None:\\n self._sICX_address.set(_address)\",\n \"def _init_ext_info(self):\\n IterativeCooperation._init_ext_info(self)\\n\\n for node in self._global_scenario.network.nodes:\\n system = self._cluster_systems[node.id][0]\\n for time_step in range(self.control_sequence_length):\\n ctrl_limit = self._cluster_ctrl_limits[node.id][time_step]\\n env_input = self._cluster_env_inputs[node.id][time_step]\\n\\n for app in system.apps:\\n for ext_node in self._global_scenario.network.nodes:\\n if node == ext_node:\\n continue\\n\\n max_dispatch_load = 0.0\\n if ext_node.is_cloud():\\n max_dispatch_load = math.inf\\n ctrl_limit.max_dispatch_load[app.id][ext_node.id] = max_dispatch_load\\n\\n env_input.generated_load[app.id][ext_node.id] = 0.0\\n env_input.additional_received_load[app.id][ext_node.id] = 0.0\\n env_input.nb_instances[app.id][ext_node.id] = 0\\n if self._global_control_input is not None:\\n nb_instances = self._global_control_input.get_max_app_placement(app.id, ext_node.id)\\n env_input.nb_instances[app.id][ext_node.id] = nb_instances\",\n \"def update_necrosis_count(self, number):\\n\\n print(\\\"controller - update_necrosis_count!\\\")\\n self.view.processing_gui.update_necrosis_count(number)\",\n \"def node_count(self, node_count):\\n\\n self._node_count = node_count\",\n \"def occupied(self):\\n self.is_occupied = 1\\n for hex in self.fon:\\n hex.add_neighbor()\\n hex.set_quality()\",\n \"def _es_push_indexes(self, content):\\n for c in self.es_clients:\\n c.create_index(content)\",\n \"def _host_in_event(self, ev):\\n self._update_nodes()\\n\\n if not self.nodes:\\n return\\n\\n for node in self.nodes:\\n if node.ip in ev.host.ipv4:\\n datapath = self.dpset.get(ev.host.port.dpid)\\n node.setPortInformation(ev.host.port.dpid, datapath, ev.host.port.port_no, ev.host.port)\\n self._install_cdnengine_matching_flow(datapath, node.ip, node.port)\\n self.logger.info('New Node connected the network. Matching rules were installed ' + node.__str__())\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def compute_ericsson_4g_value_counts(self):\\n\\n tech = \\\"4G\\\"\\n\\n # List of parameters to ignore\\n ignore_list = ['LOADID', 'VARDATE', 'DATETIME', 'REGION', 'NENAME', 'CELLID', 'ID', 'FILENAME', 'TECHNOLOGY', 'VENDOR', 'VERSION', 'NETYPE', 'CELLNAME']\\n\\n self.logger.info(\\\"Processing Ericsson baseline for {}...\\\".format(tech))\\n\\n # Get list of mos configured in process_config\\n result = self.engine.execute(text(\\\"SELECT * FROM baseline.process_config WHERE process = true AND technology = :tech AND vendor = :vendor\\\"), tech=tech, vendor='ERICSSON')\\n for row in result:\\n vendor = row['vendor']\\n technology = row['technology']\\n mo = row['mo']\\n\\n self.logger.info(\\\"vendor:{}, technology:{}, mo:{}\\\".format(vendor, technology, mo))\\n\\n # Get field names from information_schema\\n field_qry = \\\"\\\"\\\"\\n SELECT t1.column_name as field \\n FROM\\n information_schema.columns t1\\n LEFT JOIN baseline.parameter_ignore_list t2 \\n ON t1.table_name = t2.mo\\n AND t1.column_name = t2.parameter\\n WHERE \\n table_schema = 'ericsson_cm'\\n AND table_name = :mo\\n AND t2.parameter is NULL\\n AND UPPER(t1.column_name) NOT IN ('{}')\\n \\\"\\\"\\\".format(\\\"','\\\".join(ignore_list))\\n\\n field_result = self.engine.execute(text(field_qry), mo=mo)\\n\\n for f in field_result:\\n parameter = f[0]\\n\\n self.logger.info(\\\"Processing baseline for {}.{}...\\\".format(mo, parameter))\\n\\n value_qry = \\\"\\\"\\\"\\n INSERT INTO baseline.parameter_value_counts\\n (date_time, vendor, nename, mo, parameter, pvalue, occurence)\\n SELECT \\n MAX(t1.\\\"DATETIME\\\") AS date_time,\\n 'ERICSSON' as vendor,\\n t1.\\\"SubNetwork_2_id\\\" AS nename,\\n '{0}' AS mo,\\n '{1}' AS parameter,\\n t1.\\\"{1}\\\" AS pvalue,\\n COUNT(t1.\\\"{1}\\\") AS occurence\\n FROM\\n ericsson_cm.\\\"{0}\\\" t1\\n INNER JOIN cm_loads t2 on t2.pk = t1.\\\"LOADID\\\"\\n WHERE t2.is_current_load = true AND t1.\\\"{1}\\\" IS NOT NULL\\n GROUP BY \\n t1.\\\"SubNetwork_2_id\\\", t1.\\\"{1}\\\"\\n ON CONFLICT ON CONSTRAINT uq_parameter_value_counts\\n DO NOTHING\\n \\\"\\\"\\\".format(mo, parameter)\\n\\n self.engine.execute(text(value_qry))\",\n \"def test_reports_enabled_hosts_as_up(self):\\n compute1 = self.start_service('compute', host='host1')\\n compute2 = self.start_service('compute', host='host2')\\n hosts = self.scheduler.driver.hosts_up(self.context, 'compute')\\n self.assertEqual(2, len(hosts))\\n compute1.kill()\\n compute2.kill()\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def SetServerInformation(self, elastic_host, elastic_port):\\n self._elastic_db = pyelasticsearch.ElasticSearch(\\n u'http://{0:s}:{1:d}'.format(elastic_host, elastic_port))\",\n \"def iscsi_luns_every(self, iscsi_luns_every):\\n\\n self._iscsi_luns_every = iscsi_luns_every\",\n \"def cleanup_host(self):\\n\\n LOG.debug(_('XManager cleanup_host...'))\\n\\n pass\",\n \"def elf_images_every(self, elf_images_every):\\n\\n self._elf_images_every = elf_images_every\",\n \"def host_ip(self, host_ip):\\n\\n self._host_ip = host_ip\",\n \"def set_hostname(self, userid, hostname, os_version):\\n tmp_path = self._pathutils.get_guest_temp_path(userid)\\n if not os.path.exists(tmp_path):\\n os.makedirs(tmp_path)\\n tmp_file = tmp_path + '/hostname.sh'\\n\\n lnxdist = self._dist_manager.get_linux_dist(os_version)()\\n lines = lnxdist.generate_set_hostname_script(hostname)\\n with open(tmp_file, 'w') as f:\\n f.writelines(lines)\\n\\n requestData = \\\"ChangeVM \\\" + userid + \\\" punchfile \\\" + \\\\\\n tmp_file + \\\" --class x\\\"\\n LOG.debug(\\\"Punch script to guest %s to set hostname\\\" % userid)\\n\\n try:\\n self._smtclient._request(requestData)\\n except exception.SDKSMTRequestFailed as err:\\n msg = (\\\"Failed to punch set_hostname script to userid '%s'. SMT \\\"\\n \\\"error: %s\\\" % (userid, err.format_message()))\\n LOG.error(msg)\\n raise exception.SDKSMTRequestFailed(err.results, msg)\\n finally:\\n self._pathutils.clean_temp_folder(tmp_path)\",\n \"def restructure_host_cpu_data(host):\\n init_cpu_counts(host)\\n host.sockets = len(host.nodes or [])\\n host.hyperthreading = False\\n host.physical_cores = 0\\n if not host.cpus:\\n return\\n host.cpu_model = host.cpus[0].cpu_model\\n cpu_list = sorted(host.cpus, key=_sort_by_coreid)\\n for cpu in cpu_list:\\n inode = pecan.request.dbapi.inode_get(inode_id=cpu.forinodeid)\\n cpu.numa_node = inode.numa_node\\n if cpu.thread == 0:\\n host.physical_cores += 1\\n elif cpu.thread > 0:\\n host.hyperthreading = True\\n function = cpu.allocated_function or get_default_function(host)\\n host.cpu_functions[cpu.numa_node][function].append(int(cpu.cpu))\\n host.cpu_lists[cpu.numa_node].append(int(cpu.cpu))\",\n \"def update_servers(self):\\n # TODO: Optimize this to a JSONField query\\n\\n hosts_detected = 0\\n\\n for agent_id, agent_data in self.scan_results.items():\\n logger.info(\\\"Reading scan from agent id: %s\\\" % agent_id)\\n for host, p_scan in agent_data.items():\\n\\n fully_connected = False not in [\\n result['connected']\\n for port, result in p_scan.items()]\\n if fully_connected:\\n logger.debug(\\\"Host found: %s\\\" % host)\\n hosts_detected += 1\\n else:\\n logger.debug(\\\"Host not connected: %s\\\" % host)\\n continue\\n \\n ports = list(p_scan.keys())\\n server, created = ViPlatformServer.objects.update_or_create(\\n fqdn=host, server_group=self,\\n human_readable_name=\\\"Disc[%s] (P: %s)\\\" % (host, ports),\\n defaults={'active': True})\\n if created:\\n logger.info(\\\"Discovered new server: [%10s] %s\\\" % (host, server))\\n\\n # Create a servergroup notification\\n web_tasks.send_notification_task.delay(\\n text=\\\"(%s) Scan complete [%s hosts]\\\" % (\\n self.title, hosts_detected),\\n style=\\\"success\\\", lane=\\\"platform\\\")\",\n \"def compute_node_count(self, compute_node_count):\\n\\n self._compute_node_count = compute_node_count\",\n \"def compute_ericsson_2g_value_counts(self):\\n\\n tech = '2G'\\n\\n # List of parameters to ignore\\n ignore_list = ['LOADID', 'VARDATE', 'DATETIME', 'REGION', 'NENAME', 'CELLID', 'ID', 'FILENAME', 'TECHNOLOGY', 'VENDOR', 'VERSION', 'NETYPE', 'CELLNAME']\\n\\n self.logger.info(\\\"Processing Huawei baseline for {}...\\\".format(tech))\\n\\n # Get list of mos configured in process_config\\n result = self.engine.execute(text(\\\"SELECT * FROM baseline.process_config WHERE process = true AND technology = :tech AND vendor = :vendor\\\"), tech=tech, vendor='ERICSSON')\\n for row in result:\\n vendor = row['vendor']\\n technology = row['technology']\\n mo = row['mo']\\n\\n self.logger.info(\\\"vendor:{}, technology:{}, mo:{}\\\".format(vendor, technology, mo))\\n\\n # Get field names from information_schema\\n field_qry = \\\"\\\"\\\"\\n SELECT t1.column_name as field \\n FROM\\n information_schema.columns t1\\n LEFT JOIN baseline.parameter_ignore_list t2 \\n ON t1.table_name = t2.mo\\n AND t1.column_name = t2.parameter\\n WHERE \\n table_schema = 'ericsson_cm'\\n AND table_name = :mo\\n AND t2.parameter is NULL\\n AND UPPER(t1.column_name) NOT IN ('{}')\\n \\\"\\\"\\\".format(\\\"','\\\".join(ignore_list))\\n\\n field_result = self.engine.execute(text(field_qry), mo=mo)\\n\\n # self.logger.info([row[0] for row in field_result])\\n\\n # self.logger.info(field_qry)\\n\\n self.logger.info('Processing parameters...')\\n for f in field_result:\\n parameter = f[0]\\n\\n self.logger.info(\\\"Processing baseline for {}.{}...\\\".format(mo, parameter))\\n\\n value_qry = \\\"\\\"\\\"\\n INSERT INTO baseline.parameter_value_counts\\n (date_time, vendor, nename, mo, parameter, pvalue, occurence)\\n SELECT \\n MAX(t1.\\\"DATETIME\\\") AS date_time,\\n 'ERICSSON' as vendor,\\n t1.\\\"BSC_NAME\\\" AS nename,\\n '{0}' AS mo,\\n '{1}' AS parameter,\\n t1.\\\"{1}\\\" AS pvalue,\\n COUNT(t1.\\\"{1}\\\") AS occurence\\n FROM\\n ericsson_cm.\\\"{0}\\\" t1\\n INNER JOIN cm_loads t2 on t2.pk = t1.\\\"LOADID\\\"\\n WHERE t2.is_current_load = true AND t1.\\\"{1}\\\" IS NOT NULL\\n GROUP BY \\n t1.\\\"BSC_NAME\\\", t1.\\\"{1}\\\"\\n ON CONFLICT ON CONSTRAINT uq_parameter_value_counts\\n DO NOTHING\\n \\\"\\\"\\\".format(mo, parameter)\\n\\n self.engine.execute(text(value_qry))\",\n \"def cores_per_node(self, cores_per_node):\\n\\n self._cores_per_node = cores_per_node\",\n \"def number_of_atoms(self, value):\\n self._number_of_atoms = value\",\n \"def host_num_lte(self, host_num_lte):\\n\\n self._host_num_lte = host_num_lte\",\n \"def set_xp_partition(self,xp_partition):\\n # setup partition: set x grid\\n self.xp_partition = xp_partition\\n self.xp_partition.setup_x_grid(xx=[0,self.L])\\n # local copies of x and p grids\\n self.x=self.xp_partition.x\\n self.p=self.xp_partition.p\\n # allocate fmci_XP array\\n self.fmci_XP=np.zeros((self.xp_partition.nx,self.xp_partition.np))\",\n \"def set_share_all_host_disks(self, bShareAllHostDisks):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetShareAllHostDisks', self.handle, bShareAllHostDisks)\",\n \"def update_host_ovs(self, context):\\n LOG.info(_('Updating Open vSwitch host data...'))\\n LOG.debug(\\\"Current DOM: %s\\\" % self.current_dom.to_dict())\\n LOG.debug(\\\"Requested DOM: %s\\\" % self.desired_dom.to_dict())\\n\\n builder = mob.MicroOperationBuilder(context,\\n self.current_dom,\\n self.desired_dom,\\n self.rollback)\\n\\n mo_list = builder.get_micro_ops_for_update()\\n\\n # run validation\\n return self._run_micro_op_list(mo_list)\",\n \"def Hosts(self):\\n if not self._hosts:\\n hs = self._get_objects(vim.HostSystem)\\n for h in hs:\\n self._hosts[h.name] = h\\n return self._hosts\",\n \"def set_hypercubes_classes(self):\\n print(\\\"Setting the BaseGrid hypercubes' classes.\\\")\\n list_of_all_hc = list(self.hypercubes.flatten())\\n print(\\\"Number of hypercubes: \\\" + str(len(list_of_all_hc)))\\n for hypercube in list_of_all_hc:\\n hypercube.set_hypercube_class()\\n if self.child_grid:\\n self.child_grid.set_hypercubes_classes()\",\n \"def hosts_none(self, hosts_none):\\n\\n self._hosts_none = hosts_none\",\n \"def inject_hosts_files(self):\\n self.log.info(\\\"Injecting host files\\\")\\n hosts = dict()\\n for i in self.all_nodes:\\n hosts[i.name] = i.get_public_addr()\\n #add the host names to etc/hosts\\n orchestrator.inject_hostnames(hosts, delete=self.cluster_name)\\n for i in self.all_nodes:\\n i.inject_hostnames(hosts, delete=self.cluster_name)\\n self.all_nodes[0].run_command(\\\"service ganglia-monitor restart; service gmetad restart\\\", silent=True)\\n orchestrator.run_command(\\\"service ganglia-monitor restart; service gmetad restart\\\", silent=True)\",\n \"def __calc_empirical_counts__(self):\\n self.empirical_counts = np.zeros(self._features_vector_length, dtype=float)\\n for feature, freq in self.features_dict.items():\\n for index in feature:\\n self.empirical_counts[index] += freq\\n assert len(self.empirical_counts) == np.count_nonzero(self.empirical_counts), \\\"0 in empirical counts vector\\\"\",\n \"def set_host(host_index):\\n env.hosts = [public_dns_names[int(host_index)]]\\n env.password = [public_pwds[int(host_index)]]\",\n \"def get_emperor_nodes_stats(self, system):\\n path_inventory = u'%s/inventories/%s' % (self.ansible_path, self.environment)\\n runner = Runner(inventory=path_inventory, verbosity=self.verbosity)\\n hosts = runner.get_inventory(group=system)\\n self.json = []\\n resp = []\\n for host in hosts:\\n res = self.__get_stats(host)\\n vassals = res.pop(u'vassals')\\n res.pop(u'blacklist')\\n try:\\n temp = [u'%s [%s]' % (v[u'id']\\\\\\n .replace(u'/etc/uwsgi/vassals/', u'')\\\\\\n .replace(u'.ini', u'')\\\\\\n , v[u'pid']) for v in vassals]\\n res[u'vassals'] = u', '.join(temp)\\n except:\\n res[u'vassals'] = []\\n res[u'host'] = host\\n resp.append(res)\\n self.result(resp, headers=[u'host', u'pid', u'version', u'uid', u'gid', \\n u'throttle_level', u'emperor_tyrant', \\n u'vassals'])\",\n \"def scns2datacube_all_avail(self):\\n raise EODataDownException(\\\"Not implemented.\\\")\",\n \"def update(call: ServiceCall) -> None:\\n called_host = call.data[ATTR_HOST]\\n if called_host in hass.data[DOMAIN]:\\n hass.data[DOMAIN][called_host].update()\\n else:\\n for iperf3_host in hass.data[DOMAIN].values():\\n iperf3_host.update()\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def host_num_gt(self, host_num_gt):\\n\\n self._host_num_gt = host_num_gt\",\n \"def setIterationCount(self, newIterationCount):\\n \\n pass\",\n \"def szx(self, addr):\\n\\n if self.reg.zerox_flag:\\n self.reg.ip_inc()\\n self.reg.ip_inc()\",\n \"def setUp(self):\\n # Start the servers and agents\\n super().setUp()\\n\\n self.obj_class = self.params.get(\\\"dfs_oclass\\\", '/run/ior/objectclass/*')\\n self.ior_chu_trs_blk_size = self.params.get(\\n \\\"chunk_block_transfer_sizes\\\", '/run/ior/*')\\n # Fail IOR test in case of Warnings\\n self.fail_on_warning = True\\n self.server_count = len(self.hostlist_servers) * 2\\n # Create the Pool\\n self.create_pool_max_size()\\n self.update_ior_cmd_with_pool()\",\n \"def set_keys(self):\\n self.inventory_dict['csah'] = {'hosts': '{}'.format(socket.getfqdn()), 'vars': {}}\",\n \"def setNumThreads(cls, numThreads: int):\\n cls.NUMTHREADS = numThreads\",\n \"def __init__(self, es_host: str = ES_HOST, es_port: int = ES_PORT) -> None:\\n super().__init__(es_host=es_host, es_port=es_port)\",\n \"def set_hostname(self, sNewVmHostname):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetHostname', self.handle, sNewVmHostname)\",\n \"def nfvi_enable_compute_host_services(host_uuid, host_name, host_personality,\\n callback):\\n cmd_id = _compute_plugin.invoke_plugin('enable_host_services',\\n host_uuid, host_name,\\n host_personality,\\n callback=callback)\\n return cmd_id\",\n \"def set_soft_contacts_size(self, soft_contacts_size):\\n self.soft_contacts_size = soft_contacts_size\\n self.update_soft_contacts(self.soft_contacts)\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59950227","0.5260858","0.50562745","0.50244135","0.49948582","0.48857233","0.48497176","0.48385507","0.482034","0.4762812","0.47252673","0.4718971","0.47022638","0.46596396","0.46280846","0.45461932","0.45392084","0.45244592","0.45231992","0.45225677","0.45150542","0.45062536","0.45039192","0.44987655","0.4494041","0.44755217","0.44519544","0.44330424","0.44218957","0.44173533","0.44132903","0.44089934","0.44012818","0.44012818","0.43873715","0.43818653","0.43734255","0.4364015","0.4315671","0.43137312","0.43113858","0.43094453","0.43076864","0.43072915","0.43054986","0.42959276","0.42943427","0.42894644","0.42873904","0.42873216","0.4285446","0.4284587","0.42799583","0.42782387","0.42707893","0.4253175","0.42484623","0.42421266","0.4240043","0.42334592","0.42306077","0.4214661","0.42123124","0.4202023","0.4191571","0.4186944","0.41754964","0.41738024","0.41639212","0.41599646","0.4158935","0.41585052","0.41507548","0.41447908","0.4137307","0.41343182","0.413042","0.41293758","0.41240278","0.41232237","0.4113349","0.41132045","0.41123718","0.41043302","0.4093829","0.40896687","0.40797564","0.40782285","0.40782285","0.40748873","0.4068688","0.40679908","0.406477","0.4063374","0.40619245","0.40608242","0.40597436","0.4056916","0.4055356","0.40535966"],"string":"[\n \"0.59950227\",\n \"0.5260858\",\n \"0.50562745\",\n \"0.50244135\",\n \"0.49948582\",\n \"0.48857233\",\n \"0.48497176\",\n \"0.48385507\",\n \"0.482034\",\n \"0.4762812\",\n \"0.47252673\",\n \"0.4718971\",\n \"0.47022638\",\n \"0.46596396\",\n \"0.46280846\",\n \"0.45461932\",\n \"0.45392084\",\n \"0.45244592\",\n \"0.45231992\",\n \"0.45225677\",\n \"0.45150542\",\n \"0.45062536\",\n \"0.45039192\",\n \"0.44987655\",\n \"0.4494041\",\n \"0.44755217\",\n \"0.44519544\",\n \"0.44330424\",\n \"0.44218957\",\n \"0.44173533\",\n \"0.44132903\",\n \"0.44089934\",\n \"0.44012818\",\n \"0.44012818\",\n \"0.43873715\",\n \"0.43818653\",\n \"0.43734255\",\n \"0.4364015\",\n \"0.4315671\",\n \"0.43137312\",\n \"0.43113858\",\n \"0.43094453\",\n \"0.43076864\",\n \"0.43072915\",\n \"0.43054986\",\n \"0.42959276\",\n \"0.42943427\",\n \"0.42894644\",\n \"0.42873904\",\n \"0.42873216\",\n \"0.4285446\",\n \"0.4284587\",\n \"0.42799583\",\n \"0.42782387\",\n \"0.42707893\",\n \"0.4253175\",\n \"0.42484623\",\n \"0.42421266\",\n \"0.4240043\",\n \"0.42334592\",\n \"0.42306077\",\n \"0.4214661\",\n \"0.42123124\",\n \"0.4202023\",\n \"0.4191571\",\n \"0.4186944\",\n \"0.41754964\",\n \"0.41738024\",\n \"0.41639212\",\n \"0.41599646\",\n \"0.4158935\",\n \"0.41585052\",\n \"0.41507548\",\n \"0.41447908\",\n \"0.4137307\",\n \"0.41343182\",\n \"0.413042\",\n \"0.41293758\",\n \"0.41240278\",\n \"0.41232237\",\n \"0.4113349\",\n \"0.41132045\",\n \"0.41123718\",\n \"0.41043302\",\n \"0.4093829\",\n \"0.40896687\",\n \"0.40797564\",\n \"0.40782285\",\n \"0.40782285\",\n \"0.40748873\",\n \"0.4068688\",\n \"0.40679908\",\n \"0.406477\",\n \"0.4063374\",\n \"0.40619245\",\n \"0.40608242\",\n \"0.40597436\",\n \"0.4056916\",\n \"0.4055356\",\n \"0.40535966\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8037844"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19681,"cells":{"query":{"kind":"string","value":"Sets the external_group_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def local_group_count(self, local_group_count):\n\n self._local_group_count = local_group_count","def external_ids(self, external_ids):\n\n self._external_ids = external_ids","def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count","def set_all_data_external(\n self, check_data=True, external_data_folder=None\n ):\n # set blocks\n for key, block in self.blocks.items():\n file_name = os.path.split(self.filename)[1]\n block.set_all_data_external(\n file_name, check_data, external_data_folder\n )\n # set sub-packages\n for package in self._packagelist:\n package.set_all_data_external(check_data, external_data_folder)","def external_id(self, external_id):\n if external_id is not None and len(external_id) > 255:\n raise ValueError(\"Invalid value for `external_id`, length must be less than or equal to `255`\") # noqa: E501\n\n self._external_id = external_id","def total_nucleus_clients(self, total_nucleus_clients):\n\n self._total_nucleus_clients = total_nucleus_clients","def _set_group_resource(self, _g):\n\n if isinstance(_g, Server):\n return\n\n for _, sg in _g.subgroups.items():\n self._set_group_resource(sg)\n _g.vCPUs += sg.vCPUs\n _g.mem += sg.mem\n _g.local_volume_size += sg.local_volume_size","def consistency_groups_every(self, consistency_groups_every):\n\n self._consistency_groups_every = consistency_groups_every","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def external_id(self, external_id):\n\n self._external_id = external_id","def external_id(self, external_id):\n\n self._external_id = external_id","def external_id(self, external_id):\n\n self._external_id = external_id","def update_num_rois_per_group(self,num_rois_per_group=None):\n if num_rois_per_group is not None:\n self.roi_groups[0].set_num_rois(num_rois_per_group)\n self.signal_status_message.emit('Updated number of ROIs/group to {}'.format(num_rois_per_group))\n num_rois_per_group = self.roi_groups[0].get_num_rois()\n for group in self.roi_groups[1:]:\n group.set_num_rois(num_rois_per_group)\n self.signal_num_rois_per_group.emit(num_rois_per_group)\n self.send_roi_coords()","def consistency_group_num_in(self, consistency_group_num_in):\n\n self._consistency_group_num_in = consistency_group_num_in","def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count","def namespace_group_num_in(self, namespace_group_num_in):\n\n self._namespace_group_num_in = namespace_group_num_in","def namespace_groups_every(self, namespace_groups_every):\n\n self._namespace_groups_every = namespace_groups_every","def _init_ext_info(self):\n IterativeCooperation._init_ext_info(self)\n\n for node in self._global_scenario.network.nodes:\n system = self._cluster_systems[node.id][0]\n for time_step in range(self.control_sequence_length):\n ctrl_limit = self._cluster_ctrl_limits[node.id][time_step]\n env_input = self._cluster_env_inputs[node.id][time_step]\n\n for app in system.apps:\n for ext_node in self._global_scenario.network.nodes:\n if node == ext_node:\n continue\n\n max_dispatch_load = 0.0\n if ext_node.is_cloud():\n max_dispatch_load = math.inf\n ctrl_limit.max_dispatch_load[app.id][ext_node.id] = max_dispatch_load\n\n env_input.generated_load[app.id][ext_node.id] = 0.0\n env_input.additional_received_load[app.id][ext_node.id] = 0.0\n env_input.nb_instances[app.id][ext_node.id] = 0\n if self._global_control_input is not None:\n nb_instances = self._global_control_input.get_max_app_placement(app.id, ext_node.id)\n env_input.nb_instances[app.id][ext_node.id] = nb_instances","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","def update_num_roi_groups(self,num_roi_groups):\n # print('MAIA: num roi groups {}'.format(num_roi_groups))\n if num_roi_groups is not None:\n for _ in range(num_roi_groups,len(self.roi_groups)): # delete unneeded ROIs\n self.roi_groups.pop()\n for _ in range(len(self.roi_groups), num_roi_groups): # make new ROIs\n self.roi_groups.append(ROIGroup(num_images=self.num_images))\n self.signal_status_message.emit('Updated number of ROI groups to {}'.format(num_roi_groups))\n self.update_num_rois_per_group() # ensures that newly created ROI groups have the right number of ROIs\n num_roi_groups = len(self.roi_groups)\n self.signal_num_roi_groups.emit(num_roi_groups)\n # self.send_roi_coords() # this will be send when updating the number of ROIs per group anyway","def resize_eip_group_count(self, id, eip_add_count,\n client_token=None, config=None):\n path = utils.append_uri(self._get_path(), id)\n if client_token is None:\n client_token = generate_client_token()\n params = {\n b'resize': None,\n b'clientToken': client_token\n }\n body = {\n 'eipAddCount': eip_add_count\n }\n return self._send_request(http_methods.PUT,\n path, body=json.dumps(body),\n params=params, config=config)","def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def external_id(self, external_id):\n if external_id is None:\n raise ValueError(\"Invalid value for `external_id`, must not be `None`\") # noqa: E501\n\n self._external_id = external_id","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def clusters_every(self, clusters_every):\n\n self._clusters_every = clusters_every","def consistency_group_num_lte(self, consistency_group_num_lte):\n\n self._consistency_group_num_lte = consistency_group_num_lte","def set_externals_state(self, dp_name, externals_up):\n dp_conf = self._get_faucet_conf()['dps'][dp_name]\n for port_num, port_conf in dp_conf['interfaces'].items():\n if port_conf.get('loop_protect_external'):\n if externals_up:\n self.set_port_up(port_num, dp_conf.get('dp_id'))\n else:\n self.set_port_down(port_num, dp_conf.get('dp_id'))","def consistency_group_num(self, consistency_group_num):\n\n self._consistency_group_num = consistency_group_num","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def total_nucleus_clients_active(self, total_nucleus_clients_active):\n\n self._total_nucleus_clients_active = total_nucleus_clients_active","def associated_object_count(self, associated_object_count):\n self._associated_object_count = associated_object_count","def consistency_groups_some(self, consistency_groups_some):\n\n self._consistency_groups_some = consistency_groups_some","def total_nucleus_clients_inactive(self, total_nucleus_clients_inactive):\n\n self._total_nucleus_clients_inactive = total_nucleus_clients_inactive","def namespace_group_num(self, namespace_group_num):\n\n self._namespace_group_num = namespace_group_num","def setCompressorGroup(self, channel, group, unitCode=0):\n resp = self.XAPCommand('CGROUP', channel, group, unitCode=unitCode)\n return int(resp)","def vlans_every(self, vlans_every):\n\n self._vlans_every = vlans_every","def external_controller_services(self, external_controller_services):\n\n self._external_controller_services = external_controller_services","def device_count(self, device_count):\n\n self._device_count = device_count","def external_customer_id(self, external_customer_id):\n\n self._external_customer_id = external_customer_id","def setCount(self, num):\n self.count=num","def set_count(c):\n global count\n count = c","def number_of_nodes(self, number_of_nodes):\n\n self._number_of_nodes = number_of_nodes","def external_id_source(self, external_id_source):\n\n self._external_id_source = external_id_source","def update_necrosis_count(self, number):\n\n print(\"controller - update_necrosis_count!\")\n self.view.processing_gui.update_necrosis_count(number)","def setNetGroup(addr): #status: Done, not tested\r\n pass","def fusion_api_update_logical_enclosure_from_group(self, uri=None, api=None, headers=None):\n param = '/updateFromGroup'\n return self.logical_enclosure.put(body=None, uri=uri, param=param, api=api, headers=headers)","def consistency_group_num_lt(self, consistency_group_num_lt):\n\n self._consistency_group_num_lt = consistency_group_num_lt","def set_autoscaled_instances(self, instance_count: int) -> None:\n set_instances_for_marathon_service(\n service=self.service,\n instance=self.instance,\n instance_count=instance_count,\n )","def set_all_data_external(\n self, base_name, check_data=True, external_data_folder=None\n ):\n for key, dataset in self.datasets.items():\n if (\n isinstance(dataset, mfdataarray.MFArray)\n or (\n isinstance(dataset, mfdatalist.MFList)\n and dataset.structure.type == DatumType.recarray\n )\n and dataset.enabled\n ):\n file_path = f\"{base_name}_{dataset.structure.name}.txt\"\n replace_existing_external = False\n if external_data_folder is not None:\n # get simulation root path\n root_path = self._simulation_data.mfpath.get_sim_path()\n # get model relative path, if it exists\n if isinstance(self._model_or_sim, ModelInterface):\n name = self._model_or_sim.name\n rel_path = (\n self._simulation_data.mfpath.model_relative_path[\n name\n ]\n )\n if rel_path is not None:\n root_path = os.path.join(root_path, rel_path)\n full_path = os.path.join(root_path, external_data_folder)\n if not os.path.exists(full_path):\n # create new external data folder\n os.makedirs(full_path)\n file_path = os.path.join(external_data_folder, file_path)\n replace_existing_external = True\n dataset.store_as_external_file(\n file_path,\n replace_existing_external=replace_existing_external,\n check_data=check_data,\n )","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def namespace_groups_some(self, namespace_groups_some):\n\n self._namespace_groups_some = namespace_groups_some","def __init__(self, numbers, all_groups):\n\n self.numbers = numbers\n self.size = len(numbers)\n self.partitions = list(self._partition_into_unique_pairs(all_groups))\n self.calculations = list(self._perform_calculations())","def employee_count(self, employee_count):\n\n self._employee_count = employee_count","def _setintermediary_institution_56C(self, val):\n self.swift_obj.IntermediaryInstitution_C = val\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'","def external_transit_networks(self, external_transit_networks):\n\n self._external_transit_networks = external_transit_networks","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def number_of_employees(self, number_of_employees: object):\n\n self._number_of_employees = number_of_employees","def update_library_count(self, ebook_count):\n\t\tself.library_count = ebook_count","def user_count(self, user_count):\n\n self._user_count = user_count","def user_count(self, user_count):\n\n self._user_count = user_count","def vms_every(self, vms_every):\n\n self._vms_every = vms_every","def set_article_count(cls, count):\n return cls.db.set(\"article_count\", count)","def external_use(self, external_use):\n if self.local_vars_configuration.client_side_validation and external_use is None: # noqa: E501\n raise ValueError(\"Invalid value for `external_use`, must not be `None`\") # noqa: E501\n\n self._external_use = external_use","def _set_group_flag(self):\n self.flag = np.zeros(len(self), dtype=np.uint8)\n for i in range(len(self)):\n img_info = self.img_infos[i]\n if img_info['width'] / img_info['height'] > 1:\n self.flag[i] = 1","def _set_group_flag(self):\n self.flag = np.zeros(len(self), dtype=np.uint8)\n for i in range(len(self)):\n img_info = self.img_infos[i]\n if img_info['width'] / img_info['height'] > 1:\n self.flag[i] = 1","def set_autoscale_group_capacity(group, num_instances,\n attributes=('min_size', 'max_size', 'desired_capacity')):\n conn = boto.ec2.autoscale.connect_to_region(\"us-east-1\")\n group = conn.get_all_groups(names=[group])[0]\n for attrib in attributes:\n setattr(group, attrib, num_instances)\n if attributes:\n group.update()","def num_of_computing_processors(self, num_of_computing_processors):\n if (self.local_vars_configuration.client_side_validation and\n num_of_computing_processors is not None and num_of_computing_processors < -1): # noqa: E501\n raise ValueError(\"Invalid value for `num_of_computing_processors`, must be a value greater than or equal to `-1`\") # noqa: E501\n\n self._num_of_computing_processors = num_of_computing_processors","def cluster_num_in(self, cluster_num_in):\n\n self._cluster_num_in = cluster_num_in","def externally_managed(self, externally_managed):\n\n self._externally_managed = externally_managed","def load_all_groups(self):\n for _, group in self.scopes.items():\n group.update()","def set_number_of_rows(self, number_of_rows):\n self.set_value_into_input_field(self.number_of_rows_inputbox_locator, number_of_rows, True)\n global bulk_add_number_of_rows\n bulk_add_number_of_rows = int(number_of_rows)","def set_num_images(self,num_images):\n for roi in self.rois:\n roi.set_num_images(num_images)\n self.num_images = num_images","def consistency_group_num_gte(self, consistency_group_num_gte):\n\n self._consistency_group_num_gte = consistency_group_num_gte","def max_individuals(self) -> int:\n return self.group_size.upper * self.groups_allowed","def advertise_osd_count(count):\n for relid in hookenv.relation_ids('mon'):\n hookenv.relation_set(\n relation_id=relid,\n relation_settings={'bootstrapped-osds': count}\n )","def test_update_hyperflex_feature_limit_external(self):\n pass","def consistency_group(self, consistency_group):\n\n self._consistency_group = consistency_group","def consistency_group(self, consistency_group):\n\n self._consistency_group = consistency_group","def externalSubset(self, name, externalID, systemID):\n pass","def _ensure_local_neo4j_has_test_computergroup_data(neo4j_session):\n groups = tests.data.jamf.computers.GROUPS\n cartography.intel.jamf.computers.load_computer_groups(groups, neo4j_session, TEST_UPDATE_TAG)","def getNumEnergyGroups(self):\n return self.lib.numGroups","def namespace_group_num_lte(self, namespace_group_num_lte):\n\n self._namespace_group_num_lte = namespace_group_num_lte","def number_of_atoms(self, value):\n self._number_of_atoms = value","def setIterationCount(self, newIterationCount):\n \n pass","def hosts_every(self, hosts_every):\n\n self._hosts_every = hosts_every","def elf_image_num_in(self, elf_image_num_in):\n\n self._elf_image_num_in = elf_image_num_in","def _set_adj_rib_out_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"adj-rib-out-count\", rest_name=\"adj-rib-out-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"adj_rib_out_count must be of a type compatible with int32\"\"\",\n 'defined-type': \"int32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"adj-rib-out-count\", rest_name=\"adj-rib-out-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\"\"\",\n })\n\n self.__adj_rib_out_count = t\n if hasattr(self, '_set'):\n self._set()","def increment_count(self):\n self.image_count +=1\n if self.image_count > self.max_count:\n self.image_count = self.count_start # overflow","def processor_count(self, processor_count):\n\n self._processor_count = processor_count","def datacenters_every(self, datacenters_every):\n\n self._datacenters_every = datacenters_every","def _setintermediary_institution_56A(self, val):\n self.swift_obj.IntermediaryInstitution_A = val\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'","def set_top_unique_num(self, number):\n self.top_unique_num = number","def consistency_group_snapshots_every(self, consistency_group_snapshots_every):\n\n self._consistency_group_snapshots_every = consistency_group_snapshots_every","def _add_gpu(self, gpu_count):\n if not gpu_count or gpu_count == 0:\n return\n\n logger.debug(\n \"found resource request for {} GPUs. This will limit to n1 \"\n \"instance types.\".format(gpu_count)\n )\n self.workflow.default_resources.parsed[\"nvidia_gpu\"] = gpu_count\n self.workflow.default_resources.args.append(\"nvidia_gpu=%s\" % gpu_count)\n self._machine_type_prefix = self._machine_type_prefix or \"\"\n if not self._machine_type_prefix.startswith(\"n1\"):\n self._machine_type_prefix = \"n1\"","def set_pingroups(self, groups):\n self.groups = groups[:]","def test_ipam_vlan_groups_update(self):\n pass"],"string":"[\n \"def local_group_count(self, local_group_count):\\n\\n self._local_group_count = local_group_count\",\n \"def external_ids(self, external_ids):\\n\\n self._external_ids = external_ids\",\n \"def esxi_host_count(self, esxi_host_count):\\n\\n self._esxi_host_count = esxi_host_count\",\n \"def set_all_data_external(\\n self, check_data=True, external_data_folder=None\\n ):\\n # set blocks\\n for key, block in self.blocks.items():\\n file_name = os.path.split(self.filename)[1]\\n block.set_all_data_external(\\n file_name, check_data, external_data_folder\\n )\\n # set sub-packages\\n for package in self._packagelist:\\n package.set_all_data_external(check_data, external_data_folder)\",\n \"def external_id(self, external_id):\\n if external_id is not None and len(external_id) > 255:\\n raise ValueError(\\\"Invalid value for `external_id`, length must be less than or equal to `255`\\\") # noqa: E501\\n\\n self._external_id = external_id\",\n \"def total_nucleus_clients(self, total_nucleus_clients):\\n\\n self._total_nucleus_clients = total_nucleus_clients\",\n \"def _set_group_resource(self, _g):\\n\\n if isinstance(_g, Server):\\n return\\n\\n for _, sg in _g.subgroups.items():\\n self._set_group_resource(sg)\\n _g.vCPUs += sg.vCPUs\\n _g.mem += sg.mem\\n _g.local_volume_size += sg.local_volume_size\",\n \"def consistency_groups_every(self, consistency_groups_every):\\n\\n self._consistency_groups_every = consistency_groups_every\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def external_id(self, external_id):\\n\\n self._external_id = external_id\",\n \"def external_id(self, external_id):\\n\\n self._external_id = external_id\",\n \"def external_id(self, external_id):\\n\\n self._external_id = external_id\",\n \"def update_num_rois_per_group(self,num_rois_per_group=None):\\n if num_rois_per_group is not None:\\n self.roi_groups[0].set_num_rois(num_rois_per_group)\\n self.signal_status_message.emit('Updated number of ROIs/group to {}'.format(num_rois_per_group))\\n num_rois_per_group = self.roi_groups[0].get_num_rois()\\n for group in self.roi_groups[1:]:\\n group.set_num_rois(num_rois_per_group)\\n self.signal_num_rois_per_group.emit(num_rois_per_group)\\n self.send_roi_coords()\",\n \"def consistency_group_num_in(self, consistency_group_num_in):\\n\\n self._consistency_group_num_in = consistency_group_num_in\",\n \"def hyperv_host_count(self, hyperv_host_count):\\n\\n self._hyperv_host_count = hyperv_host_count\",\n \"def namespace_group_num_in(self, namespace_group_num_in):\\n\\n self._namespace_group_num_in = namespace_group_num_in\",\n \"def namespace_groups_every(self, namespace_groups_every):\\n\\n self._namespace_groups_every = namespace_groups_every\",\n \"def _init_ext_info(self):\\n IterativeCooperation._init_ext_info(self)\\n\\n for node in self._global_scenario.network.nodes:\\n system = self._cluster_systems[node.id][0]\\n for time_step in range(self.control_sequence_length):\\n ctrl_limit = self._cluster_ctrl_limits[node.id][time_step]\\n env_input = self._cluster_env_inputs[node.id][time_step]\\n\\n for app in system.apps:\\n for ext_node in self._global_scenario.network.nodes:\\n if node == ext_node:\\n continue\\n\\n max_dispatch_load = 0.0\\n if ext_node.is_cloud():\\n max_dispatch_load = math.inf\\n ctrl_limit.max_dispatch_load[app.id][ext_node.id] = max_dispatch_load\\n\\n env_input.generated_load[app.id][ext_node.id] = 0.0\\n env_input.additional_received_load[app.id][ext_node.id] = 0.0\\n env_input.nb_instances[app.id][ext_node.id] = 0\\n if self._global_control_input is not None:\\n nb_instances = self._global_control_input.get_max_app_placement(app.id, ext_node.id)\\n env_input.nb_instances[app.id][ext_node.id] = nb_instances\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def update_num_roi_groups(self,num_roi_groups):\\n # print('MAIA: num roi groups {}'.format(num_roi_groups))\\n if num_roi_groups is not None:\\n for _ in range(num_roi_groups,len(self.roi_groups)): # delete unneeded ROIs\\n self.roi_groups.pop()\\n for _ in range(len(self.roi_groups), num_roi_groups): # make new ROIs\\n self.roi_groups.append(ROIGroup(num_images=self.num_images))\\n self.signal_status_message.emit('Updated number of ROI groups to {}'.format(num_roi_groups))\\n self.update_num_rois_per_group() # ensures that newly created ROI groups have the right number of ROIs\\n num_roi_groups = len(self.roi_groups)\\n self.signal_num_roi_groups.emit(num_roi_groups)\\n # self.send_roi_coords() # this will be send when updating the number of ROIs per group anyway\",\n \"def resize_eip_group_count(self, id, eip_add_count,\\n client_token=None, config=None):\\n path = utils.append_uri(self._get_path(), id)\\n if client_token is None:\\n client_token = generate_client_token()\\n params = {\\n b'resize': None,\\n b'clientToken': client_token\\n }\\n body = {\\n 'eipAddCount': eip_add_count\\n }\\n return self._send_request(http_methods.PUT,\\n path, body=json.dumps(body),\\n params=params, config=config)\",\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def external_id(self, external_id):\\n if external_id is None:\\n raise ValueError(\\\"Invalid value for `external_id`, must not be `None`\\\") # noqa: E501\\n\\n self._external_id = external_id\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def clusters_every(self, clusters_every):\\n\\n self._clusters_every = clusters_every\",\n \"def consistency_group_num_lte(self, consistency_group_num_lte):\\n\\n self._consistency_group_num_lte = consistency_group_num_lte\",\n \"def set_externals_state(self, dp_name, externals_up):\\n dp_conf = self._get_faucet_conf()['dps'][dp_name]\\n for port_num, port_conf in dp_conf['interfaces'].items():\\n if port_conf.get('loop_protect_external'):\\n if externals_up:\\n self.set_port_up(port_num, dp_conf.get('dp_id'))\\n else:\\n self.set_port_down(port_num, dp_conf.get('dp_id'))\",\n \"def consistency_group_num(self, consistency_group_num):\\n\\n self._consistency_group_num = consistency_group_num\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def total_nucleus_clients_active(self, total_nucleus_clients_active):\\n\\n self._total_nucleus_clients_active = total_nucleus_clients_active\",\n \"def associated_object_count(self, associated_object_count):\\n self._associated_object_count = associated_object_count\",\n \"def consistency_groups_some(self, consistency_groups_some):\\n\\n self._consistency_groups_some = consistency_groups_some\",\n \"def total_nucleus_clients_inactive(self, total_nucleus_clients_inactive):\\n\\n self._total_nucleus_clients_inactive = total_nucleus_clients_inactive\",\n \"def namespace_group_num(self, namespace_group_num):\\n\\n self._namespace_group_num = namespace_group_num\",\n \"def setCompressorGroup(self, channel, group, unitCode=0):\\n resp = self.XAPCommand('CGROUP', channel, group, unitCode=unitCode)\\n return int(resp)\",\n \"def vlans_every(self, vlans_every):\\n\\n self._vlans_every = vlans_every\",\n \"def external_controller_services(self, external_controller_services):\\n\\n self._external_controller_services = external_controller_services\",\n \"def device_count(self, device_count):\\n\\n self._device_count = device_count\",\n \"def external_customer_id(self, external_customer_id):\\n\\n self._external_customer_id = external_customer_id\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def set_count(c):\\n global count\\n count = c\",\n \"def number_of_nodes(self, number_of_nodes):\\n\\n self._number_of_nodes = number_of_nodes\",\n \"def external_id_source(self, external_id_source):\\n\\n self._external_id_source = external_id_source\",\n \"def update_necrosis_count(self, number):\\n\\n print(\\\"controller - update_necrosis_count!\\\")\\n self.view.processing_gui.update_necrosis_count(number)\",\n \"def setNetGroup(addr): #status: Done, not tested\\r\\n pass\",\n \"def fusion_api_update_logical_enclosure_from_group(self, uri=None, api=None, headers=None):\\n param = '/updateFromGroup'\\n return self.logical_enclosure.put(body=None, uri=uri, param=param, api=api, headers=headers)\",\n \"def consistency_group_num_lt(self, consistency_group_num_lt):\\n\\n self._consistency_group_num_lt = consistency_group_num_lt\",\n \"def set_autoscaled_instances(self, instance_count: int) -> None:\\n set_instances_for_marathon_service(\\n service=self.service,\\n instance=self.instance,\\n instance_count=instance_count,\\n )\",\n \"def set_all_data_external(\\n self, base_name, check_data=True, external_data_folder=None\\n ):\\n for key, dataset in self.datasets.items():\\n if (\\n isinstance(dataset, mfdataarray.MFArray)\\n or (\\n isinstance(dataset, mfdatalist.MFList)\\n and dataset.structure.type == DatumType.recarray\\n )\\n and dataset.enabled\\n ):\\n file_path = f\\\"{base_name}_{dataset.structure.name}.txt\\\"\\n replace_existing_external = False\\n if external_data_folder is not None:\\n # get simulation root path\\n root_path = self._simulation_data.mfpath.get_sim_path()\\n # get model relative path, if it exists\\n if isinstance(self._model_or_sim, ModelInterface):\\n name = self._model_or_sim.name\\n rel_path = (\\n self._simulation_data.mfpath.model_relative_path[\\n name\\n ]\\n )\\n if rel_path is not None:\\n root_path = os.path.join(root_path, rel_path)\\n full_path = os.path.join(root_path, external_data_folder)\\n if not os.path.exists(full_path):\\n # create new external data folder\\n os.makedirs(full_path)\\n file_path = os.path.join(external_data_folder, file_path)\\n replace_existing_external = True\\n dataset.store_as_external_file(\\n file_path,\\n replace_existing_external=replace_existing_external,\\n check_data=check_data,\\n )\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def namespace_groups_some(self, namespace_groups_some):\\n\\n self._namespace_groups_some = namespace_groups_some\",\n \"def __init__(self, numbers, all_groups):\\n\\n self.numbers = numbers\\n self.size = len(numbers)\\n self.partitions = list(self._partition_into_unique_pairs(all_groups))\\n self.calculations = list(self._perform_calculations())\",\n \"def employee_count(self, employee_count):\\n\\n self._employee_count = employee_count\",\n \"def _setintermediary_institution_56C(self, val):\\n self.swift_obj.IntermediaryInstitution_C = val\\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'\",\n \"def external_transit_networks(self, external_transit_networks):\\n\\n self._external_transit_networks = external_transit_networks\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def number_of_employees(self, number_of_employees: object):\\n\\n self._number_of_employees = number_of_employees\",\n \"def update_library_count(self, ebook_count):\\n\\t\\tself.library_count = ebook_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def vms_every(self, vms_every):\\n\\n self._vms_every = vms_every\",\n \"def set_article_count(cls, count):\\n return cls.db.set(\\\"article_count\\\", count)\",\n \"def external_use(self, external_use):\\n if self.local_vars_configuration.client_side_validation and external_use is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `external_use`, must not be `None`\\\") # noqa: E501\\n\\n self._external_use = external_use\",\n \"def _set_group_flag(self):\\n self.flag = np.zeros(len(self), dtype=np.uint8)\\n for i in range(len(self)):\\n img_info = self.img_infos[i]\\n if img_info['width'] / img_info['height'] > 1:\\n self.flag[i] = 1\",\n \"def _set_group_flag(self):\\n self.flag = np.zeros(len(self), dtype=np.uint8)\\n for i in range(len(self)):\\n img_info = self.img_infos[i]\\n if img_info['width'] / img_info['height'] > 1:\\n self.flag[i] = 1\",\n \"def set_autoscale_group_capacity(group, num_instances,\\n attributes=('min_size', 'max_size', 'desired_capacity')):\\n conn = boto.ec2.autoscale.connect_to_region(\\\"us-east-1\\\")\\n group = conn.get_all_groups(names=[group])[0]\\n for attrib in attributes:\\n setattr(group, attrib, num_instances)\\n if attributes:\\n group.update()\",\n \"def num_of_computing_processors(self, num_of_computing_processors):\\n if (self.local_vars_configuration.client_side_validation and\\n num_of_computing_processors is not None and num_of_computing_processors < -1): # noqa: E501\\n raise ValueError(\\\"Invalid value for `num_of_computing_processors`, must be a value greater than or equal to `-1`\\\") # noqa: E501\\n\\n self._num_of_computing_processors = num_of_computing_processors\",\n \"def cluster_num_in(self, cluster_num_in):\\n\\n self._cluster_num_in = cluster_num_in\",\n \"def externally_managed(self, externally_managed):\\n\\n self._externally_managed = externally_managed\",\n \"def load_all_groups(self):\\n for _, group in self.scopes.items():\\n group.update()\",\n \"def set_number_of_rows(self, number_of_rows):\\n self.set_value_into_input_field(self.number_of_rows_inputbox_locator, number_of_rows, True)\\n global bulk_add_number_of_rows\\n bulk_add_number_of_rows = int(number_of_rows)\",\n \"def set_num_images(self,num_images):\\n for roi in self.rois:\\n roi.set_num_images(num_images)\\n self.num_images = num_images\",\n \"def consistency_group_num_gte(self, consistency_group_num_gte):\\n\\n self._consistency_group_num_gte = consistency_group_num_gte\",\n \"def max_individuals(self) -> int:\\n return self.group_size.upper * self.groups_allowed\",\n \"def advertise_osd_count(count):\\n for relid in hookenv.relation_ids('mon'):\\n hookenv.relation_set(\\n relation_id=relid,\\n relation_settings={'bootstrapped-osds': count}\\n )\",\n \"def test_update_hyperflex_feature_limit_external(self):\\n pass\",\n \"def consistency_group(self, consistency_group):\\n\\n self._consistency_group = consistency_group\",\n \"def consistency_group(self, consistency_group):\\n\\n self._consistency_group = consistency_group\",\n \"def externalSubset(self, name, externalID, systemID):\\n pass\",\n \"def _ensure_local_neo4j_has_test_computergroup_data(neo4j_session):\\n groups = tests.data.jamf.computers.GROUPS\\n cartography.intel.jamf.computers.load_computer_groups(groups, neo4j_session, TEST_UPDATE_TAG)\",\n \"def getNumEnergyGroups(self):\\n return self.lib.numGroups\",\n \"def namespace_group_num_lte(self, namespace_group_num_lte):\\n\\n self._namespace_group_num_lte = namespace_group_num_lte\",\n \"def number_of_atoms(self, value):\\n self._number_of_atoms = value\",\n \"def setIterationCount(self, newIterationCount):\\n \\n pass\",\n \"def hosts_every(self, hosts_every):\\n\\n self._hosts_every = hosts_every\",\n \"def elf_image_num_in(self, elf_image_num_in):\\n\\n self._elf_image_num_in = elf_image_num_in\",\n \"def _set_adj_rib_out_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"adj-rib-out-count\\\", rest_name=\\\"adj-rib-out-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"adj_rib_out_count must be of a type compatible with int32\\\"\\\"\\\",\\n 'defined-type': \\\"int32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"adj-rib-out-count\\\", rest_name=\\\"adj-rib-out-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__adj_rib_out_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def increment_count(self):\\n self.image_count +=1\\n if self.image_count > self.max_count:\\n self.image_count = self.count_start # overflow\",\n \"def processor_count(self, processor_count):\\n\\n self._processor_count = processor_count\",\n \"def datacenters_every(self, datacenters_every):\\n\\n self._datacenters_every = datacenters_every\",\n \"def _setintermediary_institution_56A(self, val):\\n self.swift_obj.IntermediaryInstitution_A = val\\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'\",\n \"def set_top_unique_num(self, number):\\n self.top_unique_num = number\",\n \"def consistency_group_snapshots_every(self, consistency_group_snapshots_every):\\n\\n self._consistency_group_snapshots_every = consistency_group_snapshots_every\",\n \"def _add_gpu(self, gpu_count):\\n if not gpu_count or gpu_count == 0:\\n return\\n\\n logger.debug(\\n \\\"found resource request for {} GPUs. This will limit to n1 \\\"\\n \\\"instance types.\\\".format(gpu_count)\\n )\\n self.workflow.default_resources.parsed[\\\"nvidia_gpu\\\"] = gpu_count\\n self.workflow.default_resources.args.append(\\\"nvidia_gpu=%s\\\" % gpu_count)\\n self._machine_type_prefix = self._machine_type_prefix or \\\"\\\"\\n if not self._machine_type_prefix.startswith(\\\"n1\\\"):\\n self._machine_type_prefix = \\\"n1\\\"\",\n \"def set_pingroups(self, groups):\\n self.groups = groups[:]\",\n \"def test_ipam_vlan_groups_update(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.53656894","0.4734468","0.4715213","0.4630498","0.46285418","0.4532851","0.45295992","0.44841346","0.44807005","0.44654495","0.44654495","0.44654495","0.44536784","0.4435368","0.43804","0.42757764","0.42720485","0.42300162","0.42260072","0.4207612","0.41835812","0.4180668","0.41730314","0.41576615","0.41405568","0.41390336","0.41054726","0.40963188","0.40958533","0.4085947","0.40822178","0.40820873","0.4080242","0.4073996","0.40694532","0.40688607","0.40533367","0.4049416","0.4042545","0.4039089","0.40305823","0.39743543","0.3968372","0.39637983","0.3950578","0.39476657","0.39446563","0.39376575","0.39249805","0.3919536","0.39188984","0.39188984","0.39188984","0.39158174","0.39127326","0.39112443","0.39054576","0.39029676","0.38975945","0.38975945","0.38830605","0.3875456","0.38721016","0.38721016","0.38671654","0.38472962","0.382919","0.38264847","0.38264847","0.38236186","0.38219535","0.3817405","0.3814624","0.38110042","0.38027987","0.3799369","0.3790083","0.37827715","0.37737498","0.37736997","0.3772281","0.3772281","0.3771775","0.37630862","0.37609288","0.374163","0.3741258","0.37374318","0.37335637","0.3732094","0.37301597","0.37282133","0.37270102","0.37210196","0.37189856","0.37152272","0.3713494","0.3707772","0.3704236","0.37035304"],"string":"[\n \"0.53656894\",\n \"0.4734468\",\n \"0.4715213\",\n \"0.4630498\",\n \"0.46285418\",\n \"0.4532851\",\n \"0.45295992\",\n \"0.44841346\",\n \"0.44807005\",\n \"0.44654495\",\n \"0.44654495\",\n \"0.44654495\",\n \"0.44536784\",\n \"0.4435368\",\n \"0.43804\",\n \"0.42757764\",\n \"0.42720485\",\n \"0.42300162\",\n \"0.42260072\",\n \"0.4207612\",\n \"0.41835812\",\n \"0.4180668\",\n \"0.41730314\",\n \"0.41576615\",\n \"0.41405568\",\n \"0.41390336\",\n \"0.41054726\",\n \"0.40963188\",\n \"0.40958533\",\n \"0.4085947\",\n \"0.40822178\",\n \"0.40820873\",\n \"0.4080242\",\n \"0.4073996\",\n \"0.40694532\",\n \"0.40688607\",\n \"0.40533367\",\n \"0.4049416\",\n \"0.4042545\",\n \"0.4039089\",\n \"0.40305823\",\n \"0.39743543\",\n \"0.3968372\",\n \"0.39637983\",\n \"0.3950578\",\n \"0.39476657\",\n \"0.39446563\",\n \"0.39376575\",\n \"0.39249805\",\n \"0.3919536\",\n \"0.39188984\",\n \"0.39188984\",\n \"0.39188984\",\n \"0.39158174\",\n \"0.39127326\",\n \"0.39112443\",\n \"0.39054576\",\n \"0.39029676\",\n \"0.38975945\",\n \"0.38975945\",\n \"0.38830605\",\n \"0.3875456\",\n \"0.38721016\",\n \"0.38721016\",\n \"0.38671654\",\n \"0.38472962\",\n \"0.382919\",\n \"0.38264847\",\n \"0.38264847\",\n \"0.38236186\",\n \"0.38219535\",\n \"0.3817405\",\n \"0.3814624\",\n \"0.38110042\",\n \"0.38027987\",\n \"0.3799369\",\n \"0.3790083\",\n \"0.37827715\",\n \"0.37737498\",\n \"0.37736997\",\n \"0.3772281\",\n \"0.3772281\",\n \"0.3771775\",\n \"0.37630862\",\n \"0.37609288\",\n \"0.374163\",\n \"0.3741258\",\n \"0.37374318\",\n \"0.37335637\",\n \"0.3732094\",\n \"0.37301597\",\n \"0.37282133\",\n \"0.37270102\",\n \"0.37210196\",\n \"0.37189856\",\n \"0.37152272\",\n \"0.3713494\",\n \"0.3707772\",\n \"0.3704236\",\n \"0.37035304\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75556606"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19682,"cells":{"query":{"kind":"string","value":"Sets the hyperv_host_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count","def vcpu_set(vm_hostname, count, offline=False):\n with ExitStack() as es:\n vm = es.enter_context(_get_vm(vm_hostname))\n\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\n raise NotImplementedError(\n 'This operation is not yet supported for {}'.format(\n vm.dataset_obj['datacenter_type'])\n )\n\n _check_defined(vm)\n\n if offline and not vm.is_running():\n log.info(\n '\"{}\" is already powered off, ignoring --offline.'.format(\n vm.fqdn)\n )\n offline = False\n\n if count == vm.dataset_obj['num_cpu']:\n raise Warning('CPU count is the same.')\n\n if offline:\n vm.shutdown()\n vm.set_num_cpu(count)\n if offline:\n vm.start()","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vcpus(self, vcpus):\n self._vcpus = vcpus","def host_num(self, host_num):\n\n self._host_num = host_num","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def host_count(self) -> list:\n return self.__host_count","def host_num_in(self, host_num_in):\n\n self._host_num_in = host_num_in","def set_hosts(self, hypervisor_per_cluster=False):\n\n self.conf['hosts'] = set()\n\n host_patterns, host_others = self._sift_patterns(\n self.conf.get('hosts_list')\n )\n datacenter_patterns = self.conf.get('datacenter', [])\n cluster_patterns = self.conf.get('cluster', [])\n\n if host_patterns:\n self.conf['host_pattern'] = host_patterns\n\n self.conf['hosts'] = self._get_hypervisors_from_api()\n # Filter all host specified with -H\n host_filtered = set()\n if host_others:\n host_filtered = set([\n (dc, cl, h, is_spm, is_up)\n for dc, cl, h, is_spm, is_up in self.conf['hosts']\n if h in host_others\n ])\n not_found = host_others - set(host[2] for host in host_filtered)\n if not_found != set():\n # try to resolve to ip specified hosts\n for fqdn in set(not_found):\n try:\n ipaddr = socket.gethostbyname(fqdn)\n logging.debug('%s --> %s' % (fqdn, ipaddr))\n for (dc, cl, h, is_spm, is_up) in self.conf['hosts']:\n if h == ipaddr:\n host_filtered.add((dc, cl, h, is_spm, is_up))\n not_found.remove(fqdn)\n except socket.error:\n logging.warning(\n _('Cannot resolve {host}').format(\n host=fqdn,\n )\n )\n if not_found != set():\n # try to resolve to ip known hypervisors\n for (dc, cl, h, is_spm, is_up) in self.conf['hosts']:\n try:\n ipaddr = socket.gethostbyname(h)\n logging.debug('%s --> %s' % (h, ipaddr))\n if ipaddr in host_others:\n host_filtered.add((dc, cl, h, is_spm, is_up))\n not_found.remove(ipaddr)\n except socket.error:\n logging.warning(\n _('Cannot resolve {host}').format(\n host=h,\n )\n )\n if not_found != set():\n logging.error(\n _(\n 'The following host are not listed as hypervisors: '\n '{not_listed}. Known hypervisors can be listed using '\n 'the list command'\n ).format(\n not_listed=','.join(not_found)\n )\n )\n sys.exit(ExitCodes.CRITICAL)\n\n orig_hosts = self.conf['hosts'].copy()\n\n if host_patterns:\n for pattern in host_patterns:\n host_filtered |= self._filter_hosts('host', pattern)\n if host_patterns or host_others:\n self.conf['hosts'] &= host_filtered\n\n # Intersect with hosts belonging to the data centers specified with -d\n if datacenter_patterns:\n datacenter_filtered = set()\n for pattern in datacenter_patterns:\n datacenter_filtered |= self._filter_hosts(\n 'datacenter', pattern\n )\n self.conf['hosts'] &= datacenter_filtered\n\n # Intersect with hosts belonging to the clusters specified with -c\n if cluster_patterns:\n # remove all hosts that don't match the patterns\n cluster_filtered = set()\n for pattern in cluster_patterns:\n cluster_filtered |= self._filter_hosts('cluster', pattern)\n self.conf['hosts'] &= cluster_filtered\n\n # If hypervisor_per_cluster is set, collect data only from a single\n # hypervisor per cluster; if the Spm found, collect data from it.\n if hypervisor_per_cluster:\n selected_hosts = dict()\n for dc, cluster, host, is_spm, is_up in self.conf['hosts']:\n # Always add the SPM\n if is_spm:\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\n is_up)\n # For the given cluster, if no host added yet, add it\n elif cluster.name not in selected_hosts:\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\n is_up)\n # If a host is up and the SPM isn't added yet, add this host\n elif is_up and not selected_hosts[cluster.name][3]:\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\n is_up)\n self.conf['hosts'] &= set(selected_hosts.values())\n\n # warn users if they are going to collect logs from all hosts.\n if orig_hosts and self.conf['hosts'] == orig_hosts:\n logging.warning(\n _(\n 'This ovirt-log-collector call will collect logs from '\n 'all available hosts. This may take long time, '\n 'depending on the size of your deployment'\n )\n )\n\n return bool(self.conf.get('hosts'))","def hosts_every(self, hosts_every):\n\n self._hosts_every = hosts_every","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def host_status_control(self, host_status_control):\n\n self._host_status_control = host_status_control","def init_cpu_counts(host):\n host.cpu_functions = {}\n host.cpu_lists = {}\n for s in range(0, len(host.nodes)):\n host.cpu_functions[s] = {}\n for f in CORE_FUNCTIONS:\n host.cpu_functions[s][f] = []\n host.cpu_lists[s] = []","def hosts(self, value):\n if not isinstance(value, NodeSet):\n raise TypeError(\"Invalid fio host NodeSet: {} ({})\".format(value, type(value)))\n self._hosts = value.copy()","def number_of_nodes(self, number_of_nodes):\n\n self._number_of_nodes = number_of_nodes","def restructure_host_cpu_data(host):\n init_cpu_counts(host)\n host.sockets = len(host.nodes or [])\n host.hyperthreading = False\n host.physical_cores = 0\n if not host.cpus:\n return\n host.cpu_model = host.cpus[0].cpu_model\n cpu_list = sorted(host.cpus, key=_sort_by_coreid)\n for cpu in cpu_list:\n inode = pecan.request.dbapi.inode_get(inode_id=cpu.forinodeid)\n cpu.numa_node = inode.numa_node\n if cpu.thread == 0:\n host.physical_cores += 1\n elif cpu.thread > 0:\n host.hyperthreading = True\n function = cpu.allocated_function or get_default_function(host)\n host.cpu_functions[cpu.numa_node][function].append(int(cpu.cpu))\n host.cpu_lists[cpu.numa_node].append(int(cpu.cpu))","def advertise_osd_count(count):\n for relid in hookenv.relation_ids('mon'):\n hookenv.relation_set(\n relation_id=relid,\n relation_settings={'bootstrapped-osds': count}\n )","def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count","def vms_every(self, vms_every):\n\n self._vms_every = vms_every","def update_core_allocations(host, cpu_counts):\n # Remove any previous assignments\n for s in range(0, len(host.nodes)):\n for f in CORE_FUNCTIONS:\n host.cpu_functions[s][f] = []\n # Set new assignments\n for s in range(0, len(host.nodes)):\n cpu_list = host.cpu_lists[s] if s in host.cpu_lists else []\n # Reserve for the platform first\n for i in range(0, cpu_counts[s][constants.PLATFORM_FUNCTION]):\n host.cpu_functions[s][constants.PLATFORM_FUNCTION].append(\n cpu_list.pop(0))\n # Reserve for the vswitch next\n for i in range(0, cpu_counts[s][constants.VSWITCH_FUNCTION]):\n host.cpu_functions[s][constants.VSWITCH_FUNCTION].append(\n cpu_list.pop(0))\n # Reserve for the shared next\n for i in range(0, cpu_counts[s][constants.SHARED_FUNCTION]):\n host.cpu_functions[s][constants.SHARED_FUNCTION].append(\n cpu_list.pop(0))\n for i in range(0, cpu_counts[s][constants.ISOLATED_FUNCTION]):\n host.cpu_functions[s][constants.ISOLATED_FUNCTION].append(\n cpu_list.pop(0))\n # Assign the remaining cpus to the default function for this host\n host.cpu_functions[s][get_default_function(host)] += cpu_list\n return","def host_num_lte(self, host_num_lte):\n\n self._host_num_lte = host_num_lte","def host_num_gt(self, host_num_gt):\n\n self._host_num_gt = host_num_gt","def device_count(self, device_count):\n\n self._device_count = device_count","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def add_nodes(self, count=1):\n self.log.info('Adding %d nodes' % count)\n new_nodes = []\n Node.flavor = env_vars['client_flavor']\n for i in range(count):\n #check if cluster did not previously exist\n if i == 0 and len(self.all_nodes) == 0:\n # give a floating IPv4 to the first node only\n new_guy = Node(self.cluster_name, '', len(self.all_nodes)+1, create=True, IPv4=True)\n else:\n new_guy = Node(self.cluster_name, node_type=\"\", number=len(self.all_nodes)+1, create=True)\n self.all_nodes.append(new_guy)\n new_nodes.append(new_guy)\n self.save_cluster()\n for n in new_nodes:\n n.wait_ready()\n #inject host files to everybody\n n.inject_hostnames(self.get_hosts(private=True), delete=self.cluster_name)\n n.bootstrap()\n self.log.info(\"Node %s is live \" % new_guy.name)\n #inform all\n self.inject_hosts_files()","def node_count(self, node_count):\n\n self._node_count = node_count","def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def config_ha(self, vlan_tag, vip_address):\n\n if len(self.hostnames) == 1:\n LOG.debug(\"Only one machine, doesn't need to configure HA\")\n return True\n\n interface_name = self.in_interface\n if vlan_tag:\n interface_name = \"vlan.\" + vlan_tag\n\n cmd_apv_config_virtual_iface = ADCDevice.cluster_config_virtual_interface(interface_name)\n cmd_apv_config_virtual_vip = ADCDevice.cluster_config_vip(interface_name, vip_address)\n cmd_apv_cluster_enable = ADCDevice.cluster_enable(interface_name)\n\n priority = 1\n for base_rest_url in self.base_rest_urls:\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_iface)\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_vip)\n\n priority += 10\n cmd_apv_config_virtual_prior = ADCDevice.cluster_config_priority(interface_name, priority)\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_prior)\n\n self.run_cli_extend(base_rest_url, cmd_apv_cluster_enable)","def hypervisor_version(self, hypervisor_version):\n\n self._hypervisor_version = hypervisor_version","def create_dvs(self, host_vmnics, dvs_name, num_uplinks=4):\n\n for network_obj in GetDatacenter(self.datacenter_obj).network_resources():\n if network_obj.name == dvs_name:\n self.logger.info(\"DVS {0} already exists\".format(dvs_name))\n return\n host_cfgs = []\n for host_obj, vmnics in host_vmnics.items():\n uplinks = []\n if host_obj.runtime.connectionState != \"connected\":\n self.logger.error(\n \"Host {0} is not connected. Skipped\".format(host_obj.name)\n )\n continue\n host_cfg = vim.dvs.HostMember.ConfigSpec()\n host_cfg.operation = vim.ConfigSpecOperation.add\n host_cfg.host = host_obj\n host_cfg.backing = vim.dvs.HostMember.PnicBacking()\n for pnic in GetHost(host_obj).pnics():\n for vmnic in vmnics:\n if pnic.device == vmnic:\n pnic_spec = vim.dvs.HostMember.PnicSpec()\n pnic_spec.pnicDevice = pnic.device\n uplinks.append(pnic_spec)\n host_cfg.backing.pnicSpec = uplinks\n host_cfgs.append(host_cfg)\n uplink_port_policy = vim.DistributedVirtualSwitch.NameArrayUplinkPortPolicy()\n uplnk_port_order = []\n for i in range(num_uplinks):\n name = \"uplink%d\" % (i + 1)\n uplink_port_policy.uplinkPortName.append(name)\n uplnk_port_order.append(name)\n string_policy = vim.StringPolicy()\n string_policy.value = \"failover_explicit\"\n uplink_port_order_policy = (\n vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortOrderPolicy()\n )\n # activeUplinkPort: list of active uplink ports used for load balancing\n uplink_port_order_policy.activeUplinkPort = uplnk_port_order\n team = vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortTeamingPolicy()\n team.policy = string_policy\n team.uplinkPortOrder = uplink_port_order_policy\n port_config_policy = (\n vim.dvs.VmwareDistributedVirtualSwitch.VmwarePortConfigPolicy()\n )\n port_config_policy.uplinkTeamingPolicy = team\n dvs_config_spec = vim.dvs.VmwareDistributedVirtualSwitch.ConfigSpec()\n dvs_config_spec.name = dvs_name\n dvs_config_spec.host = host_cfgs\n dvs_config_spec.defaultPortConfig = port_config_policy\n dvs_config_spec.lacpApiVersion = (\n vim.dvs.VmwareDistributedVirtualSwitch.LacpApiVersion.multipleLag\n )\n dvs_config_spec.numStandalonePorts = num_uplinks\n dvs_create_spec = vim.DistributedVirtualSwitch.CreateSpec(\n configSpec=dvs_config_spec\n )\n task = self.datacenter_obj.networkFolder.CreateDVS_Task(dvs_create_spec)\n return task","def host_version(self, host_version):\n if host_version is None:\n raise ValueError(\"Invalid value for `host_version`, must not be `None`\") # noqa: E501\n\n self._host_version = host_version","def build_lhosts(self , sws , lhost_count):\n host_count = 0\n for sw in sws:\n for i in range(lhost_count):\n host_id = host_count + 1\n host = self.addHost('h%s' % host_id)\n self.addLink(sw, host)\n host_count += 1\n return host_count","def vm_volumes_every(self, vm_volumes_every):\n\n self._vm_volumes_every = vm_volumes_every","def update_cluster_hosts(self, hosts):\n self._hosts = hosts\n self._collect_hosts_d = True","def test_vm_cpu_limitation_after_cpu_hot_plug(self):\n host = ll_vms.get_vm_host(vm_name=conf.QOS_VMS[0])\n host_cpu = ll_hosts.get_host_processing_units_number(host_name=host)\n testflow.step(\"Hotplug CPU to VM %s\", conf.QOS_VMS[0])\n vm_cpu_sockets = min(8, host_cpu)\n assert ll_vms.updateVm(\n positive=True, vm=conf.QOS_VMS[0], cpu_socket=vm_cpu_sockets\n )\n expected_dict = self.calculate_expected_values(\n load_dict=self.load_dict\n )\n assert sla_helpers.load_vm_and_check_the_load(\n load_dict=self.load_dict, expected_values=expected_dict\n )","def setCount(self, num):\n self.count=num","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","def check_core_allocations(host, cpu_counts):\n total_platform_cores = 0\n total_vswitch_cores = 0\n total_shared_cores = 0\n total_isolated_cores = 0\n for s in range(0, len(host.nodes)):\n available_cores = len(host.cpu_lists[s])\n platform_cores = cpu_counts[s][constants.PLATFORM_FUNCTION]\n vswitch_cores = cpu_counts[s][constants.VSWITCH_FUNCTION]\n shared_cores = cpu_counts[s][constants.SHARED_FUNCTION]\n isolated_cores = cpu_counts[s][constants.ISOLATED_FUNCTION]\n requested_cores = \\\n platform_cores + vswitch_cores + shared_cores + isolated_cores\n if requested_cores > available_cores:\n raise wsme.exc.ClientSideError(\n \"More total logical cores requested than present on Processor \"\n \"%s (%s cores).\" % (s, available_cores))\n total_platform_cores += platform_cores\n total_vswitch_cores += vswitch_cores\n total_shared_cores += shared_cores\n total_isolated_cores += isolated_cores\n\n # Validate Platform cores\n if ((constants.CONTROLLER in host.subfunctions) and\n (constants.WORKER in host.subfunctions)):\n if total_platform_cores < 2:\n raise wsme.exc.ClientSideError(\"%s must have at least two cores.\" %\n constants.PLATFORM_FUNCTION)\n elif total_platform_cores == 0:\n raise wsme.exc.ClientSideError(\"%s must have at least one core.\" %\n constants.PLATFORM_FUNCTION)\n for s in range(1, len(host.nodes)):\n if cpu_counts[s][constants.PLATFORM_FUNCTION] > 0:\n raise wsme.exc.ClientSideError(\n \"%s cores can only be allocated on Processor 0\" %\n constants.PLATFORM_FUNCTION)\n\n # Validate shared cores\n for s in range(0, len(host.nodes)):\n shared_cores = cpu_counts[s][constants.SHARED_FUNCTION]\n if host.hyperthreading:\n shared_cores /= 2\n if shared_cores > 1:\n raise wsme.exc.ClientSideError(\n '%s cores are limited to 1 per processor.'\n % constants.SHARED_FUNCTION)\n\n # Validate vswitch cores\n if total_vswitch_cores != 0:\n vswitch_type = cutils.get_vswitch_type(pecan.request.dbapi)\n if constants.VSWITCH_TYPE_NONE == vswitch_type:\n raise wsme.exc.ClientSideError(\n ('vSwitch cpus can only be used with a vswitch_type '\n 'specified.'))\n\n vswitch_physical_cores = total_vswitch_cores\n if host.hyperthreading:\n vswitch_physical_cores /= 2\n if vswitch_physical_cores > VSWITCH_MAX_CORES:\n raise wsme.exc.ClientSideError(\n \"The %s function can only be assigned up to %s cores.\" %\n (constants.VSWITCH_FUNCTION.lower(), VSWITCH_MAX_CORES))\n\n # Validate Isolated cores\n # We can allocate platform cores on numa 0, otherwise all isolated\n # cores must in a contiguous block after the platform cores.\n if total_isolated_cores > 0:\n if total_vswitch_cores != 0 or total_shared_cores != 0:\n raise wsme.exc.ClientSideError(\n \"%s cores can only be configured with %s and %s core types.\" %\n (constants.ISOLATED_FUNCTION, constants.PLATFORM_FUNCTION,\n constants.APPLICATION_FUNCTION))\n has_application_cpus = False\n for s in range(0, len(host.nodes)):\n numa_counts = cpu_counts[s]\n isolated_cores_requested = \\\n numa_counts[constants.ISOLATED_FUNCTION]\n if has_application_cpus and isolated_cores_requested:\n raise wsme.exc.ClientSideError(\n \"%s and %s cpus must be contiguous\" %\n (constants.PLATFORM_FUNCTION, constants.ISOLATED_FUNCTION))\n platform_cores_requested = \\\n numa_counts[constants.PLATFORM_FUNCTION]\n available_cores = len(host.cpu_lists[s])\n\n if platform_cores_requested + isolated_cores_requested \\\n != available_cores:\n has_application_cpus = True\n\n reserved_for_applications = len(host.cpus) - total_platform_cores - \\\n total_vswitch_cores\n if reserved_for_applications <= 0:\n raise wsme.exc.ClientSideError(\n \"There must be at least one unused core for %s.\" %\n constants.APPLICATION_FUNCTION)","def set_hypercubes_classes(self):\n print(\"Setting the BaseGrid hypercubes' classes.\")\n list_of_all_hc = list(self.hypercubes.flatten())\n print(\"Number of hypercubes: \" + str(len(list_of_all_hc)))\n for hypercube in list_of_all_hc:\n hypercube.set_hypercube_class()\n if self.child_grid:\n self.child_grid.set_hypercubes_classes()","def set_hosts(self, host_list: t.List[str]) -> None:\n if isinstance(host_list, str):\n host_list = [host_list.strip()]\n if not isinstance(host_list, list):\n raise TypeError(\"host_list argument must be a list of strings\")\n if not all(isinstance(host, str) for host in host_list):\n raise TypeError(\"host_list argument must be list of strings\")\n # TODO check length\n if self.batch:\n if hasattr(self, \"batch_settings\") and self.batch_settings:\n self.batch_settings.set_hostlist(host_list)\n\n if self.launcher == \"lsf\":\n for db in self.dbnodes:\n db.set_hosts(host_list)\n else:\n for host, db in zip(host_list, self.dbnodes):\n if isinstance(db.run_settings, AprunSettings):\n if not self.batch:\n db.run_settings.set_hostlist([host])\n else:\n db.run_settings.set_hostlist([host])\n\n if db.is_mpmd and hasattr(db.run_settings, \"mpmd\"):\n for i, mpmd_runsettings in enumerate(db.run_settings.mpmd):\n mpmd_runsettings.set_hostlist(host_list[i + 1])","def vsvrcount(self) :\n\t\ttry :\n\t\t\treturn self._vsvrcount\n\t\texcept Exception as e:\n\t\t\traise e","def resize(self, thread_count):\n self._thread_count = thread_count\n self._semaphore.resize(thread_count + 1)","def all_hosts(self):\n ...","def set_share_all_host_disks(self, bShareAllHostDisks):\n\t\tcall_sdk_function('PrlVmCfg_SetShareAllHostDisks', self.handle, bShareAllHostDisks)","def update_cpu(self, vm):\n try:\n cpu_spec = self.client.get_cpu(vm.backend_id)\n if (\n cpu_spec['cores_per_socket'] != vm.cores_per_socket\n or cpu_spec['count'] != vm.cores\n ):\n self.client.update_cpu(\n vm.backend_id,\n {\n 'cores_per_socket': vm.cores_per_socket,\n 'count': vm.cores,\n },\n )\n except VMwareError as e:\n raise VMwareBackendError(e)","def init_host(self, host):\n if self._drv_nodes is None:\n self.set_nodes([nova_conf.host])\n args = (drv_conf.tenant_id, drv_conf.client_id, drv_conf.client_secret,\n drv_conf.subscription_id)\n\n self.compute_client = utils.get_compute_client(*args)\n self.resource_client = utils.get_resource_client(*args)\n self.network_client = utils.get_network_client(*args)\n is_resource_created = utils.check_resource_existence(\n self.resource_client, drv_conf.resource_group)\n if not is_resource_created:\n utils.create_resource_group(\n self.resource_client, drv_conf.resource_group, drv_conf.region)\n\n self.flavor_info.update(\n utils.get_vm_sizes(self.compute_client, drv_conf.region))\n LOG.info(\"%s driver init with %s project, %s region\" %\n (self.name, drv_conf.tenant_id, drv_conf.region))","def set_all(self, host_names, ip_address):\n for host_name in host_names:\n self.set_one(host_name, ip_address)","def set_num_virtual_stages(self, num_virtual_stages: int) -> None:\n self.num_virtual_stages = num_virtual_stages","def host_num_lt(self, host_num_lt):\n\n self._host_num_lt = host_num_lt","def set_autoscaled_instances(self, instance_count: int) -> None:\n set_instances_for_marathon_service(\n service=self.service,\n instance=self.instance,\n instance_count=instance_count,\n )","def create_hosting_device_resources(self, context, complementary_id,\n tenant_id, mgmt_nw_id,\n mgmt_sec_grp_id, max_hosted):\n pass","def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\n\n self._count_update_pool_size = count_update_pool_size","def AddCpuCountFlag(parser):\n help_text = \"\"\"\\\n Whole number value indicating how many vCPUs the machine should\n contain. Each vCPU count corresponds to a N2 high-mem machine:\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\n \"\"\"\n parser.add_argument(\n '--cpu-count',\n help=help_text,\n type=int,\n choices=[2, 4, 8, 16, 32, 64],\n required=True)","def byte_count(self, byte_count):\n\n self._byte_count = byte_count","def compute_node_count(self, compute_node_count):\n\n self._compute_node_count = compute_node_count","def set_photon_counting_divs(self, n, thres):\n thres = ct.c_long(thres)\n self.lib.SetPhotonCountingDivisions(ct.c_ulong(n), ct.pointer(thres))","def _destroy_vapv(self, hostnames, lb):\n for hostname in hostnames:\n try:\n self.openstack_connector.destroy_vapv(hostname, lb)\n LOG.debug(\"\\nvAPV {} destroyed\".format(hostname))\n except Exception as e:\n LOG.error(e)","def user_count(self, user_count):\n\n self._user_count = user_count","def user_count(self, user_count):\n\n self._user_count = user_count","def update_host(self, conf, tenant_id, network_id, host_id, body):\n\t\tpass","def virtual_machines(self, virtual_machines):\n\n self._virtual_machines = virtual_machines","def init_host(self, host):\n LOG.debug(\"init_host\")\n\n self._cpc = self._client.cpcs.find(**{\n \"object-id\": CONF.dpm.cpc_object_id})\n LOG.debug(\"Matching hypervisor found %(cpcsubset_name)s for object-id \"\n \"%(cpcid)s and CPC %(cpcname)s\" %\n {'cpcsubset_name': CONF.host,\n 'cpcid': CONF.dpm.cpc_object_id,\n 'cpcname': self._cpc.properties['name']})\n\n utils.valide_host_conf(self._cpc)\n self._host = Host.Host(self._cpc, self._client)","def free_host(self, context):\n cluster_config = self._session._call_method(\n vutil, \"get_object_property\", self._cluster, \"configurationEx\")\n\n # get the group\n group = self._get_group(cluster_config)\n\n failover_hosts = []\n policy = cluster_config.dasConfig.admissionControlPolicy\n if policy and hasattr(policy, 'failoverHosts'):\n failover_hosts = set(vutil.get_moref_value(h)\n for h in policy.failoverHosts)\n\n if group is None or not getattr(group, 'host', None):\n # find a host to free\n\n # retrieve all hosts of the cluster\n host_objs = {vutil.get_moref_value(h): h\n for h in self._get_hosts_in_cluster(self._cluster)}\n vms_per_host = {h: [] for h in host_objs}\n\n # get all the vms in a cluster, because we need to find a host\n # without big VMs.