
class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			None
					lowerCamelCase_						=			None
					lowerCamelCase_						=			graph

					self._normalize_graph(lowercase						,  lowercase )
					lowerCamelCase_						=			len(lowercase )
					lowerCamelCase_						=			None



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       List[str]:
					if sources is int:
									lowerCamelCase_						=			[sources]
					if sinks is int:
									lowerCamelCase_						=			[sinks]

					if len(lowercase ) == 0 or len(lowercase ) == 0:
									return

					lowerCamelCase_						=			sources[0]
					lowerCamelCase_						=			sinks[0]

					# make fake vertex if there are more
					# than one source or sink
					if len(lowercase ) > 1 or len(lowercase ) > 1:
									lowerCamelCase_						=			0
									for i in sources:
													max_input_flow += sum(self.graph[i] )

									lowerCamelCase_						=			len(self.graph ) + 1
									for room in self.graph:
													room.insert(0						,  0 )
									self.graph.insert(0						,  [0] * size )
									for i in sources:
													lowerCamelCase_						=			max_input_flow
									lowerCamelCase_						=			0

									lowerCamelCase_						=			len(self.graph ) + 1
									for room in self.graph:
													room.append(0 )
									self.graph.append([0] * size )
									for i in sinks:
													lowerCamelCase_						=			max_input_flow
									lowerCamelCase_						=			size - 1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					if self.maximum_flow_algorithm is None:
									raise Exception("You need to set maximum flow algorithm before." )
					if self.source_index is None or self.sink_index is None:
									return 0

					self.maximum_flow_algorithm.execute()
					return self.maximum_flow_algorithm.getMaximumFlow()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			algorithm(self )




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase )				->       List[Any]:
					lowerCamelCase_						=			flow_network
					lowerCamelCase_						=			flow_network.verticesCount
					lowerCamelCase_						=			flow_network.sourceIndex
					lowerCamelCase_						=			flow_network.sinkIndex
					# it's just a reference, so you shouldn't change
					# it in your algorithms, use deep copy before doing that
					lowerCamelCase_						=			flow_network.graph
					lowerCamelCase_						=			False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					if not self.executed:
									self._algorithm()
									lowerCamelCase_						=			True



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					pass




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase )				->       Any:
					super().__init__(lowercase )
					# use this to save your result
					lowerCamelCase_						=			-1



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					if not self.executed:
									raise Exception("You should execute algorithm before using its result!" )

					return self.maximum_flow




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase )				->       str:
					super().__init__(lowercase )

					lowerCamelCase_						=			[[0] * self.verticies_count for i in range(self.verticies_count )]

					lowerCamelCase_						=			[0] * self.verticies_count
					lowerCamelCase_						=			[0] * self.verticies_count



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.verticies_count

					# push some substance to graph
					for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
									self.preflow[self.source_index][nextvertex_index] += bandwidth
									self.preflow[nextvertex_index][self.source_index] -= bandwidth
									self.excesses[nextvertex_index] += bandwidth

					# Relabel-to-front selection rule
					lowerCamelCase_						=			[
					    i
					    for i in range(self.verticies_count )
					    if i != self.source_index and i != self.sink_index
					]

					# move through list
					lowerCamelCase_						=			0
					while i < len(lowercase ):
									lowerCamelCase_						=			vertices_list[i]
									lowerCamelCase_						=			self.heights[vertex_index]
									self.process_vertex(lowercase )
									if self.heights[vertex_index] > previous_height:
													# if it was relabeled, swap elements
													# and start from 0 index
													vertices_list.insert(0						,  vertices_list.pop(lowercase ) )
													lowerCamelCase_						=			0
									else:
													i += 1

					lowerCamelCase_						=			sum(self.preflow[self.source_index] )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       List[Any]:
					while self.excesses[vertex_index] > 0:
									for neighbour_index in range(self.verticies_count ):
													# if it's neighbour and current vertex is higher
													if (
													    self.graph[vertex_index][neighbour_index]
													    - self.preflow[vertex_index][neighbour_index]
													    > 0
													    and self.heights[vertex_index] > self.heights[neighbour_index]
													):
																	self.push(lowercase						,  lowercase )

									self.relabel(lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_						=			min(
					    self.excesses[from_index]						,  self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,  )
					self.preflow[from_index][to_index] += preflow_delta
					self.preflow[to_index][from_index] -= preflow_delta
					self.excesses[from_index] -= preflow_delta
					self.excesses[to_index] += preflow_delta



