


"""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


a__    :							Dict            =					pytest.mark.integration




@require_faiss
class 			UpperCamelCase_     (			SCREAMING_SNAKE_CASE__):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    str       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A__       ) for x in np.arange(3_0       ).tolist()]}       )
						return dset




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

						__SCREAMING_SNAKE_CASE						       =    self._create_dummy_dataset()
						__SCREAMING_SNAKE_CASE						       =    dset.map(
						    lambda UpperCAmelCase__      ,      UpperCAmelCase__       : {"vecs": i * np.ones(5      ,      dtype=np.floataa       )}      ,      with_indices=A__      ,      keep_in_memory=A__       )
						__SCREAMING_SNAKE_CASE						       =    dset.add_faiss_index("vecs"      ,      batch_size=1_0_0      ,      metric_type=faiss.METRIC_INNER_PRODUCT       )
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    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      :    Union[str, Any]       )	->     int:
						import faiss

						__SCREAMING_SNAKE_CASE						       =    self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)       ) * np.arange(3_0       ).reshape(-1      ,      1       )      ,      index_name="vecs"      ,      batch_size=1_0_0      ,      metric_type=faiss.METRIC_INNER_PRODUCT      ,      )
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    dset.get_nearest_examples("vecs"      ,      np.ones(5      ,      dtype=np.floataa       )       )
						self.assertEqual(examples["filename"][0]      ,      "my_name-train_29"       )




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

						__SCREAMING_SNAKE_CASE						       =    self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)       ) * np.arange(3_0       ).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       )

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




	def   UpperCAmelCase_				(			self      :    Any       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)       ) * np.arange(3_0       ).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      :    int       )	->     str:
						from elasticsearch import Elasticsearch

						__SCREAMING_SNAKE_CASE						       =    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:
											__SCREAMING_SNAKE_CASE						       =    {"acknowledged": True}
											mocked_bulk.return_value([(True, None)] * 3_0       )
											__SCREAMING_SNAKE_CASE						       =    {"hits": {"hits": [{"_score": 1, "_id": 2_9}]}}
											__SCREAMING_SNAKE_CASE						       =    Elasticsearch()

											dset.add_elasticsearch_index("filename"      ,      es_client=A__       )
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    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[Any]       )	->     Optional[Any]:
						import faiss

						__SCREAMING_SNAKE_CASE						       =    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      ,      1_0       )

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

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




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

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




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Any:
						import faiss

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




	def   UpperCAmelCase_				(			self      :    Optional[int]       )	->     List[Any]:
						import faiss

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

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






@require_faiss
def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					import faiss

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

					__SCREAMING_SNAKE_CASE						       =    "index.faiss"
					__SCREAMING_SNAKE_CASE						       =    f"""mock://{index_name}"""
					index.save(lowerCAmelCase__  ,						storage_options=mockfs.storage_options       )
					__SCREAMING_SNAKE_CASE						       =    FaissIndex.load(lowerCAmelCase__  ,						storage_options=mockfs.storage_options       )

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




@require_elasticsearch
class 			UpperCamelCase_     (			SCREAMING_SNAKE_CASE__):

	"""simple docstring"""





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

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

											# single query with timeout
											__SCREAMING_SNAKE_CASE						       =    "foo"
											__SCREAMING_SNAKE_CASE						       =    {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    index.search(A__      ,      request_timeout=3_0       )
											self.assertEqual(scores[0]      ,      1       )
											self.assertEqual(indices[0]      ,      0       )

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

											# batched queries with timeout
											__SCREAMING_SNAKE_CASE						       =    ["foo", "bar", "foobar"]
											__SCREAMING_SNAKE_CASE						       =    {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    index.search_batch(A__      ,      request_timeout=3_0       )
											__SCREAMING_SNAKE_CASE						       =    [scores[0] for scores in total_scores]
											__SCREAMING_SNAKE_CASE						       =    [indices[0] for indices in total_indices]
											self.assertGreater(np.min(A__       )      ,      0       )
											self.assertListEqual([1, 1, 1]      ,      A__       )




"""simple docstring"""







from __future__ import annotations




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					print(f"""Vertex\tShortest Distance from vertex {src}"""       )
					for i, d in enumerate(lowerCAmelCase_       ):
										print(f"""{i}\t\t{d}"""       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					for j in range(lowerCAmelCase_       ):
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (graph[j][k] for k in ["src", "dst", "weight"])
										if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
															return True
					return False




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [float("inf"       )] * vertex_count
					__SCREAMING_SNAKE_CASE						       =    0.0

					for _ in range(vertex_count - 1       ):
										for j in range(lowerCAmelCase_       ):
															__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    (graph[j][k] for k in ["src", "dst", "weight"])

															if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
																				__SCREAMING_SNAKE_CASE						       =    distance[u] + w

					__SCREAMING_SNAKE_CASE						       =    check_negative_cycle(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					if negative_cycle_exists:
										raise Exception("Negative cycle found"       )

					return distance


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__    :							Union[str, Any]            =					int(input('''Enter number of vertices: ''').strip())
		a__    :							Any            =					int(input('''Enter number of edges: ''').strip())

		a__    :							list[dict[str, int]]            =					[{} for _ in range(E)]

		for i in range(E):
				print('''Edge ''', i + 1)
				a__      ,					a__      ,					a__    :							str            =					(
				    int(x)
				    for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
				)
				a__    :							str            =					{'''src''': src, '''dst''': dest, '''weight''': weight}

		a__    :							str            =					int(input('''\nEnter shortest path source:''').strip())
		a__    :							List[Any]            =					bellman_ford(graph, V, E, source)
		print_distance(shortest_distance, 0)