\n props = ['config.hardware.memoryMB', 'runtime.host',\n 'runtime.powerState',\n 'summary.quickStats.hostMemoryUsage']\n cluster_vms = self._vmops._list_instances_in_cluster(props)\n\n for vm_uuid, vm_props in cluster_vms:\n props = (vm_props.get('config.hardware.memoryMB', 0),\n vm_props.get('runtime.powerState', 'poweredOff'),\n vm_props.get('summary.quickStats.hostMemoryUsage', 0))\n # every host_obj is differnt, even though the value, which\n # really matters, is the same\n host_obj = vm_props.get('runtime.host')\n if not host_obj:\n continue\n\n host = vutil.get_moref_value(host_obj)\n vms_per_host.setdefault(host, []). \\\n append(props)\n\n # filter for hosts without big VMs\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\n if all(mem < CONF.largevm_mb\n for mem, state, used_mem in vms)}\n\n if not vms_per_host:\n LOG.warning('No suitable host found for freeing a host for '\n 'spawning (bigvm filter).')\n return FREE_HOST_STATE_ERROR\n\n # filter hosts which are failover hosts\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\n if h not in failover_hosts}\n\n if not vms_per_host:\n LOG.warning('No suitable host found for freeing a host for '\n 'spawning (failover host filter).')\n return FREE_HOST_STATE_ERROR\n\n # filter hosts which are in a wrong state\n result = self._session._call_method(vim_util,\n \"get_properties_for_a_collection_of_objects\",\n \"HostSystem\",\n [host_objs[h] for h in vms_per_host],\n ['summary.runtime'])\n host_states = {}\n with vutil.WithRetrieval(self._session.vim, result) as objects:\n for obj in objects:\n host_props = propset_dict(obj.propSet)\n runtime_summary = host_props['summary.runtime']\n moref_value = vutil.get_moref_value(obj.obj)\n host_states[moref_value] = (\n runtime_summary.inMaintenanceMode is False and\n runtime_summary.connectionState == \"connected\")\n\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\n if host_states[h]}\n\n if not vms_per_host:\n LOG.warning('No suitable host found for freeing a host for '\n 'spawning (host state filter).')\n return FREE_HOST_STATE_ERROR\n\n mem_per_host = {h: sum(used_mem for mem, state, used_mem in vms)\n for h, vms in vms_per_host.items()}\n\n # take the one with least memory used\n host, _ = sorted(mem_per_host.items(), key=itemgetter(1))[0]\n host_ref = host_objs[host]\n\n client_factory = self._session.vim.client.factory\n config_spec = client_factory.create('ns0:ClusterConfigSpecEx')\n\n # we need to either create the group from scratch or at least add a\n # host to it\n operation = 'add' if group is None else 'edit'\n group = cluster_util.create_host_group(client_factory,\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name,\n [host_ref], group)\n group_spec = cluster_util.create_group_spec(client_factory,\n group, operation)\n config_spec.groupSpec = [group_spec]\n\n # create the appropriate rule for VMs to leave the host\n rule_name = '{}_anti-affinity'.format(\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name)\n rule = cluster_util._get_rule(cluster_config, rule_name)\n rule_spec = cluster_util._create_cluster_group_rules_spec(\n client_factory, rule_name,\n CONF.vmware.special_spawning_vm_group,\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name,\n 'anti-affinity', rule)\n config_spec.rulesSpec = [rule_spec]\n\n cluster_util.reconfigure_cluster(self._session, self._cluster,\n config_spec)\n else:\n if len(group.host) > 1:\n LOG.warning('Found more than 1 host in spawning hostgroup.')\n host_ref = group.host[0]\n\n # check if the host is still suitable\n if vutil.get_moref_value(host_ref) in failover_hosts:\n LOG.warning('Host destined for spawning became a failover '\n 'host.')\n return FREE_HOST_STATE_ERROR\n\n runtime_summary = self._session._call_method(\n vutil, \"get_object_property\", host_ref, 'summary.runtime')\n if (runtime_summary.inMaintenanceMode is True or\n runtime_summary.connectionState != \"connected\"):\n LOG.warning('Host destined for spawning was set to '\n 'maintenance or became disconnected.')\n return FREE_HOST_STATE_ERROR\n\n # filter the VMs on the host, so we don't look at the non-movable\n # DRS-created and -owned VMs\n vcls_identifier = (constants.VCLS_EXTENSION_KEY,\n constants.VCLS_EXTENSION_TYPE_AGENT)\n vms_on_host = [\n (u, state, ref)\n for u, h, state, m, ref in self._get_vms_on_host(host_ref)\n if not m or (m.extensionKey, m.type) != vcls_identifier]\n\n # ignore partiallyAutomated VMs. They should not be big VMs as we chose\n # a host appropriately, so they should be large VMs, which we tolerate\n # next to a big VM as they get moved by the nanny\n drs_overrides = cluster_util.fetch_cluster_drs_vm_overrides(\n self._session, cluster_config=cluster_config)\n wanted_drs_override_behaviors = (\n constants.DRS_BEHAVIOR_PARTIALLY_AUTOMATED,)\n vms_on_host = [\n (u, state) for u, state, ref in vms_on_host\n if drs_overrides.get(ref) not in wanted_drs_override_behaviors]\n\n # check if there are running VMs on that host\n vms_on_host = [u for u, state in vms_on_host\n if state != 'poweredOff']\n if vms_on_host:\n # check if DRS is enabled, so freeing up can work\n if cluster_config.drsConfig.defaultVmBehavior != \\\n constants.DRS_BEHAVIOR_FULLY_AUTOMATED:\n LOG.error('DRS set to %(actual)s, expected %(expected)s.',\n {'actual': cluster_config.drsConfig.defaultVmBehavior,\n 'expected': constants.DRS_BEHAVIOR_FULLY_AUTOMATED})\n return FREE_HOST_STATE_ERROR\n\n LOG.debug('Freeing up %(host)s for spawning in progress.',\n {'host': vutil.get_moref_value(host_ref)})\n return FREE_HOST_STATE_STARTED\n\n LOG.info('Done freeing up %(host)s for spawning.',\n {'host': vutil.get_moref_value(host_ref)})\n return FREE_HOST_STATE_DONE","def update_host_ovs(self, context):\n LOG.info(_('Updating Open vSwitch host data...'))\n LOG.debug(\"Current DOM: %s\" % self.current_dom.to_dict())\n LOG.debug(\"Requested DOM: %s\" % self.desired_dom.to_dict())\n\n builder = mob.MicroOperationBuilder(context,\n self.current_dom,\n self.desired_dom,\n self.rollback)\n\n mo_list = builder.get_micro_ops_for_update()\n\n # run validation\n return self._run_micro_op_list(mo_list)","def hosts(self, hosts):\n self._hosts = hosts","def setVoxelSize(self, vxs):\n\t\tself.voxelsize = vxs\n\t\ta, b, c = vxs\n\t\tself.spacing = [1, b / a, c / a]","def heal_and_optimize(self):\n self._refresh_bridge_mappings_to_neutron()\n\n # Create ProvisionRequests for all VNICs on all (non-management client)\n # partitions...\n lpar_vnic_map = utils.list_vifs(self.adapter, self.vif_wrapper_class)\n prov_reqs = preq.ProvisionRequest.for_wrappers(self, lpar_vnic_map,\n preq.PLUG)\n # ...and mark them 'up' in neutron.\n self.provision_devices(prov_reqs)","def init_host(self, host):\n self._precreate_network()\n LOG.info(_LI(\"Create/Update Ntwork and Subnet, Done.\"))","def max_cpus_per_machine(self, max_cpus_per_machine):\n\n self._max_cpus_per_machine = max_cpus_per_machine","def vlans_every(self, vlans_every):\n\n self._vlans_every = vlans_every","def processor_count(self, processor_count):\n\n self._processor_count = processor_count","def set_hyperparameters(self, hyperparameters):\n self._hyperparameters.update(hyperparameters)\n\n if self._class:\n LOGGER.debug('Creating a new primitive instance for %s', self.name)\n self.instance = self.primitive(**self.get_hyperparameters())","def fill_host(self, data):\n check_input_params(data, self.HOST)\n self.host = data[self.HOST]","def fill_host(self, data):\n check_input_params(data, self.HOST)\n self.host = data[self.HOST]","def _set_pg_count_storage_parameters(cls, data, nodes):\n osd_num = 0\n osd_nodes = [node for node in nodes\n if 'ceph-osd' in node.all_roles]\n\n for node in osd_nodes:\n for disk in cls.get_node_volumes(node):\n for part in disk.get('volumes', []):\n if part.get('name') == 'ceph' and part.get('size', 0) > 0:\n osd_num += 1\n\n for node in data:\n storage_attrs = node['storage']\n\n pg_counts = get_pool_pg_count(\n osd_num=osd_num,\n pool_sz=int(storage_attrs['osd_pool_size']),\n ceph_version='firefly',\n volumes_ceph=storage_attrs['volumes_ceph'],\n objects_ceph=storage_attrs['objects_ceph'],\n ephemeral_ceph=storage_attrs['ephemeral_ceph'],\n images_ceph=storage_attrs['images_ceph'],\n emulate_pre_7_0=False)\n\n # Log {pool_name: pg_count} mapping\n pg_str = \", \".join(map(\"{0[0]}={0[1]}\".format, pg_counts.items()))\n logger.debug(\"Ceph: PG values {%s}\", pg_str)\n\n storage_attrs['pg_num'] = pg_counts['default_pg_num']\n storage_attrs['per_pool_pg_nums'] = pg_counts","def test_set_hosts_storage_backing_min(instance_backing, number_of_hosts):\n LOG.tc_step(\"Determine the hosts to configure\")\n hosts = host_helper.get_up_hypervisors()\n hosts_len = len(hosts)\n host_num_mapping = {\n 'all': hosts_len,\n 'two': 2,\n 'one': 1\n }\n number_of_hosts = host_num_mapping[number_of_hosts]\n\n hosts_with_backing = \\\n host_helper.get_hosts_in_storage_backing(instance_backing)\n if len(hosts_with_backing) >= number_of_hosts:\n LOG.info(\"Already have {} hosts in {} backing. Do \"\n \"nothing\".format(len(hosts_with_backing), instance_backing))\n return\n\n candidate_hosts = get_candidate_hosts(number_of_hosts=number_of_hosts)\n\n number_to_config = number_of_hosts - len(hosts_with_backing)\n hosts_to_config = list(set(candidate_hosts) -\n set(hosts_with_backing))[0:number_to_config]\n\n LOG.tc_step(\"Delete vms if any to prepare for system configuration \"\n \"change with best effort\")\n vm_helper.delete_vms(fail_ok=True)\n\n LOG.tc_step(\"Configure following hosts to {} backing: \"\n \"{}\".format(hosts_to_config, instance_backing))\n for host in hosts_to_config:\n HostsToRecover.add(host)\n host_helper.set_host_storage_backing(host=host,\n inst_backing=instance_backing,\n unlock=False,\n wait_for_configured=False)\n\n host_helper.unlock_hosts(hosts_to_config, check_hypervisor_up=True,\n fail_ok=False)\n\n LOG.tc_step(\"Waiting for hosts in {} aggregate\".format(instance_backing))\n for host in hosts_to_config:\n host_helper.wait_for_host_in_instance_backing(\n host, storage_backing=instance_backing)\n\n LOG.tc_step(\"Check number of {} hosts is at least \"\n \"{}\".format(instance_backing, number_of_hosts))\n assert number_of_hosts <= \\\n len(host_helper.get_hosts_in_storage_backing(instance_backing)), \\\n \"Number of {} hosts is less than {} after \" \\\n \"configuration\".format(instance_backing, number_of_hosts)","def clusters_every(self, clusters_every):\n\n self._clusters_every = clusters_every","def get_cpu_counts(host):\n counts = {}\n for s in range(0, len(host.nodes)):\n counts[s] = {}\n for f in CORE_FUNCTIONS:\n counts[s][f] = len(host.cpu_functions[s][f])\n return counts","def test_get_virtual_machine_count_metrics(self):\n pass","def evmcs(self, evmcs):\n\n self._evmcs = evmcs","def check_all_hosts (self, repo_version_id, version_name):\n if self.compare_versions(self.ambari_version, \"2.1.0\") < 0:\n query1 = \"SELECT chm.host_name from ClusterHostMapping chm JOIN clusters c ON c.cluster_name = '{0}';\".format(self.cluster_name)\n else:\n query1 = \"SELECT h.host_name from ClusterHostMapping chm JOIN clusters c ON c.cluster_name = '{0}' JOIN hosts h ON chm.host_id = h.host_id;\".format(self.cluster_name)\n\n if self.compare_versions(self.ambari_version, \"2.1.0\") < 0:\n query2 = \"SELECT hv.host_name, hv.state FROM host_version hv WHERE hv.repo_version_id = {0};\".format(repo_version_id)\n else:\n #query2 = \"SELECT hv.state,h.host_name FROM hosts h JOIN host_version hv ON h.host_id = hv.host_id WHERE hv.repo_version_id = {0};\".format(repo_version_id)\n query2 = \"SELECT hv.state,h.host_name, hs.health_status,hs.agent_version,(h.total_mem/1024/1024) as total_mem_gb,(hs.available_mem/1024/1024) as available_mem_gb FROM hosts h JOIN host_version hv ON h.host_id = hv.host_id JOIN hoststate hs ON h.host_id = hs.host_id WHERE hv.repo_version_id = {0} order by h.host_name;\".format(repo_version_id)\n # All cluster hosts\n host_names = set()\n self.cursor.execute(query1)\n rows = self.cursor.fetchall()\n if self.options.verbose:\n Logger.debug(query1 + \"\\n\")\n if rows and len(rows) > 0:\n host_names = set([row[0] for row in rows if len(row) == 1])\n Logger.debug(\"Hosts: {0}\".format(\", \".join(host_names)))\n\n host_name_to_state = {} # keys should be a subset of host_names\n hosts_with_repo_version_state_not_in_current = set()\n self.cursor.execute(query2 + \"\\n\")\n rows = self.cursor.fetchall()\n Logger.info(\"******************************************************************************************************************************************************\")\n Logger.info(\"\\t\\t\\t\\t\\t\\t\\tHOST(S) STATE\\t\")\n Logger.info(\"******************************************************************************************************************************************************\\n\")\n Logger.info(\"------------------------------------------------------------------------------------------------------------------------------------------------------\")\n Logger.info(\"State\\t\\tHostname\\t\\t\\t\\tHealth\\t\\tAgentVersion\\tTotalMemory\\tAvailableMemory\")\n Logger.info(\"------------------------------------------------------------------------------------------------------------------------------------------------------\")\n\n if rows and len(rows) > 0:\n for row in range(len(rows)):\n data = json.loads(rows[row][2])\n data1 = json.loads(rows[row][3])\n Logger.info(\"{0}\\t\\t{1}\\t\\t{2}\\t\\t{3}\\t\\t{4}\\t\\t{5}\".format(rows[row][0], rows[row][1], data[\"healthStatus\"], data1[\"version\"], rows[row][4], rows[row][5]))\n print (\"\\n\")\n Logger.debug(query2)\n if rows and len(rows) > 0:\n for row in rows:\n if len(row) == 6:\n host_name = row[1]\n state = row[0]\n host_name_to_state[host_name] = state\n if state.upper() != \"CURRENT\":\n hosts_with_repo_version_state_not_in_current.add(host_name)\n host_names_with_version = set(host_name_to_state.keys())\n host_names_without_version = host_names - host_names_with_version\n # Logger.info(\"\\t\\tHost(s) state Summary\")\n if len(host_names) > 0:\n if len(host_names_without_version) > 0:\n Logger.error(\"{0} host(s) do not have a Host Version for Repo Version {1}.\\n\" \\\n \"Host(s):\\n{2}\\n\".\n format(len(host_names_without_version), version_name, \", \".join(host_names_without_version)))\n\n if len(hosts_with_repo_version_state_not_in_current) > 0:\n Logger.error(\"{0} host(s) have a Host Version for Repo Version {1} but the state is not CURRENT.\\n\" \\\n \"Host(s):\\n{2}\\n\".\n format(len(hosts_with_repo_version_state_not_in_current), version_name, \", \".join(hosts_with_repo_version_state_not_in_current)))\n\n if len(host_names_without_version) == 0 and len(hosts_with_repo_version_state_not_in_current) == 0:\n Logger.info(\"Found {0} host(s) in the cluster, and all have a Host Version of CURRENT for \" \\\n \"Repo Version {1}. Things look good.\\n\".format(len(host_names), version_name))\n else:\n Logger.error(\"Make sure that all of these hosts are heartbeating, that they have the packages installed, the\\n\" \\\n \"hdp-select symlinks are correct, and that the services on these hosts have been restarated.\\n\")\n pass","def Host(self, h):\r\n\r\n self.host = h\r\n return self","def vm_templates_every(self, vm_templates_every):\n\n self._vm_templates_every = vm_templates_every","def set_hyperparameters(self, hyperparameters):\n self._hyperparameters = numpy.copy(hyperparameters)","def set_hostname(self, userid, hostname, os_version):\n tmp_path = self._pathutils.get_guest_temp_path(userid)\n if not os.path.exists(tmp_path):\n os.makedirs(tmp_path)\n tmp_file = tmp_path + '/hostname.sh'\n\n lnxdist = self._dist_manager.get_linux_dist(os_version)()\n lines = lnxdist.generate_set_hostname_script(hostname)\n with open(tmp_file, 'w') as f:\n f.writelines(lines)\n\n requestData = \"ChangeVM \" + userid + \" punchfile \" + \\\n tmp_file + \" --class x\"\n LOG.debug(\"Punch script to guest %s to set hostname\" % userid)\n\n try:\n self._smtclient._request(requestData)\n except exception.SDKSMTRequestFailed as err:\n msg = (\"Failed to punch set_hostname script to userid '%s'. SMT \"\n \"error: %s\" % (userid, err.format_message()))\n LOG.error(msg)\n raise exception.SDKSMTRequestFailed(err.results, msg)\n finally:\n self._pathutils.clean_temp_folder(tmp_path)","def _set_group_resource(self, _g):\n\n if isinstance(_g, Server):\n return\n\n for _, sg in _g.subgroups.items():\n self._set_group_resource(sg)\n _g.vCPUs += sg.vCPUs\n _g.mem += sg.mem\n _g.local_volume_size += sg.local_volume_size","def hosts_some(self, hosts_some):\n\n self._hosts_some = hosts_some","def host_ip(self, host_ip):\n\n self._host_ip = host_ip","def init_host(self, host):\n LOG.debug(\"init_host\")","def _host_in_event(self, ev):\n self._update_nodes()\n\n if not self.nodes:\n return\n\n for node in self.nodes:\n if node.ip in ev.host.ipv4:\n datapath = self.dpset.get(ev.host.port.dpid)\n node.setPortInformation(ev.host.port.dpid, datapath, ev.host.port.port_no, ev.host.port)\n self._install_cdnengine_matching_flow(datapath, node.ip, node.port)\n self.logger.info('New Node connected the network. Matching rules were installed ' + node.__str__())","def size(self):\r\n return sum(pool.size() for pool in self.host_to_pool.values())","def update_all_hosts_config(self, config_list):\n return self._put(endpoint='{}/cm/allHosts/config'.format(self.api_version),\n data=config_list).json()","def set(self, host, **kwargs):\n for p, c in self.configs_:\n if host in c.hosts_:\n c.set(host, **kwargs)\n return\n raise ValueError(\"Host %s: not found\" % host)","def getHostCount(self, **kwargs):\n\n allParams = ['scope']\n\n params = locals()\n for (key, val) in list(params['kwargs'].items()):\n if key not in allParams:\n raise TypeError(\"Got an unexpected keyword argument '%s' to method getHostCount\" % key)\n params[key] = val\n del params['kwargs']\n\n resourcePath = '/host/count'\n resourcePath = resourcePath.replace('{format}', 'json')\n method = 'GET'\n\n queryParams = {}\n headerParams = {}\n formParams = {}\n files = {}\n bodyParam = None\n\n headerParams['Accept'] = 'application/json'\n headerParams['Content-Type'] = 'application/json'\n\n\n\n\n if ('scope' in params):\n headerParams['scope'] = params['scope']\n\n\n\n\n\n\n\n\n postData = (formParams if formParams else bodyParam)\n\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\n postData, headerParams, files=files)\n\n\n if not response:\n return None\n\n responseObject = self.apiClient.deserialize(response, 'CountResult')\n return responseObject","def iscsi_luns_every(self, iscsi_luns_every):\n\n self._iscsi_luns_every = iscsi_luns_every","def set_article_count(cls, count):\n return cls.db.set(\"article_count\", count)","async def _total_players(self, ctx: Context, number: int):\n\n await self.config.guild(ctx.guild).total_players.set(number)\n\n await ctx.send(_(\"Set total players to `{}`.\").format(number))","def poll_host(self, server, obj, name):\n\n self.log.debug('found host: %s' % (name,))\n\n status = 0\n cpu_total = cpu_usage = cpu_percent = cpu_count = cpu_mhz_per_core = 0\n mem_total = mem_usage = mem_percent = 0\n vms_total = vms_running = vms_stopped = 0\n\n if '.' in name and name.count('.') != 3:\n name = name.split('.')[0]\n\n props = server._retrieve_properties_traversal(property_names=[\n 'name',\n 'summary.overallStatus',\n 'summary.quickStats.overallMemoryUsage',\n 'summary.quickStats.overallCpuUsage',\n 'summary.hardware.memorySize',\n 'summary.hardware.numCpuCores',\n 'summary.hardware.cpuMhz',\n ], from_node=obj, obj_type='HostSystem')\n\n for prop_set in props:\n for prop in prop_set.PropSet:\n pn, pv = prop.Name, prop.Val\n\n if pn == 'summary.overallStatus':\n status = HOST_STATUS.index(pv)\n elif pn == 'summary.quickStats.overallMemoryUsage':\n mem_usage = pv\n elif pn == 'summary.quickStats.overallCpuUsage':\n cpu_usage = pv\n elif pn == 'summary.hardware.memorySize':\n mem_total = pv / MB\n elif pn == 'summary.hardware.numCpuCores':\n cpu_count = pv\n elif pn == 'summary.hardware.cpuMhz':\n cpu_mhz_per_core = pv\n\n vms_total = len(server.get_registered_vms(obj))\n vms_running = len(server.get_registered_vms(obj, status='poweredOn'))\n vms_stopped = len(server.get_registered_vms(obj, status='poweredOff'))\n\n cpu_total = cpu_count * cpu_mhz_per_core\n cpu_percent = cpu_usage / float(cpu_total) * 100\n mem_percent = mem_usage / float(mem_total) * 100\n\n stats = {\n 'status': status,\n 'cpu_total': cpu_total,\n 'cpu_usage': cpu_usage,\n 'cpu_percent': cpu_percent,\n 'cpu_count': cpu_count,\n 'mem_total': mem_total,\n 'mem_usage': mem_usage,\n 'mem_percent': mem_percent,\n 'vms_total': vms_total,\n 'vms_running': vms_running,\n 'vms_stopped': vms_stopped,\n }\n\n return stats"],"string":"[\n \"def esxi_host_count(self, esxi_host_count):\\n\\n self._esxi_host_count = esxi_host_count\",\n \"def vcpu_set(vm_hostname, count, offline=False):\\n with ExitStack() as es:\\n vm = es.enter_context(_get_vm(vm_hostname))\\n\\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\\n raise NotImplementedError(\\n 'This operation is not yet supported for {}'.format(\\n vm.dataset_obj['datacenter_type'])\\n )\\n\\n _check_defined(vm)\\n\\n if offline and not vm.is_running():\\n log.info(\\n '\\\"{}\\\" is already powered off, ignoring --offline.'.format(\\n vm.fqdn)\\n )\\n offline = False\\n\\n if count == vm.dataset_obj['num_cpu']:\\n raise Warning('CPU count is the same.')\\n\\n if offline:\\n vm.shutdown()\\n vm.set_num_cpu(count)\\n if offline:\\n vm.start()\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vcpus(self, vcpus):\\n self._vcpus = vcpus\",\n \"def host_num(self, host_num):\\n\\n self._host_num = host_num\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def host_count(self) -> list:\\n return self.__host_count\",\n \"def host_num_in(self, host_num_in):\\n\\n self._host_num_in = host_num_in\",\n \"def set_hosts(self, hypervisor_per_cluster=False):\\n\\n self.conf['hosts'] = set()\\n\\n host_patterns, host_others = self._sift_patterns(\\n self.conf.get('hosts_list')\\n )\\n datacenter_patterns = self.conf.get('datacenter', [])\\n cluster_patterns = self.conf.get('cluster', [])\\n\\n if host_patterns:\\n self.conf['host_pattern'] = host_patterns\\n\\n self.conf['hosts'] = self._get_hypervisors_from_api()\\n # Filter all host specified with -H\\n host_filtered = set()\\n if host_others:\\n host_filtered = set([\\n (dc, cl, h, is_spm, is_up)\\n for dc, cl, h, is_spm, is_up in self.conf['hosts']\\n if h in host_others\\n ])\\n not_found = host_others - set(host[2] for host in host_filtered)\\n if not_found != set():\\n # try to resolve to ip specified hosts\\n for fqdn in set(not_found):\\n try:\\n ipaddr = socket.gethostbyname(fqdn)\\n logging.debug('%s --> %s' % (fqdn, ipaddr))\\n for (dc, cl, h, is_spm, is_up) in self.conf['hosts']:\\n if h == ipaddr:\\n host_filtered.add((dc, cl, h, is_spm, is_up))\\n not_found.remove(fqdn)\\n except socket.error:\\n logging.warning(\\n _('Cannot resolve {host}').format(\\n host=fqdn,\\n )\\n )\\n if not_found != set():\\n # try to resolve to ip known hypervisors\\n for (dc, cl, h, is_spm, is_up) in self.conf['hosts']:\\n try:\\n ipaddr = socket.gethostbyname(h)\\n logging.debug('%s --> %s' % (h, ipaddr))\\n if ipaddr in host_others:\\n host_filtered.add((dc, cl, h, is_spm, is_up))\\n not_found.remove(ipaddr)\\n except socket.error:\\n logging.warning(\\n _('Cannot resolve {host}').format(\\n host=h,\\n )\\n )\\n if not_found != set():\\n logging.error(\\n _(\\n 'The following host are not listed as hypervisors: '\\n '{not_listed}. Known hypervisors can be listed using '\\n 'the list command'\\n ).format(\\n not_listed=','.join(not_found)\\n )\\n )\\n sys.exit(ExitCodes.CRITICAL)\\n\\n orig_hosts = self.conf['hosts'].copy()\\n\\n if host_patterns:\\n for pattern in host_patterns:\\n host_filtered |= self._filter_hosts('host', pattern)\\n if host_patterns or host_others:\\n self.conf['hosts'] &= host_filtered\\n\\n # Intersect with hosts belonging to the data centers specified with -d\\n if datacenter_patterns:\\n datacenter_filtered = set()\\n for pattern in datacenter_patterns:\\n datacenter_filtered |= self._filter_hosts(\\n 'datacenter', pattern\\n )\\n self.conf['hosts'] &= datacenter_filtered\\n\\n # Intersect with hosts belonging to the clusters specified with -c\\n if cluster_patterns:\\n # remove all hosts that don't match the patterns\\n cluster_filtered = set()\\n for pattern in cluster_patterns:\\n cluster_filtered |= self._filter_hosts('cluster', pattern)\\n self.conf['hosts'] &= cluster_filtered\\n\\n # If hypervisor_per_cluster is set, collect data only from a single\\n # hypervisor per cluster; if the Spm found, collect data from it.\\n if hypervisor_per_cluster:\\n selected_hosts = dict()\\n for dc, cluster, host, is_spm, is_up in self.conf['hosts']:\\n # Always add the SPM\\n if is_spm:\\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\\n is_up)\\n # For the given cluster, if no host added yet, add it\\n elif cluster.name not in selected_hosts:\\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\\n is_up)\\n # If a host is up and the SPM isn't added yet, add this host\\n elif is_up and not selected_hosts[cluster.name][3]:\\n selected_hosts[cluster.name] = (dc, cluster, host, is_spm,\\n is_up)\\n self.conf['hosts'] &= set(selected_hosts.values())\\n\\n # warn users if they are going to collect logs from all hosts.\\n if orig_hosts and self.conf['hosts'] == orig_hosts:\\n logging.warning(\\n _(\\n 'This ovirt-log-collector call will collect logs from '\\n 'all available hosts. This may take long time, '\\n 'depending on the size of your deployment'\\n )\\n )\\n\\n return bool(self.conf.get('hosts'))\",\n \"def hosts_every(self, hosts_every):\\n\\n self._hosts_every = hosts_every\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def host_status_control(self, host_status_control):\\n\\n self._host_status_control = host_status_control\",\n \"def init_cpu_counts(host):\\n host.cpu_functions = {}\\n host.cpu_lists = {}\\n for s in range(0, len(host.nodes)):\\n host.cpu_functions[s] = {}\\n for f in CORE_FUNCTIONS:\\n host.cpu_functions[s][f] = []\\n host.cpu_lists[s] = []\",\n \"def hosts(self, value):\\n if not isinstance(value, NodeSet):\\n raise TypeError(\\\"Invalid fio host NodeSet: {} ({})\\\".format(value, type(value)))\\n self._hosts = value.copy()\",\n \"def number_of_nodes(self, number_of_nodes):\\n\\n self._number_of_nodes = number_of_nodes\",\n \"def restructure_host_cpu_data(host):\\n init_cpu_counts(host)\\n host.sockets = len(host.nodes or [])\\n host.hyperthreading = False\\n host.physical_cores = 0\\n if not host.cpus:\\n return\\n host.cpu_model = host.cpus[0].cpu_model\\n cpu_list = sorted(host.cpus, key=_sort_by_coreid)\\n for cpu in cpu_list:\\n inode = pecan.request.dbapi.inode_get(inode_id=cpu.forinodeid)\\n cpu.numa_node = inode.numa_node\\n if cpu.thread == 0:\\n host.physical_cores += 1\\n elif cpu.thread > 0:\\n host.hyperthreading = True\\n function = cpu.allocated_function or get_default_function(host)\\n host.cpu_functions[cpu.numa_node][function].append(int(cpu.cpu))\\n host.cpu_lists[cpu.numa_node].append(int(cpu.cpu))\",\n \"def advertise_osd_count(count):\\n for relid in hookenv.relation_ids('mon'):\\n hookenv.relation_set(\\n relation_id=relid,\\n relation_settings={'bootstrapped-osds': count}\\n )\",\n \"def external_group_count(self, external_group_count):\\n\\n self._external_group_count = external_group_count\",\n \"def vms_every(self, vms_every):\\n\\n self._vms_every = vms_every\",\n \"def update_core_allocations(host, cpu_counts):\\n # Remove any previous assignments\\n for s in range(0, len(host.nodes)):\\n for f in CORE_FUNCTIONS:\\n host.cpu_functions[s][f] = []\\n # Set new assignments\\n for s in range(0, len(host.nodes)):\\n cpu_list = host.cpu_lists[s] if s in host.cpu_lists else []\\n # Reserve for the platform first\\n for i in range(0, cpu_counts[s][constants.PLATFORM_FUNCTION]):\\n host.cpu_functions[s][constants.PLATFORM_FUNCTION].append(\\n cpu_list.pop(0))\\n # Reserve for the vswitch next\\n for i in range(0, cpu_counts[s][constants.VSWITCH_FUNCTION]):\\n host.cpu_functions[s][constants.VSWITCH_FUNCTION].append(\\n cpu_list.pop(0))\\n # Reserve for the shared next\\n for i in range(0, cpu_counts[s][constants.SHARED_FUNCTION]):\\n host.cpu_functions[s][constants.SHARED_FUNCTION].append(\\n cpu_list.pop(0))\\n for i in range(0, cpu_counts[s][constants.ISOLATED_FUNCTION]):\\n host.cpu_functions[s][constants.ISOLATED_FUNCTION].append(\\n cpu_list.pop(0))\\n # Assign the remaining cpus to the default function for this host\\n host.cpu_functions[s][get_default_function(host)] += cpu_list\\n return\",\n \"def host_num_lte(self, host_num_lte):\\n\\n self._host_num_lte = host_num_lte\",\n \"def host_num_gt(self, host_num_gt):\\n\\n self._host_num_gt = host_num_gt\",\n \"def device_count(self, device_count):\\n\\n self._device_count = device_count\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def add_nodes(self, count=1):\\n self.log.info('Adding %d nodes' % count)\\n new_nodes = []\\n Node.flavor = env_vars['client_flavor']\\n for i in range(count):\\n #check if cluster did not previously exist\\n if i == 0 and len(self.all_nodes) == 0:\\n # give a floating IPv4 to the first node only\\n new_guy = Node(self.cluster_name, '', len(self.all_nodes)+1, create=True, IPv4=True)\\n else:\\n new_guy = Node(self.cluster_name, node_type=\\\"\\\", number=len(self.all_nodes)+1, create=True)\\n self.all_nodes.append(new_guy)\\n new_nodes.append(new_guy)\\n self.save_cluster()\\n for n in new_nodes:\\n n.wait_ready()\\n #inject host files to everybody\\n n.inject_hostnames(self.get_hosts(private=True), delete=self.cluster_name)\\n n.bootstrap()\\n self.log.info(\\\"Node %s is live \\\" % new_guy.name)\\n #inform all\\n self.inject_hosts_files()\",\n \"def node_count(self, node_count):\\n\\n self._node_count = node_count\",\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def config_ha(self, vlan_tag, vip_address):\\n\\n if len(self.hostnames) == 1:\\n LOG.debug(\\\"Only one machine, doesn't need to configure HA\\\")\\n return True\\n\\n interface_name = self.in_interface\\n if vlan_tag:\\n interface_name = \\\"vlan.\\\" + vlan_tag\\n\\n cmd_apv_config_virtual_iface = ADCDevice.cluster_config_virtual_interface(interface_name)\\n cmd_apv_config_virtual_vip = ADCDevice.cluster_config_vip(interface_name, vip_address)\\n cmd_apv_cluster_enable = ADCDevice.cluster_enable(interface_name)\\n\\n priority = 1\\n for base_rest_url in self.base_rest_urls:\\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_iface)\\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_vip)\\n\\n priority += 10\\n cmd_apv_config_virtual_prior = ADCDevice.cluster_config_priority(interface_name, priority)\\n self.run_cli_extend(base_rest_url, cmd_apv_config_virtual_prior)\\n\\n self.run_cli_extend(base_rest_url, cmd_apv_cluster_enable)\",\n \"def hypervisor_version(self, hypervisor_version):\\n\\n self._hypervisor_version = hypervisor_version\",\n \"def create_dvs(self, host_vmnics, dvs_name, num_uplinks=4):\\n\\n for network_obj in GetDatacenter(self.datacenter_obj).network_resources():\\n if network_obj.name == dvs_name:\\n self.logger.info(\\\"DVS {0} already exists\\\".format(dvs_name))\\n return\\n host_cfgs = []\\n for host_obj, vmnics in host_vmnics.items():\\n uplinks = []\\n if host_obj.runtime.connectionState != \\\"connected\\\":\\n self.logger.error(\\n \\\"Host {0} is not connected. Skipped\\\".format(host_obj.name)\\n )\\n continue\\n host_cfg = vim.dvs.HostMember.ConfigSpec()\\n host_cfg.operation = vim.ConfigSpecOperation.add\\n host_cfg.host = host_obj\\n host_cfg.backing = vim.dvs.HostMember.PnicBacking()\\n for pnic in GetHost(host_obj).pnics():\\n for vmnic in vmnics:\\n if pnic.device == vmnic:\\n pnic_spec = vim.dvs.HostMember.PnicSpec()\\n pnic_spec.pnicDevice = pnic.device\\n uplinks.append(pnic_spec)\\n host_cfg.backing.pnicSpec = uplinks\\n host_cfgs.append(host_cfg)\\n uplink_port_policy = vim.DistributedVirtualSwitch.NameArrayUplinkPortPolicy()\\n uplnk_port_order = []\\n for i in range(num_uplinks):\\n name = \\\"uplink%d\\\" % (i + 1)\\n uplink_port_policy.uplinkPortName.append(name)\\n uplnk_port_order.append(name)\\n string_policy = vim.StringPolicy()\\n string_policy.value = \\\"failover_explicit\\\"\\n uplink_port_order_policy = (\\n vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortOrderPolicy()\\n )\\n # activeUplinkPort: list of active uplink ports used for load balancing\\n uplink_port_order_policy.activeUplinkPort = uplnk_port_order\\n team = vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortTeamingPolicy()\\n team.policy = string_policy\\n team.uplinkPortOrder = uplink_port_order_policy\\n port_config_policy = (\\n vim.dvs.VmwareDistributedVirtualSwitch.VmwarePortConfigPolicy()\\n )\\n port_config_policy.uplinkTeamingPolicy = team\\n dvs_config_spec = vim.dvs.VmwareDistributedVirtualSwitch.ConfigSpec()\\n dvs_config_spec.name = dvs_name\\n dvs_config_spec.host = host_cfgs\\n dvs_config_spec.defaultPortConfig = port_config_policy\\n dvs_config_spec.lacpApiVersion = (\\n vim.dvs.VmwareDistributedVirtualSwitch.LacpApiVersion.multipleLag\\n )\\n dvs_config_spec.numStandalonePorts = num_uplinks\\n dvs_create_spec = vim.DistributedVirtualSwitch.CreateSpec(\\n configSpec=dvs_config_spec\\n )\\n task = self.datacenter_obj.networkFolder.CreateDVS_Task(dvs_create_spec)\\n return task\",\n \"def host_version(self, host_version):\\n if host_version is None:\\n raise ValueError(\\\"Invalid value for `host_version`, must not be `None`\\\") # noqa: E501\\n\\n self._host_version = host_version\",\n \"def build_lhosts(self , sws , lhost_count):\\n host_count = 0\\n for sw in sws:\\n for i in range(lhost_count):\\n host_id = host_count + 1\\n host = self.addHost('h%s' % host_id)\\n self.addLink(sw, host)\\n host_count += 1\\n return host_count\",\n \"def vm_volumes_every(self, vm_volumes_every):\\n\\n self._vm_volumes_every = vm_volumes_every\",\n \"def update_cluster_hosts(self, hosts):\\n self._hosts = hosts\\n self._collect_hosts_d = True\",\n \"def test_vm_cpu_limitation_after_cpu_hot_plug(self):\\n host = ll_vms.get_vm_host(vm_name=conf.QOS_VMS[0])\\n host_cpu = ll_hosts.get_host_processing_units_number(host_name=host)\\n testflow.step(\\\"Hotplug CPU to VM %s\\\", conf.QOS_VMS[0])\\n vm_cpu_sockets = min(8, host_cpu)\\n assert ll_vms.updateVm(\\n positive=True, vm=conf.QOS_VMS[0], cpu_socket=vm_cpu_sockets\\n )\\n expected_dict = self.calculate_expected_values(\\n load_dict=self.load_dict\\n )\\n assert sla_helpers.load_vm_and_check_the_load(\\n load_dict=self.load_dict, expected_values=expected_dict\\n )\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def check_core_allocations(host, cpu_counts):\\n total_platform_cores = 0\\n total_vswitch_cores = 0\\n total_shared_cores = 0\\n total_isolated_cores = 0\\n for s in range(0, len(host.nodes)):\\n available_cores = len(host.cpu_lists[s])\\n platform_cores = cpu_counts[s][constants.PLATFORM_FUNCTION]\\n vswitch_cores = cpu_counts[s][constants.VSWITCH_FUNCTION]\\n shared_cores = cpu_counts[s][constants.SHARED_FUNCTION]\\n isolated_cores = cpu_counts[s][constants.ISOLATED_FUNCTION]\\n requested_cores = \\\\\\n platform_cores + vswitch_cores + shared_cores + isolated_cores\\n if requested_cores > available_cores:\\n raise wsme.exc.ClientSideError(\\n \\\"More total logical cores requested than present on Processor \\\"\\n \\\"%s (%s cores).\\\" % (s, available_cores))\\n total_platform_cores += platform_cores\\n total_vswitch_cores += vswitch_cores\\n total_shared_cores += shared_cores\\n total_isolated_cores += isolated_cores\\n\\n # Validate Platform cores\\n if ((constants.CONTROLLER in host.subfunctions) and\\n (constants.WORKER in host.subfunctions)):\\n if total_platform_cores < 2:\\n raise wsme.exc.ClientSideError(\\\"%s must have at least two cores.\\\" %\\n constants.PLATFORM_FUNCTION)\\n elif total_platform_cores == 0:\\n raise wsme.exc.ClientSideError(\\\"%s must have at least one core.\\\" %\\n constants.PLATFORM_FUNCTION)\\n for s in range(1, len(host.nodes)):\\n if cpu_counts[s][constants.PLATFORM_FUNCTION] > 0:\\n raise wsme.exc.ClientSideError(\\n \\\"%s cores can only be allocated on Processor 0\\\" %\\n constants.PLATFORM_FUNCTION)\\n\\n # Validate shared cores\\n for s in range(0, len(host.nodes)):\\n shared_cores = cpu_counts[s][constants.SHARED_FUNCTION]\\n if host.hyperthreading:\\n shared_cores /= 2\\n if shared_cores > 1:\\n raise wsme.exc.ClientSideError(\\n '%s cores are limited to 1 per processor.'\\n % constants.SHARED_FUNCTION)\\n\\n # Validate vswitch cores\\n if total_vswitch_cores != 0:\\n vswitch_type = cutils.get_vswitch_type(pecan.request.dbapi)\\n if constants.VSWITCH_TYPE_NONE == vswitch_type:\\n raise wsme.exc.ClientSideError(\\n ('vSwitch cpus can only be used with a vswitch_type '\\n 'specified.'))\\n\\n vswitch_physical_cores = total_vswitch_cores\\n if host.hyperthreading:\\n vswitch_physical_cores /= 2\\n if vswitch_physical_cores > VSWITCH_MAX_CORES:\\n raise wsme.exc.ClientSideError(\\n \\\"The %s function can only be assigned up to %s cores.\\\" %\\n (constants.VSWITCH_FUNCTION.lower(), VSWITCH_MAX_CORES))\\n\\n # Validate Isolated cores\\n # We can allocate platform cores on numa 0, otherwise all isolated\\n # cores must in a contiguous block after the platform cores.\\n if total_isolated_cores > 0:\\n if total_vswitch_cores != 0 or total_shared_cores != 0:\\n raise wsme.exc.ClientSideError(\\n \\\"%s cores can only be configured with %s and %s core types.\\\" %\\n (constants.ISOLATED_FUNCTION, constants.PLATFORM_FUNCTION,\\n constants.APPLICATION_FUNCTION))\\n has_application_cpus = False\\n for s in range(0, len(host.nodes)):\\n numa_counts = cpu_counts[s]\\n isolated_cores_requested = \\\\\\n numa_counts[constants.ISOLATED_FUNCTION]\\n if has_application_cpus and isolated_cores_requested:\\n raise wsme.exc.ClientSideError(\\n \\\"%s and %s cpus must be contiguous\\\" %\\n (constants.PLATFORM_FUNCTION, constants.ISOLATED_FUNCTION))\\n platform_cores_requested = \\\\\\n numa_counts[constants.PLATFORM_FUNCTION]\\n available_cores = len(host.cpu_lists[s])\\n\\n if platform_cores_requested + isolated_cores_requested \\\\\\n != available_cores:\\n has_application_cpus = True\\n\\n reserved_for_applications = len(host.cpus) - total_platform_cores - \\\\\\n total_vswitch_cores\\n if reserved_for_applications <= 0:\\n raise wsme.exc.ClientSideError(\\n \\\"There must be at least one unused core for %s.\\\" %\\n constants.APPLICATION_FUNCTION)\",\n \"def set_hypercubes_classes(self):\\n print(\\\"Setting the BaseGrid hypercubes' classes.\\\")\\n list_of_all_hc = list(self.hypercubes.flatten())\\n print(\\\"Number of hypercubes: \\\" + str(len(list_of_all_hc)))\\n for hypercube in list_of_all_hc:\\n hypercube.set_hypercube_class()\\n if self.child_grid:\\n self.child_grid.set_hypercubes_classes()\",\n \"def set_hosts(self, host_list: t.List[str]) -> None:\\n if isinstance(host_list, str):\\n host_list = [host_list.strip()]\\n if not isinstance(host_list, list):\\n raise TypeError(\\\"host_list argument must be a list of strings\\\")\\n if not all(isinstance(host, str) for host in host_list):\\n raise TypeError(\\\"host_list argument must be list of strings\\\")\\n # TODO check length\\n if self.batch:\\n if hasattr(self, \\\"batch_settings\\\") and self.batch_settings:\\n self.batch_settings.set_hostlist(host_list)\\n\\n if self.launcher == \\\"lsf\\\":\\n for db in self.dbnodes:\\n db.set_hosts(host_list)\\n else:\\n for host, db in zip(host_list, self.dbnodes):\\n if isinstance(db.run_settings, AprunSettings):\\n if not self.batch:\\n db.run_settings.set_hostlist([host])\\n else:\\n db.run_settings.set_hostlist([host])\\n\\n if db.is_mpmd and hasattr(db.run_settings, \\\"mpmd\\\"):\\n for i, mpmd_runsettings in enumerate(db.run_settings.mpmd):\\n mpmd_runsettings.set_hostlist(host_list[i + 1])\",\n \"def vsvrcount(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._vsvrcount\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def resize(self, thread_count):\\n self._thread_count = thread_count\\n self._semaphore.resize(thread_count + 1)\",\n \"def all_hosts(self):\\n ...\",\n \"def set_share_all_host_disks(self, bShareAllHostDisks):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetShareAllHostDisks', self.handle, bShareAllHostDisks)\",\n \"def update_cpu(self, vm):\\n try:\\n cpu_spec = self.client.get_cpu(vm.backend_id)\\n if (\\n cpu_spec['cores_per_socket'] != vm.cores_per_socket\\n or cpu_spec['count'] != vm.cores\\n ):\\n self.client.update_cpu(\\n vm.backend_id,\\n {\\n 'cores_per_socket': vm.cores_per_socket,\\n 'count': vm.cores,\\n },\\n )\\n except VMwareError as e:\\n raise VMwareBackendError(e)\",\n \"def init_host(self, host):\\n if self._drv_nodes is None:\\n self.set_nodes([nova_conf.host])\\n args = (drv_conf.tenant_id, drv_conf.client_id, drv_conf.client_secret,\\n drv_conf.subscription_id)\\n\\n self.compute_client = utils.get_compute_client(*args)\\n self.resource_client = utils.get_resource_client(*args)\\n self.network_client = utils.get_network_client(*args)\\n is_resource_created = utils.check_resource_existence(\\n self.resource_client, drv_conf.resource_group)\\n if not is_resource_created:\\n utils.create_resource_group(\\n self.resource_client, drv_conf.resource_group, drv_conf.region)\\n\\n self.flavor_info.update(\\n utils.get_vm_sizes(self.compute_client, drv_conf.region))\\n LOG.info(\\\"%s driver init with %s project, %s region\\\" %\\n (self.name, drv_conf.tenant_id, drv_conf.region))\",\n \"def set_all(self, host_names, ip_address):\\n for host_name in host_names:\\n self.set_one(host_name, ip_address)\",\n \"def set_num_virtual_stages(self, num_virtual_stages: int) -> None:\\n self.num_virtual_stages = num_virtual_stages\",\n \"def host_num_lt(self, host_num_lt):\\n\\n self._host_num_lt = host_num_lt\",\n \"def set_autoscaled_instances(self, instance_count: int) -> None:\\n set_instances_for_marathon_service(\\n service=self.service,\\n instance=self.instance,\\n instance_count=instance_count,\\n )\",\n \"def create_hosting_device_resources(self, context, complementary_id,\\n tenant_id, mgmt_nw_id,\\n mgmt_sec_grp_id, max_hosted):\\n pass\",\n \"def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\\n\\n self._count_update_pool_size = count_update_pool_size\",\n \"def AddCpuCountFlag(parser):\\n help_text = \\\"\\\"\\\"\\\\\\n Whole number value indicating how many vCPUs the machine should\\n contain. Each vCPU count corresponds to a N2 high-mem machine:\\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\\n \\\"\\\"\\\"\\n parser.add_argument(\\n '--cpu-count',\\n help=help_text,\\n type=int,\\n choices=[2, 4, 8, 16, 32, 64],\\n required=True)\",\n \"def byte_count(self, byte_count):\\n\\n self._byte_count = byte_count\",\n \"def compute_node_count(self, compute_node_count):\\n\\n self._compute_node_count = compute_node_count\",\n \"def set_photon_counting_divs(self, n, thres):\\n thres = ct.c_long(thres)\\n self.lib.SetPhotonCountingDivisions(ct.c_ulong(n), ct.pointer(thres))\",\n \"def _destroy_vapv(self, hostnames, lb):\\n for hostname in hostnames:\\n try:\\n self.openstack_connector.destroy_vapv(hostname, lb)\\n LOG.debug(\\\"\\\\nvAPV {} destroyed\\\".format(hostname))\\n except Exception as e:\\n LOG.error(e)\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def update_host(self, conf, tenant_id, network_id, host_id, body):\\n\\t\\tpass\",\n \"def virtual_machines(self, virtual_machines):\\n\\n self._virtual_machines = virtual_machines\",\n \"def init_host(self, host):\\n LOG.debug(\\\"init_host\\\")\\n\\n self._cpc = self._client.cpcs.find(**{\\n \\\"object-id\\\": CONF.dpm.cpc_object_id})\\n LOG.debug(\\\"Matching hypervisor found %(cpcsubset_name)s for object-id \\\"\\n \\\"%(cpcid)s and CPC %(cpcname)s\\\" %\\n {'cpcsubset_name': CONF.host,\\n 'cpcid': CONF.dpm.cpc_object_id,\\n 'cpcname': self._cpc.properties['name']})\\n\\n utils.valide_host_conf(self._cpc)\\n self._host = Host.Host(self._cpc, self._client)\",\n \"def free_host(self, context):\\n cluster_config = self._session._call_method(\\n vutil, \\\"get_object_property\\\", self._cluster, \\\"configurationEx\\\")\\n\\n # get the group\\n group = self._get_group(cluster_config)\\n\\n failover_hosts = []\\n policy = cluster_config.dasConfig.admissionControlPolicy\\n if policy and hasattr(policy, 'failoverHosts'):\\n failover_hosts = set(vutil.get_moref_value(h)\\n for h in policy.failoverHosts)\\n\\n if group is None or not getattr(group, 'host', None):\\n # find a host to free\\n\\n # retrieve all hosts of the cluster\\n host_objs = {vutil.get_moref_value(h): h\\n for h in self._get_hosts_in_cluster(self._cluster)}\\n vms_per_host = {h: [] for h in host_objs}\\n\\n # get all the vms in a cluster, because we need to find a host\\n # without big VMs.\\n props = ['config.hardware.memoryMB', 'runtime.host',\\n 'runtime.powerState',\\n 'summary.quickStats.hostMemoryUsage']\\n cluster_vms = self._vmops._list_instances_in_cluster(props)\\n\\n for vm_uuid, vm_props in cluster_vms:\\n props = (vm_props.get('config.hardware.memoryMB', 0),\\n vm_props.get('runtime.powerState', 'poweredOff'),\\n vm_props.get('summary.quickStats.hostMemoryUsage', 0))\\n # every host_obj is differnt, even though the value, which\\n # really matters, is the same\\n host_obj = vm_props.get('runtime.host')\\n if not host_obj:\\n continue\\n\\n host = vutil.get_moref_value(host_obj)\\n vms_per_host.setdefault(host, []). \\\\\\n append(props)\\n\\n # filter for hosts without big VMs\\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\\n if all(mem < CONF.largevm_mb\\n for mem, state, used_mem in vms)}\\n\\n if not vms_per_host:\\n LOG.warning('No suitable host found for freeing a host for '\\n 'spawning (bigvm filter).')\\n return FREE_HOST_STATE_ERROR\\n\\n # filter hosts which are failover hosts\\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\\n if h not in failover_hosts}\\n\\n if not vms_per_host:\\n LOG.warning('No suitable host found for freeing a host for '\\n 'spawning (failover host filter).')\\n return FREE_HOST_STATE_ERROR\\n\\n # filter hosts which are in a wrong state\\n result = self._session._call_method(vim_util,\\n \\\"get_properties_for_a_collection_of_objects\\\",\\n \\\"HostSystem\\\",\\n [host_objs[h] for h in vms_per_host],\\n ['summary.runtime'])\\n host_states = {}\\n with vutil.WithRetrieval(self._session.vim, result) as objects:\\n for obj in objects:\\n host_props = propset_dict(obj.propSet)\\n runtime_summary = host_props['summary.runtime']\\n moref_value = vutil.get_moref_value(obj.obj)\\n host_states[moref_value] = (\\n runtime_summary.inMaintenanceMode is False and\\n runtime_summary.connectionState == \\\"connected\\\")\\n\\n vms_per_host = {h: vms for h, vms in vms_per_host.items()\\n if host_states[h]}\\n\\n if not vms_per_host:\\n LOG.warning('No suitable host found for freeing a host for '\\n 'spawning (host state filter).')\\n return FREE_HOST_STATE_ERROR\\n\\n mem_per_host = {h: sum(used_mem for mem, state, used_mem in vms)\\n for h, vms in vms_per_host.items()}\\n\\n # take the one with least memory used\\n host, _ = sorted(mem_per_host.items(), key=itemgetter(1))[0]\\n host_ref = host_objs[host]\\n\\n client_factory = self._session.vim.client.factory\\n config_spec = client_factory.create('ns0:ClusterConfigSpecEx')\\n\\n # we need to either create the group from scratch or at least add a\\n # host to it\\n operation = 'add' if group is None else 'edit'\\n group = cluster_util.create_host_group(client_factory,\\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name,\\n [host_ref], group)\\n group_spec = cluster_util.create_group_spec(client_factory,\\n group, operation)\\n config_spec.groupSpec = [group_spec]\\n\\n # create the appropriate rule for VMs to leave the host\\n rule_name = '{}_anti-affinity'.format(\\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name)\\n rule = cluster_util._get_rule(cluster_config, rule_name)\\n rule_spec = cluster_util._create_cluster_group_rules_spec(\\n client_factory, rule_name,\\n CONF.vmware.special_spawning_vm_group,\\n CONF.vmware.bigvm_deployment_free_host_hostgroup_name,\\n 'anti-affinity', rule)\\n config_spec.rulesSpec = [rule_spec]\\n\\n cluster_util.reconfigure_cluster(self._session, self._cluster,\\n config_spec)\\n else:\\n if len(group.host) > 1:\\n LOG.warning('Found more than 1 host in spawning hostgroup.')\\n host_ref = group.host[0]\\n\\n # check if the host is still suitable\\n if vutil.get_moref_value(host_ref) in failover_hosts:\\n LOG.warning('Host destined for spawning became a failover '\\n 'host.')\\n return FREE_HOST_STATE_ERROR\\n\\n runtime_summary = self._session._call_method(\\n vutil, \\\"get_object_property\\\", host_ref, 'summary.runtime')\\n if (runtime_summary.inMaintenanceMode is True or\\n runtime_summary.connectionState != \\\"connected\\\"):\\n LOG.warning('Host destined for spawning was set to '\\n 'maintenance or became disconnected.')\\n return FREE_HOST_STATE_ERROR\\n\\n # filter the VMs on the host, so we don't look at the non-movable\\n # DRS-created and -owned VMs\\n vcls_identifier = (constants.VCLS_EXTENSION_KEY,\\n constants.VCLS_EXTENSION_TYPE_AGENT)\\n vms_on_host = [\\n (u, state, ref)\\n for u, h, state, m, ref in self._get_vms_on_host(host_ref)\\n if not m or (m.extensionKey, m.type) != vcls_identifier]\\n\\n # ignore partiallyAutomated VMs. They should not be big VMs as we chose\\n # a host appropriately, so they should be large VMs, which we tolerate\\n # next to a big VM as they get moved by the nanny\\n drs_overrides = cluster_util.fetch_cluster_drs_vm_overrides(\\n self._session, cluster_config=cluster_config)\\n wanted_drs_override_behaviors = (\\n constants.DRS_BEHAVIOR_PARTIALLY_AUTOMATED,)\\n vms_on_host = [\\n (u, state) for u, state, ref in vms_on_host\\n if drs_overrides.get(ref) not in wanted_drs_override_behaviors]\\n\\n # check if there are running VMs on that host\\n vms_on_host = [u for u, state in vms_on_host\\n if state != 'poweredOff']\\n if vms_on_host:\\n # check if DRS is enabled, so freeing up can work\\n if cluster_config.drsConfig.defaultVmBehavior != \\\\\\n constants.DRS_BEHAVIOR_FULLY_AUTOMATED:\\n LOG.error('DRS set to %(actual)s, expected %(expected)s.',\\n {'actual': cluster_config.drsConfig.defaultVmBehavior,\\n 'expected': constants.DRS_BEHAVIOR_FULLY_AUTOMATED})\\n return FREE_HOST_STATE_ERROR\\n\\n LOG.debug('Freeing up %(host)s for spawning in progress.',\\n {'host': vutil.get_moref_value(host_ref)})\\n return FREE_HOST_STATE_STARTED\\n\\n LOG.info('Done freeing up %(host)s for spawning.',\\n {'host': vutil.get_moref_value(host_ref)})\\n return FREE_HOST_STATE_DONE\",\n \"def update_host_ovs(self, context):\\n LOG.info(_('Updating Open vSwitch host data...'))\\n LOG.debug(\\\"Current DOM: %s\\\" % self.current_dom.to_dict())\\n LOG.debug(\\\"Requested DOM: %s\\\" % self.desired_dom.to_dict())\\n\\n builder = mob.MicroOperationBuilder(context,\\n self.current_dom,\\n self.desired_dom,\\n self.rollback)\\n\\n mo_list = builder.get_micro_ops_for_update()\\n\\n # run validation\\n return self._run_micro_op_list(mo_list)\",\n \"def hosts(self, hosts):\\n self._hosts = hosts\",\n \"def setVoxelSize(self, vxs):\\n\\t\\tself.voxelsize = vxs\\n\\t\\ta, b, c = vxs\\n\\t\\tself.spacing = [1, b / a, c / a]\",\n \"def heal_and_optimize(self):\\n self._refresh_bridge_mappings_to_neutron()\\n\\n # Create ProvisionRequests for all VNICs on all (non-management client)\\n # partitions...\\n lpar_vnic_map = utils.list_vifs(self.adapter, self.vif_wrapper_class)\\n prov_reqs = preq.ProvisionRequest.for_wrappers(self, lpar_vnic_map,\\n preq.PLUG)\\n # ...and mark them 'up' in neutron.\\n self.provision_devices(prov_reqs)\",\n \"def init_host(self, host):\\n self._precreate_network()\\n LOG.info(_LI(\\\"Create/Update Ntwork and Subnet, Done.\\\"))\",\n \"def max_cpus_per_machine(self, max_cpus_per_machine):\\n\\n self._max_cpus_per_machine = max_cpus_per_machine\",\n \"def vlans_every(self, vlans_every):\\n\\n self._vlans_every = vlans_every\",\n \"def processor_count(self, processor_count):\\n\\n self._processor_count = processor_count\",\n \"def set_hyperparameters(self, hyperparameters):\\n self._hyperparameters.update(hyperparameters)\\n\\n if self._class:\\n LOGGER.debug('Creating a new primitive instance for %s', self.name)\\n self.instance = self.primitive(**self.get_hyperparameters())\",\n \"def fill_host(self, data):\\n check_input_params(data, self.HOST)\\n self.host = data[self.HOST]\",\n \"def fill_host(self, data):\\n check_input_params(data, self.HOST)\\n self.host = data[self.HOST]\",\n \"def _set_pg_count_storage_parameters(cls, data, nodes):\\n osd_num = 0\\n osd_nodes = [node for node in nodes\\n if 'ceph-osd' in node.all_roles]\\n\\n for node in osd_nodes:\\n for disk in cls.get_node_volumes(node):\\n for part in disk.get('volumes', []):\\n if part.get('name') == 'ceph' and part.get('size', 0) > 0:\\n osd_num += 1\\n\\n for node in data:\\n storage_attrs = node['storage']\\n\\n pg_counts = get_pool_pg_count(\\n osd_num=osd_num,\\n pool_sz=int(storage_attrs['osd_pool_size']),\\n ceph_version='firefly',\\n volumes_ceph=storage_attrs['volumes_ceph'],\\n objects_ceph=storage_attrs['objects_ceph'],\\n ephemeral_ceph=storage_attrs['ephemeral_ceph'],\\n images_ceph=storage_attrs['images_ceph'],\\n emulate_pre_7_0=False)\\n\\n # Log {pool_name: pg_count} mapping\\n pg_str = \\\", \\\".join(map(\\\"{0[0]}={0[1]}\\\".format, pg_counts.items()))\\n logger.debug(\\\"Ceph: PG values {%s}\\\", pg_str)\\n\\n storage_attrs['pg_num'] = pg_counts['default_pg_num']\\n storage_attrs['per_pool_pg_nums'] = pg_counts\",\n \"def test_set_hosts_storage_backing_min(instance_backing, number_of_hosts):\\n LOG.tc_step(\\\"Determine the hosts to configure\\\")\\n hosts = host_helper.get_up_hypervisors()\\n hosts_len = len(hosts)\\n host_num_mapping = {\\n 'all': hosts_len,\\n 'two': 2,\\n 'one': 1\\n }\\n number_of_hosts = host_num_mapping[number_of_hosts]\\n\\n hosts_with_backing = \\\\\\n host_helper.get_hosts_in_storage_backing(instance_backing)\\n if len(hosts_with_backing) >= number_of_hosts:\\n LOG.info(\\\"Already have {} hosts in {} backing. Do \\\"\\n \\\"nothing\\\".format(len(hosts_with_backing), instance_backing))\\n return\\n\\n candidate_hosts = get_candidate_hosts(number_of_hosts=number_of_hosts)\\n\\n number_to_config = number_of_hosts - len(hosts_with_backing)\\n hosts_to_config = list(set(candidate_hosts) -\\n set(hosts_with_backing))[0:number_to_config]\\n\\n LOG.tc_step(\\\"Delete vms if any to prepare for system configuration \\\"\\n \\\"change with best effort\\\")\\n vm_helper.delete_vms(fail_ok=True)\\n\\n LOG.tc_step(\\\"Configure following hosts to {} backing: \\\"\\n \\\"{}\\\".format(hosts_to_config, instance_backing))\\n for host in hosts_to_config:\\n HostsToRecover.add(host)\\n host_helper.set_host_storage_backing(host=host,\\n inst_backing=instance_backing,\\n unlock=False,\\n wait_for_configured=False)\\n\\n host_helper.unlock_hosts(hosts_to_config, check_hypervisor_up=True,\\n fail_ok=False)\\n\\n LOG.tc_step(\\\"Waiting for hosts in {} aggregate\\\".format(instance_backing))\\n for host in hosts_to_config:\\n host_helper.wait_for_host_in_instance_backing(\\n host, storage_backing=instance_backing)\\n\\n LOG.tc_step(\\\"Check number of {} hosts is at least \\\"\\n \\\"{}\\\".format(instance_backing, number_of_hosts))\\n assert number_of_hosts <= \\\\\\n len(host_helper.get_hosts_in_storage_backing(instance_backing)), \\\\\\n \\\"Number of {} hosts is less than {} after \\\" \\\\\\n \\\"configuration\\\".format(instance_backing, number_of_hosts)\",\n \"def clusters_every(self, clusters_every):\\n\\n self._clusters_every = clusters_every\",\n \"def get_cpu_counts(host):\\n counts = {}\\n for s in range(0, len(host.nodes)):\\n counts[s] = {}\\n for f in CORE_FUNCTIONS:\\n counts[s][f] = len(host.cpu_functions[s][f])\\n return counts\",\n \"def test_get_virtual_machine_count_metrics(self):\\n pass\",\n \"def evmcs(self, evmcs):\\n\\n self._evmcs = evmcs\",\n \"def check_all_hosts (self, repo_version_id, version_name):\\n if self.compare_versions(self.ambari_version, \\\"2.1.0\\\") < 0:\\n query1 = \\\"SELECT chm.host_name from ClusterHostMapping chm JOIN clusters c ON c.cluster_name = '{0}';\\\".format(self.cluster_name)\\n else:\\n query1 = \\\"SELECT h.host_name from ClusterHostMapping chm JOIN clusters c ON c.cluster_name = '{0}' JOIN hosts h ON chm.host_id = h.host_id;\\\".format(self.cluster_name)\\n\\n if self.compare_versions(self.ambari_version, \\\"2.1.0\\\") < 0:\\n query2 = \\\"SELECT hv.host_name, hv.state FROM host_version hv WHERE hv.repo_version_id = {0};\\\".format(repo_version_id)\\n else:\\n #query2 = \\\"SELECT hv.state,h.host_name FROM hosts h JOIN host_version hv ON h.host_id = hv.host_id WHERE hv.repo_version_id = {0};\\\".format(repo_version_id)\\n query2 = \\\"SELECT hv.state,h.host_name, hs.health_status,hs.agent_version,(h.total_mem/1024/1024) as total_mem_gb,(hs.available_mem/1024/1024) as available_mem_gb FROM hosts h JOIN host_version hv ON h.host_id = hv.host_id JOIN hoststate hs ON h.host_id = hs.host_id WHERE hv.repo_version_id = {0} order by h.host_name;\\\".format(repo_version_id)\\n # All cluster hosts\\n host_names = set()\\n self.cursor.execute(query1)\\n rows = self.cursor.fetchall()\\n if self.options.verbose:\\n Logger.debug(query1 + \\\"\\\\n\\\")\\n if rows and len(rows) > 0:\\n host_names = set([row[0] for row in rows if len(row) == 1])\\n Logger.debug(\\\"Hosts: {0}\\\".format(\\\", \\\".join(host_names)))\\n\\n host_name_to_state = {} # keys should be a subset of host_names\\n hosts_with_repo_version_state_not_in_current = set()\\n self.cursor.execute(query2 + \\\"\\\\n\\\")\\n rows = self.cursor.fetchall()\\n Logger.info(\\\"******************************************************************************************************************************************************\\\")\\n Logger.info(\\\"\\\\t\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tHOST(S) STATE\\\\t\\\")\\n Logger.info(\\\"******************************************************************************************************************************************************\\\\n\\\")\\n Logger.info(\\\"------------------------------------------------------------------------------------------------------------------------------------------------------\\\")\\n Logger.info(\\\"State\\\\t\\\\tHostname\\\\t\\\\t\\\\t\\\\tHealth\\\\t\\\\tAgentVersion\\\\tTotalMemory\\\\tAvailableMemory\\\")\\n Logger.info(\\\"------------------------------------------------------------------------------------------------------------------------------------------------------\\\")\\n\\n if rows and len(rows) > 0:\\n for row in range(len(rows)):\\n data = json.loads(rows[row][2])\\n data1 = json.loads(rows[row][3])\\n Logger.info(\\\"{0}\\\\t\\\\t{1}\\\\t\\\\t{2}\\\\t\\\\t{3}\\\\t\\\\t{4}\\\\t\\\\t{5}\\\".format(rows[row][0], rows[row][1], data[\\\"healthStatus\\\"], data1[\\\"version\\\"], rows[row][4], rows[row][5]))\\n print (\\\"\\\\n\\\")\\n Logger.debug(query2)\\n if rows and len(rows) > 0:\\n for row in rows:\\n if len(row) == 6:\\n host_name = row[1]\\n state = row[0]\\n host_name_to_state[host_name] = state\\n if state.upper() != \\\"CURRENT\\\":\\n hosts_with_repo_version_state_not_in_current.add(host_name)\\n host_names_with_version = set(host_name_to_state.keys())\\n host_names_without_version = host_names - host_names_with_version\\n # Logger.info(\\\"\\\\t\\\\tHost(s) state Summary\\\")\\n if len(host_names) > 0:\\n if len(host_names_without_version) > 0:\\n Logger.error(\\\"{0} host(s) do not have a Host Version for Repo Version {1}.\\\\n\\\" \\\\\\n \\\"Host(s):\\\\n{2}\\\\n\\\".\\n format(len(host_names_without_version), version_name, \\\", \\\".join(host_names_without_version)))\\n\\n if len(hosts_with_repo_version_state_not_in_current) > 0:\\n Logger.error(\\\"{0} host(s) have a Host Version for Repo Version {1} but the state is not CURRENT.\\\\n\\\" \\\\\\n \\\"Host(s):\\\\n{2}\\\\n\\\".\\n format(len(hosts_with_repo_version_state_not_in_current), version_name, \\\", \\\".join(hosts_with_repo_version_state_not_in_current)))\\n\\n if len(host_names_without_version) == 0 and len(hosts_with_repo_version_state_not_in_current) == 0:\\n Logger.info(\\\"Found {0} host(s) in the cluster, and all have a Host Version of CURRENT for \\\" \\\\\\n \\\"Repo Version {1}. Things look good.\\\\n\\\".format(len(host_names), version_name))\\n else:\\n Logger.error(\\\"Make sure that all of these hosts are heartbeating, that they have the packages installed, the\\\\n\\\" \\\\\\n \\\"hdp-select symlinks are correct, and that the services on these hosts have been restarated.\\\\n\\\")\\n pass\",\n \"def Host(self, h):\\r\\n\\r\\n self.host = h\\r\\n return self\",\n \"def vm_templates_every(self, vm_templates_every):\\n\\n self._vm_templates_every = vm_templates_every\",\n \"def set_hyperparameters(self, hyperparameters):\\n self._hyperparameters = numpy.copy(hyperparameters)\",\n \"def set_hostname(self, userid, hostname, os_version):\\n tmp_path = self._pathutils.get_guest_temp_path(userid)\\n if not os.path.exists(tmp_path):\\n os.makedirs(tmp_path)\\n tmp_file = tmp_path + '/hostname.sh'\\n\\n lnxdist = self._dist_manager.get_linux_dist(os_version)()\\n lines = lnxdist.generate_set_hostname_script(hostname)\\n with open(tmp_file, 'w') as f:\\n f.writelines(lines)\\n\\n requestData = \\\"ChangeVM \\\" + userid + \\\" punchfile \\\" + \\\\\\n tmp_file + \\\" --class x\\\"\\n LOG.debug(\\\"Punch script to guest %s to set hostname\\\" % userid)\\n\\n try:\\n self._smtclient._request(requestData)\\n except exception.SDKSMTRequestFailed as err:\\n msg = (\\\"Failed to punch set_hostname script to userid '%s'. SMT \\\"\\n \\\"error: %s\\\" % (userid, err.format_message()))\\n LOG.error(msg)\\n raise exception.SDKSMTRequestFailed(err.results, msg)\\n finally:\\n self._pathutils.clean_temp_folder(tmp_path)\",\n \"def _set_group_resource(self, _g):\\n\\n if isinstance(_g, Server):\\n return\\n\\n for _, sg in _g.subgroups.items():\\n self._set_group_resource(sg)\\n _g.vCPUs += sg.vCPUs\\n _g.mem += sg.mem\\n _g.local_volume_size += sg.local_volume_size\",\n \"def hosts_some(self, hosts_some):\\n\\n self._hosts_some = hosts_some\",\n \"def host_ip(self, host_ip):\\n\\n self._host_ip = host_ip\",\n \"def init_host(self, host):\\n LOG.debug(\\\"init_host\\\")\",\n \"def _host_in_event(self, ev):\\n self._update_nodes()\\n\\n if not self.nodes:\\n return\\n\\n for node in self.nodes:\\n if node.ip in ev.host.ipv4:\\n datapath = self.dpset.get(ev.host.port.dpid)\\n node.setPortInformation(ev.host.port.dpid, datapath, ev.host.port.port_no, ev.host.port)\\n self._install_cdnengine_matching_flow(datapath, node.ip, node.port)\\n self.logger.info('New Node connected the network. Matching rules were installed ' + node.__str__())\",\n \"def size(self):\\r\\n return sum(pool.size() for pool in self.host_to_pool.values())\",\n \"def update_all_hosts_config(self, config_list):\\n return self._put(endpoint='{}/cm/allHosts/config'.format(self.api_version),\\n data=config_list).json()\",\n \"def set(self, host, **kwargs):\\n for p, c in self.configs_:\\n if host in c.hosts_:\\n c.set(host, **kwargs)\\n return\\n raise ValueError(\\\"Host %s: not found\\\" % host)\",\n \"def getHostCount(self, **kwargs):\\n\\n allParams = ['scope']\\n\\n params = locals()\\n for (key, val) in list(params['kwargs'].items()):\\n if key not in allParams:\\n raise TypeError(\\\"Got an unexpected keyword argument '%s' to method getHostCount\\\" % key)\\n params[key] = val\\n del params['kwargs']\\n\\n resourcePath = '/host/count'\\n resourcePath = resourcePath.replace('{format}', 'json')\\n method = 'GET'\\n\\n queryParams = {}\\n headerParams = {}\\n formParams = {}\\n files = {}\\n bodyParam = None\\n\\n headerParams['Accept'] = 'application/json'\\n headerParams['Content-Type'] = 'application/json'\\n\\n\\n\\n\\n if ('scope' in params):\\n headerParams['scope'] = params['scope']\\n\\n\\n\\n\\n\\n\\n\\n\\n postData = (formParams if formParams else bodyParam)\\n\\n response = self.apiClient.callAPI(resourcePath, method, queryParams,\\n postData, headerParams, files=files)\\n\\n\\n if not response:\\n return None\\n\\n responseObject = self.apiClient.deserialize(response, 'CountResult')\\n return responseObject\",\n \"def iscsi_luns_every(self, iscsi_luns_every):\\n\\n self._iscsi_luns_every = iscsi_luns_every\",\n \"def set_article_count(cls, count):\\n return cls.db.set(\\\"article_count\\\", count)\",\n \"async def _total_players(self, ctx: Context, number: int):\\n\\n await self.config.guild(ctx.guild).total_players.set(number)\\n\\n await ctx.send(_(\\\"Set total players to `{}`.\\\").format(number))\",\n \"def poll_host(self, server, obj, name):\\n\\n self.log.debug('found host: %s' % (name,))\\n\\n status = 0\\n cpu_total = cpu_usage = cpu_percent = cpu_count = cpu_mhz_per_core = 0\\n mem_total = mem_usage = mem_percent = 0\\n vms_total = vms_running = vms_stopped = 0\\n\\n if '.' in name and name.count('.') != 3:\\n name = name.split('.')