






import mpmath  # for roots of unity
import numpy as np






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__=None       ,A__=None		):
						# Input as list
						_A	:				Any   =      list(poly_a or [0]		)[:]
						_A	:				List[Any]   =      list(poly_b or [0]		)[:]

						# Remove leading zero coefficients
						while self.polyA[-1] == 0:
										self.polyA.pop()
						_A	:				List[str]   =      len(self.polyA		)

						while self.polyB[-1] == 0:
										self.polyB.pop()
						_A	:				int   =      len(self.polyB		)

						# Add 0 to make lengths equal a power of 2
						_A	:				Optional[Any]   =      int(
						    2 ** np.ceil(np.loga(len(self.polyA		) + len(self.polyB		) - 1		)		)		)

						while len(self.polyA		) < self.c_max_length:
										self.polyA.append(0		)
						while len(self.polyB		) < self.c_max_length:
										self.polyB.append(0		)
						# A complex root used for the fourier transform
						_A	:				str   =      complex(mpmath.root(x=1       ,n=self.c_max_length       ,k=1		)		)

						# The product
						_A	:				Union[str, Any]   =      self.__multiply()







		def  A__      (					self       ,A__		):
						_A	:				Tuple   =      [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
						# Corner case
						if len(A__		) <= 1:
										return dft[0]
						#
						_A	:				Union[str, Any]   =      self.c_max_length // 2
						while next_ncol > 0:
										_A	:				Dict   =      [[] for i in range(A__		)]
										_A	:				Optional[int]   =      self.root**next_ncol

										# First half of next step
										_A	:				int   =      1
										for j in range(self.c_max_length // (next_ncol * 2)		):
														for i in range(A__		):
																		new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]		)
														current_root *= root
										# Second half of next step
										_A	:				int   =      1
										for j in range(self.c_max_length // (next_ncol * 2)		):
														for i in range(A__		):
																		new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]		)
														current_root *= root
										# Update
										_A	:				Any   =      new_dft
										_A	:				List[str]   =      next_ncol // 2
						return dft[0]







		def  A__      (					self		):
						_A	:				Tuple   =      self.__dft('''A'''		)
						_A	:				Tuple   =      self.__dft('''B'''		)
						_A	:				Dict   =      [[dft_a[i] * dft_b[i] for i in range(self.c_max_length		)]]
						del dft_a
						del dft_b

						# Corner Case
						if len(inverce_c[0]		) <= 1:
										return inverce_c[0]
						# Inverse DFT
						_A	:				Tuple   =      2
						while next_ncol <= self.c_max_length:
										_A	:				Optional[Any]   =      [[] for i in range(A__		)]
										_A	:				int   =      self.root ** (next_ncol // 2)
										_A	:				int   =      1
										# First half of next step
										for j in range(self.c_max_length // next_ncol		):
														for i in range(next_ncol // 2		):
																		# Even positions
																		new_inverse_c[i].append(
																		    (
																		        inverce_c[i][j]
																		        + inverce_c[i][j + self.c_max_length // next_ncol]
																		    )
																		    / 2		)
																		# Odd positions
																		new_inverse_c[i + next_ncol // 2].append(
																		    (
																		        inverce_c[i][j]
																		        - inverce_c[i][j + self.c_max_length // next_ncol]
																		    )
																		    / (2 * current_root)		)
														current_root *= root
										# Update
										_A	:				Tuple   =      new_inverse_c
										next_ncol *= 2
						# Unpack
						_A	:				List[str]   =      [round(x[0].real       ,8		) + round(x[0].imag       ,8		) * 1J for x in inverce_c]

