
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							:Dict							 =				pytest.mark.integration





@require_faiss
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					import faiss

					__A   			= self._create_dummy_dataset()
					__A   			= dset.map(
					    lambda A							,A : {"vecs": i * np.ones(5							,dtype=np.floataa )}							,with_indices=A							,keep_in_memory=A )
					__A   			= dset.add_faiss_index("vecs"							,batch_size=1_00							,metric_type=faiss.METRIC_INNER_PRODUCT )
					__A			,							__A   			= 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 				UpperCamelCase_  (       self					: Tuple ):
					import faiss

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






	def 				UpperCamelCase_  (       self					: List[str] ):
					import faiss

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






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= 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 				UpperCamelCase_  (       self					: Dict ):
					from elasticsearch import Elasticsearch

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

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





@require_faiss
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Optional[int] ):
					import faiss

					__A   			= 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   			= np.zeros(5							,dtype=np.floataa )
					__A   			= 1
					__A			,							__A   			= 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   			= np.eye(5							,dtype=np.floataa )[::-1]
					__A			,							__A   			= index.search_batch(A )
					self.assertRaises(A							,index.search_batch							,queries[0] )
					__A   			= [scores[0] for scores in total_scores]
					__A   			= [indices[0] for indices in total_indices]
					self.assertGreater(np.min(A )							,0 )
					self.assertListEqual([4, 3, 2, 1, 0]							,A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					import faiss

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






	def 				UpperCamelCase_  (       self					: Any ):
					import faiss

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







	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					import faiss

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

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





@require_faiss
def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				import faiss

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

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

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





@require_elasticsearch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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

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

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

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

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


import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   input_matrix.conj().T						)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

				while not convergence:
								# Multiple matrix by the vector.
								__A   			= np.dot(a_		,   a_						)
								# Normalize the resulting output vector.
								__A   			= w / np.linalg.norm(a_						)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__A   			= vector.conj().T if is_complex else vector.T
								__A   			= np.dot(a_		,   np.dot(a_		,   a_						)						)

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