[0]\\n\\n props = server._retrieve_properties_traversal(property_names=[\\n 'name',\\n 'summary.overallStatus',\\n 'summary.quickStats.overallMemoryUsage',\\n 'summary.quickStats.overallCpuUsage',\\n 'summary.hardware.memorySize',\\n 'summary.hardware.numCpuCores',\\n 'summary.hardware.cpuMhz',\\n ], from_node=obj, obj_type='HostSystem')\\n\\n for prop_set in props:\\n for prop in prop_set.PropSet:\\n pn, pv = prop.Name, prop.Val\\n\\n if pn == 'summary.overallStatus':\\n status = HOST_STATUS.index(pv)\\n elif pn == 'summary.quickStats.overallMemoryUsage':\\n mem_usage = pv\\n elif pn == 'summary.quickStats.overallCpuUsage':\\n cpu_usage = pv\\n elif pn == 'summary.hardware.memorySize':\\n mem_total = pv / MB\\n elif pn == 'summary.hardware.numCpuCores':\\n cpu_count = pv\\n elif pn == 'summary.hardware.cpuMhz':\\n cpu_mhz_per_core = pv\\n\\n vms_total = len(server.get_registered_vms(obj))\\n vms_running = len(server.get_registered_vms(obj, status='poweredOn'))\\n vms_stopped = len(server.get_registered_vms(obj, status='poweredOff'))\\n\\n cpu_total = cpu_count * cpu_mhz_per_core\\n cpu_percent = cpu_usage / float(cpu_total) * 100\\n mem_percent = mem_usage / float(mem_total) * 100\\n\\n stats = {\\n 'status': status,\\n 'cpu_total': cpu_total,\\n 'cpu_usage': cpu_usage,\\n 'cpu_percent': cpu_percent,\\n 'cpu_count': cpu_count,\\n 'mem_total': mem_total,\\n 'mem_usage': mem_usage,\\n 'mem_percent': mem_percent,\\n 'vms_total': vms_total,\\n 'vms_running': vms_running,\\n 'vms_stopped': vms_stopped,\\n }\\n\\n return stats\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66588247","0.5818077","0.5640094","0.5640094","0.5394924","0.5279623","0.5277185","0.52716357","0.5202823","0.50858754","0.5027976","0.4993971","0.4947955","0.4938431","0.48995036","0.4849338","0.4816406","0.4814211","0.48059392","0.47629017","0.47593567","0.4754853","0.47468898","0.4743043","0.46774286","0.46355253","0.46348038","0.46311286","0.4624137","0.4622257","0.46133512","0.4607854","0.45836878","0.45467642","0.4545559","0.45281798","0.4519975","0.4517403","0.45134997","0.45108777","0.45090604","0.45085707","0.44889688","0.4487499","0.44844756","0.44759995","0.44755483","0.44698033","0.44608602","0.44593254","0.44563645","0.44543663","0.444307","0.4438199","0.4429109","0.44280148","0.44014725","0.43967745","0.43945417","0.43845063","0.43845063","0.43664223","0.4363821","0.43528908","0.43515792","0.43434575","0.4332184","0.43241364","0.432296","0.43184853","0.43063697","0.43061328","0.4303232","0.42911157","0.42886743","0.42886743","0.42850313","0.42845556","0.42736575","0.42734736","0.4270853","0.42688978","0.4268784","0.42644992","0.42551","0.42548218","0.4242691","0.42411265","0.42325348","0.42294964","0.4229271","0.4217806","0.42120352","0.4207362","0.4204293","0.4202125","0.420103","0.41986752","0.41902122","0.4188711"],"string":"[\n \"0.66588247\",\n \"0.5818077\",\n \"0.5640094\",\n \"0.5640094\",\n \"0.5394924\",\n \"0.5279623\",\n \"0.5277185\",\n \"0.52716357\",\n \"0.5202823\",\n \"0.50858754\",\n \"0.5027976\",\n \"0.4993971\",\n \"0.4947955\",\n \"0.4938431\",\n \"0.48995036\",\n \"0.4849338\",\n \"0.4816406\",\n \"0.4814211\",\n \"0.48059392\",\n \"0.47629017\",\n \"0.47593567\",\n \"0.4754853\",\n \"0.47468898\",\n \"0.4743043\",\n \"0.46774286\",\n \"0.46355253\",\n \"0.46348038\",\n \"0.46311286\",\n \"0.4624137\",\n \"0.4622257\",\n \"0.46133512\",\n \"0.4607854\",\n \"0.45836878\",\n \"0.45467642\",\n \"0.4545559\",\n \"0.45281798\",\n \"0.4519975\",\n \"0.4517403\",\n \"0.45134997\",\n \"0.45108777\",\n \"0.45090604\",\n \"0.45085707\",\n \"0.44889688\",\n \"0.4487499\",\n \"0.44844756\",\n \"0.44759995\",\n \"0.44755483\",\n \"0.44698033\",\n \"0.44608602\",\n \"0.44593254\",\n \"0.44563645\",\n \"0.44543663\",\n \"0.444307\",\n \"0.4438199\",\n \"0.4429109\",\n \"0.44280148\",\n \"0.44014725\",\n \"0.43967745\",\n \"0.43945417\",\n \"0.43845063\",\n \"0.43845063\",\n \"0.43664223\",\n \"0.4363821\",\n \"0.43528908\",\n \"0.43515792\",\n \"0.43434575\",\n \"0.4332184\",\n \"0.43241364\",\n \"0.432296\",\n \"0.43184853\",\n \"0.43063697\",\n \"0.43061328\",\n \"0.4303232\",\n \"0.42911157\",\n \"0.42886743\",\n \"0.42886743\",\n \"0.42850313\",\n \"0.42845556\",\n \"0.42736575\",\n \"0.42734736\",\n \"0.4270853\",\n \"0.42688978\",\n \"0.4268784\",\n \"0.42644992\",\n \"0.42551\",\n \"0.42548218\",\n \"0.4242691\",\n \"0.42411265\",\n \"0.42325348\",\n \"0.42294964\",\n \"0.4229271\",\n \"0.4217806\",\n \"0.42120352\",\n \"0.4207362\",\n \"0.4204293\",\n \"0.4202125\",\n \"0.420103\",\n \"0.41986752\",\n \"0.41902122\",\n \"0.4188711\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7839512"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19683,"cells":{"query":{"kind":"string","value":"Sets the local_group_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def local_group_count(self, local_group_count):\n\n self._local_group_count = local_group_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count","def _set_group_resource(self, _g):\n\n if isinstance(_g, Server):\n return\n\n for _, sg in _g.subgroups.items():\n self._set_group_resource(sg)\n _g.vCPUs += sg.vCPUs\n _g.mem += sg.mem\n _g.local_volume_size += sg.local_volume_size","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def __init__(__self__, *,\n local_ssd_count: Optional[pulumi.Input[int]] = None):\n if local_ssd_count is not None:\n pulumi.set(__self__, \"local_ssd_count\", local_ssd_count)","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def local_id(self, local_id):\n\n self._local_id = local_id","def local_id(self, local_id):\n\n self._local_id = local_id","def local_id(self, local_id):\n\n self._local_id = local_id","def local_id(self, local_id):\n\n self._local_id = local_id","def namespace_group_num_in(self, namespace_group_num_in):\n\n self._namespace_group_num_in = namespace_group_num_in","def local_category(self, local_category: str):\n\n self._local_category = local_category","def local_id_in(self, local_id_in):\n\n self._local_id_in = local_id_in","def local_id_in(self, local_id_in):\n\n self._local_id_in = local_id_in","def local_id_in(self, local_id_in):\n\n self._local_id_in = local_id_in","def local_id_in(self, local_id_in):\n\n self._local_id_in = local_id_in","def _ensure_local_neo4j_has_test_computergroup_data(neo4j_session):\n groups = tests.data.jamf.computers.GROUPS\n cartography.intel.jamf.computers.load_computer_groups(groups, neo4j_session, TEST_UPDATE_TAG)","def local_id_gte(self, local_id_gte):\n\n self._local_id_gte = local_id_gte","def local_id_gte(self, local_id_gte):\n\n self._local_id_gte = local_id_gte","def local_id_gte(self, local_id_gte):\n\n self._local_id_gte = local_id_gte","def local_id_gte(self, local_id_gte):\n\n self._local_id_gte = local_id_gte","def total_nucleus_clients(self, total_nucleus_clients):\n\n self._total_nucleus_clients = total_nucleus_clients","def _set_group_weight(self, _group):\n\n if self.resource.CPU_avail > 0:\n _group.vCPU_weight = float(_group.vCPUs) / float(self.resource.CPU_avail)\n else:\n if _group.vCPUs > 0:\n _group.vCPU_weight = 1.0\n else:\n _group.vCPU_weight = 0.0\n\n if self.resource.mem_avail > 0:\n _group.mem_weight = float(_group.mem) / float(self.resource.mem_avail)\n else:\n if _group.mem > 0:\n _group.mem_weight = 1.0\n else:\n _group.mem_weight = 0.0\n\n if self.resource.local_disk_avail > 0:\n _group.local_volume_weight = float(_group.local_volume_size) / float(self.resource.local_disk_avail)\n else:\n if _group.local_volume_size > 0:\n _group.local_volume_weight = 1.0\n else:\n _group.local_volume_weight = 0.0\n\n for _, sg in _group.subgroups.items():\n if isinstance(sg, Group):\n self._set_group_weight(sg)","def local_id_gt(self, local_id_gt):\n\n self._local_id_gt = local_id_gt","def local_id_gt(self, local_id_gt):\n\n self._local_id_gt = local_id_gt","def local_id_gt(self, local_id_gt):\n\n self._local_id_gt = local_id_gt","def local_id_gt(self, local_id_gt):\n\n self._local_id_gt = local_id_gt","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def local_id(self, local_id):\n if self.local_vars_configuration.client_side_validation and local_id is None: # noqa: E501\n raise ValueError(\"Invalid value for `local_id`, must not be `None`\") # noqa: E501\n\n self._local_id = local_id","def update_num_rois_per_group(self,num_rois_per_group=None):\n if num_rois_per_group is not None:\n self.roi_groups[0].set_num_rois(num_rois_per_group)\n self.signal_status_message.emit('Updated number of ROIs/group to {}'.format(num_rois_per_group))\n num_rois_per_group = self.roi_groups[0].get_num_rois()\n for group in self.roi_groups[1:]:\n group.set_num_rois(num_rois_per_group)\n self.signal_num_rois_per_group.emit(num_rois_per_group)\n self.send_roi_coords()","def local_id_lte(self, local_id_lte):\n\n self._local_id_lte = local_id_lte","def local_id_lte(self, local_id_lte):\n\n self._local_id_lte = local_id_lte","def local_id_lte(self, local_id_lte):\n\n self._local_id_lte = local_id_lte","def local_id_lte(self, local_id_lte):\n\n self._local_id_lte = local_id_lte","def local_id_lt(self, local_id_lt):\n\n self._local_id_lt = local_id_lt","def local_id_lt(self, local_id_lt):\n\n self._local_id_lt = local_id_lt","def local_id_lt(self, local_id_lt):\n\n self._local_id_lt = local_id_lt","def local_id_lt(self, local_id_lt):\n\n self._local_id_lt = local_id_lt","def load_all_groups(self):\n for _, group in self.scopes.items():\n group.update()","def setNetGroup(addr): #status: Done, not tested\r\n pass","def consistency_group_num_in(self, consistency_group_num_in):\n\n self._consistency_group_num_in = consistency_group_num_in","def namespace_group_num(self, namespace_group_num):\n\n self._namespace_group_num = namespace_group_num","def update_global_identifiers(self, universe_test):\n self.cellNum += 1\n self.surfaceNum += 1\n self.materialNum += 1\n if universe_test:\n self.universe += 1","def set_group(self, group):\n self._group = group","def set_main_group(self, sub_group):\n self.attrs[\"MAIN_GROUP\"] = sub_group","def local_time(self, local_time: SmartNvmeLocalTime):\n\n self._local_time = local_time","def localip(self, localip) :\n\t\ttry :\n\t\t\tself._localip = localip\n\t\texcept Exception as e:\n\t\t\traise e","def testPutNetworkLocalIp(self):\n models.System.objects.all().delete()\n self._saveSystem()\n old_count = models.Network.objects.count()\n self._put('inventory/networks/1/',\n data=testsxml.network_put_xml_opt_ip_addr % \"169.254.4.4\",\n username=\"admin\", password=\"password\")\n self.assertEquals(old_count, models.Network.objects.count())\n\n self._put('inventory/networks/1/',\n data=testsxml.network_put_xml_opt_ip_addr % \"4.4.4.4\",\n username=\"admin\", password=\"password\")\n self.assertEquals(old_count + 1, models.Network.objects.count())","def set_total_threads(self, total_thread: int) -> 'General':\n self.total_threads = total_thread\n return self","def set_group(self, group: str) -> None:\n self.group = group","def namespace_groups_every(self, namespace_groups_every):\n\n self._namespace_groups_every = namespace_groups_every","def setCompressorGroup(self, channel, group, unitCode=0):\n resp = self.XAPCommand('CGROUP', channel, group, unitCode=unitCode)\n return int(resp)","def set_total_lui(self, valeurs):\r\n \r\n self._total_lui = valeurs","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def set_pingroups(self, groups):\n self.groups = groups[:]","def all_inclusive(self, all_inclusive):\n\n self._all_inclusive = all_inclusive","def get_affiliation_for_local_group(self, local_group):\n affiliation = self.localgroupaffiliation_set.filter(\n local_group=local_group\n ).first()\n return affiliation","def _update_initial_limits(self, nominal_new=None):\n nominal_new = dict() if nominal_new is None else nominal_new\n self._initial_limits.update(nominal_new)","def set_country_groups_grid_settings(self, grid_settings):\n self.set_grid_settings(self.country_groups_grid_div_id, grid_settings)","def reset_service_group(self, loadbal_id, group_id):\r\n _filter = NestedDict({})\r\n _filter['virtualServers']['id'] = query_filter(group_id)\r\n\r\n kwargs = NestedDict({})\r\n kwargs['filter'] = _filter.to_dict()\r\n kwargs['mask'] = 'mask[serviceGroups]'\r\n\r\n virtual_servers = self.lb_svc.getVirtualServers(id=loadbal_id,\r\n **kwargs)\r\n actual_id = virtual_servers[0]['serviceGroups'][0]['id']\r\n\r\n svc = self.client['Network_Application_Delivery_Controller'\r\n '_LoadBalancer_Service_Group']\r\n return svc.kickAllConnections(id=actual_id)","def get_count_rem_local_ips(self):\n return len(self.remaining_local_ips)","def reset(self, default=False):\n self.local.clear()\n if default:\n for group in self.Group:\n self.system.remove(group.value)","def clear_group(self):\n # Implemented from template for osid.resource.ResourceForm.clear_group_template\n if (self.get_group_metadata().is_read_only() or\n self.get_group_metadata().is_required()):\n raise errors.NoAccess()\n self._my_map['group'] = self._group_default","def CountLocal():\r\n return _hiew.HiewGate_Names_CountLocal()","def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def set_country_group_scope(self, country_group_scope):\n self.single_selection_from_kendo_dropdown(self.country_group_scope_dropdown_locator, country_group_scope)","def set_group_name(self, name):\n self.groupname = name","def setGroup(self, group):\n\t\tself.config.GROUP = group","def consistency_group_num_gte(self, consistency_group_num_gte):\n\n self._consistency_group_num_gte = consistency_group_num_gte","def dist_init(\n local_rank: int,\n num_procs: int,\n *func_args: list[Any],\n **func_kwargs: dict[str, Any],\n ) -> None:\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n os.environ['MASTER_PORT'] = '29503'\n os.environ['LOCAL_RANK'] = str(local_rank)\n # NOTE: unit tests don't support multi-node so\n # local_rank == global rank\n os.environ['RANK'] = str(local_rank)\n os.environ['WORLD_SIZE'] = str(num_procs)\n\n dist.init_process_group('gloo')\n\n run_func(*func_args, **func_kwargs)\n\n # Keep faster ranks from exiting and breaking process group\n dist.barrier()","def consistency_groups_every(self, consistency_groups_every):\n\n self._consistency_groups_every = consistency_groups_every","def namespace_group_num_gte(self, namespace_group_num_gte):\n\n self._namespace_group_num_gte = namespace_group_num_gte","def make_grp(self, name='grp', v=False):\n self.base[name] = self.get_group_array(v=v) #np.zeros(len(self.base), dtype=int)#self.get_group_array()","def test_ipam_vlan_groups_partial_update(self):\n pass","def consistency_group_num_lte(self, consistency_group_num_lte):\n\n self._consistency_group_num_lte = consistency_group_num_lte","def setGroups(self, user):\n self.grouplist.setGroups(user)","def get_new_local_ips(self, count: int=1):\n\n if count <= 0:\n return []\n\n # add more unused local ips to the pool, if needed\n while len(self.unused_local_ips) < count and self.expand_unused_local_ips() == True:\n pass\n\n unused_local_ips = self.unused_local_ips\n uncertain_local_ips = self.uncertain_local_ips\n count_certain = min(count, len(unused_local_ips))\n retr_local_ips = []\n\n for _ in range(0, count_certain):\n random_local_ip = choice(sorted(unused_local_ips))\n retr_local_ips.append(str(random_local_ip))\n unused_local_ips.remove(random_local_ip)\n\n # retrieve uncertain local ips\n if count_certain < count:\n count_uncertain = count - count_certain\n\n # check if new uncertain IPs have to be created\n if len(uncertain_local_ips) < count_uncertain:\n ipspace_multiplier = self.UNCERTAIN_IPSPACE_MULTIPLIER\n\n max_new_ip = self.max_uncertain_local_ip.to_int() + ipspace_multiplier * count_uncertain\n\n count_new_ips = max_new_ip - self.max_uncertain_local_ip.to_int()\n\n # create ipspace_multiplier * count_uncertain new uncertain local IP addresses\n last_gen_ip = None\n for i in range(1, count_new_ips + 1):\n ip = IPAddress.from_int(self.max_uncertain_local_ip.to_int() + i)\n # exclude the definite broadcast address\n if self.priv_ip_segment:\n if ip.to_int() >= self.priv_ip_segment.last_address().to_int():\n break\n uncertain_local_ips.add(ip)\n last_gen_ip = ip\n self.max_uncertain_local_ip = last_gen_ip\n\n # choose the uncertain IPs to return\n total_uncertain = min(count_uncertain, len(uncertain_local_ips))\n for _ in range(0, total_uncertain):\n random_local_ip = choice(sorted(uncertain_local_ips))\n retr_local_ips.append(str(random_local_ip))\n uncertain_local_ips.remove(random_local_ip)\n \n return retr_local_ips","def namespace_group_num_lte(self, namespace_group_num_lte):\n\n self._namespace_group_num_lte = namespace_group_num_lte","def namespace_group_num_lt(self, namespace_group_num_lt):\n\n self._namespace_group_num_lt = namespace_group_num_lt","def local_id_starts_with(self, local_id_starts_with):\n\n self._local_id_starts_with = local_id_starts_with","def local_id_starts_with(self, local_id_starts_with):\n\n self._local_id_starts_with = local_id_starts_with","def local_id_starts_with(self, local_id_starts_with):\n\n self._local_id_starts_with = local_id_starts_with","def local_id_starts_with(self, local_id_starts_with):\n\n self._local_id_starts_with = local_id_starts_with","def update_num_roi_groups(self,num_roi_groups):\n # print('MAIA: num roi groups {}'.format(num_roi_groups))\n if num_roi_groups is not None:\n for _ in range(num_roi_groups,len(self.roi_groups)): # delete unneeded ROIs\n self.roi_groups.pop()\n for _ in range(len(self.roi_groups), num_roi_groups): # make new ROIs\n self.roi_groups.append(ROIGroup(num_images=self.num_images))\n self.signal_status_message.emit('Updated number of ROI groups to {}'.format(num_roi_groups))\n self.update_num_rois_per_group() # ensures that newly created ROI groups have the right number of ROIs\n num_roi_groups = len(self.roi_groups)\n self.signal_num_roi_groups.emit(num_roi_groups)\n # self.send_roi_coords() # this will be send when updating the number of ROIs per group anyway","def init(\n self,\n fsdp_root: \"FullyShardedDataParallel\",\n process_group: dist.ProcessGroup,\n ) -> None:\n self.process_group = process_group\n self.rank = process_group.rank()\n self.world_size = process_group.size()\n # Fix an order over the handles, which should be the same across ranks\n for fsdp_module in fsdp_root.fsdp_modules(fsdp_root):\n for handle in fsdp_module._handles:\n index = len(self.all_handles)\n self.all_handles.append(handle)\n self.handle_to_handle_index[handle] = index\n self.flat_param_to_prefixed_param_names = cast(\n Dict[FlatParameter, List[str]],\n _get_param_to_unflat_param_names(fsdp_root),\n )\n # TODO (awgu): We can broadcast the metadata of rank 0's `all_handles`\n # to check that all ranks have the same handles in the same order.\n # https://github.com/pytorch/pytorch/issues/79620","def Update(self, local_path_info):\n if not local_path_info:\n return\n for priority_group in local_path_info._path_priority_groups:\n group_list = []\n for path in priority_group:\n if not self.IsPathInLocalPaths(path):\n group_list.append(path)\n if group_list:\n self._path_priority_groups.append(group_list)","def set_count(c):\n global count\n count = c","def nfs_inodes_every(self, nfs_inodes_every):\n\n self._nfs_inodes_every = nfs_inodes_every","def local_connections_enabled(self, local_connections_enabled):\n\n self._local_connections_enabled = local_connections_enabled","async def async_set_multiroom_group(self, multiroom_group):\n self._multiroom_group = multiroom_group","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def consistency_group_num_lt(self, consistency_group_num_lt):\n\n self._consistency_group_num_lt = consistency_group_num_lt","def local(self):\n logging.info(__name__ + ' : Set control to local & locked')\n self.set_remote_status(0)","def with_group(self, group):\n\t\tself.variables['group'] = group\n\t\treturn self","def load_group(self):\n items = self.group_list.selectedItems()\n counter = 0\n for item in items:\n if counter == 0:\n self.parent.load_group(item.text())\n else:\n self.parent.add_group_data(item.text())\n counter += 1","def user_count(self, user_count):\n\n self._user_count = user_count","def user_count(self, user_count):\n\n self._user_count = user_count","def test_ipam_vlan_groups_update(self):\n pass"],"string":"[\n \"def external_group_count(self, external_group_count):\\n\\n self._external_group_count = external_group_count\",\n \"def _set_group_resource(self, _g):\\n\\n if isinstance(_g, Server):\\n return\\n\\n for _, sg in _g.subgroups.items():\\n self._set_group_resource(sg)\\n _g.vCPUs += sg.vCPUs\\n _g.mem += sg.mem\\n _g.local_volume_size += sg.local_volume_size\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def __init__(__self__, *,\\n local_ssd_count: Optional[pulumi.Input[int]] = None):\\n if local_ssd_count is not None:\\n pulumi.set(__self__, \\\"local_ssd_count\\\", local_ssd_count)\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def local_id(self, local_id):\\n\\n self._local_id = local_id\",\n \"def local_id(self, local_id):\\n\\n self._local_id = local_id\",\n \"def local_id(self, local_id):\\n\\n self._local_id = local_id\",\n \"def local_id(self, local_id):\\n\\n self._local_id = local_id\",\n \"def namespace_group_num_in(self, namespace_group_num_in):\\n\\n self._namespace_group_num_in = namespace_group_num_in\",\n \"def local_category(self, local_category: str):\\n\\n self._local_category = local_category\",\n \"def local_id_in(self, local_id_in):\\n\\n self._local_id_in = local_id_in\",\n \"def local_id_in(self, local_id_in):\\n\\n self._local_id_in = local_id_in\",\n \"def local_id_in(self, local_id_in):\\n\\n self._local_id_in = local_id_in\",\n \"def local_id_in(self, local_id_in):\\n\\n self._local_id_in = local_id_in\",\n \"def _ensure_local_neo4j_has_test_computergroup_data(neo4j_session):\\n groups = tests.data.jamf.computers.GROUPS\\n cartography.intel.jamf.computers.load_computer_groups(groups, neo4j_session, TEST_UPDATE_TAG)\",\n \"def local_id_gte(self, local_id_gte):\\n\\n self._local_id_gte = local_id_gte\",\n \"def local_id_gte(self, local_id_gte):\\n\\n self._local_id_gte = local_id_gte\",\n \"def local_id_gte(self, local_id_gte):\\n\\n self._local_id_gte = local_id_gte\",\n \"def local_id_gte(self, local_id_gte):\\n\\n self._local_id_gte = local_id_gte\",\n \"def total_nucleus_clients(self, total_nucleus_clients):\\n\\n self._total_nucleus_clients = total_nucleus_clients\",\n \"def _set_group_weight(self, _group):\\n\\n if self.resource.CPU_avail > 0:\\n _group.vCPU_weight = float(_group.vCPUs) / float(self.resource.CPU_avail)\\n else:\\n if _group.vCPUs > 0:\\n _group.vCPU_weight = 1.0\\n else:\\n _group.vCPU_weight = 0.0\\n\\n if self.resource.mem_avail > 0:\\n _group.mem_weight = float(_group.mem) / float(self.resource.mem_avail)\\n else:\\n if _group.mem > 0:\\n _group.mem_weight = 1.0\\n else:\\n _group.mem_weight = 0.0\\n\\n if self.resource.local_disk_avail > 0:\\n _group.local_volume_weight = float(_group.local_volume_size) / float(self.resource.local_disk_avail)\\n else:\\n if _group.local_volume_size > 0:\\n _group.local_volume_weight = 1.0\\n else:\\n _group.local_volume_weight = 0.0\\n\\n for _, sg in _group.subgroups.items():\\n if isinstance(sg, Group):\\n self._set_group_weight(sg)\",\n \"def local_id_gt(self, local_id_gt):\\n\\n self._local_id_gt = local_id_gt\",\n \"def local_id_gt(self, local_id_gt):\\n\\n self._local_id_gt = local_id_gt\",\n \"def local_id_gt(self, local_id_gt):\\n\\n self._local_id_gt = local_id_gt\",\n \"def local_id_gt(self, local_id_gt):\\n\\n self._local_id_gt = local_id_gt\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def local_id(self, local_id):\\n if self.local_vars_configuration.client_side_validation and local_id is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `local_id`, must not be `None`\\\") # noqa: E501\\n\\n self._local_id = local_id\",\n \"def update_num_rois_per_group(self,num_rois_per_group=None):\\n if num_rois_per_group is not None:\\n self.roi_groups[0].set_num_rois(num_rois_per_group)\\n self.signal_status_message.emit('Updated number of ROIs/group to {}'.format(num_rois_per_group))\\n num_rois_per_group = self.roi_groups[0].get_num_rois()\\n for group in self.roi_groups[1:]:\\n group.set_num_rois(num_rois_per_group)\\n self.signal_num_rois_per_group.emit(num_rois_per_group)\\n self.send_roi_coords()\",\n \"def local_id_lte(self, local_id_lte):\\n\\n self._local_id_lte = local_id_lte\",\n \"def local_id_lte(self, local_id_lte):\\n\\n self._local_id_lte = local_id_lte\",\n \"def local_id_lte(self, local_id_lte):\\n\\n self._local_id_lte = local_id_lte\",\n \"def local_id_lte(self, local_id_lte):\\n\\n self._local_id_lte = local_id_lte\",\n \"def local_id_lt(self, local_id_lt):\\n\\n self._local_id_lt = local_id_lt\",\n \"def local_id_lt(self, local_id_lt):\\n\\n self._local_id_lt = local_id_lt\",\n \"def local_id_lt(self, local_id_lt):\\n\\n self._local_id_lt = local_id_lt\",\n \"def local_id_lt(self, local_id_lt):\\n\\n self._local_id_lt = local_id_lt\",\n \"def load_all_groups(self):\\n for _, group in self.scopes.items():\\n group.update()\",\n \"def setNetGroup(addr): #status: Done, not tested\\r\\n pass\",\n \"def consistency_group_num_in(self, consistency_group_num_in):\\n\\n self._consistency_group_num_in = consistency_group_num_in\",\n \"def namespace_group_num(self, namespace_group_num):\\n\\n self._namespace_group_num = namespace_group_num\",\n \"def update_global_identifiers(self, universe_test):\\n self.cellNum += 1\\n self.surfaceNum += 1\\n self.materialNum += 1\\n if universe_test:\\n self.universe += 1\",\n \"def set_group(self, group):\\n self._group = group\",\n \"def set_main_group(self, sub_group):\\n self.attrs[\\\"MAIN_GROUP\\\"] = sub_group\",\n \"def local_time(self, local_time: SmartNvmeLocalTime):\\n\\n self._local_time = local_time\",\n \"def localip(self, localip) :\\n\\t\\ttry :\\n\\t\\t\\tself._localip = localip\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def testPutNetworkLocalIp(self):\\n models.System.objects.all().delete()\\n self._saveSystem()\\n old_count = models.Network.objects.count()\\n self._put('inventory/networks/1/',\\n data=testsxml.network_put_xml_opt_ip_addr % \\\"169.254.4.4\\\",\\n username=\\\"admin\\\", password=\\\"password\\\")\\n self.assertEquals(old_count, models.Network.objects.count())\\n\\n self._put('inventory/networks/1/',\\n data=testsxml.network_put_xml_opt_ip_addr % \\\"4.4.4.4\\\",\\n username=\\\"admin\\\", password=\\\"password\\\")\\n self.assertEquals(old_count + 1, models.Network.objects.count())\",\n \"def set_total_threads(self, total_thread: int) -> 'General':\\n self.total_threads = total_thread\\n return self\",\n \"def set_group(self, group: str) -> None:\\n self.group = group\",\n \"def namespace_groups_every(self, namespace_groups_every):\\n\\n self._namespace_groups_every = namespace_groups_every\",\n \"def setCompressorGroup(self, channel, group, unitCode=0):\\n resp = self.XAPCommand('CGROUP', channel, group, unitCode=unitCode)\\n return int(resp)\",\n \"def set_total_lui(self, valeurs):\\r\\n \\r\\n self._total_lui = valeurs\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def set_pingroups(self, groups):\\n self.groups = groups[:]\",\n \"def all_inclusive(self, all_inclusive):\\n\\n self._all_inclusive = all_inclusive\",\n \"def get_affiliation_for_local_group(self, local_group):\\n affiliation = self.localgroupaffiliation_set.filter(\\n local_group=local_group\\n ).first()\\n return affiliation\",\n \"def _update_initial_limits(self, nominal_new=None):\\n nominal_new = dict() if nominal_new is None else nominal_new\\n self._initial_limits.update(nominal_new)\",\n \"def set_country_groups_grid_settings(self, grid_settings):\\n self.set_grid_settings(self.country_groups_grid_div_id, grid_settings)\",\n \"def reset_service_group(self, loadbal_id, group_id):\\r\\n _filter = NestedDict({})\\r\\n _filter['virtualServers']['id'] = query_filter(group_id)\\r\\n\\r\\n kwargs = NestedDict({})\\r\\n kwargs['filter'] = _filter.to_dict()\\r\\n kwargs['mask'] = 'mask[serviceGroups]'\\r\\n\\r\\n virtual_servers = self.lb_svc.getVirtualServers(id=loadbal_id,\\r\\n **kwargs)\\r\\n actual_id = virtual_servers[0]['serviceGroups'][0]['id']\\r\\n\\r\\n svc = self.client['Network_Application_Delivery_Controller'\\r\\n '_LoadBalancer_Service_Group']\\r\\n return svc.kickAllConnections(id=actual_id)\",\n \"def get_count_rem_local_ips(self):\\n return len(self.remaining_local_ips)\",\n \"def reset(self, default=False):\\n self.local.clear()\\n if default:\\n for group in self.Group:\\n self.system.remove(group.value)\",\n \"def clear_group(self):\\n # Implemented from template for osid.resource.ResourceForm.clear_group_template\\n if (self.get_group_metadata().is_read_only() or\\n self.get_group_metadata().is_required()):\\n raise errors.NoAccess()\\n self._my_map['group'] = self._group_default\",\n \"def CountLocal():\\r\\n return _hiew.HiewGate_Names_CountLocal()\",\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def set_country_group_scope(self, country_group_scope):\\n self.single_selection_from_kendo_dropdown(self.country_group_scope_dropdown_locator, country_group_scope)\",\n \"def set_group_name(self, name):\\n self.groupname = name\",\n \"def setGroup(self, group):\\n\\t\\tself.config.GROUP = group\",\n \"def consistency_group_num_gte(self, consistency_group_num_gte):\\n\\n self._consistency_group_num_gte = consistency_group_num_gte\",\n \"def dist_init(\\n local_rank: int,\\n num_procs: int,\\n *func_args: list[Any],\\n **func_kwargs: dict[str, Any],\\n ) -> None:\\n os.environ['MASTER_ADDR'] = '127.0.0.1'\\n os.environ['MASTER_PORT'] = '29503'\\n os.environ['LOCAL_RANK'] = str(local_rank)\\n # NOTE: unit tests don't support multi-node so\\n # local_rank == global rank\\n os.environ['RANK'] = str(local_rank)\\n os.environ['WORLD_SIZE'] = str(num_procs)\\n\\n dist.init_process_group('gloo')\\n\\n run_func(*func_args, **func_kwargs)\\n\\n # Keep faster ranks from exiting and breaking process group\\n dist.barrier()\",\n \"def consistency_groups_every(self, consistency_groups_every):\\n\\n self._consistency_groups_every = consistency_groups_every\",\n \"def namespace_group_num_gte(self, namespace_group_num_gte):\\n\\n self._namespace_group_num_gte = namespace_group_num_gte\",\n \"def make_grp(self, name='grp', v=False):\\n self.base[name] = self.get_group_array(v=v) #np.zeros(len(self.base), dtype=int)#self.get_group_array()\",\n \"def test_ipam_vlan_groups_partial_update(self):\\n pass\",\n \"def consistency_group_num_lte(self, consistency_group_num_lte):\\n\\n self._consistency_group_num_lte = consistency_group_num_lte\",\n \"def setGroups(self, user):\\n self.grouplist.setGroups(user)\",\n \"def get_new_local_ips(self, count: int=1):\\n\\n if count <= 0:\\n return []\\n\\n # add more unused local ips to the pool, if needed\\n while len(self.unused_local_ips) < count and self.expand_unused_local_ips() == True:\\n pass\\n\\n unused_local_ips = self.unused_local_ips\\n uncertain_local_ips = self.uncertain_local_ips\\n count_certain = min(count, len(unused_local_ips))\\n retr_local_ips = []\\n\\n for _ in range(0, count_certain):\\n random_local_ip = choice(sorted(unused_local_ips))\\n retr_local_ips.append(str(random_local_ip))\\n unused_local_ips.remove(random_local_ip)\\n\\n # retrieve uncertain local ips\\n if count_certain < count:\\n count_uncertain = count - count_certain\\n\\n # check if new uncertain IPs have to be created\\n if len(uncertain_local_ips) < count_uncertain:\\n ipspace_multiplier = self.UNCERTAIN_IPSPACE_MULTIPLIER\\n\\n max_new_ip = self.max_uncertain_local_ip.to_int() + ipspace_multiplier * count_uncertain\\n\\n count_new_ips = max_new_ip - self.max_uncertain_local_ip.to_int()\\n\\n # create ipspace_multiplier * count_uncertain new uncertain local IP addresses\\n last_gen_ip = None\\n for i in range(1, count_new_ips + 1):\\n ip = IPAddress.from_int(self.max_uncertain_local_ip.to_int() + i)\\n # exclude the definite broadcast address\\n if self.priv_ip_segment:\\n if ip.to_int() >= self.priv_ip_segment.last_address().to_int():\\n break\\n uncertain_local_ips.add(ip)\\n last_gen_ip = ip\\n self.max_uncertain_local_ip = last_gen_ip\\n\\n # choose the uncertain IPs to return\\n total_uncertain = min(count_uncertain, len(uncertain_local_ips))\\n for _ in range(0, total_uncertain):\\n random_local_ip = choice(sorted(uncertain_local_ips))\\n retr_local_ips.append(str(random_local_ip))\\n uncertain_local_ips.remove(random_local_ip)\\n \\n return retr_local_ips\",\n \"def namespace_group_num_lte(self, namespace_group_num_lte):\\n\\n self._namespace_group_num_lte = namespace_group_num_lte\",\n \"def namespace_group_num_lt(self, namespace_group_num_lt):\\n\\n self._namespace_group_num_lt = namespace_group_num_lt\",\n \"def local_id_starts_with(self, local_id_starts_with):\\n\\n self._local_id_starts_with = local_id_starts_with\",\n \"def local_id_starts_with(self, local_id_starts_with):\\n\\n self._local_id_starts_with = local_id_starts_with\",\n \"def local_id_starts_with(self, local_id_starts_with):\\n\\n self._local_id_starts_with = local_id_starts_with\",\n \"def local_id_starts_with(self, local_id_starts_with):\\n\\n self._local_id_starts_with = local_id_starts_with\",\n \"def update_num_roi_groups(self,num_roi_groups):\\n # print('MAIA: num roi groups {}'.format(num_roi_groups))\\n if num_roi_groups is not None:\\n for _ in range(num_roi_groups,len(self.roi_groups)): # delete unneeded ROIs\\n self.roi_groups.pop()\\n for _ in range(len(self.roi_groups), num_roi_groups): # make new ROIs\\n self.roi_groups.append(ROIGroup(num_images=self.num_images))\\n self.signal_status_message.emit('Updated number of ROI groups to {}'.format(num_roi_groups))\\n self.update_num_rois_per_group() # ensures that newly created ROI groups have the right number of ROIs\\n num_roi_groups = len(self.roi_groups)\\n self.signal_num_roi_groups.emit(num_roi_groups)\\n # self.send_roi_coords() # this will be send when updating the number of ROIs per group anyway\",\n \"def init(\\n self,\\n fsdp_root: \\\"FullyShardedDataParallel\\\",\\n process_group: dist.ProcessGroup,\\n ) -> None:\\n self.process_group = process_group\\n self.rank = process_group.rank()\\n self.world_size = process_group.size()\\n # Fix an order over the handles, which should be the same across ranks\\n for fsdp_module in fsdp_root.fsdp_modules(fsdp_root):\\n for handle in fsdp_module._handles:\\n index = len(self.all_handles)\\n self.all_handles.append(handle)\\n self.handle_to_handle_index[handle] = index\\n self.flat_param_to_prefixed_param_names = cast(\\n Dict[FlatParameter, List[str]],\\n _get_param_to_unflat_param_names(fsdp_root),\\n )\\n # TODO (awgu): We can broadcast the metadata of rank 0's `all_handles`\\n # to check that all ranks have the same handles in the same order.\\n # https://github.com/pytorch/pytorch/issues/79620\",\n \"def Update(self, local_path_info):\\n if not local_path_info:\\n return\\n for priority_group in local_path_info._path_priority_groups:\\n group_list = []\\n for path in priority_group:\\n if not self.IsPathInLocalPaths(path):\\n group_list.append(path)\\n if group_list:\\n self._path_priority_groups.append(group_list)\",\n \"def set_count(c):\\n global count\\n count = c\",\n \"def nfs_inodes_every(self, nfs_inodes_every):\\n\\n self._nfs_inodes_every = nfs_inodes_every\",\n \"def local_connections_enabled(self, local_connections_enabled):\\n\\n self._local_connections_enabled = local_connections_enabled\",\n \"async def async_set_multiroom_group(self, multiroom_group):\\n self._multiroom_group = multiroom_group\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def consistency_group_num_lt(self, consistency_group_num_lt):\\n\\n self._consistency_group_num_lt = consistency_group_num_lt\",\n \"def local(self):\\n logging.info(__name__ + ' : Set control to local & locked')\\n self.set_remote_status(0)\",\n \"def with_group(self, group):\\n\\t\\tself.variables['group'] = group\\n\\t\\treturn self\",\n \"def load_group(self):\\n items = self.group_list.selectedItems()\\n counter = 0\\n for item in items:\\n if counter == 0:\\n self.parent.load_group(item.text())\\n else:\\n self.parent.add_group_data(item.text())\\n counter += 1\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def test_ipam_vlan_groups_update(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5947293","0.509726","0.4911288","0.4911288","0.4911288","0.4881376","0.4801572","0.4801572","0.4801572","0.4801572","0.46374658","0.46280244","0.46024618","0.46024618","0.46024618","0.46024618","0.45948556","0.45816755","0.45816755","0.45816755","0.45816755","0.45652","0.45283303","0.4524158","0.4524158","0.4524158","0.4524158","0.44918805","0.44677573","0.44227064","0.43963397","0.43963397","0.43963397","0.43963397","0.439217","0.439217","0.439217","0.439217","0.43795747","0.43759686","0.434321","0.43350238","0.4330463","0.43142387","0.43140855","0.4313436","0.42855412","0.42720234","0.42593807","0.42398882","0.4229959","0.42172506","0.42157906","0.41537178","0.41537178","0.4151466","0.41459924","0.41323942","0.41160932","0.41118267","0.41092014","0.41051117","0.41046256","0.4102136","0.41013408","0.4099224","0.40937084","0.40834883","0.40829247","0.4077221","0.40621087","0.4060848","0.40438548","0.40403676","0.40390682","0.402112","0.4018822","0.40165043","0.40130153","0.40121773","0.40042952","0.40042952","0.40042952","0.40042952","0.39940384","0.39869294","0.39755583","0.39751625","0.39734754","0.39727563","0.3963821","0.3954032","0.3941217","0.39381272","0.3935059","0.3933567","0.3933055","0.39329037","0.39329037","0.39239335"],"string":"[\n \"0.5947293\",\n \"0.509726\",\n \"0.4911288\",\n \"0.4911288\",\n \"0.4911288\",\n \"0.4881376\",\n \"0.4801572\",\n \"0.4801572\",\n \"0.4801572\",\n \"0.4801572\",\n \"0.46374658\",\n \"0.46280244\",\n \"0.46024618\",\n \"0.46024618\",\n \"0.46024618\",\n \"0.46024618\",\n \"0.45948556\",\n \"0.45816755\",\n \"0.45816755\",\n \"0.45816755\",\n \"0.45816755\",\n \"0.45652\",\n \"0.45283303\",\n \"0.4524158\",\n \"0.4524158\",\n \"0.4524158\",\n \"0.4524158\",\n \"0.44918805\",\n \"0.44677573\",\n \"0.44227064\",\n \"0.43963397\",\n \"0.43963397\",\n \"0.43963397\",\n \"0.43963397\",\n \"0.439217\",\n \"0.439217\",\n \"0.439217\",\n \"0.439217\",\n \"0.43795747\",\n \"0.43759686\",\n \"0.434321\",\n \"0.43350238\",\n \"0.4330463\",\n \"0.43142387\",\n \"0.43140855\",\n \"0.4313436\",\n \"0.42855412\",\n \"0.42720234\",\n \"0.42593807\",\n \"0.42398882\",\n \"0.4229959\",\n \"0.42172506\",\n \"0.42157906\",\n \"0.41537178\",\n \"0.41537178\",\n \"0.4151466\",\n \"0.41459924\",\n \"0.41323942\",\n \"0.41160932\",\n \"0.41118267\",\n \"0.41092014\",\n \"0.41051117\",\n \"0.41046256\",\n \"0.4102136\",\n \"0.41013408\",\n \"0.4099224\",\n \"0.40937084\",\n \"0.40834883\",\n \"0.40829247\",\n \"0.4077221\",\n \"0.40621087\",\n \"0.4060848\",\n \"0.40438548\",\n \"0.40403676\",\n \"0.40390682\",\n \"0.402112\",\n \"0.4018822\",\n \"0.40165043\",\n \"0.40130153\",\n \"0.40121773\",\n \"0.40042952\",\n \"0.40042952\",\n \"0.40042952\",\n \"0.40042952\",\n \"0.39940384\",\n \"0.39869294\",\n \"0.39755583\",\n \"0.39751625\",\n \"0.39734754\",\n \"0.39727563\",\n \"0.3963821\",\n \"0.3954032\",\n \"0.3941217\",\n \"0.39381272\",\n \"0.3935059\",\n \"0.3933567\",\n \"0.3933055\",\n \"0.39329037\",\n \"0.39329037\",\n \"0.39239335\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76894385"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19684,"cells":{"query":{"kind":"string","value":"Sets the standard_catalog_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def standard_catalog_count(self, standard_catalog_count):\n\n self._standard_catalog_count = standard_catalog_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count","def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count","def advanced_catalog_count(self, advanced_catalog_count):\n\n self._advanced_catalog_count = advanced_catalog_count","def setNumberOfTraces(self,numberOfTraces: int) -> None:\n\n if not self.debug:\n self.myFieldFox.write(\"CALC:PAR:COUN \" + str(numberOfTraces))\n\n return","def count_all_catalogs(self):\n return self.manager.count_entities(ModelCatalog)","def custom_compliance_standard_id(self, custom_compliance_standard_id):\n\n self._custom_compliance_standard_id = custom_compliance_standard_id","def _set_readcount_related_data_to_zero(self):\n self.total_read_count = 0\n self.perfect_read_count = 0\n self.RISCC_genome_side_aligned_reads = {}\n self.RISCC_genome_side_unaligned_reads = {}\n self.sequences_counts_positions_errors = {}\n # TODO should all this really be readcount-related? Well, it IS, but when I have a multi-dataset mutant, do I really want to keep the seq/position/count details and the genome-side RISCC read data per dataset rather than total? Hard to tell, really. In a perfect world I wouldn't be doing multiple RISCC datasets anyway!","def solid_surface_density_CL2013_given_physical_catalog(sssp_per_sys, max_core_mass=10.):\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\n core_mass_all = np.copy(sssp_per_sys['mass_all'][sssp_per_sys['a_all'] > 0])\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\n sigma_all = solid_surface_density_CL2013(core_mass_all, a_all)\n return sigma_all, a_all","def count_all_catalog_services(self):\n return self.manager.count_entities(ModelEndpoint)","def setCompoundCount(self, count):\n return self._set(compoundCount=count)","def categoria_svs(self, categoria_svs):\n\n self._categoria_svs = categoria_svs","def catalog_id(self, catalog_id):\n self._catalog_id = catalog_id","def count_standard_residues(self):\n n = 0\n for na in self.iter_standard_residues():\n n += 1\n return n","def catalog_merge(self, catalog_cols=None):\n\n for cluster_info in self._catalog_dictionary.values():\n # Array element names\n catalog_idx = cluster_info['SPT_cat_idx']\n se_catalog = cluster_info['catalog']\n\n # Replace the existing SPT_ID in the SExtractor catalog with the official cluster ID.\n # se_catalog.columns[0].name = 'SPT_ID'\n # del se_catalog['SPT_ID']\n\n # Then replace the column values with the official ID.\n se_catalog['SPT_ID'] = self._spt_catalog['SPT_ID'][catalog_idx]\n\n # Add the SZ center coordinates to the catalog\n se_catalog['SZ_RA'] = self._spt_catalog['RA'][catalog_idx]\n se_catalog['SZ_DEC'] = self._spt_catalog['DEC'][catalog_idx]\n\n # For all requested columns from the master catalog add the value to all columns in the SExtractor catalog.\n if catalog_cols is not None:\n for col_name in catalog_cols:\n se_catalog[col_name] = self._spt_catalog[col_name][catalog_idx]\n\n cluster_info['catalog'] = se_catalog","def __init__(self,\n advanced_catalog_count=None,\n bm_catalog_count=None,\n container_catalog_count=None,\n esxi_host_count=None,\n external_group_count=None,\n hyperv_host_count=None,\n local_group_count=None,\n standard_catalog_count=None,\n user_count=None,\n vdc_count=None,\n vm_count=None,\n guid=None,\n local_vars_configuration=None): # noqa: E501 # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration()\n self.local_vars_configuration = local_vars_configuration\n\n self._advanced_catalog_count = None\n self._bm_catalog_count = None\n self._container_catalog_count = None\n self._esxi_host_count = None\n self._external_group_count = None\n self._hyperv_host_count = None\n self._local_group_count = None\n self._standard_catalog_count = None\n self._user_count = None\n self._vdc_count = None\n self._vm_count = None\n self._guid = None\n self.discriminator = None\n\n if advanced_catalog_count is not None:\n self.advanced_catalog_count = advanced_catalog_count\n if bm_catalog_count is not None:\n self.bm_catalog_count = bm_catalog_count\n if container_catalog_count is not None:\n self.container_catalog_count = container_catalog_count\n if esxi_host_count is not None:\n self.esxi_host_count = esxi_host_count\n if external_group_count is not None:\n self.external_group_count = external_group_count\n if hyperv_host_count is not None:\n self.hyperv_host_count = hyperv_host_count\n if local_group_count is not None:\n self.local_group_count = local_group_count\n if standard_catalog_count is not None:\n self.standard_catalog_count = standard_catalog_count\n if user_count is not None:\n self.user_count = user_count\n if vdc_count is not None:\n self.vdc_count = vdc_count\n if vm_count is not None:\n self.vm_count = vm_count\n if guid is not None:\n self.guid = guid","def _set_catalog(self, catalog: cat.Catalog) -> None:\n self._catalog_interface = CatalogInterface(catalog)\n self._catalog = catalog","def limit_size(self, catalog):\n if len(catalog)<=self.limit:\n return catalog\n mem = {}\n for instance in catalog:\n if (instance['vCpu'], math.ceil(instance['memory'])) not in mem:\n mem[(instance['vCpu'], math.ceil(instance['memory']))] = instance\n out = [val for val in mem.values()]\n if len(out)>self.limit:\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return out[:self.limit]\n return out","def set_nb_clusters(self):\n \n print(\"Finding the optimal number of clusters...\")\n \n sample = ro.r.matrix(self.df[self.df[\"filename\"].between(1, 4)][\"active_power\"].to_numpy())\n \n r=ro.r(\"\"\"\n check = function(matrix) {\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\n\n n_clust = n_clust$data\n\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\n return(max_cluster)\n }\n \"\"\")\n\n result = r(sample)\n self.conf[\"nb_clust\"] = int(result[0])\n \n print(f\"Optimal number of clusters is {self.conf['nb_clust']}\\n\")","def solid_surface_density_nHill_given_physical_catalog(sssp_per_sys, sssp, max_core_mass=10., n=10.):\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\n core_mass_all = np.copy(sssp_per_sys['mass_all'])\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\n sigma_all = solid_surface_density_nHill(core_mass_all, sssp_per_sys['a_all'], Mstar=sssp['Mstar_all'][:,None], n=n)[sssp_per_sys['a_all'] > 0]\n return sigma_all, a_all","def solid_surface_density_CL2013_given_observed_catalog(sss_per_sys, max_core_mass=10.):\n a_obs_per_sys = gen.a_from_P(sss_per_sys['P_obs'], sss_per_sys['Mstar_obs'][:,None])\n a_obs = a_obs_per_sys[sss_per_sys['P_obs'] > 0]\n radii_obs = sss_per_sys['radii_obs'][sss_per_sys['P_obs'] > 0]\n core_mass_obs = generate_planet_mass_from_radius_Ning2018_table_above_lognormal_mass_earthlike_rocky_below_vec(radii_obs)\n core_mass_obs[core_mass_obs > max_core_mass] = max_core_mass\n sigma_obs = solid_surface_density_CL2013(core_mass_obs, a_obs)\n return sigma_obs, core_mass_obs, a_obs","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def _0_cluster_profile(self, _0_cluster_profile):\n\n self.__0_cluster_profile = _0_cluster_profile","def update_scalar_oids(self, new_scalar_oids):\n # type: (List[OID]) -> None\n if not self._is_cache_enabled():\n return\n # Do not update if we are already using scalar oids cache.\n if self._use_scalar_oids_cache:\n return\n self._all_scalar_oids = new_scalar_oids\n self._use_scalar_oids_cache = True\n self._last_ts = time.time()","def update_necrosis_count(self, number):\n\n print(\"controller - update_necrosis_count!\")\n self.view.processing_gui.update_necrosis_count(number)","def set_rate_catalog(self, rate_catalog):\n self.single_selection_from_kendo_dropdown(self.rate_catalog_kendo_dropdown_locator, rate_catalog)","def setupCPUStandard(solver):\n #Adjust blocks for boundary conditions\n makeReadBlocksStandard(solver,solver.operating)\n solver.cpu.set_globals(*solver.globals)\n #Creating sets for cpu calculation\n standardSet = [(x+solver.operating,y+solver.operating) for x,y in numpy.ndindex(solver.blocksize[:-1])]\n #Initializing CPU on standard\n cshape = solver.sharedArray[solver.blocks[0][1]].shape if solver.blocks else (0,)\n solver.standard.initializeCPU(solver.cpu,standardSet,solver.intermediate-1,cshape)","def modeScaler(self, latestCount):\n \n try:\n # Accumulate new sample data.\n self.__accumCts += latestCount\n \n # Increment runtime counter.\n self.__runtime += 1\n \n except:\n raise\n \n return","def set_n_kinetics(self, n):\n self.lib.SetNumberKinetics(ct.c_int(n))","def solid_surface_density_S2014_given_physical_catalog(sssp_per_sys, sssp, max_core_mass=10.):\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\n core_mass_all = np.copy(sssp_per_sys['mass_all'])\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\n sigma_all = solid_surface_density_S2014(core_mass_all, sssp_per_sys['radii_all'], sssp_per_sys['a_all'], Mstar=sssp['Mstar_all'][:,None])[sssp_per_sys['a_all'] > 0]\n return sigma_all, a_all","def system_wide(self, system_wide):\n\n self._system_wide = system_wide","def zero_scanners(self):\n self.logger.info('Zero all Scanners.')\n self.anc350_instrument.zero_scanners()","def set_num_cores(self, ncores, caching = True, dynamic=False):\n self._cores = ncores\n self._caching = caching\n self._dynamic = dynamic","def cluster_num(self, cluster_num):\n\n self._cluster_num = cluster_num","def custom_compliance_standard(self, custom_compliance_standard):\n\n self._custom_compliance_standard = custom_compliance_standard","def test_api_ucs_get_catalog(self):\n api_data = request(\"get\", \"/sys\")\n self.assertEqual(api_data['status'], 200,\n 'Incorrect HTTP return code, expected 200, got:' + str(api_data['status']))\n total_elements = 0\n for elementTypes in api_data[\"json\"]:\n for element in api_data[\"json\"][str(elementTypes)]:\n api_data_c = request(\"get\", \"/catalog\",\n query={\"identifier\": element[\"relative_path\"].strip(\"/\")})\n self.assertEqual(api_data_c['status'], 200,\n 'Incorrect HTTP return code, expected 200, got:' +\n str(api_data_c['status']))\n total_elements += 1\n self.assertGreater(total_elements, 0, \"Zero catalog elements found\")\n # TO DO: deeper check on the catalog data","def n_cs(self):\n return self._configurations[0].n_cs","def system_vlan_num(self, system_vlan_num):\n\n self._system_vlan_num = system_vlan_num","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def setCount(self, num):\n self.count=num","def set_cores(self, cores):\n self.cores = cores\n return","def custom_compliance_standard_id(self):\n return self._custom_compliance_standard_id","def apply(self, catalog):\n out = []\n for product in catalog:\n valid = True\n for key, filtr in self.filters.items():\n valid = valid and filtr(product[key])\n if valid:\n out.append(product)\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\n return self.limit_size(out)","def reset(self):\n for s in self.subsystems:\n s.uptime = 1","def get_root_catalog_ids(self):\n # Implemented from kitosid template for -\n # osid.resource.BinHierarchySession.get_root_bin_ids\n return self._get_provider_session('catalog_hierarchy_session').get_root_catalog_ids()","def _reset_collection(base: pymongo.database.Database, collection: str) -> None:\n logger.info(f'Resetting all data related to \"{collection}\" collection...')\n nb_removed = base[collection].delete_many({}).deleted_count\n logger.info(f\"{nb_removed} records deleted.\")\n\n logger.info(f'Resetting counters.\"{collection}\".')\n nb_removed = base[\"counters\"].delete_many({\"_id\": collection}).deleted_count\n logger.info(f\"{nb_removed} counter records deleted\")","def n_cs(self):\n pass","def cluster_num_lte(self, cluster_num_lte):\n\n self._cluster_num_lte = cluster_num_lte","def dvs_vcenter_multiple_nics(self):\n self.show_step(1)\n self.env.revert_snapshot(\"dvs_vcenter_systest_setup\")\n cluster_id = self.fuel_web.get_last_created_cluster()\n\n os_ip = self.fuel_web.get_public_vip(cluster_id)\n os_conn = os_actions.OpenStackActions(\n os_ip, SERVTEST_USERNAME,\n SERVTEST_PASSWORD,\n SERVTEST_TENANT)\n\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\n networks = []\n router = os_conn.get_router(os_conn.get_network(self.ext_net_name))\n\n self.show_step(2)\n self.show_step(3)\n for net in self.net_data:\n network = os_conn.create_network(network_name=net.keys()[0],\n tenant_id=tenant.id)['network']\n\n logger.info('Create subnet {}'.format(net.keys()[0]))\n subnet = os_conn.create_subnet(subnet_name=net.keys()[0],\n network_id=network['id'],\n cidr=net[net.keys()[0]],\n ip_version=4)\n\n # Check that network is created.\n assert_true(\n os_conn.get_network(network['name'])['id'] == network['id'])\n os_conn.add_router_interface(\n router_id=router[\"id\"],\n subnet_id=subnet[\"id\"])\n networks.append(network)\n\n nics = [{'net-id': network['id']} for network in networks]\n\n # Add rules for ssh and ping\n os_conn.goodbye_security()\n\n _s_groups = os_conn.neutron.list_security_groups()\n _srv_tenant_id = os_conn.get_tenant(SERVTEST_TENANT).id\n default_sg = [sg for sg in _s_groups['security_groups']\n if sg['tenant_id'] == _srv_tenant_id and\n sg['name'] == 'default'][0]\n\n self.show_step(4)\n instances = openstack.create_instances(\n os_conn=os_conn,\n nics=nics,\n security_groups=[default_sg['name']])\n openstack.verify_instance_state(os_conn)\n\n self.show_step(5)\n for instance in instances:\n for net in networks:\n assert_true(os_conn.get_nova_instance_ip(\n instance, net_name=net['name']) is not None)\n\n net_1_name = self.net_data[0].keys()[0]\n net_2_name = self.net_data[1].keys()[0]\n ips = {\n net_1_name: {'ips': [], 'access_point_ip': ''},\n net_2_name: {'ips': [], 'access_point_ip': ''}\n }\n\n for net in networks:\n ips[net['name']]['ips'] = map(\n (lambda x:\n os_conn.get_nova_instance_ip(x, net_name=net['name'])),\n instances)\n access_point, access_point_ip = openstack.create_access_point(\n os_conn=os_conn,\n nics=[{'net-id': net['id']}],\n security_groups=[default_sg['name']])\n ips[net['name']]['access_point_ip'] = access_point_ip\n\n logger.info(pretty_log(ips))\n\n self.show_step(6)\n cmds = [\"sudo /bin/ip link set up dev eth1\",\n \"sudo /sbin/cirros-dhcpc up eth1\"]\n access_point_ip = ips[net_1_name]['access_point_ip']\n for ip in ips[net_1_name]['ips']:\n openstack.remote_execute_command(access_point_ip, ip, cmds[0])\n openstack.remote_execute_command(access_point_ip, ip, cmds[1])\n\n self.show_step(7)\n for net in networks:\n inst_ips = ips[net['name']]['ips']\n access_point_ip = ips[net['name']]['access_point_ip']\n ip_pair = {ip: [v for v in inst_ips if v != ip] for ip in inst_ips}\n openstack.check_connection_through_host(access_point_ip,\n ip_pair,\n timeout=60 * 5,\n interval=10)","def initialize(self, dataset, session, limit=None):\n if self.global_mean.is_required:\n # If a value is already provided, it has priority\n if self.global_mean.value is not None:\n self.global_mean.assign(self.global_mean.value, session)\n # Otherwise, if it has not been restored, compute it\n if not self.global_mean.is_restored(session):\n print(\"---------------------------------\")\n print(\"Preprocessing... Calculating mean over all dataset \"\n \"(this may take long)...\")\n self._compute_global_mean(dataset, session, limit)\n print(\"Mean: \" + str(self.global_mean.value) + \" (To avoid \"\n \"repetitive computation, add it to argument 'mean' of \"\n \"`add_featurewise_zero_center`)\")\n if self.global_std.is_required:\n # If a value is already provided, it has priority\n if self.global_std.value is not None:\n self.global_std.assign(self.global_std.value, session)\n # Otherwise, if it has not been restored, compute it\n if not self.global_std.is_restored(session):\n print(\"---------------------------------\")\n print(\"Preprocessing... Calculating std over all dataset \"\n \"(this may take long)...\")\n self._compute_global_std(dataset, session, limit)\n print(\"STD: \" + str(self.global_std.value) + \" (To avoid \"\n \"repetitive computation, add it to argument 'std' of \"\n \"`add_featurewise_stdnorm`)\")\n if self.global_pc.is_required:\n # If a value is already provided, it has priority\n if self.global_pc.value is not None:\n self.global_pc.assign(self.global_pc.value, session)\n # Otherwise, if it has not been restored, compute it\n if not self.global_pc.is_restored(session):\n print(\"---------------------------------\")\n print(\"Preprocessing... PCA over all dataset \"\n \"(this may take long)...\")\n self._compute_global_pc(dataset, session, limit)\n with open('PC.pkl', 'wb') as f:\n pickle.dump(self.global_pc.value, f)\n print(\"PC saved to 'PC.pkl' (To avoid repetitive computation, \"\n \"load this pickle file and assign its value to 'pc' \"\n \"argument of `add_zca_whitening`)\")","def reset_zoc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset zoc until parent_block_count is set\")\n zoc_len = np.prod(self.parent_block_count)\n self.zoc = np.zeros(zoc_len, dtype=np.int32)","def clusters_none(self, clusters_none):\n\n self._clusters_none = clusters_none","def set_circuit_fav_count(self, circuit_id, number):\n key = ':'.join(\n [CIRCUIT_NMBR_FAVS_1, \n str(circuit_id), \n CIRCUIT_NMBR_FAVS_2]\n ) \n self.RS.set(key, number)","def top10(self, top10: List[Word]):\n\n self._top10 = top10","def set_card_standard(self, title, text, smallImageUrl=None,\n largeImageUrl=None):\n self.response.card.type = 'Standard'\n self.response.card.title = title\n self.response.card.text = text\n if smallImageUrl:\n self.response.card.image.smallImageUrl = smallImageUrl\n if largeImageUrl:\n self.response.card.image.largeImageUrl = largeImageUrl","def initialize(self, number_of_topics, random=False):\n print(\"Initializing...\")\n\n if random:\n self.initialize_randomly(number_of_topics)\n else:\n self.initialize_uniformly(number_of_topics)","def system_capex(self):\n\n topside = self.config[\"offshore_substation_topside\"][\"unit_cost\"]\n substructure = self.config[\"offshore_substation_substructure\"][\"unit_cost\"]\n mooring = self.config[\"offshore_substation_substructure\"][\"mooring_cost\"]\n\n return self.num_substations * (topside + substructure + mooring)","def SetDefaultPerfMode(self):\n if not self._device.old_interface.IsRootEnabled():\n return\n product_model = self._device.GetProp('ro.product.model')\n governor_mode = {\n 'GT-I9300': 'pegasusq',\n 'Galaxy Nexus': 'interactive',\n 'Nexus 4': 'ondemand',\n 'Nexus 7': 'interactive',\n 'Nexus 10': 'interactive'\n }.get(product_model, 'ondemand')\n self._SetScalingGovernorInternal(governor_mode)\n self._ForceAllCpusOnline(False)","def solid_surface_density_RC2014_given_physical_catalog(sssp_per_sys, max_core_mass=10.):\n mult_all = sssp_per_sys['Mtot_all']\n a_all_2p = []\n mult_all_2p = []\n sigma_all_2p = []\n for i in np.arange(len(mult_all))[mult_all > 1]: # only consider multi-planet systems\n a_sys = sssp_per_sys['a_all'][i]\n core_mass_sys = np.copy(sssp_per_sys['mass_all'][i][a_sys > 0])\n core_mass_sys[core_mass_sys > max_core_mass] = max_core_mass\n a_sys = a_sys[a_sys > 0]\n a_all_2p += list(a_sys)\n mult_all_2p += [len(a_sys)]*len(a_sys)\n sigma_all_2p += list(solid_surface_density_system_RC2014(core_mass_sys, a_sys))\n a_all_2p = np.array(a_all_2p)\n mult_all_2p = np.array(mult_all_2p)\n sigma_all_2p = np.array(sigma_all_2p)\n return sigma_all_2p, a_all_2p, mult_all_2p","def create_catalog_v0(self, create_catalog_request, **kwargs):\n # type: (CreateCatalogRequest_f3cdf8bb, **Any) -> Union[ApiResponse, object, BadRequestError_a8ac8b44, CatalogDetails_912693fa, Error_d660d58]\n operation_name = \"create_catalog_v0\"\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'create_catalog_request' is set\n if ('create_catalog_request' not in params) or (params['create_catalog_request'] is None):\n raise ValueError(\n \"Missing the required parameter `create_catalog_request` when calling `\" + operation_name + \"`\")\n\n resource_path = '/v0/catalogs'\n resource_path = resource_path.replace('{format}', 'json')\n\n path_params = {} # type: Dict\n\n query_params = [] # type: List\n\n header_params = [] # type: List\n\n body_params = None\n if 'create_catalog_request' in params:\n body_params = params['create_catalog_request']\n header_params.append(('Content-type', 'application/json'))\n header_params.append(('User-Agent', self.user_agent))\n\n # Response Type\n full_response = False\n if 'full_response' in params:\n full_response = params['full_response']\n\n # Authentication setting\n access_token = self._lwa_service_client.get_access_token_from_refresh_token()\n authorization_value = \"Bearer \" + access_token\n header_params.append(('Authorization', authorization_value))\n\n error_definitions = [] # type: List\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\", status_code=201, message=\"Catalog created.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.bad_request_error.BadRequestError\", status_code=400, message=\"Server cannot process the request due to a client error.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=401, message=\"The auth token is invalid/expired or doesn't have access to the resource.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.bad_request_error.BadRequestError\", status_code=403, message=\"The operation being requested is not allowed.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=404, message=\"The resource being requested is not found.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=429, message=\"Exceed the permitted request limit. Throttling criteria includes total requests, per API, ClientId, and CustomerId.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=500, message=\"Internal Server Error.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=503, message=\"Service Unavailable.\"))\n\n api_response = self.invoke(\n method=\"POST\",\n endpoint=self._api_endpoint,\n path=resource_path,\n path_params=path_params,\n query_params=query_params,\n header_params=header_params,\n body=body_params,\n response_definitions=error_definitions,\n response_type=\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\")\n\n if full_response:\n return api_response\n return api_response.body","def _set_catalog_view(self, session):\n if self._catalog_view == COMPARATIVE:\n try:\n session.use_comparative_catalog_view()\n except AttributeError:\n pass\n else:\n try:\n session.use_plenary_catalog_view()\n except AttributeError:\n pass","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def collectInitialeccnStatistics(self, folder, databaseFilename, multiplicityFactor = 1.0, deformedNuclei = False):\n typeCollections = ((1, 'sn'), (2,'en'))\n for ecc_id, ecc_type_name in typeCollections:\n db = SqliteDB(path.join(folder, databaseFilename % ecc_type_name))\n # first write the ecc_id_lookup table, makes sure there is only one such table\n if db.createTableIfNotExists(\"ecc_id_lookup\", ((\"ecc_id\",\"integer\"), (\"ecc_type_name\",\"text\"))):\n db.insertIntoTable(\"ecc_id_lookup\", (ecc_id, ecc_type_name))\n\n # next create the eccentricities and collisionParameters table\n db.createTableIfNotExists(\"eccentricities\", ((\"event_id\",\"integer\"), (\"ecc_id\", \"integer\"), (\"n\",\"integer\"), (\"ecc_real\",\"real\"), (\"ecc_imag\",\"real\")))\n db.createTableIfNotExists(\"collisionParameters\", ((\"event_id\",\"integer\"), (\"Npart\", \"integer\"), (\"Ncoll\",\"integer\"), (\"b\",\"real\"), (\"total_entropy\",\"real\")))\n if(deformedNuclei):\n db.createTableIfNotExists(\"deformationParameters\", ((\"event_id\",\"integer\"), (\"cosTheta1\", \"real\"), (\"phi1\",\"real\"), (\"cosTheta2\",\"real\"), (\"phi2\",\"real\")))\n\n # the big loop\n for iorder in range(1,10):\n data = loadtxt(path.join(folder, '%s_ecc_eccp_%d.dat' %(ecc_type_name, iorder)))\n if iorder == 1:\n Npart = data[:,4]\n Ncoll = data[:,5]\n dSdy = data[:,6]/multiplicityFactor #scale out the multiplicity factor used in superMC\n b = data[:,7]\n for event_id in range(len(Npart)):\n db.insertIntoTable(\"collisionParameters\", (event_id, int(Npart[event_id]), int(Ncoll[event_id]), float(b[event_id]), float(dSdy[event_id])))\n if(deformedNuclei):\n cosTheta1 = data[:,8]\n phi1 = data[:,9]\n cosTheta2 = data[:,10]\n phi2 = data[:,11]\n for event_id in range(len(Npart)):\n db.insertIntoTable(\"deformationParameters\", (event_id, float(cosTheta1[event_id]), float(phi1[event_id]), float(cosTheta2[event_id]), float(phi2[event_id])))\n eccReal = data[:,2]\n eccImag = data[:,3]\n for event_id in range(len(eccReal)):\n db.insertIntoTable(\"eccentricities\",(event_id, ecc_id, iorder, float(eccReal[event_id]), float(eccImag[event_id])))\n\n # close connection to commit changes\n db.closeConnection()","def clusters_every(self, clusters_every):\n\n self._clusters_every = clusters_every","def testHybridVariability(self):\n cat_name = os.path.join(self.scratch_dir, 'hybridTestCatalog.dat')\n makeHybridTable(database=self.variability_db)\n myDB = CatalogDBObject.from_objid('hybridTest', database=self.variability_db)\n myCatalog = StellarVariabilityCatalogWithTest(myDB, obs_metadata=self.obs_metadata)\n\n myCatalog.write_catalog(cat_name, chunk_size=1000)\n self.verify_catalogs(cat_name)\n\n if os.path.exists(cat_name):\n os.unlink(cat_name)\n\n # make sure order of mixin inheritance does not matter\n myCatalog = OtherVariabilityCatalogWithTest(myDB, obs_metadata=self.obs_metadata)\n myCatalog.write_catalog(cat_name, chunk_size=1000)\n self.verify_catalogs(cat_name)\n\n if os.path.exists(cat_name):\n os.unlink(cat_name)\n\n # make sure that, if a catalog does not contain a variability method,\n # an error is thrown; verify that it contains the correct error message\n myCatalog = StellarVariabilityCatalog(myDB, obs_metadata=self.obs_metadata)\n\n with self.assertRaises(RuntimeError) as context:\n myCatalog.write_catalog(cat_name)\n\n if os.path.exists(cat_name):\n os.unlink(cat_name)\n\n expectedMessage = \"Your InstanceCatalog does not contain a variability method\"\n expectedMessage += \" corresponding to 'testVar'\"\n self.assertEqual(context.exception.args[0], expectedMessage)","def reload_systemwide_ca_store(self):\n\n raise NotImplementedError()","def modify_standard_value():\n file_list = traverse_logs(args.log_path)\n for file in file_list:\n model, fail_flag, result, loss_result = analysis(file)\n if int(fail_flag) == 1:\n print(\"{} running failed in paddle develop!\".format(model))\n else:\n print(\"result:{}\".format(result))\n print(\"model:{}\".format(model))\n standard_record = os.path.join(args.standard_path, model + '.txt')\n with open(standard_record, 'r') as f:\n for line in f:\n standard_result = line.strip('\\n')\n print(\"Setting paddle develop result as standard value.\")\n command = 'sed -i \"s/{}/{}/g\" {}'.format(standard_result, result, standard_record)\n os.system(command)","def system_vlan_num_lte(self, system_vlan_num_lte):\n\n self._system_vlan_num_lte = system_vlan_num_lte","def get_catalog_size() -> int:\n return len(gift_catalog)","def reset_arrays(self):\n super().reset_arrays()\n self.bins = self.bin_array\n self.occs = np.zeros(len(self.bins),dtype=int) if np.size(self.bins) else []","def add_root_catalog(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinHierarchyDesignSession.add_root_bin\n self._get_provider_session('catalog_hierarchy_design_session').add_root_catalog(*args, **kwargs)","def set_circuit_remix_count(self, circuit_id, number):\n key = ':'.join(\n [CIRCUIT_NMBR_RMX_1, \n str(circuit_id), \n CIRCUIT_NMBR_RMX_2]\n )\n self.RS.set(key, number)","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def adjust_n_clusters(self, n_clusters):\n\t\tself.estimator = KMeans(init='k-means++', n_clusters=n_clusters)","def Cls(self):\n self.Bus.Write_uInt8(self.Address,0x10,0x00)","def get_catalog_v0(self, catalog_id, **kwargs):\n # type: (str, **Any) -> Union[ApiResponse, object, BadRequestError_a8ac8b44, CatalogDetails_912693fa, Error_d660d58]\n operation_name = \"get_catalog_v0\"\n params = locals()\n for key, val in six.iteritems(params['kwargs']):\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'catalog_id' is set\n if ('catalog_id' not in params) or (params['catalog_id'] is None):\n raise ValueError(\n \"Missing the required parameter `catalog_id` when calling `\" + operation_name + \"`\")\n\n resource_path = '/v0/catalogs/{catalogId}'\n resource_path = resource_path.replace('{format}', 'json')\n\n path_params = {} # type: Dict\n if 'catalog_id' in params:\n path_params['catalogId'] = params['catalog_id']\n\n query_params = [] # type: List\n\n header_params = [] # type: List\n\n body_params = None\n header_params.append(('Content-type', 'application/json'))\n header_params.append(('User-Agent', self.user_agent))\n\n # Response Type\n full_response = False\n if 'full_response' in params:\n full_response = params['full_response']\n\n # Authentication setting\n access_token = self._lwa_service_client.get_access_token_from_refresh_token()\n authorization_value = \"Bearer \" + access_token\n header_params.append(('Authorization', authorization_value))\n\n error_definitions = [] # type: List\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\", status_code=200, message=\"Successful operation.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.bad_request_error.BadRequestError\", status_code=400, message=\"Server cannot process the request due to a client error.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=401, message=\"The auth token is invalid/expired or doesn't have access to the resource.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.bad_request_error.BadRequestError\", status_code=403, message=\"The operation being requested is not allowed.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=404, message=\"The resource being requested is not found.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=429, message=\"Exceed the permitted request limit. Throttling criteria includes total requests, per API, ClientId, and CustomerId.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=500, message=\"Internal Server Error.\"))\n error_definitions.append(ServiceClientResponse(response_type=\"ask_smapi_model.v0.error.Error\", status_code=503, message=\"Service Unavailable.\"))\n\n api_response = self.invoke(\n method=\"GET\",\n endpoint=self._api_endpoint,\n path=resource_path,\n path_params=path_params,\n query_params=query_params,\n header_params=header_params,\n body=body_params,\n response_definitions=error_definitions,\n response_type=\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\")\n\n if full_response:\n return api_response\n return api_response.body","def maxCODQty(self, maxCODQty):\n\n self._maxCODQty = maxCODQty","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","def most_common(self, number=10):\n\n words_full_list = []\n\n for string in self.__corpora:\n words_full_list += string.split()\n\n print(Counter(words_full_list).most_common(number))","def set_num_rois(self,num_rois):\n for _ in range(num_rois,len(self.rois)): # delete unneeded ROIs\n self.rois.pop()\n for _ in range(len(self.rois), num_rois): # make new ROIs\n self.rois.append(ROI(1,1,4,4,num_images=self.num_images))","def setACSIndex(self, index):\n\n n_csets = self._n_csets\n if n_csets == 0:\n self._acsi = 0\n if not isinstance(index, int):\n raise TypeError('index must be an integer')\n if n_csets <= index or n_csets < abs(index):\n raise IndexError('coordinate set index is out of range')\n if index < 0:\n index += n_csets\n self._acsi = index","def test_standardize_volpianoN(self):\n lib = mei2volpiano.MEItoVolpiano()\n f1 = \"./resources/neume_mei/016r_reviewed.mei\"\n f2 = \"./resources/neume_mei/CDN-Hsmu_M2149.L4_003r.mei\"\n f3 = \"./resources/neume_mei/CDN-Hsmu_M2149.L4_003v.mei\"\n\n files = [f1, f2, f3]\n\n for i, element in enumerate(n_standard):\n func = lib.convert_mei_volpiano(files[i])\n func = lib.standardize_volpiano(func) \n self.assertTrue(func, w_standard[i])","def n_cf(self):\n return self.meta.n_caps - self.n_cs","def add_centroid(self, new_cen):\n \n self.add_vector(\n new_cen.name,\n new_cen.vector_cnt,\n new_cen.centroid_vector)","def n_cs(self):\n return np.size(self._cs, 0)","def set_number_of_sentences(self):\n self.number_of_sentences = int(self.num_sentences.get())","def set_indoor_air_quality_baseline(self,\n co2_equivalent,\n total_volatile_organic_compounds):\n if co2_equivalent == 0 and total_volatile_organic_compounds == 0:\n raise RuntimeError('Invalid baseline')\n buffer = []\n for value in [total_volatile_organic_compounds, co2_equivalent]:\n arr = [value >> 8, value & 0xFF]\n arr.append(generate_crc(arr))\n buffer += arr\n self._i2c_read_words_from_cmd(command=[0x20, 0x1e] + buffer, reply_size=0, delay=0.01)","def setSicxSupply(self) -> None:\n self._sICX_supply.set(self.sICX_score.totalSupply())","def test_get_catalogue_lengh(self):\n s1 = System()\n self.assertEqual(s1.get_catalogue_lengh(), 0)\n b1 = Books(\"1984\", \"George Orwell\", \"Harvill Secker\", \"1949\", \"0123456789123\")\n s1.add_resource(b1)\n self.assertEqual(s1.get_catalogue_lengh(), 1)","def base_reserve_0():\n print('Setting base reserve to 0')\n upgrade('basereserve', 'base_reserve_in_stroops', 0)","def lcs(self, RTSummary , SystemSummary):\n\n RTSummary_len = len(RTSummary)\n SystemSummary_len = len(SystemSummary)\n\n LCS = [[None]*(SystemSummary_len+1) for i in range(RTSummary_len+1)]\n\n for i in range(RTSummary_len+1):\n for j in range(SystemSummary_len+1):\n if i == 0 or j == 0 :\n LCS[i][j] = 0\n elif RTSummary[i-1] == SystemSummary[j-1]:\n LCS[i][j] = LCS[i-1][j-1]+1\n else:\n LCS[i][j] = max(LCS[i-1][j] , LCS[i][j-1])\n\n return LCS[RTSummary_len][SystemSummary_len]","def remove_root_catalog(self, *args, **kwargs):\n # Implemented from kitosid template for -\n # osid.resource.BinHierarchyDesignSession.remove_root_bin\n self._get_provider_session('catalog_hierarchy_design_session').remove_root_catalog(*args, **kwargs)","def ucat(catalogName, subarray=DEFAULT) :\n stripCat = catalogName.strip() \n if stripCat == \"\": catalogName = 'NONE'\n else : catalogName = stripCat\n multiSubarray('ucat', subarray, catalogName)","def total_nucleus_clients(self, total_nucleus_clients):\n\n self._total_nucleus_clients = total_nucleus_clients","def get_sectors_per_cluster(self):\n\n\t\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\n\t\tif sectors_per_cluster_base == 0:\n\t\t\traise BootSectorException('Invalid cluster size (zero)')\n\n\t\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\n\t\t\tsectors_per_cluster_real = sectors_per_cluster_base\n\t\telse:\n\t\t\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\n\t\t\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\n\n\t\treturn sectors_per_cluster_real","def datacenter_num_lte(self, datacenter_num_lte):\n\n self._datacenter_num_lte = datacenter_num_lte","def reset_cbc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset cbc until parent_block_count is set\")\n cbc_len = np.prod(self.parent_block_count)\n self.cbc = np.ones(cbc_len, dtype=np.uint32)","def reset_cbc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset cbc until parent_block_count is set\")\n cbc_len = np.prod(self.parent_block_count)\n self.cbc = np.ones(cbc_len, dtype=np.uint32)","def reset_cbc(self):\n if not self.parent_block_count:\n raise ValueError(\"cannot reset cbc until parent_block_count is set\")\n cbc_len = np.prod(self.parent_block_count)\n self.cbc = np.ones(cbc_len, dtype=np.uint32)","def set_photon_counting_thres(self, mini, maxi):\n self.lib.SetPhotonCountingThreshold(ct.c_long(mini), ct.c_long(maxi))","def handleSingleCoreOverride(self):\n try:\n if int(self.step.data.application.multicore.numberOfCores) > 1:\n self.step.data.application.multicore.numberOfCores = 1\n except AttributeError:\n pass\n\n try:\n if int(self.step.data.application.multicore.eventStreams) > 0:\n self.step.data.application.multicore.eventStreams = 0\n except AttributeError:\n pass\n\n return"],"string":"[\n \"def container_catalog_count(self, container_catalog_count):\\n\\n self._container_catalog_count = container_catalog_count\",\n \"def bm_catalog_count(self, bm_catalog_count):\\n\\n self._bm_catalog_count = bm_catalog_count\",\n \"def advanced_catalog_count(self, advanced_catalog_count):\\n\\n self._advanced_catalog_count = advanced_catalog_count\",\n \"def setNumberOfTraces(self,numberOfTraces: int) -> None:\\n\\n if not self.debug:\\n self.myFieldFox.write(\\\"CALC:PAR:COUN \\\" + str(numberOfTraces))\\n\\n return\",\n \"def count_all_catalogs(self):\\n return self.manager.count_entities(ModelCatalog)\",\n \"def custom_compliance_standard_id(self, custom_compliance_standard_id):\\n\\n self._custom_compliance_standard_id = custom_compliance_standard_id\",\n \"def _set_readcount_related_data_to_zero(self):\\n self.total_read_count = 0\\n self.perfect_read_count = 0\\n self.RISCC_genome_side_aligned_reads = {}\\n self.RISCC_genome_side_unaligned_reads = {}\\n self.sequences_counts_positions_errors = {}\\n # TODO should all this really be readcount-related? Well, it IS, but when I have a multi-dataset mutant, do I really want to keep the seq/position/count details and the genome-side RISCC read data per dataset rather than total? Hard to tell, really. In a perfect world I wouldn't be doing multiple RISCC datasets anyway!\",\n \"def solid_surface_density_CL2013_given_physical_catalog(sssp_per_sys, max_core_mass=10.):\\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\\n core_mass_all = np.copy(sssp_per_sys['mass_all'][sssp_per_sys['a_all'] > 0])\\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\\n sigma_all = solid_surface_density_CL2013(core_mass_all, a_all)\\n return sigma_all, a_all\",\n \"def count_all_catalog_services(self):\\n return self.manager.count_entities(ModelEndpoint)\",\n \"def setCompoundCount(self, count):\\n return self._set(compoundCount=count)\",\n \"def categoria_svs(self, categoria_svs):\\n\\n self._categoria_svs = categoria_svs\",\n \"def catalog_id(self, catalog_id):\\n self._catalog_id = catalog_id\",\n \"def count_standard_residues(self):\\n n = 0\\n for na in self.iter_standard_residues():\\n n += 1\\n return n\",\n \"def catalog_merge(self, catalog_cols=None):\\n\\n for cluster_info in self._catalog_dictionary.values():\\n # Array element names\\n catalog_idx = cluster_info['SPT_cat_idx']\\n se_catalog = cluster_info['catalog']\\n\\n # Replace the existing SPT_ID in the SExtractor catalog with the official cluster ID.\\n # se_catalog.columns[0].name = 'SPT_ID'\\n # del se_catalog['SPT_ID']\\n\\n # Then replace the column values with the official ID.\\n se_catalog['SPT_ID'] = self._spt_catalog['SPT_ID'][catalog_idx]\\n\\n # Add the SZ center coordinates to the catalog\\n se_catalog['SZ_RA'] = self._spt_catalog['RA'][catalog_idx]\\n se_catalog['SZ_DEC'] = self._spt_catalog['DEC'][catalog_idx]\\n\\n # For all requested columns from the master catalog add the value to all columns in the SExtractor catalog.\\n if catalog_cols is not None:\\n for col_name in catalog_cols:\\n se_catalog[col_name] = self._spt_catalog[col_name][catalog_idx]\\n\\n cluster_info['catalog'] = se_catalog\",\n \"def __init__(self,\\n advanced_catalog_count=None,\\n bm_catalog_count=None,\\n container_catalog_count=None,\\n esxi_host_count=None,\\n external_group_count=None,\\n hyperv_host_count=None,\\n local_group_count=None,\\n standard_catalog_count=None,\\n user_count=None,\\n vdc_count=None,\\n vm_count=None,\\n guid=None,\\n local_vars_configuration=None): # noqa: E501 # noqa: E501\\n if local_vars_configuration is None:\\n local_vars_configuration = Configuration()\\n self.local_vars_configuration = local_vars_configuration\\n\\n self._advanced_catalog_count = None\\n self._bm_catalog_count = None\\n self._container_catalog_count = None\\n self._esxi_host_count = None\\n self._external_group_count = None\\n self._hyperv_host_count = None\\n self._local_group_count = None\\n self._standard_catalog_count = None\\n self._user_count = None\\n self._vdc_count = None\\n self._vm_count = None\\n self._guid = None\\n self.discriminator = None\\n\\n if advanced_catalog_count is not None:\\n self.advanced_catalog_count = advanced_catalog_count\\n if bm_catalog_count is not None:\\n self.bm_catalog_count = bm_catalog_count\\n if container_catalog_count is not None:\\n self.container_catalog_count = container_catalog_count\\n if esxi_host_count is not None:\\n self.esxi_host_count = esxi_host_count\\n if external_group_count is not None:\\n self.external_group_count = external_group_count\\n if hyperv_host_count is not None:\\n self.hyperv_host_count = hyperv_host_count\\n if local_group_count is not None:\\n self.local_group_count = local_group_count\\n if standard_catalog_count is not None:\\n self.standard_catalog_count = standard_catalog_count\\n if user_count is not None:\\n self.user_count = user_count\\n if vdc_count is not None:\\n self.vdc_count = vdc_count\\n if vm_count is not None:\\n self.vm_count = vm_count\\n if guid is not None:\\n self.guid = guid\",\n \"def _set_catalog(self, catalog: cat.Catalog) -> None:\\n self._catalog_interface = CatalogInterface(catalog)\\n self._catalog = catalog\",\n \"def limit_size(self, catalog):\\n if len(catalog)<=self.limit:\\n return catalog\\n mem = {}\\n for instance in catalog:\\n if (instance['vCpu'], math.ceil(instance['memory'])) not in mem:\\n mem[(instance['vCpu'], math.ceil(instance['memory']))] = instance\\n out = [val for val in mem.values()]\\n if len(out)>self.limit:\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return out[:self.limit]\\n return out\",\n \"def set_nb_clusters(self):\\n \\n print(\\\"Finding the optimal number of clusters...\\\")\\n \\n sample = ro.r.matrix(self.df[self.df[\\\"filename\\\"].between(1, 4)][\\\"active_power\\\"].to_numpy())\\n \\n r=ro.r(\\\"\\\"\\\"\\n check = function(matrix) {\\n n_clust = fviz_nbclust(matrix, kmeans, k.max = 15)\\n\\n n_clust = n_clust$data\\n\\n max_cluster = as.numeric(n_clust$clusters[which.max(n_clust$y)])\\n return(max_cluster)\\n }\\n \\\"\\\"\\\")\\n\\n result = r(sample)\\n self.conf[\\\"nb_clust\\\"] = int(result[0])\\n \\n print(f\\\"Optimal number of clusters is {self.conf['nb_clust']}\\\\n\\\")\",\n \"def solid_surface_density_nHill_given_physical_catalog(sssp_per_sys, sssp, max_core_mass=10., n=10.):\\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\\n core_mass_all = np.copy(sssp_per_sys['mass_all'])\\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\\n sigma_all = solid_surface_density_nHill(core_mass_all, sssp_per_sys['a_all'], Mstar=sssp['Mstar_all'][:,None], n=n)[sssp_per_sys['a_all'] > 0]\\n return sigma_all, a_all\",\n \"def solid_surface_density_CL2013_given_observed_catalog(sss_per_sys, max_core_mass=10.):\\n a_obs_per_sys = gen.a_from_P(sss_per_sys['P_obs'], sss_per_sys['Mstar_obs'][:,None])\\n a_obs = a_obs_per_sys[sss_per_sys['P_obs'] > 0]\\n radii_obs = sss_per_sys['radii_obs'][sss_per_sys['P_obs'] > 0]\\n core_mass_obs = generate_planet_mass_from_radius_Ning2018_table_above_lognormal_mass_earthlike_rocky_below_vec(radii_obs)\\n core_mass_obs[core_mass_obs > max_core_mass] = max_core_mass\\n sigma_obs = solid_surface_density_CL2013(core_mass_obs, a_obs)\\n return sigma_obs, core_mass_obs, a_obs\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def _0_cluster_profile(self, _0_cluster_profile):\\n\\n self.__0_cluster_profile = _0_cluster_profile\",\n \"def update_scalar_oids(self, new_scalar_oids):\\n # type: (List[OID]) -> None\\n if not self._is_cache_enabled():\\n return\\n # Do not update if we are already using scalar oids cache.\\n if self._use_scalar_oids_cache:\\n return\\n self._all_scalar_oids = new_scalar_oids\\n self._use_scalar_oids_cache = True\\n self._last_ts = time.time()\",\n \"def update_necrosis_count(self, number):\\n\\n print(\\\"controller - update_necrosis_count!\\\")\\n self.view.processing_gui.update_necrosis_count(number)\",\n \"def set_rate_catalog(self, rate_catalog):\\n self.single_selection_from_kendo_dropdown(self.rate_catalog_kendo_dropdown_locator, rate_catalog)\",\n \"def setupCPUStandard(solver):\\n #Adjust blocks for boundary conditions\\n makeReadBlocksStandard(solver,solver.operating)\\n solver.cpu.set_globals(*solver.globals)\\n #Creating sets for cpu calculation\\n standardSet = [(x+solver.operating,y+solver.operating) for x,y in numpy.ndindex(solver.blocksize[:-1])]\\n #Initializing CPU on standard\\n cshape = solver.sharedArray[solver.blocks[0][1]].shape if solver.blocks else (0,)\\n solver.standard.initializeCPU(solver.cpu,standardSet,solver.intermediate-1,cshape)\",\n \"def modeScaler(self, latestCount):\\n \\n try:\\n # Accumulate new sample data.\\n self.__accumCts += latestCount\\n \\n # Increment runtime counter.\\n self.__runtime += 1\\n \\n except:\\n raise\\n \\n return\",\n \"def set_n_kinetics(self, n):\\n self.lib.SetNumberKinetics(ct.c_int(n))\",\n \"def solid_surface_density_S2014_given_physical_catalog(sssp_per_sys, sssp, max_core_mass=10.):\\n a_all = sssp_per_sys['a_all'][sssp_per_sys['a_all'] > 0]\\n core_mass_all = np.copy(sssp_per_sys['mass_all'])\\n core_mass_all[core_mass_all > max_core_mass] = max_core_mass\\n sigma_all = solid_surface_density_S2014(core_mass_all, sssp_per_sys['radii_all'], sssp_per_sys['a_all'], Mstar=sssp['Mstar_all'][:,None])[sssp_per_sys['a_all'] > 0]\\n return sigma_all, a_all\",\n \"def system_wide(self, system_wide):\\n\\n self._system_wide = system_wide\",\n \"def zero_scanners(self):\\n self.logger.info('Zero all Scanners.')