

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_			:					List[Any]    			=     pytest.mark.integration



@require_faiss
class        _a		(__magic_name__							):


					'''simple docstring'''






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






					def     __A     (					self     ):
						import faiss

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

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






					def     __A     (					self     ):
						import faiss

						A__				:       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     )

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






					def     __A     (					self     ):
						A__				:       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     ):
						from elasticsearch import Elasticsearch

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

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



@require_faiss
class        _a		(__magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						import faiss

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

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






					def     __A     (					self     ):
						import faiss

						A__				:       List[str]	  =						FaissIndex(string_factory="""Flat"""     )
						index.add_vectors(np.eye(5       ,			dtype=np.floataa     )     )
						self.assertIsInstance(index.faiss_index       ,			faiss.IndexFlat     )
						A__				:       Union[str, 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__     ):
							A__				:       List[Any]	  =						FaissIndex(string_factory="""Flat"""       ,			custom_index=faiss.IndexFlat(5     )     )






					def     __A     (					self     ):
						import faiss

						A__				:       List[str]	  =						faiss.IndexFlat(5     )
						A__				:       int	  =						FaissIndex(custom_index=A__     )
						index.add_vectors(np.eye(5       ,			dtype=np.floataa     )     )
						self.assertIsInstance(index.faiss_index       ,			faiss.IndexFlat     )






					def     __A     (					self     ):
						import faiss

						A__				:       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     )
							A__				:       Optional[Any]	  =						FaissIndex.load(tmp_file.name     )
						os.unlink(tmp_file.name     )

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



@require_faiss
def 		UpperCamelCase   (lowercase_:					List[Any]   )	-> int:
	import faiss

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

	A__				:       str	  =						"""index.faiss"""
	A__				:       Optional[int]	  =						f"""mock://{index_name}"""
	index.save(lowercase_       , storage_options=mockfs.storage_options   )
	A__				:       Tuple	  =						FaissIndex.load(lowercase_       , storage_options=mockfs.storage_options   )

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



@require_elasticsearch
class        _a		(__magic_name__							):


					'''simple docstring'''






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

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

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

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

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






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



