





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      	=		pytest.mark.integration
@require_faiss
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def UpperCAmelCase_				(      self      :      int	)							->    Any:







							"""simple docstring"""


							snake_case_		:			str											=	Dataset.from_dict({'filename': ['my_name-train' + '_' + str(_A	) for x in np.arange(30	).tolist()]}	)
							return dset






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







							"""simple docstring"""


							import faiss

							snake_case_		:			Dataset											=	self._create_dummy_dataset()
							snake_case_		:			Any											=	dset.map(
							    lambda _A		,   _A	: {"vecs": i * np.ones(5		,   dtype=np.floataa	)}		,   with_indices=_A		,   keep_in_memory=_A	)
							snake_case_		:			int											=	dset.add_faiss_index('vecs'		,   batch_size=100		,   metric_type=faiss.METRIC_INNER_PRODUCT	)
							snake_case_						,snake_case_		:			List[str]											=	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      :      List[str]	)							->    str:







							"""simple docstring"""


							import faiss

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






						def UpperCAmelCase_				(      self      :      str	)							->    List[str]:







							"""simple docstring"""


							import faiss

							snake_case_		:			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	)

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






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			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 UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							from elasticsearch import Elasticsearch

							snake_case_		:			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:
								snake_case_		:			Optional[Any]											=	{'acknowledged': True}
								mocked_bulk.return_value([(True, None)] * 30	)
								snake_case_		:			Any											=	{'hits': {'hits': [{'_score': 1, '_id': 29}]}}
								snake_case_		:			Any											=	Elasticsearch()

								dset.add_elasticsearch_index('filename'		,   es_client=_A	)
								snake_case_						,snake_case_		:			Optional[Any]											=	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 UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							import faiss

							snake_case_		:			Optional[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
							snake_case_		:			Union[str, Any]											=	np.zeros(5		,   dtype=np.floataa	)
							snake_case_		:			Optional[Any]											=	1
							snake_case_						,snake_case_		:			Tuple											=	index.search(_A	)
							self.assertRaises(_A		,   index.search		,   query.reshape(-1		,   1	)	)
							self.assertGreater(scores[0]		,   0	)
							self.assertEqual(indices[0]		,   1	)

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






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							import faiss

							snake_case_		:			List[Any]											=	FaissIndex(string_factory='Flat'	)
							index.add_vectors(np.eye(5		,   dtype=np.floataa	)	)
							self.assertIsInstance(index.faiss_index		,   faiss.IndexFlat	)
							snake_case_		:			List[Any]											=	FaissIndex(string_factory='LSH'	)
							index.add_vectors(np.eye(5		,   dtype=np.floataa	)	)
							self.assertIsInstance(index.faiss_index		,   faiss.IndexLSH	)
							with self.assertRaises(_A	):
								snake_case_		:			int											=	FaissIndex(string_factory='Flat'		,   custom_index=faiss.IndexFlat(5	)	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    List[str]:







							"""simple docstring"""


							import faiss

							snake_case_		:			Union[str, Any]											=	faiss.IndexFlat(5	)
							snake_case_		:			Optional[int]											=	FaissIndex(custom_index=_A	)
							index.add_vectors(np.eye(5		,   dtype=np.floataa	)	)
							self.assertIsInstance(index.faiss_index		,   faiss.IndexFlat	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    Any:







							"""simple docstring"""


							import faiss

							snake_case_		:			List[str]											=	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	)
								snake_case_		:			str											=	FaissIndex.load(tmp_file.name	)
							os.unlink(tmp_file.name	)

							snake_case_		:			int											=	np.zeros(5		,   dtype=np.floataa	)
							snake_case_		:			List[str]											=	1
							snake_case_						,snake_case_		:			str											=	index.search(_A	)
							self.assertGreater(scores[0]		,   0	)
							self.assertEqual(indices[0]		,   1	)


@require_faiss
def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	import faiss

	snake_case_		:			List[str]											=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT   )
	index.add_vectors(np.eye(5   , dtype=np.floataa   )   )

	snake_case_		:			List[str]											=	'index.faiss'
	snake_case_		:			int											=	f"""mock://{index_name}"""
	index.save(__a   , storage_options=mockfs.storage_options   )
	snake_case_		:			Union[str, Any]											=	FaissIndex.load(__a   , storage_options=mockfs.storage_options   )

	snake_case_		:			Union[str, Any]											=	np.zeros(5   , dtype=np.floataa   )
	snake_case_		:			List[str]											=	1
	snake_case_						,snake_case_		:			List[Any]											=	index.search(__a   )
	assert scores[0] > 0
	assert indices[0] == 1


@require_elasticsearch
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[str]:







							"""simple docstring"""


							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:
								snake_case_		:			Dict											=	Elasticsearch()
								snake_case_		:			List[Any]											=	{'acknowledged': True}
								snake_case_		:			Dict											=	ElasticSearchIndex(es_client=_A	)
								mocked_bulk.return_value([(True, None)] * 3	)
								index.add_documents(['foo', 'bar', 'foobar']	)

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

								# single query with timeout
								snake_case_		:			int											=	'foo'
								snake_case_		:			Any											=	{'hits': {'hits': [{'_score': 1, '_id': 0}]}}
								snake_case_						,snake_case_		:			List[Any]											=	index.search(_A		,   request_timeout=30	)
								self.assertEqual(scores[0]		,   1	)
								self.assertEqual(indices[0]		,   0	)

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

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








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