\\n self.anc350_instrument.zero_scanners()\",\n \"def set_num_cores(self, ncores, caching = True, dynamic=False):\\n self._cores = ncores\\n self._caching = caching\\n self._dynamic = dynamic\",\n \"def cluster_num(self, cluster_num):\\n\\n self._cluster_num = cluster_num\",\n \"def custom_compliance_standard(self, custom_compliance_standard):\\n\\n self._custom_compliance_standard = custom_compliance_standard\",\n \"def test_api_ucs_get_catalog(self):\\n api_data = request(\\\"get\\\", \\\"/sys\\\")\\n self.assertEqual(api_data['status'], 200,\\n 'Incorrect HTTP return code, expected 200, got:' + str(api_data['status']))\\n total_elements = 0\\n for elementTypes in api_data[\\\"json\\\"]:\\n for element in api_data[\\\"json\\\"][str(elementTypes)]:\\n api_data_c = request(\\\"get\\\", \\\"/catalog\\\",\\n query={\\\"identifier\\\": element[\\\"relative_path\\\"].strip(\\\"/\\\")})\\n self.assertEqual(api_data_c['status'], 200,\\n 'Incorrect HTTP return code, expected 200, got:' +\\n str(api_data_c['status']))\\n total_elements += 1\\n self.assertGreater(total_elements, 0, \\\"Zero catalog elements found\\\")\\n # TO DO: deeper check on the catalog data\",\n \"def n_cs(self):\\n return self._configurations[0].n_cs\",\n \"def system_vlan_num(self, system_vlan_num):\\n\\n self._system_vlan_num = system_vlan_num\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def set_cores(self, cores):\\n self.cores = cores\\n return\",\n \"def custom_compliance_standard_id(self):\\n return self._custom_compliance_standard_id\",\n \"def apply(self, catalog):\\n out = []\\n for product in catalog:\\n valid = True\\n for key, filtr in self.filters.items():\\n valid = valid and filtr(product[key])\\n if valid:\\n out.append(product)\\n out = sorted(out, key=lambda x: x['onDemandUsdPrice'])\\n return self.limit_size(out)\",\n \"def reset(self):\\n for s in self.subsystems:\\n s.uptime = 1\",\n \"def get_root_catalog_ids(self):\\n # Implemented from kitosid template for -\\n # osid.resource.BinHierarchySession.get_root_bin_ids\\n return self._get_provider_session('catalog_hierarchy_session').get_root_catalog_ids()\",\n \"def _reset_collection(base: pymongo.database.Database, collection: str) -> None:\\n logger.info(f'Resetting all data related to \\\"{collection}\\\" collection...')\\n nb_removed = base[collection].delete_many({}).deleted_count\\n logger.info(f\\\"{nb_removed} records deleted.\\\")\\n\\n logger.info(f'Resetting counters.\\\"{collection}\\\".')\\n nb_removed = base[\\\"counters\\\"].delete_many({\\\"_id\\\": collection}).deleted_count\\n logger.info(f\\\"{nb_removed} counter records deleted\\\")\",\n \"def n_cs(self):\\n pass\",\n \"def cluster_num_lte(self, cluster_num_lte):\\n\\n self._cluster_num_lte = cluster_num_lte\",\n \"def dvs_vcenter_multiple_nics(self):\\n self.show_step(1)\\n self.env.revert_snapshot(\\\"dvs_vcenter_systest_setup\\\")\\n cluster_id = self.fuel_web.get_last_created_cluster()\\n\\n os_ip = self.fuel_web.get_public_vip(cluster_id)\\n os_conn = os_actions.OpenStackActions(\\n os_ip, SERVTEST_USERNAME,\\n SERVTEST_PASSWORD,\\n SERVTEST_TENANT)\\n\\n tenant = os_conn.get_tenant(SERVTEST_TENANT)\\n networks = []\\n router = os_conn.get_router(os_conn.get_network(self.ext_net_name))\\n\\n self.show_step(2)\\n self.show_step(3)\\n for net in self.net_data:\\n network = os_conn.create_network(network_name=net.keys()[0],\\n tenant_id=tenant.id)['network']\\n\\n logger.info('Create subnet {}'.format(net.keys()[0]))\\n subnet = os_conn.create_subnet(subnet_name=net.keys()[0],\\n network_id=network['id'],\\n cidr=net[net.keys()[0]],\\n ip_version=4)\\n\\n # Check that network is created.\\n assert_true(\\n os_conn.get_network(network['name'])['id'] == network['id'])\\n os_conn.add_router_interface(\\n router_id=router[\\\"id\\\"],\\n subnet_id=subnet[\\\"id\\\"])\\n networks.append(network)\\n\\n nics = [{'net-id': network['id']} for network in networks]\\n\\n # Add rules for ssh and ping\\n os_conn.goodbye_security()\\n\\n _s_groups = os_conn.neutron.list_security_groups()\\n _srv_tenant_id = os_conn.get_tenant(SERVTEST_TENANT).id\\n default_sg = [sg for sg in _s_groups['security_groups']\\n if sg['tenant_id'] == _srv_tenant_id and\\n sg['name'] == 'default'][0]\\n\\n self.show_step(4)\\n instances = openstack.create_instances(\\n os_conn=os_conn,\\n nics=nics,\\n security_groups=[default_sg['name']])\\n openstack.verify_instance_state(os_conn)\\n\\n self.show_step(5)\\n for instance in instances:\\n for net in networks:\\n assert_true(os_conn.get_nova_instance_ip(\\n instance, net_name=net['name']) is not None)\\n\\n net_1_name = self.net_data[0].keys()[0]\\n net_2_name = self.net_data[1].keys()[0]\\n ips = {\\n net_1_name: {'ips': [], 'access_point_ip': ''},\\n net_2_name: {'ips': [], 'access_point_ip': ''}\\n }\\n\\n for net in networks:\\n ips[net['name']]['ips'] = map(\\n (lambda x:\\n os_conn.get_nova_instance_ip(x, net_name=net['name'])),\\n instances)\\n access_point, access_point_ip = openstack.create_access_point(\\n os_conn=os_conn,\\n nics=[{'net-id': net['id']}],\\n security_groups=[default_sg['name']])\\n ips[net['name']]['access_point_ip'] = access_point_ip\\n\\n logger.info(pretty_log(ips))\\n\\n self.show_step(6)\\n cmds = [\\\"sudo /bin/ip link set up dev eth1\\\",\\n \\\"sudo /sbin/cirros-dhcpc up eth1\\\"]\\n access_point_ip = ips[net_1_name]['access_point_ip']\\n for ip in ips[net_1_name]['ips']:\\n openstack.remote_execute_command(access_point_ip, ip, cmds[0])\\n openstack.remote_execute_command(access_point_ip, ip, cmds[1])\\n\\n self.show_step(7)\\n for net in networks:\\n inst_ips = ips[net['name']]['ips']\\n access_point_ip = ips[net['name']]['access_point_ip']\\n ip_pair = {ip: [v for v in inst_ips if v != ip] for ip in inst_ips}\\n openstack.check_connection_through_host(access_point_ip,\\n ip_pair,\\n timeout=60 * 5,\\n interval=10)\",\n \"def initialize(self, dataset, session, limit=None):\\n if self.global_mean.is_required:\\n # If a value is already provided, it has priority\\n if self.global_mean.value is not None:\\n self.global_mean.assign(self.global_mean.value, session)\\n # Otherwise, if it has not been restored, compute it\\n if not self.global_mean.is_restored(session):\\n print(\\\"---------------------------------\\\")\\n print(\\\"Preprocessing... Calculating mean over all dataset \\\"\\n \\\"(this may take long)...\\\")\\n self._compute_global_mean(dataset, session, limit)\\n print(\\\"Mean: \\\" + str(self.global_mean.value) + \\\" (To avoid \\\"\\n \\\"repetitive computation, add it to argument 'mean' of \\\"\\n \\\"`add_featurewise_zero_center`)\\\")\\n if self.global_std.is_required:\\n # If a value is already provided, it has priority\\n if self.global_std.value is not None:\\n self.global_std.assign(self.global_std.value, session)\\n # Otherwise, if it has not been restored, compute it\\n if not self.global_std.is_restored(session):\\n print(\\\"---------------------------------\\\")\\n print(\\\"Preprocessing... Calculating std over all dataset \\\"\\n \\\"(this may take long)...\\\")\\n self._compute_global_std(dataset, session, limit)\\n print(\\\"STD: \\\" + str(self.global_std.value) + \\\" (To avoid \\\"\\n \\\"repetitive computation, add it to argument 'std' of \\\"\\n \\\"`add_featurewise_stdnorm`)\\\")\\n if self.global_pc.is_required:\\n # If a value is already provided, it has priority\\n if self.global_pc.value is not None:\\n self.global_pc.assign(self.global_pc.value, session)\\n # Otherwise, if it has not been restored, compute it\\n if not self.global_pc.is_restored(session):\\n print(\\\"---------------------------------\\\")\\n print(\\\"Preprocessing... PCA over all dataset \\\"\\n \\\"(this may take long)...\\\")\\n self._compute_global_pc(dataset, session, limit)\\n with open('PC.pkl', 'wb') as f:\\n pickle.dump(self.global_pc.value, f)\\n print(\\\"PC saved to 'PC.pkl' (To avoid repetitive computation, \\\"\\n \\\"load this pickle file and assign its value to 'pc' \\\"\\n \\\"argument of `add_zca_whitening`)\\\")\",\n \"def reset_zoc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset zoc until parent_block_count is set\\\")\\n zoc_len = np.prod(self.parent_block_count)\\n self.zoc = np.zeros(zoc_len, dtype=np.int32)\",\n \"def clusters_none(self, clusters_none):\\n\\n self._clusters_none = clusters_none\",\n \"def set_circuit_fav_count(self, circuit_id, number):\\n key = ':'.join(\\n [CIRCUIT_NMBR_FAVS_1, \\n str(circuit_id), \\n CIRCUIT_NMBR_FAVS_2]\\n ) \\n self.RS.set(key, number)\",\n \"def top10(self, top10: List[Word]):\\n\\n self._top10 = top10\",\n \"def set_card_standard(self, title, text, smallImageUrl=None,\\n largeImageUrl=None):\\n self.response.card.type = 'Standard'\\n self.response.card.title = title\\n self.response.card.text = text\\n if smallImageUrl:\\n self.response.card.image.smallImageUrl = smallImageUrl\\n if largeImageUrl:\\n self.response.card.image.largeImageUrl = largeImageUrl\",\n \"def initialize(self, number_of_topics, random=False):\\n print(\\\"Initializing...\\\")\\n\\n if random:\\n self.initialize_randomly(number_of_topics)\\n else:\\n self.initialize_uniformly(number_of_topics)\",\n \"def system_capex(self):\\n\\n topside = self.config[\\\"offshore_substation_topside\\\"][\\\"unit_cost\\\"]\\n substructure = self.config[\\\"offshore_substation_substructure\\\"][\\\"unit_cost\\\"]\\n mooring = self.config[\\\"offshore_substation_substructure\\\"][\\\"mooring_cost\\\"]\\n\\n return self.num_substations * (topside + substructure + mooring)\",\n \"def SetDefaultPerfMode(self):\\n if not self._device.old_interface.IsRootEnabled():\\n return\\n product_model = self._device.GetProp('ro.product.model')\\n governor_mode = {\\n 'GT-I9300': 'pegasusq',\\n 'Galaxy Nexus': 'interactive',\\n 'Nexus 4': 'ondemand',\\n 'Nexus 7': 'interactive',\\n 'Nexus 10': 'interactive'\\n }.get(product_model, 'ondemand')\\n self._SetScalingGovernorInternal(governor_mode)\\n self._ForceAllCpusOnline(False)\",\n \"def solid_surface_density_RC2014_given_physical_catalog(sssp_per_sys, max_core_mass=10.):\\n mult_all = sssp_per_sys['Mtot_all']\\n a_all_2p = []\\n mult_all_2p = []\\n sigma_all_2p = []\\n for i in np.arange(len(mult_all))[mult_all > 1]: # only consider multi-planet systems\\n a_sys = sssp_per_sys['a_all'][i]\\n core_mass_sys = np.copy(sssp_per_sys['mass_all'][i][a_sys > 0])\\n core_mass_sys[core_mass_sys > max_core_mass] = max_core_mass\\n a_sys = a_sys[a_sys > 0]\\n a_all_2p += list(a_sys)\\n mult_all_2p += [len(a_sys)]*len(a_sys)\\n sigma_all_2p += list(solid_surface_density_system_RC2014(core_mass_sys, a_sys))\\n a_all_2p = np.array(a_all_2p)\\n mult_all_2p = np.array(mult_all_2p)\\n sigma_all_2p = np.array(sigma_all_2p)\\n return sigma_all_2p, a_all_2p, mult_all_2p\",\n \"def create_catalog_v0(self, create_catalog_request, **kwargs):\\n # type: (CreateCatalogRequest_f3cdf8bb, **Any) -> Union[ApiResponse, object, BadRequestError_a8ac8b44, CatalogDetails_912693fa, Error_d660d58]\\n operation_name = \\\"create_catalog_v0\\\"\\n params = locals()\\n for key, val in six.iteritems(params['kwargs']):\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'create_catalog_request' is set\\n if ('create_catalog_request' not in params) or (params['create_catalog_request'] is None):\\n raise ValueError(\\n \\\"Missing the required parameter `create_catalog_request` when calling `\\\" + operation_name + \\\"`\\\")\\n\\n resource_path = '/v0/catalogs'\\n resource_path = resource_path.replace('{format}', 'json')\\n\\n path_params = {} # type: Dict\\n\\n query_params = [] # type: List\\n\\n header_params = [] # type: List\\n\\n body_params = None\\n if 'create_catalog_request' in params:\\n body_params = params['create_catalog_request']\\n header_params.append(('Content-type', 'application/json'))\\n header_params.append(('User-Agent', self.user_agent))\\n\\n # Response Type\\n full_response = False\\n if 'full_response' in params:\\n full_response = params['full_response']\\n\\n # Authentication setting\\n access_token = self._lwa_service_client.get_access_token_from_refresh_token()\\n authorization_value = \\\"Bearer \\\" + access_token\\n header_params.append(('Authorization', authorization_value))\\n\\n error_definitions = [] # type: List\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\\\", status_code=201, message=\\\"Catalog created.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.bad_request_error.BadRequestError\\\", status_code=400, message=\\\"Server cannot process the request due to a client error.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=401, message=\\\"The auth token is invalid/expired or doesn't have access to the resource.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.bad_request_error.BadRequestError\\\", status_code=403, message=\\\"The operation being requested is not allowed.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=404, message=\\\"The resource being requested is not found.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=429, message=\\\"Exceed the permitted request limit. Throttling criteria includes total requests, per API, ClientId, and CustomerId.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=500, message=\\\"Internal Server Error.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=503, message=\\\"Service Unavailable.\\\"))\\n\\n api_response = self.invoke(\\n method=\\\"POST\\\",\\n endpoint=self._api_endpoint,\\n path=resource_path,\\n path_params=path_params,\\n query_params=query_params,\\n header_params=header_params,\\n body=body_params,\\n response_definitions=error_definitions,\\n response_type=\\\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\\\")\\n\\n if full_response:\\n return api_response\\n return api_response.body\",\n \"def _set_catalog_view(self, session):\\n if self._catalog_view == COMPARATIVE:\\n try:\\n session.use_comparative_catalog_view()\\n except AttributeError:\\n pass\\n else:\\n try:\\n session.use_plenary_catalog_view()\\n except AttributeError:\\n pass\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def collectInitialeccnStatistics(self, folder, databaseFilename, multiplicityFactor = 1.0, deformedNuclei = False):\\n typeCollections = ((1, 'sn'), (2,'en'))\\n for ecc_id, ecc_type_name in typeCollections:\\n db = SqliteDB(path.join(folder, databaseFilename % ecc_type_name))\\n # first write the ecc_id_lookup table, makes sure there is only one such table\\n if db.createTableIfNotExists(\\\"ecc_id_lookup\\\", ((\\\"ecc_id\\\",\\\"integer\\\"), (\\\"ecc_type_name\\\",\\\"text\\\"))):\\n db.insertIntoTable(\\\"ecc_id_lookup\\\", (ecc_id, ecc_type_name))\\n\\n # next create the eccentricities and collisionParameters table\\n db.createTableIfNotExists(\\\"eccentricities\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"ecc_id\\\", \\\"integer\\\"), (\\\"n\\\",\\\"integer\\\"), (\\\"ecc_real\\\",\\\"real\\\"), (\\\"ecc_imag\\\",\\\"real\\\")))\\n db.createTableIfNotExists(\\\"collisionParameters\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"Npart\\\", \\\"integer\\\"), (\\\"Ncoll\\\",\\\"integer\\\"), (\\\"b\\\",\\\"real\\\"), (\\\"total_entropy\\\",\\\"real\\\")))\\n if(deformedNuclei):\\n db.createTableIfNotExists(\\\"deformationParameters\\\", ((\\\"event_id\\\",\\\"integer\\\"), (\\\"cosTheta1\\\", \\\"real\\\"), (\\\"phi1\\\",\\\"real\\\"), (\\\"cosTheta2\\\",\\\"real\\\"), (\\\"phi2\\\",\\\"real\\\")))\\n\\n # the big loop\\n for iorder in range(1,10):\\n data = loadtxt(path.join(folder, '%s_ecc_eccp_%d.dat' %(ecc_type_name, iorder)))\\n if iorder == 1:\\n Npart = data[:,4]\\n Ncoll = data[:,5]\\n dSdy = data[:,6]/multiplicityFactor #scale out the multiplicity factor used in superMC\\n b = data[:,7]\\n for event_id in range(len(Npart)):\\n db.insertIntoTable(\\\"collisionParameters\\\", (event_id, int(Npart[event_id]), int(Ncoll[event_id]), float(b[event_id]), float(dSdy[event_id])))\\n if(deformedNuclei):\\n cosTheta1 = data[:,8]\\n phi1 = data[:,9]\\n cosTheta2 = data[:,10]\\n phi2 = data[:,11]\\n for event_id in range(len(Npart)):\\n db.insertIntoTable(\\\"deformationParameters\\\", (event_id, float(cosTheta1[event_id]), float(phi1[event_id]), float(cosTheta2[event_id]), float(phi2[event_id])))\\n eccReal = data[:,2]\\n eccImag = data[:,3]\\n for event_id in range(len(eccReal)):\\n db.insertIntoTable(\\\"eccentricities\\\",(event_id, ecc_id, iorder, float(eccReal[event_id]), float(eccImag[event_id])))\\n\\n # close connection to commit changes\\n db.closeConnection()\",\n \"def clusters_every(self, clusters_every):\\n\\n self._clusters_every = clusters_every\",\n \"def testHybridVariability(self):\\n cat_name = os.path.join(self.scratch_dir, 'hybridTestCatalog.dat')\\n makeHybridTable(database=self.variability_db)\\n myDB = CatalogDBObject.from_objid('hybridTest', database=self.variability_db)\\n myCatalog = StellarVariabilityCatalogWithTest(myDB, obs_metadata=self.obs_metadata)\\n\\n myCatalog.write_catalog(cat_name, chunk_size=1000)\\n self.verify_catalogs(cat_name)\\n\\n if os.path.exists(cat_name):\\n os.unlink(cat_name)\\n\\n # make sure order of mixin inheritance does not matter\\n myCatalog = OtherVariabilityCatalogWithTest(myDB, obs_metadata=self.obs_metadata)\\n myCatalog.write_catalog(cat_name, chunk_size=1000)\\n self.verify_catalogs(cat_name)\\n\\n if os.path.exists(cat_name):\\n os.unlink(cat_name)\\n\\n # make sure that, if a catalog does not contain a variability method,\\n # an error is thrown; verify that it contains the correct error message\\n myCatalog = StellarVariabilityCatalog(myDB, obs_metadata=self.obs_metadata)\\n\\n with self.assertRaises(RuntimeError) as context:\\n myCatalog.write_catalog(cat_name)\\n\\n if os.path.exists(cat_name):\\n os.unlink(cat_name)\\n\\n expectedMessage = \\\"Your InstanceCatalog does not contain a variability method\\\"\\n expectedMessage += \\\" corresponding to 'testVar'\\\"\\n self.assertEqual(context.exception.args[0], expectedMessage)\",\n \"def reload_systemwide_ca_store(self):\\n\\n raise NotImplementedError()\",\n \"def modify_standard_value():\\n file_list = traverse_logs(args.log_path)\\n for file in file_list:\\n model, fail_flag, result, loss_result = analysis(file)\\n if int(fail_flag) == 1:\\n print(\\\"{} running failed in paddle develop!\\\".format(model))\\n else:\\n print(\\\"result:{}\\\".format(result))\\n print(\\\"model:{}\\\".format(model))\\n standard_record = os.path.join(args.standard_path, model + '.txt')\\n with open(standard_record, 'r') as f:\\n for line in f:\\n standard_result = line.strip('\\\\n')\\n print(\\\"Setting paddle develop result as standard value.\\\")\\n command = 'sed -i \\\"s/{}/{}/g\\\" {}'.format(standard_result, result, standard_record)\\n os.system(command)\",\n \"def system_vlan_num_lte(self, system_vlan_num_lte):\\n\\n self._system_vlan_num_lte = system_vlan_num_lte\",\n \"def get_catalog_size() -> int:\\n return len(gift_catalog)\",\n \"def reset_arrays(self):\\n super().reset_arrays()\\n self.bins = self.bin_array\\n self.occs = np.zeros(len(self.bins),dtype=int) if np.size(self.bins) else []\",\n \"def add_root_catalog(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinHierarchyDesignSession.add_root_bin\\n self._get_provider_session('catalog_hierarchy_design_session').add_root_catalog(*args, **kwargs)\",\n \"def set_circuit_remix_count(self, circuit_id, number):\\n key = ':'.join(\\n [CIRCUIT_NMBR_RMX_1, \\n str(circuit_id), \\n CIRCUIT_NMBR_RMX_2]\\n )\\n self.RS.set(key, number)\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def adjust_n_clusters(self, n_clusters):\\n\\t\\tself.estimator = KMeans(init='k-means++', n_clusters=n_clusters)\",\n \"def Cls(self):\\n self.Bus.Write_uInt8(self.Address,0x10,0x00)\",\n \"def get_catalog_v0(self, catalog_id, **kwargs):\\n # type: (str, **Any) -> Union[ApiResponse, object, BadRequestError_a8ac8b44, CatalogDetails_912693fa, Error_d660d58]\\n operation_name = \\\"get_catalog_v0\\\"\\n params = locals()\\n for key, val in six.iteritems(params['kwargs']):\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'catalog_id' is set\\n if ('catalog_id' not in params) or (params['catalog_id'] is None):\\n raise ValueError(\\n \\\"Missing the required parameter `catalog_id` when calling `\\\" + operation_name + \\\"`\\\")\\n\\n resource_path = '/v0/catalogs/{catalogId}'\\n resource_path = resource_path.replace('{format}', 'json')\\n\\n path_params = {} # type: Dict\\n if 'catalog_id' in params:\\n path_params['catalogId'] = params['catalog_id']\\n\\n query_params = [] # type: List\\n\\n header_params = [] # type: List\\n\\n body_params = None\\n header_params.append(('Content-type', 'application/json'))\\n header_params.append(('User-Agent', self.user_agent))\\n\\n # Response Type\\n full_response = False\\n if 'full_response' in params:\\n full_response = params['full_response']\\n\\n # Authentication setting\\n access_token = self._lwa_service_client.get_access_token_from_refresh_token()\\n authorization_value = \\\"Bearer \\\" + access_token\\n header_params.append(('Authorization', authorization_value))\\n\\n error_definitions = [] # type: List\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\\\", status_code=200, message=\\\"Successful operation.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.bad_request_error.BadRequestError\\\", status_code=400, message=\\\"Server cannot process the request due to a client error.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=401, message=\\\"The auth token is invalid/expired or doesn't have access to the resource.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.bad_request_error.BadRequestError\\\", status_code=403, message=\\\"The operation being requested is not allowed.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=404, message=\\\"The resource being requested is not found.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=429, message=\\\"Exceed the permitted request limit. Throttling criteria includes total requests, per API, ClientId, and CustomerId.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=500, message=\\\"Internal Server Error.\\\"))\\n error_definitions.append(ServiceClientResponse(response_type=\\\"ask_smapi_model.v0.error.Error\\\", status_code=503, message=\\\"Service Unavailable.\\\"))\\n\\n api_response = self.invoke(\\n method=\\\"GET\\\",\\n endpoint=self._api_endpoint,\\n path=resource_path,\\n path_params=path_params,\\n query_params=query_params,\\n header_params=header_params,\\n body=body_params,\\n response_definitions=error_definitions,\\n response_type=\\\"ask_smapi_model.v0.catalog.catalog_details.CatalogDetails\\\")\\n\\n if full_response:\\n return api_response\\n return api_response.body\",\n \"def maxCODQty(self, maxCODQty):\\n\\n self._maxCODQty = maxCODQty\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def most_common(self, number=10):\\n\\n words_full_list = []\\n\\n for string in self.__corpora:\\n words_full_list += string.split()\\n\\n print(Counter(words_full_list).most_common(number))\",\n \"def set_num_rois(self,num_rois):\\n for _ in range(num_rois,len(self.rois)): # delete unneeded ROIs\\n self.rois.pop()\\n for _ in range(len(self.rois), num_rois): # make new ROIs\\n self.rois.append(ROI(1,1,4,4,num_images=self.num_images))\",\n \"def setACSIndex(self, index):\\n\\n n_csets = self._n_csets\\n if n_csets == 0:\\n self._acsi = 0\\n if not isinstance(index, int):\\n raise TypeError('index must be an integer')\\n if n_csets <= index or n_csets < abs(index):\\n raise IndexError('coordinate set index is out of range')\\n if index < 0:\\n index += n_csets\\n self._acsi = index\",\n \"def test_standardize_volpianoN(self):\\n lib = mei2volpiano.MEItoVolpiano()\\n f1 = \\\"./resources/neume_mei/016r_reviewed.mei\\\"\\n f2 = \\\"./resources/neume_mei/CDN-Hsmu_M2149.L4_003r.mei\\\"\\n f3 = \\\"./resources/neume_mei/CDN-Hsmu_M2149.L4_003v.mei\\\"\\n\\n files = [f1, f2, f3]\\n\\n for i, element in enumerate(n_standard):\\n func = lib.convert_mei_volpiano(files[i])\\n func = lib.standardize_volpiano(func) \\n self.assertTrue(func, w_standard[i])\",\n \"def n_cf(self):\\n return self.meta.n_caps - self.n_cs\",\n \"def add_centroid(self, new_cen):\\n \\n self.add_vector(\\n new_cen.name,\\n new_cen.vector_cnt,\\n new_cen.centroid_vector)\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def set_number_of_sentences(self):\\n self.number_of_sentences = int(self.num_sentences.get())\",\n \"def set_indoor_air_quality_baseline(self,\\n co2_equivalent,\\n total_volatile_organic_compounds):\\n if co2_equivalent == 0 and total_volatile_organic_compounds == 0:\\n raise RuntimeError('Invalid baseline')\\n buffer = []\\n for value in [total_volatile_organic_compounds, co2_equivalent]:\\n arr = [value >> 8, value & 0xFF]\\n arr.append(generate_crc(arr))\\n buffer += arr\\n self._i2c_read_words_from_cmd(command=[0x20, 0x1e] + buffer, reply_size=0, delay=0.01)\",\n \"def setSicxSupply(self) -> None:\\n self._sICX_supply.set(self.sICX_score.totalSupply())\",\n \"def test_get_catalogue_lengh(self):\\n s1 = System()\\n self.assertEqual(s1.get_catalogue_lengh(), 0)\\n b1 = Books(\\\"1984\\\", \\\"George Orwell\\\", \\\"Harvill Secker\\\", \\\"1949\\\", \\\"0123456789123\\\")\\n s1.add_resource(b1)\\n self.assertEqual(s1.get_catalogue_lengh(), 1)\",\n \"def base_reserve_0():\\n print('Setting base reserve to 0')\\n upgrade('basereserve', 'base_reserve_in_stroops', 0)\",\n \"def lcs(self, RTSummary , SystemSummary):\\n\\n RTSummary_len = len(RTSummary)\\n SystemSummary_len = len(SystemSummary)\\n\\n LCS = [[None]*(SystemSummary_len+1) for i in range(RTSummary_len+1)]\\n\\n for i in range(RTSummary_len+1):\\n for j in range(SystemSummary_len+1):\\n if i == 0 or j == 0 :\\n LCS[i][j] = 0\\n elif RTSummary[i-1] == SystemSummary[j-1]:\\n LCS[i][j] = LCS[i-1][j-1]+1\\n else:\\n LCS[i][j] = max(LCS[i-1][j] , LCS[i][j-1])\\n\\n return LCS[RTSummary_len][SystemSummary_len]\",\n \"def remove_root_catalog(self, *args, **kwargs):\\n # Implemented from kitosid template for -\\n # osid.resource.BinHierarchyDesignSession.remove_root_bin\\n self._get_provider_session('catalog_hierarchy_design_session').remove_root_catalog(*args, **kwargs)\",\n \"def ucat(catalogName, subarray=DEFAULT) :\\n stripCat = catalogName.strip() \\n if stripCat == \\\"\\\": catalogName = 'NONE'\\n else : catalogName = stripCat\\n multiSubarray('ucat', subarray, catalogName)\",\n \"def total_nucleus_clients(self, total_nucleus_clients):\\n\\n self._total_nucleus_clients = total_nucleus_clients\",\n \"def get_sectors_per_cluster(self):\\n\\n\\t\\tsectors_per_cluster_base = struct.unpack('B', self.boot_sector_data[13 : 14])[0]\\n\\t\\tif sectors_per_cluster_base == 0:\\n\\t\\t\\traise BootSectorException('Invalid cluster size (zero)')\\n\\n\\t\\tif sectors_per_cluster_base <= 0x80: # Although 0x80 is a signed value, it's used as an unsigned one.\\n\\t\\t\\tsectors_per_cluster_real = sectors_per_cluster_base\\n\\t\\telse:\\n\\t\\t\\tsectors_per_cluster_base = struct.unpack('b', self.boot_sector_data[13 : 14])[0] # Read this again as a signed value.\\n\\t\\t\\tsectors_per_cluster_real = 1 << abs(sectors_per_cluster_base)\\n\\n\\t\\treturn sectors_per_cluster_real\",\n \"def datacenter_num_lte(self, datacenter_num_lte):\\n\\n self._datacenter_num_lte = datacenter_num_lte\",\n \"def reset_cbc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset cbc until parent_block_count is set\\\")\\n cbc_len = np.prod(self.parent_block_count)\\n self.cbc = np.ones(cbc_len, dtype=np.uint32)\",\n \"def reset_cbc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset cbc until parent_block_count is set\\\")\\n cbc_len = np.prod(self.parent_block_count)\\n self.cbc = np.ones(cbc_len, dtype=np.uint32)\",\n \"def reset_cbc(self):\\n if not self.parent_block_count:\\n raise ValueError(\\\"cannot reset cbc until parent_block_count is set\\\")\\n cbc_len = np.prod(self.parent_block_count)\\n self.cbc = np.ones(cbc_len, dtype=np.uint32)\",\n \"def set_photon_counting_thres(self, mini, maxi):\\n self.lib.SetPhotonCountingThreshold(ct.c_long(mini), ct.c_long(maxi))\",\n \"def handleSingleCoreOverride(self):\\n try:\\n if int(self.step.data.application.multicore.numberOfCores) > 1:\\n self.step.data.application.multicore.numberOfCores = 1\\n except AttributeError:\\n pass\\n\\n try:\\n if int(self.step.data.application.multicore.eventStreams) > 0:\\n self.step.data.application.multicore.eventStreams = 0\\n except AttributeError:\\n pass\\n\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57423514","0.5616781","0.50827426","0.4542607","0.44935066","0.44344246","0.43874955","0.43789217","0.4358185","0.43167907","0.43059853","0.4270202","0.42586276","0.4214198","0.42093727","0.4138831","0.41344634","0.41310257","0.41012546","0.40627837","0.4059922","0.40574533","0.40550974","0.40492907","0.4037851","0.40317857","0.40211806","0.4020201","0.40056854","0.400553","0.40044743","0.39967847","0.3992009","0.3976084","0.39599797","0.39549953","0.39525732","0.3951251","0.3949576","0.3946078","0.3940418","0.39373615","0.39210784","0.39057","0.39019692","0.38917434","0.38882422","0.3881441","0.3875783","0.38721833","0.38566655","0.38511926","0.38462958","0.38329396","0.38279888","0.38256803","0.38185054","0.38159114","0.38119397","0.38099408","0.38099334","0.37993577","0.37916973","0.3774629","0.37726456","0.37667224","0.37604317","0.3759894","0.37594533","0.3749897","0.37463742","0.37447938","0.37445375","0.3740825","0.37407428","0.37365147","0.3731953","0.37266043","0.3723844","0.37186626","0.37177423","0.37173542","0.37173247","0.3717138","0.37120903","0.37118116","0.3710949","0.3707811","0.37065193","0.3705338","0.36975023","0.3695867","0.36946145","0.36801162","0.3677117","0.36743388","0.36743388","0.36743388","0.36680052","0.36649746"],"string":"[\n \"0.57423514\",\n \"0.5616781\",\n \"0.50827426\",\n \"0.4542607\",\n \"0.44935066\",\n \"0.44344246\",\n \"0.43874955\",\n \"0.43789217\",\n \"0.4358185\",\n \"0.43167907\",\n \"0.43059853\",\n \"0.4270202\",\n \"0.42586276\",\n \"0.4214198\",\n \"0.42093727\",\n \"0.4138831\",\n \"0.41344634\",\n \"0.41310257\",\n \"0.41012546\",\n \"0.40627837\",\n \"0.4059922\",\n \"0.40574533\",\n \"0.40550974\",\n \"0.40492907\",\n \"0.4037851\",\n \"0.40317857\",\n \"0.40211806\",\n \"0.4020201\",\n \"0.40056854\",\n \"0.400553\",\n \"0.40044743\",\n \"0.39967847\",\n \"0.3992009\",\n \"0.3976084\",\n \"0.39599797\",\n \"0.39549953\",\n \"0.39525732\",\n \"0.3951251\",\n \"0.3949576\",\n \"0.3946078\",\n \"0.3940418\",\n \"0.39373615\",\n \"0.39210784\",\n \"0.39057\",\n \"0.39019692\",\n \"0.38917434\",\n \"0.38882422\",\n \"0.3881441\",\n \"0.3875783\",\n \"0.38721833\",\n \"0.38566655\",\n \"0.38511926\",\n \"0.38462958\",\n \"0.38329396\",\n \"0.38279888\",\n \"0.38256803\",\n \"0.38185054\",\n \"0.38159114\",\n \"0.38119397\",\n \"0.38099408\",\n \"0.38099334\",\n \"0.37993577\",\n \"0.37916973\",\n \"0.3774629\",\n \"0.37726456\",\n \"0.37667224\",\n \"0.37604317\",\n \"0.3759894\",\n \"0.37594533\",\n \"0.3749897\",\n \"0.37463742\",\n \"0.37447938\",\n \"0.37445375\",\n \"0.3740825\",\n \"0.37407428\",\n \"0.37365147\",\n \"0.3731953\",\n \"0.37266043\",\n \"0.3723844\",\n \"0.37186626\",\n \"0.37177423\",\n \"0.37173542\",\n \"0.37173247\",\n \"0.3717138\",\n \"0.37120903\",\n \"0.37118116\",\n \"0.3710949\",\n \"0.3707811\",\n \"0.37065193\",\n \"0.3705338\",\n \"0.36975023\",\n \"0.3695867\",\n \"0.36946145\",\n \"0.36801162\",\n \"0.3677117\",\n \"0.36743388\",\n \"0.36743388\",\n \"0.36743388\",\n \"0.36680052\",\n \"0.36649746\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81387305"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19685,"cells":{"query":{"kind":"string","value":"Sets the user_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def user_count(self, user_count):\n\n self._user_count = user_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def AddUser(self, usercount, user):\n for i in range(usercount):\n login = string.replace(user[i]['Login'], ' ', '')\n home = self.__homeprefix + login[0] + '/' + login\n action = 'userman -A ' + login + ' -p ' + user[i]['Passwd'] + ' -u ' + str(user[i]['UID']) + \\\n ' -g ' + str(user[i]['GID']) + ' -H ' + home + ' -s ' + user[i]['Shell'] \n output = commands.getstatusoutput(action)\n print output\n updatecount, update = self.__sqlData[\"UPDATE AccUser SET ToDo = 0 WHERE Login = '%s'\" % (login)]","def n_users(self):\n if self._n_users is None:\n self._n_users = len(self.user_unique_vals)\n return self._n_users","def user_capacity(self, user_capacity: SmartSsdUserCapacity):\n\n self._user_capacity = user_capacity","def _set_usr_traceroute_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-traceroute-count\", rest_name=\"usr-traceroute-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_traceroute_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-traceroute-count\", rest_name=\"usr-traceroute-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_traceroute_count = t\n if hasattr(self, '_set'):\n self._set()","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def set_count(self, count):\n self._count = count","def setCount(self, num):\n self.count=num","def count_total_each_user():\r\n trans = transaction.begin()\r\n user_list = UserMgr.get_list(active=True)\r\n for user in user_list:\r\n StatBookmarkMgr.count_user_bookmarks(user.username)\r\n trans.commit()","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def device_count(self, device_count):\n\n self._device_count = device_count","def users(self, users):\n if (self.local_vars_configuration.client_side_validation and\n users is not None and not isinstance(users, int)):\n raise ValueError(\"Parameter `users` must be an integer\") # noqa: E501\n\n self._users = users","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","async def connected_users_count(self, event):\n print(\"PublicChatConsumer\", \"connected_users_count\",\n event[\"connected_users_count\"])\n await self.send_json({\n \"msg_type\": MSG_TYPE_CONNECTED_USERS_COUNT,\n \"connected_users_count\": event[\"connected_users_count\"]\n })","def add_user(self, u: \"Node\") -> None:\n\n if u not in self.users_:\n self.users_[u] = 0\n self.users_[u] += 1","def set_count(self, count):\n\n\t\tif count is not None and not isinstance(count, int):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\n\t\t\n\t\tself.__count = count\n\t\tself.__key_modified['count'] = 1","def count_users(self):\n return self.get_session.query(func.count(self.user_model.id)).scalar()","def setuservisits_and_allitems(self, uservisits=None, allitems=None):\n if uservisits is not None:\n self._uservisits = uservisits\n if allitems is not None:\n self._allitems = allitems","def number_users_active(self) -> int:\r\n unique_users = {\r\n row['user']\r\n for row in self.rows\r\n }\r\n\r\n return len(unique_users)","def users(self, users):\n\n self._users = users","def users(self, users):\n\n self._users = users","def users(self, users):\n\n self._users = users","def count(self, value):\n \n self._count = int(value)","def count(self, count: int) -> None:\n self._count = count","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def count(self, count: int):\n\n self._count = count","def get_all_users_count(khoros_object):\n liql_query = 'SELECT count(*) FROM users'\n api_response = liql.perform_query(khoros_object, liql_query=liql_query, verify_success=True)\n return int(api_response['data']['count'])","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def user_id(self, user_id):\n\n self._user_id = user_id","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def getUserCount(self):\n logger.debug('Getting the number of users discovered...')\n return get_text(get_element_by_css(\"span[data-nsmodule='usersdiscovered']\"))","async def connected_user_count(self, event):\n\t\t# Send a message down to the client\n\t\tprint(\"DocumentChatConsumer: connected_user_count: count: \" + str(event[\"connected_user_count\"]))\n\t\tawait self.send_json(\n\t\t\t{\n\t\t\t\t\"msg_type\": MSG_TYPE_CONNECTED_USER_COUNT,\n\t\t\t\t\"connected_user_count\": event[\"connected_user_count\"]\n\t\t\t},\n\t\t)","def _add_data_to_user(self, user, number):\n self._users_numbers[user]['sum'] += number\n self._users_numbers[user]['counter'] += 1\n self._save_users_data_to_json()","def associated_object_count(self, associated_object_count):\n self._associated_object_count = associated_object_count","def update(self, user):\n\n\t\tif self == user.classroom:\n\t\t\treturn\n\n\t\tself.size += user.classroom.size\n\t\tuser.set_classroom(self)","def updateUsers(self):\n sel = self.userbox.GetValue()\n usernames = ['None'] + c.getUserNames()\n self.userbox.SetItems(usernames)\n if sel in self.userbox.GetItems():\n self.userbox.SetValue(sel)\n if sel == 'None':\n self.btnUserSettings.Enable(False)\n else:\n self.btnUserSettings.Enable(True)\n else:\n self.userbox.SetSelection(0)\n self.btnUserSettings.Enable(False)","def set_user(self, user_model):\n\n self.user_model = user_model\n return self","def update_users_in_range(self, user_list):\r\n self.users_in_range = []\r\n for user in user_list:\r\n if user != self:\r\n for freq_range in user.currently_used_frequencies:\r\n received_power = self.calculate_signal_power(user, freq_range)\r\n if received_power > settings.power_threshold:\r\n tmp_freq = freq_range[:]\r\n if tmp_freq not in self.users_in_range:\r\n self.users_in_range.append(tmp_freq)","def set_user(self, user: User):\n self.__user = user","def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count","def total_users(user):\n user_count = User.objects.filter(is_active=True).count()\n\n return NumberResponse(user_count, 'Total number of users')","def node_count(self, node_count):\n\n self._node_count = node_count","async def set_aces_used(card_list, user_id):\n separator = ','\n cards = separator.join(card_list)\n await ex.conn.execute(\"UPDATE blackjack.currentstatus SET acesused = $1 WHERE userid = $2\", cards, user_id)","def getViewPortUserCount(self):\n logger.debug('Getting map view port user count...')\n elements = get_elements_by_css(\".leaflet-marker-icon.srcCluster\")\n users = 0\n for element in elements:\n users += int(get_text(element))\n return users","def processor_count(self, processor_count):\n\n self._processor_count = processor_count","def voterContactCount(self, user):\n return self.votercontact_set.filter(user=user).count()","def setClassCount(self, count):\n\n return self._set(classCount=count)","def select_user(self, number):\n\n if len(self.users) < 1:\n vk_common.dprint(1, \"Массив пользователей пуст.\")\n return -1\n\n vk_common.dprint(2, vk_common.func_name(), \"Список пользователей при входе: \", self.users)\n self.user_id = self.users[number-1][0]\n self.item_ids = self.users[number-1][1]\n vk_common.dprint(2, f\"Выбран пользователь с id = {self.user_id}\")\n\n rows, err = vk_db.get_photo_info_by_user_id(self.user_id)\n if err != 0:\n vk_common.dprint(1, \"Ошибка получения имен изображений.\")\n return -2\n\n vk_common.dprint(2, rows)\n\n self.top_images = rows\n\n err = self.load_image()\n if err != 0:\n vk_common.dprint(1, \"Ошибка при загрузке изображений из БД на форму\")\n return -2\n\n return 0","def set_total_lui(self, valeurs):\r\n \r\n self._total_lui = valeurs","def all_users(self):\n return range(self.n_users)","def set_user(self, user):\r\n self.user = user","def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count","def ugc_limit(self, ugc_limit):\n\n self._ugc_limit = ugc_limit","def employee_count(self, employee_count):\n\n self._employee_count = employee_count","def setusers(self, users=None):\n if users:\n self.users = users\n return\n import jsb.lib.users as u\n if not u.users: u.users_boot()\n self.users = u.users","def put_in_all_user_data(user: dict):\n all_user_data[user.id] = user","def assign_user_features(self, user_data):\n assert \"user\" in user_data.columns, \"Data must contain `user` column.\"\n user_data = user_data.drop_duplicates(subset=[\"user\"], keep=\"last\")\n user_row_idx, user_id_mask = get_row_id_masks(\n user_data[\"user\"], self.user_unique_vals\n )\n self.user_sparse_unique = update_new_sparse_feats(\n user_data,\n user_row_idx,\n user_id_mask,\n self.user_sparse_unique,\n self.sparse_unique_vals,\n self.multi_sparse_unique_vals,\n self.user_sparse_col,\n self.col_name_mapping,\n self.sparse_offset,\n )\n self.user_dense_unique = update_new_dense_feats(\n user_data,\n user_row_idx,\n user_id_mask,\n self.user_dense_unique,\n self.user_dense_col,\n )","def user_settings(self, user_settings):\n\n self._user_settings = user_settings","def update_user_data(self, new_user: User):\n self.user_data.update_user_data(new_user)","def service_user_id(self, service_user_id):\n\n self._service_user_id = service_user_id","def io_throttle_count(self, io_throttle_count):\n\n self._io_throttle_count = io_throttle_count","def set_user_config(self, data):\n config = self.read_config_obj(self.account_file)\n for key, value in data.items():\n config.set(self.user, str(key), value)\n\n self.write_config(self.account_file, config)","def apply_user_change(self, change_count: int, user: get_user_model()) -> bool:\n if user is None or change_count == 0:\n return False\n\n try:\n existing_card = UserOwnedCard.objects.get(\n card_localisation=self, owner=user\n )\n if change_count < 0 and abs(change_count) >= existing_card.count:\n # If the count is below 1 than there is no point thinking that the user \"owns\"\n # the card anymore, so just delete the record\n change_count = -existing_card.count\n existing_card.delete()\n else:\n existing_card.count += change_count\n existing_card.clean()\n existing_card.save()\n except UserOwnedCard.DoesNotExist:\n if change_count < 0:\n # You can't subtract cards when you don' have any\n return False\n new_ownership = UserOwnedCard(\n count=change_count, owner=user, card_localisation=self\n )\n new_ownership.clean()\n new_ownership.save()\n\n change = UserCardChange(\n card_localisation=self,\n owner=user,\n difference=change_count,\n date=datetime.datetime.now(),\n )\n change.clean()\n change.save()\n return True","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def user(self, user):\n\n self._user = user","def askSymplecticToClearUsers():\r\n #description\r\n #\r\n xml_filename = SymplecticXMLClearUser.__createXMLFileForClear()\r\n symplectic_response = SymplecticXMLClearUser.__postClearUsersXMLFileToSymplectic(xml_filename)\r\n cleared_count = SymplecticXMLClearUser.__extractUserClearedCountFromResponse(symplectic_response)\r\n return cleared_count","def request_count(self, request_count):\n\n self._request_count = request_count","def user_capacity(self) -> SmartSsdUserCapacity:\n return self._user_capacity","def user(self, user):\n self.user_id = user.get_id()","def fillData(self):\n self.users = c.getUserNames()\n self.userlist.SetItems(self.users)","def unseen_count_for(self, user):\r\n return self.filter(user=user, unseen=True).count()","def total_nucleus_clients(self, total_nucleus_clients):\n\n self._total_nucleus_clients = total_nucleus_clients","def set_su_user_id(self, su_user_id):\n su_user = self.available_users().get(id=su_user_id)\n self.set_su_user(su_user)","def active_member_count(self, active_member_count):\n\n self._active_member_count = active_member_count"],"string":"[\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def AddUser(self, usercount, user):\\n for i in range(usercount):\\n login = string.replace(user[i]['Login'], ' ', '')\\n home = self.__homeprefix + login[0] + '/' + login\\n action = 'userman -A ' + login + ' -p ' + user[i]['Passwd'] + ' -u ' + str(user[i]['UID']) + \\\\\\n ' -g ' + str(user[i]['GID']) + ' -H ' + home + ' -s ' + user[i]['Shell'] \\n output = commands.getstatusoutput(action)\\n print output\\n updatecount, update = self.__sqlData[\\\"UPDATE AccUser SET ToDo = 0 WHERE Login = '%s'\\\" % (login)]\",\n \"def n_users(self):\\n if self._n_users is None:\\n self._n_users = len(self.user_unique_vals)\\n return self._n_users\",\n \"def user_capacity(self, user_capacity: SmartSsdUserCapacity):\\n\\n self._user_capacity = user_capacity\",\n \"def _set_usr_traceroute_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-traceroute-count\\\", rest_name=\\\"usr-traceroute-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_traceroute_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-traceroute-count\\\", rest_name=\\\"usr-traceroute-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_traceroute_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def set_count(self, count):\\n self._count = count\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def count_total_each_user():\\r\\n trans = transaction.begin()\\r\\n user_list = UserMgr.get_list(active=True)\\r\\n for user in user_list:\\r\\n StatBookmarkMgr.count_user_bookmarks(user.username)\\r\\n trans.commit()\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def device_count(self, device_count):\\n\\n self._device_count = device_count\",\n \"def users(self, users):\\n if (self.local_vars_configuration.client_side_validation and\\n users is not None and not isinstance(users, int)):\\n raise ValueError(\\\"Parameter `users` must be an integer\\\") # noqa: E501\\n\\n self._users = users\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"async def connected_users_count(self, event):\\n print(\\\"PublicChatConsumer\\\", \\\"connected_users_count\\\",\\n event[\\\"connected_users_count\\\"])\\n await self.send_json({\\n \\\"msg_type\\\": MSG_TYPE_CONNECTED_USERS_COUNT,\\n \\\"connected_users_count\\\": event[\\\"connected_users_count\\\"]\\n })\",\n \"def add_user(self, u: \\\"Node\\\") -> None:\\n\\n if u not in self.users_:\\n self.users_[u] = 0\\n self.users_[u] += 1\",\n \"def set_count(self, count):\\n\\n\\t\\tif count is not None and not isinstance(count, int):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\\n\\t\\t\\n\\t\\tself.__count = count\\n\\t\\tself.__key_modified['count'] = 1\",\n \"def count_users(self):\\n return self.get_session.query(func.count(self.user_model.id)).scalar()\",\n \"def setuservisits_and_allitems(self, uservisits=None, allitems=None):\\n if uservisits is not None:\\n self._uservisits = uservisits\\n if allitems is not None:\\n self._allitems = allitems\",\n \"def number_users_active(self) -> int:\\r\\n unique_users = {\\r\\n row['user']\\r\\n for row in self.rows\\r\\n }\\r\\n\\r\\n return len(unique_users)\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def users(self, users):\\n\\n self._users = users\",\n \"def count(self, value):\\n \\n self._count = int(value)\",\n \"def count(self, count: int) -> None:\\n self._count = count\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def count(self, count: int):\\n\\n self._count = count\",\n \"def get_all_users_count(khoros_object):\\n liql_query = 'SELECT count(*) FROM users'\\n api_response = liql.perform_query(khoros_object, liql_query=liql_query, verify_success=True)\\n return int(api_response['data']['count'])\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def user_id(self, user_id):\\n\\n self._user_id = user_id\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def getUserCount(self):\\n logger.debug('Getting the number of users discovered...')\\n return get_text(get_element_by_css(\\\"span[data-nsmodule='usersdiscovered']\\\"))\",\n \"async def connected_user_count(self, event):\\n\\t\\t# Send a message down to the client\\n\\t\\tprint(\\\"DocumentChatConsumer: connected_user_count: count: \\\" + str(event[\\\"connected_user_count\\\"]))\\n\\t\\tawait self.send_json(\\n\\t\\t\\t{\\n\\t\\t\\t\\t\\\"msg_type\\\": MSG_TYPE_CONNECTED_USER_COUNT,\\n\\t\\t\\t\\t\\\"connected_user_count\\\": event[\\\"connected_user_count\\\"]\\n\\t\\t\\t},\\n\\t\\t)\",\n \"def _add_data_to_user(self, user, number):\\n self._users_numbers[user]['sum'] += number\\n self._users_numbers[user]['counter'] += 1\\n self._save_users_data_to_json()\",\n \"def associated_object_count(self, associated_object_count):\\n self._associated_object_count = associated_object_count\",\n \"def update(self, user):\\n\\n\\t\\tif self == user.classroom:\\n\\t\\t\\treturn\\n\\n\\t\\tself.size += user.classroom.size\\n\\t\\tuser.set_classroom(self)\",\n \"def updateUsers(self):\\n sel = self.userbox.GetValue()\\n usernames = ['None'] + c.getUserNames()\\n self.userbox.SetItems(usernames)\\n if sel in self.userbox.GetItems():\\n self.userbox.SetValue(sel)\\n if sel == 'None':\\n self.btnUserSettings.Enable(False)\\n else:\\n self.btnUserSettings.Enable(True)\\n else:\\n self.userbox.SetSelection(0)\\n self.btnUserSettings.Enable(False)\",\n \"def set_user(self, user_model):\\n\\n self.user_model = user_model\\n return self\",\n \"def update_users_in_range(self, user_list):\\r\\n self.users_in_range = []\\r\\n for user in user_list:\\r\\n if user != self:\\r\\n for freq_range in user.currently_used_frequencies:\\r\\n received_power = self.calculate_signal_power(user, freq_range)\\r\\n if received_power > settings.power_threshold:\\r\\n tmp_freq = freq_range[:]\\r\\n if tmp_freq not in self.users_in_range:\\r\\n self.users_in_range.append(tmp_freq)\",\n \"def set_user(self, user: User):\\n self.__user = user\",\n \"def esxi_host_count(self, esxi_host_count):\\n\\n self._esxi_host_count = esxi_host_count\",\n \"def total_users(user):\\n user_count = User.objects.filter(is_active=True).count()\\n\\n return NumberResponse(user_count, 'Total number of users')\",\n \"def node_count(self, node_count):\\n\\n self._node_count = node_count\",\n \"async def set_aces_used(card_list, user_id):\\n separator = ','\\n cards = separator.join(card_list)\\n await ex.conn.execute(\\\"UPDATE blackjack.currentstatus SET acesused = $1 WHERE userid = $2\\\", cards, user_id)\",\n \"def getViewPortUserCount(self):\\n logger.debug('Getting map view port user count...')\\n elements = get_elements_by_css(\\\".leaflet-marker-icon.srcCluster\\\")\\n users = 0\\n for element in elements:\\n users += int(get_text(element))\\n return users\",\n \"def processor_count(self, processor_count):\\n\\n self._processor_count = processor_count\",\n \"def voterContactCount(self, user):\\n return self.votercontact_set.filter(user=user).count()\",\n \"def setClassCount(self, count):\\n\\n return self._set(classCount=count)\",\n \"def select_user(self, number):\\n\\n if len(self.users) < 1:\\n vk_common.dprint(1, \\\"Массив пользователей пуст.\\\")\\n return -1\\n\\n vk_common.dprint(2, vk_common.func_name(), \\\"Список пользователей при входе: \\\", self.users)\\n self.user_id = self.users[number-1][0]\\n self.item_ids = self.users[number-1][1]\\n vk_common.dprint(2, f\\\"Выбран пользователь с id = {self.user_id}\\\")\\n\\n rows, err = vk_db.get_photo_info_by_user_id(self.user_id)\\n if err != 0:\\n vk_common.dprint(1, \\\"Ошибка получения имен изображений.\\\")\\n return -2\\n\\n vk_common.dprint(2, rows)\\n\\n self.top_images = rows\\n\\n err = self.load_image()\\n if err != 0:\\n vk_common.dprint(1, \\\"Ошибка при загрузке изображений из БД на форму\\\")\\n return -2\\n\\n return 0\",\n \"def set_total_lui(self, valeurs):\\r\\n \\r\\n self._total_lui = valeurs\",\n \"def all_users(self):\\n return range(self.n_users)\",\n \"def set_user(self, user):\\r\\n self.user = user\",\n \"def hyperv_host_count(self, hyperv_host_count):\\n\\n self._hyperv_host_count = hyperv_host_count\",\n \"def ugc_limit(self, ugc_limit):\\n\\n self._ugc_limit = ugc_limit\",\n \"def employee_count(self, employee_count):\\n\\n self._employee_count = employee_count\",\n \"def setusers(self, users=None):\\n if users:\\n self.users = users\\n return\\n import jsb.lib.users as u\\n if not u.users: u.users_boot()\\n self.users = u.users\",\n \"def put_in_all_user_data(user: dict):\\n all_user_data[user.id] = user\",\n \"def assign_user_features(self, user_data):\\n assert \\\"user\\\" in user_data.columns, \\\"Data must contain `user` column.\\\"\\n user_data = user_data.drop_duplicates(subset=[\\\"user\\\"], keep=\\\"last\\\")\\n user_row_idx, user_id_mask = get_row_id_masks(\\n user_data[\\\"user\\\"], self.user_unique_vals\\n )\\n self.user_sparse_unique = update_new_sparse_feats(\\n user_data,\\n user_row_idx,\\n user_id_mask,\\n self.user_sparse_unique,\\n self.sparse_unique_vals,\\n self.multi_sparse_unique_vals,\\n self.user_sparse_col,\\n self.col_name_mapping,\\n self.sparse_offset,\\n )\\n self.user_dense_unique = update_new_dense_feats(\\n user_data,\\n user_row_idx,\\n user_id_mask,\\n self.user_dense_unique,\\n self.user_dense_col,\\n )\",\n \"def user_settings(self, user_settings):\\n\\n self._user_settings = user_settings\",\n \"def update_user_data(self, new_user: User):\\n self.user_data.update_user_data(new_user)\",\n \"def service_user_id(self, service_user_id):\\n\\n self._service_user_id = service_user_id\",\n \"def io_throttle_count(self, io_throttle_count):\\n\\n self._io_throttle_count = io_throttle_count\",\n \"def set_user_config(self, data):\\n config = self.read_config_obj(self.account_file)\\n for key, value in data.items():\\n config.set(self.user, str(key), value)\\n\\n self.write_config(self.account_file, config)\",\n \"def apply_user_change(self, change_count: int, user: get_user_model()) -> bool:\\n if user is None or change_count == 0:\\n return False\\n\\n try:\\n existing_card = UserOwnedCard.objects.get(\\n card_localisation=self, owner=user\\n )\\n if change_count < 0 and abs(change_count) >= existing_card.count:\\n # If the count is below 1 than there is no point thinking that the user \\\"owns\\\"\\n # the card anymore, so just delete the record\\n change_count = -existing_card.count\\n existing_card.delete()\\n else:\\n existing_card.count += change_count\\n existing_card.clean()\\n existing_card.save()\\n except UserOwnedCard.DoesNotExist:\\n if change_count < 0:\\n # You can't subtract cards when you don' have any\\n return False\\n new_ownership = UserOwnedCard(\\n count=change_count, owner=user, card_localisation=self\\n )\\n new_ownership.clean()\\n new_ownership.save()\\n\\n change = UserCardChange(\\n card_localisation=self,\\n owner=user,\\n difference=change_count,\\n date=datetime.datetime.now(),\\n )\\n change.clean()\\n change.save()\\n return True\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def user(self, user):\\n\\n self._user = user\",\n \"def askSymplecticToClearUsers():\\r\\n #description\\r\\n #\\r\\n xml_filename = SymplecticXMLClearUser.__createXMLFileForClear()\\r\\n symplectic_response = SymplecticXMLClearUser.__postClearUsersXMLFileToSymplectic(xml_filename)\\r\\n cleared_count = SymplecticXMLClearUser.__extractUserClearedCountFromResponse(symplectic_response)\\r\\n return cleared_count\",\n \"def request_count(self, request_count):\\n\\n self._request_count = request_count\",\n \"def user_capacity(self) -> SmartSsdUserCapacity:\\n return self._user_capacity\",\n \"def user(self, user):\\n self.user_id = user.get_id()\",\n \"def fillData(self):\\n self.users = c.getUserNames()\\n self.userlist.SetItems(self.users)\",\n \"def unseen_count_for(self, user):\\r\\n return self.filter(user=user, unseen=True).count()\",\n \"def total_nucleus_clients(self, total_nucleus_clients):\\n\\n self._total_nucleus_clients = total_nucleus_clients\",\n \"def set_su_user_id(self, su_user_id):\\n su_user = self.available_users().get(id=su_user_id)\\n self.set_su_user(su_user)\",\n \"def active_member_count(self, active_member_count):\\n\\n self._active_member_count = active_member_count\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61215466","0.5793492","0.54216063","0.54033273","0.532203","0.5165332","0.5099306","0.5092787","0.5068327","0.5068107","0.5022238","0.5022238","0.49769667","0.49539578","0.49485537","0.4900824","0.48729518","0.48636237","0.48355436","0.4805139","0.48023763","0.47965187","0.47965187","0.47965187","0.47733536","0.47679263","0.47635502","0.47627205","0.47581416","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47561386","0.47446612","0.47446612","0.47446612","0.47446612","0.474453","0.47425005","0.47411022","0.47365952","0.47301656","0.4729367","0.47203237","0.47106665","0.46952266","0.46798438","0.46709055","0.46671787","0.46609694","0.46552417","0.46520334","0.4651607","0.4634078","0.4634024","0.46264008","0.46189025","0.4618106","0.46073672","0.4585823","0.45779988","0.4568628","0.45587832","0.45548382","0.4545803","0.4543744","0.45415312","0.45386884","0.45330027","0.45277643","0.45073718","0.45058048","0.45058048","0.45058048","0.45058048","0.45058048","0.45058048","0.45058048","0.45058048","0.45058048","0.4497514","0.44910604","0.44745684","0.44702366","0.4469752","0.44503826","0.44489822","0.4436614","0.44342273"],"string":"[\n \"0.61215466\",\n \"0.5793492\",\n \"0.54216063\",\n \"0.54033273\",\n \"0.532203\",\n \"0.5165332\",\n \"0.5099306\",\n \"0.5092787\",\n \"0.5068327\",\n \"0.5068107\",\n \"0.5022238\",\n \"0.5022238\",\n \"0.49769667\",\n \"0.49539578\",\n \"0.49485537\",\n \"0.4900824\",\n \"0.48729518\",\n \"0.48636237\",\n \"0.48355436\",\n \"0.4805139\",\n \"0.48023763\",\n \"0.47965187\",\n \"0.47965187\",\n \"0.47965187\",\n \"0.47733536\",\n \"0.47679263\",\n \"0.47635502\",\n \"0.47627205\",\n \"0.47581416\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47561386\",\n \"0.47446612\",\n \"0.47446612\",\n \"0.47446612\",\n \"0.47446612\",\n \"0.474453\",\n \"0.47425005\",\n \"0.47411022\",\n \"0.47365952\",\n \"0.47301656\",\n \"0.4729367\",\n \"0.47203237\",\n \"0.47106665\",\n \"0.46952266\",\n \"0.46798438\",\n \"0.46709055\",\n \"0.46671787\",\n \"0.46609694\",\n \"0.46552417\",\n \"0.46520334\",\n \"0.4651607\",\n \"0.4634078\",\n \"0.4634024\",\n \"0.46264008\",\n \"0.46189025\",\n \"0.4618106\",\n \"0.46073672\",\n \"0.4585823\",\n \"0.45779988\",\n \"0.4568628\",\n \"0.45587832\",\n \"0.45548382\",\n \"0.4545803\",\n \"0.4543744\",\n \"0.45415312\",\n \"0.45386884\",\n \"0.45330027\",\n \"0.45277643\",\n \"0.45073718\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.45058048\",\n \"0.4497514\",\n \"0.44910604\",\n \"0.44745684\",\n \"0.44702366\",\n \"0.4469752\",\n \"0.44503826\",\n \"0.44489822\",\n \"0.4436614\",\n \"0.44342273\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72169346"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":19686,"cells":{"query":{"kind":"string","value":"Sets the vdc_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def vm_count(self, vm_count):\n\n self._vm_count = vm_count","def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","def device_count(self, device_count):\n\n self._device_count = device_count","def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count","def vsvrcount(self) :\n\t\ttry :\n\t\t\treturn self._vsvrcount\n\t\texcept Exception as e:\n\t\t\traise e","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def vcpus(self, vcpus):\n self._vcpus = vcpus","def vcn_id(self, vcn_id):\n self._vcn_id = vcn_id","def count(self, count: int):\n\n self._count = count","def setCompoundCount(self, count):\n return self._set(compoundCount=count)","def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count","def count(self, count: int) -> None:\n self._count = count","def get_vdcs(self):\n if self._check_for_7k():\n self.logger.debug('Getting VDC information from {}'.format(self.host))\n vdcxml = self._ncc.nxoscli('show vdc')\n vdcparsed = _begin_parse(vdcxml)\n vdcschema = parse_get_nsmap(vdcparsed)\n showvdc = parse_xml_heirarchy('ROW_vdc', ['vdc_id', 'vdc_name', 'state'], vdcschema,\n vdcparsed)\n vdcs = {}\n for v in showvdc:\n self.logger.debug(\n 'VDC {} {} {} on {}'.format(v['vdc_id'], v['vdc_name'], v['state'], self.host))\n vdcs[v['vdc_name']] = VDC(**v)\n if v['vdc_id'] == '1':\n self.default_vdc = v['vdc_name']\n self.vdcs = vdcs\n self.logger.debug(vdcs)","def set_count(self, count):\n self._count = count","def vcpu_set(vm_hostname, count, offline=False):\n with ExitStack() as es:\n vm = es.enter_context(_get_vm(vm_hostname))\n\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\n raise NotImplementedError(\n 'This operation is not yet supported for {}'.format(\n vm.dataset_obj['datacenter_type'])\n )\n\n _check_defined(vm)\n\n if offline and not vm.is_running():\n log.info(\n '\"{}\" is already powered off, ignoring --offline.'.format(\n vm.fqdn)\n )\n offline = False\n\n if count == vm.dataset_obj['num_cpu']:\n raise Warning('CPU count is the same.')\n\n if offline:\n vm.shutdown()\n vm.set_num_cpu(count)\n if offline:\n vm.start()","def advertise_osd_count(count):\n for relid in hookenv.relation_ids('mon'):\n hookenv.relation_set(\n relation_id=relid,\n relation_settings={'bootstrapped-osds': count}\n )","def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count","def set_count(self, count):\n\n\t\tif count is not None and not isinstance(count, int):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\n\t\t\n\t\tself.__count = count\n\t\tself.__key_modified['count'] = 1","def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count","def set_count(c):\n global count\n count = c","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def setCount(self, num):\n self.count=num","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def user_count(self, user_count):\n\n self._user_count = user_count","def user_count(self, user_count):\n\n self._user_count = user_count","def byte_count(self, byte_count):\n\n self._byte_count = byte_count","def vdses_every(self, vdses_every):\n\n self._vdses_every = vdses_every","def data_center_count(self) -> int:\n return pulumi.get(self, \"data_center_count\")","def count(self, value):\n \n self._count = int(value)","def advanced_catalog_count(self, advanced_catalog_count):\n\n self._advanced_catalog_count = advanced_catalog_count","def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count","def set_segments(self, segments):\n self.send_command(Command.SET_SEGMENT_COUNT, [segments])","def set_num_channels(count):\r\n check_mixer()\r\n global _numchanneldata, _channeldata\r\n if count > _numchanneldata:\r\n _channeldata.extend([ChannelData() for i in\r\n range(count - _numchanneldata)])\r\n _numchanneldata = count\r\n sdl.Mix_AllocateChannels(count)","def update_necrosis_count(self, number):\n\n print(\"controller - update_necrosis_count!\")\n self.view.processing_gui.update_necrosis_count(number)","def associated_object_count(self, associated_object_count):\n self._associated_object_count = associated_object_count","def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def vds_num(self, vds_num):\n\n self._vds_num = vds_num","def set_dark_counts_auto_iv(self, dark_counts):\n if self.NUMBER_OF_DETECTORS != len(dark_counts):\n raise ValueError('Dark counts not the same lenght as number of detectors')\n else:\n msg =json.dumps(dict(command=\"DarkCountsAutoIV\", label=\"DarkCountsAutoIV\", value=dark_counts))\n self.talk.send(msg)","async def connected_users_count(self, event):\n print(\"PublicChatConsumer\", \"connected_users_count\",\n event[\"connected_users_count\"])\n await self.send_json({\n \"msg_type\": MSG_TYPE_CONNECTED_USERS_COUNT,\n \"connected_users_count\": event[\"connected_users_count\"]\n })","def _set_adj_rib_out_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"adj-rib-out-count\", rest_name=\"adj-rib-out-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"adj_rib_out_count must be of a type compatible with int32\"\"\",\n 'defined-type': \"int32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"adj-rib-out-count\", rest_name=\"adj-rib-out-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\"\"\",\n })\n\n self.__adj_rib_out_count = t\n if hasattr(self, '_set'):\n self._set()","def standard_catalog_count(self, standard_catalog_count):\n\n self._standard_catalog_count = standard_catalog_count","def updateBotCounts(self, nextCard):\n nextVal = dnUtil.getValue(nextCard)\n state = self.getState()\n counts = self.getCounts(state)\n newCount = counts.copy()\n for value in dnUtil.valuesList:\n if counts[value][2] == 0:\n continue\n update = self.updateCount(value, nextVal, counts[value])\n newCount[value] = update\n self.setCounts(newCount)","def total_nucleus_clients(self, total_nucleus_clients):\n\n self._total_nucleus_clients = total_nucleus_clients","def count(self, count):\n if count is not None and count < 0:\n raise ValueError(\"Invalid value for `count`, must be a value greater than or equal to `0`\")\n\n self._count = count","def count(self):\n return self.vcount","def set_city_count(self, city_count):\n self.city_count = city_count","def set_article_count(cls, count):\n return cls.db.set(\"article_count\", count)","def processor_count(self, processor_count):\n\n self._processor_count = processor_count","def set_ecuacion_constitutiva(self, param_con, ec_con_id):\n self.param_con = param_con\n self.ecucon_id = ec_con_id\n self.ecuacion_constitutiva = self.ecuaciones_constitutivas(ec_con_id)","def node_count(self, node_count):\n\n self._node_count = node_count","def setClassCount(self, count):\n\n return self._set(classCount=count)","def setVoxelSize(self, vxs):\n\t\tself.voxelsize = vxs\n\t\ta, b, c = vxs\n\t\tself.spacing = [1, b / a, c / a]","def bvc(self, arg):\n\n self.pc += arg if not self.p & const.FLAG_OVERFLOW else 0\n self.pc = c_uint16(self.pc).value","def vms_every(self, vms_every):\n\n self._vms_every = vms_every","def get_devs_count(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetDevsCount', self.handle)","def vlans_every(self, vlans_every):\n\n self._vlans_every = vlans_every","def __init__(self,\n advanced_catalog_count=None,\n bm_catalog_count=None,\n container_catalog_count=None,\n esxi_host_count=None,\n external_group_count=None,\n hyperv_host_count=None,\n local_group_count=None,\n standard_catalog_count=None,\n user_count=None,\n vdc_count=None,\n vm_count=None,\n guid=None,\n local_vars_configuration=None): # noqa: E501 # noqa: E501\n if local_vars_configuration is None:\n local_vars_configuration = Configuration()\n self.local_vars_configuration = local_vars_configuration\n\n self._advanced_catalog_count = None\n self._bm_catalog_count = None\n self._container_catalog_count = None\n self._esxi_host_count = None\n self._external_group_count = None\n self._hyperv_host_count = None\n self._local_group_count = None\n self._standard_catalog_count = None\n self._user_count = None\n self._vdc_count = None\n self._vm_count = None\n self._guid = None\n self.discriminator = None\n\n if advanced_catalog_count is not None:\n self.advanced_catalog_count = advanced_catalog_count\n if bm_catalog_count is not None:\n self.bm_catalog_count = bm_catalog_count\n if container_catalog_count is not None:\n self.container_catalog_count = container_catalog_count\n if esxi_host_count is not None:\n self.esxi_host_count = esxi_host_count\n if external_group_count is not None:\n self.external_group_count = external_group_count\n if hyperv_host_count is not None:\n self.hyperv_host_count = hyperv_host_count\n if local_group_count is not None:\n self.local_group_count = local_group_count\n if standard_catalog_count is not None:\n self.standard_catalog_count = standard_catalog_count\n if user_count is not None:\n self.user_count = user_count\n if vdc_count is not None:\n self.vdc_count = vdc_count\n if vm_count is not None:\n self.vm_count = vm_count\n if guid is not None:\n self.guid = guid","def scale_datanode(self, datanode_count):\n call([Command.docker_compose, \"-f\", self.docker_compose_file,\n \"up\", \"-d\", \"--scale\", \"datanode=\" + datanode_count])","def setScheduleCount(self, count):\n DPxSetDinSchedCount(count)","def setup_measurement_count_and_average_samples(self, measurement_count, average_samples):\n measurement_count_prefix = self._base_pv_name + \":\" + self._slot_number + \":\" + MEASUREMENT_COUNT\n self._run_cmd(CAPUT + \" \" + str(measurement_count_prefix) + \" \" + str(measurement_count))\n\n average_sample_prefix = self._base_pv_name + \":\" + self._slot_number + \":\" + AVERAGE_COUNT\n self._run_cmd(CAPUT + \" \" + str(average_sample_prefix) + \" \" + str(average_samples))","def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\n\n self._count_update_pool_size = count_update_pool_size","def n_cs(self):\n return np.size(self._cs, 0)","def set_count2(self, count2):\n self._count2 = count2","def count_codon_all(self):\n return Counter(list(self))","def get_generic_scsi_devs_count(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetGenericScsiDevsCount', self.handle)","def _set_usr_traceroute_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-traceroute-count\", rest_name=\"usr-traceroute-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_traceroute_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-traceroute-count\", rest_name=\"usr-traceroute-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_traceroute_count = t\n if hasattr(self, '_set'):\n self._set()","def count(self, value):\n raise AttributeError(\"count is read only\")","def get_interfaces(self, vdc=None):\n\n for vdcname in vdc:\n self.switchto_vdc(vdcname)\n self.logger.debug(\"Getting interfaces in vdc {} on {}\".format(self.current_vdc, self.host))\n intdict = Interface._parseallinterface(**self._interfaces_xml_builder())\n self.logger.debug('intdict: {}'.format(str(intdict)))\n if self.vdcs[vdcname].interfaces is None:\n intobj = {}\n for interface in intdict:\n self.logger.debug(\n \"Creating interface object: {} for vdc {}\".format(str(intdict[interface]), self.current_vdc))\n intobj[interface.title()] = Interface(**intdict[interface])\n self.vdcs[self.current_vdc].set_interfaces(intobj)\n self.logger.debug(self.vdcs[self.current_vdc].interfaces)\n else:\n for interface in intdict:\n self.logger.debug(\n \"Updating interface object: {} for vdc {}\".format(str(intdict[interface]), self.current_vdc))\n if interface.title() in self.vdcs[self.current_vdc].interfaces:\n self.vdcs[self.current_vdc].interfaces[interface.title()].update(**intdict[interface])\n else:\n self.vdcs[self.current_vdc].interfaces[interface.title()] = Interface(**intdict[interface])","def set(self, v):\n self.components = v.components","def update_library_count(self, ebook_count):\n\t\tself.library_count = ebook_count","def BVC(self, value):\n if not self.reg.V:\n self.reg.PC += value","def dvs_vcenter_bvt(self):\n self.env.revert_snapshot(\"ready_with_9_slaves\")\n\n plugin.install_dvs_plugin(\n self.env.d_env.get_admin_remote())\n\n # Configure cluster with 2 vcenter clusters and vcenter glance\n cluster_id = self.fuel_web.create_cluster(\n name=self.__class__.__name__,\n mode=DEPLOYMENT_MODE,\n settings={\n \"net_provider\": 'neutron',\n \"net_segment_type\": NEUTRON_SEGMENT_TYPE,\n 'images_ceph': True,\n 'volumes_ceph': True,\n 'objects_ceph': True,\n 'volumes_lvm': False\n }\n )\n plugin.enable_plugin(cluster_id, self.fuel_web)\n\n # Assign role to node\n self.fuel_web.update_nodes(\n cluster_id,\n {'slave-01': ['controller'],\n 'slave-02': ['controller'],\n 'slave-03': ['controller'],\n 'slave-04': ['compute', 'ceph-osd'],\n 'slave-05': ['compute', 'ceph-osd'],\n 'slave-06': ['compute', 'ceph-osd'],\n 'slave-07': ['compute-vmware', 'cinder-vmware']}\n )\n\n # Configure VMWare vCenter settings\n target_node_2 = self.node_name('slave-07')\n self.fuel_web.vcenter_configure(\n cluster_id,\n target_node_2=target_node_2,\n multiclusters=True\n )\n\n self.fuel_web.verify_network(cluster_id, timeout=60 * 15)\n self.fuel_web.deploy_cluster_wait(cluster_id, timeout=3600 * 3)\n\n self.fuel_web.run_ostf(\n cluster_id=cluster_id, test_sets=['smoke'])\n\n self.env.make_snapshot(\"dvs_bvt\", is_make=True)","def update(\n self,\n Count=None,\n Dhcp4EchoRelayInfo=None,\n Dhcp6IaType=None,\n Enabled=None,\n IpAddress=None,\n IpAddressIncrement=None,\n IpAddressPoolIncrement=None,\n IpAddressPrefix=None,\n IpAddressPrefixIncrement=None,\n IpAddressPrefixPoolIncrement=None,\n IpDns1=None,\n IpDns2=None,\n IpGateway=None,\n IpGatewayIncrement=None,\n IpPrefix=None,\n IpType=None,\n Name=None,\n PrefixCount=None,\n PrefixLength=None,\n ServerAddress=None,\n ServerAddressIncrement=None,\n ServerCount=None,\n ServerGateway=None,\n ServerGatewayIncrement=None,\n ServerPrefix=None,\n UseRapidCommit=None,\n ):\n # type: (int, bool, str, bool, str, str, str, str, str, str, str, str, str, str, int, str, str, int, int, str, str, int, str, str, int, bool) -> DhcpServerRange\n return self._update(self._map_locals(self._SDM_ATT_MAP, locals()))","def _setintermediary_institution_56C(self, val):\n self.swift_obj.IntermediaryInstitution_C = val\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'","def set_vector(self, i_vec, nda):\n if i_vec<0 or i_vec>len(self.cumulative_length.nda)-1:\n print('VectorOfVectors: Error: bad i_vec', i_vec)\n return \n if len(nda.shape) != 1:\n print('VectorOfVectors: Error: nda had bad shape', nda.shape)\n return\n start = 0 if i_vec == 0 else self.cumulative_length.nda[i_vec-1]\n end = start + len(nda)\n while end >= len(self.flattened_data.nda):\n self.flattened_data.nda.resize(2*len(self.flattened_data.nda))\n self.flattened_data.nda[start:end] = nda\n self.cumulative_length.nda[i_vec] = end","def setSplitCount(self, count):\n pass","def cria_carro_volks(self):\n\n self.total_de_carros_volks += 1\n print(\"Carro Volks #\", self.total_de_carros_volks, \" criado\")","def Fill(self, v: 'unsigned int const &') -> \"void\":\n return _itkArray2DPython.itkArray2DUI_Fill(self, v)","def GetVendorCount(self):\n regionVectorData = self.VectorData[self.SelectedRegion]\n return regionVectorData['Vendors']","def _update_count(self):\n self._count = len(self._items)","def set_disk(self, number: int, count: int) -> None:\n if count and count > 0:\n self.disc_count = count\n else:\n self.disc_count = None\n if number and number > 0:\n if self.disc_count and number > self.disc_count:\n # The disc number cannot be greater than the total disc number.\n self.disc_number = self.disc_count\n self.disc_number = number\n return\n self.disc_number = None","def AddCpuCountFlag(parser):\n help_text = \"\"\"\\\n Whole number value indicating how many vCPUs the machine should\n contain. Each vCPU count corresponds to a N2 high-mem machine:\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\n \"\"\"\n parser.add_argument(\n '--cpu-count',\n help=help_text,\n type=int,\n choices=[2, 4, 8, 16, 32, 64],\n required=True)","def compute_node_count(self, compute_node_count):\n\n self._compute_node_count = compute_node_count","def create_dvs(self, host_vmnics, dvs_name, num_uplinks=4):\n\n for network_obj in GetDatacenter(self.datacenter_obj).network_resources():\n if network_obj.name == dvs_name:\n self.logger.info(\"DVS {0} already exists\".format(dvs_name))\n return\n host_cfgs = []\n for host_obj, vmnics in host_vmnics.items():\n uplinks = []\n if host_obj.runtime.connectionState != \"connected\":\n self.logger.error(\n \"Host {0} is not connected. Skipped\".format(host_obj.name)\n )\n continue\n host_cfg = vim.dvs.HostMember.ConfigSpec()\n host_cfg.operation = vim.ConfigSpecOperation.add\n host_cfg.host = host_obj\n host_cfg.backing = vim.dvs.HostMember.PnicBacking()\n for pnic in GetHost(host_obj).pnics():\n for vmnic in vmnics:\n if pnic.device == vmnic:\n pnic_spec = vim.dvs.HostMember.PnicSpec()\n pnic_spec.pnicDevice = pnic.device\n uplinks.append(pnic_spec)\n host_cfg.backing.pnicSpec = uplinks\n host_cfgs.append(host_cfg)\n uplink_port_policy = vim.DistributedVirtualSwitch.NameArrayUplinkPortPolicy()\n uplnk_port_order = []\n for i in range(num_uplinks):\n name = \"uplink%d\" % (i + 1)\n uplink_port_policy.uplinkPortName.append(name)\n uplnk_port_order.append(name)\n string_policy = vim.StringPolicy()\n string_policy.value = \"failover_explicit\"\n uplink_port_order_policy = (\n vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortOrderPolicy()\n )\n # activeUplinkPort: list of active uplink ports used for load balancing\n uplink_port_order_policy.activeUplinkPort = uplnk_port_order\n team = vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortTeamingPolicy()\n team.policy = string_policy\n team.uplinkPortOrder = uplink_port_order_policy\n port_config_policy = (\n vim.dvs.VmwareDistributedVirtualSwitch.VmwarePortConfigPolicy()\n )\n port_config_policy.uplinkTeamingPolicy = team\n dvs_config_spec = vim.dvs.VmwareDistributedVirtualSwitch.ConfigSpec()\n dvs_config_spec.name = dvs_name\n dvs_config_spec.host = host_cfgs\n dvs_config_spec.defaultPortConfig = port_config_policy\n dvs_config_spec.lacpApiVersion = (\n vim.dvs.VmwareDistributedVirtualSwitch.LacpApiVersion.multipleLag\n )\n dvs_config_spec.numStandalonePorts = num_uplinks\n dvs_create_spec = vim.DistributedVirtualSwitch.CreateSpec(\n configSpec=dvs_config_spec\n )\n task = self.datacenter_obj.networkFolder.CreateDVS_Task(dvs_create_spec)\n return task","def datacenter_num(self, datacenter_num):\n\n self._datacenter_num = datacenter_num","def add_count(self, denom: CashDenomination, count: int) -> None:\n if not self.