

'''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


UpperCamelCase_						:      Dict       =						pytest.mark.integration

@require_faiss
class        _a    (						SCREAMING_SNAKE_CASE__							):






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






     def     _lowercase     (			self   )							->      List[Any]:
          import faiss

          _snake_case							=				self._create_dummy_dataset()
          _snake_case							=				dset.map(
              lambda _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   : {"vecs": i * np.ones(5 ,dtype=np.floataa   )} ,with_indices=A__ ,keep_in_memory=A__   )
          _snake_case							=				dset.add_faiss_index("vecs" ,batch_size=100 ,metric_type=faiss.METRIC_INNER_PRODUCT   )
          _snake_case							, _snake_case							=				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   )							->      int:
          import faiss

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






     def     _lowercase     (			self   )							->      List[Any]:
          import faiss

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






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				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   )							->      str:
          from elasticsearch import Elasticsearch

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

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







@require_faiss
class        _a    (						SCREAMING_SNAKE_CASE__							):






     def     _lowercase     (			self   )							->      Optional[int]:
          import faiss

          _snake_case							=				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							=				np.zeros(5 ,dtype=np.floataa   )
          _snake_case							=				1
          _snake_case							, _snake_case							=				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							=				np.eye(5 ,dtype=np.floataa   )[::-1]
          _snake_case							, _snake_case							=				index.search_batch(A__   )
          self.assertRaises(A__ ,index.search_batch ,queries[0]   )
          _snake_case							=				[scores[0] for scores in total_scores]
          _snake_case							=				[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]:
          import faiss

          _snake_case							=				FaissIndex(string_factory="Flat"   )
          index.add_vectors(np.eye(5 ,dtype=np.floataa   )   )
          self.assertIsInstance(index.faiss_index ,faiss.IndexFlat   )
          _snake_case							=				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							=				FaissIndex(string_factory="Flat" ,custom_index=faiss.IndexFlat(5   )   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          import faiss

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






     def     _lowercase     (			self   )							->      Dict:
          import faiss

          _snake_case							=				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							=				FaissIndex.load(tmp_file.name   )
          os.unlink(tmp_file.name   )

          _snake_case							=				np.zeros(5 ,dtype=np.floataa   )
          _snake_case							=				1
          _snake_case							, _snake_case							=				index.search(A__   )
          self.assertGreater(scores[0] ,0   )
          self.assertEqual(indices[0] ,1   )







@require_faiss
def      __a     (       _UpperCamelCase:   List[str]   )						->			Tuple:

     """simple docstring"""




     import faiss

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

     _snake_case							=				"index.faiss"
     _snake_case							=				F"""mock://{index_name}"""
     index.save(lowerCAmelCase__ ,						storage_options=mockfs.storage_options   )
     _snake_case							=				FaissIndex.load(lowerCAmelCase__ ,						storage_options=mockfs.storage_options   )

     _snake_case							=				np.zeros(5 ,						dtype=np.floataa   )
     _snake_case							=				1
     _snake_case							, _snake_case							=				index.search(lowerCAmelCase__   )
     assert scores[0] > 0
     assert indices[0] == 1





@require_elasticsearch
class        _a    (						SCREAMING_SNAKE_CASE__							):






     def     _lowercase     (			self   )							->      Optional[int]:
          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							=				Elasticsearch()
               _snake_case							=				{"acknowledged": True}
               _snake_case							=				ElasticSearchIndex(es_client=A__   )
               mocked_bulk.return_value([(True, None)] * 3   )
               index.add_documents(["foo", "bar", "foobar"]   )

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

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

               # batched queries
               _snake_case							=				["foo", "bar", "foobar"]
               _snake_case							=				{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
               _snake_case							, _snake_case							=				index.search_batch(A__   )
               _snake_case							=				[scores[0] for scores in total_scores]
               _snake_case							=				[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							=				["foo", "bar", "foobar"]
               _snake_case							=				{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
               _snake_case							, _snake_case							=				index.search_batch(A__ ,request_timeout=30   )
               _snake_case							=				[scores[0] for scores in total_scores]
               _snake_case							=				[indices[0] for indices in total_indices]
               self.assertGreater(np.min(A__   ) ,0   )
               self.assertListEqual([1, 1, 1] ,A__   )








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


