(self.merges_file   ,   """w"""   ,   encoding="""utf-8"""  ) as fp:
  											fp.write("""\n""".join(snake_case_  )  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,   """dpr_tokenizer"""  )  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,   """bart_tokenizer"""  )  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									shutil.rmtree(self.tmpdirname  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Tuple   				=					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 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_=1_2_3_4_5  ):
  									_lowerCAmelCase       :  List[str]   				=					self.get_dummy_dataset()
  									_lowerCAmelCase       :  Union[str, Any]   				=					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:
  											_lowerCAmelCase       :  Dict   				=					dataset
  											_lowerCAmelCase       :  Optional[Any]   				=					RagPyTorchDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   )
  											if init_retrieval:
  													retriever.init_retrieval(snake_case_  )
  									return retriever
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_  ):
  									# Have to run in local mode because sys.path modifications at top of
  									# file are not propogated to remote workers.
  									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
  									ray.init(local_mode=snake_case_  )
  									_lowerCAmelCase       :  Dict   				=					RagConfig(
  									    retrieval_vector_size=self.retrieval_vector_size   ,   question_encoder=DPRConfig().to_dict()   ,   generator=BartConfig().to_dict()   ,   )
  									_lowerCAmelCase       :  Union[str, Any]   				=					ray.remote(snake_case_  )
  									_lowerCAmelCase       :  int   				=					[remote_cls.remote() for _ in range(1  )]
  									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""  ) as mock_load_dataset:
  											_lowerCAmelCase       :  str   				=					self.get_dummy_dataset()
  											_lowerCAmelCase       :  int   				=					RagRayDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   retrieval_workers=snake_case_   ,   )
  											if init_retrieval:
  													retriever.init_retrieval()
  									return retriever
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_=1_2_3_4_5  ):
  									_lowerCAmelCase       :  Optional[int]   				=					self.get_dummy_dataset()
  									_lowerCAmelCase       :  str   				=					RagConfig(
  									    retrieval_vector_size=self.retrieval_vector_size   ,   question_encoder=DPRConfig().to_dict()   ,   generator=BartConfig().to_dict()   ,   index_name="""custom"""   ,   )
  									if from_disk:
  											_lowerCAmelCase       :  List[Any]   				=					os.path.join(self.tmpdirname   ,   """dataset"""  )
  											_lowerCAmelCase       :  Dict   				=					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
  											_lowerCAmelCase       :  Optional[Any]   				=					RagPyTorchDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   )
  									else:
  											_lowerCAmelCase       :  List[Any]   				=					RagPyTorchDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   index=CustomHFIndex(config.retrieval_vector_size   ,   snake_case_  )   ,   )
  									if init_retrieval:
  											retriever.init_retrieval(snake_case_  )
  									return retriever
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
  									# Have to run in local mode because sys.path modifications at top of
  									# file are not propogated to remote workers.
  									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
  									ray.init(local_mode=snake_case_  )
  									_lowerCAmelCase       :  Optional[Any]   				=					self.get_dummy_dataset()
  									_lowerCAmelCase       :  List[Any]   				=					RagConfig(
  									    retrieval_vector_size=self.retrieval_vector_size   ,   question_encoder=DPRConfig().to_dict()   ,   generator=BartConfig().to_dict()   ,   index_name="""custom"""   ,   )
  									_lowerCAmelCase       :  List[Any]   				=					ray.remote(snake_case_  )
  									_lowerCAmelCase       :  int   				=					[remote_cls.remote() for _ in range(1  )]
  									if from_disk:
  											_lowerCAmelCase       :  Tuple   				=					os.path.join(self.tmpdirname   ,   """dataset"""  )
  											_lowerCAmelCase       :  Dict   				=					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
  											_lowerCAmelCase       :  Dict   				=					RagRayDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   retrieval_workers=snake_case_   ,   index=CustomHFIndex.load_from_disk(
  											        vector_size=config.retrieval_vector_size   ,   dataset_path=config.passages_path   ,   index_path=config.index_path   ,   )   ,   )
  									else:
  											_lowerCAmelCase       :  Dict   				=					RagRayDistributedRetriever(
  											    snake_case_   ,   question_encoder_tokenizer=self.get_dpr_tokenizer()   ,   generator_tokenizer=self.get_bart_tokenizer()   ,   retrieval_workers=snake_case_   ,   index=CustomHFIndex(config.retrieval_vector_size   ,   snake_case_  )   ,   )
  									if init_retrieval:
  											retriever.init_retrieval()
  									return retriever
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_  ):
  									_lowerCAmelCase       :  List[str]   				=					retriever.retrieve(snake_case_   ,   n_docs=snake_case_  )
  									self.assertEqual(retrieved_doc_embeds.shape   ,   (2, n_docs, self.retrieval_vector_size)  )
  									self.assertEqual(len(snake_case_  )   ,   2  )
  									self.assertEqual(sorted(doc_dicts[0]  )   ,   ["""embeddings""", """id""", """text""", """title"""]  )
  									self.assertEqual(len(doc_dicts[0]["""id"""]  )   ,   snake_case_  )
  									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 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Union[str, Any]   				=					1
  									_lowerCAmelCase       :  int   				=					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=snake_case_  )   ,   snake_case_   ,   snake_case_  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Optional[int]   				=					1
  									_lowerCAmelCase       :  Tuple   				=					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=snake_case_   ,   from_disk=snake_case_  )   ,   snake_case_   ,   snake_case_   ,   )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  str   				=					1
  									_lowerCAmelCase       :  Dict   				=					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=snake_case_   ,   from_disk=snake_case_  )   ,   snake_case_   ,   snake_case_   ,   )
  
  
  
  
  							@require_ray
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Any   				=					1
  									_lowerCAmelCase       :  Dict   				=					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=snake_case_  )   ,   snake_case_   ,   snake_case_  )
  									ray.shutdown()
  
  
  
  
  							@require_ray
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  str   				=					1
  									_lowerCAmelCase       :  List[str]   				=					np.array(
  									    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,   dtype=np.floataa  )
  									with self.assertRaises(snake_case_  ):
  											self.distributed_retriever_check(
  											    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case_   ,   from_disk=snake_case_  )   ,   snake_case_   ,   snake_case_   ,   )
  									ray.shutdown()
  
  
  
  
  
  
  
  							@require_ray
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  List[str]   				=					1
  									_lowerCAmelCase       :  Dict   				=					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=snake_case_   ,   from_disk=snake_case_  )   ,   snake_case_   ,   snake_case_  )
  									ray.shutdown()
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCamelCase_     			=       {
      """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
       UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DeformableDetrForObjectDetection""",
           """DeformableDetrModel""",
           """DeformableDetrPreTrainedModel""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  