__open:\n raise RuntimeError(\"Cash drawer must be open to modify.\")\n if count < 0:\n raise ValueError(\"Count must not be negative.\")\n self.__contents[denom] += count","def set_cov(self):\n v_mpart = self.d_vars['MPart']\n n_mpart = len(v_mpart)\n for p in combinations_with_replacement(range(n_mpart), 2):\n self.add_parameter('Cov', p[0], p[1])\n\n m_cov = np.zeros((n_mpart, n_mpart))\n return m_cov","def get_vendors(self, count: int = 10) -> list:\n return list(itertools.islice(self.client.vendors.get_all_generator(), count))","def train_UCSDped2():\r\n # type: () -> None\r\n dataset_name = \"ucsd_ped2\"\r\n #\r\n # lam_svdd_opt(dataset_name)\r\n # window_size_opt(dataset_name)\r\n latent_code_size_opt(dataset_name)","def vm_volumes_every(self, vm_volumes_every):\n\n self._vm_volumes_every = vm_volumes_every","def bvs(self, arg):\n\n self.pc += arg if self.p & const.FLAG_OVERFLOW else 0\n self.pc = c_uint16(self.pc).value","def datacenters_every(self, datacenters_every):\n\n self._datacenters_every = datacenters_every","def test_0004(self):\n vdc_name = config['vcloud']['vdc']\n vapp_name = config['vcloud']['vapp']\n the_vdc = self.vca.get_vdc(vdc_name)\n assert the_vdc\n assert the_vdc.get_name() == vdc_name\n the_vapp = self.vca.get_vapp(the_vdc, vapp_name)\n assert the_vapp\n assert the_vapp.me.get_status() == 8","def ugc_limit(self, ugc_limit):\n\n self._ugc_limit = ugc_limit","def set_circuit_fav_count(self, circuit_id, number):\n key = ':'.join(\n [CIRCUIT_NMBR_FAVS_1, \n str(circuit_id), \n CIRCUIT_NMBR_FAVS_2]\n ) \n self.RS.set(key, number)"],"string":"[\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def vm_count(self, vm_count):\\n\\n self._vm_count = vm_count\",\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def device_count(self, device_count):\\n\\n self._device_count = device_count\",\n \"def hyperv_host_count(self, hyperv_host_count):\\n\\n self._hyperv_host_count = hyperv_host_count\",\n \"def vsvrcount(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._vsvrcount\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def vcpus(self, vcpus):\\n self._vcpus = vcpus\",\n \"def vcn_id(self, vcn_id):\\n self._vcn_id = vcn_id\",\n \"def count(self, count: int):\\n\\n self._count = count\",\n \"def setCompoundCount(self, count):\\n return self._set(compoundCount=count)\",\n \"def container_catalog_count(self, container_catalog_count):\\n\\n self._container_catalog_count = container_catalog_count\",\n \"def count(self, count: int) -> None:\\n self._count = count\",\n \"def get_vdcs(self):\\n if self._check_for_7k():\\n self.logger.debug('Getting VDC information from {}'.format(self.host))\\n vdcxml = self._ncc.nxoscli('show vdc')\\n vdcparsed = _begin_parse(vdcxml)\\n vdcschema = parse_get_nsmap(vdcparsed)\\n showvdc = parse_xml_heirarchy('ROW_vdc', ['vdc_id', 'vdc_name', 'state'], vdcschema,\\n vdcparsed)\\n vdcs = {}\\n for v in showvdc:\\n self.logger.debug(\\n 'VDC {} {} {} on {}'.format(v['vdc_id'], v['vdc_name'], v['state'], self.host))\\n vdcs[v['vdc_name']] = VDC(**v)\\n if v['vdc_id'] == '1':\\n self.default_vdc = v['vdc_name']\\n self.vdcs = vdcs\\n self.logger.debug(vdcs)\",\n \"def set_count(self, count):\\n self._count = count\",\n \"def vcpu_set(vm_hostname, count, offline=False):\\n with ExitStack() as es:\\n vm = es.enter_context(_get_vm(vm_hostname))\\n\\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\\n raise NotImplementedError(\\n 'This operation is not yet supported for {}'.format(\\n vm.dataset_obj['datacenter_type'])\\n )\\n\\n _check_defined(vm)\\n\\n if offline and not vm.is_running():\\n log.info(\\n '\\\"{}\\\" is already powered off, ignoring --offline.'.format(\\n vm.fqdn)\\n )\\n offline = False\\n\\n if count == vm.dataset_obj['num_cpu']:\\n raise Warning('CPU count is the same.')\\n\\n if offline:\\n vm.shutdown()\\n vm.set_num_cpu(count)\\n if offline:\\n vm.start()\",\n \"def advertise_osd_count(count):\\n for relid in hookenv.relation_ids('mon'):\\n hookenv.relation_set(\\n relation_id=relid,\\n relation_settings={'bootstrapped-osds': count}\\n )\",\n \"def bm_catalog_count(self, bm_catalog_count):\\n\\n self._bm_catalog_count = bm_catalog_count\",\n \"def set_count(self, count):\\n\\n\\t\\tif count is not None and not isinstance(count, int):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\\n\\t\\t\\n\\t\\tself.__count = count\\n\\t\\tself.__key_modified['count'] = 1\",\n \"def esxi_host_count(self, esxi_host_count):\\n\\n self._esxi_host_count = esxi_host_count\",\n \"def set_count(c):\\n global count\\n count = c\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def byte_count(self, byte_count):\\n\\n self._byte_count = byte_count\",\n \"def vdses_every(self, vdses_every):\\n\\n self._vdses_every = vdses_every\",\n \"def data_center_count(self) -> int:\\n return pulumi.get(self, \\\"data_center_count\\\")\",\n \"def count(self, value):\\n \\n self._count = int(value)\",\n \"def advanced_catalog_count(self, advanced_catalog_count):\\n\\n self._advanced_catalog_count = advanced_catalog_count\",\n \"def external_group_count(self, external_group_count):\\n\\n self._external_group_count = external_group_count\",\n \"def set_segments(self, segments):\\n self.send_command(Command.SET_SEGMENT_COUNT, [segments])\",\n \"def set_num_channels(count):\\r\\n check_mixer()\\r\\n global _numchanneldata, _channeldata\\r\\n if count > _numchanneldata:\\r\\n _channeldata.extend([ChannelData() for i in\\r\\n range(count - _numchanneldata)])\\r\\n _numchanneldata = count\\r\\n sdl.Mix_AllocateChannels(count)\",\n \"def update_necrosis_count(self, number):\\n\\n print(\\\"controller - update_necrosis_count!\\\")\\n self.view.processing_gui.update_necrosis_count(number)\",\n \"def associated_object_count(self, associated_object_count):\\n self._associated_object_count = associated_object_count\",\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def vds_num(self, vds_num):\\n\\n self._vds_num = vds_num\",\n \"def set_dark_counts_auto_iv(self, dark_counts):\\n if self.NUMBER_OF_DETECTORS != len(dark_counts):\\n raise ValueError('Dark counts not the same lenght as number of detectors')\\n else:\\n msg =json.dumps(dict(command=\\\"DarkCountsAutoIV\\\", label=\\\"DarkCountsAutoIV\\\", value=dark_counts))\\n self.talk.send(msg)\",\n \"async def connected_users_count(self, event):\\n print(\\\"PublicChatConsumer\\\", \\\"connected_users_count\\\",\\n event[\\\"connected_users_count\\\"])\\n await self.send_json({\\n \\\"msg_type\\\": MSG_TYPE_CONNECTED_USERS_COUNT,\\n \\\"connected_users_count\\\": event[\\\"connected_users_count\\\"]\\n })\",\n \"def _set_adj_rib_out_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"adj-rib-out-count\\\", rest_name=\\\"adj-rib-out-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"adj_rib_out_count must be of a type compatible with int32\\\"\\\"\\\",\\n 'defined-type': \\\"int32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"adj-rib-out-count\\\", rest_name=\\\"adj-rib-out-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__adj_rib_out_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def standard_catalog_count(self, standard_catalog_count):\\n\\n self._standard_catalog_count = standard_catalog_count\",\n \"def updateBotCounts(self, nextCard):\\n nextVal = dnUtil.getValue(nextCard)\\n state = self.getState()\\n counts = self.getCounts(state)\\n newCount = counts.copy()\\n for value in dnUtil.valuesList:\\n if counts[value][2] == 0:\\n continue\\n update = self.updateCount(value, nextVal, counts[value])\\n newCount[value] = update\\n self.setCounts(newCount)\",\n \"def total_nucleus_clients(self, total_nucleus_clients):\\n\\n self._total_nucleus_clients = total_nucleus_clients\",\n \"def count(self, count):\\n if count is not None and count < 0:\\n raise ValueError(\\\"Invalid value for `count`, must be a value greater than or equal to `0`\\\")\\n\\n self._count = count\",\n \"def count(self):\\n return self.vcount\",\n \"def set_city_count(self, city_count):\\n self.city_count = city_count\",\n \"def set_article_count(cls, count):\\n return cls.db.set(\\\"article_count\\\", count)\",\n \"def processor_count(self, processor_count):\\n\\n self._processor_count = processor_count\",\n \"def set_ecuacion_constitutiva(self, param_con, ec_con_id):\\n self.param_con = param_con\\n self.ecucon_id = ec_con_id\\n self.ecuacion_constitutiva = self.ecuaciones_constitutivas(ec_con_id)\",\n \"def node_count(self, node_count):\\n\\n self._node_count = node_count\",\n \"def setClassCount(self, count):\\n\\n return self._set(classCount=count)\",\n \"def setVoxelSize(self, vxs):\\n\\t\\tself.voxelsize = vxs\\n\\t\\ta, b, c = vxs\\n\\t\\tself.spacing = [1, b / a, c / a]\",\n \"def bvc(self, arg):\\n\\n self.pc += arg if not self.p & const.FLAG_OVERFLOW else 0\\n self.pc = c_uint16(self.pc).value\",\n \"def vms_every(self, vms_every):\\n\\n self._vms_every = vms_every\",\n \"def get_devs_count(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetDevsCount', self.handle)\",\n \"def vlans_every(self, vlans_every):\\n\\n self._vlans_every = vlans_every\",\n \"def __init__(self,\\n advanced_catalog_count=None,\\n bm_catalog_count=None,\\n container_catalog_count=None,\\n esxi_host_count=None,\\n external_group_count=None,\\n hyperv_host_count=None,\\n local_group_count=None,\\n standard_catalog_count=None,\\n user_count=None,\\n vdc_count=None,\\n vm_count=None,\\n guid=None,\\n local_vars_configuration=None): # noqa: E501 # noqa: E501\\n if local_vars_configuration is None:\\n local_vars_configuration = Configuration()\\n self.local_vars_configuration = local_vars_configuration\\n\\n self._advanced_catalog_count = None\\n self._bm_catalog_count = None\\n self._container_catalog_count = None\\n self._esxi_host_count = None\\n self._external_group_count = None\\n self._hyperv_host_count = None\\n self._local_group_count = None\\n self._standard_catalog_count = None\\n self._user_count = None\\n self._vdc_count = None\\n self._vm_count = None\\n self._guid = None\\n self.discriminator = None\\n\\n if advanced_catalog_count is not None:\\n self.advanced_catalog_count = advanced_catalog_count\\n if bm_catalog_count is not None:\\n self.bm_catalog_count = bm_catalog_count\\n if container_catalog_count is not None:\\n self.container_catalog_count = container_catalog_count\\n if esxi_host_count is not None:\\n self.esxi_host_count = esxi_host_count\\n if external_group_count is not None:\\n self.external_group_count = external_group_count\\n if hyperv_host_count is not None:\\n self.hyperv_host_count = hyperv_host_count\\n if local_group_count is not None:\\n self.local_group_count = local_group_count\\n if standard_catalog_count is not None:\\n self.standard_catalog_count = standard_catalog_count\\n if user_count is not None:\\n self.user_count = user_count\\n if vdc_count is not None:\\n self.vdc_count = vdc_count\\n if vm_count is not None:\\n self.vm_count = vm_count\\n if guid is not None:\\n self.guid = guid\",\n \"def scale_datanode(self, datanode_count):\\n call([Command.docker_compose, \\\"-f\\\", self.docker_compose_file,\\n \\\"up\\\", \\\"-d\\\", \\\"--scale\\\", \\\"datanode=\\\" + datanode_count])\",\n \"def setScheduleCount(self, count):\\n DPxSetDinSchedCount(count)\",\n \"def setup_measurement_count_and_average_samples(self, measurement_count, average_samples):\\n measurement_count_prefix = self._base_pv_name + \\\":\\\" + self._slot_number + \\\":\\\" + MEASUREMENT_COUNT\\n self._run_cmd(CAPUT + \\\" \\\" + str(measurement_count_prefix) + \\\" \\\" + str(measurement_count))\\n\\n average_sample_prefix = self._base_pv_name + \\\":\\\" + self._slot_number + \\\":\\\" + AVERAGE_COUNT\\n self._run_cmd(CAPUT + \\\" \\\" + str(average_sample_prefix) + \\\" \\\" + str(average_samples))\",\n \"def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\\n\\n self._count_update_pool_size = count_update_pool_size\",\n \"def n_cs(self):\\n return np.size(self._cs, 0)\",\n \"def set_count2(self, count2):\\n self._count2 = count2\",\n \"def count_codon_all(self):\\n return Counter(list(self))\",\n \"def get_generic_scsi_devs_count(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetGenericScsiDevsCount', self.handle)\",\n \"def _set_usr_traceroute_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-traceroute-count\\\", rest_name=\\\"usr-traceroute-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_traceroute_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-traceroute-count\\\", rest_name=\\\"usr-traceroute-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_traceroute_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def count(self, value):\\n raise AttributeError(\\\"count is read only\\\")\",\n \"def get_interfaces(self, vdc=None):\\n\\n for vdcname in vdc:\\n self.switchto_vdc(vdcname)\\n self.logger.debug(\\\"Getting interfaces in vdc {} on {}\\\".format(self.current_vdc, self.host))\\n intdict = Interface._parseallinterface(**self._interfaces_xml_builder())\\n self.logger.debug('intdict: {}'.format(str(intdict)))\\n if self.vdcs[vdcname].interfaces is None:\\n intobj = {}\\n for interface in intdict:\\n self.logger.debug(\\n \\\"Creating interface object: {} for vdc {}\\\".format(str(intdict[interface]), self.current_vdc))\\n intobj[interface.title()] = Interface(**intdict[interface])\\n self.vdcs[self.current_vdc].set_interfaces(intobj)\\n self.logger.debug(self.vdcs[self.current_vdc].interfaces)\\n else:\\n for interface in intdict:\\n self.logger.debug(\\n \\\"Updating interface object: {} for vdc {}\\\".format(str(intdict[interface]), self.current_vdc))\\n if interface.title() in self.vdcs[self.current_vdc].interfaces:\\n self.vdcs[self.current_vdc].interfaces[interface.title()].update(**intdict[interface])\\n else:\\n self.vdcs[self.current_vdc].interfaces[interface.title()] = Interface(**intdict[interface])\",\n \"def set(self, v):\\n self.components = v.components\",\n \"def update_library_count(self, ebook_count):\\n\\t\\tself.library_count = ebook_count\",\n \"def BVC(self, value):\\n if not self.reg.V:\\n self.reg.PC += value\",\n \"def dvs_vcenter_bvt(self):\\n self.env.revert_snapshot(\\\"ready_with_9_slaves\\\")\\n\\n plugin.install_dvs_plugin(\\n self.env.d_env.get_admin_remote())\\n\\n # Configure cluster with 2 vcenter clusters and vcenter glance\\n cluster_id = self.fuel_web.create_cluster(\\n name=self.__class__.__name__,\\n mode=DEPLOYMENT_MODE,\\n settings={\\n \\\"net_provider\\\": 'neutron',\\n \\\"net_segment_type\\\": NEUTRON_SEGMENT_TYPE,\\n 'images_ceph': True,\\n 'volumes_ceph': True,\\n 'objects_ceph': True,\\n 'volumes_lvm': False\\n }\\n )\\n plugin.enable_plugin(cluster_id, self.fuel_web)\\n\\n # Assign role to node\\n self.fuel_web.update_nodes(\\n cluster_id,\\n {'slave-01': ['controller'],\\n 'slave-02': ['controller'],\\n 'slave-03': ['controller'],\\n 'slave-04': ['compute', 'ceph-osd'],\\n 'slave-05': ['compute', 'ceph-osd'],\\n 'slave-06': ['compute', 'ceph-osd'],\\n 'slave-07': ['compute-vmware', 'cinder-vmware']}\\n )\\n\\n # Configure VMWare vCenter settings\\n target_node_2 = self.node_name('slave-07')\\n self.fuel_web.vcenter_configure(\\n cluster_id,\\n target_node_2=target_node_2,\\n multiclusters=True\\n )\\n\\n self.fuel_web.verify_network(cluster_id, timeout=60 * 15)\\n self.fuel_web.deploy_cluster_wait(cluster_id, timeout=3600 * 3)\\n\\n self.fuel_web.run_ostf(\\n cluster_id=cluster_id, test_sets=['smoke'])\\n\\n self.env.make_snapshot(\\\"dvs_bvt\\\", is_make=True)\",\n \"def update(\\n self,\\n Count=None,\\n Dhcp4EchoRelayInfo=None,\\n Dhcp6IaType=None,\\n Enabled=None,\\n IpAddress=None,\\n IpAddressIncrement=None,\\n IpAddressPoolIncrement=None,\\n IpAddressPrefix=None,\\n IpAddressPrefixIncrement=None,\\n IpAddressPrefixPoolIncrement=None,\\n IpDns1=None,\\n IpDns2=None,\\n IpGateway=None,\\n IpGatewayIncrement=None,\\n IpPrefix=None,\\n IpType=None,\\n Name=None,\\n PrefixCount=None,\\n PrefixLength=None,\\n ServerAddress=None,\\n ServerAddressIncrement=None,\\n ServerCount=None,\\n ServerGateway=None,\\n ServerGatewayIncrement=None,\\n ServerPrefix=None,\\n UseRapidCommit=None,\\n ):\\n # type: (int, bool, str, bool, str, str, str, str, str, str, str, str, str, str, int, str, str, int, int, str, str, int, str, str, int, bool) -> DhcpServerRange\\n return self._update(self._map_locals(self._SDM_ATT_MAP, locals()))\",\n \"def _setintermediary_institution_56C(self, val):\\n self.swift_obj.IntermediaryInstitution_C = val\\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'\",\n \"def set_vector(self, i_vec, nda):\\n if i_vec<0 or i_vec>len(self.cumulative_length.nda)-1:\\n print('VectorOfVectors: Error: bad i_vec', i_vec)\\n return \\n if len(nda.shape) != 1:\\n print('VectorOfVectors: Error: nda had bad shape', nda.shape)\\n return\\n start = 0 if i_vec == 0 else self.cumulative_length.nda[i_vec-1]\\n end = start + len(nda)\\n while end >= len(self.flattened_data.nda):\\n self.flattened_data.nda.resize(2*len(self.flattened_data.nda))\\n self.flattened_data.nda[start:end] = nda\\n self.cumulative_length.nda[i_vec] = end\",\n \"def setSplitCount(self, count):\\n pass\",\n \"def cria_carro_volks(self):\\n\\n self.total_de_carros_volks += 1\\n print(\\\"Carro Volks #\\\", self.total_de_carros_volks, \\\" criado\\\")\",\n \"def Fill(self, v: 'unsigned int const &') -> \\\"void\\\":\\n return _itkArray2DPython.itkArray2DUI_Fill(self, v)\",\n \"def GetVendorCount(self):\\n regionVectorData = self.VectorData[self.SelectedRegion]\\n return regionVectorData['Vendors']\",\n \"def _update_count(self):\\n self._count = len(self._items)\",\n \"def set_disk(self, number: int, count: int) -> None:\\n if count and count > 0:\\n self.disc_count = count\\n else:\\n self.disc_count = None\\n if number and number > 0:\\n if self.disc_count and number > self.disc_count:\\n # The disc number cannot be greater than the total disc number.\\n self.disc_number = self.disc_count\\n self.disc_number = number\\n return\\n self.disc_number = None\",\n \"def AddCpuCountFlag(parser):\\n help_text = \\\"\\\"\\\"\\\\\\n Whole number value indicating how many vCPUs the machine should\\n contain. Each vCPU count corresponds to a N2 high-mem machine:\\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\\n \\\"\\\"\\\"\\n parser.add_argument(\\n '--cpu-count',\\n help=help_text,\\n type=int,\\n choices=[2, 4, 8, 16, 32, 64],\\n required=True)\",\n \"def compute_node_count(self, compute_node_count):\\n\\n self._compute_node_count = compute_node_count\",\n \"def create_dvs(self, host_vmnics, dvs_name, num_uplinks=4):\\n\\n for network_obj in GetDatacenter(self.datacenter_obj).network_resources():\\n if network_obj.name == dvs_name:\\n self.logger.info(\\\"DVS {0} already exists\\\".format(dvs_name))\\n return\\n host_cfgs = []\\n for host_obj, vmnics in host_vmnics.items():\\n uplinks = []\\n if host_obj.runtime.connectionState != \\\"connected\\\":\\n self.logger.error(\\n \\\"Host {0} is not connected. Skipped\\\".format(host_obj.name)\\n )\\n continue\\n host_cfg = vim.dvs.HostMember.ConfigSpec()\\n host_cfg.operation = vim.ConfigSpecOperation.add\\n host_cfg.host = host_obj\\n host_cfg.backing = vim.dvs.HostMember.PnicBacking()\\n for pnic in GetHost(host_obj).pnics():\\n for vmnic in vmnics:\\n if pnic.device == vmnic:\\n pnic_spec = vim.dvs.HostMember.PnicSpec()\\n pnic_spec.pnicDevice = pnic.device\\n uplinks.append(pnic_spec)\\n host_cfg.backing.pnicSpec = uplinks\\n host_cfgs.append(host_cfg)\\n uplink_port_policy = vim.DistributedVirtualSwitch.NameArrayUplinkPortPolicy()\\n uplnk_port_order = []\\n for i in range(num_uplinks):\\n name = \\\"uplink%d\\\" % (i + 1)\\n uplink_port_policy.uplinkPortName.append(name)\\n uplnk_port_order.append(name)\\n string_policy = vim.StringPolicy()\\n string_policy.value = \\\"failover_explicit\\\"\\n uplink_port_order_policy = (\\n vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortOrderPolicy()\\n )\\n # activeUplinkPort: list of active uplink ports used for load balancing\\n uplink_port_order_policy.activeUplinkPort = uplnk_port_order\\n team = vim.dvs.VmwareDistributedVirtualSwitch.UplinkPortTeamingPolicy()\\n team.policy = string_policy\\n team.uplinkPortOrder = uplink_port_order_policy\\n port_config_policy = (\\n vim.dvs.VmwareDistributedVirtualSwitch.VmwarePortConfigPolicy()\\n )\\n port_config_policy.uplinkTeamingPolicy = team\\n dvs_config_spec = vim.dvs.VmwareDistributedVirtualSwitch.ConfigSpec()\\n dvs_config_spec.name = dvs_name\\n dvs_config_spec.host = host_cfgs\\n dvs_config_spec.defaultPortConfig = port_config_policy\\n dvs_config_spec.lacpApiVersion = (\\n vim.dvs.VmwareDistributedVirtualSwitch.LacpApiVersion.multipleLag\\n )\\n dvs_config_spec.numStandalonePorts = num_uplinks\\n dvs_create_spec = vim.DistributedVirtualSwitch.CreateSpec(\\n configSpec=dvs_config_spec\\n )\\n task = self.datacenter_obj.networkFolder.CreateDVS_Task(dvs_create_spec)\\n return task\",\n \"def datacenter_num(self, datacenter_num):\\n\\n self._datacenter_num = datacenter_num\",\n \"def add_count(self, denom: CashDenomination, count: int) -> None:\\n if not self.__open:\\n raise RuntimeError(\\\"Cash drawer must be open to modify.\\\")\\n if count < 0:\\n raise ValueError(\\\"Count must not be negative.\\\")\\n self.__contents[denom] += count\",\n \"def set_cov(self):\\n v_mpart = self.d_vars['MPart']\\n n_mpart = len(v_mpart)\\n for p in combinations_with_replacement(range(n_mpart), 2):\\n self.add_parameter('Cov', p[0], p[1])\\n\\n m_cov = np.zeros((n_mpart, n_mpart))\\n return m_cov\",\n \"def get_vendors(self, count: int = 10) -> list:\\n return list(itertools.islice(self.client.vendors.get_all_generator(), count))\",\n \"def train_UCSDped2():\\r\\n # type: () -> None\\r\\n dataset_name = \\\"ucsd_ped2\\\"\\r\\n #\\r\\n # lam_svdd_opt(dataset_name)\\r\\n # window_size_opt(dataset_name)\\r\\n latent_code_size_opt(dataset_name)\",\n \"def vm_volumes_every(self, vm_volumes_every):\\n\\n self._vm_volumes_every = vm_volumes_every\",\n \"def bvs(self, arg):\\n\\n self.pc += arg if self.p & const.FLAG_OVERFLOW else 0\\n self.pc = c_uint16(self.pc).value\",\n \"def datacenters_every(self, datacenters_every):\\n\\n self._datacenters_every = datacenters_every\",\n \"def test_0004(self):\\n vdc_name = config['vcloud']['vdc']\\n vapp_name = config['vcloud']['vapp']\\n the_vdc = self.vca.get_vdc(vdc_name)\\n assert the_vdc\\n assert the_vdc.get_name() == vdc_name\\n the_vapp = self.vca.get_vapp(the_vdc, vapp_name)\\n assert the_vapp\\n assert the_vapp.me.get_status() == 8\",\n \"def ugc_limit(self, ugc_limit):\\n\\n self._ugc_limit = ugc_limit\",\n \"def set_circuit_fav_count(self, circuit_id, number):\\n key = ':'.join(\\n [CIRCUIT_NMBR_FAVS_1, \\n str(circuit_id), \\n CIRCUIT_NMBR_FAVS_2]\\n ) \\n self.RS.set(key, number)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5437181","0.5437181","0.531096","0.5136232","0.50647324","0.49089286","0.47968695","0.4710327","0.46992522","0.46300593","0.46219018","0.46113285","0.46049786","0.4604565","0.45997623","0.45524865","0.45503348","0.45477548","0.45165822","0.45023248","0.44992244","0.44801655","0.44748098","0.44748098","0.44748098","0.44748098","0.44720647","0.44547394","0.4446141","0.4446141","0.4445781","0.44168583","0.43541402","0.43365797","0.43356487","0.4307151","0.4276885","0.42749685","0.42586842","0.4252607","0.42426","0.41977087","0.41881773","0.41812283","0.4175024","0.41690266","0.4163723","0.41613597","0.41532195","0.41337466","0.41150963","0.41068912","0.41032296","0.40951684","0.40918177","0.4064274","0.40548438","0.4040551","0.4034796","0.40196005","0.39985427","0.39942905","0.39893594","0.39877778","0.39834842","0.3981607","0.39803755","0.39693084","0.39619368","0.39582324","0.3957399","0.3943926","0.39431322","0.39396206","0.39308605","0.39246505","0.3916545","0.3905759","0.39031744","0.3903021","0.38794726","0.38615456","0.3860743","0.38599592","0.38580918","0.3850577","0.38492998","0.38446137","0.38428223","0.3820782","0.3818791","0.38140044","0.38138017","0.38005188","0.37962702","0.37959042","0.37943393","0.3793173","0.37889713","0.37869158"],"string":"[\n \"0.5437181\",\n \"0.5437181\",\n \"0.531096\",\n \"0.5136232\",\n \"0.50647324\",\n \"0.49089286\",\n \"0.47968695\",\n \"0.4710327\",\n \"0.46992522\",\n \"0.46300593\",\n \"0.46219018\",\n \"0.46113285\",\n \"0.46049786\",\n \"0.4604565\",\n \"0.45997623\",\n \"0.45524865\",\n \"0.45503348\",\n \"0.45477548\",\n \"0.45165822\",\n \"0.45023248\",\n \"0.44992244\",\n \"0.44801655\",\n \"0.44748098\",\n \"0.44748098\",\n \"0.44748098\",\n \"0.44748098\",\n \"0.44720647\",\n \"0.44547394\",\n \"0.4446141\",\n \"0.4446141\",\n \"0.4445781\",\n \"0.44168583\",\n \"0.43541402\",\n \"0.43365797\",\n \"0.43356487\",\n \"0.4307151\",\n \"0.4276885\",\n \"0.42749685\",\n \"0.42586842\",\n \"0.4252607\",\n \"0.42426\",\n \"0.41977087\",\n \"0.41881773\",\n \"0.41812283\",\n \"0.4175024\",\n \"0.41690266\",\n \"0.4163723\",\n \"0.41613597\",\n \"0.41532195\",\n \"0.41337466\",\n \"0.41150963\",\n \"0.41068912\",\n \"0.41032296\",\n \"0.40951684\",\n \"0.40918177\",\n \"0.4064274\",\n \"0.40548438\",\n \"0.4040551\",\n \"0.4034796\",\n \"0.40196005\",\n \"0.39985427\",\n \"0.39942905\",\n \"0.39893594\",\n \"0.39877778\",\n \"0.39834842\",\n \"0.3981607\",\n \"0.39803755\",\n \"0.39693084\",\n \"0.39619368\",\n \"0.39582324\",\n \"0.3957399\",\n \"0.3943926\",\n \"0.39431322\",\n \"0.39396206\",\n \"0.39308605\",\n \"0.39246505\",\n \"0.3916545\",\n \"0.3905759\",\n \"0.39031744\",\n \"0.3903021\",\n \"0.38794726\",\n \"0.38615456\",\n \"0.3860743\",\n \"0.38599592\",\n \"0.38580918\",\n \"0.3850577\",\n \"0.38492998\",\n \"0.38446137\",\n \"0.38428223\",\n \"0.3820782\",\n \"0.3818791\",\n \"0.38140044\",\n \"0.38138017\",\n \"0.38005188\",\n \"0.37962702\",\n \"0.37959042\",\n \"0.37943393\",\n \"0.3793173\",\n \"0.37889713\",\n \"0.37869158\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7397312"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19687,"cells":{"query":{"kind":"string","value":"Sets the vm_count of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def vm_count(self, vm_count):\n\n self._vm_count = vm_count"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)","def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count","def lun_count(self, lun_count):\n\n self._lun_count = lun_count","def vcpus(self, vcpus):\n self._vcpus = vcpus","def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count","def vcpu_set(vm_hostname, count, offline=False):\n with ExitStack() as es:\n vm = es.enter_context(_get_vm(vm_hostname))\n\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\n raise NotImplementedError(\n 'This operation is not yet supported for {}'.format(\n vm.dataset_obj['datacenter_type'])\n )\n\n _check_defined(vm)\n\n if offline and not vm.is_running():\n log.info(\n '\"{}\" is already powered off, ignoring --offline.'.format(\n vm.fqdn)\n )\n offline = False\n\n if count == vm.dataset_obj['num_cpu']:\n raise Warning('CPU count is the same.')\n\n if offline:\n vm.shutdown()\n vm.set_num_cpu(count)\n if offline:\n vm.start()","def vms_every(self, vms_every):\n\n self._vms_every = vms_every","def set_count(self, count, asset=None):\n self._set_property('pc:count', count, asset)","def vm_num(self, vm_num):\n\n self._vm_num = vm_num","def vm_volumes_every(self, vm_volumes_every):\n\n self._vm_volumes_every = vm_volumes_every","def vm_num_in(self, vm_num_in):\n\n self._vm_num_in = vm_num_in","def setCount(self, num):\n self.count=num","def user_count(self, user_count):\n\n self._user_count = user_count","def user_count(self, user_count):\n\n self._user_count = user_count","def vm(self, vm):\n\n self._vm = vm","def processor_count(self, processor_count):\n\n self._processor_count = processor_count","def node_count(self, node_count):\n\n self._node_count = node_count","def set_num_virtual_stages(self, num_virtual_stages: int) -> None:\n self.num_virtual_stages = num_virtual_stages","def set_count(self, count):\n self._count = count","def test_vm_count():\n assert environments.vm_count() > 0, 'Total VM count should be over 1.'\n count = 0\n for l in list(environments.data):\n e = environments[l]\n count += e.vm_count\n msg = ('VM count mismatch. Environments says: ' +\n str(environments.vm_count()) +\n ', actual count: ' + str(count))\n assert count == environments.vm_count(), msg","def vlans_every(self, vlans_every):\n\n self._vlans_every = vlans_every","def virtual_machines(self, virtual_machines):\n\n self._virtual_machines = virtual_machines","def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count","def device_count(self, device_count):\n\n self._device_count = device_count","def vsvrcount(self) :\n\t\ttry :\n\t\t\treturn self._vsvrcount\n\t\texcept Exception as e:\n\t\t\traise e","def count(self, count: int):\n\n self._count = count","def count(self, count: int) -> None:\n self._count = count","def update_cpu(self, vm):\n try:\n cpu_spec = self.client.get_cpu(vm.backend_id)\n if (\n cpu_spec['cores_per_socket'] != vm.cores_per_socket\n or cpu_spec['count'] != vm.cores\n ):\n self.client.update_cpu(\n vm.backend_id,\n {\n 'cores_per_socket': vm.cores_per_socket,\n 'count': vm.cores,\n },\n )\n except VMwareError as e:\n raise VMwareBackendError(e)","def AddCpuCountFlag(parser):\n help_text = \"\"\"\\\n Whole number value indicating how many vCPUs the machine should\n contain. Each vCPU count corresponds to a N2 high-mem machine:\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\n \"\"\"\n parser.add_argument(\n '--cpu-count',\n help=help_text,\n type=int,\n choices=[2, 4, 8, 16, 32, 64],\n required=True)","def vm_templates_every(self, vm_templates_every):\n\n self._vm_templates_every = vm_templates_every","def setMancount(self, cnt):\n self.__mancount=cnt","def set_entity_count(cls, count):\n return cls.db.set(\"entity_count\", count)","def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def count(self, count):\n\n self._count = count","def number_of_nodes(self, number_of_nodes):\n\n self._number_of_nodes = number_of_nodes","def block_count(self, block_count):\n\n self._block_count = block_count","def set_count(self, count):\n\n\t\tif count is not None and not isinstance(count, int):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\n\t\t\n\t\tself.__count = count\n\t\tself.__key_modified['count'] = 1","def cpus(self, num_of_cpus):\n\n config_spec = vim.vm.ConfigSpec()\n config_spec.numCPUs = num_of_cpus\n return self.vm_obj.ReconfigVM_Task(config_spec)","def update_virtual_machine(self, vm):\n self.update_cpu(vm)\n self.update_memory(vm)\n signals.vm_updated.send(self.__class__, vm=vm)","def vm_vlan_num_in(self, vm_vlan_num_in):\n\n self._vm_vlan_num_in = vm_vlan_num_in","def vdses_every(self, vdses_every):\n\n self._vdses_every = vdses_every","def byte_count(self, byte_count):\n\n self._byte_count = byte_count","def set_city_count(self, city_count):\n self.city_count = city_count","def set_article_count(cls, count):\n return cls.db.set(\"article_count\", count)","def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"usr_ping_count must be of a type compatible with uint32\"\"\",\n 'defined-type': \"uint32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\"\"\",\n })\n\n self.__usr_ping_count = t\n if hasattr(self, '_set'):\n self._set()","def vms_some(self, vms_some):\n\n self._vms_some = vms_some","def set_cpu_limit(self, nVmCpuLimit):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuLimit', self.handle, nVmCpuLimit)","def setVoxelSize(self, vxs):\n\t\tself.voxelsize = vxs\n\t\ta, b, c = vxs\n\t\tself.spacing = [1, b / a, c / a]","def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\n\n self._count_update_pool_size = count_update_pool_size","def rescan_virtual_machines(self):\n vms = self._get_objects(vim.VirtualMachine)\n for vm in vms:\n if not self._vms.has_key(vm.name):\n self._vms[vm.name] = VirtualMachine(vm, self.service_instance)","def test_get_virtual_machine_count_metrics(self):\n pass","def setClassCount(self, count):\n\n return self._set(classCount=count)","def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count","def test_svm_vs_vm_count():\n assert environments.svms() >= environments.vm_count()","def test_svm_count():\n assert environments.svms() > 0\n count = 0\n for l in list(environments.data):\n e = environments[l]\n count += e.svms\n msg = ('SVM count mismatch. Environments says: ' +\n str(environments.svms()) +\n ', actual count: ' + str(count))\n assert count == environments.svms(), msg","def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count","def vcpus(self):\n return self._vcpus","def max_cpus_per_machine(self, max_cpus_per_machine):\n\n self._max_cpus_per_machine = max_cpus_per_machine","def vm_num_lte(self, vm_num_lte):\n\n self._vm_num_lte = vm_num_lte","def vm_snapshots_every(self, vm_snapshots_every):\n\n self._vm_snapshots_every = vm_snapshots_every","def vm_vlan_num(self, vm_vlan_num):\n\n self._vm_vlan_num = vm_vlan_num","def process_count(self, process_count):\n\n self._process_count = process_count","def compute_node_count(self, compute_node_count):\n\n self._compute_node_count = compute_node_count","def vm_num_gt(self, vm_num_gt):\n\n self._vm_num_gt = vm_num_gt","def set_total_threads(self, total_thread: int) -> 'General':\n self.total_threads = total_thread\n return self","def set_total(self, valeurs):\r\n \r\n self._total = valeurs","def check_vm_count(system, warn=10, crit=15, **kwargs):\n logger = kwargs[\"logger\"]\n vm_count = len(system.list_vms())\n logger.info(\"Checking threshold status for instance count\")\n check_threshold(vm_count, warn, crit, logger)","def set_total_lui(self, valeurs):\r\n \r\n self._total_lui = valeurs","def count(self, value):\n \n self._count = int(value)","def setIterationCount(self, newIterationCount):\n \n pass","def employee_count(self, employee_count):\n\n self._employee_count = employee_count","def vm_template_num_in(self, vm_template_num_in):\n\n self._vm_template_num_in = vm_template_num_in","def test_get_virtual_machine_count_metrics1(self):\n pass","def message_count_limit(self, message_count_limit: ConfigNodePropertyInteger):\n\n self._message_count_limit = message_count_limit","def count(self):\n return self.vcount","def set_autoscaled_instances(self, instance_count: int) -> None:\n set_instances_for_marathon_service(\n service=self.service,\n instance=self.instance,\n instance_count=instance_count,\n )","def set_count(c):\n global count\n count = c","def associated_object_count(self, associated_object_count):\n self._associated_object_count = associated_object_count","def update_necrosis_count(self, number):\n\n print(\"controller - update_necrosis_count!\")\n self.view.processing_gui.update_necrosis_count(number)","def vm_volume_num_in(self, vm_volume_num_in):\n\n self._vm_volume_num_in = vm_volume_num_in","def vm_template_num(self, vm_template_num):\n\n self._vm_template_num = vm_template_num","def __init__(__self__, *,\n count: Optional[pulumi.Input[int]] = None,\n count_i_pv6: Optional[pulumi.Input[int]] = None):\n if count is None:\n count = 1\n if count is not None:\n pulumi.set(__self__, \"count\", count)\n if count_i_pv6 is None:\n count_i_pv6 = 0\n if count_i_pv6 is not None:\n pulumi.set(__self__, \"count_i_pv6\", count_i_pv6)","def test_vm_cpu_limitation(self):\n testflow.step(\"Migrate VM %s\", conf.QOS_VMS[0])\n assert ll_vms.migrateVm(positive=True, vm=conf.QOS_VMS[0])\n expected_dict = self.calculate_expected_values(\n load_dict=self.load_dict\n )\n assert sla_helpers.load_vm_and_check_the_load(\n load_dict=self.load_dict, expected_values=expected_dict\n )","def update_count(self, model, view):\r\n view.SetLabel(str(len(model)))","def get_cpu_count(self):\n\t\treturn call_sdk_function('PrlVmCfg_GetCpuCount', self.handle)","def count(self, count):\n if count is not None and count < 0:\n raise ValueError(\"Invalid value for `count`, must be a value greater than or equal to `0`\")\n\n self._count = count","def vm_vlan_num_gt(self, vm_vlan_num_gt):\n\n self._vm_vlan_num_gt = vm_vlan_num_gt","def _update_count(self):\n self._count = len(self._items)","def advertise_osd_count(count):\n for relid in hookenv.relation_ids('mon'):\n hookenv.relation_set(\n relation_id=relid,\n relation_settings={'bootstrapped-osds': count}\n )","def number_of_atoms(self, value):\n self._number_of_atoms = value","def iscsi_luns_every(self, iscsi_luns_every):\n\n self._iscsi_luns_every = iscsi_luns_every","def _override_size(vm_):\n vm_size = get_size(vm_)\n\n if \"cores\" in vm_:\n vm_size[\"cores\"] = vm_[\"cores\"]\n\n if \"ram\" in vm_:\n vm_size[\"ram\"] = vm_[\"ram\"]\n\n return vm_size","def message_count(self, message_count):\r\n\r\n self._message_count = message_count","def setCompoundCount(self, count):\n return self._set(compoundCount=count)","def system_vlan_num_in(self, system_vlan_num_in):\n\n self._system_vlan_num_in = system_vlan_num_in","def nvmf_subsystems_every(self, nvmf_subsystems_every):\n\n self._nvmf_subsystems_every = nvmf_subsystems_every"],"string":"[\n \"def set_cpu_count(self, nVmCpuCount):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)\",\n \"def vdc_count(self, vdc_count):\\n\\n self._vdc_count = vdc_count\",\n \"def lun_count(self, lun_count):\\n\\n self._lun_count = lun_count\",\n \"def vcpus(self, vcpus):\\n self._vcpus = vcpus\",\n \"def hyperv_host_count(self, hyperv_host_count):\\n\\n self._hyperv_host_count = hyperv_host_count\",\n \"def vcpu_set(vm_hostname, count, offline=False):\\n with ExitStack() as es:\\n vm = es.enter_context(_get_vm(vm_hostname))\\n\\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\\n raise NotImplementedError(\\n 'This operation is not yet supported for {}'.format(\\n vm.dataset_obj['datacenter_type'])\\n )\\n\\n _check_defined(vm)\\n\\n if offline and not vm.is_running():\\n log.info(\\n '\\\"{}\\\" is already powered off, ignoring --offline.'.format(\\n vm.fqdn)\\n )\\n offline = False\\n\\n if count == vm.dataset_obj['num_cpu']:\\n raise Warning('CPU count is the same.')\\n\\n if offline:\\n vm.shutdown()\\n vm.set_num_cpu(count)\\n if offline:\\n vm.start()\",\n \"def vms_every(self, vms_every):\\n\\n self._vms_every = vms_every\",\n \"def set_count(self, count, asset=None):\\n self._set_property('pc:count', count, asset)\",\n \"def vm_num(self, vm_num):\\n\\n self._vm_num = vm_num\",\n \"def vm_volumes_every(self, vm_volumes_every):\\n\\n self._vm_volumes_every = vm_volumes_every\",\n \"def vm_num_in(self, vm_num_in):\\n\\n self._vm_num_in = vm_num_in\",\n \"def setCount(self, num):\\n self.count=num\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def user_count(self, user_count):\\n\\n self._user_count = user_count\",\n \"def vm(self, vm):\\n\\n self._vm = vm\",\n \"def processor_count(self, processor_count):\\n\\n self._processor_count = processor_count\",\n \"def node_count(self, node_count):\\n\\n self._node_count = node_count\",\n \"def set_num_virtual_stages(self, num_virtual_stages: int) -> None:\\n self.num_virtual_stages = num_virtual_stages\",\n \"def set_count(self, count):\\n self._count = count\",\n \"def test_vm_count():\\n assert environments.vm_count() > 0, 'Total VM count should be over 1.'\\n count = 0\\n for l in list(environments.data):\\n e = environments[l]\\n count += e.vm_count\\n msg = ('VM count mismatch. Environments says: ' +\\n str(environments.vm_count()) +\\n ', actual count: ' + str(count))\\n assert count == environments.vm_count(), msg\",\n \"def vlans_every(self, vlans_every):\\n\\n self._vlans_every = vlans_every\",\n \"def virtual_machines(self, virtual_machines):\\n\\n self._virtual_machines = virtual_machines\",\n \"def bm_catalog_count(self, bm_catalog_count):\\n\\n self._bm_catalog_count = bm_catalog_count\",\n \"def device_count(self, device_count):\\n\\n self._device_count = device_count\",\n \"def vsvrcount(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._vsvrcount\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def count(self, count: int):\\n\\n self._count = count\",\n \"def count(self, count: int) -> None:\\n self._count = count\",\n \"def update_cpu(self, vm):\\n try:\\n cpu_spec = self.client.get_cpu(vm.backend_id)\\n if (\\n cpu_spec['cores_per_socket'] != vm.cores_per_socket\\n or cpu_spec['count'] != vm.cores\\n ):\\n self.client.update_cpu(\\n vm.backend_id,\\n {\\n 'cores_per_socket': vm.cores_per_socket,\\n 'count': vm.cores,\\n },\\n )\\n except VMwareError as e:\\n raise VMwareBackendError(e)\",\n \"def AddCpuCountFlag(parser):\\n help_text = \\\"\\\"\\\"\\\\\\n Whole number value indicating how many vCPUs the machine should\\n contain. Each vCPU count corresponds to a N2 high-mem machine:\\n (https://cloud.google.com/compute/docs/general-purpose-machines#n2_machines).\\n \\\"\\\"\\\"\\n parser.add_argument(\\n '--cpu-count',\\n help=help_text,\\n type=int,\\n choices=[2, 4, 8, 16, 32, 64],\\n required=True)\",\n \"def vm_templates_every(self, vm_templates_every):\\n\\n self._vm_templates_every = vm_templates_every\",\n \"def setMancount(self, cnt):\\n self.__mancount=cnt\",\n \"def set_entity_count(cls, count):\\n return cls.db.set(\\\"entity_count\\\", count)\",\n \"def esxi_host_count(self, esxi_host_count):\\n\\n self._esxi_host_count = esxi_host_count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def count(self, count):\\n\\n self._count = count\",\n \"def number_of_nodes(self, number_of_nodes):\\n\\n self._number_of_nodes = number_of_nodes\",\n \"def block_count(self, block_count):\\n\\n self._block_count = block_count\",\n \"def set_count(self, count):\\n\\n\\t\\tif count is not None and not isinstance(count, int):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: count EXPECTED TYPE: int', None, None)\\n\\t\\t\\n\\t\\tself.__count = count\\n\\t\\tself.__key_modified['count'] = 1\",\n \"def cpus(self, num_of_cpus):\\n\\n config_spec = vim.vm.ConfigSpec()\\n config_spec.numCPUs = num_of_cpus\\n return self.vm_obj.ReconfigVM_Task(config_spec)\",\n \"def update_virtual_machine(self, vm):\\n self.update_cpu(vm)\\n self.update_memory(vm)\\n signals.vm_updated.send(self.__class__, vm=vm)\",\n \"def vm_vlan_num_in(self, vm_vlan_num_in):\\n\\n self._vm_vlan_num_in = vm_vlan_num_in\",\n \"def vdses_every(self, vdses_every):\\n\\n self._vdses_every = vdses_every\",\n \"def byte_count(self, byte_count):\\n\\n self._byte_count = byte_count\",\n \"def set_city_count(self, city_count):\\n self.city_count = city_count\",\n \"def set_article_count(cls, count):\\n return cls.db.set(\\\"article_count\\\", count)\",\n \"def _set_usr_ping_count(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"usr_ping_count must be of a type compatible with uint32\\\"\\\"\\\",\\n 'defined-type': \\\"uint32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\\\"usr-ping-count\\\", rest_name=\\\"usr-ping-count\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__usr_ping_count = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def vms_some(self, vms_some):\\n\\n self._vms_some = vms_some\",\n \"def set_cpu_limit(self, nVmCpuLimit):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetCpuLimit', self.handle, nVmCpuLimit)\",\n \"def setVoxelSize(self, vxs):\\n\\t\\tself.voxelsize = vxs\\n\\t\\ta, b, c = vxs\\n\\t\\tself.spacing = [1, b / a, c / a]\",\n \"def count_update_pool_size(self, count_update_pool_size: ConfigNodePropertyInteger):\\n\\n self._count_update_pool_size = count_update_pool_size\",\n \"def rescan_virtual_machines(self):\\n vms = self._get_objects(vim.VirtualMachine)\\n for vm in vms:\\n if not self._vms.has_key(vm.name):\\n self._vms[vm.name] = VirtualMachine(vm, self.service_instance)\",\n \"def test_get_virtual_machine_count_metrics(self):\\n pass\",\n \"def setClassCount(self, count):\\n\\n return self._set(classCount=count)\",\n \"def external_group_count(self, external_group_count):\\n\\n self._external_group_count = external_group_count\",\n \"def test_svm_vs_vm_count():\\n assert environments.svms() >= environments.vm_count()\",\n \"def test_svm_count():\\n assert environments.svms() > 0\\n count = 0\\n for l in list(environments.data):\\n e = environments[l]\\n count += e.svms\\n msg = ('SVM count mismatch. Environments says: ' +\\n str(environments.svms()) +\\n ', actual count: ' + str(count))\\n assert count == environments.svms(), msg\",\n \"def container_catalog_count(self, container_catalog_count):\\n\\n self._container_catalog_count = container_catalog_count\",\n \"def vcpus(self):\\n return self._vcpus\",\n \"def max_cpus_per_machine(self, max_cpus_per_machine):\\n\\n self._max_cpus_per_machine = max_cpus_per_machine\",\n \"def vm_num_lte(self, vm_num_lte):\\n\\n self._vm_num_lte = vm_num_lte\",\n \"def vm_snapshots_every(self, vm_snapshots_every):\\n\\n self._vm_snapshots_every = vm_snapshots_every\",\n \"def vm_vlan_num(self, vm_vlan_num):\\n\\n self._vm_vlan_num = vm_vlan_num\",\n \"def process_count(self, process_count):\\n\\n self._process_count = process_count\",\n \"def compute_node_count(self, compute_node_count):\\n\\n self._compute_node_count = compute_node_count\",\n \"def vm_num_gt(self, vm_num_gt):\\n\\n self._vm_num_gt = vm_num_gt\",\n \"def set_total_threads(self, total_thread: int) -> 'General':\\n self.total_threads = total_thread\\n return self\",\n \"def set_total(self, valeurs):\\r\\n \\r\\n self._total = valeurs\",\n \"def check_vm_count(system, warn=10, crit=15, **kwargs):\\n logger = kwargs[\\\"logger\\\"]\\n vm_count = len(system.list_vms())\\n logger.info(\\\"Checking threshold status for instance count\\\")\\n check_threshold(vm_count, warn, crit, logger)\",\n \"def set_total_lui(self, valeurs):\\r\\n \\r\\n self._total_lui = valeurs\",\n \"def count(self, value):\\n \\n self._count = int(value)\",\n \"def setIterationCount(self, newIterationCount):\\n \\n pass\",\n \"def employee_count(self, employee_count):\\n\\n self._employee_count = employee_count\",\n \"def vm_template_num_in(self, vm_template_num_in):\\n\\n self._vm_template_num_in = vm_template_num_in\",\n \"def test_get_virtual_machine_count_metrics1(self):\\n pass\",\n \"def message_count_limit(self, message_count_limit: ConfigNodePropertyInteger):\\n\\n self._message_count_limit = message_count_limit\",\n \"def count(self):\\n return self.vcount\",\n \"def set_autoscaled_instances(self, instance_count: int) -> None:\\n set_instances_for_marathon_service(\\n service=self.service,\\n instance=self.instance,\\n instance_count=instance_count,\\n )\",\n \"def set_count(c):\\n global count\\n count = c\",\n \"def associated_object_count(self, associated_object_count):\\n self._associated_object_count = associated_object_count\",\n \"def update_necrosis_count(self, number):\\n\\n print(\\\"controller - update_necrosis_count!\\\")\\n self.view.processing_gui.update_necrosis_count(number)\",\n \"def vm_volume_num_in(self, vm_volume_num_in):\\n\\n self._vm_volume_num_in = vm_volume_num_in\",\n \"def vm_template_num(self, vm_template_num):\\n\\n self._vm_template_num = vm_template_num\",\n \"def __init__(__self__, *,\\n count: Optional[pulumi.Input[int]] = None,\\n count_i_pv6: Optional[pulumi.Input[int]] = None):\\n if count is None:\\n count = 1\\n if count is not None:\\n pulumi.set(__self__, \\\"count\\\", count)\\n if count_i_pv6 is None:\\n count_i_pv6 = 0\\n if count_i_pv6 is not None:\\n pulumi.set(__self__, \\\"count_i_pv6\\\", count_i_pv6)\",\n \"def test_vm_cpu_limitation(self):\\n testflow.step(\\\"Migrate VM %s\\\", conf.QOS_VMS[0])\\n assert ll_vms.migrateVm(positive=True, vm=conf.QOS_VMS[0])\\n expected_dict = self.calculate_expected_values(\\n load_dict=self.load_dict\\n )\\n assert sla_helpers.load_vm_and_check_the_load(\\n load_dict=self.load_dict, expected_values=expected_dict\\n )\",\n \"def update_count(self, model, view):\\r\\n view.SetLabel(str(len(model)))\",\n \"def get_cpu_count(self):\\n\\t\\treturn call_sdk_function('PrlVmCfg_GetCpuCount', self.handle)\",\n \"def count(self, count):\\n if count is not None and count < 0:\\n raise ValueError(\\\"Invalid value for `count`, must be a value greater than or equal to `0`\\\")\\n\\n self._count = count\",\n \"def vm_vlan_num_gt(self, vm_vlan_num_gt):\\n\\n self._vm_vlan_num_gt = vm_vlan_num_gt\",\n \"def _update_count(self):\\n self._count = len(self._items)\",\n \"def advertise_osd_count(count):\\n for relid in hookenv.relation_ids('mon'):\\n hookenv.relation_set(\\n relation_id=relid,\\n relation_settings={'bootstrapped-osds': count}\\n )\",\n \"def number_of_atoms(self, value):\\n self._number_of_atoms = value\",\n \"def iscsi_luns_every(self, iscsi_luns_every):\\n\\n self._iscsi_luns_every = iscsi_luns_every\",\n \"def _override_size(vm_):\\n vm_size = get_size(vm_)\\n\\n if \\\"cores\\\" in vm_:\\n vm_size[\\\"cores\\\"] = vm_[\\\"cores\\\"]\\n\\n if \\\"ram\\\" in vm_:\\n vm_size[\\\"ram\\\"] = vm_[\\\"ram\\\"]\\n\\n return vm_size\",\n \"def message_count(self, message_count):\\r\\n\\r\\n self._message_count = message_count\",\n \"def setCompoundCount(self, count):\\n return self._set(compoundCount=count)\",\n \"def system_vlan_num_in(self, system_vlan_num_in):\\n\\n self._system_vlan_num_in = system_vlan_num_in\",\n \"def nvmf_subsystems_every(self, nvmf_subsystems_every):\\n\\n self._nvmf_subsystems_every = nvmf_subsystems_every\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60274035","0.5922383","0.5739843","0.5628235","0.56280166","0.5574185","0.55255973","0.53965175","0.5285271","0.5197171","0.5193007","0.5076765","0.5026433","0.5026433","0.50207126","0.49862435","0.4984859","0.49740753","0.4924906","0.49171144","0.49162132","0.49112612","0.4900573","0.4875653","0.48574725","0.48341277","0.48284873","0.48186758","0.47809905","0.47798416","0.4770054","0.47610223","0.4759049","0.47489318","0.47489318","0.47489318","0.47489318","0.46929714","0.46531188","0.46493146","0.4635295","0.45835835","0.45646325","0.4561363","0.45567495","0.45557117","0.45534486","0.4552669","0.45287088","0.45283163","0.4525902","0.45034432","0.4502465","0.4493251","0.44797173","0.44746408","0.4459433","0.44433868","0.4434257","0.44239432","0.44220236","0.4420086","0.44070697","0.43996075","0.4393896","0.43855596","0.43764907","0.43748498","0.43729144","0.4372554","0.4365599","0.43543807","0.43493813","0.43487358","0.43473068","0.43472806","0.43446586","0.43421152","0.43414435","0.43404806","0.43338346","0.43323565","0.4318917","0.43092486","0.42985716","0.42971927","0.42775577","0.42704964","0.4267612","0.42671812","0.42637256","0.4261935","0.4261867","0.42516118","0.42416233","0.42403227","0.42215115","0.42210478","0.42185864"],"string":"[\n \"0.60274035\",\n \"0.5922383\",\n \"0.5739843\",\n \"0.5628235\",\n \"0.56280166\",\n \"0.5574185\",\n \"0.55255973\",\n \"0.53965175\",\n \"0.5285271\",\n \"0.5197171\",\n \"0.5193007\",\n \"0.5076765\",\n \"0.5026433\",\n \"0.5026433\",\n \"0.50207126\",\n \"0.49862435\",\n \"0.4984859\",\n \"0.49740753\",\n \"0.4924906\",\n \"0.49171144\",\n \"0.49162132\",\n \"0.49112612\",\n \"0.4900573\",\n \"0.4875653\",\n \"0.48574725\",\n \"0.48341277\",\n \"0.48284873\",\n \"0.48186758\",\n \"0.47809905\",\n \"0.47798416\",\n \"0.4770054\",\n \"0.47610223\",\n \"0.4759049\",\n \"0.47489318\",\n \"0.47489318\",\n \"0.47489318\",\n \"0.47489318\",\n \"0.46929714\",\n \"0.46531188\",\n \"0.46493146\",\n \"0.4635295\",\n \"0.45835835\",\n \"0.45646325\",\n \"0.4561363\",\n \"0.45567495\",\n \"0.45557117\",\n \"0.45534486\",\n \"0.4552669\",\n \"0.45287088\",\n \"0.45283163\",\n \"0.4525902\",\n \"0.45034432\",\n \"0.4502465\",\n \"0.4493251\",\n \"0.44797173\",\n \"0.44746408\",\n \"0.4459433\",\n \"0.44433868\",\n \"0.4434257\",\n \"0.44239432\",\n \"0.44220236\",\n \"0.4420086\",\n \"0.44070697\",\n \"0.43996075\",\n \"0.4393896\",\n \"0.43855596\",\n \"0.43764907\",\n \"0.43748498\",\n \"0.43729144\",\n \"0.4372554\",\n \"0.4365599\",\n \"0.43543807\",\n \"0.43493813\",\n \"0.43487358\",\n \"0.43473068\",\n \"0.43472806\",\n \"0.43446586\",\n \"0.43421152\",\n \"0.43414435\",\n \"0.43404806\",\n \"0.43338346\",\n \"0.43323565\",\n \"0.4318917\",\n \"0.43092486\",\n \"0.42985716\",\n \"0.42971927\",\n \"0.42775577\",\n \"0.42704964\",\n \"0.4267612\",\n \"0.42671812\",\n \"0.42637256\",\n \"0.4261935\",\n \"0.4261867\",\n \"0.42516118\",\n \"0.42416233\",\n \"0.42403227\",\n \"0.42215115\",\n \"0.42210478\",\n \"0.42185864\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7385598"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":19688,"cells":{"query":{"kind":"string","value":"Sets the guid of this IaasUcsdManagedInfraAllOf."},"document":{"kind":"string","value":"def guid(self, guid):\n\n self._guid = guid"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def guid(self, guid):\n self._guid = guid","def cmd_set_guid(argv,o):\n LibIBOpts.setup(o);\n (args,values) = o.parse_args(argv);\n lib = LibIBOpts(o,args,values,3,(tmpl_target,tmpl_int,tmpl_port_guid));\n\n if len(values) != 3:\n raise CmdError(\"Not enough arguments\");\n\n with lib.get_umad_for_target(values[0],gmp=True) as umad:\n set_cm = set_guid(lib.path.DLID,values[2],values[1]);\n ret = umad.SubnAdmSet(set_cm,umad.end_port.sa_path);\n ret.printer(sys.stdout);","def set_space_guid(self, space_guid):\n res = self._cc.spaces(space_guid).get()\n self._space = res.resource\n\n res = self._cc.request(self._space.organization_url).get()\n self._org = res.resource\n return self","def script_set_device(self,udid=None):\n self.desired_caps['udid'] = udid;","def bios_uuid(self, bios_uuid):\n\n self._bios_uuid = bios_uuid","def set_uuid(self, sNewVmUuid):\n\t\tcall_sdk_function('PrlVmCfg_SetUuid', self.handle, sNewVmUuid)","def udh(self, udh):\n\n self._udh = udh","def __initializeEntityIfNotPresent(self, umls_cui):\n if not (self.__inStructure(umls_cui)):\n self.structure[umls_cui] = [None, [], [], [], []]","def identify(self):\n if self.cur_uid is None:\n return\n self.ola_thread.rdm_set(self.universe.get(), self.cur_uid, 0, \n \"IDENTIFY_DEVICE\", \n lambda b, s, uid = self.cur_uid:self._rdm_set_complete(uid, b, s), \n [self.id_state.get()])","def write_uid(self, uid):\n self.check_validity()\n\n uid = int(uid)\n\n self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_WRITE_UID, (uid,), 'I', 0, '')","def __eq__(self, other):\n if not isinstance(other, IaasUcsdManagedInfraAllOf):\n return False\n\n return self.to_dict() == other.to_dict()","def unique_identifier(self, unique_identifier):\n\n self._unique_identifier = unique_identifier","def set_identifier(self, uid):\n\n self.uid = uid\n\n self.set_unique_metadata('DC', 'identifier', self.uid, {'id': self.IDENTIFIER_ID})","def reset_uid():\n NNModel._UID_BASE = -1","def bus_ob_id(self, bus_ob_id):\n\n self._bus_ob_id = bus_ob_id","def bus_ob_id(self, bus_ob_id):\n\n self._bus_ob_id = bus_ob_id","def _fix_guid(config, guid):\n if config['dir_guid_source'] == 'objectGUID':\n return str(\n uuid.UUID(bytes_le=guid)\n )\n else:\n return guid","def industry(self, industry):\n\n self._industry = industry","def Set(self, *args):\n return _itkRGBAPixelPython.itkRGBAPixelUC_Set(self, *args)","def uom(self, uom):\n\n self._uom = uom","def _setintermediary_institution_56C(self, val):\n self.swift_obj.IntermediaryInstitution_C = val\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'","def uuid(self, uuid):\n\n self._uuid = uuid","def uuid(self, uuid):\n\n self._uuid = uuid","def uuid(self, uuid):\n\n self._uuid = uuid","def uuid(self, uuid):\n\n self._uuid = uuid","def industry(self, industry: str):\n\n self._industry = industry","def guid(self) -> str:\n return pulumi.get(self, \"guid\")","def guid(self) -> str:\n return pulumi.get(self, \"guid\")","def _set_clear_tm_voq_stat_ing_all_egr_ifname(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=clear_tm_voq_stat_ing_all_egr_ifname.clear_tm_voq_stat_ing_all_egr_ifname, is_leaf=True, yang_name=\"clear-tm-voq-stat-ing-all-egr-ifname\", rest_name=\"clear-tm-voq-stat-ing-all-egr-ifname\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmVoqClearIntf'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"clear_tm_voq_stat_ing_all_egr_ifname must be of a type compatible with rpc\"\"\",\n 'defined-type': \"rpc\",\n 'generated-type': \"\"\"YANGDynClass(base=clear_tm_voq_stat_ing_all_egr_ifname.clear_tm_voq_stat_ing_all_egr_ifname, is_leaf=True, yang_name=\"clear-tm-voq-stat-ing-all-egr-ifname\", rest_name=\"clear-tm-voq-stat-ing-all-egr-ifname\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmVoqClearIntf'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\"\"\",\n })\n\n self.__clear_tm_voq_stat_ing_all_egr_ifname = t\n if hasattr(self, '_set'):\n self._set()","def _setintermediary_institution_56A(self, val):\n self.swift_obj.IntermediaryInstitution_A = val\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'","def set(self, U):\n pass","def set(self, U):\n pass","def setMyIndustryData(self, prefix):\n abr = string.upper(prefix + self.key)\n for id, myIndustryData in self.myParent.industrydata.iteritems():\n if myIndustryData.abr == abr:\n self.myIndustryData = myIndustryData\n return","def uuid(self, uuid: str):\n \n self._uuid = uuid","def nucleus_security_id(self, nucleus_security_id):\n\n self._nucleus_security_id = nucleus_security_id","def guid(self):\n return self._guid","def __init__ (self, uid=None):\n assert (uid is None) or (self.uid() == uid), 'UniqueIdentifier: ctor %s, actual %s' % (uid, self.uid())\n self.__associatedObjects = set()","def set_bios_settings(self, data=None):\n\n if not data:\n raise exception.SDFlexError(\"Could not apply settings with\"\n \" empty data\")\n sushy_system = self._get_sushy_system()\n\n try:\n for key in data.keys():\n sushy_system.bios.set_attribute(key, data[key])\n except sushy.exceptions.SushyError as e:\n message_extended_info = e.body.get('@Message.ExtendedInfo')\n error_message = message_extended_info[0]['Message']\n\n msg = (self._(\"Setting the value of Bios attribute \"\n \"'%(atrribute)s' is not succesfull. \"\n \"Error: %(error)s\") %\n {'error': str(error_message), 'atrribute': key})\n LOG.debug(msg)\n raise exception.SDFlexError(msg)","def uni_type(self, uni_type):\n self._uni_type = uni_type","def iscsi_luns_some(self, iscsi_luns_some):\n\n self._iscsi_luns_some = iscsi_luns_some","def uuid(self, uuid: str):\n\n self._uuid = uuid","def set_uuid(self, device):\n import uuid\n\n return str(uuid.uuid4())","def read_guid(self):\n return ''.join(self.hextostring(i) for i in self.read('bytes:16'))","def fetchguid(self):\n if self['guid']:\n request = self._connection.get('people/{0}.json'.format(self['guid']))\n self._postproc(request)\n self._fetchstream()\n else:\n raise errors.UserError('GUID not set')","def OrganizationSpecificOamPduData(self):\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.vport.protocols.organizationspecificoampdudata_583be9df6a7e96b61036baf2444ee057 import OrganizationSpecificOamPduData\n return OrganizationSpecificOamPduData(self)","def _setintermediary_institution_56D(self, val):\n self.swift_obj.IntermediaryInstitution_D = val\n self.swift_obj.IntermediaryInstitution_D.swiftTag = '56D'","def AddMissingDevice(self, guid_to_code_map: Dict[str, str]) -> None:\n self._missing_devices.update(guid_to_code_map)","def set_region_of_interest(self, roi: UserRoi):\n value = roi.to_struct()\n Utils.check(VL53L1X_C_LIBRARY.VL53L1_SetUserROI(self.dev, byref(value)))","def uuid(self, value):\n self.unique_id = UUID(str(value)).hex","def build_uuid(self, build_uuid):\n\n self._build_uuid = build_uuid","def target_industry(self, target_industry):\n\n self._target_industry = target_industry","def untagAll(self, authenticationToken, guid):\r\n pass","def update_organizational_id(cls, aws_cloud_account_id: str, body: CloudAccountUpdateOrganizationalUnitId) -> Dict:\n\t\tpass","def reset_u(self, random=False):\n if (random):\n self.u = self._u_naught_simple()\n else:\n self.u = self._u_naught()","def cluster_uuid(self, cluster_uuid):\n\n self._cluster_uuid = cluster_uuid","def fusion_api_set_power_device_uid_state(self, body, uri, api=None, headers=None):\n return self.pd.update(body=body, uri=uri, api=api, headers=headers, param='/uidState')","def _set_clear_tm_voq_stat_slot_id_egr_all(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=clear_tm_voq_stat_slot_id_egr_all.clear_tm_voq_stat_slot_id_egr_all, is_leaf=True, yang_name=\"clear-tm-voq-stat-slot-id-egr-all\", rest_name=\"clear-tm-voq-stat-slot-id-egr-all\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmCpuVoqClearGrp'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"clear_tm_voq_stat_slot_id_egr_all must be of a type compatible with rpc\"\"\",\n 'defined-type': \"rpc\",\n 'generated-type': \"\"\"YANGDynClass(base=clear_tm_voq_stat_slot_id_egr_all.clear_tm_voq_stat_slot_id_egr_all, is_leaf=True, yang_name=\"clear-tm-voq-stat-slot-id-egr-all\", rest_name=\"clear-tm-voq-stat-slot-id-egr-all\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmCpuVoqClearGrp'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\"\"\",\n })\n\n self.__clear_tm_voq_stat_slot_id_egr_all = t\n if hasattr(self, '_set'):\n self._set()","def _newClusterId(self):\n return self.guidGenerator.new_id()","def set_id(self, uid):\n self.nccl_id = uid\n return self.nccl_id","def cal_guid(self):\n return 'setup' + str(self.id) + '@lnldb'","def clear_all_keys(self):\n message = WriteMessage()\n message.write_uint8(constants.request.SSH_AGENTC_REMOVE_ALL_IDENTITIES)\n self.connection.send_message(message.data)\n self._await_operation_result()","def id_estagio(self, id_estagio):\n self._id_estagio = id_estagio","def write_uid(self, uid):\n uid = int(uid)\n\n self.ipcon.send_request(self, BrickletBarometerV2.FUNCTION_WRITE_UID, (uid,), 'I', '')","def uid(self, value):\n self._uid = value","def _set_ub(o, d):\n o.setub(d)","def unique_id(self):\n return _spacegrant_swig.invert_bit_sptr_unique_id(self)","def ReadUInt160(self):\n return UInt160(data=bytearray(self.ReadBytes(20)))","def __init__(self, val, offset, mask=0xffffffff, offmask=0):\n flags = TC_U32_TERMINAL\n nkeys = 1\n sel = self.tc_u32_sel.pack(flags, 0, nkeys, 0, 0, 0, 0, 0)\n key = self.tc_u32_key.pack(mask, val, offset, offmask)\n data = sel+key\n super(u32_selector, self).__init__(TCA_U32_SEL, data)","def create_ou(self, ou_dn, description=None, name=None, sd=None):\n m = {\"dn\": ou_dn,\n \"objectClass\": \"organizationalUnit\"}\n\n if description:\n m[\"description\"] = description\n if name:\n m[\"name\"] = name\n\n if sd:\n m[\"nTSecurityDescriptor\"] = ndr_pack(sd)\n self.add(m)","def iscsi_luns_every(self, iscsi_luns_every):\n\n self._iscsi_luns_every = iscsi_luns_every","def test_update_bios_unit(self):\n pass","def set_reservation_target(self, uid: str):\n\n self.driver.get(f\"https://myrecsports.usc.edu/booking/{uid}\")","def _ctrl_hum_set(self, osrs_h):\n data = osrs_h & 0x7\n self._bus.write_byte_data(self.addr, self.CTRL_HUM,\n data)","def unique_key(self, unique_key):\n\n self._unique_key = unique_key","def __init__(self,address,InUseBy=None,Bus = None,Ident=''):\n self.Ident = Ident\n self.InUseBy = InUseBy\n VersionStrip =Ident.split(' ')[0].lower()\n if Ident !='' and VersionStrip in I2C.DeviceList:\n self.InUseBy = I2C.DeviceList[VersionStrip](Bus,address)\n else:\n self.Type=None\n if self.InUseBy!=None:\n self.InUseBy.Ident=VersionStrip","def remove_guid(self, guid):\n super(CachingKBucket, self).remove_guid(guid)\n self.fill_from_cache()","def set_emanuel_usa(self, if_id=1, intensity=np.arange(0, 121, 5),\n v_thresh=25.7, v_half=74.7, scale=1.0):\n if v_half <= v_thresh:\n LOGGER.error('Shape parameters out of range: v_half <= v_thresh.')\n raise ValueError\n if v_thresh < 0 or v_half < 0:\n LOGGER.error('Negative shape parameter.')\n raise ValueError\n if scale > 1 or scale <= 0:\n LOGGER.error('Scale parameter out of range.')\n raise ValueError\n\n self.name = 'Emanuel 2011'\n self.id = if_id\n self.intensity_unit = 'm/s'\n self.intensity = intensity\n self.paa = np.ones(intensity.shape)\n v_temp = (self.intensity - v_thresh) / (v_half - v_thresh)\n v_temp[v_temp < 0] = 0\n self.mdd = v_temp**3 / (1 + v_temp**3)\n self.mdd *= scale","def test_call_default_params_and_lower_id(self):\r\n\r\n app = Usearch610DeNovoOtuPicker(\r\n params={'save_intermediate_files': False,\r\n 'output_dir': self.output_dir,\r\n 'remove_usearch_logs': True,\r\n 'percent_id': 0.95\r\n })\r\n\r\n obs_clusters = app(self.tmp_seq_filepath_97perc_id)\r\n\r\n # All seqs should fall into a single cluster\r\n expected_clusters = {'denovo0': ['usearch_ecoli_seq',\r\n 'usearch_ecoli_seq_2bp_change', 'usearch_ecoli_seq_1bp_change']}\r\n\r\n self.assertItemsEqual(obs_clusters, expected_clusters)","def uf(self, uf):\n self._uf = uf","async def _async_set_unique_id(self, unique_id: str) -> None:\n await self.async_set_unique_id(unique_id)\n self._abort_if_unique_id_configured()","def datacenter_uuid(self) -> str:\n return self.__datacenter_uuid","def datacenter_uuid(self) -> str:\n return self.__datacenter_uuid","def datacenter_uuid(self) -> str:\n return self.__datacenter_uuid","def subject_uuid(self, subject_uuid):\r\n\r\n self._subject_uuid = subject_uuid","def set_all_pwm(self, on, off):\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_L, on & 0xFF)\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_H, on >> 8)\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_L, off & 0xFF)\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_H, off >> 8)","def setResourceApplicationDataEntry(self, authenticationToken, guid, key, value):\r\n pass","def _set_unique_id(self, json_request):\n values = []\n for field in value_fields:\n value = json_request.get(field, '')\n values.append(quote(self.fully_decode_uri(value), safe=''))\n if len(values) == 1:\n self.unique_id = values[0]\n elif len(values) == 2:\n self.unique_id = self.build_summary(values[0], values[1])\n elif len(values) == 1:\n self.unique_id = self.build_summary(values[0], values[1], values[2])","def set_su_user_id(self, su_user_id):\n su_user = self.available_users().get(id=su_user_id)\n self.set_su_user(su_user)","def set_unicast_address(self,serial,address):\n self.set_TX_address(0xff)\n if type(serial) is not str: serial=struct.pack('<BBBHBB',*serial)\n if type(address) is not str: address=chr(address)\n self.send_multicast('\\xb2'+serial+address)","def __init__(self, guid=str(uuid.uuid4()), speciesId=Species, caughtLat=0, caughtLong=0, consumption=Consumption):\n self.guid = guid\n self.speciesId = speciesId\n self.caughtLat = caughtLat\n self.caughtLong = caughtLong\n self.consumption = consumption","def ata_smart_data(self, ata_smart_data: SmartSsdAtaSmartData):\n\n self._ata_smart_data = ata_smart_data","def uid(self, uid):\n\n self._uid = uid","def uid(self, uid):\n\n self._uid = uid","def uid(self, uid):\n\n self._uid = uid","def uid(self, uid):\n\n self._uid = uid","def __init__(self, guidstr=None):\n v_prim.__init__(self)\n self._vs_length = 16\n self._vs_value = '\\x00' * 16\n self._vs_fmt = '16s'\n self._guid_fields = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\n if guidstr is not None:\n self._parseGuidStr(guidstr)","def ata_security(self, ata_security: SmartSsdAtaSecurity):\n\n self._ata_security = ata_security","def SetGPU(id):\n global option\n option['device_id'] = id"],"string":"[\n \"def guid(self, guid):\\n self._guid = guid\",\n \"def cmd_set_guid(argv,o):\\n LibIBOpts.setup(o);\\n (args,values) = o.parse_args(argv);\\n lib = LibIBOpts(o,args,values,3,(tmpl_target,tmpl_int,tmpl_port_guid));\\n\\n if len(values) != 3:\\n raise CmdError(\\\"Not enough arguments\\\");\\n\\n with lib.get_umad_for_target(values[0],gmp=True) as umad:\\n set_cm = set_guid(lib.path.DLID,values[2],values[1]);\\n ret = umad.SubnAdmSet(set_cm,umad.end_port.sa_path);\\n ret.printer(sys.stdout);\",\n \"def set_space_guid(self, space_guid):\\n res = self._cc.spaces(space_guid).get()\\n self._space = res.resource\\n\\n res = self._cc.request(self._space.organization_url).get()\\n self._org = res.resource\\n return self\",\n \"def script_set_device(self,udid=None):\\n self.desired_caps['udid'] = udid;\",\n \"def bios_uuid(self, bios_uuid):\\n\\n self._bios_uuid = bios_uuid\",\n \"def set_uuid(self, sNewVmUuid):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetUuid', self.handle, sNewVmUuid)\",\n \"def udh(self, udh):\\n\\n self._udh = udh\",\n \"def __initializeEntityIfNotPresent(self, umls_cui):\\n if not (self.__inStructure(umls_cui)):\\n self.structure[umls_cui] = [None, [], [], [], []]\",\n \"def identify(self):\\n if self.cur_uid is None:\\n return\\n self.ola_thread.rdm_set(self.universe.get(), self.cur_uid, 0, \\n \\\"IDENTIFY_DEVICE\\\", \\n lambda b, s, uid = self.cur_uid:self._rdm_set_complete(uid, b, s), \\n [self.id_state.get()])\",\n \"def write_uid(self, uid):\\n self.check_validity()\\n\\n uid = int(uid)\\n\\n self.ipcon.send_request(self, BrickletIndustrialDualAnalogInV2.FUNCTION_WRITE_UID, (uid,), 'I', 0, '')\",\n \"def __eq__(self, other):\\n if not isinstance(other, IaasUcsdManagedInfraAllOf):\\n return False\\n\\n return self.to_dict() == other.to_dict()\",\n \"def unique_identifier(self, unique_identifier):\\n\\n self._unique_identifier = unique_identifier\",\n \"def set_identifier(self, uid):\\n\\n self.uid = uid\\n\\n self.set_unique_metadata('DC', 'identifier', self.uid, {'id': self.IDENTIFIER_ID})\",\n \"def reset_uid():\\n NNModel._UID_BASE = -1\",\n \"def bus_ob_id(self, bus_ob_id):\\n\\n self._bus_ob_id = bus_ob_id\",\n \"def bus_ob_id(self, bus_ob_id):\\n\\n self._bus_ob_id = bus_ob_id\",\n \"def _fix_guid(config, guid):\\n if config['dir_guid_source'] == 'objectGUID':\\n return str(\\n uuid.UUID(bytes_le=guid)\\n )\\n else:\\n return guid\",\n \"def industry(self, industry):\\n\\n self._industry = industry\",\n \"def Set(self, *args):\\n return _itkRGBAPixelPython.itkRGBAPixelUC_Set(self, *args)\",\n \"def uom(self, uom):\\n\\n self._uom = uom\",\n \"def _setintermediary_institution_56C(self, val):\\n self.swift_obj.IntermediaryInstitution_C = val\\n self.swift_obj.IntermediaryInstitution_C.swiftTag = '56C'\",\n \"def uuid(self, uuid):\\n\\n self._uuid = uuid\",\n \"def uuid(self, uuid):\\n\\n self._uuid = uuid\",\n \"def uuid(self, uuid):\\n\\n self._uuid = uuid\",\n \"def uuid(self, uuid):\\n\\n self._uuid = uuid\",\n \"def industry(self, industry: str):\\n\\n self._industry = industry\",\n \"def guid(self) -> str:\\n return pulumi.get(self, \\\"guid\\\")\",\n \"def guid(self) -> str:\\n return pulumi.get(self, \\\"guid\\\")\",\n \"def _set_clear_tm_voq_stat_ing_all_egr_ifname(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=clear_tm_voq_stat_ing_all_egr_ifname.clear_tm_voq_stat_ing_all_egr_ifname, is_leaf=True, yang_name=\\\"clear-tm-voq-stat-ing-all-egr-ifname\\\", rest_name=\\\"clear-tm-voq-stat-ing-all-egr-ifname\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmVoqClearIntf'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"clear_tm_voq_stat_ing_all_egr_ifname must be of a type compatible with rpc\\\"\\\"\\\",\\n 'defined-type': \\\"rpc\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=clear_tm_voq_stat_ing_all_egr_ifname.clear_tm_voq_stat_ing_all_egr_ifname, is_leaf=True, yang_name=\\\"clear-tm-voq-stat-ing-all-egr-ifname\\\", rest_name=\\\"clear-tm-voq-stat-ing-all-egr-ifname\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmVoqClearIntf'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__clear_tm_voq_stat_ing_all_egr_ifname = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _setintermediary_institution_56A(self, val):\\n self.swift_obj.IntermediaryInstitution_A = val\\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'\",\n \"def set(self, U):\\n pass\",\n \"def set(self, U):\\n pass\",\n \"def setMyIndustryData(self, prefix):\\n abr = string.upper(prefix + self.key)\\n for id, myIndustryData in self.myParent.industrydata.iteritems():\\n if myIndustryData.abr == abr:\\n self.myIndustryData = myIndustryData\\n return\",\n \"def uuid(self, uuid: str):\\n \\n self._uuid = uuid\",\n \"def nucleus_security_id(self, nucleus_security_id):\\n\\n self._nucleus_security_id = nucleus_security_id\",\n \"def guid(self):\\n return self._guid\",\n \"def __init__ (self, uid=None):\\n assert (uid is None) or (self.uid() == uid), 'UniqueIdentifier: ctor %s, actual %s' % (uid, self.uid())\\n self.__associatedObjects = set()\",\n \"def set_bios_settings(self, data=None):\\n\\n if not data:\\n raise exception.SDFlexError(\\\"Could not apply settings with\\\"\\n \\\" empty data\\\")\\n sushy_system = self._get_sushy_system()\\n\\n try:\\n for key in data.keys():\\n sushy_system.bios.set_attribute(key, data[key])\\n except sushy.exceptions.SushyError as e:\\n message_extended_info = e.body.get('@Message.ExtendedInfo')\\n error_message = message_extended_info[0]['Message']\\n\\n msg = (self._(\\\"Setting the value of Bios attribute \\\"\\n \\\"'%(atrribute)s' is not succesfull. \\\"\\n \\\"Error: %(error)s\\\") %\\n {'error': str(error_message), 'atrribute': key})\\n LOG.debug(msg)\\n raise exception.SDFlexError(msg)\",\n \"def uni_type(self, uni_type):\\n self._uni_type = uni_type\",\n \"def iscsi_luns_some(self, iscsi_luns_some):\\n\\n self._iscsi_luns_some = iscsi_luns_some\",\n \"def uuid(self, uuid: str):\\n\\n self._uuid = uuid\",\n \"def set_uuid(self, device):\\n import uuid\\n\\n return str(uuid.uuid4())\",\n \"def read_guid(self):\\n return ''.join(self.hextostring(i) for i in self.read('bytes:16'))\",\n \"def fetchguid(self):\\n if self['guid']:\\n request = self._connection.get('people/{0}.json'.format(self['guid']))\\n self._postproc(request)\\n self._fetchstream()\\n else:\\n raise errors.UserError('GUID not set')\",\n \"def OrganizationSpecificOamPduData(self):\\n from ixnetwork_restpy.testplatform.sessions.ixnetwork.vport.protocols.organizationspecificoampdudata_583be9df6a7e96b61036baf2444ee057 import OrganizationSpecificOamPduData\\n return OrganizationSpecificOamPduData(self)\",\n \"def _setintermediary_institution_56D(self, val):\\n self.swift_obj.IntermediaryInstitution_D = val\\n self.swift_obj.IntermediaryInstitution_D.swiftTag = '56D'\",\n \"def AddMissingDevice(self, guid_to_code_map: Dict[str, str]) -> None:\\n self._missing_devices.update(guid_to_code_map)\",\n \"def set_region_of_interest(self, roi: UserRoi):\\n value = roi.to_struct()\\n Utils.check(VL53L1X_C_LIBRARY.VL53L1_SetUserROI(self.dev, byref(value)))\",\n \"def uuid(self, value):\\n self.unique_id = UUID(str(value)).hex\",\n \"def build_uuid(self, build_uuid):\\n\\n self._build_uuid = build_uuid\",\n \"def target_industry(self, target_industry):\\n\\n self._target_industry = target_industry\",\n \"def untagAll(self, authenticationToken, guid):\\r\\n pass\",\n \"def update_organizational_id(cls, aws_cloud_account_id: str, body: CloudAccountUpdateOrganizationalUnitId) -> Dict:\\n\\t\\tpass\",\n \"def reset_u(self, random=False):\\n if (random):\\n self.u = self._u_naught_simple()\\n else:\\n self.u = self._u_naught()\",\n \"def cluster_uuid(self, cluster_uuid):\\n\\n self._cluster_uuid = cluster_uuid\",\n \"def fusion_api_set_power_device_uid_state(self, body, uri, api=None, headers=None):\\n return self.pd.update(body=body, uri=uri, api=api, headers=headers, param='/uidState')\",\n \"def _set_clear_tm_voq_stat_slot_id_egr_all(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=clear_tm_voq_stat_slot_id_egr_all.clear_tm_voq_stat_slot_id_egr_all, is_leaf=True, yang_name=\\\"clear-tm-voq-stat-slot-id-egr-all\\\", rest_name=\\\"clear-tm-voq-stat-slot-id-egr-all\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmCpuVoqClearGrp'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"clear_tm_voq_stat_slot_id_egr_all must be of a type compatible with rpc\\\"\\\"\\\",\\n 'defined-type': \\\"rpc\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=clear_tm_voq_stat_slot_id_egr_all.clear_tm_voq_stat_slot_id_egr_all, is_leaf=True, yang_name=\\\"clear-tm-voq-stat-slot-id-egr-all\\\", rest_name=\\\"clear-tm-voq-stat-slot-id-egr-all\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'tmCpuVoqClearGrp'}}, namespace='urn:brocade.com:mgmt:brocade-tm-stats', defining_module='brocade-tm-stats', yang_type='rpc', is_config=True)\\\"\\\"\\\",\\n })\\n\\n self.