




"""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_						=				pytest.mark.integration






@require_faiss
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   import faiss

   _snake_case							:		Dataset								=  self._create_dummy_dataset()
   _snake_case							:		Any								=  dset.map(
       lambda a_,      a_		: {"vecs": i * np.ones(5,      dtype=np.floataa		)},      with_indices=a_,      keep_in_memory=a_		)
   _snake_case							:		List[Any]								=  dset.add_faiss_index("""vecs""",      batch_size=100,      metric_type=faiss.METRIC_INNER_PRODUCT		)
   _snake_case		,					_snake_case							:		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  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   import faiss

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


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   import faiss

   _snake_case							:		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		)

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


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		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  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   from elasticsearch import Elasticsearch

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

     dset.add_elasticsearch_index("""filename""",      es_client=a_		)
     _snake_case		,					_snake_case							:		int								=  dset.get_nearest_examples("""filename""",      """my_name-train_29"""		)
     self.assertEqual(examples["""filename"""][0],      """my_name-train_29"""		)







@require_faiss
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   import faiss

   _snake_case							:		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
   _snake_case							:		List[Any]								=  np.zeros(5,      dtype=np.floataa		)
   _snake_case							:		List[str]								=  1
   _snake_case		,					_snake_case							:		str								=  index.search(a_		)
   self.assertRaises(a_,      index.search,      query.reshape(-1,      1		)		)
   self.assertGreater(scores[0],      0		)
   self.assertEqual(indices[0],      1		)

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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   import faiss

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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   import faiss

   _snake_case							:		Any								=  faiss.IndexFlat(5		)
   _snake_case							:		Tuple								=  FaissIndex(custom_index=a_		)
   index.add_vectors(np.eye(5,      dtype=np.floataa		)		)
   self.assertIsInstance(index.faiss_index,      faiss.IndexFlat		)


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   import faiss

   _snake_case							:		List[str]								=  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		)
     _snake_case							:		str								=  FaissIndex.load(tmp_file.name		)
   os.unlink(tmp_file.name		)

   _snake_case							:		int								=  np.zeros(5,      dtype=np.floataa		)
   _snake_case							:		List[str]								=  1
   _snake_case		,					_snake_case							:		List[Any]								=  index.search(a_		)
   self.assertGreater(scores[0],      0		)
   self.assertEqual(indices[0],      1		)







@require_faiss
def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  import faiss

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

  _snake_case							:		Any								=  """index.faiss"""
  _snake_case							:		int								=  F"mock://{index_name}"
  index.save(snake_case__		,   storage_options=mockfs.storage_options    )
  _snake_case							:		Optional[Any]								=  FaissIndex.load(snake_case__		,   storage_options=mockfs.storage_options    )

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






@require_elasticsearch
class 					lowercase(     __a ):







 '''simple docstring'''





 def  UpperCamelCase_   (				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:
     _snake_case							:		Tuple								=  Elasticsearch()
     _snake_case							:		Union[str, Any]								=  {"""acknowledged""": True}
     _snake_case							:		List[str]								=  ElasticSearchIndex(es_client=a_		)
     mocked_bulk.return_value([(True, None)] * 3		)
     index.add_documents(["""foo""", """bar""", """foobar"""]		)

     # single query
     _snake_case							:		str								=  """foo"""
     _snake_case							:		Dict								=  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
     _snake_case		,					_snake_case							:		Tuple								=  index.search(a_		)
     self.assertEqual(scores[0],      1		)
     self.assertEqual(indices[0],      0		)

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

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

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








"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A_						=				r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''






@add_start_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


 def __init__(				self:		Union[str, Any],      a_:		int=None,      a_:		Tuple=True,      a_:		Optional[int]=None,      a_:		List[str]=None,      a_:		int=None,      a_:		Optional[Any]=None,      a_:		List[str]=None,      a_:		Optional[Any]=" / ",      a_:		Tuple=" // ",      a_:		List[Any]=5,      a_:		Dict=300,      a_:		Tuple=768,      a_:		Optional[Any]=8,      a_:		int="wiki_dpr",      a_:		Any="train",      a_:		Optional[int]="compressed",      a_:		Optional[int]=None,      a_:		List[Any]=None,      a_:		Optional[Any]=False,      a_:		str=False,      a_:		Dict=0.0,      a_:		Union[str, Any]=True,      a_:		Union[str, Any]=False,      a_:		str=False,      a_:		List[str]=False,      a_:		Union[str, Any]=True,      a_:		Any=None,      **a_:		List[Any],      ):




   '''simple docstring'''



   super().__init__(
       bos_token_id=a_,      pad_token_id=a_,      eos_token_id=a_,      decoder_start_token_id=a_,      forced_eos_token_id=a_,      is_encoder_decoder=a_,      prefix=a_,      vocab_size=a_,      **a_,      )
   assert (
       "question_encoder" in kwargs and "generator" in kwargs
   ), "Config has to be initialized with question_encoder and generator config"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



   return cls(question_encoder=question_encoder_config.to_dict(),      generator=generator_config.to_dict(),      **a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output