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Dict:
					lowerCamelCase_						=			None
					for to_index in range(self.verticies_count ):
									if (
									    self.graph[vertex_index][to_index]
									    - self.preflow[vertex_index][to_index]
									    > 0
									) and (min_height is None or self.heights[to_index] < min_height):
													lowerCamelCase_						=			self.heights[to_index]

					if min_height is not None:
									lowerCamelCase_						=			min_height + 1


if __name__ == "__main__":
			__A				=[0]
			__A				=[3]
			# graph = [
			#     [0, 0, 4, 6, 0, 0],
			#     [0, 0, 5, 2, 0, 0],
			#     [0, 0, 0, 0, 4, 4],
			#     [0, 0, 0, 0, 6, 6],
			#     [0, 0, 0, 0, 0, 0],
			#     [0, 0, 0, 0, 0, 0],
			# ]
			__A				=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

			# prepare our network
			__A				=FlowNetwork(graph, entrances, exits)
			# set algorithm
			flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
			# and calculate
			__A				=flow_network.find_maximum_flow()

			print(F"""maximum flow is {maximum_flow}""")




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				=pytest.mark.integration




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					import faiss

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

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



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

					lowerCamelCase_						=			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=lowercase ) as tmp_file:
									dset.save_faiss_index("vecs"						,  tmp_file.name )
									dset.load_faiss_index("vecs2"						,  tmp_file.name )
					os.unlink(tmp_file.name )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			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(lowercase						,  partial(dset.get_nearest_examples						,  "vecs2"						,  np.ones(5						,  dtype=np.floataa ) ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					from elasticsearch import Elasticsearch

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

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




@require_faiss
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					import faiss

					lowerCamelCase_						=			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
					lowerCamelCase_						=			np.zeros(5						,  dtype=np.floataa )
					lowerCamelCase_						=			1
					lowerCamelCase_	,    lowerCamelCase_						=			index.search(lowercase )
					self.assertRaises(lowercase						,  index.search						,  query.reshape(-1						,  1 ) )
					self.assertGreater(scores[0]						,  0 )
					self.assertEqual(indices[0]						,  1 )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					import faiss

					lowerCamelCase_						=			FaissIndex(string_factory="Flat" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexFlat )
					lowerCamelCase_						=			FaissIndex(string_factory="LSH" )
					index.add_vectors(np.eye(5						,  dtype=np.floataa ) )
					self.assertIsInstance(index.faiss_index						,  faiss.IndexLSH )
					with self.assertRaises(lowercase ):
									lowerCamelCase_						=			FaissIndex(string_factory="Flat"						,  custom_index=faiss.IndexFlat(5 ) )



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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					import faiss

					lowerCamelCase_						=			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=lowercase ) as tmp_file:
									index.save(tmp_file.name )
									lowerCamelCase_						=			FaissIndex.load(tmp_file.name )
					os.unlink(tmp_file.name )

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





@require_faiss
def 			lowerCamelCase_ (				lowerCamelCase__    ):
				import faiss

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

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

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





@require_elasticsearch
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					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:
									lowerCamelCase_						=			Elasticsearch()
									lowerCamelCase_						=			{"acknowledged": True}
									lowerCamelCase_						=			ElasticSearchIndex(es_client=lowercase )
									mocked_bulk.return_value([(True, None)] * 3 )
									index.add_documents(["foo", "bar", "foobar"] )

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

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

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

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