__clear_tm_voq_stat_slot_id_egr_all = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def _newClusterId(self):\\n return self.guidGenerator.new_id()\",\n \"def set_id(self, uid):\\n self.nccl_id = uid\\n return self.nccl_id\",\n \"def cal_guid(self):\\n return 'setup' + str(self.id) + '@lnldb'\",\n \"def clear_all_keys(self):\\n message = WriteMessage()\\n message.write_uint8(constants.request.SSH_AGENTC_REMOVE_ALL_IDENTITIES)\\n self.connection.send_message(message.data)\\n self._await_operation_result()\",\n \"def id_estagio(self, id_estagio):\\n self._id_estagio = id_estagio\",\n \"def write_uid(self, uid):\\n uid = int(uid)\\n\\n self.ipcon.send_request(self, BrickletBarometerV2.FUNCTION_WRITE_UID, (uid,), 'I', '')\",\n \"def uid(self, value):\\n self._uid = value\",\n \"def _set_ub(o, d):\\n o.setub(d)\",\n \"def unique_id(self):\\n return _spacegrant_swig.invert_bit_sptr_unique_id(self)\",\n \"def ReadUInt160(self):\\n return UInt160(data=bytearray(self.ReadBytes(20)))\",\n \"def __init__(self, val, offset, mask=0xffffffff, offmask=0):\\n flags = TC_U32_TERMINAL\\n nkeys = 1\\n sel = self.tc_u32_sel.pack(flags, 0, nkeys, 0, 0, 0, 0, 0)\\n key = self.tc_u32_key.pack(mask, val, offset, offmask)\\n data = sel+key\\n super(u32_selector, self).__init__(TCA_U32_SEL, data)\",\n \"def create_ou(self, ou_dn, description=None, name=None, sd=None):\\n m = {\\\"dn\\\": ou_dn,\\n \\\"objectClass\\\": \\\"organizationalUnit\\\"}\\n\\n if description:\\n m[\\\"description\\\"] = description\\n if name:\\n m[\\\"name\\\"] = name\\n\\n if sd:\\n m[\\\"nTSecurityDescriptor\\\"] = ndr_pack(sd)\\n self.add(m)\",\n \"def iscsi_luns_every(self, iscsi_luns_every):\\n\\n self._iscsi_luns_every = iscsi_luns_every\",\n \"def test_update_bios_unit(self):\\n pass\",\n \"def set_reservation_target(self, uid: str):\\n\\n self.driver.get(f\\\"https://myrecsports.usc.edu/booking/{uid}\\\")\",\n \"def _ctrl_hum_set(self, osrs_h):\\n data = osrs_h & 0x7\\n self._bus.write_byte_data(self.addr, self.CTRL_HUM,\\n data)\",\n \"def unique_key(self, unique_key):\\n\\n self._unique_key = unique_key\",\n \"def __init__(self,address,InUseBy=None,Bus = None,Ident=''):\\n self.Ident = Ident\\n self.InUseBy = InUseBy\\n VersionStrip =Ident.split(' ')[0].lower()\\n if Ident !='' and VersionStrip in I2C.DeviceList:\\n self.InUseBy = I2C.DeviceList[VersionStrip](Bus,address)\\n else:\\n self.Type=None\\n if self.InUseBy!=None:\\n self.InUseBy.Ident=VersionStrip\",\n \"def remove_guid(self, guid):\\n super(CachingKBucket, self).remove_guid(guid)\\n self.fill_from_cache()\",\n \"def set_emanuel_usa(self, if_id=1, intensity=np.arange(0, 121, 5),\\n v_thresh=25.7, v_half=74.7, scale=1.0):\\n if v_half <= v_thresh:\\n LOGGER.error('Shape parameters out of range: v_half <= v_thresh.')\\n raise ValueError\\n if v_thresh < 0 or v_half < 0:\\n LOGGER.error('Negative shape parameter.')\\n raise ValueError\\n if scale > 1 or scale <= 0:\\n LOGGER.error('Scale parameter out of range.')\\n raise ValueError\\n\\n self.name = 'Emanuel 2011'\\n self.id = if_id\\n self.intensity_unit = 'm/s'\\n self.intensity = intensity\\n self.paa = np.ones(intensity.shape)\\n v_temp = (self.intensity - v_thresh) / (v_half - v_thresh)\\n v_temp[v_temp < 0] = 0\\n self.mdd = v_temp**3 / (1 + v_temp**3)\\n self.mdd *= scale\",\n \"def test_call_default_params_and_lower_id(self):\\r\\n\\r\\n app = Usearch610DeNovoOtuPicker(\\r\\n params={'save_intermediate_files': False,\\r\\n 'output_dir': self.output_dir,\\r\\n 'remove_usearch_logs': True,\\r\\n 'percent_id': 0.95\\r\\n })\\r\\n\\r\\n obs_clusters = app(self.tmp_seq_filepath_97perc_id)\\r\\n\\r\\n # All seqs should fall into a single cluster\\r\\n expected_clusters = {'denovo0': ['usearch_ecoli_seq',\\r\\n 'usearch_ecoli_seq_2bp_change', 'usearch_ecoli_seq_1bp_change']}\\r\\n\\r\\n self.assertItemsEqual(obs_clusters, expected_clusters)\",\n \"def uf(self, uf):\\n self._uf = uf\",\n \"async def _async_set_unique_id(self, unique_id: str) -> None:\\n await self.async_set_unique_id(unique_id)\\n self._abort_if_unique_id_configured()\",\n \"def datacenter_uuid(self) -> str:\\n return self.__datacenter_uuid\",\n \"def datacenter_uuid(self) -> str:\\n return self.__datacenter_uuid\",\n \"def datacenter_uuid(self) -> str:\\n return self.__datacenter_uuid\",\n \"def subject_uuid(self, subject_uuid):\\r\\n\\r\\n self._subject_uuid = subject_uuid\",\n \"def set_all_pwm(self, on, off):\\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_L, on & 0xFF)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_ON_H, on >> 8)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_L, off & 0xFF)\\n self.i2cBus.write_byte_data(self.address, ALL_LED_OFF_H, off >> 8)\",\n \"def setResourceApplicationDataEntry(self, authenticationToken, guid, key, value):\\r\\n pass\",\n \"def _set_unique_id(self, json_request):\\n values = []\\n for field in value_fields:\\n value = json_request.get(field, '')\\n values.append(quote(self.fully_decode_uri(value), safe=''))\\n if len(values) == 1:\\n self.unique_id = values[0]\\n elif len(values) == 2:\\n self.unique_id = self.build_summary(values[0], values[1])\\n elif len(values) == 1:\\n self.unique_id = self.build_summary(values[0], values[1], values[2])\",\n \"def set_su_user_id(self, su_user_id):\\n su_user = self.available_users().get(id=su_user_id)\\n self.set_su_user(su_user)\",\n \"def set_unicast_address(self,serial,address):\\n self.set_TX_address(0xff)\\n if type(serial) is not str: serial=struct.pack('<BBBHBB',*serial)\\n if type(address) is not str: address=chr(address)\\n self.send_multicast('\\\\xb2'+serial+address)\",\n \"def __init__(self, guid=str(uuid.uuid4()), speciesId=Species, caughtLat=0, caughtLong=0, consumption=Consumption):\\n self.guid = guid\\n self.speciesId = speciesId\\n self.caughtLat = caughtLat\\n self.caughtLong = caughtLong\\n self.consumption = consumption\",\n \"def ata_smart_data(self, ata_smart_data: SmartSsdAtaSmartData):\\n\\n self._ata_smart_data = ata_smart_data\",\n \"def uid(self, uid):\\n\\n self._uid = uid\",\n \"def uid(self, uid):\\n\\n self._uid = uid\",\n \"def uid(self, uid):\\n\\n self._uid = uid\",\n \"def uid(self, uid):\\n\\n self._uid = uid\",\n \"def __init__(self, guidstr=None):\\n v_prim.__init__(self)\\n self._vs_length = 16\\n self._vs_value = '\\\\x00' * 16\\n self._vs_fmt = '16s'\\n self._guid_fields = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)\\n if guidstr is not None:\\n self._parseGuidStr(guidstr)\",\n \"def ata_security(self, ata_security: SmartSsdAtaSecurity):\\n\\n self._ata_security = ata_security\",\n \"def SetGPU(id):\\n global option\\n option['device_id'] = id\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.37902427\",\n \"0.37895024\",\n \"0.37850833\",\n \"0.37788847\",\n \"0.37750697\",\n \"0.37688375\",\n \"0.3766933\",\n \"0.37600803\",\n \"0.3755167\",\n \"0.37541404\",\n \"0.37528715\",\n \"0.37474844\",\n \"0.37472358\",\n \"0.37401593\",\n \"0.37241748\",\n \"0.37225947\",\n \"0.37211615\",\n \"0.37191603\",\n \"0.3718509\",\n \"0.37178355\",\n \"0.3713277\",\n \"0.37131667\",\n \"0.37104148\",\n \"0.36964458\",\n \"0.3696017\",\n \"0.3696017\",\n \"0.3696017\",\n \"0.3693258\",\n \"0.3684022\",\n \"0.36757788\",\n \"0.36747757\",\n \"0.36740634\",\n \"0.36738557\",\n \"0.36711934\",\n \"0.36649916\",\n \"0.36598083\",\n \"0.36598083\",\n \"0.36598083\",\n \"0.36598083\",\n \"0.36551163\",\n \"0.36538586\",\n \"0.36530462\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5306522"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":19689,"cells":{"query":{"kind":"string","value":"Returns the model properties as a dict"},"document":{"kind":"string","value":"def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.openapi_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(\n map(lambda x: x.to_dict()\n if hasattr(x, \"to_dict\") else x, value))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(\n map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()))\n else:\n result[attr] = value\n\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def to_dict(self):\n return self.properties","def to_dict(self):\n return self.properties","def get_properties(self):\n return self.properties","def asdict(self):\n return self._prop_dict","def json(self):\n rv = {\n prop: getattr(self, prop)\n for prop in self.__properties__\n if prop in vars(self)\n }\n rv.update(self._props)\n return rv","def get_properties(self):\n return self.properties","def get_properties():","def getProperties():","def properties(self):\r\n if self._properties is None:\r\n res = self._con.get(self._url, {'f':'json'})\r\n self._properties = PropertyMap(res)\r\n return self._properties","def properties(self):\r\n if self._properties is None:\r\n res = self._con.get(self._url, {'f':'json'})\r\n self._properties = PropertyMap(res)\r\n return self._properties","def getProperties(self):\n return self.properties","def __properties__(self) -> dict:\r\n parameters = [\r\n d for d in dir(self) if (d[0] != \"_\") and (d.count(\"set\") == 0)\r\n and (d.count(\"_c\") == 0) and (d.count(\"_f\") == 0)\r\n ]\r\n\r\n return self.__as_json__(parameters)","def json_properties(self):\n attributes = []\n all = vars(self)\n for var in all:\n if var[:1] != '_':\n attributes.append(var)\n if isinstance(self, db.Model):\n properties = self.properties().keys()\n for property in properties:\n if property[:1] != '_':\n attributes.append(property)\n return attributes","def properties(self) -> Any:\n return pulumi.get(self, \"properties\")","def _properties(self) -> dict[str, dict[str, str]]:\n schema = self.schema(by_alias=False)\n if schema.get('properties') is not None:\n return schema.get('properties', {})\n return schema.get('definitions', {}).get(self.__class__.__name__, {}).get('properties', {})","def get_model_properties(self):\n properties = {}\n\n filename = self._get_data_filename(\"modelargs.json\")\n with open(filename, \"r\") as f:\n results = json.loads(f.read())\n properties[\"image_size\"] = results.get(\"image_size\")\n properties[\"num_classes\"] = results.get(\"num_classes\")\n properties[\"model\"] = results.get(\"model\")\n properties[\"name\"] = results.get(\"name\")\n properties[\"filter_size\"] = results.get(\"filter_size\", 3)\n properties[\"increase_factor\"] = results.get(\"increase_factor\", 0)\n self.model = properties[\"name\"] # regardless of the name of the folder, this will get the proper model name (i.e. <modelname>.cntk)\n\n # optional property\n properties[\"trainer\"] = results.get(\"trainer\", \"CNTK 2.2\")\n\n self._ensure_model_file()\n properties[\"size_mb\"] = round(os.path.getsize(self.model_file) / (1000 * 1000))\n\n return properties","def as_dict(self):\n result = {}\n for attr in self.__attr:\n result[attr] = getattr(self, attr)\n return result","def to_dict_model(self) -> dict:\n return dict((key, getattr(self, key)) for key in self.__mapper__.c.keys())","def get_properties():\n properties = dict()\n properties['size'] = list()\n properties['color'] = list()\n properties['quality'] = list()\n u = models.Size.query.all()\n for i in u:\n properties['size'].append(i.size_name)\n u = models.Color.query.all()\n for i in u:\n properties['color'].append(i.color_name)\n u = models.Quality.query.all()\n for i in u:\n properties['quality'].append(i.quality_name)\n return make_response(jsonify(properties))","def get_modelDict(self):\n return self.__modelDict","def attributes(self):\n return dict(self.__attributes)","def properties(self):\n return self._properties","def properties(self):\n return self._properties","def to_dict(self):\n result = {}\n for p in self.json_properties():\n value = getattr(self, p)\n if isinstance(value, datetime.datetime):\n value = value.strftime('%s%f')[:-3]\n result[Jsonifiable.transform_to_camelcase(p)] = value\n return result","def properties(self):\n return self._props","def properties(self):\n pass","def to_dict(self):\n d = {}\n for attr in self.__class__.attributes:\n d[attr] = getattr(self, attr)\n return d","def properties_get(self):\n return self._get('properties')","def _collect_properties(self):\n properties = {\n 'userid': self.user_id,\n 'title': self.get_fullname()\n }\n if not self.ogds_user:\n return properties\n\n for attribute_name in self.ogds_user_attributes:\n value = getattr(self.ogds_user, attribute_name)\n properties[attribute_name] = value\n return properties","def getPropertyDict(self):\n \n d = self.getChild('__properties')\n if d:\n return d.getDict()\n else:\n return {}","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def to_dict(self, include=None):\n _MODEL = type(self)\n repr_dict = {}\n if include is None:\n include = []\n for name, prop in _MODEL._properties.iteritems():\n if hasattr(prop, 'public') and getattr(prop, 'public', False):\n include.append(name)\n\n for name in include:\n # check if this property is even allowed to be public\n # or has a value set\n if not hasattr(self, name):\n continue\n\n value = getattr(self, name)\n if type(getattr(_MODEL, name)) == ndb.StructuredProperty:\n if isinstance(value, list):\n items = []\n for item in value:\n items.append(item.to_dict(include=None))\n repr_dict[name] = items\n else:\n repr_dict[name] = value.to_dict(include=None)\n elif isinstance(value, date):\n repr_dict[name] = value.isoformat()\n elif isinstance(value, ndb.Key):\n repr_dict[name] = value.urlsafe()\n else:\n repr_dict[name] = value\n\n if self._key:\n repr_dict['key'] = self.get_key_urlsafe()\n return repr_dict","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k == 'POSSIBLE_METHODS':\n continue\n if k == 'keysamplers':\n properties[k] = [i.to_dict() for i in self.__dict__[k] if hasattr(i,'to_dict')]\n elif k in {'pooler'}:\n properties[k] = self.__dict__[k].to_dict()\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def get_all_properties(cls):\n return ['key', 'id'] + _.keys(cls._properties)","def get_properties(self):\n return self.name, self.author, self.description, self.fmu_type, self.version, self.guid, self.tool, self.numStates","def properties(self):\n\n return self._properties","def properties(self):","def properties(self):","def properties(self):","def ToDict(self):\n atributes_dictionary = {}\n for key, value in self.__dict__.iteritems():\n atributes_dictionary[key] = value\n return atributes_dictionary","def modelPropertiesDictionary(sql_row_list):\n \n properties_dictionary = \\\n {\n \"id\": sql_row_list[0],\n \"name\": sql_row_list[1],\n \"last_deploy_timestamp\": sql_row_list[2],\n \"active_version\": sql_row_list[3],\n \"build_id\": sql_row_list[4]\n };\n\n return properties_dictionary;","def as_dict(self):\n data = dict()\n for name in self.fields:\n val = getattr(self, name)\n if isinstance(val, Model):\n val = val.as_dict()\n elif isinstance(val, list) and val and isinstance(val[0], Model):\n val = [sub.as_dict() for sub in val]\n data[name] = val\n return data","def to_dict(self):\n if self._dict is not None:\n return self._dict\n\n result = {}\n for key in self.ATTRIBUTES:\n value = getattr(self, key)\n if value:\n result[key] = value\n self._dict = result\n return result","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def to_dict(self):\n _dict = {}\n for f in self._meta.fields:\n if f.name == 'created':\n _dict[f.name] = str(f.value_from_object(self))\n else:\n _dict[f.name] = f.value_from_object(self)\n\n return _dict","def to_dict(self):\r\n return self.__dict__","def properties(self):\n return None","def properties(self):\n return None","def to_dict(self):\n return attr.asdict(self)","def _get_model_state(self) -> dict:\n return dict(model=self.model, kwargs=self._model_kwargs)","def as_dict(self):\n return self.__dict__","def dictify(self):\n return {\n \"name\" : self.name,\n \"lastname\" : self.lastname,\n \"phone\" : self.phone,\n \"email\" : self.email\n }","def to_dict(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n # \"created_by\": self.created_by,\n # \"created_on\": self.created_on,\n # \"modified_by\": self.modified_by,\n # \"modified_on\": self.modified_on\n }","def properties(self):\r\n return resources.Properties(self)","def attributes(self):\n params = self.model.param_array\n return {'parameters': params}","def properties(self, pk):\n return JsonResponse(self._get_properties(pk))","def to_dict(self):\n return vars(self)","def to_dict(self):\n\n # Check if is the right instance.\n if isinstance(self, db.Model):\n # construct a dictionary from column names and values.\n dict_representation = {c.name: getattr(self, c.name) for c in self.__table__.columns}\n return dict_representation\n else:\n raise AttributeError(type(self).__name__ + \" is not instance of \" + db.Model.__name__)","def bson_properties(self):\n return []","def to_dict(self):\n return {\n \"id\": self.id,\n \"name\": self.name\n }","def get_dict(self):\n return","def to_dict(self):\n return to_dict(self.__dict__)","def to_json(self):\n properties = self.to_dict()\n if isinstance(self, db.Model):\n properties['id'] = unicode(self.key().id())\n return json.dumps(properties)","def to_dict(self):","def get_attributes(self) -> Dict[str, str]:\n pass","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def config(self) -> ModelConfigDict:\n return self.config_obj.to_dict()","def properties(self):\n return self.properties_with_uid[1:]","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def serialise(self):\n return {\n 'id': self.id,\n 'category_id': self.category_id,\n 'name': self.name,\n 'description': self.description,\n 'quantity': self.quantity,\n 'price': self.price,\n 'user_id': self.user_id\n }","def getPropertiesAll():","def get_all_properties(self) -> dict:\n return self._request(\n \"post\",\n URL,\n json=attr.asdict(\n Body(\"getAllProperties\", API_VERSION),\n filter=attr.filters.exclude(attr.fields(Body).params),\n ),\n )","def model_info(self):\n if not self._model_info:\n self._load_model_info()\n try:\n data = json.loads(self._model_info)\n except (TypeError, ValueError):\n data = {}\n return data","def to_dict(self):\n return {\n 'name': self.get_name(),\n 'description': self.get_description()\n }","def serialize(self):\n return {\n 'id' : self.id,\n 'name' : self.name,\n }","def serialize(self):\n return {\n 'id' : self.id,\n 'name' : self.name,\n }","def as_dict(self):\n return self.__dict__","def to_dict(self):\r\n\r\n return {\r\n 'product_id': self.product_id,\r\n 'product_name': self.product_name\r\n }","def serialize(self):\n return {\n 'name' : self.name,\n 'id' : self.id,\n }","def to_dict(self) -> dict:","def asdict(self):\n return attr.asdict(self)","def getDict(self):\n res = {}\n for attr, value in self.__dict__.iteritems():\n if type(attr) is IntType or type(attr) is StringType or type(attr) is LongType or type(attr) is UnicodeType:\n res[attr] = value\n elif isinstance(attr, datetime.datetime):\n res[attr] = value.isoformat('-')\n \n return res","def attributes(self):\n return self.__dict.keys()","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def as_dict(self):\n return {c.key: getattr(self, c.key)\n for c in inspect(self).mapper.column_attrs}"],"string":"[\n \"def to_dict(self):\\n return self.properties\",\n \"def to_dict(self):\\n return self.properties\",\n \"def get_properties(self):\\n return self.properties\",\n \"def asdict(self):\\n return self._prop_dict\",\n \"def json(self):\\n rv = {\\n prop: getattr(self, prop)\\n for prop in self.__properties__\\n if prop in vars(self)\\n }\\n rv.update(self._props)\\n return rv\",\n \"def get_properties(self):\\n return self.properties\",\n \"def get_properties():\",\n \"def getProperties():\",\n \"def properties(self):\\r\\n if self._properties is None:\\r\\n res = self._con.get(self._url, {'f':'json'})\\r\\n self._properties = PropertyMap(res)\\r\\n return self._properties\",\n \"def properties(self):\\r\\n if self._properties is None:\\r\\n res = self._con.get(self._url, {'f':'json'})\\r\\n self._properties = PropertyMap(res)\\r\\n return self._properties\",\n \"def getProperties(self):\\n return self.properties\",\n \"def __properties__(self) -> dict:\\r\\n parameters = [\\r\\n d for d in dir(self) if (d[0] != \\\"_\\\") and (d.count(\\\"set\\\") == 0)\\r\\n and (d.count(\\\"_c\\\") == 0) and (d.count(\\\"_f\\\") == 0)\\r\\n ]\\r\\n\\r\\n return self.__as_json__(parameters)\",\n \"def json_properties(self):\\n attributes = []\\n all = vars(self)\\n for var in all:\\n if var[:1] != '_':\\n attributes.append(var)\\n if isinstance(self, db.Model):\\n properties = self.properties().keys()\\n for property in properties:\\n if property[:1] != '_':\\n attributes.append(property)\\n return attributes\",\n \"def properties(self) -> Any:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def _properties(self) -> dict[str, dict[str, str]]:\\n schema = self.schema(by_alias=False)\\n if schema.get('properties') is not None:\\n return schema.get('properties', {})\\n return schema.get('definitions', {}).get(self.__class__.__name__, {}).get('properties', {})\",\n \"def get_model_properties(self):\\n properties = {}\\n\\n filename = self._get_data_filename(\\\"modelargs.json\\\")\\n with open(filename, \\\"r\\\") as f:\\n results = json.loads(f.read())\\n properties[\\\"image_size\\\"] = results.get(\\\"image_size\\\")\\n properties[\\\"num_classes\\\"] = results.get(\\\"num_classes\\\")\\n properties[\\\"model\\\"] = results.get(\\\"model\\\")\\n properties[\\\"name\\\"] = results.get(\\\"name\\\")\\n properties[\\\"filter_size\\\"] = results.get(\\\"filter_size\\\", 3)\\n properties[\\\"increase_factor\\\"] = results.get(\\\"increase_factor\\\", 0)\\n self.model = properties[\\\"name\\\"] # regardless of the name of the folder, this will get the proper model name (i.e. <modelname>.cntk)\\n\\n # optional property\\n properties[\\\"trainer\\\"] = results.get(\\\"trainer\\\", \\\"CNTK 2.2\\\")\\n\\n self._ensure_model_file()\\n properties[\\\"size_mb\\\"] = round(os.path.getsize(self.model_file) / (1000 * 1000))\\n\\n return properties\",\n \"def as_dict(self):\\n result = {}\\n for attr in self.__attr:\\n result[attr] = getattr(self, attr)\\n return result\",\n \"def to_dict_model(self) -> dict:\\n return dict((key, getattr(self, key)) for key in self.__mapper__.c.keys())\",\n \"def get_properties():\\n properties = dict()\\n properties['size'] = list()\\n properties['color'] = list()\\n properties['quality'] = list()\\n u = models.Size.query.all()\\n for i in u:\\n properties['size'].append(i.size_name)\\n u = models.Color.query.all()\\n for i in u:\\n properties['color'].append(i.color_name)\\n u = models.Quality.query.all()\\n for i in u:\\n properties['quality'].append(i.quality_name)\\n return make_response(jsonify(properties))\",\n \"def get_modelDict(self):\\n return self.__modelDict\",\n \"def attributes(self):\\n return dict(self.__attributes)\",\n \"def properties(self):\\n return self._properties\",\n \"def properties(self):\\n return self._properties\",\n \"def to_dict(self):\\n result = {}\\n for p in self.json_properties():\\n value = getattr(self, p)\\n if isinstance(value, datetime.datetime):\\n value = value.strftime('%s%f')[:-3]\\n result[Jsonifiable.transform_to_camelcase(p)] = value\\n return result\",\n \"def properties(self):\\n return self._props\",\n \"def properties(self):\\n pass\",\n \"def to_dict(self):\\n d = {}\\n for attr in self.__class__.attributes:\\n d[attr] = getattr(self, attr)\\n return d\",\n \"def properties_get(self):\\n return self._get('properties')\",\n \"def _collect_properties(self):\\n properties = {\\n 'userid': self.user_id,\\n 'title': self.get_fullname()\\n }\\n if not self.ogds_user:\\n return properties\\n\\n for attribute_name in self.ogds_user_attributes:\\n value = getattr(self.ogds_user, attribute_name)\\n properties[attribute_name] = value\\n return properties\",\n \"def getPropertyDict(self):\\n \\n d = self.getChild('__properties')\\n if d:\\n return d.getDict()\\n else:\\n return {}\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def to_dict(self, include=None):\\n _MODEL = type(self)\\n repr_dict = {}\\n if include is None:\\n include = []\\n for name, prop in _MODEL._properties.iteritems():\\n if hasattr(prop, 'public') and getattr(prop, 'public', False):\\n include.append(name)\\n\\n for name in include:\\n # check if this property is even allowed to be public\\n # or has a value set\\n if not hasattr(self, name):\\n continue\\n\\n value = getattr(self, name)\\n if type(getattr(_MODEL, name)) == ndb.StructuredProperty:\\n if isinstance(value, list):\\n items = []\\n for item in value:\\n items.append(item.to_dict(include=None))\\n repr_dict[name] = items\\n else:\\n repr_dict[name] = value.to_dict(include=None)\\n elif isinstance(value, date):\\n repr_dict[name] = value.isoformat()\\n elif isinstance(value, ndb.Key):\\n repr_dict[name] = value.urlsafe()\\n else:\\n repr_dict[name] = value\\n\\n if self._key:\\n repr_dict['key'] = self.get_key_urlsafe()\\n return repr_dict\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k == 'POSSIBLE_METHODS':\\n continue\\n if k == 'keysamplers':\\n properties[k] = [i.to_dict() for i in self.__dict__[k] if hasattr(i,'to_dict')]\\n elif k in {'pooler'}:\\n properties[k] = self.__dict__[k].to_dict()\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def get_all_properties(cls):\\n return ['key', 'id'] + _.keys(cls._properties)\",\n \"def get_properties(self):\\n return self.name, self.author, self.description, self.fmu_type, self.version, self.guid, self.tool, self.numStates\",\n \"def properties(self):\\n\\n return self._properties\",\n \"def properties(self):\",\n \"def properties(self):\",\n \"def properties(self):\",\n \"def ToDict(self):\\n atributes_dictionary = {}\\n for key, value in self.__dict__.iteritems():\\n atributes_dictionary[key] = value\\n return atributes_dictionary\",\n \"def modelPropertiesDictionary(sql_row_list):\\n \\n properties_dictionary = \\\\\\n {\\n \\\"id\\\": sql_row_list[0],\\n \\\"name\\\": sql_row_list[1],\\n \\\"last_deploy_timestamp\\\": sql_row_list[2],\\n \\\"active_version\\\": sql_row_list[3],\\n \\\"build_id\\\": sql_row_list[4]\\n };\\n\\n return properties_dictionary;\",\n \"def as_dict(self):\\n data = dict()\\n for name in self.fields:\\n val = getattr(self, name)\\n if isinstance(val, Model):\\n val = val.as_dict()\\n elif isinstance(val, list) and val and isinstance(val[0], Model):\\n val = [sub.as_dict() for sub in val]\\n data[name] = val\\n return data\",\n \"def to_dict(self):\\n if self._dict is not None:\\n return self._dict\\n\\n result = {}\\n for key in self.ATTRIBUTES:\\n value = getattr(self, key)\\n if value:\\n result[key] = value\\n self._dict = result\\n return result\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def to_dict(self):\\n _dict = {}\\n for f in self._meta.fields:\\n if f.name == 'created':\\n _dict[f.name] = str(f.value_from_object(self))\\n else:\\n _dict[f.name] = f.value_from_object(self)\\n\\n return _dict\",\n \"def to_dict(self):\\r\\n return self.__dict__\",\n \"def properties(self):\\n return None\",\n \"def properties(self):\\n return None\",\n \"def to_dict(self):\\n return attr.asdict(self)\",\n \"def _get_model_state(self) -> dict:\\n return dict(model=self.model, kwargs=self._model_kwargs)\",\n \"def as_dict(self):\\n return self.__dict__\",\n \"def dictify(self):\\n return {\\n \\\"name\\\" : self.name,\\n \\\"lastname\\\" : self.lastname,\\n \\\"phone\\\" : self.phone,\\n \\\"email\\\" : self.email\\n }\",\n \"def to_dict(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n # \\\"created_by\\\": self.created_by,\\n # \\\"created_on\\\": self.created_on,\\n # \\\"modified_by\\\": self.modified_by,\\n # \\\"modified_on\\\": self.modified_on\\n }\",\n \"def properties(self):\\r\\n return resources.Properties(self)\",\n \"def attributes(self):\\n params = self.model.param_array\\n return {'parameters': params}\",\n \"def properties(self, pk):\\n return JsonResponse(self._get_properties(pk))\",\n \"def to_dict(self):\\n return vars(self)\",\n \"def to_dict(self):\\n\\n # Check if is the right instance.\\n if isinstance(self, db.Model):\\n # construct a dictionary from column names and values.\\n dict_representation = {c.name: getattr(self, c.name) for c in self.__table__.columns}\\n return dict_representation\\n else:\\n raise AttributeError(type(self).__name__ + \\\" is not instance of \\\" + db.Model.__name__)\",\n \"def bson_properties(self):\\n return []\",\n \"def to_dict(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name\\n }\",\n \"def get_dict(self):\\n return\",\n \"def to_dict(self):\\n return to_dict(self.__dict__)\",\n \"def to_json(self):\\n properties = self.to_dict()\\n if isinstance(self, db.Model):\\n properties['id'] = unicode(self.key().id())\\n return json.dumps(properties)\",\n \"def to_dict(self):\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def config(self) -> ModelConfigDict:\\n return self.config_obj.to_dict()\",\n \"def properties(self):\\n return self.properties_with_uid[1:]\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def serialise(self):\\n return {\\n 'id': self.id,\\n 'category_id': self.category_id,\\n 'name': self.name,\\n 'description': self.description,\\n 'quantity': self.quantity,\\n 'price': self.price,\\n 'user_id': self.user_id\\n }\",\n \"def getPropertiesAll():\",\n \"def get_all_properties(self) -> dict:\\n return self._request(\\n \\\"post\\\",\\n URL,\\n json=attr.asdict(\\n Body(\\\"getAllProperties\\\", API_VERSION),\\n filter=attr.filters.exclude(attr.fields(Body).params),\\n ),\\n )\",\n \"def model_info(self):\\n if not self._model_info:\\n self._load_model_info()\\n try:\\n data = json.loads(self._model_info)\\n except (TypeError, ValueError):\\n data = {}\\n return data\",\n \"def to_dict(self):\\n return {\\n 'name': self.get_name(),\\n 'description': self.get_description()\\n }\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'name' : self.name,\\n }\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'name' : self.name,\\n }\",\n \"def as_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\r\\n\\r\\n return {\\r\\n 'product_id': self.product_id,\\r\\n 'product_name': self.product_name\\r\\n }\",\n \"def serialize(self):\\n return {\\n 'name' : self.name,\\n 'id' : self.id,\\n }\",\n \"def to_dict(self) -> dict:\",\n \"def asdict(self):\\n return attr.asdict(self)\",\n \"def getDict(self):\\n res = {}\\n for attr, value in self.__dict__.iteritems():\\n if type(attr) is IntType or type(attr) is StringType or type(attr) is LongType or type(attr) is UnicodeType:\\n res[attr] = value\\n elif isinstance(attr, datetime.datetime):\\n res[attr] = value.isoformat('-')\\n \\n return res\",\n \"def attributes(self):\\n return self.__dict.keys()\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def as_dict(self):\\n return {c.key: getattr(self, c.key)\\n for c in inspect(self).mapper.column_attrs}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.775081","0.775081","0.7338297","0.73333067","0.7296288","0.7277521","0.7159291","0.71573424","0.714855","0.714855","0.71273863","0.71261555","0.7122034","0.71070296","0.7060047","0.7043975","0.7032398","0.7023222","0.6964265","0.6958815","0.68987364","0.68804616","0.68804616","0.6857846","0.6840596","0.6811852","0.67992616","0.6790346","0.675877","0.67524505","0.67404795","0.67404795","0.67404795","0.67353433","0.6711996","0.669751","0.66947263","0.66887134","0.6680157","0.6680157","0.6680157","0.66795","0.6655938","0.6650065","0.66330093","0.66183764","0.66183764","0.6615774","0.6603685","0.65902025","0.65902025","0.6588644","0.65867186","0.6586305","0.6581893","0.65793556","0.6577974","0.65770143","0.6571875","0.6565409","0.65633625","0.6539638","0.6539251","0.65283597","0.65242255","0.6513062","0.65099394","0.65070164","0.65064347","0.65064347","0.65064347","0.65064347","0.65064347","0.65064347","0.6504425","0.65015763","0.6494163","0.6494163","0.6494163","0.64857966","0.64744276","0.6469791","0.6468503","0.6452789","0.64525396","0.64525396","0.6429155","0.6429088","0.6426443","0.64198464","0.6419703","0.64170074","0.64132357","0.64102525","0.64102525","0.64102525","0.64102525","0.64102525","0.64102525","0.64102525","0.64029527"],"string":"[\n \"0.775081\",\n \"0.775081\",\n \"0.7338297\",\n \"0.73333067\",\n \"0.7296288\",\n \"0.7277521\",\n \"0.7159291\",\n \"0.71573424\",\n \"0.714855\",\n \"0.714855\",\n \"0.71273863\",\n \"0.71261555\",\n \"0.7122034\",\n \"0.71070296\",\n \"0.7060047\",\n \"0.7043975\",\n \"0.7032398\",\n \"0.7023222\",\n \"0.6964265\",\n \"0.6958815\",\n \"0.68987364\",\n \"0.68804616\",\n \"0.68804616\",\n \"0.6857846\",\n \"0.6840596\",\n \"0.6811852\",\n \"0.67992616\",\n \"0.6790346\",\n \"0.675877\",\n \"0.67524505\",\n \"0.67404795\",\n \"0.67404795\",\n \"0.67404795\",\n \"0.67353433\",\n \"0.6711996\",\n \"0.669751\",\n \"0.66947263\",\n \"0.66887134\",\n \"0.6680157\",\n \"0.6680157\",\n \"0.6680157\",\n \"0.66795\",\n \"0.6655938\",\n \"0.6650065\",\n \"0.66330093\",\n \"0.66183764\",\n \"0.66183764\",\n \"0.6615774\",\n \"0.6603685\",\n \"0.65902025\",\n \"0.65902025\",\n \"0.6588644\",\n \"0.65867186\",\n \"0.6586305\",\n \"0.6581893\",\n \"0.65793556\",\n \"0.6577974\",\n \"0.65770143\",\n \"0.6571875\",\n \"0.6565409\",\n \"0.65633625\",\n \"0.6539638\",\n \"0.6539251\",\n \"0.65283597\",\n \"0.65242255\",\n \"0.6513062\",\n \"0.65099394\",\n \"0.65070164\",\n \"0.65064347\",\n \"0.65064347\",\n \"0.65064347\",\n \"0.65064347\",\n \"0.65064347\",\n \"0.65064347\",\n \"0.6504425\",\n \"0.65015763\",\n \"0.6494163\",\n \"0.6494163\",\n \"0.6494163\",\n \"0.64857966\",\n \"0.64744276\",\n \"0.6469791\",\n \"0.6468503\",\n \"0.6452789\",\n \"0.64525396\",\n \"0.64525396\",\n \"0.6429155\",\n \"0.6429088\",\n \"0.6426443\",\n \"0.64198464\",\n \"0.6419703\",\n \"0.64170074\",\n \"0.64132357\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64102525\",\n \"0.64029527\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":19690,"cells":{"query":{"kind":"string","value":"Returns the string representation of the model"},"document":{"kind":"string","value":"def to_str(self):\n return pprint.pformat(self.to_dict())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __str__(self):\n return super().__str__() + self.model.__str__()","def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n model_fields = get_model_fields(\n opts.model,\n foreign=False,\n m2m=False,\n exclude=self.exclude_from_str\n )\n # TODO: replace the above with the below to remove the get_model_fields call:\n # model_fields = [\n # f for f in opts.get_fields()\n # if f.concrete\n # and not (f.primary_key or f.is_relation or f.name in self.exclude_from_str)\n # ]\n result = \" \".join(\n [\n str(fld.value_from_object(self))\n for fld in model_fields\n if fld.value_from_object(self)\n ]\n )\n return result.strip() or super().__str__()","def __str__(self):\n return '%s%s' % (self.name, ' - %s' % self.model if self.model else '')","def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"","def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"","def __repr__(self):\n\n mod = f\"{self.__class__.__name__} Model\"\n try:\n mod += f': {self.filename}'\n except AttributeError:\n pass\n s = [mod]\n for name, v in self.metadata.items():\n s += [f\"{name:16} : {v}\"]\n return '\\n'.join(s)","def __str__(self):\n \n res = ['>>> Model %(model_name)s <<<']\n res.append('')\n res.append('Independent parameters:')\n res.append('-----------------------')\n res.append('')","def __str__(self):\n return \"DataModel(name={},attributes={},description={})\".format(\n self.name, {a.name: str(a) for a in self.attributes}, self.description\n )","def model_info(self) -> str:\n return self._model_info(self.model).decode(\"utf-8\")","def __str__(self):\n return str(self.serialize())","def __str__ (self) :\n\n return self.as_string()","def __str__(self):\n\n return self.toString()","def __str__(self):\n msg = [\n f'{self.model=}',\n f'{self.field=}',\n f'{self.fxx=}',\n f'{self.date=}',\n f'{self.priority=}',\n ]\n return '\\n'.join(msg)","def __str__(self):\n model = self._meta.verbose_name.title()\n title = self.extended_object.get_title()\n return f\"{model:s}: {title:s}\"","def __repr__(self):\n return grid_search_to_str(self.model)","def __str__(self):\n return self.toString()","def __str__(self):\n return str(self.__dict__)","def __str__(self):\n return str(self.__dict__)","def to_representation(self) -> str:\n raise NotImplementedError()","def __str__(self):\n return str(self.obj)","def __str__(self):\n return self.make_flat()","def dump_model(self):","def __str__(self):\n return str(self.__dict__['_obj'])","def __str__(self) -> str:\n model_str = [\"\\nModel info:\\n\", \" Unimodal encoder:\\n\"]\n\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_encoder[modality]}\")\n\n model_str.append(\"\\n\\n Unimodal decoder:\\n\")\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_decoder[modality]}\")\n\n if self.multimodal_decoder is not None:\n model_str.append(\"\\n\\n Multimodal decoder:\\n\")\n model_str.append(f\" {self.multimodal_decoder}\")\n\n return \"\".join(model_str)","def __repr__(self):\n s = 'text model name: ' + self.name + '\\n'\n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\n'\n s += ' most common words: ' + str(self.common_word) + '\\n'\n\n return s","def to_string(self):\r\n return self.__str__()","def __repr__(self):\n return '<ModelSignature(model_name=%r)>' % self.model_name","def __repr__(self):\n return '<ModelSignature(model_name=%r)>' % self.model_name","def __str__(self):\n return str(self.get_data())","def __str__(self):\n return f\"model {self._name}\"","def __str__(self):\n\n return self.raw_field","def __repr__(self):\n \n s = 'text model name: ' + self.name + '\\n' \n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\n'\n s += ' number of word stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of commas counts: ' + str(len(self.commas_per_sentence)) + '\\n'\n return s","def serialize(self):\n\n\t\treturn str(self)","def __str__(self):\n return self.get_str()","def serialize(self):\n\n return str(self)","def __str__(self) -> str:\n if self.name_field:\n return str(getattr(self, self.name_field))\n # noinspection PyUnresolvedReferences\n data = [\n # Collect the string representations of related objects.\n # getattr(self, fk_field.attname) and\n # fk_field.value_from_object(self) would only return the primary\n # key of the related object.\n str(getattr(self, fk_field.name))\n for fk_field in get_model_fields(\n self._meta.model, base=False, foreign=True, m2m=False\n )\n if not fk_field.null\n ]\n if len(data) < 2:\n # Cannot build a more meaningful representation than the default.\n return super().__str__()\n else:\n template = \"{}\" + \" ({})\" * (len(data) - 1)\n return template.format(*data)","def __str__(self):\n return self.s","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __repr__(self):\n\n # info string\n info = self.model.__repr__()\n info += \"\\n=========================\\n\"\n info += f\"Train data length:\\t\\t{ len(self.train_dataset) }\\n\"\n info += f\"Eval sata length:\\t\\t{ len(self.eval_dataset) }\\n\"\n info += f\"Optimizer:\\t\\t\\t\\t{ str(self.optimizer).split('(')[0] }\\n\"\n info += f\"Criterion:\\t\\t\\t\\t{ str(self.criterion).split('(')[0] }\\n\"\n info += f\"Training Environment:\\t{ self.device.type }\\n\"\n info += f\"Show information:\\t\\t{ 'True' if self.info else 'False' }\\n\"\n info += \"=========================\\n\"\n\n return info","def __repr__(self):\n s = 'text model name: ' + self.name + '\\n'\n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths))\\\n + '\\n'\n s += ' number of punctuation types: ' + str(len(self.punctuation))\n return s","def dumps(self, indent=0):\n outstr = \" \"*indent + \"MewloDbModel object '{0}' attribute values:\\n\".format(self.__class__.__name__)\n public_props = (name for name in dir(object) if not name.startswith('_'))\n for name in public_props:\n outstr += \" \"*indent + \"{0}: {1}\\n\".format(name, str(getattr(self,name)))\n return outstr","def __str__(self):\n model = self._meta.verbose_name.title()\n title = self.title or str(_(\"Empty title\"))\n\n return f\"{model:s}: {title:s}\"","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()"],"string":"[\n \"def __str__(self):\\n return super().__str__() + self.model.__str__()\",\n \"def __str__(self) -> str:\\n # noinspection PyUnresolvedReferences\\n opts = self._meta\\n if self.name_field:\\n result = str(opts.get_field(self.name_field).value_from_object(self))\\n else:\\n model_fields = get_model_fields(\\n opts.model,\\n foreign=False,\\n m2m=False,\\n exclude=self.exclude_from_str\\n )\\n # TODO: replace the above with the below to remove the get_model_fields call:\\n # model_fields = [\\n # f for f in opts.get_fields()\\n # if f.concrete\\n # and not (f.primary_key or f.is_relation or f.name in self.exclude_from_str)\\n # ]\\n result = \\\" \\\".join(\\n [\\n str(fld.value_from_object(self))\\n for fld in model_fields\\n if fld.value_from_object(self)\\n ]\\n )\\n return result.strip() or super().__str__()\",\n \"def __str__(self):\\n return '%s%s' % (self.name, ' - %s' % self.model if self.model else '')\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n return f\\\"{model:s}: {self.name:s}\\\"\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n return f\\\"{model:s}: {self.name:s}\\\"\",\n \"def __repr__(self):\\n\\n mod = f\\\"{self.__class__.__name__} Model\\\"\\n try:\\n mod += f': {self.filename}'\\n except AttributeError:\\n pass\\n s = [mod]\\n for name, v in self.metadata.items():\\n s += [f\\\"{name:16} : {v}\\\"]\\n return '\\\\n'.join(s)\",\n \"def __str__(self):\\n \\n res = ['>>> Model %(model_name)s <<<']\\n res.append('')\\n res.append('Independent parameters:')\\n res.append('-----------------------')\\n res.append('')\",\n \"def __str__(self):\\n return \\\"DataModel(name={},attributes={},description={})\\\".format(\\n self.name, {a.name: str(a) for a in self.attributes}, self.description\\n )\",\n \"def model_info(self) -> str:\\n return self._model_info(self.model).decode(\\\"utf-8\\\")\",\n \"def __str__(self):\\n return str(self.serialize())\",\n \"def __str__ (self) :\\n\\n return self.as_string()\",\n \"def __str__(self):\\n\\n return self.toString()\",\n \"def __str__(self):\\n msg = [\\n f'{self.model=}',\\n f'{self.field=}',\\n f'{self.fxx=}',\\n f'{self.date=}',\\n f'{self.priority=}',\\n ]\\n return '\\\\n'.join(msg)\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n title = self.extended_object.get_title()\\n return f\\\"{model:s}: {title:s}\\\"\",\n \"def __repr__(self):\\n return grid_search_to_str(self.model)\",\n \"def __str__(self):\\n return self.toString()\",\n \"def __str__(self):\\n return str(self.__dict__)\",\n \"def __str__(self):\\n return str(self.__dict__)\",\n \"def to_representation(self) -> str:\\n raise NotImplementedError()\",\n \"def __str__(self):\\n return str(self.obj)\",\n \"def __str__(self):\\n return self.make_flat()\",\n \"def dump_model(self):\",\n \"def __str__(self):\\n return str(self.__dict__['_obj'])\",\n \"def __str__(self) -> str:\\n model_str = [\\\"\\\\nModel info:\\\\n\\\", \\\" Unimodal encoder:\\\\n\\\"]\\n\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_encoder[modality]}\\\")\\n\\n model_str.append(\\\"\\\\n\\\\n Unimodal decoder:\\\\n\\\")\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_decoder[modality]}\\\")\\n\\n if self.multimodal_decoder is not None:\\n model_str.append(\\\"\\\\n\\\\n Multimodal decoder:\\\\n\\\")\\n model_str.append(f\\\" {self.multimodal_decoder}\\\")\\n\\n return \\\"\\\".join(model_str)\",\n \"def __repr__(self):\\n s = 'text model name: ' + self.name + '\\\\n'\\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\\\n'\\n s += ' most common words: ' + str(self.common_word) + '\\\\n'\\n\\n return s\",\n \"def to_string(self):\\r\\n return self.__str__()\",\n \"def __repr__(self):\\n return '<ModelSignature(model_name=%r)>' % self.model_name\",\n \"def __repr__(self):\\n return '<ModelSignature(model_name=%r)>' % self.model_name\",\n \"def __str__(self):\\n return str(self.get_data())\",\n \"def __str__(self):\\n return f\\\"model {self._name}\\\"\",\n \"def __str__(self):\\n\\n return self.raw_field\",\n \"def __repr__(self):\\n \\n s = 'text model name: ' + self.name + '\\\\n' \\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\\\n'\\n s += ' number of word stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of commas counts: ' + str(len(self.commas_per_sentence)) + '\\\\n'\\n return s\",\n \"def serialize(self):\\n\\n\\t\\treturn str(self)\",\n \"def __str__(self):\\n return self.get_str()\",\n \"def serialize(self):\\n\\n return str(self)\",\n \"def __str__(self) -> str:\\n if self.name_field:\\n return str(getattr(self, self.name_field))\\n # noinspection PyUnresolvedReferences\\n data = [\\n # Collect the string representations of related objects.\\n # getattr(self, fk_field.attname) and\\n # fk_field.value_from_object(self) would only return the primary\\n # key of the related object.\\n str(getattr(self, fk_field.name))\\n for fk_field in get_model_fields(\\n self._meta.model, base=False, foreign=True, m2m=False\\n )\\n if not fk_field.null\\n ]\\n if len(data) < 2:\\n # Cannot build a more meaningful representation than the default.\\n return super().__str__()\\n else:\\n template = \\\"{}\\\" + \\\" ({})\\\" * (len(data) - 1)\\n return template.format(*data)\",\n \"def __str__(self):\\n return self.s\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __repr__(self):\\n\\n # info string\\n info = self.model.__repr__()\\n info += \\\"\\\\n=========================\\\\n\\\"\\n info += f\\\"Train data length:\\\\t\\\\t{ len(self.train_dataset) }\\\\n\\\"\\n info += f\\\"Eval sata length:\\\\t\\\\t{ len(self.eval_dataset) }\\\\n\\\"\\n info += f\\\"Optimizer:\\\\t\\\\t\\\\t\\\\t{ str(self.optimizer).split('(')[0] }\\\\n\\\"\\n info += f\\\"Criterion:\\\\t\\\\t\\\\t\\\\t{ str(self.criterion).split('(')[0] }\\\\n\\\"\\n info += f\\\"Training Environment:\\\\t{ self.device.type }\\\\n\\\"\\n info += f\\\"Show information:\\\\t\\\\t{ 'True' if self.info else 'False' }\\\\n\\\"\\n info += \\\"=========================\\\\n\\\"\\n\\n return info\",\n \"def __repr__(self):\\n s = 'text model name: ' + self.name + '\\\\n'\\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths))\\\\\\n + '\\\\n'\\n s += ' number of punctuation types: ' + str(len(self.punctuation))\\n return s\",\n \"def dumps(self, indent=0):\\n outstr = \\\" \\\"*indent + \\\"MewloDbModel object '{0}' attribute values:\\\\n\\\".format(self.__class__.__name__)\\n public_props = (name for name in dir(object) if not name.startswith('_'))\\n for name in public_props:\\n outstr += \\\" \\\"*indent + \\\"{0}: {1}\\\\n\\\".format(name, str(getattr(self,name)))\\n return outstr\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n title = self.title or str(_(\\\"Empty title\\\"))\\n\\n return f\\\"{model:s}: {title:s}\\\"\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def 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\"0.85856134\",\n \"0.7814518\",\n \"0.77898884\",\n \"0.7751367\",\n \"0.7751367\",\n \"0.7712228\",\n \"0.76981676\",\n \"0.76700574\",\n \"0.7651133\",\n \"0.7597206\",\n \"0.75800353\",\n \"0.7568254\",\n \"0.7538184\",\n \"0.75228703\",\n \"0.7515832\",\n \"0.7498764\",\n \"0.74850684\",\n \"0.74850684\",\n \"0.7467648\",\n \"0.74488163\",\n \"0.7442643\",\n \"0.74416703\",\n \"0.7433768\",\n \"0.7411771\",\n \"0.7405439\",\n \"0.7379557\",\n \"0.7361716\",\n \"0.7361716\",\n \"0.732774\",\n \"0.7325511\",\n \"0.732528\",\n \"0.73097324\",\n \"0.73078936\",\n \"0.73001266\",\n \"0.7296789\",\n \"0.7292791\",\n \"0.7289445\",\n \"0.7287187\",\n \"0.7287187\",\n \"0.7287187\",\n \"0.7287187\",\n \"0.7287187\",\n \"0.7279803\",\n \"0.7261615\",\n \"0.7250399\",\n \"0.7244789\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\",\n \"0.7223068\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":19691,"cells":{"query":{"kind":"string","value":"For `print` and `pprint`"},"document":{"kind":"string","value":"def __repr__(self):\n return self.to_str()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)","def print_out():\n pass","def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)","def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)","def _printable(self):\n pass","def _print_custom(self):\n pass","def pypprint(*args, **kwargs): # type: ignore\n from typing import Iterable\n\n if len(args) != 1:\n print(*args, **kwargs)\n return\n x = args[0]\n if isinstance(x, dict):\n for k, v in x.items():\n print(f\"{k}:\", v, **kwargs)\n elif isinstance(x, Iterable) and not isinstance(x, str):\n for i in x:\n print(i, **kwargs)\n else:\n print(x, **kwargs)","def pprint(self):\n # just here for defining the interface; work is done in subclasses\n pass","def print(*args, **kwargs):\n with P_LOCK:\n __builtins__.print(*args, **kwargs)","def print(self):\n # Your implementation here","def p(value):\n pp.pprint(value)","def static_print(*args, __p=print, **kwargs):\n __p(*args, **kwargs)","def print(self, *args, **kwargs):\n print(*args, **kwargs)","def pprint(self):\n print(self.pprint_str())","def pprint(obj):\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\n # Skip callables\n if hasattr(getattr(obj, argname), '__call__'):\n continue\n print(\"{} : {}\".format(argname, getattr(obj, argname)))","def print_(self, s: str) -> None:","def my_pprint(obj, intend = 0):\n if isinstance(obj, dict):\n for key, value in obj.items():\n print(intend*\" \"+str(key)+\" : \")\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, list):\n for value in obj:\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, bytes):\n print(\"<binary data>\")\n \n else:\n try:\n print(intend*\" \"+str(obj))\n except UnicodeDecodeError:\n print(intend*\" \"\"<?>\")","def test_print(chikin):\n chikin.print()","def _print(self, text):\n\t\tif self.verbose:\n\t\t\tprint text","def out(*args):\r\n print(*args)","def __pprint(object, stream=None, indent=1, width=80, depth=None):\n printer = PrettyPrinterExt(\n stream=stream, indent=indent, width=width, depth=depth)\n printer.pprint(object)","def DumpPprint(data):\n #NOTE(g): Import is done here, instead of the top of the file, to not require this module if it is not used\n import pprint\n \n text = pprint.pformat(data)\n \n return text","def repl_print_statements():\n pass","def test_03_pass_print(self):\n print('Hello World!')","def p(self):\n self.printstdout = True","def print(*args, **kwargs):\n new_args = []\n for arg in args:\n if builtins.isinstance(arg, models.Point):\n new_args.append(\"({0}, {1})\".format(arg.x, arg.y))\n else:\n new_args.append(arg)\n\n builtins.print(*new_args, **kwargs)","def real_print(*args, **kwargs):\n\n kwargs.setdefault('file', real_stdout)\n _python_print_function(*args, **kwargs)","def to_print_out(self):\n self.error_throw('output')\n\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\n self.output_div('print')\n else:\n self.output('print')","def debug_print(debug_data):\n if DEBUG_MODE == \"true\":\n pp.pprint(debug_data)","def print(self):\r\n self.print_avec_separateur()","def pprint(self):\r\n for i in self.items():\r\n print '%s => %r'%i","def pprint(self, parameter_s=''):\n ptformatter = self.shell.display_formatter.formatters['text/plain']\n ptformatter.pprint = bool(1 - ptformatter.pprint)\n print('Pretty printing has been turned',\n ['OFF','ON'][ptformatter.pprint])","def print(self):\n print(self.pretty_str())","def test_print4(self):\n writer = StringIO()\n collatz_print(writer, 1, 1, 1)\n self.assertEqual(writer.getvalue(), \"1 1 1\\n\")","def eprint(*args, **kwargs):\n\tprint(*args, file=sys.stderr, **kwargs)","def _print(self, *args, **kwargs) -> None:\n # Only print in verbose mode\n if self._verbose:\n arglist = list(args)\n arglist[0] = f\"[buddy-{self._experiment_name}] {args[0]}\"\n print(*arglist, **kwargs)","def use_pypprint_for_implicit_print(self) -> None:\n if self.implicit_print is not None:\n self.implicit_print.func.id = \"pypprint\" # type: ignore\n # Make sure we import it later\n self.undefined.add(\"pypprint\")","def test_print(self):\n writer = StringIO()\n collatz_print(writer, 1, 10, 20)\n self.assertEqual(writer.getvalue(), \"1 10 20\\n\")","def pprint(self):\n return pformat(repr(self))","def printer(message):\n if VERBOSITY:\n pprint(message)","def rec_print(p):\n if len(p) == 0:\n return\n t = p.pop(0)\n print t\n rec_print(p)","def pformat(object):\r\n return PrettyPrinter().pformat(object)","def printc(*a, **kw):\n print(*a, **kw)","def pr(x):\n Card.print_pretty_cards(x)","def debug_print(self, *content):\n if self.debug:\n print(*content)","def pprint(object, stream=None):\r\n printer = PrettyPrinter(stream=stream)\r\n printer.pprint(object)","def safe_print(*objs, errors=\"replace\"):\n\tprint(*(to_stdout(str(o), errors) for o in objs))","def magic_Pprint(self, parameter_s=''):\n \n self.shell.outputcache.Pprint = 1 - self.shell.outputcache.Pprint\n print 'Pretty printing has been turned', \\\n ['OFF','ON'][self.shell.outputcache.Pprint]","def print_output(tree):\n print_value(tree)\n print_tree(tree)","def _Print(self, t):\n self.RaiseError(t, \"Print not supported\")","def vprint(*args, **kwargs ):\n\n forceprint = False\n for key in kwargs:\n if key == \"forceprint\":\n forceprint =kwargs[key]\n \n line = ''\n if debug or forceprint : \n for arg in args:\n line += str(arg) +\" \"\n log = open(exepath + 'pyframe.log', 'a') \n log.write(line + \"\\n\")\n log.close() \n print line","def eprint(*pargs, **kargs):\n print('\\u001b[31m', end='', file=sys.stderr)\n print(*pargs, file=sys.stderr, **kargs)\n print('\\u001b[0m', end='', file=sys.stderr)","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def _print(self, *args):\n return _ida_hexrays.qstring_printer_t__print(self, *args)","def pprint(self):\n def pprintStr(node):\n s = \"(\" + str(node.value) \n for action in node.children:\n s = s + \", \" + pprintStr(node.children[action])\n s = s + \")\"\n return s\n\n print pprintStr(self)","def hook_print():\n sys.stdout = PrintHook()","def cmdPrint( self, *args):\n return self.cmd( *args, **{ 'verbose': True } )","def print_list(self):\r\n pass","def debugprint(obj, depth=-1, print_type=False,\r\n file=None, ids='CHAR', stop_on_name=False):\r\n if file == 'str':\r\n _file = StringIO()\r\n elif file is None:\r\n _file = sys.stdout\r\n else:\r\n _file = file\r\n done = dict()\r\n results_to_print = []\r\n order = []\r\n if isinstance(obj, gof.Variable):\r\n results_to_print.append(obj)\r\n elif isinstance(obj, gof.Apply):\r\n results_to_print.extend(obj.outputs)\r\n elif isinstance(obj, Function):\r\n results_to_print.extend(obj.maker.fgraph.outputs)\r\n order = obj.maker.fgraph.toposort()\r\n elif isinstance(obj, (list, tuple)):\r\n results_to_print.extend(obj)\r\n elif isinstance(obj, gof.FunctionGraph):\r\n results_to_print.extend(obj.outputs)\r\n order = obj.toposort()\r\n elif isinstance(obj, (int, long, float, numpy.ndarray)):\r\n print obj\r\n else:\r\n raise TypeError(\"debugprint cannot print an object of this type\", obj)\r\n for r in results_to_print:\r\n debugmode.debugprint(r, depth=depth, done=done, print_type=print_type,\r\n file=_file, order=order, ids=ids,\r\n stop_on_name=stop_on_name)\r\n if file is _file:\r\n return file\r\n elif file == 'str':\r\n return _file.getvalue()\r\n else:\r\n _file.flush()","def _get_print_fn(file=sys.stdout):\n def _print_fn(op, xin,):\n for attr in op.attrs:\n temp = getattr(xin, attr)\n if callable(temp):\n pmsg = temp()\n else:\n pmsg = temp\n print(op.message, attr, '=', pmsg, file=file)\n return _print_fn","def test_print1(self):\n writer = StringIO()\n collatz_print(writer, 100, 200, 125)\n self.assertEqual(writer.getvalue(), \"100 200 125\\n\")","def printOutput(self):\n pass","def _print(self, *args):\n return _ida_hexrays.cnumber_t__print(self, *args)","def setPrint():\n (e,d,sr,sw) = codecs.lookup('utf-8')\n unicode_to_utf8 = sw(sys.stdout)\n sys.stdout = unicode_to_utf8","def pr(string, verbose):\n if(verbose):\n print(string)","def print(*args, sep=\" \"):\n pass","def printv(self, *arg):\n if self.verbose:\n print(*arg)","def print(self):\n\n print(self)","def _p(self, *args, level=2, **kwargs):\n if self._verbosity >= level:\n print(*args, **kwargs)","def test_print2(self):\n writer = StringIO()\n collatz_print(writer, 201, 210, 89)\n self.assertEqual(writer.getvalue(), \"201 210 89\\n\")","def print_pointers(self):\n\n ### FILL IN ###","def foo_printer(self):\n print(\"\\nHi I'm {}\".format(self.foo))","def printed(method):\n\t\tdef wrapper(cls, *args):\n\t\t\tif cls.verbose:\n\t\t\t\treturn method(cls, *args)\n\t\treturn wrapper","def print_me(self, tabs=0, tab=' '):\n pre = tab*tabs\n print(pre+'Producer:')\n print(pre+' produces:', self._produces)\n print(pre+' consumes:', self._consumes)\n print(pre+' transfer:', self._transfer)\n print(pre+' capacity:', self._capacity)","def _print(cls, quad):\n\t\tprint(\"\\nLIGHT OUTPUT:\\n<<<<{}>>>>\".format(ast.literal_eval(str(cls.get_address_value(quad.result)))))\n\t\tprint(\"END\")\n\n\t\tvar = cls.get_address_value(quad.result)\n\t\tif isinstance(var, collections.Iterable):\n\t\t\tprint(\"DEEP COPY\")\n\t\t\tcls.print_queue.enqueue(copy.deepcopy(var))\n\t\telse:\n\t\t\tcls.print_queue.enqueue(var)","def printout(*args, **kwargs):\n console_print(sys.stdout, *args, **kwargs)","def pprint(x):\n if is_theano_object(x):\n return _gettheano().printing.pprint(x)\n else:\n return str(x)","def PrettyPrint(self):\r\n print(self.data)\r\n return","def print(self):\n self.print_avec_separateur(\" \")","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def pprint_helper(self, angle, indent):\n # just here for defining the interface; work is done in subclasses\n pass","def _pprint(params, offset=0, printer=repr):\n # Do a multi-line justified repr:\n param_names = [p for p in params.keys() if p is not \"cost\"]\n param_names.sort()\n\n params_list = list()\n this_line_length = offset\n line_sep = ',\\n' + (1 + offset // 2) * ' '\n for i, name in enumerate(param_names):\n value = params[name]\n if isinstance(value, float):\n this_repr = '%s=%s' % (name, str(value))\n else:\n this_repr = '%s=%s' % (name, printer(value))\n if len(this_repr) > 500:\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\n if i > 0:\n if (this_line_length + len(this_repr) >= 75 or '\\n' in this_repr):\n params_list.append(line_sep)\n this_line_length = len(line_sep)\n else:\n params_list.append(', ')\n this_line_length += 2\n params_list.append(this_repr)\n this_line_length += len(this_repr)\n # options = np.get_printoptions()\n # np.set_printoptions(**options)\n lines = ''.join(params_list)\n # Strip trailing space to avoid nightmare in doctests\n lines = '\\n'.join(l.rstrip(' ') for l in lines.split('\\n'))\n return lines","def init_printing(pretty_print=True, order=None, use_unicode=None):\n if pretty_print:\n stringify_func = lambda arg: pretty(arg, order=order, use_unicode=use_unicode)\n else:\n stringify_func = sstrrepr\n\n try:\n import IPython\n\n ip = IPython.ipapi.get()\n\n if ip is not None:\n def result_display(self, arg):\n \"\"\"IPython's pretty-printer display hook.\n\n This function was adapted from:\n\n ipython/IPython/hooks.py:155\n\n \"\"\"\n if self.rc.pprint:\n out = stringify_func(arg)\n\n if '\\n' in out:\n print\n\n print out\n else:\n print repr(arg)\n\n ip.set_hook('result_display', result_display)\n return\n except ImportError:\n pass\n\n import __builtin__, sys\n\n def displayhook(arg):\n \"\"\"Python's pretty-printer display hook.\n\n This function was adapted from:\n\n http://www.python.org/dev/peps/pep-0217/\n\n \"\"\"\n if arg is not None:\n __builtin__._ = None\n print stringify_func(arg)\n __builtin__._ = arg\n\n sys.displayhook = displayhook","def print_verbose(self) -> None:\n print(self)\n if self.meta is not None:\n print(self.meta.__repr__())","def _print(self, *args):\n return _ida_hexrays.cinsn_t__print(self, *args)","def my_print(self):\n if self.__size == 0:\n print(\"\")\n return\n [print(\"\") for x in range(0, self.__position[1])]\n for i in range(0, self.__size):\n [print(\" \", end=\"\") for i in range(0, self.__position[0])]\n [print(\"#\", end=\"\") for j in range(0, self.__size)]\n print(\"\")","def sequential_print_statements():\n pass","def print_post():\n print('| | |'),","def print_(*args, **kwargs):\r\n fp = kwargs.pop(\"file\", sys.stdout)\r\n if fp is None:\r\n return\r\n def write(data):\r\n if not isinstance(data, basestring):\r\n data = str(data)\r\n fp.write(data)\r\n want_unicode = False\r\n sep = kwargs.pop(\"sep\", None)\r\n if sep is not None:\r\n if isinstance(sep, unicode):\r\n want_unicode = True\r\n elif not isinstance(sep, str):\r\n raise TypeError(\"sep must be None or a string\")\r\n end = kwargs.pop(\"end\", None)\r\n if end is not None:\r\n if isinstance(end, unicode):\r\n want_unicode = True\r\n elif not isinstance(end, str):\r\n raise TypeError(\"end must be None or a string\")\r\n if kwargs:\r\n raise TypeError(\"invalid keyword arguments to print()\")\r\n if not want_unicode:\r\n for arg in args:\r\n if isinstance(arg, unicode):\r\n want_unicode = True\r\n break\r\n if want_unicode:\r\n newline = unicode(\"\\n\")\r\n space = unicode(\" \")\r\n else:\r\n newline = \"\\n\"\r\n space = \" \"\r\n if sep is None:\r\n sep = space\r\n if end is None:\r\n end = newline\r\n for i, arg in enumerate(args):\r\n if i:\r\n write(sep)\r\n write(arg)\r\n write(end)","def print_(*args, **kwargs):\r\n fp = kwargs.pop(\"file\", sys.stdout)\r\n if fp is None:\r\n return\r\n def write(data):\r\n if not isinstance(data, basestring):\r\n data = str(data)\r\n fp.write(data)\r\n want_unicode = False\r\n sep = kwargs.pop(\"sep\", None)\r\n if sep is not None:\r\n if isinstance(sep, unicode):\r\n want_unicode = True\r\n elif not isinstance(sep, str):\r\n raise TypeError(\"sep must be None or a string\")\r\n end = kwargs.pop(\"end\", None)\r\n if end is not None:\r\n if isinstance(end, unicode):\r\n want_unicode = True\r\n elif not isinstance(end, str):\r\n raise TypeError(\"end must be None or a string\")\r\n if kwargs:\r\n raise TypeError(\"invalid keyword arguments to print()\")\r\n if not want_unicode:\r\n for arg in args:\r\n if isinstance(arg, unicode):\r\n want_unicode = True\r\n break\r\n if want_unicode:\r\n newline = unicode(\"\\n\")\r\n space = unicode(\" \")\r\n else:\r\n newline = \"\\n\"\r\n space = \" \"\r\n if sep is None:\r\n sep = space\r\n if end is None:\r\n end = newline\r\n for i, arg in enumerate(args):\r\n if i:\r\n write(sep)\r\n write(arg)\r\n write(end)","def debugprint(r, prefix='', depth=-1, done=None, print_type=False,\r\n file=sys.stdout, print_destroy_map=False,\r\n print_view_map=False, order=None, ids='CHAR',\r\n stop_on_name=False, prefix_child=None):\r\n if depth == 0:\r\n return\r\n\r\n if order is None:\r\n order = []\r\n\r\n if done is None:\r\n done = dict()\r\n\r\n if print_type:\r\n type_str = ' <%s>' % r.type\r\n else:\r\n type_str = ''\r\n\r\n if prefix_child is None:\r\n prefix_child = prefix\r\n\r\n def get_id_str(obj):\r\n if obj in done:\r\n id_str = done[obj]\r\n elif ids == \"id\":\r\n id_str = \"[@%s]\" % str(id(r))\r\n elif ids == \"int\":\r\n id_str = \"[@%s]\" % str(len(done))\r\n elif ids == \"CHAR\":\r\n id_str = \"[@%s]\" % char_from_number(len(done))\r\n elif ids == \"\":\r\n id_str = \"\"\r\n done[obj] = id_str\r\n return id_str\r\n\r\n if hasattr(r.owner, 'op'):\r\n # this variable is the output of computation,\r\n # so just print out the apply\r\n a = r.owner\r\n\r\n r_name = getattr(r, 'name', '')\r\n # normally if the name isn't set, it'll be None, so\r\n # r_name is None here\r\n if r_name is None:\r\n r_name = ''\r\n\r\n if print_destroy_map:\r\n destroy_map_str = str(getattr(r.owner.op, 'destroy_map', ''))\r\n else:\r\n destroy_map_str = ''\r\n\r\n if print_view_map:\r\n view_map_str = str(getattr(r.owner.op, 'view_map', ''))\r\n else:\r\n view_map_str = ''\r\n if destroy_map_str and destroy_map_str != '{}':\r\n destroy_map_str = 'd=' + destroy_map_str\r\n if view_map_str and view_map_str != '{}':\r\n view_map_str = 'v=' + view_map_str\r\n\r\n o = ''\r\n if order:\r\n o = str(order.index(r.owner))\r\n already_printed = a in done # get_id_str put it in the dict\r\n id_str = get_id_str(a)\r\n\r\n if len(a.outputs) == 1:\r\n print >> file, '%s%s %s%s \\'%s\\' %s %s %s' % (prefix, a.op,\r\n id_str,\r\n type_str, r_name,\r\n destroy_map_str,\r\n view_map_str,\r\n o)\r\n else:\r\n print >> file, '%s%s.%i %s%s \\'%s\\' %s %s %s' % (prefix, a.op,\r\n a.outputs.index(r),\r\n id_str, type_str,\r\n r_name,\r\n destroy_map_str,\r\n view_map_str,\r\n o)\r\n if not already_printed:\r\n if (not stop_on_name or\r\n not (hasattr(r, 'name') and r.name is not None)):\r\n new_prefix = prefix_child + ' |'\r\n new_prefix_child = prefix_child + ' |'\r\n for idx, i in enumerate(a.inputs):\r\n if idx == len(a.inputs) - 1:\r\n new_prefix_child = prefix_child + ' '\r\n\r\n debugprint(i, new_prefix, depth=depth - 1, done=done,\r\n print_type=print_type, file=file, order=order,\r\n ids=ids, stop_on_name=stop_on_name,\r\n prefix_child=new_prefix_child)\r\n else:\r\n #this is an input variable\r\n id_str = get_id_str(r)\r\n print >> file, '%s%s %s%s' % (prefix, r, id_str, type_str)\r\n\r\n return file","def bpprint(self, out=None):\n if out is None:\n out = sys.stdout\n print(self.bpformat(), file=out)","def vprint(expr, **settings):\n\n outstr = vsprint(expr, **settings)\n\n import builtins\n if (outstr != 'None'):\n builtins._ = outstr\n print(outstr)","def _pprint(params, offset=0, printer=repr):\n # Do a multi-line justified repr:\n options = numpy.get_printoptions()\n numpy.set_printoptions(precision=5, threshold=64, edgeitems=2)\n params_list = list()\n this_line_length = offset\n line_sep = ',\\n' + (1 + offset // 2) * ' '\n for i, (k, v) in enumerate(sorted(params.items())):\n if isinstance(v, float):\n # use str for representing floating point numbers\n # this way we get consistent representation across\n # architectures and versions.\n this_repr = '%s=%s' % (k, str(v))\n else:\n # use repr of the rest\n this_repr = '%s=%s' % (k, printer(v))\n if len(this_repr) > 500:\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\n if i > 0:\n if this_line_length + len(this_repr) >= 75 or '\\n' in this_repr:\n params_list.append(line_sep)\n this_line_length = len(line_sep)\n else:\n params_list.append(', ')\n this_line_length += 2\n params_list.append(this_repr)\n this_line_length += len(this_repr)\n\n numpy.set_printoptions(**options)\n lines = ''.join(params_list)\n # Strip trailing space to avoid nightmare in doctests\n lines = '\\n'.join(l.rstrip(' ') for l in lines.split('\\n'))\n return lines","def print_(*args, **kwargs):\n fp = kwargs.pop(\"file\", sys.stdout)\n if fp is None:\n return\n\n def write(data):\n if not isinstance(data, basestring):\n data = str(data)\n fp.write(data)\n want_unicode = False\n sep = kwargs.pop(\"sep\", None)\n if sep is not None:\n if isinstance(sep, unicode):\n want_unicode = True\n elif not isinstance(sep, str):\n raise TypeError(\"sep must be None or a string\")\n end = kwargs.pop(\"end\", None)\n if end is not None:\n if isinstance(end, unicode):\n want_unicode = True\n elif not isinstance(end, str):\n raise TypeError(\"end must be None or a string\")\n if kwargs:\n raise TypeError(\"invalid keyword arguments to print()\")\n if not want_unicode:\n for arg in args:\n if isinstance(arg, unicode):\n want_unicode = True\n break\n if want_unicode:\n newline = unicode(\"\\n\")\n space = unicode(\" \")\n else:\n newline = \"\\n\"\n space = \" \"\n if sep is None:\n sep = space\n if end is None:\n end = newline\n for i, arg in enumerate(args):\n if i:\n write(sep)\n write(arg)\n write(end)"],"string":"[\n \"def pprint(*args, **kwargs):\\n if PRINTING:\\n print(*args, **kwargs)\",\n \"def print_out():\\n pass\",\n \"def custom_print(*objects):\\n print(*objects, sep=OFS, end=ORS)\",\n \"def _print(self, *args):\\n return _ida_hexrays.vd_printer_t__print(self, *args)\",\n \"def _printable(self):\\n pass\",\n \"def _print_custom(self):\\n pass\",\n \"def pypprint(*args, **kwargs): # type: ignore\\n from typing import Iterable\\n\\n if len(args) != 1:\\n print(*args, **kwargs)\\n return\\n x = args[0]\\n if isinstance(x, dict):\\n for k, v in x.items():\\n print(f\\\"{k}:\\\", v, **kwargs)\\n elif isinstance(x, Iterable) and not isinstance(x, str):\\n for i in x:\\n print(i, **kwargs)\\n else:\\n print(x, **kwargs)\",\n \"def pprint(self):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def print(*args, **kwargs):\\n with P_LOCK:\\n __builtins__.print(*args, **kwargs)\",\n \"def print(self):\\n # Your implementation here\",\n \"def p(value):\\n pp.pprint(value)\",\n \"def static_print(*args, __p=print, **kwargs):\\n __p(*args, **kwargs)\",\n \"def print(self, *args, **kwargs):\\n print(*args, **kwargs)\",\n \"def pprint(self):\\n print(self.pprint_str())\",\n \"def pprint(obj):\\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\\n # Skip callables\\n if hasattr(getattr(obj, argname), '__call__'):\\n continue\\n print(\\\"{} : {}\\\".format(argname, getattr(obj, argname)))\",\n \"def print_(self, s: str) -> None:\",\n \"def my_pprint(obj, intend = 0):\\n if isinstance(obj, dict):\\n for key, value in obj.items():\\n print(intend*\\\" \\\"+str(key)+\\\" : \\\")\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, list):\\n for value in obj:\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, bytes):\\n print(\\\"<binary data>\\\")\\n \\n else:\\n try:\\n print(intend*\\\" \\\"+str(obj))\\n except UnicodeDecodeError:\\n print(intend*\\\" \\\"\\\"<?>\\\")\",\n \"def test_print(chikin):\\n chikin.print()\",\n \"def _print(self, text):\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint text\",\n \"def out(*args):\\r\\n print(*args)\",\n \"def __pprint(object, stream=None, indent=1, width=80, depth=None):\\n printer = PrettyPrinterExt(\\n stream=stream, indent=indent, width=width, depth=depth)\\n printer.pprint(object)\",\n \"def DumpPprint(data):\\n #NOTE(g): Import is done here, instead of the top of the file, to not require this module if it is not used\\n import pprint\\n \\n text = pprint.pformat(data)\\n \\n return text\",\n \"def repl_print_statements():\\n pass\",\n \"def test_03_pass_print(self):\\n print('Hello World!')