						# Remove leading 0's
						while inverce_c[-1] == 0:
										inverce_c.pop()
						return inverce_c







		def __str__(					self		):
						_A	:				Optional[int]   =      '''A = ''' + ''' + '''.join(
						    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]		)		)
						_A	:				Optional[int]   =      '''B = ''' + ''' + '''.join(
						    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]		)		)
						_A	:				Optional[int]   =      '''A*B = ''' + ''' + '''.join(
						    f"""{coef}*x^{i}""" for coef, i in enumerate(self.product		)		)

						return f"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
	import doctest

	doctest.testmod()











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			:	Optional[Any]								=pytest.mark.integration






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						_A	:				Any   =      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	:				List[str]   =      dset.map(
						    lambda A__       ,A__		: {"vecs": i * np.ones(5       ,dtype=np.floataa		)}       ,with_indices=A__       ,keep_in_memory=A__		)
						_A	:				Tuple   =      dset.add_faiss_index('''vecs'''       ,batch_size=100       ,metric_type=faiss.METRIC_INNER_PRODUCT		)
						_A  ,				_A	:				List[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		):
						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	:				Optional[int]   =      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	:				Optional[Any]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
										_A	:				Optional[Any]   =      Elasticsearch()

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






@require_faiss
class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self		):
						import faiss

						_A	:				Union[str, 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	:				Dict   =      1
						_A  ,				_A	:				Optional[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
						_A	:				Optional[Any]   =      np.eye(5       ,dtype=np.floataa		)[::-1]
						_A  ,				_A	:				Dict   =      index.search_batch(A__		)
						self.assertRaises(A__       ,index.search_batch       ,queries[0]		)
						_A	:				Tuple   =      [scores[0] for scores in total_scores]
						_A	:				List[str]   =      [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[Any]   =      FaissIndex(string_factory='''Flat'''		)
						index.add_vectors(np.eye(5       ,dtype=np.floataa		)		)
						self.assertIsInstance(index.faiss_index       ,faiss.IndexFlat		)
						_A	:				str   =      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[str]   =      FaissIndex(string_factory='''Flat'''       ,custom_index=faiss.IndexFlat(5		)		)







		def  A__      (					self		):
						import faiss

						_A	:				Any   =      faiss.IndexFlat(5		)
						_A	:				Any   =      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	:				Optional[int]   =      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	:				Tuple   =      np.zeros(5       ,dtype=np.floataa		)
						_A	:				Tuple   =      1
						_A  ,				_A	:				Union[str, Any]   =      index.search(A__		)
						self.assertGreater(scores[0]       ,0		)
						self.assertEqual(indices[0]       ,1		)



@require_faiss
def  a__ (__lowercase							:List[Any]   )		-> int:
				import faiss

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

				_A	:				List[Any]   =      '''index.faiss'''
				_A	:				Any   =      f"""mock://{index_name}"""
				index.save(__lowercase					,  storage_options=mockfs.storage_options   )
				_A	:				Dict   =      FaissIndex.load(__lowercase					,  storage_options=mockfs.storage_options   )

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






@require_elasticsearch
class 						UpperCAmelCase__ (							__snake_case							):







		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	:				Tuple   =      Elasticsearch()
										_A	:				Tuple   =      {'''acknowledged''': True}
										_A	:				int   =      ElasticSearchIndex(es_client=A__		)
										mocked_bulk.return_value([(True, None)] * 3		)
										index.add_documents(['''foo''', '''bar''', '''foobar''']		)

										# single query
										_A	:				Dict   =      '''foo'''
										_A	:				Optional[Any]   =      {'''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	:				Union[str, Any]   =      '''foo'''
										_A	:				Optional[int]   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
										_A  ,				_A	:				Optional[int]   =      index.search(A__       ,request_timeout=30		)
										self.assertEqual(scores[0]       ,1		)
										self.assertEqual(indices[0]       ,0		)

										# batched queries
										_A	:				Dict   =      ['''foo''', '''bar''', '''foobar''']
										_A	:				Dict   =      {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
										_A  ,				_A	:				Dict   =      index.search_batch(A__		)
										_A	:				Union[str, Any]   =      [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__		)

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



