'''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


__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  pytest.mark.integration

@require_faiss
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

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




      def      _A	(       self						:				Tuple       ):
             import faiss

             _UpperCAmelCase     :  Dataset							      =    self._create_dummy_dataset()
             _UpperCAmelCase     :  Dict							      =    dset.map(
                 lambda A						,					A       : {"vecs": i * np.ones(5						,					dtype=np.floataa       )}						,					with_indices=A						,					keep_in_memory=A       )
             _UpperCAmelCase     :  Tuple							      =    dset.add_faiss_index("vecs"						,					batch_size=100						,					metric_type=faiss.METRIC_INNER_PRODUCT       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    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[str]       ):
             import faiss

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




      def      _A	(       self						:				Tuple       ):
             import faiss

             _UpperCAmelCase     :  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       )

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




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  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      _A	(       self						:				str       ):
             from elasticsearch import Elasticsearch

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

                    dset.add_elasticsearch_index("filename"						,					es_client=A       )
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    dset.get_nearest_examples("filename"						,					"my_name-train_29"       )
                    self.assertEqual(examples["filename"][0]						,					"my_name-train_29"       )

@require_faiss
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				int       ):
             import faiss

             _UpperCAmelCase     :  Tuple							      =    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
             _UpperCAmelCase     :  Optional[Any]							      =    np.zeros(5						,					dtype=np.floataa       )
             _UpperCAmelCase     :  int							      =    1
             _UpperCAmelCase      ,  _UpperCAmelCase     :  int							      =    index.search(A       )
             self.assertRaises(A						,					index.search						,					query.reshape(-1						,					1       )       )
             self.assertGreater(scores[0]						,					0       )
             self.assertEqual(indices[0]						,					1       )

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




      def      _A	(       self						:				Any       ):
             import faiss

             _UpperCAmelCase     :  Tuple							      =    FaissIndex(string_factory="Flat"       )
             index.add_vectors(np.eye(5						,					dtype=np.floataa       )       )
             self.assertIsInstance(index.faiss_index						,					faiss.IndexFlat       )
             _UpperCAmelCase     :  Optional[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       ):
                    _UpperCAmelCase     :  List[str]							      =    FaissIndex(string_factory="Flat"						,					custom_index=faiss.IndexFlat(5       )       )




      def      _A	(       self						:				Any       ):
             import faiss

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





      def      _A	(       self						:				Any       ):
             import faiss

             _UpperCAmelCase     :  Union[str, Any]							      =    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       )
                    _UpperCAmelCase     :  List[Any]							      =    FaissIndex.load(tmp_file.name       )
             os.unlink(tmp_file.name       )

             _UpperCAmelCase     :  Optional[Any]							      =    np.zeros(5						,					dtype=np.floataa       )
             _UpperCAmelCase     :  List[Any]							      =    1
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    index.search(A       )
             self.assertGreater(scores[0]						,					0       )
             self.assertEqual(indices[0]						,					1       )

@require_faiss
def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]    )    ->    Union[str, Any]:


       """simple docstring"""







       import faiss

       _UpperCAmelCase     :  Union[str, Any]							      =    FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT    )
       index.add_vectors(np.eye(5			,	dtype=np.floataa    )    )

       _UpperCAmelCase     :  List[str]							      =    "index.faiss"
       _UpperCAmelCase     :  Tuple							      =    F"""mock://{index_name}"""
       index.save(_UpperCAmelCase			,	storage_options=mockfs.storage_options    )
       _UpperCAmelCase     :  str							      =    FaissIndex.load(_UpperCAmelCase			,	storage_options=mockfs.storage_options    )

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


@require_elasticsearch
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				List[str]       ):
             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:
                    _UpperCAmelCase     :  Optional[int]							      =    Elasticsearch()
                    _UpperCAmelCase     :  Any							      =    {"acknowledged": True}
                    _UpperCAmelCase     :  Optional[Any]							      =    ElasticSearchIndex(es_client=A       )
                    mocked_bulk.return_value([(True, None)] * 3       )
                    index.add_documents(["foo", "bar", "foobar"]       )

                    # single query
                    _UpperCAmelCase     :  Optional[int]							      =    "foo"
                    _UpperCAmelCase     :  Dict							      =    {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    index.search(A       )
                    self.assertEqual(scores[0]						,					1       )
                    self.assertEqual(indices[0]						,					0       )

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

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

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


'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