\",\n \"def p(self):\\n self.printstdout = True\",\n \"def print(*args, **kwargs):\\n new_args = []\\n for arg in args:\\n if builtins.isinstance(arg, models.Point):\\n new_args.append(\\\"({0}, {1})\\\".format(arg.x, arg.y))\\n else:\\n new_args.append(arg)\\n\\n builtins.print(*new_args, **kwargs)\",\n \"def real_print(*args, **kwargs):\\n\\n kwargs.setdefault('file', real_stdout)\\n _python_print_function(*args, **kwargs)\",\n \"def to_print_out(self):\\n self.error_throw('output')\\n\\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\\n self.output_div('print')\\n else:\\n self.output('print')\",\n \"def debug_print(debug_data):\\n if DEBUG_MODE == \\\"true\\\":\\n pp.pprint(debug_data)\",\n \"def print(self):\\r\\n self.print_avec_separateur()\",\n \"def pprint(self):\\r\\n for i in self.items():\\r\\n print '%s => %r'%i\",\n \"def pprint(self, parameter_s=''):\\n ptformatter = self.shell.display_formatter.formatters['text/plain']\\n ptformatter.pprint = bool(1 - ptformatter.pprint)\\n print('Pretty printing has been turned',\\n ['OFF','ON'][ptformatter.pprint])\",\n \"def print(self):\\n print(self.pretty_str())\",\n \"def test_print4(self):\\n writer = StringIO()\\n collatz_print(writer, 1, 1, 1)\\n self.assertEqual(writer.getvalue(), \\\"1 1 1\\\\n\\\")\",\n \"def eprint(*args, **kwargs):\\n\\tprint(*args, file=sys.stderr, **kwargs)\",\n \"def _print(self, *args, **kwargs) -> None:\\n # Only print in verbose mode\\n if self._verbose:\\n arglist = list(args)\\n arglist[0] = f\\\"[buddy-{self._experiment_name}] {args[0]}\\\"\\n print(*arglist, **kwargs)\",\n \"def use_pypprint_for_implicit_print(self) -> None:\\n if self.implicit_print is not None:\\n self.implicit_print.func.id = \\\"pypprint\\\" # type: ignore\\n # Make sure we import it later\\n self.undefined.add(\\\"pypprint\\\")\",\n \"def test_print(self):\\n writer = StringIO()\\n collatz_print(writer, 1, 10, 20)\\n self.assertEqual(writer.getvalue(), \\\"1 10 20\\\\n\\\")\",\n \"def pprint(self):\\n return pformat(repr(self))\",\n \"def printer(message):\\n if VERBOSITY:\\n pprint(message)\",\n \"def rec_print(p):\\n if len(p) == 0:\\n return\\n t = p.pop(0)\\n print t\\n rec_print(p)\",\n \"def pformat(object):\\r\\n return PrettyPrinter().pformat(object)\",\n \"def printc(*a, **kw):\\n print(*a, **kw)\",\n \"def pr(x):\\n Card.print_pretty_cards(x)\",\n \"def debug_print(self, *content):\\n if self.debug:\\n print(*content)\",\n \"def pprint(object, stream=None):\\r\\n printer = PrettyPrinter(stream=stream)\\r\\n printer.pprint(object)\",\n \"def safe_print(*objs, errors=\\\"replace\\\"):\\n\\tprint(*(to_stdout(str(o), errors) for o in objs))\",\n \"def magic_Pprint(self, parameter_s=''):\\n \\n self.shell.outputcache.Pprint = 1 - self.shell.outputcache.Pprint\\n print 'Pretty printing has been turned', \\\\\\n ['OFF','ON'][self.shell.outputcache.Pprint]\",\n \"def print_output(tree):\\n print_value(tree)\\n print_tree(tree)\",\n \"def _Print(self, t):\\n self.RaiseError(t, \\\"Print not supported\\\")\",\n \"def vprint(*args, **kwargs ):\\n\\n forceprint = False\\n for key in kwargs:\\n if key == \\\"forceprint\\\":\\n forceprint =kwargs[key]\\n \\n line = ''\\n if debug or forceprint : \\n for arg in args:\\n line += str(arg) +\\\" \\\"\\n log = open(exepath + 'pyframe.log', 'a') \\n log.write(line + \\\"\\\\n\\\")\\n log.close() \\n print line\",\n \"def eprint(*pargs, **kargs):\\n print('\\\\u001b[31m', end='', file=sys.stderr)\\n print(*pargs, file=sys.stderr, **kargs)\\n print('\\\\u001b[0m', end='', file=sys.stderr)\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def _print(self, *args):\\n return _ida_hexrays.qstring_printer_t__print(self, *args)\",\n \"def pprint(self):\\n def pprintStr(node):\\n s = \\\"(\\\" + str(node.value) \\n for action in node.children:\\n s = s + \\\", \\\" + pprintStr(node.children[action])\\n s = s + \\\")\\\"\\n return s\\n\\n print pprintStr(self)\",\n \"def hook_print():\\n sys.stdout = PrintHook()\",\n \"def cmdPrint( self, *args):\\n return self.cmd( *args, **{ 'verbose': True } )\",\n \"def print_list(self):\\r\\n pass\",\n \"def debugprint(obj, depth=-1, print_type=False,\\r\\n file=None, ids='CHAR', stop_on_name=False):\\r\\n if file == 'str':\\r\\n _file = StringIO()\\r\\n elif file is None:\\r\\n _file = sys.stdout\\r\\n else:\\r\\n _file = file\\r\\n done = dict()\\r\\n results_to_print = []\\r\\n order = []\\r\\n if isinstance(obj, gof.Variable):\\r\\n results_to_print.append(obj)\\r\\n elif isinstance(obj, gof.Apply):\\r\\n results_to_print.extend(obj.outputs)\\r\\n elif isinstance(obj, Function):\\r\\n results_to_print.extend(obj.maker.fgraph.outputs)\\r\\n order = obj.maker.fgraph.toposort()\\r\\n elif isinstance(obj, (list, tuple)):\\r\\n results_to_print.extend(obj)\\r\\n elif isinstance(obj, gof.FunctionGraph):\\r\\n results_to_print.extend(obj.outputs)\\r\\n order = obj.toposort()\\r\\n elif isinstance(obj, (int, long, float, numpy.ndarray)):\\r\\n print obj\\r\\n else:\\r\\n raise TypeError(\\\"debugprint cannot print an object of this type\\\", obj)\\r\\n for r in results_to_print:\\r\\n debugmode.debugprint(r, depth=depth, done=done, print_type=print_type,\\r\\n file=_file, order=order, ids=ids,\\r\\n stop_on_name=stop_on_name)\\r\\n if file is _file:\\r\\n return file\\r\\n elif file == 'str':\\r\\n return _file.getvalue()\\r\\n else:\\r\\n _file.flush()\",\n \"def _get_print_fn(file=sys.stdout):\\n def _print_fn(op, xin,):\\n for attr in op.attrs:\\n temp = getattr(xin, attr)\\n if callable(temp):\\n pmsg = temp()\\n else:\\n pmsg = temp\\n print(op.message, attr, '=', pmsg, file=file)\\n return _print_fn\",\n \"def test_print1(self):\\n writer = StringIO()\\n collatz_print(writer, 100, 200, 125)\\n self.assertEqual(writer.getvalue(), \\\"100 200 125\\\\n\\\")\",\n \"def printOutput(self):\\n pass\",\n \"def _print(self, *args):\\n return _ida_hexrays.cnumber_t__print(self, *args)\",\n \"def setPrint():\\n (e,d,sr,sw) = codecs.lookup('utf-8')\\n unicode_to_utf8 = sw(sys.stdout)\\n sys.stdout = unicode_to_utf8\",\n \"def pr(string, verbose):\\n if(verbose):\\n print(string)\",\n \"def print(*args, sep=\\\" \\\"):\\n pass\",\n \"def printv(self, *arg):\\n if self.verbose:\\n print(*arg)\",\n \"def print(self):\\n\\n print(self)\",\n \"def _p(self, *args, level=2, **kwargs):\\n if self._verbosity >= level:\\n print(*args, **kwargs)\",\n \"def test_print2(self):\\n writer = StringIO()\\n collatz_print(writer, 201, 210, 89)\\n self.assertEqual(writer.getvalue(), \\\"201 210 89\\\\n\\\")\",\n \"def print_pointers(self):\\n\\n ### FILL IN ###\",\n \"def foo_printer(self):\\n print(\\\"\\\\nHi I'm {}\\\".format(self.foo))\",\n \"def printed(method):\\n\\t\\tdef wrapper(cls, *args):\\n\\t\\t\\tif cls.verbose:\\n\\t\\t\\t\\treturn method(cls, *args)\\n\\t\\treturn wrapper\",\n \"def print_me(self, tabs=0, tab=' '):\\n pre = tab*tabs\\n print(pre+'Producer:')\\n print(pre+' produces:', self._produces)\\n print(pre+' consumes:', self._consumes)\\n print(pre+' transfer:', self._transfer)\\n print(pre+' capacity:', self._capacity)\",\n \"def _print(cls, quad):\\n\\t\\tprint(\\\"\\\\nLIGHT OUTPUT:\\\\n<<<<{}>>>>\\\".format(ast.literal_eval(str(cls.get_address_value(quad.result)))))\\n\\t\\tprint(\\\"END\\\")\\n\\n\\t\\tvar = cls.get_address_value(quad.result)\\n\\t\\tif isinstance(var, collections.Iterable):\\n\\t\\t\\tprint(\\\"DEEP COPY\\\")\\n\\t\\t\\tcls.print_queue.enqueue(copy.deepcopy(var))\\n\\t\\telse:\\n\\t\\t\\tcls.print_queue.enqueue(var)\",\n \"def printout(*args, **kwargs):\\n console_print(sys.stdout, *args, **kwargs)\",\n \"def pprint(x):\\n if is_theano_object(x):\\n return _gettheano().printing.pprint(x)\\n else:\\n return str(x)\",\n \"def PrettyPrint(self):\\r\\n print(self.data)\\r\\n return\",\n \"def print(self):\\n self.print_avec_separateur(\\\" \\\")\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def pprint_helper(self, angle, indent):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def _pprint(params, offset=0, printer=repr):\\n # Do a multi-line justified repr:\\n param_names = [p for p in params.keys() if p is not \\\"cost\\\"]\\n param_names.sort()\\n\\n params_list = list()\\n this_line_length = offset\\n line_sep = ',\\\\n' + (1 + offset // 2) * ' '\\n for i, name in enumerate(param_names):\\n value = params[name]\\n if isinstance(value, float):\\n this_repr = '%s=%s' % (name, str(value))\\n else:\\n this_repr = '%s=%s' % (name, printer(value))\\n if len(this_repr) > 500:\\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\\n if i > 0:\\n if (this_line_length + len(this_repr) >= 75 or '\\\\n' in this_repr):\\n params_list.append(line_sep)\\n this_line_length = len(line_sep)\\n else:\\n params_list.append(', ')\\n this_line_length += 2\\n params_list.append(this_repr)\\n this_line_length += len(this_repr)\\n # options = np.get_printoptions()\\n # np.set_printoptions(**options)\\n lines = ''.join(params_list)\\n # Strip trailing space to avoid nightmare in doctests\\n lines = '\\\\n'.join(l.rstrip(' ') for l in lines.split('\\\\n'))\\n return lines\",\n \"def init_printing(pretty_print=True, order=None, use_unicode=None):\\n if pretty_print:\\n stringify_func = lambda arg: pretty(arg, order=order, use_unicode=use_unicode)\\n else:\\n stringify_func = sstrrepr\\n\\n try:\\n import IPython\\n\\n ip = IPython.ipapi.get()\\n\\n if ip is not None:\\n def result_display(self, arg):\\n \\\"\\\"\\\"IPython's pretty-printer display hook.\\n\\n This function was adapted from:\\n\\n ipython/IPython/hooks.py:155\\n\\n \\\"\\\"\\\"\\n if self.rc.pprint:\\n out = stringify_func(arg)\\n\\n if '\\\\n' in out:\\n print\\n\\n print out\\n else:\\n print repr(arg)\\n\\n ip.set_hook('result_display', result_display)\\n return\\n except ImportError:\\n pass\\n\\n import __builtin__, sys\\n\\n def displayhook(arg):\\n \\\"\\\"\\\"Python's pretty-printer display hook.\\n\\n This function was adapted from:\\n\\n http://www.python.org/dev/peps/pep-0217/\\n\\n \\\"\\\"\\\"\\n if arg is not None:\\n __builtin__._ = None\\n print stringify_func(arg)\\n __builtin__._ = arg\\n\\n sys.displayhook = displayhook\",\n \"def print_verbose(self) -> None:\\n print(self)\\n if self.meta is not None:\\n print(self.meta.__repr__())\",\n \"def _print(self, *args):\\n return _ida_hexrays.cinsn_t__print(self, *args)\",\n \"def my_print(self):\\n if self.__size == 0:\\n print(\\\"\\\")\\n return\\n [print(\\\"\\\") for x in range(0, self.__position[1])]\\n for i in range(0, self.__size):\\n [print(\\\" \\\", end=\\\"\\\") for i in range(0, self.__position[0])]\\n [print(\\\"#\\\", end=\\\"\\\") for j in range(0, self.__size)]\\n print(\\\"\\\")\",\n \"def sequential_print_statements():\\n pass\",\n \"def print_post():\\n print('| | |'),\",\n \"def print_(*args, **kwargs):\\r\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\r\\n if fp is None:\\r\\n return\\r\\n def write(data):\\r\\n if not isinstance(data, basestring):\\r\\n data = str(data)\\r\\n fp.write(data)\\r\\n want_unicode = False\\r\\n sep = kwargs.pop(\\\"sep\\\", None)\\r\\n if sep is not None:\\r\\n if isinstance(sep, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(sep, str):\\r\\n raise TypeError(\\\"sep must be None or a string\\\")\\r\\n end = kwargs.pop(\\\"end\\\", None)\\r\\n if end is not None:\\r\\n if isinstance(end, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(end, str):\\r\\n raise TypeError(\\\"end must be None or a string\\\")\\r\\n if kwargs:\\r\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\r\\n if not want_unicode:\\r\\n for arg in args:\\r\\n if isinstance(arg, unicode):\\r\\n want_unicode = True\\r\\n break\\r\\n if want_unicode:\\r\\n newline = unicode(\\\"\\\\n\\\")\\r\\n space = unicode(\\\" \\\")\\r\\n else:\\r\\n newline = \\\"\\\\n\\\"\\r\\n space = \\\" \\\"\\r\\n if sep is None:\\r\\n sep = space\\r\\n if end is None:\\r\\n end = newline\\r\\n for i, arg in enumerate(args):\\r\\n if i:\\r\\n write(sep)\\r\\n write(arg)\\r\\n write(end)\",\n \"def print_(*args, **kwargs):\\r\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\r\\n if fp is None:\\r\\n return\\r\\n def write(data):\\r\\n if not isinstance(data, basestring):\\r\\n data = str(data)\\r\\n fp.write(data)\\r\\n want_unicode = False\\r\\n sep = kwargs.pop(\\\"sep\\\", None)\\r\\n if sep is not None:\\r\\n if isinstance(sep, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(sep, str):\\r\\n raise TypeError(\\\"sep must be None or a string\\\")\\r\\n end = kwargs.pop(\\\"end\\\", None)\\r\\n if end is not None:\\r\\n if isinstance(end, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(end, str):\\r\\n raise TypeError(\\\"end must be None or a string\\\")\\r\\n if kwargs:\\r\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\r\\n if not want_unicode:\\r\\n for arg in args:\\r\\n if isinstance(arg, unicode):\\r\\n want_unicode = True\\r\\n break\\r\\n if want_unicode:\\r\\n newline = unicode(\\\"\\\\n\\\")\\r\\n space = unicode(\\\" \\\")\\r\\n else:\\r\\n newline = \\\"\\\\n\\\"\\r\\n space = \\\" \\\"\\r\\n if sep is None:\\r\\n sep = space\\r\\n if end is None:\\r\\n end = newline\\r\\n for i, arg in enumerate(args):\\r\\n if i:\\r\\n write(sep)\\r\\n write(arg)\\r\\n write(end)\",\n \"def debugprint(r, prefix='', depth=-1, done=None, print_type=False,\\r\\n file=sys.stdout, print_destroy_map=False,\\r\\n print_view_map=False, order=None, ids='CHAR',\\r\\n stop_on_name=False, prefix_child=None):\\r\\n if depth == 0:\\r\\n return\\r\\n\\r\\n if order is None:\\r\\n order = []\\r\\n\\r\\n if done is None:\\r\\n done = dict()\\r\\n\\r\\n if print_type:\\r\\n type_str = ' <%s>' % r.type\\r\\n else:\\r\\n type_str = ''\\r\\n\\r\\n if prefix_child is None:\\r\\n prefix_child = prefix\\r\\n\\r\\n def get_id_str(obj):\\r\\n if obj in done:\\r\\n id_str = done[obj]\\r\\n elif ids == \\\"id\\\":\\r\\n id_str = \\\"[@%s]\\\" % str(id(r))\\r\\n elif ids == \\\"int\\\":\\r\\n id_str = \\\"[@%s]\\\" % str(len(done))\\r\\n elif ids == \\\"CHAR\\\":\\r\\n id_str = \\\"[@%s]\\\" % char_from_number(len(done))\\r\\n elif ids == \\\"\\\":\\r\\n id_str = \\\"\\\"\\r\\n done[obj] = id_str\\r\\n return id_str\\r\\n\\r\\n if hasattr(r.owner, 'op'):\\r\\n # this variable is the output of computation,\\r\\n # so just print out the apply\\r\\n a = r.owner\\r\\n\\r\\n r_name = getattr(r, 'name', '')\\r\\n # normally if the name isn't set, it'll be None, so\\r\\n # r_name is None here\\r\\n if r_name is None:\\r\\n r_name = ''\\r\\n\\r\\n if print_destroy_map:\\r\\n destroy_map_str = str(getattr(r.owner.op, 'destroy_map', ''))\\r\\n else:\\r\\n destroy_map_str = ''\\r\\n\\r\\n if print_view_map:\\r\\n view_map_str = str(getattr(r.owner.op, 'view_map', ''))\\r\\n else:\\r\\n view_map_str = ''\\r\\n if destroy_map_str and destroy_map_str != '{}':\\r\\n destroy_map_str = 'd=' + destroy_map_str\\r\\n if view_map_str and view_map_str != '{}':\\r\\n view_map_str = 'v=' + view_map_str\\r\\n\\r\\n o = ''\\r\\n if order:\\r\\n o = str(order.index(r.owner))\\r\\n already_printed = a in done # get_id_str put it in the dict\\r\\n id_str = get_id_str(a)\\r\\n\\r\\n if len(a.outputs) == 1:\\r\\n print >> file, '%s%s %s%s \\\\'%s\\\\' %s %s %s' % (prefix, a.op,\\r\\n id_str,\\r\\n type_str, r_name,\\r\\n destroy_map_str,\\r\\n view_map_str,\\r\\n o)\\r\\n else:\\r\\n print >> file, '%s%s.%i %s%s \\\\'%s\\\\' %s %s %s' % (prefix, a.op,\\r\\n a.outputs.index(r),\\r\\n id_str, type_str,\\r\\n r_name,\\r\\n destroy_map_str,\\r\\n view_map_str,\\r\\n o)\\r\\n if not already_printed:\\r\\n if (not stop_on_name or\\r\\n not (hasattr(r, 'name') and r.name is not None)):\\r\\n new_prefix = prefix_child + ' |'\\r\\n new_prefix_child = prefix_child + ' |'\\r\\n for idx, i in enumerate(a.inputs):\\r\\n if idx == len(a.inputs) - 1:\\r\\n new_prefix_child = prefix_child + ' '\\r\\n\\r\\n debugprint(i, new_prefix, depth=depth - 1, done=done,\\r\\n print_type=print_type, file=file, order=order,\\r\\n ids=ids, stop_on_name=stop_on_name,\\r\\n prefix_child=new_prefix_child)\\r\\n else:\\r\\n #this is an input variable\\r\\n id_str = get_id_str(r)\\r\\n print >> file, '%s%s %s%s' % (prefix, r, id_str, type_str)\\r\\n\\r\\n return file\",\n \"def bpprint(self, out=None):\\n if out is None:\\n out = sys.stdout\\n print(self.bpformat(), file=out)\",\n \"def vprint(expr, **settings):\\n\\n outstr = vsprint(expr, **settings)\\n\\n import builtins\\n if (outstr != 'None'):\\n builtins._ = outstr\\n print(outstr)\",\n \"def _pprint(params, offset=0, printer=repr):\\n # Do a multi-line justified repr:\\n options = numpy.get_printoptions()\\n numpy.set_printoptions(precision=5, threshold=64, edgeitems=2)\\n params_list = list()\\n this_line_length = offset\\n line_sep = ',\\\\n' + (1 + offset // 2) * ' '\\n for i, (k, v) in enumerate(sorted(params.items())):\\n if isinstance(v, float):\\n # use str for representing floating point numbers\\n # this way we get consistent representation across\\n # architectures and versions.\\n this_repr = '%s=%s' % (k, str(v))\\n else:\\n # use repr of the rest\\n this_repr = '%s=%s' % (k, printer(v))\\n if len(this_repr) > 500:\\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\\n if i > 0:\\n if this_line_length + len(this_repr) >= 75 or '\\\\n' in this_repr:\\n params_list.append(line_sep)\\n this_line_length = len(line_sep)\\n else:\\n params_list.append(', ')\\n this_line_length += 2\\n params_list.append(this_repr)\\n this_line_length += len(this_repr)\\n\\n numpy.set_printoptions(**options)\\n lines = ''.join(params_list)\\n # Strip trailing space to avoid nightmare in doctests\\n lines = '\\\\n'.join(l.rstrip(' ') for l in lines.split('\\\\n'))\\n return lines\",\n \"def print_(*args, **kwargs):\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\n if fp is None:\\n return\\n\\n def write(data):\\n if not isinstance(data, basestring):\\n data = str(data)\\n fp.write(data)\\n want_unicode = False\\n sep = kwargs.pop(\\\"sep\\\", None)\\n if sep is not None:\\n if isinstance(sep, unicode):\\n want_unicode = True\\n elif not isinstance(sep, str):\\n raise TypeError(\\\"sep must be None or a string\\\")\\n end = kwargs.pop(\\\"end\\\", None)\\n if end is not None:\\n if isinstance(end, unicode):\\n want_unicode = True\\n elif not isinstance(end, str):\\n raise TypeError(\\\"end must be None or a string\\\")\\n if kwargs:\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\n if not want_unicode:\\n for arg in args:\\n if isinstance(arg, unicode):\\n want_unicode = True\\n break\\n if want_unicode:\\n newline = unicode(\\\"\\\\n\\\")\\n space = unicode(\\\" \\\")\\n else:\\n newline = \\\"\\\\n\\\"\\n space = \\\" \\\"\\n if sep is None:\\n sep = space\\n if end is None:\\n end = newline\\n for i, arg in enumerate(args):\\n if i:\\n write(sep)\\n write(arg)\\n write(end)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75577617","0.73375154","0.6986672","0.698475","0.6944995","0.692333","0.6899106","0.6898902","0.68146646","0.6806209","0.6753795","0.67497987","0.6744008","0.6700308","0.6691256","0.6674591","0.6658083","0.66091245","0.6606931","0.6601862","0.6563738","0.6561717","0.65549695","0.6494838","0.6473391","0.64491546","0.6411177","0.6340302","0.6339321","0.6335031","0.6332035","0.6315847","0.631272","0.6297732","0.62969106","0.6283717","0.6279154","0.6271603","0.62673396","0.6265511","0.62629336","0.6258366","0.6258278","0.62501305","0.6248315","0.62459755","0.6244254","0.6242083","0.62393075","0.62156516","0.6208198","0.62068796","0.62062824","0.62062824","0.6194123","0.6189738","0.6183852","0.6183035","0.61697906","0.61614454","0.6160741","0.61544997","0.61528033","0.6150831","0.6147288","0.61380607","0.613793","0.61300766","0.61278135","0.6125416","0.6114217","0.61126333","0.6100682","0.60998785","0.6096818","0.6081694","0.6076982","0.6072701","0.6060028","0.60581726","0.6055044","0.6055044","0.6055044","0.6055044","0.6055044","0.6055044","0.6043662","0.6037599","0.60336643","0.6030174","0.60290223","0.60242903","0.6016989","0.6004274","0.60005474","0.60005474","0.60003483","0.599558","0.59923434","0.5979316","0.59777945"],"string":"[\n \"0.75577617\",\n \"0.73375154\",\n \"0.6986672\",\n \"0.698475\",\n \"0.6944995\",\n \"0.692333\",\n \"0.6899106\",\n \"0.6898902\",\n \"0.68146646\",\n \"0.6806209\",\n \"0.6753795\",\n \"0.67497987\",\n \"0.6744008\",\n \"0.6700308\",\n \"0.6691256\",\n \"0.6674591\",\n \"0.6658083\",\n \"0.66091245\",\n \"0.6606931\",\n \"0.6601862\",\n \"0.6563738\",\n \"0.6561717\",\n \"0.65549695\",\n \"0.6494838\",\n \"0.6473391\",\n \"0.64491546\",\n \"0.6411177\",\n \"0.6340302\",\n \"0.6339321\",\n \"0.6335031\",\n \"0.6332035\",\n \"0.6315847\",\n \"0.631272\",\n \"0.6297732\",\n \"0.62969106\",\n \"0.6283717\",\n \"0.6279154\",\n \"0.6271603\",\n \"0.62673396\",\n \"0.6265511\",\n \"0.62629336\",\n \"0.6258366\",\n \"0.6258278\",\n \"0.62501305\",\n \"0.6248315\",\n \"0.62459755\",\n \"0.6244254\",\n \"0.6242083\",\n \"0.62393075\",\n \"0.62156516\",\n \"0.6208198\",\n \"0.62068796\",\n \"0.62062824\",\n \"0.62062824\",\n \"0.6194123\",\n \"0.6189738\",\n \"0.6183852\",\n \"0.6183035\",\n \"0.61697906\",\n \"0.61614454\",\n \"0.6160741\",\n \"0.61544997\",\n \"0.61528033\",\n \"0.6150831\",\n \"0.6147288\",\n \"0.61380607\",\n \"0.613793\",\n \"0.61300766\",\n \"0.61278135\",\n \"0.6125416\",\n \"0.6114217\",\n \"0.61126333\",\n \"0.6100682\",\n \"0.60998785\",\n \"0.6096818\",\n \"0.6081694\",\n \"0.6076982\",\n \"0.6072701\",\n \"0.6060028\",\n \"0.60581726\",\n \"0.6055044\",\n \"0.6055044\",\n \"0.6055044\",\n \"0.6055044\",\n \"0.6055044\",\n \"0.6055044\",\n \"0.6043662\",\n \"0.6037599\",\n \"0.60336643\",\n \"0.6030174\",\n \"0.60290223\",\n \"0.60242903\",\n \"0.6016989\",\n \"0.6004274\",\n \"0.60005474\",\n \"0.60005474\",\n \"0.60003483\",\n \"0.599558\",\n \"0.59923434\",\n \"0.5979316\",\n \"0.59777945\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":19692,"cells":{"query":{"kind":"string","value":"Returns true if both objects are equal"},"document":{"kind":"string","value":"def __eq__(self, other):\n if not isinstance(other, IaasUcsdManagedInfraAllOf):\n return False\n\n return self.to_dict() == other.to_dict()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __eq__(self, other):\n return are_equal(self, other)","def __eq__(self, other):\n return are_equal(self, other)","def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False","def __eq__(self, other):\n if isinstance(self, other.__class__):\n return self.__dict__ == other.__dict__\n return False","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self.__dict__ == other.__dict__\n return False","def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self.__dict__ == other.__dict__\n return False","def __eq__(self, other):\r\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n # Ensure same class and values match\n if isinstance(other, self.__class__):\n return self.__dict__ == other.__dict__\n else:\n return False","def is_equal(self, a, b):\n return a is b","def is_equal(self, a, b):\n return a == b","def __eq__(self, other):\n return self is other","def __eq__(self, other):\n return self is other","def __eq__(self, other):\r\n if isinstance(other, self.__class__):\r\n return self.__dict__ == other.__dict__\r\n else:\r\n return False","def is_equal(o1: object, o2: object) -> bool:\n if o1 is None and o2 is None:\n return True\n if o1 is None:\n return False\n return o1 == o2","def __eq__(self,other):\n return self is other","def is_equal(self, a, b):\n return a.X[0] == b.X[0]","def __eq__(self, other):\n return type(self) == type(other) and self.id == other.id","def __eq__(self, other) -> bool:\n if json.dumps(self.data,sort_keys=True) == json.dumps(other.data,sort_keys=True):\n return True\n else:\n return False","def __eq__(self, other):\n if not isinstance(other, Single2HaObject):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.__dict__ == other","def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self.__hash__() == other.__hash__()\n return False","def __eq__(self, other):\n if self.__class__ != other.__class__:\n return False\n if self.primary != other.primary:\n return False\n return True","def __eq__(self, other) -> bool:\n if other is None:\n return False\n return self.__hash__() == other.__hash__()","def __eq__(self, other):\n if not isinstance(other, ObjectInfo):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self: _TT, other: object) -> bool:\n return self.eq(other) # type: ignore","def __eq__(self, other):\n return id(self) == id(other)","def __eq__(self, other) -> bool:\n return type(self) == type(other) and \\\n self._id == other.id and \\\n self.code == other.code and \\\n self.name == other.name and \\\n self.gender == other.gender and \\\n self.date_of_birth == other.date_of_birth","def equals(self, other): # -> bool:\n ...","def equals(self, obj: object) -> bool:\n ...","def __eq__(self, other):\n for attr in self._attrs_to_save:\n try:\n if getattr(self, attr) != getattr(other, attr):\n return False\n except AttributeError:\n return False\n return True","def __eq__(self, other):\n if type(other) is type(self):\n return (self.x == other.x and self.y == other.y and self.z == other.z)\n return False","def __eq__(self, other):\n if isinstance(other, self.__class__):\n return self.x == other.x and self.y == other.y\n return False","def __eq__(self, other: object) -> bool:\n if not isinstance(other, self.__class__):\n return NotImplemented\n\n return (\n self.name,\n self.submit_at,\n self.subreddit,\n self.title,\n self.body_template,\n ) == (\n other.name,\n other.submit_at,\n other.subreddit,\n other.title,\n other.body_template,\n )","def __eq__(self, other):\n # Check that we share the same class as this object\n if not isinstance(other, type(self)):\n return False\n\n return hash(self) == hash(other)","def __eq__(self, other):\n if not isinstance(other, PreviewObjectAutofill):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return equal(self, other)","def __eq__(self, other: Any) -> bool:\n return self.__class__ is other.__class__ and self.identifier == other.identifier","def __eq__(self, other):\n return self.__id == other.get_id()","def __eq__ (self, other):\n if type(self) == type(other):\n return self._m == other._m\n else:\n return False","def __eq__(self, other):\n if not isinstance(other, Referent):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n return self.properties == other.properties","def __eq__(self, other):\n return self.items() == other.items()","def __eq__(self, other):\n return hash(self) == hash(other)","def __eq__(self, other):\n return hash(self) == hash(other)","def __eq__(self, other):\n return hash(self) == hash(other)","def __eq__(self, other):\n return self.x == other.x and self.y == other.y","def __eq__(self, other):\n\n if self is other:\n return True\n return hash(self) == hash(other)","def __eq__(self, other):\n if other._field1 == self._field1:\n return True\n return False","def same_as(self, other):\n return super().__eq__(other)","def __eq__(self, other):\n try:\n return other and \\\n self.id == other.id\n\n except AttributeError:\n return False","def __eq__(self, other):\r\n\t\treturn self._to_pylist() == other._to_pylist()","def __eq__(self, other):\n if not isinstance(other, Fiddle):\n return False\n\n return self.__dict__ == other.__dict__"],"string":"[\n \"def __eq__(self, other):\\n return are_equal(self, other)\",\n \"def __eq__(self, other):\\n return are_equal(self, other)\",\n \"def __eq__(self,other):\\n try: return self.object==other.object and isinstance(self,type(other))\\n except: return False\",\n \"def __eq__(self, other):\\n if isinstance(self, other.__class__):\\n return self.__dict__ == other.__dict__\\n return False\",\n \"def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.__dict__ == 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\"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84560555","0.83913696","0.8143685","0.81403786","0.8131871","0.8093231","0.80915105","0.80915105","0.80915105","0.8084564","0.8084564","0.8075547","0.8075547","0.8065006","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657","0.80417657"],"string":"[\n \"0.84560555\",\n \"0.83913696\",\n \"0.8143685\",\n \"0.81403786\",\n \"0.8131871\",\n \"0.8093231\",\n \"0.80915105\",\n \"0.80915105\",\n \"0.80915105\",\n \"0.8084564\",\n \"0.8084564\",\n \"0.8075547\",\n \"0.8075547\",\n \"0.8065006\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\",\n \"0.80417657\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":19694,"cells":{"query":{"kind":"string","value":"Formats ``path`` with the rank zero values."},"document":{"kind":"string","value":"def _format_path_with_rank_zero(path: str) -> str:\n return path.format(\n rank=0,\n local_rank=0,\n node_rank=0,\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_local_rank_zero_path(path: Optional[str]) -> str:\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\n paths = dist.all_gather_object(path)\n local_rank_zero_path = paths[local_rank_zero]\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\n return local_rank_zero_path","def _format_path_with_current_rank(path: str) -> str:\n return path.format(\n rank=dist.get_global_rank(),\n local_rank=dist.get_local_rank(),\n node_rank=dist.get_node_rank(),\n )","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, int):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def path_str(path):\n\toutput = \"PATH: \"\n\tif path:\n\t\tfor i in path:\n\t\t\toutput += str(i.data) + \" -> \"\n\telse:\n\t\toutput += \"Empty\"\n\treturn output","def path_to_string(path: Path) -> str:\n assert_continuous(path)\n\n pieces = [\"M {} {}\".format(path[0].p0[0], path[0].p0[1])]\n for curve in iter(path): # iter cast not strictly necessary\n piece = \"C {} {} {} {} {} {}\".format(\n int(round(curve.c0[0])), int(round(curve.c0[1])),\n int(round(curve.c1[0])), int(round(curve.c1[1])),\n int(round(curve.p1[0])), int(round(curve.p1[1]))\n )\n pieces.append(piece)\n\n return \" \".join(pieces)","def format_path(path):\n if len(path) > 1:\n result = [crayons.yellow(path[0].name)]\n\n previous = path[0]\n for item in path[1:]:\n result.append(' -> ')\n result.append(crayons.yellow(item.name))\n result.append(': Line ')\n result.append(crayons.cyan(str(item.is_imported_from[previous.full_path][0])))\n previous = item\n result.append(' =>> ')\n\n result.append(crayons.magenta(path[0].name))\n return ''.join(str(x) for x in result)\n else:\n return ''","def encodePath(self, path):\n codedPath = []\n for x, y, pickedRewards, holding in path:\n rewardsList = sorted(list(pickedRewards))\n codedPath.append((self.coordToState[(x, y, tuple(rewardsList), holding)], 0))\n return codedPath","def print_rank_zero(*args, **kwargs) -> None:\n print(*args, **kwargs)","def _GeneratePathStr(path):\n return ((len(path) - 1) * ' ') + path[-1] if path else ''","def empty_path() -> dict[str, int]:\n return {\"UP\": 0, \"DOWN\": 0, \"LEFT\": 0, \"RIGHT\": 0}","def zero_val(self):\r\n self.piDD = {\"[0]\": None}\r\n self.top_node = \"[0]\"\r\n self.dim = 0","def zero(self, value):\n raise NotImplementedError","def count_paths_with_zero_intervals(self):\n zeros = []\n for path in self.paths:\n # print(\"Checking path {}\".format(path))\n has_zero = 0\n for arc in path:\n # lb = self.arc_info[arc][\"lower_bound\"]\n # ub = self.arc_info[arc][\"upper_bound\"]\n # print(\"{} {} interval\".format(lb,ub))\n if (self.arc_info[arc][\"upper_bound\"] -\n self.arc_info[arc][\"lower_bound\"]) == 0:\n has_zero = 1\n zeros.append(has_zero)\n print(zeros)\n return(sum(zeros))","def test_format_throughput_for_when_available_is_zero(self):\n self.assertEqual(format_throughput(0, 7), \"7/∞\")\n self.assertEqual(format_throughput(0), \"N/A\")","def format_path(path):\n if not path:\n return path\n\n path = re.sub(r'/+', '/', path)\n\n if path == '/':\n return (u\"\" if isinstance(path, unicode) else \"\")\n else:\n return '/' + path.strip('/')","def normalizePath(path):\n if path == None or len(path) == 0 or path == '/':\n return '/'\n buff = '/' + path if path[0] != '/' else path\n return buff.replace('//', '/')","def format_path(path):\n return path if path.endswith('/') else path + '/'","def zero(klass):\n return RatTerm(RatNum(0, 1), 0)","def corrTIFPath(path, symbol, value): \n reg_expr_search = '(\\%s+)'%str(symbol)\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\n str_replace = str_format.format(int(value))\n\n return re.sub(reg_expr_search, str_replace, path)","def path(filename, path):\n\n # If the line is not empty:\n if len(path) > 0:\n # Open the file for appending\n with open(filename, \"a\") as file:\n # Define format string\n write = \"{:.2f},{:.2f},{:d},{:d},{:d},\\n\"\n\n # Find the first point\n first = path[0]\n # Write the first point with \"no extruding\" option\n file.write(write.format(float(first[1][0]), float(first[1][1]), 0, 0, 0))\n\n # For each line in the path\n for i, line in enumerate(path):\n # If line isn't a repeated point\n if True or (line[1][0] != line[2][0]) and (line[1][1] != line[2][1]):\n\n # If the line is somewhere in the middle of the list write it with \"extruding\" option\n if i < len(path) - 1:\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 0, 0))\n\n # If the line is the last of the path, write it with \"extruding\" and \"end of island\" options\n else:\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 1, 0))","def reset_path(self):\n for i in self.grid:\n for y in i:\n y.g = 0\n y.h = 0\n y.f = 0\n y.parent = None\n y.visited = False","def clean_path(path):\n return resolved_path(path)","def _isolated_path_format(self, path):\n if self._root_dir.is_parent_of(path):\n return '%s:%s' % (\n self._root_dir,\n self._api.path.join(*path.pieces[len(self._root_dir.pieces):])\n )\n else:\n assert path == self._root_dir, \\\n \"isolated path must be equal to or within %s\" % self._root_dir\n return '%s:.' % self._root_dir","def sign_of_path(path):\n vectors = [(a[0] - b[0], a[1] - b[1]) for b, a in pairwise(path)]\n sign_exp = 0\n for idx, vector in enumerate(vectors):\n if vector == (0, 1):\n sign_exp += len([v for v in vectors[idx + 1:] if v == (1, 0)])\n return (-1) ** (sign_exp)","def printPath(path):\n result =''\n for i in range(len(path)):\n result = result + str(path[i])\n if i != len(path) -1:\n result = result + '->'\n return result","def printPath(path):\r\n result = ''\r\n for i in range(len(path)):\r\n result = result + str(path[i])\r\n if i != len(path) - 1:\r\n result = result + '->'\r\n return result","def calcPath(self):\n return None","def write_zero_expression(output_file, file_contents, line):\n output_file.write('%s\\t' % line)\n for index, column in enumerate(['0'] * len(file_contents[0][1:])):\n output_file.write(column)\n if index < (len(file_contents[0][1:]) - 1):\n output_file.write('\\t')\n output_file.write('\\n')","def zero(cls: Type[_R]) -> _R:\n return cls(\"0.0.0\")","def printPath(path):\n result = ''\n for i in range(len(path)):\n result = result + str(path[i])\n if i != len(path) - 1:\n result = result + '->'\n return result","def prepare_path(self,path):\n debug.info(4,\"Set path: \" + str(path))\n\n # This is marked for debug\n path.set_path()\n\n # For debugging... if the path failed to route.\n if False or path==None:\n self.write_debug_gds()\n\n # First, simplify the path for\n #debug.info(1,str(self.path)) \n contracted_path = self.contract_path(path)\n debug.info(3,\"Contracted path: \" + str(contracted_path))\n \n return contracted_path","def print_path(self, path, marks = []):\n\n result = ''\n\n for y in range(1, self.height + 1):\n for x in range(1, self.width + 1):\n # Draw top line\n if (x, y - 1) in self.get_reachables(x, y):\n result += '+ '\n else: result += '+--'\n\n result += '+\\n'\n\n for x in range(1, self.width + 1):\n # Draw horizontal passage\n if (x - 1, y) in self.get_reachables(x, y):\n result += ' '\n else: result += '|'\n\n\n if (x, y) in path:\n if (x, y) in path[-1:]:\n result += '(X'\n else: result += ' x'\n elif (x, y) in marks:\n result += ' #'\n else: result += ' '\n\n result += '|\\n'\n\n if y == self.height:\n for x in range(1, self.width + 1):\n # Draw bottom line\n result += '+--'\n\n return result + '+'","def remove_constant_points(path):\n z = path\n while \"UD\" in z or \"DU\" in z or \"LR\" in z or \"RL\" in z:\n z = z.replace(\"UD\", \"\")\n z = z.replace(\"DU\", \"\")\n z = z.replace(\"LR\", \"\")\n z = z.replace(\"RL\", \"\")\n return z","def get_full_res_path(path):\n path = re.sub(r'fill=((\\d)+x(\\d)+)\\/', '', path)\n return re.sub(r'fit=((\\d+)x(\\d+))?\\/', 'fit=100000x100000/', path)","def increment_path(path, exist_ok=False):\n path = Path(path) # model_dir(./model)\n if (path.exists() and exist_ok) or (not path.exists()):\n return str(path)\n else:\n dirs = glob.glob(f\"{path}*\") # 지정한 패턴에 맞는 파일을 불러옴\n matches = [re.search(rf\"%s(\\d+)\" % path.stem, d) for d in dirs]\n i = [int(m.groups()[0]) for m in matches if m]\n n = max(i) + 1 if i else 2\n return f\"{path}{n}\" # 뒷 부분에 숫자 + 1을 하여 return","def conversion_binaire(self, binary_path):\r\n if self.node_leaf():\r\n return self.name + ':' + str(binary_path) + '\\n'\r\n else:\r\n path = self.zero_son.conversion_binaire(str(binary_path) + '0')\r\n path += self.one_son.conversion_binaire(str(binary_path) + '1')\r\n #path determined by recursion\r\n return path","def normalizeNativePath(path: unicode) -> unicode:\n ...","def fix_path(self):\n paths = self.data['path'].tolist()\n prefixes = [re.findall(r'[A-Z\\-0-9]+', path) for path in paths]\n prefix_good = [str(prefix[0]) + \".json\" for prefix in prefixes]\n self.data['path'] = prefix_good","def path_convert(self):\n pub_path = Exp_msg()\n for i in self.path:\n epoint = Cordi()\n (epoint.x, epoint.y) = i\n pub_path.bliss.append(epoint)\n return(pub_path)","def tree_zeros_like(tree_x):\n return tree_map(jnp.zeros_like, tree_x)","def add_zeros(i, length): # format index in photos.html\n return (\"{:0>\" + str(max(len(str(length)), 2)) + \"d}\").format(i)","def s_path(self, position: Vector) -> float:\n return self.path_coordinates(position)[0]","def matrix_string_zeros(self, size, prefix = ''):\n if size == 1: # Not indexed\n return np.array2string(np.array(0.))\n if len(size) == 1:\n size.append(1)\n mat = np.zeros(size)\n return np.array2string(mat, prefix = prefix, sign = ' ', separator = ',')","def getPreZero(i):\n pre_zero = ''\n if bool(i % 10) != bool(i == 0):\n pre_zero = '0000'\n if round(((i % 100) / 10), 1):\n pre_zero = '000'\n if round(((i % 1000) / 100), 1):\n pre_zero = '00'\n if round(((i % 10000) / 1000), 1):\n pre_zero = '0'\n return pre_zero","def preorder_label(tree: Tree, position: Tree.Position, depth: int, path: [int]):\n label = \".\".join([str(j+1) for j in path])\n print(2 * depth * \" \", label, str(position.element()))\n\n path.append(0)\n for c in tree.children(position):\n preorder_label(tree, c, depth + 1, path)\n path[-1] += 1\n path.pop()","def re_zero_to_datum(self):\n self.device.re_zero_to_datum()\n return \"OK\"","def depth_zero_finder(self, node):\n for depth, prev_node in self.m_stack.lineage_with_level():\n for typ in (markers.lower, markers.upper, markers.ints,\n markers.roman):\n if prev_node.label[-1] in typ and node.label[-1] in typ:\n typ = list(typ)\n prev_idx = typ.index(prev_node.label[-1])\n current_idx = typ.index(node.label[-1])\n if current_idx == prev_idx + 1:\n return depth\n return self.depth + 1","def _format(path, arformat):\n if isinstance(path, unicode):\n path = path.encode('utf-8')\n\n if path.startswith('#1/'):\n if not arformat:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name starts with special for format!')\n\n if len(path) >= 16:\n if arformat is None:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name too long for format!')\n\n if ' ' in path:\n if not arformat:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name contains forbidden character for format!')\n\n if arformat is None:\n arformat = AR_FORMAT_SIMPLE\n\n return arformat","def init_zero(cls, h):\n shapes = Checkpoint.make_shaped_arrays(h)\n return jax.tree_util.tree_map(lambda s: np.zeros(s.shape, s.dtype), shapes)","def nullValueToZero(self) -> None:\n self.cpp.nullValueToZero()","def _normalize_path(path):\n\n i = 0\n for c in path:\n if c != \"/\":\n break\n i = i + 1\n\n if i:\n return path[(i - 1) :]\n\n return path","def init_zero(cls, h):\n shapes = QuantizedCheckpoint.make_shaped_arrays(h)\n return jax.tree_util.tree_map(lambda s: np.zeros(s.shape, s.dtype), shapes)","def acquisition_number_of(path_to_func_or_anat_or_json: Path) -> str:\n\n return path_to_func_or_anat_or_json.stem.split(\"_\")[-1].zfill(2)","def increment_path(path, exist_ok=False):\n path = Path(path)\n if (path.exists() and exist_ok) or (not path.exists()):\n return str(path)\n else:\n dirs = glob.glob(f\"{path}*\")\n matches = [re.search(rf\"%s(\\d+)\" % path.stem, d) for d in dirs]\n i = [int(m.groups()[0]) for m in matches if m]\n n = max(i) + 1 if i else 2\n return f\"{path}{n}\"","def number_from_path(path):\n try:\n num = int(path[len(basename) + 1:-len(ext)])\n except ValueError:\n return 0\n return num","def format(self, path=None):\n if not path:\n path = self.path\n print(path, end=\"\")\n FORMATTER.parse_file(path)\n FORMATTER.write_file(path)\n print(\" Done\")","def condense_zero_units(css):\n log.debug(\"Condensing all zeroes on values.\")\n return re.sub(r\"([\\s:])(0)(px|em|%|in|q|ch|cm|mm|pc|pt|ex|rem|s|ms|\"\n r\"deg|grad|rad|turn|vw|vh|vmin|vmax|fr)\", r\"\\1\\2\", css)","def check_level(pathname):\n parts = pathname.split(':')\n if len(parts) > 1:\n first_part = parts.pop(0)\n remainder = ':'.join(parts)\n if check_int(first_part):\n return int(first_part), remainder\n # if anything goes wrong just fail back to assuming the whole\n # thing is a path without a weight prefix.\n return 0, pathname","def zero(self):\n raise NotImplementedError(\"Not implemented yet.\")","def path_to_str(path):\n if hasattr(path, '__fspath__'):\n path = as_str_any(path.__fspath__())\n return path","def display_path(self, path):\n graph = path.graph\n if not graph:\n return\n for v in sorted(graph.vertices()):\n p = graph.get_vertex_attribute(v, 'xy')\n x, y = to_geometry(p[0]), to_geometry(p[1])\n print('define v{} ellipse 2 2 c_vertex {} {}'.format(v, x, y))\n #print('define v{0}t text {0} 14 white {1} {2}'.format(v, x, y))\n for u, v in graph.edges():\n print('define - link v{} v{} 1 c_edge'.format(u, v))\n # NOTE: this code assumes paths will not move indefinitely\n print('fix /./')","def generateZeros(num_rows, format, dim=None):\n \n np_dtype, data_len, np_fmt = formatLookup(format)\n try:\n d = np.zeros(num_rows, dtype=np_dtype)\n except ValueError:\n d = np.zeros(num_rows, dtype=np_dtype.strip('_'))\n return d","def delete_closing_zero(model_version: str) -> str:\r\n if model_version[-2:] == \".0\":\r\n return model_version[:-2]\r\n return model_version","def __check_path__(path):\n\n def seq_iter(iterable):\n result = []\n for p in iterable:\n if isinstance(p, Iterable) and \\\n not isinstance(p, (basestring, tuple)):\n result += seq_iter(p)\n else:\n result.append(p)\n\n return result\n\n if isinstance(path, (basestring, int, float, complex, NoneType)):\n return path,\n else:\n return tuple(seq_iter(path))","def _fill_with_zeroes(order, rows, zero=None):\n assert len(rows) > 0\n index = dict(rows)\n if zero is None:\n array = np.array(tuple(index.values()))\n if len(array.shape) == 1:\n zero = array.dtype.type()\n return np.array([index.get(k, zero) for k in order])","def path_to_string(path, separator):\n i = 0\n path_string = \"Path :\" + separator + \"[\"\n while i < len(path):\n if isinstance(path[i], Firewall.Firewall):\n path_string += path[i].hostname\n elif isinstance(path[i], Ip.Ip):\n path_string += path[i].to_string()\n\n if i < len(path) - 1:\n path_string += \",\" + separator\n i += 1\n path_string += \"]\"\n\n return path_string","def reconstruct_object(path):\n if len(path) == 0:\n return '%s'\n else:\n # The indexed query on `path` below is the means by which we recurse\n # Every iteration pushes it closer to a length of 0 and, thus, bottoming out\n return '{{%s: {recons}}}'.format(recons=reconstruct_object(path[1:]))","def find_zero(self):\n\n for key, val in self.sudoku_board.items():\n if val == 0:\n return key\n return \"\"","def zero_init(self, shape):\n return np.zeros((shape[0],shape[1]))","def get_unoriented_sorted_str(path):\n all_rc_path = []\n for p in path:\n if p[-1] != \"'\": p = p+\"'\"\n all_rc_path.append(p)\n return \"\".join(sorted(all_rc_path))","def zeros(cls, shape, domain, *, fmt='sparse'):\n return cls.from_rep(SDM.zeros(shape, domain))","def visit_path(self, node, children):\n # Rut is zero by default\n path = {node.rule_name: {'lane': children[0], 'rut': children.results.get('notch', [0])[0]} }\n return path # { path: { lane: <lane_num>, rut: <rut_displacement> }","def zero_weight():\n return Weight(kg=0)","def zero_calibrationn(self):\n self.link.write(self._calibrateZeroSequence)","def zero_K(self):\n # print('zero_K axnode: ', self.cell.axnode)\n i = 0\n for node in self.cell.axnode:\n for seg in node:\n if i == 0:\n print(f\"KLT: {seg.klt.gbar:.6f} mho/cm2\")\n print(f\"KCNQ: {seg.kcnq.gbar:.6f} mho/cm2\")\n print(f\"KHT: {seg.kht.gbar:.6f} mho/cm2\")\n i = 1\n # seg.klt.gbar = 0e-3\n seg.kcnq.gbar = 0e-3\n # seg.kcnq.phi_m = seg.kcnq.phi_m - 20.\n # seg.kht.gbar = 0e-3\n # seg.kht.vshift = -20.\n pass","def breseq_pipeline_output_empty(tmp_path)->Path:\n\n\tparent_folder = checkdir(tmp_path / \"parent_folder\")\n\n\tsample_1_folder = checkdir(parent_folder / \"sample1\")\n\tsample_1_folder_output = checkdir(sample_1_folder / \"output\")\n\tsample_1_folder_data = checkdir(sample_1_folder / \"data\")\n\n\tsample_2_folder = checkdir(parent_folder / \"sample2\")\n\tsample_2_folder_breseq = checkdir(sample_2_folder / \"breseq\")\n\tsample_2_folder_output = checkdir(sample_2_folder_breseq / \"output\")\n\tsample_2_folder_data = checkdir(sample_2_folder_breseq / \"data\")\n\n\tsample_3_folder = checkdir(parent_folder / \"AU1234_ABC\")\n\tsample_3_folder_breseq = checkdir(sample_3_folder / \"breseq_output\")\n\tsample_3_folder_output = checkdir(sample_3_folder_breseq / \"output\")\n\tsample_3_folder_data = checkdir(sample_3_folder_breseq / \"data\")\n\t\n\treturn parent_folder","def _formatFreq(self, value, pos):\n inv = 999\n if value:\n inv = 1/value\n return \"1/%0.2f\" % inv","def path_to_message(self, mapdata, path):\n rospy.loginfo(\"Returning a Path message\")\n pathObj = Path()\n pathObj.header.frame_id = '/map'\n pathObj.poses = PathPlanner.path_to_poses(mapdata, path)\n return pathObj","def __str__(self):\n if self.denominator ==1:\n return f'{self.numerator}'\n elif self.denominator == 0 and self.numerator ==0:\n if self.inf_size > 0:\n return f'Infinity with size of {self.inf_size}'\n else:\n return f'Negative infinity with size of {-self.inf_size}'\n elif self.numerator ==0:\n return '0'\n else:\n return f'{self.numerator}/{self.denominator}'","def __init__(self, path):\n assert isinstance(path, plumbum.LocalPath), path\n assert path.exists()\n self.path = path\n super().__init__(path.stem)","def zero(self):\n q = pinocchio.neutral(self.model)\n v = np.zeros(self.model.nv)\n return np.concatenate([q.flat, v])","def path_cost(path):\n # path = [state, (action, total_cost), state, ... ]\n if len(path) < 2:\n return 0\n else:\n return path[-2][-1]","def as_string(path: pathlib.Path) -> str:\n return path.as_posix()","def str_mks0(self) -> str:\n if hasattr(self, \"mks0\"):\n return f\"MKS={self.mks0} *={self.mks0_}\"\n else:\n return \"-\"","def path_serializer(obj: PurePath, **_: Any) -> str:\n return obj.as_posix()","def path(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:","def __str__(self):\n if self._rank is None:\n rank_str = \"\"\n else:\n rank_str = str(self._rank + 1)\n\n if self._file is None:\n file_str = \"\"\n else:\n file_str = chr(self._file + 97)\n\n return file_str + rank_str","def shift_zero(line):\n for index_i in range(0, len(line)):\n if line[index_i] != 0:\n key = line[index_i]\n index_j = index_i-1\n while index_j >= 0 and line[index_j] == 0:\n line[index_j+1] = line[index_j]\n index_j = index_j-1\n line[index_j+1] = key","def empty_paths(self):\n self.paths[:]","def zero(self):\n return np.zeros([self.nx])","def __init__(self, path: str) -> None:\n self.root = Node(\n None, path\n )\n self.depth = 0\n self.add_node(self.root)","def convert_path_type(path):\n if isinstance(path, Path):\n np_path = path_to_array(path)[:,0]\n elif isinstance(path, Trajectory):\n np_path = trajectory_to_array(path)[:,0]\n elif isinstance(path, (list, np.ndarray)):\n np_path = np.array(path)\n else:\n raise ValueError(\"Invalid type for `path` argument. Must be an array-like type.\")\n\n return np_path","def osnorm(self):\n import os\n if os.sep=='/' and \"\\\\\" in str(self):\n return Path(os.path.normpath(str(self).replace('\\\\','/' )))\n elif os.sep=='\\\\' and \"/\" in str(self):\n return Path(os.path.normpath(str(self).replace('/','\\\\' )))\n else:\n return self.norm()","def intialize_hierarchy_paths(self):\n\n leaf_nodes = [node[0] for node in self.tree.out_degree if node[1] == 0]\n paths = [self.tree_utils.determine_path_to_root([node]) for node in leaf_nodes]\n\n # Normalize paths per level in hierarchy - currently the nodes are of increasing number throughout the tree.\n normalized_paths = [self.tree_utils.normalize_path_from_root_per_level(path) for path in paths]\n\n normalized_encoder = {'Root': {'original_key': 0, 'derived_key': 0}}\n normalized_decoder = { 0: {'original_key': 0, 'value': 'Root'}}\n decoder = dict(self.tree.nodes(data=\"name\"))\n encoder = dict([(value, key) for key, value in decoder.items()])\n\n #initiaize encoders\n for path, normalized_path in zip(paths, normalized_paths):\n key = path[-1]\n derived_key = normalized_path[-1]\n if key in leaf_nodes:\n normalized_encoder[decoder[key]] = {'original_key': key, 'derived_key': derived_key}\n normalized_decoder[derived_key] = {'original_key': key, 'value': decoder[key]}\n\n oov_path = [[0, 0, 0]]\n normalized_paths = oov_path + normalized_paths\n\n #Align length of paths if necessary\n longest_path = max([len(path) for path in normalized_paths])\n\n # Sort paths ascending\n sorted_normalized_paths = []\n for i in range(len(normalized_paths)):\n found_path = normalized_paths[0]\n for path in normalized_paths:\n for found_node, node in zip(found_path,path):\n if found_node > node:\n found_path = path\n break\n\n if not (found_path is None):\n sorted_normalized_paths.append(found_path)\n normalized_paths.remove(found_path)\n\n return normalized_encoder, normalized_decoder, sorted_normalized_paths","def get_none_zero_list(line):\n new_line = []\n for dummy_i in range(0,len(line)):\n if line[dummy_i] != 0:\n new_line.append(line[dummy_i])\n return new_line","def printtable(dict):\n for dirname in dict:\n if dict[dirname][2] != 0:\n sys.stdout.write(\"{0:4} {1:4} {2:4} {3}\\n\".format(dict[dirname][2], dict[dirname][0], dict[dirname][1], dirname))","def read_max_angular_momentum(path):\n with open(path, 'r') as fd:\n line = fd.readline()\n if line[0] == '@':\n # Extended format\n fd.readline()\n l = 3\n pos = 9\n else:\n # Simple format:\n l = 2\n pos = 7\n\n # Sometimes there ar commas, sometimes not:\n line = fd.readline().replace(',', ' ')\n\n occs = [float(f) for f in line.split()[pos:pos + l + 1]]\n for f in occs:\n if f > 0.0:\n return l\n l -= 1"],"string":"[\n \"def _get_local_rank_zero_path(path: Optional[str]) -> str:\\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\\n paths = dist.all_gather_object(path)\\n local_rank_zero_path = paths[local_rank_zero]\\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\\n return local_rank_zero_path\",\n \"def _format_path_with_current_rank(path: str) -> str:\\n return path.format(\\n rank=dist.get_global_rank(),\\n local_rank=dist.get_local_rank(),\\n node_rank=dist.get_node_rank(),\\n )\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, int):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def path_str(path):\\n\\toutput = \\\"PATH: \\\"\\n\\tif path:\\n\\t\\tfor i in path:\\n\\t\\t\\toutput += str(i.data) + \\\" -> \\\"\\n\\telse:\\n\\t\\toutput += \\\"Empty\\\"\\n\\treturn output\",\n \"def path_to_string(path: Path) -> str:\\n assert_continuous(path)\\n\\n pieces = [\\\"M {} {}\\\".format(path[0].p0[0], path[0].p0[1])]\\n for curve in iter(path): # iter cast not strictly necessary\\n piece = \\\"C {} {} {} {} {} {}\\\".format(\\n int(round(curve.c0[0])), int(round(curve.c0[1])),\\n int(round(curve.c1[0])), int(round(curve.c1[1])),\\n int(round(curve.p1[0])), int(round(curve.p1[1]))\\n )\\n pieces.append(piece)\\n\\n return \\\" \\\".join(pieces)\",\n \"def format_path(path):\\n if len(path) > 1:\\n result = [crayons.yellow(path[0].name)]\\n\\n previous = path[0]\\n for item in path[1:]:\\n result.append(' -> ')\\n result.append(crayons.yellow(item.name))\\n result.append(': Line ')\\n result.append(crayons.cyan(str(item.is_imported_from[previous.full_path][0])))\\n previous = item\\n result.append(' =>> ')\\n\\n result.append(crayons.magenta(path[0].name))\\n return ''.join(str(x) for x in result)\\n else:\\n return ''\",\n \"def encodePath(self, path):\\n codedPath = []\\n for x, y, pickedRewards, holding in path:\\n rewardsList = sorted(list(pickedRewards))\\n codedPath.append((self.coordToState[(x, y, tuple(rewardsList), holding)], 0))\\n return codedPath\",\n \"def print_rank_zero(*args, **kwargs) -> None:\\n print(*args, **kwargs)\",\n \"def _GeneratePathStr(path):\\n return ((len(path) - 1) * ' ') + path[-1] if path else ''\",\n \"def empty_path() -> dict[str, int]:\\n return {\\\"UP\\\": 0, \\\"DOWN\\\": 0, \\\"LEFT\\\": 0, \\\"RIGHT\\\": 0}\",\n \"def zero_val(self):\\r\\n self.piDD = {\\\"[0]\\\": None}\\r\\n self.top_node = \\\"[0]\\\"\\r\\n self.dim = 0\",\n \"def zero(self, value):\\n raise NotImplementedError\",\n \"def count_paths_with_zero_intervals(self):\\n zeros = []\\n for path in self.paths:\\n # print(\\\"Checking path {}\\\".format(path))\\n has_zero = 0\\n for arc in path:\\n # lb = self.arc_info[arc][\\\"lower_bound\\\"]\\n # ub = self.arc_info[arc][\\\"upper_bound\\\"]\\n # print(\\\"{} {} interval\\\".format(lb,ub))\\n if (self.arc_info[arc][\\\"upper_bound\\\"] -\\n self.arc_info[arc][\\\"lower_bound\\\"]) == 0:\\n has_zero = 1\\n zeros.append(has_zero)\\n print(zeros)\\n return(sum(zeros))\",\n \"def test_format_throughput_for_when_available_is_zero(self):\\n self.assertEqual(format_throughput(0, 7), \\\"7/∞\\\")\\n self.assertEqual(format_throughput(0), \\\"N/A\\\")\",\n \"def format_path(path):\\n if not path:\\n return path\\n\\n path = re.sub(r'/+', '/', path)\\n\\n if path == '/':\\n return (u\\\"\\\" if isinstance(path, unicode) else \\\"\\\")\\n else:\\n return '/' + path.strip('/')\",\n \"def normalizePath(path):\\n if path == None or len(path) == 0 or path == '/':\\n return '/'\\n buff = '/' + path if path[0] != '/' else path\\n return buff.replace('//', '/')\",\n \"def format_path(path):\\n return path if path.endswith('/') else path + '/'\",\n \"def zero(klass):\\n return RatTerm(RatNum(0, 1), 0)\",\n \"def corrTIFPath(path, symbol, value): \\n reg_expr_search = '(\\\\%s+)'%str(symbol)\\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\\n str_replace = str_format.format(int(value))\\n\\n return re.sub(reg_expr_search, str_replace, path)\",\n \"def path(filename, path):\\n\\n # If the line is not empty:\\n if len(path) > 0:\\n # Open the file for appending\\n with open(filename, \\\"a\\\") as file:\\n # Define format string\\n write = \\\"{:.2f},{:.2f},{:d},{:d},{:d},\\\\n\\\"\\n\\n # Find the first point\\n first = path[0]\\n # Write the first point with \\\"no extruding\\\" option\\n file.write(write.format(float(first[1][0]), float(first[1][1]), 0, 0, 0))\\n\\n # For each line in the path\\n for i, line in enumerate(path):\\n # If line isn't a repeated point\\n if True or (line[1][0] != line[2][0]) and (line[1][1] != line[2][1]):\\n\\n # If the line is somewhere in the middle of the list write it with \\\"extruding\\\" option\\n if i < len(path) - 1:\\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 0, 0))\\n\\n # If the line is the last of the path, write it with \\\"extruding\\\" and \\\"end of island\\\" options\\n else:\\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 1, 0))\",\n \"def reset_path(self):\\n for i in self.grid:\\n for y in i:\\n y.g = 0\\n y.h = 0\\n y.f = 0\\n y.parent = None\\n y.visited = False\",\n \"def clean_path(path):\\n return resolved_path(path)\",\n \"def _isolated_path_format(self, path):\\n if self._root_dir.is_parent_of(path):\\n return '%s:%s' % (\\n self._root_dir,\\n self._api.path.join(*path.pieces[len(self._root_dir.pieces):])\\n )\\n else:\\n assert path == self._root_dir, \\\\\\n \\\"isolated path must be equal to or within %s\\\" % self._root_dir\\n return '%s:.' % self._root_dir\",\n \"def sign_of_path(path):\\n vectors = [(a[0] - b[0], a[1] - b[1]) for b, a in pairwise(path)]\\n sign_exp = 0\\n for idx, vector in enumerate(vectors):\\n if vector == (0, 1):\\n sign_exp += len([v for v in vectors[idx + 1:] if v == (1, 0)])\\n return (-1) ** (sign_exp)\",\n \"def printPath(path):\\n result =''\\n for i in range(len(path)):\\n result = result + str(path[i])\\n if i != len(path) -1:\\n result = result + '->'\\n return result\",\n \"def printPath(path):\\r\\n result = ''\\r\\n for i in range(len(path)):\\r\\n result = result + str(path[i])\\r\\n if i != len(path) - 1:\\r\\n result = result + '->'\\r\\n return result\",\n \"def calcPath(self):\\n return None\",\n \"def write_zero_expression(output_file, file_contents, line):\\n output_file.write('%s\\\\t' % line)\\n for index, column in enumerate(['0'] * len(file_contents[0][1:])):\\n output_file.write(column)\\n if index < (len(file_contents[0][1:]) - 1):\\n output_file.write('\\\\t')\\n output_file.write('\\\\n')\",\n \"def zero(cls: Type[_R]) -> _R:\\n return cls(\\\"0.0.0\\\")\",\n \"def printPath(path):\\n result = ''\\n for i in range(len(path)):\\n result = result + str(path[i])\\n if i != len(path) - 1:\\n result = result + '->'\\n return result\",\n \"def prepare_path(self,path):\\n debug.info(4,\\\"Set path: \\\" + str(path))\\n\\n # This is marked for debug\\n path.set_path()\\n\\n # For debugging... if the path failed to route.\\n if False or path==None:\\n self.write_debug_gds()\\n\\n # First, simplify the path for\\n #debug.info(1,str(self.path)) \\n contracted_path = self.contract_path(path)\\n debug.info(3,\\\"Contracted path: \\\" + str(contracted_path))\\n \\n return contracted_path\",\n \"def print_path(self, path, marks = []):\\n\\n result = ''\\n\\n for y in range(1, self.height + 1):\\n for x in range(1, self.width + 1):\\n # Draw top line\\n if (x, y - 1) in self.get_reachables(x, y):\\n result += '+ '\\n else: result += '+--'\\n\\n result += '+\\\\n'\\n\\n for x in range(1, self.width + 1):\\n # Draw horizontal passage\\n if (x - 1, y) in self.get_reachables(x, y):\\n result += ' '\\n else: result += '|'\\n\\n\\n if (x, y) in path:\\n if (x, y) in path[-1:]:\\n result += '(X'\\n else: result += ' x'\\n elif (x, y) in marks:\\n result += ' #'\\n else: result += ' '\\n\\n result += '|\\\\n'\\n\\n if y == self.height:\\n for x in range(1, self.width + 1):\\n # Draw bottom line\\n result += '+--'\\n\\n return result + '+'\",\n \"def remove_constant_points(path):\\n z = path\\n while \\\"UD\\\" in z or \\\"DU\\\" in z or \\\"LR\\\" in z or \\\"RL\\\" in z:\\n z = z.replace(\\\"UD\\\", \\\"\\\")\\n z = z.replace(\\\"DU\\\", \\\"\\\")\\n z = z.replace(\\\"LR\\\", \\\"\\\")\\n z = z.replace(\\\"RL\\\", \\\"\\\")\\n return z\",\n \"def get_full_res_path(path):\\n path = re.sub(r'fill=((\\\\d)+x(\\\\d)+)\\\\/', '', path)\\n return re.sub(r'fit=((\\\\d+)x(\\\\d+))?\\\\/', 'fit=100000x100000/', path)\",\n \"def increment_path(path, exist_ok=False):\\n path = Path(path) # model_dir(./model)\\n if (path.exists() and exist_ok) or (not path.exists()):\\n return str(path)\\n else:\\n dirs = glob.glob(f\\\"{path}*\\\") # 지정한 패턴에 맞는 파일을 불러옴\\n matches = [re.search(rf\\\"%s(\\\\d+)\\\" % path.stem, d) for d in dirs]\\n i = [int(m.groups()[0]) for m in matches if m]\\n n = max(i) + 1 if i else 2\\n return f\\\"{path}{n}\\\" # 뒷 부분에 숫자 + 1을 하여 return\",\n \"def conversion_binaire(self, binary_path):\\r\\n if self.node_leaf():\\r\\n return self.name + ':' + str(binary_path) + '\\\\n'\\r\\n else:\\r\\n path = self.zero_son.conversion_binaire(str(binary_path) + '0')\\r\\n path += self.one_son.conversion_binaire(str(binary_path) + '1')\\r\\n #path determined by recursion\\r\\n return path\",\n \"def normalizeNativePath(path: unicode) -> unicode:\\n ...\",\n \"def fix_path(self):\\n paths = self.data['path'].tolist()\\n prefixes = [re.findall(r'[A-Z\\\\-0-9]+', path) for path in paths]\\n prefix_good = [str(prefix[0]) + \\\".json\\\" for prefix in prefixes]\\n self.data['path'] = prefix_good\",\n \"def path_convert(self):\\n pub_path = Exp_msg()\\n for i in self.path:\\n epoint = Cordi()\\n (epoint.x, epoint.y) = i\\n pub_path.bliss.append(epoint)\\n return(pub_path)\",\n \"def tree_zeros_like(tree_x):\\n return tree_map(jnp.zeros_like, tree_x)\",\n \"def add_zeros(i, length): # format index in photos.html\\n return (\\\"{:0>\\\" + str(max(len(str(length)), 2)) + \\\"d}\\\").format(i)\",\n \"def s_path(self, position: Vector) -> float:\\n return self.path_coordinates(position)[0]\",\n \"def matrix_string_zeros(self, size, prefix = ''):\\n if size == 1: # Not indexed\\n return np.array2string(np.array(0.))\\n if len(size) == 1:\\n size.append(1)\\n mat = np.zeros(size)\\n return np.array2string(mat, prefix = prefix, sign = ' ', separator = ',')\",\n \"def getPreZero(i):\\n pre_zero = ''\\n if bool(i % 10) != bool(i == 0):\\n pre_zero = '0000'\\n if round(((i % 100) / 10), 1):\\n pre_zero = '000'\\n if round(((i % 1000) / 100), 1):\\n pre_zero = '00'\\n if round(((i % 10000) / 1000), 1):\\n pre_zero = '0'\\n return pre_zero\",\n \"def preorder_label(tree: Tree, position: Tree.Position, depth: int, path: [int]):\\n label = \\\".\\\".join([str(j+1) for j in path])\\n print(2 * depth * \\\" \\\", label, str(position.element()))\\n\\n path.append(0)\\n for c in tree.children(position):\\n preorder_label(tree, c, depth + 1, path)\\n path[-1] += 1\\n path.pop()\",\n \"def re_zero_to_datum(self):\\n self.device.re_zero_to_datum()\\n return \\\"OK\\\"\",\n \"def depth_zero_finder(self, node):\\n for depth, prev_node in self.m_stack.lineage_with_level():\\n for typ in (markers.lower, markers.upper, markers.ints,\\n markers.roman):\\n if prev_node.label[-1] in typ and node.label[-1] in typ:\\n typ = list(typ)\\n prev_idx = typ.index(prev_node.label[-1])\\n current_idx = typ.index(node.label[-1])\\n if current_idx == prev_idx + 1:\\n return depth\\n return self.depth + 1\",\n \"def _format(path, arformat):\\n if isinstance(path, unicode):\\n path = path.encode('utf-8')\\n\\n if path.startswith('#1/'):\\n if not arformat:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name starts with special for format!')\\n\\n if len(path) >= 16:\\n if arformat is None:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name too long for format!')\\n\\n if ' ' in path:\\n if not arformat:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name contains forbidden character for format!')\\n\\n if arformat is None:\\n arformat = AR_FORMAT_SIMPLE\\n\\n return arformat\",\n \"def init_zero(cls, h):\\n shapes = Checkpoint.make_shaped_arrays(h)\\n return jax.tree_util.tree_map(lambda s: np.zeros(s.shape, s.dtype), shapes)\",\n \"def nullValueToZero(self) -> None:\\n self.cpp.nullValueToZero()\",\n \"def _normalize_path(path):\\n\\n i = 0\\n for c in path:\\n if c != \\\"/\\\":\\n break\\n i = i + 1\\n\\n if i:\\n return path[(i - 1) :]\\n\\n return path\",\n \"def init_zero(cls, h):\\n shapes = QuantizedCheckpoint.make_shaped_arrays(h)\\n return jax.tree_util.tree_map(lambda s: np.zeros(s.shape, s.dtype), shapes)\",\n \"def acquisition_number_of(path_to_func_or_anat_or_json: Path) -> str:\\n\\n return path_to_func_or_anat_or_json.stem.split(\\\"_\\\")[-1].zfill(2)\",\n \"def increment_path(path, exist_ok=False):\\n path = Path(path)\\n if (path.exists() and exist_ok) or (not path.exists()):\\n return str(path)\\n else:\\n dirs = glob.glob(f\\\"{path}*\\\")\\n matches = [re.search(rf\\\"%s(\\\\d+)\\\" % path.stem, d) for d in dirs]\\n i = [int(m.groups()[0]) for m in matches if m]\\n n = max(i) + 1 if i else 2\\n return f\\\"{path}{n}\\\"\",\n \"def number_from_path(path):\\n try:\\n num = int(path[len(basename) + 1:-len(ext)])\\n except ValueError:\\n return 0\\n return num\",\n \"def format(self, path=None):\\n if not path:\\n path = self.path\\n print(path, end=\\\"\\\")\\n FORMATTER.parse_file(path)\\n FORMATTER.write_file(path)\\n print(\\\" Done\\\")\",\n \"def condense_zero_units(css):\\n log.debug(\\\"Condensing all zeroes on values.\\\")\\n return re.sub(r\\\"([\\\\s:])(0)(px|em|%|in|q|ch|cm|mm|pc|pt|ex|rem|s|ms|\\\"\\n r\\\"deg|grad|rad|turn|vw|vh|vmin|vmax|fr)\\\", r\\\"\\\\1\\\\2\\\", css)\",\n \"def check_level(pathname):\\n parts = pathname.split(':')\\n if len(parts) > 1:\\n first_part = parts.pop(0)\\n remainder = ':'.join(parts)\\n if check_int(first_part):\\n return int(first_part), remainder\\n # if anything goes wrong just fail back to assuming the whole\\n # thing is a path without a weight prefix.\\n return 0, pathname\",\n \"def zero(self):\\n raise NotImplementedError(\\\"Not implemented yet.\\\")\",\n \"def path_to_str(path):\\n if hasattr(path, '__fspath__'):\\n path = as_str_any(path.__fspath__())\\n return path\",\n \"def display_path(self, path):\\n graph = path.graph\\n if not graph:\\n return\\n for v in sorted(graph.vertices()):\\n p = graph.get_vertex_attribute(v, 'xy')\\n x, y = to_geometry(p[0]), to_geometry(p[1])\\n print('define v{} ellipse 2 2 c_vertex {} {}'.format(v, x, y))\\n #print('define v{0}t text {0} 14 white {1} {2}'.format(v, x, y))\\n for u, v in graph.edges():\\n print('define - link v{} v{} 1 c_edge'.format(u, v))\\n # NOTE: this code assumes paths will not move indefinitely\\n print('fix /./')\",\n \"def generateZeros(num_rows, format, dim=None):\\n \\n np_dtype, data_len, np_fmt = formatLookup(format)\\n try:\\n d = np.zeros(num_rows, dtype=np_dtype)\\n except ValueError:\\n d = np.zeros(num_rows, dtype=np_dtype.strip('_'))\\n return d\",\n \"def delete_closing_zero(model_version: str) -> str:\\r\\n if model_version[-2:] == \\\".0\\\":\\r\\n return model_version[:-2]\\r\\n return model_version\",\n \"def __check_path__(path):\\n\\n def seq_iter(iterable):\\n result = []\\n for p in iterable:\\n if isinstance(p, Iterable) and \\\\\\n not isinstance(p, (basestring, tuple)):\\n result += seq_iter(p)\\n else:\\n result.append(p)\\n\\n return result\\n\\n if isinstance(path, (basestring, int, float, complex, NoneType)):\\n return path,\\n else:\\n return tuple(seq_iter(path))\",\n \"def _fill_with_zeroes(order, rows, zero=None):\\n assert len(rows) > 0\\n index = dict(rows)\\n if zero is None:\\n array = np.array(tuple(index.values()))\\n if len(array.shape) == 1:\\n zero = array.dtype.type()\\n return np.array([index.get(k, zero) for k in order])\",\n \"def path_to_string(path, separator):\\n i = 0\\n path_string = \\\"Path :\\\" + separator + \\\"[\\\"\\n while i < len(path):\\n if isinstance(path[i], Firewall.Firewall):\\n path_string += path[i].hostname\\n elif isinstance(path[i], Ip.Ip):\\n path_string += path[i].to_string()\\n\\n if i < len(path) - 1:\\n path_string += \\\",\\\" + separator\\n i += 1\\n path_string += \\\"]\\\"\\n\\n return path_string\",\n \"def reconstruct_object(path):\\n if len(path) == 0:\\n return '%s'\\n else:\\n # The indexed query on `path` below is the means by which we recurse\\n # Every iteration pushes it closer to a length of 0 and, thus, bottoming out\\n return '{{%s: {recons}}}'.format(recons=reconstruct_object(path[1:]))\",\n \"def find_zero(self):\\n\\n for key, val in self.sudoku_board.items():\\n if val == 0:\\n return key\\n return \\\"\\\"\",\n \"def zero_init(self, shape):\\n return np.zeros((shape[0],shape[1]))\",\n \"def get_unoriented_sorted_str(path):\\n all_rc_path = []\\n for p in path:\\n if p[-1] != \\\"'\\\": p = p+\\\"'\\\"\\n all_rc_path.append(p)\\n return \\\"\\\".join(sorted(all_rc_path))\",\n \"def zeros(cls, shape, domain, *, fmt='sparse'):\\n return cls.from_rep(SDM.zeros(shape, domain))\",\n \"def visit_path(self, node, children):\\n # Rut is zero by default\\n path = {node.rule_name: {'lane': children[0], 'rut': children.results.get('notch', [0])[0]} }\\n return path # { path: { lane: <lane_num>, rut: <rut_displacement> }\",\n \"def zero_weight():\\n return Weight(kg=0)\",\n \"def zero_calibrationn(self):\\n self.link.write(self._calibrateZeroSequence)\",\n \"def zero_K(self):\\n # print('zero_K axnode: ', self.cell.axnode)\\n i = 0\\n for node in self.cell.axnode:\\n for seg in node:\\n if i == 0:\\n print(f\\\"KLT: {seg.klt.gbar:.6f} mho/cm2\\\")\\n print(f\\\"KCNQ: {seg.kcnq.gbar:.6f} mho/cm2\\\")\\n print(f\\\"KHT: {seg.kht.gbar:.6f} mho/cm2\\\")\\n i = 1\\n # seg.klt.gbar = 0e-3\\n seg.kcnq.gbar = 0e-3\\n # seg.kcnq.phi_m = seg.kcnq.phi_m - 20.\\n # seg.kht.gbar = 0e-3\\n # seg.kht.vshift = -20.\\n pass\",\n \"def breseq_pipeline_output_empty(tmp_path)->Path:\\n\\n\\tparent_folder = checkdir(tmp_path / \\\"parent_folder\\\")\\n\\n\\tsample_1_folder = checkdir(parent_folder / \\\"sample1\\\")\\n\\tsample_1_folder_output = checkdir(sample_1_folder / \\\"output\\\")\\n\\tsample_1_folder_data = checkdir(sample_1_folder / \\\"data\\\")\\n\\n\\tsample_2_folder = checkdir(parent_folder / \\\"sample2\\\")\\n\\tsample_2_folder_breseq = checkdir(sample_2_folder / \\\"breseq\\\")\\n\\tsample_2_folder_output = checkdir(sample_2_folder_breseq / \\\"output\\\")\\n\\tsample_2_folder_data = checkdir(sample_2_folder_breseq / \\\"data\\\")\\n\\n\\tsample_3_folder = checkdir(parent_folder / \\\"AU1234_ABC\\\")\\n\\tsample_3_folder_breseq = checkdir(sample_3_folder / \\\"breseq_output\\\")\\n\\tsample_3_folder_output = checkdir(sample_3_folder_breseq / \\\"output\\\")\\n\\tsample_3_folder_data = checkdir(sample_3_folder_breseq / \\\"data\\\")\\n\\t\\n\\treturn parent_folder\",\n \"def _formatFreq(self, value, pos):\\n inv = 999\\n if value:\\n inv = 1/value\\n return \\\"1/%0.2f\\\" % inv\",\n \"def path_to_message(self, mapdata, path):\\n rospy.loginfo(\\\"Returning a Path message\\\")\\n pathObj = Path()\\n pathObj.header.frame_id = '/map'\\n pathObj.poses = PathPlanner.path_to_poses(mapdata, path)\\n return pathObj\",\n \"def __str__(self):\\n if self.denominator ==1:\\n return f'{self.numerator}'\\n elif self.denominator == 0 and self.numerator ==0:\\n if self.inf_size > 0:\\n return f'Infinity with size of {self.inf_size}'\\n else:\\n return f'Negative infinity with size of {-self.inf_size}'\\n elif self.numerator ==0:\\n return '0'\\n else:\\n return f'{self.numerator}/{self.denominator}'\",\n \"def __init__(self, path):\\n assert isinstance(path, plumbum.LocalPath), path\\n assert path.exists()\\n self.path = path\\n super().__init__(path.stem)\",\n \"def zero(self):\\n q = pinocchio.neutral(self.model)\\n v = np.zeros(self.model.nv)\\n return np.concatenate([q.flat, v])\",\n \"def path_cost(path):\\n # path = [state, (action, total_cost), state, ... ]\\n if len(path) < 2:\\n return 0\\n else:\\n return path[-2][-1]\",\n \"def as_string(path: pathlib.Path) -> str:\\n return path.as_posix()\",\n \"def str_mks0(self) -> str:\\n if hasattr(self, \\\"mks0\\\"):\\n return f\\\"MKS={self.mks0} *={self.mks0_}\\\"\\n else:\\n return \\\"-\\\"\",\n \"def path_serializer(obj: PurePath, **_: Any) -> str:\\n return obj.as_posix()\",\n \"def path(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:\",\n \"def __str__(self):\\n if self._rank is None:\\n rank_str = \\\"\\\"\\n else:\\n rank_str = str(self._rank + 1)\\n\\n if self._file is None:\\n file_str = \\\"\\\"\\n else:\\n file_str = chr(self._file + 97)\\n\\n return file_str + rank_str\",\n \"def shift_zero(line):\\n for index_i in range(0, len(line)):\\n if line[index_i] != 0:\\n key = line[index_i]\\n index_j = index_i-1\\n while index_j >= 0 and line[index_j] == 0:\\n line[index_j+1] = line[index_j]\\n index_j = index_j-1\\n line[index_j+1] = key\",\n \"def empty_paths(self):\\n self.paths[:]\",\n \"def zero(self):\\n return np.zeros([self.nx])\",\n \"def __init__(self, path: str) -> None:\\n self.root = Node(\\n None, path\\n )\\n self.depth = 0\\n self.add_node(self.root)\",\n \"def convert_path_type(path):\\n if isinstance(path, Path):\\n np_path = path_to_array(path)[:,0]\\n elif isinstance(path, Trajectory):\\n np_path = trajectory_to_array(path)[:,0]\\n elif isinstance(path, (list, np.ndarray)):\\n np_path = np.array(path)\\n else:\\n raise ValueError(\\\"Invalid type for `path` argument. Must be an array-like type.\\\")\\n\\n return np_path\",\n \"def osnorm(self):\\n import os\\n if os.sep=='/' and \\\"\\\\\\\\\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('\\\\\\\\','/' )))\\n elif os.sep=='\\\\\\\\' and \\\"/\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('/','\\\\\\\\' )))\\n else:\\n return self.norm()\",\n \"def intialize_hierarchy_paths(self):\\n\\n leaf_nodes = [node[0] for node in self.tree.out_degree if node[1] == 0]\\n paths = [self.tree_utils.determine_path_to_root([node]) for node in leaf_nodes]\\n\\n # Normalize paths per level in hierarchy - currently the nodes are of increasing number throughout the tree.\\n normalized_paths = [self.tree_utils.normalize_path_from_root_per_level(path) for path in paths]\\n\\n normalized_encoder = {'Root': {'original_key': 0, 'derived_key': 0}}\\n normalized_decoder = { 0: {'original_key': 0, 'value': 'Root'}}\\n decoder = dict(self.tree.nodes(data=\\\"name\\\"))\\n encoder = dict([(value, key) for key, value in decoder.items()])\\n\\n #initiaize encoders\\n for path, normalized_path in zip(paths, normalized_paths):\\n key = path[-1]\\n derived_key = normalized_path[-1]\\n if key in leaf_nodes:\\n normalized_encoder[decoder[key]] = {'original_key': key, 'derived_key': derived_key}\\n normalized_decoder[derived_key] = {'original_key': key, 'value': decoder[key]}\\n\\n oov_path = [[0, 0, 0]]\\n normalized_paths = oov_path + normalized_paths\\n\\n #Align length of paths if necessary\\n longest_path = max([len(path) for path in normalized_paths])\\n\\n # Sort paths ascending\\n sorted_normalized_paths = []\\n for i in range(len(normalized_paths)):\\n found_path = normalized_paths[0]\\n for path in normalized_paths:\\n for found_node, node in zip(found_path,path):\\n if found_node > node:\\n found_path = path\\n break\\n\\n if not (found_path is None):\\n sorted_normalized_paths.append(found_path)\\n normalized_paths.remove(found_path)\\n\\n return normalized_encoder, normalized_decoder, sorted_normalized_paths\",\n \"def get_none_zero_list(line):\\n new_line = []\\n for dummy_i in range(0,len(line)):\\n if line[dummy_i] != 0:\\n new_line.append(line[dummy_i])\\n return new_line\",\n \"def printtable(dict):\\n for dirname in dict:\\n if dict[dirname][2] != 0:\\n sys.stdout.write(\\\"{0:4} {1:4} {2:4} {3}\\\\n\\\".format(dict[dirname][2], dict[dirname][0], dict[dirname][1], dirname))\",\n \"def read_max_angular_momentum(path):\\n with open(path, 'r') as fd:\\n line = fd.readline()\\n if line[0] == '@':\\n # Extended format\\n fd.readline()\\n l = 3\\n pos = 9\\n else:\\n # Simple format:\\n l = 2\\n pos = 7\\n\\n # Sometimes there ar commas, sometimes not:\\n line = fd.readline().replace(',', ' ')\\n\\n occs = [float(f) for f in line.split()[pos:pos + l + 1]]\\n for f in occs:\\n if f > 0.0:\\n return l\\n l -= 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6743314","0.6682493","0.5183608","0.51139396","0.51139396","0.51139396","0.50459886","0.49452367","0.49336353","0.49094725","0.4861403","0.48274943","0.48119223","0.48062816","0.47794765","0.47726187","0.4767327","0.47632933","0.47430956","0.4704121","0.46915564","0.46808624","0.4668043","0.46600246","0.4647841","0.4640059","0.46351165","0.46247953","0.46155828","0.46115085","0.4599557","0.45666465","0.45630538","0.45582542","0.45496866","0.4545896","0.45318523","0.45265162","0.45261645","0.4518331","0.45130572","0.4497321","0.4489022","0.44723892","0.44621518","0.44590864","0.4453727","0.4450475","0.44434693","0.4440004","0.44390807","0.44293237","0.44261694","0.44256437","0.44209683","0.4420218","0.4418616","0.43939805","0.4392476","0.43830863","0.437298","0.43563136","0.43477064","0.43277788","0.43271685","0.43256435","0.43214783","0.43208247","0.43140548","0.43026328","0.42953375","0.42948848","0.42940664","0.42918393","0.42906305","0.42703867","0.42697307","0.42684162","0.42623934","0.42617244","0.4254081","0.42517498","0.42477402","0.424657","0.42450994","0.42390305","0.42375946","0.4235163","0.423078","0.42292738","0.4219898","0.42191407","0.42122215","0.4210337","0.4206064","0.41985694","0.419753","0.41958377","0.4192526","0.41888762"],"string":"[\n \"0.6743314\",\n \"0.6682493\",\n \"0.5183608\",\n \"0.51139396\",\n \"0.51139396\",\n \"0.51139396\",\n \"0.50459886\",\n \"0.49452367\",\n \"0.49336353\",\n \"0.49094725\",\n \"0.4861403\",\n \"0.48274943\",\n \"0.48119223\",\n \"0.48062816\",\n \"0.47794765\",\n \"0.47726187\",\n \"0.4767327\",\n \"0.47632933\",\n \"0.47430956\",\n \"0.4704121\",\n \"0.46915564\",\n \"0.46808624\",\n \"0.4668043\",\n \"0.46600246\",\n \"0.4647841\",\n \"0.4640059\",\n \"0.46351165\",\n \"0.46247953\",\n \"0.46155828\",\n \"0.46115085\",\n \"0.4599557\",\n \"0.45666465\",\n \"0.45630538\",\n \"0.45582542\",\n \"0.45496866\",\n \"0.4545896\",\n \"0.45318523\",\n \"0.45265162\",\n \"0.45261645\",\n \"0.4518331\",\n \"0.45130572\",\n \"0.4497321\",\n \"0.4489022\",\n \"0.44723892\",\n \"0.44621518\",\n \"0.44590864\",\n \"0.4453727\",\n \"0.4450475\",\n \"0.44434693\",\n \"0.4440004\",\n \"0.44390807\",\n \"0.44293237\",\n \"0.44261694\",\n \"0.44256437\",\n \"0.44209683\",\n \"0.4420218\",\n \"0.4418616\",\n \"0.43939805\",\n \"0.4392476\",\n \"0.43830863\",\n \"0.437298\",\n \"0.43563136\",\n \"0.43477064\",\n \"0.43277788\",\n \"0.43271685\",\n \"0.43256435\",\n \"0.43214783\",\n \"0.43208247\",\n \"0.43140548\",\n \"0.43026328\",\n \"0.42953375\",\n \"0.42948848\",\n \"0.42940664\",\n \"0.42918393\",\n \"0.42906305\",\n \"0.42703867\",\n \"0.42697307\",\n \"0.42684162\",\n \"0.42623934\",\n \"0.42617244\",\n \"0.4254081\",\n \"0.42517498\",\n \"0.42477402\",\n \"0.424657\",\n \"0.42450994\",\n \"0.42390305\",\n \"0.42375946\",\n \"0.4235163\",\n \"0.423078\",\n \"0.42292738\",\n \"0.4219898\",\n \"0.42191407\",\n \"0.42122215\",\n \"0.4210337\",\n \"0.4206064\",\n \"0.41985694\",\n \"0.419753\",\n \"0.41958377\",\n \"0.4192526\",\n \"0.41888762\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.890424"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19695,"cells":{"query":{"kind":"string","value":"Formats ``path`` formatted with the current rank values."},"document":{"kind":"string","value":"def _format_path_with_current_rank(path: str) -> str:\n return path.format(\n rank=dist.get_global_rank(),\n local_rank=dist.get_local_rank(),\n node_rank=dist.get_node_rank(),\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _format_path_with_rank_zero(path: str) -> str:\n return path.format(\n rank=0,\n local_rank=0,\n node_rank=0,\n )","def format_path(path):\n if len(path) > 1:\n result = [crayons.yellow(path[0].name)]\n\n previous = path[0]\n for item in path[1:]:\n result.append(' -> ')\n result.append(crayons.yellow(item.name))\n result.append(': Line ')\n result.append(crayons.cyan(str(item.is_imported_from[previous.full_path][0])))\n previous = item\n result.append(' =>> ')\n\n result.append(crayons.magenta(path[0].name))\n return ''.join(str(x) for x in result)\n else:\n return ''","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, int):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def format(self, path=None):\n if not path:\n path = self.path\n print(path, end=\"\")\n FORMATTER.parse_file(path)\n FORMATTER.write_file(path)\n print(\" Done\")","def print_path(self, path, marks = []):\n\n result = ''\n\n for y in range(1, self.height + 1):\n for x in range(1, self.width + 1):\n # Draw top line\n if (x, y - 1) in self.get_reachables(x, y):\n result += '+ '\n else: result += '+--'\n\n result += '+\\n'\n\n for x in range(1, self.width + 1):\n # Draw horizontal passage\n if (x - 1, y) in self.get_reachables(x, y):\n result += ' '\n else: result += '|'\n\n\n if (x, y) in path:\n if (x, y) in path[-1:]:\n result += '(X'\n else: result += ' x'\n elif (x, y) in marks:\n result += ' #'\n else: result += ' '\n\n result += '|\\n'\n\n if y == self.height:\n for x in range(1, self.width + 1):\n # Draw bottom line\n result += '+--'\n\n return result + '+'","def _isolated_path_format(self, path):\n if self._root_dir.is_parent_of(path):\n return '%s:%s' % (\n self._root_dir,\n self._api.path.join(*path.pieces[len(self._root_dir.pieces):])\n )\n else:\n assert path == self._root_dir, \\\n \"isolated path must be equal to or within %s\" % self._root_dir\n return '%s:.' % self._root_dir","def format_path(path):\n return path if path.endswith('/') else path + '/'","def _pretty_path(path: Sequence[BaseField]) -> str:\n # pylint: disable=protected-access\n return \"< \" + \" -> \".join(f\"'{field._resolve_field_name()}' ({type(field).