




"""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


_lowerCamelCase  :			Union[str, Any]        =       pytest.mark.integration

@require_faiss
class        lowercase      (    _UpperCamelCase):







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__a       ) for x in np.arange(30       ).tolist()]}       )
					return dset







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					import faiss

					A_  :				Dataset	   =		self._create_dummy_dataset()
					A_  :				Union[str, Any]	   =		dset.map(
					    lambda _lowerCamelCase      ,     _lowerCamelCase       : {"vecs": i * np.ones(5      ,     dtype=np.floataa       )}      ,     with_indices=__a      ,     keep_in_memory=__a       )
					A_  :				Dict	   =		dset.add_faiss_index('''vecs'''      ,     batch_size=1_00      ,     metric_type=faiss.METRIC_INNER_PRODUCT       )
					A_  :				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 					a_  (							self   :					int       ):



					"""simple docstring"""

					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=1_00      ,     metric_type=faiss.METRIC_INNER_PRODUCT      ,     )
					A_  :				str	   =		dset.get_nearest_examples('''vecs'''      ,     np.ones(5      ,     dtype=np.floataa       )       )
					self.assertEqual(examples['''filename'''][0]      ,     '''my_name-train_29'''       )







				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					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_  :				List[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]       ):



					"""simple docstring"""

					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   :					Tuple       ):



					"""simple docstring"""

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

						dset.add_elasticsearch_index('''filename'''      ,     es_client=__a       )
						A_  :				Dict	   =		dset.get_nearest_examples('''filename'''      ,     '''my_name-train_29'''       )
						self.assertEqual(examples['''filename'''][0]      ,     '''my_name-train_29'''       )

@require_faiss
class        lowercase      (    _UpperCamelCase):







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					import faiss

					A_  :				List[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_  :				Optional[int]	   =		np.zeros(5      ,     dtype=np.floataa       )
					A_  :				Dict	   =		1
					A_  :				Optional[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_  :				Optional[int]	   =		np.eye(5      ,     dtype=np.floataa       )[::-1]
					A_  :				Optional[Any]	   =		index.search_batch(__a       )
					self.assertRaises(__a      ,     index.search_batch      ,     queries[0]       )
					A_  :				str	   =		[scores[0] for scores in total_scores]
					A_  :				Optional[Any]	   =		[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       ):



					"""simple docstring"""

					import faiss

					A_  :				str	   =		FaissIndex(string_factory='''Flat'''       )
					index.add_vectors(np.eye(5      ,     dtype=np.floataa       )       )
					self.assertIsInstance(index.faiss_index      ,     faiss.IndexFlat       )
					A_  :				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   :					Optional[int]       ):



					"""simple docstring"""

					import faiss

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







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					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_  :				str	   =		FaissIndex.load(tmp_file.name       )
					os.unlink(tmp_file.name       )

					A_  :				Dict	   =		np.zeros(5      ,     dtype=np.floataa       )
					A_  :				Union[str, Any]	   =		1
					A_  :				Tuple	   =		index.search(__a       )
					self.assertGreater(scores[0]      ,     0       )
					self.assertEqual(indices[0]      ,     1       )

@require_faiss
def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	import faiss

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

	A_  :				Any	   =		"index.faiss"
	A_  :				List[Any]	   =		f"""mock://{index_name}"""
	index.save(lowercase_  ,      storage_options=mockfs.storage_options	)
	A_  :				int	   =		FaissIndex.load(lowercase_  ,      storage_options=mockfs.storage_options	)

	A_  :				Dict	   =		np.zeros(5  ,      dtype=np.floataa	)
	A_  :				str	   =		1
	A_  :				Tuple	   =		index.search(lowercase_	)
	assert scores[0] > 0
	assert indices[0] == 1

@require_elasticsearch
class        lowercase      (    _UpperCamelCase):







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

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

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

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

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












"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





