'''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	:       Any						       =	pytest.mark.integration






@require_faiss
class 						A   (   UpperCAmelCase   ):





							def       snake_case__						(      self				:							Dict	)							->	Dict:
								__UpperCAmelCase							=			Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(__a	) for x in np.arange(3_0	).tolist()]}	)
								return dset





							def       snake_case__						(      self				:							str	)							->	int:
								import faiss

								__UpperCAmelCase							=			self._create_dummy_dataset()
								__UpperCAmelCase							=			dset.map(
								    lambda __a     ,		__a	: {"vecs": i * np.ones(5     ,		dtype=np.floataa	)}     ,		with_indices=__a     ,		keep_in_memory=__a	)
								__UpperCAmelCase							=			dset.add_faiss_index('''vecs'''     ,		batch_size=1_0_0     ,		metric_type=faiss.METRIC_INNER_PRODUCT	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			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       snake_case__						(      self				:							Any	)							->	List[Any]:
								import faiss

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





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

								__UpperCAmelCase							=			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	)

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





							def       snake_case__						(      self				:							Dict	)							->	List[str]:
								__UpperCAmelCase							=			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       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								from elasticsearch import Elasticsearch

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

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






@require_faiss
class 						A   (   UpperCAmelCase   ):





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

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

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





							def       snake_case__						(      self				:							int	)							->	int:
								import faiss

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





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

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






							def       snake_case__						(      self				:							int	)							->	Any:
								import faiss

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

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





@require_faiss
def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	import faiss

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

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

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






@require_elasticsearch
class 						A   (   UpperCAmelCase   ):





							def       snake_case__						(      self				:							Union[str, Any]	)							->	str:
								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:
									__UpperCAmelCase							=			Elasticsearch()
									__UpperCAmelCase							=			{'''acknowledged''': True}
									__UpperCAmelCase							=			ElasticSearchIndex(es_client=__a	)
									mocked_bulk.return_value([(True, None)] * 3	)
									index.add_documents(['''foo''', '''bar''', '''foobar''']	)

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

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

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

									# batched queries with timeout
									__UpperCAmelCase							=			['''foo''', '''bar''', '''foobar''']
									__UpperCAmelCase							=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
									__UpperCAmelCase				,			__UpperCAmelCase							=			index.search_batch(__a     ,		request_timeout=3_0	)
									__UpperCAmelCase							=			[scores[0] for scores in total_scores]
									__UpperCAmelCase							=			[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

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			-1

		for i in range(UpperCamelCase__       ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(UpperCamelCase__       )

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
					print("[TEST CASE 01]")

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       Dict						       =	calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
										    F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
										)
					print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
					print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