__name__})\" for field in path) + \" >\"","def printPath(path):\r\n result = ''\r\n for i in range(len(path)):\r\n result = result + str(path[i])\r\n if i != len(path) - 1:\r\n result = result + '->'\r\n return result","def printPath(path):\n result =''\n for i in range(len(path)):\n result = result + str(path[i])\n if i != len(path) -1:\n result = result + '->'\n return result","def format_path(path):\n if not path:\n return path\n\n path = re.sub(r'/+', '/', path)\n\n if path == '/':\n return (u\"\" if isinstance(path, unicode) else \"\")\n else:\n return '/' + path.strip('/')","def _formatPath(directoryPath, filePath):\n return directoryPath + \"\\\\\" + filePath","def printPath(path):\n result = ''\n for i in range(len(path)):\n result = result + str(path[i])\n if i != len(path) - 1:\n result = result + '->'\n return result","def _pretty_json_path(self, path):\r\n segments = path.split('.')\r\n\r\n def builder(prev, cur):\r\n if re.match(cur):\r\n return \"{0}[]\".format(prev)\r\n return \"{0}.{1}\".format(prev, cur)\r\n\r\n segments = reduce(builder, segments)\r\n return segments","def format_path(path_string, selection):\n return path_string.format(selection, selection.namespace())","def pretty_path(path):\n return path.replace(REPO_DIR + '/', '')","def _get_local_rank_zero_path(path: Optional[str]) -> str:\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\n paths = dist.all_gather_object(path)\n local_rank_zero_path = paths[local_rank_zero]\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\n return local_rank_zero_path","def path_to_string(path: Path) -> str:\n assert_continuous(path)\n\n pieces = [\"M {} {}\".format(path[0].p0[0], path[0].p0[1])]\n for curve in iter(path): # iter cast not strictly necessary\n piece = \"C {} {} {} {} {} {}\".format(\n int(round(curve.c0[0])), int(round(curve.c0[1])),\n int(round(curve.c1[0])), int(round(curve.c1[1])),\n int(round(curve.p1[0])), int(round(curve.p1[1]))\n )\n pieces.append(piece)\n\n return \" \".join(pieces)","def __str__(self):\n if self._rank is None:\n rank_str = \"\"\n else:\n rank_str = str(self._rank + 1)\n\n if self._file is None:\n file_str = \"\"\n else:\n file_str = chr(self._file + 97)\n\n return file_str + rank_str","def path_str(path):\n\toutput = \"PATH: \"\n\tif path:\n\t\tfor i in path:\n\t\t\toutput += str(i.data) + \" -> \"\n\telse:\n\t\toutput += \"Empty\"\n\treturn output","def corrTIFPath(path, symbol, value): \n reg_expr_search = '(\\%s+)'%str(symbol)\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\n str_replace = str_format.format(int(value))\n\n return re.sub(reg_expr_search, str_replace, path)","def display_path(self, path):\n graph = path.graph\n if not graph:\n return\n for v in sorted(graph.vertices()):\n p = graph.get_vertex_attribute(v, 'xy')\n x, y = to_geometry(p[0]), to_geometry(p[1])\n print('define v{} ellipse 2 2 c_vertex {} {}'.format(v, x, y))\n #print('define v{0}t text {0} 14 white {1} {2}'.format(v, x, y))\n for u, v in graph.edges():\n print('define - link v{} v{} 1 c_edge'.format(u, v))\n # NOTE: this code assumes paths will not move indefinitely\n print('fix /./')","def __str__(self):\n return '{0}'.format(self.path.name[2:])","def path_name(self, path):\r\n ind = path.rfind(\"/\") + 1\r\n return (path[:ind], path[ind:])","def __rank_from_int_to_str(rank: int) -> str:\n return str(rank + 1)","def format_path(s,\n path=None,\n replace_long_filename=False):\n # TODO: could possibly simplify by moving representation logic to FileNode\n replaced_path_name = False\n if path is not None:\n if s.startswith(path):\n replaced_path_name = True\n s = s[len(path)+1:]\n if replace_long_filename:\n head, tail = os.path.split(s)\n name_prefix = head.replace('/','_')\n if '/' in head and len(name_prefix) > 0:\n s = s.replace(name_prefix, '. . . ')\n if replaced_path_name:\n s = \"[DIR]/\" + s\n return \"/\\\\n\".join(s.split('/'))","def addRankToLine(self,cols,rank):\n line = \"\"\n ann = \";RankScore=\" + str(self.family_id) + \":\" + str(rank)\n for c in cols:\n line = line + c\n if cols.index(c) == 7: # 0-based index\n line = line + ann\n if cols.index(c) < len(cols):\n line = line + \"\\t\"\n print(line)","def __fspath__(self):\n return str(self)","def __str__(self):\n return '{0}'.format(self.path.name[8:], )","def __str__(self):\n return super().formatter(\"r301 '{oldPath}', '/{new}'\")","def format_path (in_path):\n return os.path.realpath(os.path.expanduser(in_path))","def composePath(self,splitedPath):\n # 027 It is used anywhere?? Nope!! Remove!\n\n self.debug.printHeader()\n return os.sep.join(splitedPath)","def encodePath(self, path):\n codedPath = []\n for x, y, pickedRewards, holding in path:\n rewardsList = sorted(list(pickedRewards))\n codedPath.append((self.coordToState[(x, y, tuple(rewardsList), holding)], 0))\n return codedPath","def increment_path(path, exist_ok=False):\n path = Path(path) # model_dir(./model)\n if (path.exists() and exist_ok) or (not path.exists()):\n return str(path)\n else:\n dirs = glob.glob(f\"{path}*\") # 지정한 패턴에 맞는 파일을 불러옴\n matches = [re.search(rf\"%s(\\d+)\" % path.stem, d) for d in dirs]\n i = [int(m.groups()[0]) for m in matches if m]\n n = max(i) + 1 if i else 2\n return f\"{path}{n}\" # 뒷 부분에 숫자 + 1을 하여 return","def set_new_path(self, path):\r\n path = Path(path)\r\n self.tree.setRootIndex(self.model.index(str(path)))\r\n # to display correcly / on windows and \\ everywhere else\r\n if platform.system() == \"windows\":\r\n self.path_viewer.setText(path.as_posix())\r\n else:\r\n self.path_viewer.setText(str(path))","def _GeneratePathStr(path):\n return ((len(path) - 1) * ' ') + path[-1] if path else ''","def path(self, *path):\n path = list(filter(None, path))\n path = self.remove_prefix(path)\n items = [self.prefix_] + path\n return self.delimiter.join(items)","def _format(path, arformat):\n if isinstance(path, unicode):\n path = path.encode('utf-8')\n\n if path.startswith('#1/'):\n if not arformat:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name starts with special for format!')\n\n if len(path) >= 16:\n if arformat is None:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name too long for format!')\n\n if ' ' in path:\n if not arformat:\n arformat = AR_FORMAT_BSD\n elif arformat is AR_FORMAT_SIMPLE:\n raise IOError('File name contains forbidden character for format!')\n\n if arformat is None:\n arformat = AR_FORMAT_SIMPLE\n\n return arformat","def format_score(att, hts):\n return str(att) + \"/\" + str(hts)","def increment_path(path, exist_ok=False):\n path = Path(path)\n if (path.exists() and exist_ok) or (not path.exists()):\n return str(path)\n else:\n dirs = glob.glob(f\"{path}*\")\n matches = [re.search(rf\"%s(\\d+)\" % path.stem, d) for d in dirs]\n i = [int(m.groups()[0]) for m in matches if m]\n n = max(i) + 1 if i else 2\n return f\"{path}{n}\"","def getpath(self, path):\n return self._join(path)","def path_to_string(path, separator):\n i = 0\n path_string = \"Path :\" + separator + \"[\"\n while i < len(path):\n if isinstance(path[i], Firewall.Firewall):\n path_string += path[i].hostname\n elif isinstance(path[i], Ip.Ip):\n path_string += path[i].to_string()\n\n if i < len(path) - 1:\n path_string += \",\" + separator\n i += 1\n path_string += \"]\"\n\n return path_string","def format_path(folder, project_name=\"FCCH\", ext=\".xlsx\"):\n timestamp = str(datetime.datetime.now()).replace(\":\", \"_\")\n path = f\"{folder}Project {project_name} Report_{timestamp}.{ext}\"\n return path","def _glob_precip_stats_dirpath(reanalysis):\n fmt = os.path.join( os.path.split( os.path.split( filepath[reanalysis]['path'] )[0] )[0], '????', '??')\n return fmt.format('PRECTOT')","def path_name(self):","def pretty_print_path(self):\n # full path of personData file\n path = self.scenario_path + \"/output/personData_\" + str(self.iteration) + \".csv\"\n\n # path to persons data for excel spreadsheet output\n filename = os.path.split(path)[1]\n directory = os.path.split(os.path.split(path)[0])[1]\n model_run_name = os.path.split(os.path.split(os.path.split(path)[0])[0])[1] # model run name\n filepath_pretty = model_run_name + \"/\" + directory + \"/\" + filename\n\n return filepath_pretty","def __str__(self):\n return \"\\n\".join(self.path)","def printPath(self, simulationIndex):\n path = self.getPath(simulationIndex);\n\n print(\"The path for the\", simulationIndex, \"-th simulation is the following:\")\n print()\n print('\\n'.join('{:.3}'.format(realization) for realization in path))\n print()","def _formatFreq(self, value, pos):\n inv = 999\n if value:\n inv = 1/value\n return \"1/%0.2f\" % inv","def path_format(self):\n return '{}{}'.format(\n self.config['serve_at'],\n self.sub_base_pod_path)","def __str__(self):\n return str(self.rank)","def rank(self, path):\n\t\twords = self._weight.keys()\n\t\tlast_position = dict(zip(words, [None] * len(words)))\n\t\tdistances = dict(zip(words, [dict(last_position) for x in range(len(words))]))\n\n\t\t# scan for words in the file, store distance between words\n\t\tcounter = 0\n\t\tfor line in open(path, 'r'):\n\t\t\ttry:\n\t\t\t\ttokens = line.lower().split()\n\t\t\t\tfor token in tokens:\n\t\t\t\t\tfor word in words:\n\t\t\t\t\t\tif token == word:\n\t\t\t\t\t\t\tfor reference in words:\n\t\t\t\t\t\t\t\tif last_position[reference] is not None:\n\t\t\t\t\t\t\t\t\tdistances[word][reference] = counter - last_position[reference]\n\t\t\t\t\t\t\tlast_position[word] = counter\n\t\t\t\t\tcounter += 1\n\t\t\texcept InvalidArgument:\n\t\t\t\tpass\n\n\t\t# calculate the final score\n\t\tscore = 0.0\n\t\tmax_score = 1.0\n\t\tfor word in words:\n\t\t\tfor reference in words:\n\t\t\t\tdistance = distances[word][reference]\n\t\t\t\tif distance:\n\t\t\t\t\tscore += self._weight[word] * self._weight[reference] / distance\n\t\t\t\t\tmax_score += self._weight[word] * self._weight[reference]\n\t\treturn 100 * math.pow(float(score) / max_score, WISHFUL_THINKING_EXPONENT)","def format_filename(self, data, row):\n return self.filename.format(**self.filename_formatters(data, row))","def print_path(self):\n\n grid = tg.Graph.grid_graph(self.graph.rows,self.graph.cols)\n #tg.draw_grid(self.draw_edges_alt,self.graph.rows,self.graph.cols,grid)\n tg.draw_grid(self.edges,self.graph.rows,self.graph.cols,grid)","def update_path():\n #TODO update path information\n pass","def print_info_for_path(path):\n import json\n import sys\n json.dump(gather_info_for_path(path), sys.stdout)","def quote_path(path):\n return \"[%s]\" % \",\".join(\"'%s'\" % p for p in path)","def _path_to_string(path):\n return '.'.join(path)","def print_path(path, index):\r\n\r\n print(\"Printing trace for puzzle no. {0}\".format(index))\r\n print_puzzle(path[0][0])\r\n for i in range(1, len(path)):\r\n movement = get_move(path[i-1][1], path[i][1])\r\n\r\n moved_tile = get_value(path[i-1][0], path[i][1])\r\n print(i, \": move \", moved_tile, \" \", movement, sep=\"\")\r\n print_puzzle(path[i][0])\r\n print('')","def get_full_path(self):\n return self.path_display","def prettyfypath(path):\n home = os.path.expanduser(\"~/\")\n home_n = pathnormalize(home)\n path_n = pathnormalize(path)\n if path_n.startswith(home_n):\n path = os.path.join(\"~\", os.path.relpath(path, home))\n return path","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?',1)[0]\n path = path.split('#',1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = filter(None, words)\n path = str(port_number)\n for word in words:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if word in (os.curdir, os.pardir): continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def _ref_name_from_path(self, path: str) -> str:\n prefix = \"%s/\" % self._path\n assert path.startswith(prefix)\n return path[len(prefix) :]","def get_rank(ngrams_set, path):\r\n pmi_sorted = sorted(set([float(tup[3]) for tup in ngrams_set]), reverse=True)\r\n pmi_ranking = {}\r\n for i in range(len(pmi_sorted)):\r\n pmi_ranking[pmi_sorted[i]] = i + 1\r\n\r\n tscore_sorted = sorted(set([float(tup[4]) for tup in ngrams_set]), reverse=True)\r\n tscore_ranking = {}\r\n for i in range(len(tscore_sorted)):\r\n tscore_ranking[tscore_sorted[i]] = i + 1\r\n\r\n logDice_sorted = sorted(set([float(tup[2]) for tup in ngrams_set]), reverse=True)\r\n logDice_ranking = {}\r\n for i in range(len(logDice_sorted)):\r\n logDice_ranking[logDice_sorted[i]] = i + 1\r\n\r\n with open(path[:-4] + '_ranked.csv', 'a', encoding='utf-8') as f:\r\n f.write('\\t'.join(['collocation_tags', 'raw_frequency', 'log_Dice', 'PMI', 'tscore',\r\n 'pmi_rank', 'tsc_rank', 'logD_rank', 'summary t-sc + pmi', 'summary logD + pmi']) + '\\n')\r\n for collocation_scored in ngrams_set:\r\n collocation_tags, raw_frequency, log_Dice, PMI, tscore = collocation_scored\r\n tsc_rank, pmi_rank, logd_rank, summarytscpmi, summarylogdpmi = ranked(pmi_ranking, tscore_ranking,\r\n logDice_ranking, tscore, PMI, log_Dice)\r\n f.write('\\t'.join([collocation_tags, raw_frequency,\r\n log_Dice, PMI, tscore, str(pmi_rank), str(tsc_rank), str(logd_rank),\r\n str(summarytscpmi), str(summarylogdpmi)]) + '\\n')","def as_string(path: pathlib.Path) -> str:\n return path.as_posix()","def path(self):\n\t\treturn os.path.join(*self._string_values(limit=4))","def _get_path_category(self, path: Path) -> str:\n if str(path).startswith(\"/\"):\n return \"global\"\n elif str(path).startswith(\"~\"):\n return \"local\"\n\n return \"custom\"","def __pathEdited(self, path):\n if self._completer and not self._completer.popup().isVisible():\n self._completer.setRootPath(Utilities.toNativeSeparators(path))","def path_convert(self):\n pub_path = Exp_msg()\n for i in self.path:\n epoint = Cordi()\n (epoint.x, epoint.y) = i\n pub_path.bliss.append(epoint)\n return(pub_path)","def format_folder_path(folder_path):\n if folder_path[-1] != '/':\n folder_path += '/'\n\n return folder_path","def warriorPath1_2():\n with open('stories/warrior/warrior_path1_2.txt') as f:\n path1_2 = f.read()\n print(path1_2.format(NAME))","def number_title(line, current_levels):\n level = header_level(line)\n if not level > 0:\n raise ValueError(\"level must be > 0: {}\".format(level))\n if len(current_levels) < level:\n while len(current_levels) < level:\n current_levels.append(1)\n else:\n current_levels = current_levels[:level]\n current_levels[-1] += 1\n hashes, title = line.split(\" \", 1)\n numbers = \".\".join([str(i) for i in current_levels]) + \".\"\n new = \" \".join([hashes, numbers, title])\n return new, current_levels","def base_path_format(self):\n return '{}{}'.format(\n self._base_path_format,\n self.sub_base_pod_path)","def get_output_str(self):\n out_str = \"\"\n path_size = len(self.path)\n for i in range(0, path_size):\n out_str += self.path[i].flight_num\n if i == path_size - 1:\n out_str += \"\\n\"\n else:\n out_str += \",\"\n return out_str","def format_path(file: str) -> str:\n return os.path.abspath([file.replace('/', os.path.sep)][0])","def displayPathToPrincess(n, grid):\n for i, row in enumerate(grid):\n if 'p' in row:\n princess = [i, row.index('p')]\n if 'm' in row:\n me = [i, row.index('m')]\n\n r, c = find_path(princess, me)\n path = [r]\n path.append(c)\n return '\\n'.join(path)","def filtrat(line):\n if \"/\" not in line:\n vert = [int(x) for x in line.split(' ')]\n return vert\n else:\n return","def format(self) -> str:","def path(self) -> str:\r\n path = []\r\n path.append(self._item[\"text\"])\r\n current_item: str = self._id\r\n\r\n while (parent := self._tree.parent(current_item)) != \"\":\r\n tree_item = self._tree.item(parent)\r\n path.append(tree_item[\"text\"])\r\n current_item = parent\r\n\r\n return REGISTRY_PATH_SEPARATOR.join(reversed(path))","def displayname(self):\n if self.path.is_dir():\n if (is_uuid(self.path.parts[-1])):\n self.is_uuid_folder = True\n return self.path.name + '/'\n elif is_proj(self.path.parts[-1]):\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\n return self.path.name","def formatPath(a):\n return \"\".join([\" %s \" % cmd + \" \".join([str(p) for p in params]) for cmd, params in a])","def path(self):\n p = self\n\n name = [p.name()]\n offsets = set([p._offset])\n while p.has_parent_key():\n p = p.parent_key()\n if p._offset in offsets:\n name.append(\"[path cycle]\")\n break\n name.append(p.name())\n offsets.add(p._offset)\n return '\\\\'.join(reversed(name))","def path(filename, path):\n\n # If the line is not empty:\n if len(path) > 0:\n # Open the file for appending\n with open(filename, \"a\") as file:\n # Define format string\n write = \"{:.2f},{:.2f},{:d},{:d},{:d},\\n\"\n\n # Find the first point\n first = path[0]\n # Write the first point with \"no extruding\" option\n file.write(write.format(float(first[1][0]), float(first[1][1]), 0, 0, 0))\n\n # For each line in the path\n for i, line in enumerate(path):\n # If line isn't a repeated point\n if True or (line[1][0] != line[2][0]) and (line[1][1] != line[2][1]):\n\n # If the line is somewhere in the middle of the list write it with \"extruding\" option\n if i < len(path) - 1:\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 0, 0))\n\n # If the line is the last of the path, write it with \"extruding\" and \"end of island\" options\n else:\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 1, 0))","def completePath(path):\n return os.getcwd() + convertString(path)","def label(item):\n if isinstance(item, Path):\n if item.is_dir():\n return f\"{Fore.BLUE}{Style.BRIGHT}{item}{Style.RESET_ALL}\"\n return f\"{Style.BRIGHT}{Fore.BLUE}{item.parent}/{Fore.MAGENTA}{item.name}{Style.RESET_ALL}\"\n return str(item)","def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\r\n path_template = get_template_from_path(path)\r\n path = get_path_from_template(path_template, path_type)\r\n return path","def get_unoriented_sorted_str(path):\n all_rc_path = []\n for p in path:\n if p[-1] != \"'\": p = p+\"'\"\n all_rc_path.append(p)\n return \"\".join(sorted(all_rc_path))","def path_child(path):\n return path_to_str(parse_path(path)[1:])","def format_line(cls, reponame, package_counts, percent, repomd):\n return '{0} {1} {2} {3}'.format(reponame, package_counts, percent, repomd)","def reformat(self):\n\t\told_path = os.path.join( self.path, self.init_str )\n\t\tnew_path = os.path.join( self.path, self.reorder() )\n\t\tos.rename(old_path,new_path)","def reconstructPath(came_from, current):\n path = [current]\n while current in came_from:\n current = came_from[current]\n path.append(current)\n print(f\"path: {path}\")\n return path","def preorder_label(tree: Tree, position: Tree.Position, depth: int, path: [int]):\n label = \".\".join([str(j+1) for j in path])\n print(2 * depth * \" \", label, str(position.element()))\n\n path.append(0)\n for c in tree.children(position):\n preorder_label(tree, c, depth + 1, path)\n path[-1] += 1\n path.pop()","def jsonpath_to_xpath(path):\n return '/' + path.replace('.', \"/\")","def normalizePath(path):\n if path == None or len(path) == 0 or path == '/':\n return '/'\n buff = '/' + path if path[0] != '/' else path\n return buff.replace('//', '/')","def warriorPath1_1_2():\n with open('stories/warrior/warrior_path1_1_2.txt') as f:\n path1_1_2 = f.read()\n print(path1_1_2.format(NAME))","def format_project_ref_string(repo_path):\n\n repo = ProjectRepo(repo_path, __project_repo_default_remote__, __project_repo_default_ref__)\n local_commits = repo.new_commits()\n upstream_commits = repo.new_commits(upstream=True)\n no_local_commits = local_commits == 0 or local_commits == '0'\n no_upstream_commits = upstream_commits == 0 or upstream_commits == '0'\n if no_local_commits and no_upstream_commits:\n status = ''\n else:\n local_commits_output = colored('+' + str(local_commits), 'yellow')\n upstream_commits_output = colored('-' + str(upstream_commits), 'red')\n status = '[' + local_commits_output + '/' + upstream_commits_output + ']'\n\n if repo.is_detached():\n current_ref = repo.sha(short=True)\n return colored('(HEAD @ ' + current_ref + ')', 'magenta')\n current_branch = repo.current_branch()\n return colored('(' + current_branch + ')', 'magenta') + status","def ppath(path):\n import glob\n slug, user = os.path.basename(path).split('-')\n \n if os.path.exists(path):\n with open(path) as f: date = f.read().strip()\n subdir = uploads.format(slug=slug, user=user, date=date)\n pyfile = glob.glob(os.path.join(subdir, '*.py'))\n if len(pyfile) == 1: pyfile = pyfile[0] # HACK: assumes single-file submissions\n else: pyfile = None\n dst = os.path.join(subdir, '.autograde')\n else:\n subdir, pyfile, dst = None, None, None\n \n afb = uploads.format(slug=slug, user=user, date='.autofeedback')\n lfb = uploads.format(slug=slug, user=user, date='.latefeedback')\n \n return subdir, pyfile, dst, afb, lfb, slug"],"string":"[\n \"def _format_path_with_rank_zero(path: str) -> str:\\n return path.format(\\n rank=0,\\n local_rank=0,\\n node_rank=0,\\n )\",\n \"def format_path(path):\\n if len(path) > 1:\\n result = [crayons.yellow(path[0].name)]\\n\\n previous = path[0]\\n for item in path[1:]:\\n result.append(' -> ')\\n result.append(crayons.yellow(item.name))\\n result.append(': Line ')\\n result.append(crayons.cyan(str(item.is_imported_from[previous.full_path][0])))\\n previous = item\\n result.append(' =>> ')\\n\\n result.append(crayons.magenta(path[0].name))\\n return ''.join(str(x) for x in result)\\n else:\\n return ''\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, int):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def format(self, path=None):\\n if not path:\\n path = self.path\\n print(path, end=\\\"\\\")\\n FORMATTER.parse_file(path)\\n FORMATTER.write_file(path)\\n print(\\\" Done\\\")\",\n \"def print_path(self, path, marks = []):\\n\\n result = ''\\n\\n for y in range(1, self.height + 1):\\n for x in range(1, self.width + 1):\\n # Draw top line\\n if (x, y - 1) in self.get_reachables(x, y):\\n result += '+ '\\n else: result += '+--'\\n\\n result += '+\\\\n'\\n\\n for x in range(1, self.width + 1):\\n # Draw horizontal passage\\n if (x - 1, y) in self.get_reachables(x, y):\\n result += ' '\\n else: result += '|'\\n\\n\\n if (x, y) in path:\\n if (x, y) in path[-1:]:\\n result += '(X'\\n else: result += ' x'\\n elif (x, y) in marks:\\n result += ' #'\\n else: result += ' '\\n\\n result += '|\\\\n'\\n\\n if y == self.height:\\n for x in range(1, self.width + 1):\\n # Draw bottom line\\n result += '+--'\\n\\n return result + '+'\",\n \"def _isolated_path_format(self, path):\\n if self._root_dir.is_parent_of(path):\\n return '%s:%s' % (\\n self._root_dir,\\n self._api.path.join(*path.pieces[len(self._root_dir.pieces):])\\n )\\n else:\\n assert path == self._root_dir, \\\\\\n \\\"isolated path must be equal to or within %s\\\" % self._root_dir\\n return '%s:.' % self._root_dir\",\n \"def format_path(path):\\n return path if path.endswith('/') else path + '/'\",\n \"def _pretty_path(path: Sequence[BaseField]) -> str:\\n # pylint: disable=protected-access\\n return \\\"< \\\" + \\\" -> \\\".join(f\\\"'{field._resolve_field_name()}' ({type(field).__name__})\\\" for field in path) + \\\" >\\\"\",\n \"def printPath(path):\\r\\n result = ''\\r\\n for i in range(len(path)):\\r\\n result = result + str(path[i])\\r\\n if i != len(path) - 1:\\r\\n result = result + '->'\\r\\n return result\",\n \"def printPath(path):\\n result =''\\n for i in range(len(path)):\\n result = result + str(path[i])\\n if i != len(path) -1:\\n result = result + '->'\\n return result\",\n \"def format_path(path):\\n if not path:\\n return path\\n\\n path = re.sub(r'/+', '/', path)\\n\\n if path == '/':\\n return (u\\\"\\\" if isinstance(path, unicode) else \\\"\\\")\\n else:\\n return '/' + path.strip('/')\",\n \"def _formatPath(directoryPath, filePath):\\n return directoryPath + \\\"\\\\\\\\\\\" + filePath\",\n \"def printPath(path):\\n result = ''\\n for i in range(len(path)):\\n result = result + str(path[i])\\n if i != len(path) - 1:\\n result = result + '->'\\n return result\",\n \"def _pretty_json_path(self, path):\\r\\n segments = path.split('.')\\r\\n\\r\\n def builder(prev, cur):\\r\\n if re.match(cur):\\r\\n return \\\"{0}[]\\\".format(prev)\\r\\n return \\\"{0}.{1}\\\".format(prev, cur)\\r\\n\\r\\n segments = reduce(builder, segments)\\r\\n return segments\",\n \"def format_path(path_string, selection):\\n return path_string.format(selection, selection.namespace())\",\n \"def pretty_path(path):\\n return path.replace(REPO_DIR + '/', '')\",\n \"def _get_local_rank_zero_path(path: Optional[str]) -> str:\\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\\n paths = dist.all_gather_object(path)\\n local_rank_zero_path = paths[local_rank_zero]\\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\\n return local_rank_zero_path\",\n \"def path_to_string(path: Path) -> str:\\n assert_continuous(path)\\n\\n pieces = [\\\"M {} {}\\\".format(path[0].p0[0], path[0].p0[1])]\\n for curve in iter(path): # iter cast not strictly necessary\\n piece = \\\"C {} {} {} {} {} {}\\\".format(\\n int(round(curve.c0[0])), int(round(curve.c0[1])),\\n int(round(curve.c1[0])), int(round(curve.c1[1])),\\n int(round(curve.p1[0])), int(round(curve.p1[1]))\\n )\\n pieces.append(piece)\\n\\n return \\\" \\\".join(pieces)\",\n \"def __str__(self):\\n if self._rank is None:\\n rank_str = \\\"\\\"\\n else:\\n rank_str = str(self._rank + 1)\\n\\n if self._file is None:\\n file_str = \\\"\\\"\\n else:\\n file_str = chr(self._file + 97)\\n\\n return file_str + rank_str\",\n \"def path_str(path):\\n\\toutput = \\\"PATH: \\\"\\n\\tif path:\\n\\t\\tfor i in path:\\n\\t\\t\\toutput += str(i.data) + \\\" -> \\\"\\n\\telse:\\n\\t\\toutput += \\\"Empty\\\"\\n\\treturn output\",\n \"def corrTIFPath(path, symbol, value): \\n reg_expr_search = '(\\\\%s+)'%str(symbol)\\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\\n str_replace = str_format.format(int(value))\\n\\n return re.sub(reg_expr_search, str_replace, path)\",\n \"def display_path(self, path):\\n graph = path.graph\\n if not graph:\\n return\\n for v in sorted(graph.vertices()):\\n p = graph.get_vertex_attribute(v, 'xy')\\n x, y = to_geometry(p[0]), to_geometry(p[1])\\n print('define v{} ellipse 2 2 c_vertex {} {}'.format(v, x, y))\\n #print('define v{0}t text {0} 14 white {1} {2}'.format(v, x, y))\\n for u, v in graph.edges():\\n print('define - link v{} v{} 1 c_edge'.format(u, v))\\n # NOTE: this code assumes paths will not move indefinitely\\n print('fix /./')\",\n \"def __str__(self):\\n return '{0}'.format(self.path.name[2:])\",\n \"def path_name(self, path):\\r\\n ind = path.rfind(\\\"/\\\") + 1\\r\\n return (path[:ind], path[ind:])\",\n \"def __rank_from_int_to_str(rank: int) -> str:\\n return str(rank + 1)\",\n \"def format_path(s,\\n path=None,\\n replace_long_filename=False):\\n # TODO: could possibly simplify by moving representation logic to FileNode\\n replaced_path_name = False\\n if path is not None:\\n if s.startswith(path):\\n replaced_path_name = True\\n s = s[len(path)+1:]\\n if replace_long_filename:\\n head, tail = os.path.split(s)\\n name_prefix = head.replace('/','_')\\n if '/' in head and len(name_prefix) > 0:\\n s = s.replace(name_prefix, '. . . ')\\n if replaced_path_name:\\n s = \\\"[DIR]/\\\" + s\\n return \\\"/\\\\\\\\n\\\".join(s.split('/'))\",\n \"def addRankToLine(self,cols,rank):\\n line = \\\"\\\"\\n ann = \\\";RankScore=\\\" + str(self.family_id) + \\\":\\\" + str(rank)\\n for c in cols:\\n line = line + c\\n if cols.index(c) == 7: # 0-based index\\n line = line + ann\\n if cols.index(c) < len(cols):\\n line = line + \\\"\\\\t\\\"\\n print(line)\",\n \"def __fspath__(self):\\n return str(self)\",\n \"def __str__(self):\\n return '{0}'.format(self.path.name[8:], )\",\n \"def __str__(self):\\n return super().formatter(\\\"r301 '{oldPath}', '/{new}'\\\")\",\n \"def format_path (in_path):\\n return os.path.realpath(os.path.expanduser(in_path))\",\n \"def composePath(self,splitedPath):\\n # 027 It is used anywhere?? Nope!! Remove!\\n\\n self.debug.printHeader()\\n return os.sep.join(splitedPath)\",\n \"def encodePath(self, path):\\n codedPath = []\\n for x, y, pickedRewards, holding in path:\\n rewardsList = sorted(list(pickedRewards))\\n codedPath.append((self.coordToState[(x, y, tuple(rewardsList), holding)], 0))\\n return codedPath\",\n \"def increment_path(path, exist_ok=False):\\n path = Path(path) # model_dir(./model)\\n if (path.exists() and exist_ok) or (not path.exists()):\\n return str(path)\\n else:\\n dirs = glob.glob(f\\\"{path}*\\\") # 지정한 패턴에 맞는 파일을 불러옴\\n matches = [re.search(rf\\\"%s(\\\\d+)\\\" % path.stem, d) for d in dirs]\\n i = [int(m.groups()[0]) for m in matches if m]\\n n = max(i) + 1 if i else 2\\n return f\\\"{path}{n}\\\" # 뒷 부분에 숫자 + 1을 하여 return\",\n \"def set_new_path(self, path):\\r\\n path = Path(path)\\r\\n self.tree.setRootIndex(self.model.index(str(path)))\\r\\n # to display correcly / on windows and \\\\ everywhere else\\r\\n if platform.system() == \\\"windows\\\":\\r\\n self.path_viewer.setText(path.as_posix())\\r\\n else:\\r\\n self.path_viewer.setText(str(path))\",\n \"def _GeneratePathStr(path):\\n return ((len(path) - 1) * ' ') + path[-1] if path else ''\",\n \"def path(self, *path):\\n path = list(filter(None, path))\\n path = self.remove_prefix(path)\\n items = [self.prefix_] + path\\n return self.delimiter.join(items)\",\n \"def _format(path, arformat):\\n if isinstance(path, unicode):\\n path = path.encode('utf-8')\\n\\n if path.startswith('#1/'):\\n if not arformat:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name starts with special for format!')\\n\\n if len(path) >= 16:\\n if arformat is None:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name too long for format!')\\n\\n if ' ' in path:\\n if not arformat:\\n arformat = AR_FORMAT_BSD\\n elif arformat is AR_FORMAT_SIMPLE:\\n raise IOError('File name contains forbidden character for format!')\\n\\n if arformat is None:\\n arformat = AR_FORMAT_SIMPLE\\n\\n return arformat\",\n \"def format_score(att, hts):\\n return str(att) + \\\"/\\\" + str(hts)\",\n \"def increment_path(path, exist_ok=False):\\n path = Path(path)\\n if (path.exists() and exist_ok) or (not path.exists()):\\n return str(path)\\n else:\\n dirs = glob.glob(f\\\"{path}*\\\")\\n matches = [re.search(rf\\\"%s(\\\\d+)\\\" % path.stem, d) for d in dirs]\\n i = [int(m.groups()[0]) for m in matches if m]\\n n = max(i) + 1 if i else 2\\n return f\\\"{path}{n}\\\"\",\n \"def getpath(self, path):\\n return self._join(path)\",\n \"def path_to_string(path, separator):\\n i = 0\\n path_string = \\\"Path :\\\" + separator + \\\"[\\\"\\n while i < len(path):\\n if isinstance(path[i], Firewall.Firewall):\\n path_string += path[i].hostname\\n elif isinstance(path[i], Ip.Ip):\\n path_string += path[i].to_string()\\n\\n if i < len(path) - 1:\\n path_string += \\\",\\\" + separator\\n i += 1\\n path_string += \\\"]\\\"\\n\\n return path_string\",\n \"def format_path(folder, project_name=\\\"FCCH\\\", ext=\\\".xlsx\\\"):\\n timestamp = str(datetime.datetime.now()).replace(\\\":\\\", \\\"_\\\")\\n path = f\\\"{folder}Project {project_name} Report_{timestamp}.{ext}\\\"\\n return path\",\n \"def _glob_precip_stats_dirpath(reanalysis):\\n fmt = os.path.join( os.path.split( os.path.split( filepath[reanalysis]['path'] )[0] )[0], '????', '??')\\n return fmt.format('PRECTOT')\",\n \"def path_name(self):\",\n \"def pretty_print_path(self):\\n # full path of personData file\\n path = self.scenario_path + \\\"/output/personData_\\\" + str(self.iteration) + \\\".csv\\\"\\n\\n # path to persons data for excel spreadsheet output\\n filename = os.path.split(path)[1]\\n directory = os.path.split(os.path.split(path)[0])[1]\\n model_run_name = os.path.split(os.path.split(os.path.split(path)[0])[0])[1] # model run name\\n filepath_pretty = model_run_name + \\\"/\\\" + directory + \\\"/\\\" + filename\\n\\n return filepath_pretty\",\n \"def __str__(self):\\n return \\\"\\\\n\\\".join(self.path)\",\n \"def printPath(self, simulationIndex):\\n path = self.getPath(simulationIndex);\\n\\n print(\\\"The path for the\\\", simulationIndex, \\\"-th simulation is the following:\\\")\\n print()\\n print('\\\\n'.join('{:.3}'.format(realization) for realization in path))\\n print()\",\n \"def _formatFreq(self, value, pos):\\n inv = 999\\n if value:\\n inv = 1/value\\n return \\\"1/%0.2f\\\" % inv\",\n \"def path_format(self):\\n return '{}{}'.format(\\n self.config['serve_at'],\\n self.sub_base_pod_path)\",\n \"def __str__(self):\\n return str(self.rank)\",\n \"def rank(self, path):\\n\\t\\twords = self._weight.keys()\\n\\t\\tlast_position = dict(zip(words, [None] * len(words)))\\n\\t\\tdistances = dict(zip(words, [dict(last_position) for x in range(len(words))]))\\n\\n\\t\\t# scan for words in the file, store distance between words\\n\\t\\tcounter = 0\\n\\t\\tfor line in open(path, 'r'):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttokens = line.lower().split()\\n\\t\\t\\t\\tfor token in tokens:\\n\\t\\t\\t\\t\\tfor word in words:\\n\\t\\t\\t\\t\\t\\tif token == word:\\n\\t\\t\\t\\t\\t\\t\\tfor reference in words:\\n\\t\\t\\t\\t\\t\\t\\t\\tif last_position[reference] is not None:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tdistances[word][reference] = counter - last_position[reference]\\n\\t\\t\\t\\t\\t\\t\\tlast_position[word] = counter\\n\\t\\t\\t\\t\\tcounter += 1\\n\\t\\t\\texcept InvalidArgument:\\n\\t\\t\\t\\tpass\\n\\n\\t\\t# calculate the final score\\n\\t\\tscore = 0.0\\n\\t\\tmax_score = 1.0\\n\\t\\tfor word in words:\\n\\t\\t\\tfor reference in words:\\n\\t\\t\\t\\tdistance = distances[word][reference]\\n\\t\\t\\t\\tif distance:\\n\\t\\t\\t\\t\\tscore += self._weight[word] * self._weight[reference] / distance\\n\\t\\t\\t\\t\\tmax_score += self._weight[word] * self._weight[reference]\\n\\t\\treturn 100 * math.pow(float(score) / max_score, WISHFUL_THINKING_EXPONENT)\",\n \"def format_filename(self, data, row):\\n return self.filename.format(**self.filename_formatters(data, row))\",\n \"def print_path(self):\\n\\n grid = tg.Graph.grid_graph(self.graph.rows,self.graph.cols)\\n #tg.draw_grid(self.draw_edges_alt,self.graph.rows,self.graph.cols,grid)\\n tg.draw_grid(self.edges,self.graph.rows,self.graph.cols,grid)\",\n \"def update_path():\\n #TODO update path information\\n pass\",\n \"def print_info_for_path(path):\\n import json\\n import sys\\n json.dump(gather_info_for_path(path), sys.stdout)\",\n \"def quote_path(path):\\n return \\\"[%s]\\\" % \\\",\\\".join(\\\"'%s'\\\" % p for p in path)\",\n \"def _path_to_string(path):\\n return '.'.join(path)\",\n \"def print_path(path, index):\\r\\n\\r\\n print(\\\"Printing trace for puzzle no. {0}\\\".format(index))\\r\\n print_puzzle(path[0][0])\\r\\n for i in range(1, len(path)):\\r\\n movement = get_move(path[i-1][1], path[i][1])\\r\\n\\r\\n moved_tile = get_value(path[i-1][0], path[i][1])\\r\\n print(i, \\\": move \\\", moved_tile, \\\" \\\", movement, sep=\\\"\\\")\\r\\n print_puzzle(path[i][0])\\r\\n print('')\",\n \"def get_full_path(self):\\n return self.path_display\",\n \"def prettyfypath(path):\\n home = os.path.expanduser(\\\"~/\\\")\\n home_n = pathnormalize(home)\\n path_n = pathnormalize(path)\\n if path_n.startswith(home_n):\\n path = os.path.join(\\\"~\\\", os.path.relpath(path, home))\\n return path\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?',1)[0]\\n path = path.split('#',1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n path = posixpath.normpath(urllib.unquote(path))\\n words = path.split('/')\\n words = filter(None, words)\\n path = str(port_number)\\n for word in words:\\n drive, word = os.path.splitdrive(word)\\n head, word = os.path.split(word)\\n if word in (os.curdir, os.pardir): continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def _ref_name_from_path(self, path: str) -> str:\\n prefix = \\\"%s/\\\" % self._path\\n assert path.startswith(prefix)\\n return path[len(prefix) :]\",\n \"def get_rank(ngrams_set, path):\\r\\n pmi_sorted = sorted(set([float(tup[3]) for tup in ngrams_set]), reverse=True)\\r\\n pmi_ranking = {}\\r\\n for i in range(len(pmi_sorted)):\\r\\n pmi_ranking[pmi_sorted[i]] = i + 1\\r\\n\\r\\n tscore_sorted = sorted(set([float(tup[4]) for tup in ngrams_set]), reverse=True)\\r\\n tscore_ranking = {}\\r\\n for i in range(len(tscore_sorted)):\\r\\n tscore_ranking[tscore_sorted[i]] = i + 1\\r\\n\\r\\n logDice_sorted = sorted(set([float(tup[2]) for tup in ngrams_set]), reverse=True)\\r\\n logDice_ranking = {}\\r\\n for i in range(len(logDice_sorted)):\\r\\n logDice_ranking[logDice_sorted[i]] = i + 1\\r\\n\\r\\n with open(path[:-4] + '_ranked.csv', 'a', encoding='utf-8') as f:\\r\\n f.write('\\\\t'.join(['collocation_tags', 'raw_frequency', 'log_Dice', 'PMI', 'tscore',\\r\\n 'pmi_rank', 'tsc_rank', 'logD_rank', 'summary t-sc + pmi', 'summary logD + pmi']) + '\\\\n')\\r\\n for collocation_scored in ngrams_set:\\r\\n collocation_tags, raw_frequency, log_Dice, PMI, tscore = collocation_scored\\r\\n tsc_rank, pmi_rank, logd_rank, summarytscpmi, summarylogdpmi = ranked(pmi_ranking, tscore_ranking,\\r\\n logDice_ranking, tscore, PMI, log_Dice)\\r\\n f.write('\\\\t'.join([collocation_tags, raw_frequency,\\r\\n log_Dice, PMI, tscore, str(pmi_rank), str(tsc_rank), str(logd_rank),\\r\\n str(summarytscpmi), str(summarylogdpmi)]) + '\\\\n')\",\n \"def as_string(path: pathlib.Path) -> str:\\n return path.as_posix()\",\n \"def path(self):\\n\\t\\treturn os.path.join(*self._string_values(limit=4))\",\n \"def _get_path_category(self, path: Path) -> str:\\n if str(path).startswith(\\\"/\\\"):\\n return \\\"global\\\"\\n elif str(path).startswith(\\\"~\\\"):\\n return \\\"local\\\"\\n\\n return \\\"custom\\\"\",\n \"def __pathEdited(self, path):\\n if self._completer and not self._completer.popup().isVisible():\\n self._completer.setRootPath(Utilities.toNativeSeparators(path))\",\n \"def path_convert(self):\\n pub_path = Exp_msg()\\n for i in self.path:\\n epoint = Cordi()\\n (epoint.x, epoint.y) = i\\n pub_path.bliss.append(epoint)\\n return(pub_path)\",\n \"def format_folder_path(folder_path):\\n if folder_path[-1] != '/':\\n folder_path += '/'\\n\\n return folder_path\",\n \"def warriorPath1_2():\\n with open('stories/warrior/warrior_path1_2.txt') as f:\\n path1_2 = f.read()\\n print(path1_2.format(NAME))\",\n \"def number_title(line, current_levels):\\n level = header_level(line)\\n if not level > 0:\\n raise ValueError(\\\"level must be > 0: {}\\\".format(level))\\n if len(current_levels) < level:\\n while len(current_levels) < level:\\n current_levels.append(1)\\n else:\\n current_levels = current_levels[:level]\\n current_levels[-1] += 1\\n hashes, title = line.split(\\\" \\\", 1)\\n numbers = \\\".\\\".join([str(i) for i in current_levels]) + \\\".\\\"\\n new = \\\" \\\".join([hashes, numbers, title])\\n return new, current_levels\",\n \"def base_path_format(self):\\n return '{}{}'.format(\\n self._base_path_format,\\n self.sub_base_pod_path)\",\n \"def get_output_str(self):\\n out_str = \\\"\\\"\\n path_size = len(self.path)\\n for i in range(0, path_size):\\n out_str += self.path[i].flight_num\\n if i == path_size - 1:\\n out_str += \\\"\\\\n\\\"\\n else:\\n out_str += \\\",\\\"\\n return out_str\",\n \"def format_path(file: str) -> str:\\n return os.path.abspath([file.replace('/', os.path.sep)][0])\",\n \"def displayPathToPrincess(n, grid):\\n for i, row in enumerate(grid):\\n if 'p' in row:\\n princess = [i, row.index('p')]\\n if 'm' in row:\\n me = [i, row.index('m')]\\n\\n r, c = find_path(princess, me)\\n path = [r]\\n path.append(c)\\n return '\\\\n'.join(path)\",\n \"def filtrat(line):\\n if \\\"/\\\" not in line:\\n vert = [int(x) for x in line.split(' ')]\\n return vert\\n else:\\n return\",\n \"def format(self) -> str:\",\n \"def path(self) -> str:\\r\\n path = []\\r\\n path.append(self._item[\\\"text\\\"])\\r\\n current_item: str = self._id\\r\\n\\r\\n while (parent := self._tree.parent(current_item)) != \\\"\\\":\\r\\n tree_item = self._tree.item(parent)\\r\\n path.append(tree_item[\\\"text\\\"])\\r\\n current_item = parent\\r\\n\\r\\n return REGISTRY_PATH_SEPARATOR.join(reversed(path))\",\n \"def displayname(self):\\n if self.path.is_dir():\\n if (is_uuid(self.path.parts[-1])):\\n self.is_uuid_folder = True\\n return self.path.name + '/'\\n elif is_proj(self.path.parts[-1]):\\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\\n return self.path.name\",\n \"def formatPath(a):\\n return \\\"\\\".join([\\\" %s \\\" % cmd + \\\" \\\".join([str(p) for p in params]) for cmd, params in a])\",\n \"def path(self):\\n p = self\\n\\n name = [p.name()]\\n offsets = set([p._offset])\\n while p.has_parent_key():\\n p = p.parent_key()\\n if p._offset in offsets:\\n name.append(\\\"[path cycle]\\\")\\n break\\n name.append(p.name())\\n offsets.add(p._offset)\\n return '\\\\\\\\'.join(reversed(name))\",\n \"def path(filename, path):\\n\\n # If the line is not empty:\\n if len(path) > 0:\\n # Open the file for appending\\n with open(filename, \\\"a\\\") as file:\\n # Define format string\\n write = \\\"{:.2f},{:.2f},{:d},{:d},{:d},\\\\n\\\"\\n\\n # Find the first point\\n first = path[0]\\n # Write the first point with \\\"no extruding\\\" option\\n file.write(write.format(float(first[1][0]), float(first[1][1]), 0, 0, 0))\\n\\n # For each line in the path\\n for i, line in enumerate(path):\\n # If line isn't a repeated point\\n if True or (line[1][0] != line[2][0]) and (line[1][1] != line[2][1]):\\n\\n # If the line is somewhere in the middle of the list write it with \\\"extruding\\\" option\\n if i < len(path) - 1:\\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 0, 0))\\n\\n # If the line is the last of the path, write it with \\\"extruding\\\" and \\\"end of island\\\" options\\n else:\\n file.write(write.format(float(line[2][0]), float(line[2][1]), 1, 1, 0))\",\n \"def completePath(path):\\n return os.getcwd() + convertString(path)\",\n \"def label(item):\\n if isinstance(item, Path):\\n if item.is_dir():\\n return f\\\"{Fore.BLUE}{Style.BRIGHT}{item}{Style.RESET_ALL}\\\"\\n return f\\\"{Style.BRIGHT}{Fore.BLUE}{item.parent}/{Fore.MAGENTA}{item.name}{Style.RESET_ALL}\\\"\\n return str(item)\",\n \"def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\\r\\n path_template = get_template_from_path(path)\\r\\n path = get_path_from_template(path_template, path_type)\\r\\n return path\",\n \"def get_unoriented_sorted_str(path):\\n all_rc_path = []\\n for p in path:\\n if p[-1] != \\\"'\\\": p = p+\\\"'\\\"\\n all_rc_path.append(p)\\n return \\\"\\\".join(sorted(all_rc_path))\",\n \"def path_child(path):\\n return path_to_str(parse_path(path)[1:])\",\n \"def format_line(cls, reponame, package_counts, percent, repomd):\\n return '{0} {1} {2} {3}'.format(reponame, package_counts, percent, repomd)\",\n \"def reformat(self):\\n\\t\\told_path = os.path.join( self.path, self.init_str )\\n\\t\\tnew_path = os.path.join( self.path, self.reorder() )\\n\\t\\tos.rename(old_path,new_path)\",\n \"def reconstructPath(came_from, current):\\n path = [current]\\n while current in came_from:\\n current = came_from[current]\\n path.append(current)\\n print(f\\\"path: {path}\\\")\\n return path\",\n \"def preorder_label(tree: Tree, position: Tree.Position, depth: int, path: [int]):\\n label = \\\".\\\".join([str(j+1) for j in path])\\n print(2 * depth * \\\" \\\", label, str(position.element()))\\n\\n path.append(0)\\n for c in tree.children(position):\\n preorder_label(tree, c, depth + 1, path)\\n path[-1] += 1\\n path.pop()\",\n \"def jsonpath_to_xpath(path):\\n return '/' + path.replace('.', \\\"/\\\")\",\n \"def normalizePath(path):\\n if path == None or len(path) == 0 or path == '/':\\n return '/'\\n buff = '/' + path if path[0] != '/' else path\\n return buff.replace('//', '/')\",\n \"def warriorPath1_1_2():\\n with open('stories/warrior/warrior_path1_1_2.txt') as f:\\n path1_1_2 = f.read()\\n print(path1_1_2.format(NAME))\",\n \"def format_project_ref_string(repo_path):\\n\\n repo = ProjectRepo(repo_path, __project_repo_default_remote__, __project_repo_default_ref__)\\n local_commits = repo.new_commits()\\n upstream_commits = repo.new_commits(upstream=True)\\n no_local_commits = local_commits == 0 or local_commits == '0'\\n no_upstream_commits = upstream_commits == 0 or upstream_commits == '0'\\n if no_local_commits and no_upstream_commits:\\n status = ''\\n else:\\n local_commits_output = colored('+' + str(local_commits), 'yellow')\\n upstream_commits_output = colored('-' + str(upstream_commits), 'red')\\n status = '[' + local_commits_output + '/' + upstream_commits_output + ']'\\n\\n if repo.is_detached():\\n current_ref = repo.sha(short=True)\\n return colored('(HEAD @ ' + current_ref + ')', 'magenta')\\n current_branch = repo.current_branch()\\n return colored('(' + current_branch + ')', 'magenta') + status\",\n \"def ppath(path):\\n import glob\\n slug, user = os.path.basename(path).split('-')\\n \\n if os.path.exists(path):\\n with open(path) as f: date = f.read().strip()\\n subdir = uploads.format(slug=slug, user=user, date=date)\\n pyfile = glob.glob(os.path.join(subdir, '*.py'))\\n if len(pyfile) == 1: pyfile = pyfile[0] # HACK: assumes single-file submissions\\n else: pyfile = None\\n dst = os.path.join(subdir, '.autograde')\\n else:\\n subdir, pyfile, dst = None, None, None\\n \\n afb = uploads.format(slug=slug, user=user, date='.autofeedback')\\n lfb = uploads.format(slug=slug, user=user, date='.latefeedback')\\n \\n return subdir, pyfile, dst, afb, lfb, slug\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7308434","0.6294355","0.6030438","0.58486265","0.58486265","0.58486265","0.5666408","0.5501491","0.54912573","0.5491005","0.5487094","0.5456914","0.5407116","0.53897357","0.5379467","0.5378536","0.53413504","0.5290244","0.5282369","0.5271615","0.52611613","0.52607363","0.52605337","0.522763","0.51928437","0.5153218","0.5116245","0.5106497","0.5103367","0.50885874","0.5087625","0.50651187","0.5051881","0.5047732","0.5014185","0.5010094","0.49990556","0.49817336","0.49740565","0.4961702","0.48930678","0.48391438","0.48252362","0.48186532","0.48109812","0.48057044","0.47945052","0.47910303","0.4784972","0.47837585","0.47812173","0.47728217","0.47642198","0.47625372","0.47505784","0.4747475","0.4742767","0.4738397","0.4733506","0.47326627","0.47205234","0.4719588","0.47169122","0.47161528","0.47102958","0.4704459","0.46999487","0.46999282","0.4696646","0.4691826","0.46850115","0.4683074","0.4673242","0.46662304","0.46652934","0.46611506","0.46604207","0.46602872","0.4648902","0.46410853","0.46398318","0.46221304","0.46191216","0.46177202","0.46137986","0.460885","0.46004322","0.45915654","0.45892537","0.4583368","0.45811537","0.45701757","0.45672566","0.45575333","0.45560887","0.45512113","0.45505318","0.45414165","0.45386794","0.45336744"],"string":"[\n \"0.7308434\",\n \"0.6294355\",\n \"0.6030438\",\n \"0.58486265\",\n \"0.58486265\",\n \"0.58486265\",\n \"0.5666408\",\n \"0.5501491\",\n \"0.54912573\",\n \"0.5491005\",\n \"0.5487094\",\n \"0.5456914\",\n \"0.5407116\",\n \"0.53897357\",\n \"0.5379467\",\n \"0.5378536\",\n \"0.53413504\",\n \"0.5290244\",\n \"0.5282369\",\n \"0.5271615\",\n \"0.52611613\",\n \"0.52607363\",\n \"0.52605337\",\n \"0.522763\",\n \"0.51928437\",\n \"0.5153218\",\n \"0.5116245\",\n \"0.5106497\",\n \"0.5103367\",\n \"0.50885874\",\n \"0.5087625\",\n \"0.50651187\",\n \"0.5051881\",\n \"0.5047732\",\n \"0.5014185\",\n \"0.5010094\",\n \"0.49990556\",\n \"0.49817336\",\n \"0.49740565\",\n \"0.4961702\",\n \"0.48930678\",\n \"0.48391438\",\n \"0.48252362\",\n \"0.48186532\",\n \"0.48109812\",\n \"0.48057044\",\n \"0.47945052\",\n \"0.47910303\",\n \"0.4784972\",\n \"0.47837585\",\n \"0.47812173\",\n \"0.47728217\",\n \"0.47642198\",\n \"0.47625372\",\n \"0.47505784\",\n \"0.4747475\",\n \"0.4742767\",\n \"0.4738397\",\n \"0.4733506\",\n \"0.47326627\",\n \"0.47205234\",\n \"0.4719588\",\n \"0.47169122\",\n \"0.47161528\",\n \"0.47102958\",\n \"0.4704459\",\n \"0.46999487\",\n \"0.46999282\",\n \"0.4696646\",\n \"0.4691826\",\n \"0.46850115\",\n \"0.4683074\",\n \"0.4673242\",\n \"0.46662304\",\n \"0.46652934\",\n \"0.46611506\",\n \"0.46604207\",\n \"0.46602872\",\n \"0.4648902\",\n \"0.46410853\",\n \"0.46398318\",\n \"0.46221304\",\n \"0.46191216\",\n \"0.46177202\",\n \"0.46137986\",\n \"0.460885\",\n \"0.46004322\",\n \"0.45915654\",\n \"0.45892537\",\n \"0.4583368\",\n \"0.45811537\",\n \"0.45701757\",\n \"0.45672566\",\n \"0.45575333\",\n \"0.45560887\",\n \"0.45512113\",\n \"0.45505318\",\n \"0.45414165\",\n \"0.45386794\",\n \"0.45336744\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8522862"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19696,"cells":{"query":{"kind":"string","value":"Load a checkpoint from a local file, URI, or cloud object store into ``state``."},"document":{"kind":"string","value":"def load_checkpoint(\n path: str,\n state: State,\n logger: Logger,\n object_store: Optional[Union[ObjectStore, LoggerDestination]] = None,\n load_weights_only: bool = False,\n strict_model_weights: bool = False,\n progress_bar: bool = True,\n ignore_keys: Optional[Union[List[str], Callable[[Dict], None]]] = None,\n exclude_algorithms: Optional[List[str]] = None,\n algorithm_passes: Optional[List[AlgorithmPass]] = None,\n):\n # Download the checkpoint to the node-local folder\n log.debug('Loading checkpoint at %s', path)\n # Each node gets one unique folder to store checkpoints that is shared amongst all local ranks in that node.\n # If fsdp sharded state_dicts is enabled then EVERY rank gets a unique checkpoint folder.\n needs_unique_checkpoint_folder = state.fsdp_sharded_state_dict_enabled or dist.get_local_rank() == 0\n tempdir_ctx = tempfile.TemporaryDirectory() if needs_unique_checkpoint_folder else contextlib.nullcontext(None)\n with tempdir_ctx as tempdir:\n try:\n # Get the path to the proper checkpoint folder corresponding to the current rank's node.\n # If fsdp_sharded_state_dict_enabled then just use that rank's unique tempdir.\n node_checkpoint_folder = (tempdir\n if state.fsdp_sharded_state_dict_enabled else _get_local_rank_zero_path(tempdir))\n assert node_checkpoint_folder is not None\n\n composer_states_filepath, extracted_checkpoint_folder, extracted_rank_n = download_checkpoint(\n path=path,\n node_checkpoint_folder=node_checkpoint_folder,\n object_store=object_store,\n progress_bar=progress_bar,\n fsdp_sharded_state_dict_enabled=state.fsdp_sharded_state_dict_enabled,\n deepspeed_sharded_checkpoint=is_model_deepspeed(state.model),\n )\n rng_state_dicts = _restore_checkpoint(\n state,\n logger,\n composer_states_filepath,\n extracted_rank_n,\n extracted_checkpoint_folder,\n load_weights_only=load_weights_only,\n strict_model_weights=strict_model_weights,\n ignore_keys=ignore_keys,\n exclude_algorithms=exclude_algorithms,\n algorithm_passes=algorithm_passes,\n )\n finally:\n # Wait for all ranks to finish restoring the checkpoint before releasing the tempdir, since tempdir can\n # be a shared resource between nodes.\n dist.barrier()\n\n log.info('%s loaded from %s', 'Model weights' if load_weights_only else 'Trainer checkpoint', path)\n return rng_state_dicts"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_checkpoint(self, checkpoint: str, **kwargs) -> None:\n with open(checkpoint, \"rb\") as f:\n state = SafePickle.load(f)\n\n state_id = ray.put(state)\n ray.get([worker.set_state.remote(state_id, **kwargs) for worker in self.remote_workers])","def load_from_checkpoint(self, path):\n print(f'# loading trainer state from {path}')\n checkpoint = torch.load(path)\n self.load(checkpoint)","def load_checkpoint(self, checkpoint_path, continue_from_epoch=True):\n print(\"Loading checkpoint: {}\".format(checkpoint_path))\n state = torch.load(checkpoint_path)\n self.model.load_state_dict(state['state_dict'])\n self.optimizer.load_state_dict(state['optim_dict'])\n\n if continue_from_epoch:\n self.epoch = state['epoch']","def load_checkpoint(self, file):\n \"\"\"Load \"\"\"\n chkpnt = torch.load(file)\n self.load_state_dict(chkpnt['model_state_dict'])","def load_checkpoint(self, checkpoint_path=None):\n if checkpoint_path is None:\n checkpoint_path = self.get_latest_path()\n\n if os.path.isfile(checkpoint_path):\n key = 'cuda' if torch.cuda.is_available() else 'cpu'\n checkpoint = torch.load(checkpoint_path, map_location=key)\n self.network.load_state_dict(checkpoint['network'])\n self.network_target.load_state_dict(checkpoint['network_target'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n\n print('checkpoint loaded at {}'.format(checkpoint_path))\n else:\n raise OSError(\"Checkpoint file not found.\")","def load(self):\r\n checkpoint = torch.load(self.checkpoint_path,\r\n map_location=self.device)\r\n self.load_state_dict(checkpoint)\r\n del checkpoint","def load(self):\n checkpoint = torch.load(self.checkpoint_path,\n map_location=self.net.device)\n self.load_state_dict(checkpoint)\n del checkpoint","def load_checkpoint(self, session, model_dir):\n assert self.params.cp_load == True, (\"cp_load must be set to true to load a checkpoint\")\n self.loader.restore(session, model_dir)","def load_checkpoint(checkpoint_path, model, optimizer=None):\n if not os.path.exists(checkpoint_path):\n raise IOError(f\"Checkpoint '{checkpoint_path}' does not exist\")\n\n state = torch.load(checkpoint_path)\n try:\n model.load_state_dict(state['model_state_dict'])\n except BaseException as e:\n print('Failed to do something: ' + str(e))\n\n if optimizer is not None:\n try:\n optimizer.load_state_dict(state['optimizer_state_dict'])\n except Exception as e:\n print(e)\n\n return state","def load_checkpoint(fpath):\n if fpath is None:\n raise ValueError('File path is None')\n if not osp.exists(fpath):\n raise FileNotFoundError('File is not found at \"{}\"'.format(fpath))\n map_location = None if torch.cuda.is_available() else 'cpu'\n try:\n checkpoint = torch.load(fpath, map_location=map_location)\n except UnicodeDecodeError:\n pickle.load = partial(pickle.load, encoding=\"latin1\")\n pickle.Unpickler = partial(pickle.Unpickler, encoding=\"latin1\")\n checkpoint = torch.load(\n fpath, pickle_module=pickle, map_location=map_location\n )\n except Exception:\n print('Unable to load checkpoint from \"{}\"'.format(fpath))\n raise\n return checkpoint","def load_checkpoint(self):\n if self.params.resume_from is not None and os.path.exists(self.params.resume_from):\n try:\n LOG('Loading Checkpoint at %s' % self.params.resume_from)\n ckpt = torch.load(self.params.resume_from)\n self.epoch = ckpt['epoch']\n try:\n self.train_loss = ckpt['train_loss']\n self.val_loss = ckpt['val_loss']\n except:\n self.train_loss = []\n self.val_loss = []\n self.network.load_state_dict(ckpt['state_dict'])\n self.opt.load_state_dict(ckpt['optimizer'])\n LOG('Checkpoint Loaded!')\n LOG('Current Epoch: %d' % self.epoch)\n self.ckpt_flag = True\n except:\n WARNING('Cannot load checkpoint from %s. Start loading pre-trained model......' % self.params.resume_from)\n else:\n WARNING('Checkpoint do not exists. Start loading pre-trained model......')","def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\n return torch.load(checkpoint_path, *args, **kwargs)","def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\n return torch.load(checkpoint_path, *args, **kwargs)","def load_checkpoint(self, checkpoint: Dict[str, OrderedDict]):\n self.model.load_state_dict(checkpoint[\"model_state_dict\"])\n self.optimizer.load_state_dict(checkpoint[\"optimizer_state_dict\"])\n return self","def load_checkpoint(checkpoint_path, model, optimizer=None,\n model_key='model_state_dict', optimizer_key='optimizer_state_dict'):\n if not os.path.exists(checkpoint_path):\n raise IOError(f\"Checkpoint '{checkpoint_path}' does not exist\")\n\n state = torch.load(checkpoint_path, map_location='cpu')\n model.load_state_dict(state[model_key])\n\n if optimizer is not None:\n optimizer.load_state_dict(state[optimizer_key])\n\n return state","def load_model_from_checkpoint(self, path: str):\n ckpt = torch.load(path, map_location='cpu')\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\n self.net_q.head.load_state_dict(ckpt['head'])\n self.net_ps.load_state_dict(ckpt['net_ps'])\n self.net_k.load_state_dict(ckpt['net_k'])\n self.queue.load_state_dict(ckpt['queue'])\n self.optimizer.load_state_dict(ckpt['optimizer'])\n if 'scheduler' in ckpt:\n self.scheduler.load_stae_dict(ckpt['scheduler'])\n self.move_optimizer_states(self.optimizer, self.local_rank)","def load_from_checkpoint(self, chkpt, section=None):\n if section is None:\n section = self.name\n self.load_state_dict(chkpt[section])","def load(self, checkpoint_dir):\n print(\"\\nReading Checkpoints.....\\n\\n\")\n model_dir = \"%s\" % (\"cnn\") # give the model name by label_size\n checkpoint_dir = os.path.join(checkpoint_dir, model_dir)\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n \n # Check the checkpoint is exist\n if ckpt and ckpt.model_checkpoint_path:\n ckpt_path = str(ckpt.model_checkpoint_path) # convert the unicode to string\n self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path))\n print(\"\\n Checkpoint Loading Success! %s\\n\\n\"% ckpt_path)\n else:\n print(\"\\n! Checkpoint Loading Failed \\n\\n\")","def load_checkpoint(self, label):\n model_dir = os.path.join(\n config.results_dir, config.experiment_name, 'checkpoints')\n model_file = os.path.join(model_dir, '{}_net.pth.tar'.format(label))\n\n print(\"Loading model from {}\".format(model_file))\n model_dict = torch.load(model_file)\n\n self.use_cuda = model_dict['use_cuda']\n if self.use_cuda:\n self.net.cuda()\n\n self.net.load_state_dict(model_dict['net_state_dict'])","def load(loadname, checkpoint=None):\n ckpt_dir = \"./models/tf_ckpt_\" + loadname + \"/\"\n if checkpoint is not None:\n status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))\n status.assert_consumed()\n print(\"Loaded checkpoint\")\n else:\n print(\"Not Loading any checkpoint\")\n print(\"Starting training from initial configuration\")","def _load_checkpoint(filename, map_location=None):\n if filename.startswith('modelzoo://'):\n warnings.warn('The URL scheme of \"modelzoo://\" is deprecated, please '\n 'use \"torchvision://\" instead')\n model_urls = get_torchvision_models()\n model_name = filename[11:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('torchvision://'):\n model_urls = get_torchvision_models()\n model_name = filename[14:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('open-mmlab://'):\n model_urls = get_external_models()\n model_name = filename[13:]\n deprecated_urls = get_deprecated_model_names()\n if model_name in deprecated_urls:\n warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '\n f'of open-mmlab://{deprecated_urls[model_name]}')\n model_name = deprecated_urls[model_name]\n model_url = model_urls[model_name]\n # check if is url\n if model_url.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(model_url)\n else:\n filename = osp.join(_get_mmcv_home(), model_url)\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n elif filename.startswith('mmcls://'):\n model_urls = get_mmcls_models()\n model_name = filename[8:]\n checkpoint = load_url_dist(model_urls[model_name])\n checkpoint = _process_mmcls_checkpoint(checkpoint)\n elif filename.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(filename)\n elif filename.startswith('pavi://'):\n model_path = filename[7:]\n checkpoint = load_pavimodel_dist(model_path, map_location=map_location)\n elif filename.startswith('s3://'):\n checkpoint = load_fileclient_dist(\n filename, backend='ceph', map_location=map_location)\n else:\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n return checkpoint","def load_checkpoint(checkpoint, model, optimizer=None):\n if not os.path.exists(checkpoint):\n raise (\"File doesn't exist {}\".format(checkpoint))\n checkpoint = torch.load(checkpoint)\n model.load_state_dict(checkpoint['state_dict'])\n\n if optimizer:\n optimizer.load_state_dict(checkpoint['optim_dict'])\n\n return checkpoint","def load(cls, path):\n print(f'load checkpoint from {path}')\n if torch.cuda.is_available():\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))\n model = torch.load(os.path.join(path, cls.MODEL_NAME))\n else:\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME), map_location=lambda storage, loc: storage)\n model = torch.load(os.path.join(path, cls.MODEL_NAME), map_location=lambda storage, loc: storage)\n \n # model.flatten_parameters() # make RNN parameters contiguous\n optimizer = resume_checkpoint['optimizer']\n return Checkpoint(\n model=model, \n optimizer=optimizer,\n epoch=resume_checkpoint['epoch'],\n path=path\n )","def load_checkpoint(self, checkpoint_dir):\r\n\r\n if not os.path.exists(checkpoint_dir):\r\n raise Exception('No checkpoint directory <%s>' % checkpoint_dir)\r\n\r\n path = os.path.join(checkpoint_dir, 'model.pt')\r\n self.model.load_state_dict(torch.load(path, self.device))\r\n self.update()","def load_from_path(self, checkpoint_dir):\n\n vars = self.save_var_names\n saver = tf.train.Saver(vars)\n\n def load_aux(ckpt_path):\n \"\"\"Helper function to not repeat the same code in the following lines.\"\"\"\n\n ckpt_name = os.path.basename(ckpt_path)\n saver.restore(self.sess, ckpt_path)\n counter = int(next(re.finditer(\"(\\d+)(?!.*\\d)\", ckpt_name)).group(0))\n self.counter = counter\n print(\" [*] Loaded {}\".format(ckpt_name))\n return True, counter\n\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n try:\n if ckpt and ckpt.model_checkpoint_path:\n ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\n return load_aux(os.path.join(checkpoint_dir, ckpt_name))\n else:\n print(\n \" [!] Failed to find a checkpoint within directory {}\".format(\n FLAGS.ckpt_path))\n return False, 0\n except:\n print(\" [!] Failed to find a checkpoint, Exception!\")\n return False, 0","def load_states(self, checkpoint):\n raise NotImplementedError()","def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None):\n checkpoint = _load_checkpoint(filename, map_location)\n if not isinstance(checkpoint, dict):\n raise RuntimeError(f'No state_dict found in checkpoint file {filename}')\n if 'state_dict' in checkpoint:\n state_dict_tmp = checkpoint['state_dict']\n else:\n state_dict_tmp = checkpoint\n state_dict = OrderedDict()\n for k, v in state_dict_tmp.items():\n if k.startswith('module.backbone.'):\n state_dict[k[16:]] = v\n elif k.startswith('module.'):\n state_dict[k[7:]] = v\n elif k.startswith('backbone.'):\n state_dict[k[9:]] = v\n else:\n state_dict[k] = v\n load_state_dict(model, state_dict, strict, logger)\n return checkpoint","def load_ckpt(self, name=None):\r\n name = name if name == 'latest' else \"ckpt_epoch{}\".format(name)\r\n load_path = os.path.join(self.model_dir, \"{}.pth\".format(name))\r\n if not os.path.exists(load_path):\r\n raise ValueError(\"Checkpoint {} not exists.\".format(load_path))\r\n\r\n checkpoint = torch.load(load_path)\r\n print(\"Checkpoint loaded from {}\".format(load_path))\r\n if isinstance(self.net, nn.DataParallel):\r\n self.net.module.load_state_dict(checkpoint['model_state_dict'])\r\n else:\r\n self.net.load_state_dict(checkpoint['model_state_dict'])\r\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\r\n self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\r\n self.clock.restore_checkpoint(checkpoint['clock'])","def load_checkpoint(tag, params, model):\r\n file_name = os.path.join(\"saved_models\", params.path, tag + \".pt\")\r\n logger.info(\"Load checkpoint from %s\" % file_name)\r\n if os.path.exists(file_name):\r\n checkpoint = torch.load(file_name, map_location='cpu')\r\n params.training_id = checkpoint['training_id']\r\n logger.info(checkpoint['training_id'])\r\n model.global_step = checkpoint['global_step']\r\n model.load_state_dict(checkpoint['model'])\r\n for i, optimizer in enumerate(model.optimizers):\r\n optimizer.load_state_dict(checkpoint['optimizers'][i])\r\n else:\r\n raise Exception(\"Checkpoint not found.\")","def load_state(self, fname: str) -> _TrainingState:\n training_state = None\n with open(fname, \"rb\") as fp:\n training_state = pickle.load(fp)\n return training_state","def load_checkpoint(filename: str) -> CheckpointData:\n return torch.load(filename)","def load_checkpoint(checkpoint, model, optimizer=None):\n if not os.path.exists(checkpoint):\n raise (\"File doesn't exist {}\".format(checkpoint))\n checkpoint = torch.load(checkpoint, map_location=torch.device('cpu'))\n model.load_state_dict(checkpoint['state_dict'])\n\n if optimizer:\n optimizer.load_state_dict(checkpoint['optim_dict'])\n\n return checkpoint","def load_checkpoint(checkpoint_path, model, optimizer=None):\n if not os.path.exists(checkpoint_path):\n raise IOError(\"Checkpoint '{}' does not exist\".format(checkpoint_path))\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else 'cpu')\n state = torch.load(checkpoint_path, map_location=\"cuda:0\")\n model.load_state_dict(state['model_state_dict'])\n\n if optimizer is not None:\n optimizer.load_state_dict(state['optimizer_state_dict'])\n\n return state","def load(self, filename):\n\n c = torch.load(filename)\n\n if type(c) is dict:\n sd = c['state_dict']\n self.net.load_state_dict(sd)\n if 'monitors' in c: # Remove the branching eventually\n self.monitors = c['monitors']\n else:\n self.monitors = {'loss_train': c['train_monitor'], 'loss_val': c['val_monitor'],\n 'accu_train': MetricHistory(), 'accu_val': MetricHistory()}\n if 'optimizer' in c: # Remove the branching eventually\n self.optimizer.load_state_dict(c['optimizer'])\n else:\n raise RuntimeError('Unsupported checkpoint. (Not a dict)')\n\n self.parent = filename\n self.last_checkpoint = filename\n self.start_epoch = self.monitors['loss_train'].num_epochs","def load_checkpoint(filename, from_gpu=True):\r\n assert os.path.exists(filename)\r\n if from_gpu:\r\n return torch.load(filename)\r\n else:\r\n return torch.load(filename, map_location=lambda storage, loc: storage)","def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\n print(\"Load checkpoint from: {}\".format(path))\n state_dict, optimizer_state = _load(path)\n\n model.load_dict(state_dict)\n if not reset_optimizer and optimizer is not None:\n if optimizer_state is not None:\n print(\"[loading] Load optimizer state from {}\".format(path))\n optimizer.load(optimizer_state)\n\n return model","def load_checkpoint(model, save_path):\n model.load_state_dict(torch.load(save_path))","def load_checkpoint(self):\n checkpoin_path = self.get_checkpoint_path()\n _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)","def load(self, path):\n\n filename = self.__path(path, prefix=None)\n chkpt = th.load(filename, map_location=\"cpu\") # TODO: check behavior\n\n if self.model is not None and chkpt[\"model\"] is not None:\n log.debug(\"Loading model state dict\")\n self.model.load_state_dict(chkpt[\"model\"])\n\n if \"optimizers\" in chkpt.keys():\n if self.optimizers is not None and chkpt[\"optimizers\"] is not None:\n try:\n for opt, state in zip(self.optimizers,\n chkpt[\"optimizers\"]):\n log.debug(\"Loading optimizers state dict for %s\", opt)\n opt.load_state_dict(state)\n except:\n # We do not raise an error here, e.g. in case the user simply\n # changes optimizer\n log.warning(\"Could not load optimizer state dicts, \"\n \"starting from scratch\")\n\n if \"schedulers\" in chkpt.keys():\n if self.schedulers is not None and chkpt[\"schedulers\"] is not None:\n try:\n for s, state in zip(self.schedulers,\n chkpt[\"schedulers\"]):\n log.debug(\"Loading scheduler state dict for %s\", s)\n s.load_state_dict(state)\n except:\n log.warning(\"Could not load scheduler state dicts, \"\n \"starting from scratch\")\n\n log.debug(\"Loaded checkpoint \\\"{}\\\"\".format(filename))\n return tuple(chkpt[k] for k in [\"extras\", \"meta\"])","def load_actor(self, checkpoint):\n \n model = torch.load(checkpoint)\n self.actor_local.load_state_dict(model)","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def load_checkpoint(cfg, args):\n checkpoint_iteration = args.checkpoint\n bucket = connect_to_bucket(args.bucket)\n # load actual checkpoint\n if not os.path.isdir(cfg.OUTPUT_DIR):\n os.mkdir(cfg.OUTPUT_DIR)\n blob = bucket.blob(cfg.OUTPUT_DIR + \"/model_\" + str(checkpoint_iteration) + \".pth\")\n blob.download_to_filename(cfg.OUTPUT_DIR + \"/model_\" + str(checkpoint_iteration) + \".pth\")\n if args.resume:\n # also write last checkpoint file for when --resume statement, model gets checkpoint name from this file\n with open(cfg.OUTPUT_DIR + \"/last_checkpoint\", \"w\") as file:\n file.write(\"model_\" + str(checkpoint_iteration) + \".pth\")\n # return statement not clean, but useful for inference code\n return checkpoint_iteration, bucket","def loadCheckpoint(self, file):\n if isinstance(file, str):\n with open(file, 'rb') as f:\n self.context.loadCheckpoint(f.read())\n else:\n self.context.loadCheckpoint(file.read())","def load_model(self, ckpt_fn):\n checkpoint = torch.load(ckpt_fn)\n self.net_.load_state_dict(checkpoint[\"model\"])\n self.optimizer_.load_state_dict(checkpoint[\"optimizer\"])\n self.epoch_ = checkpoint[\"epoch\"]\n self.global_step_ = checkpoint[\"global_step\"]\n self.model_samples_ = deque(checkpoint[\"model_samples\"])\n self.sampler.load_state_dict(checkpoint[\"sampler_state\"])\n self.ais_loss.load_state_dict(checkpoint[\"ais_state\"])\n self.replay_prob = checkpoint[\"replay_prob\"]\n self.max_replay = checkpoint[\"max_replay\"]","def load_checkpoint(checkpoint, model, optimizer=None):\n model_state_dict, optimizer_state_dict = torch.load(checkpoint)\n model.load_state_dict(model_state_dict)\n\n if optimizer is not None:\n optimizer.load_state_dict(optimizer_state_dict)","def load(self, checkpoint_dir=None):\n\n if checkpoint_dir is None:\n checkpoint_dir = FLAGS.checkpoint_dir\n\n checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)\n\n return self.load_from_path(checkpoint_dir)","def _load_checkpoint(cls, model: DeviceAwareModule, checkpoint_path: Path,\n key_in_state_dict: str, use_gpu: bool) -> int:\n logging.info(f\"Loading checkpoint {checkpoint_path}\")\n checkpoint = ModelAndInfo.read_checkpoint(checkpoint_path, use_gpu)\n\n try:\n state_dict = checkpoint[key_in_state_dict]\n except KeyError:\n logging.error(f\"Key {key_in_state_dict} not found in checkpoint\")\n return False\n\n if isinstance(model, torch.nn.DataParallel):\n result = model.module.load_state_dict(state_dict, strict=False)\n else:\n result = model.load_state_dict(state_dict, strict=False)\n\n if result.missing_keys:\n logging.warning(f\"Missing keys in model checkpoint: {result.missing_keys}\")\n if result.unexpected_keys:\n logging.warning(f\"Unexpected keys in model checkpoint: {result.unexpected_keys}\")\n\n return checkpoint[ModelAndInfo.EPOCH_KEY]","def load_checkpoint(checkpoint_file: pl.Path) -> Optional[Dict[str, Any]]:\n if checkpoint_file.exists():\n logger.info(f\"Loading checkpoint {checkpoint_file}.\")\n checkpoint = torch.load(str(checkpoint_file))\n logger.info(f\"Done loading checkpoint from epoch {checkpoint['epoch']}.\")\n else:\n logger.warning(f\"No {checkpoint_file} checkpoint file found. Starting normal.\")\n return checkpoint","def restore(self, checkpoint_path):\n start_time = time.time()\n latest_checkpoint = train_util.get_latest_chekpoint(checkpoint_path)\n if latest_checkpoint is not None:\n checkpoint = tf.train.Checkpoint(model=self)\n checkpoint.restore(latest_checkpoint).expect_partial()\n logging.info('Loaded checkpoint %s', latest_checkpoint)\n logging.info('Loading model took %.1f seconds', time.time() - start_time)\n else:\n logging.info('Could not find checkpoint to load at %s, skipping.',\n checkpoint_path)","def load_checkpoint(path: str, use_cuda: bool = True) -> dict:\n assert os.path.isfile(path), \"Checkpoint %s not found\" % path\n checkpoint = torch.load(path, map_location=\"cuda\" if use_cuda else \"cpu\")\n return checkpoint","def load_checkpoint(self, model):\n print(f\"load model {self.save_model_path}\")\n model.load_state_dict(torch.load(self.save_model_path))","def load_model_states_from_checkpoint(model, filename, tag, from_gpu=True):\r\n assert os.path.exists(filename)\r\n if from_gpu:\r\n checkpoint = torch.load(filename)\r\n else:\r\n checkpoint = torch.load(filename, map_location=lambda storage, loc: storage)\r\n model.load_state_dict(checkpoint[tag])","def load_checkpoint(self, path: str = '', train: bool = True) -> int:\n\n if not path:\n dir_ = os.path.dirname(os.path.realpath('__file__'))\n path = os.path.join(dir_, 'model.pt')\n\n try:\n ckpt = torch.load(path)\n except FileNotFoundError:\n return 0\n else:\n print('Loaded model at epoch: ', end='')\n\n self.load_state_dict(ckpt['model_state_dict'])\n self.actor_optimizer.load_state_dict(ckpt['ac_optim_dict'])\n self.critic_optimizer.load_state_dict(ckpt['critic_optim_dict'])\n epoch = ckpt['epoch']\n\n print(epoch)\n\n if not train:\n self.eval()\n else:\n self.train()\n\n return epoch","def load_checkpoint(args, trainer, epoch_itr):\n os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)\n checkpoint_path = os.path.join(args.save_dir, 'checkpoints', args.restore_file)\n if os.path.isfile(checkpoint_path):\n extra_state = trainer.load_checkpoint(checkpoint_path)\n if extra_state is not None:\n # replay train iterator to match checkpoint\n epoch_itr.load_state_dict(extra_state['train_iterator'])\n\n print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(\n checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))\n\n trainer.lr_step(epoch_itr.epoch)\n trainer.lr_step_update(trainer.get_num_updates())\n if 'best' in extra_state:\n save_checkpoint.best = extra_state['best']","def _load(checkpoint_path):\n state_dict, optimizer_state = dg.load_persistables(dirname=checkpoint_path)\n return state_dict, optimizer_state","def load_from_checkpoint(results_dir, load_fn, args):\n ckpt_dir = os.path.join(results_dir, \"tb\", \"version_0\", \"checkpoints\")\n files = os.listdir(ckpt_dir)\n assert len(files) > 0, \"Checkpoint directory is empty\"\n ckpt_path = os.path.join(ckpt_dir, files[-1])\n model = load_fn(checkpoint_path=ckpt_path, args=args)\n return model","def load_checkpoint(filepath):\n checkpoint = torch.load(filepath)\n \n arch = checkpoint['arch']\n if arch == 'vgg':\n model = models.vgg16(pretrained=True)\n elif arch == 'densenet':\n model = models.densenet121(pretrained=True) \n \n model.class_to_idx = checkpoint['class_to_idx']\n model.classifier = checkpoint['classifier']\n model.classifier.load_sate_dict = checkpoint['classifier_state_dict']\n model.optimizer = checkpoint['optimizer_state_dict']\n model.input_size = checkpoint['input_size']\n model.output_size = checkpoint['output_size']\n \n return model","def parse_checkpoint(checkpoint_path):\n with gfile.Open(checkpoint_path, 'rb') as fp:\n raw_contents = fp.read()\n if raw_contents.startswith(b'model_checkpoint_path'):\n raise ValueError(\n 'Attempting to restore a TensorFlow checkpoint as a native T5X '\n f'checkpoint. Path: {checkpoint_path}')\n return serialization.msgpack_restore(raw_contents)","def load_pretrained(model, fname, optimizer=None):\n if os.path.isfile(fname):\n print(\"=> loading checkpoint '{}'\".format(fname))\n checkpoint = torch.load(fname)\n model.load_state_dict(checkpoint['state_dict'])\n if optimizer is not None:\n optimizer.load_state_dict(checkpoint['optimizer'])\n return model, optimizer, checkpoint['epoch']\n else:\n return model\n else:\n raise Exception(\"=> no checkpoint found at '{}'\".format(fname))","def load_checkpoint(checkpoint_dir, epoch, iteration):\n path = opj(checkpoint_dir, str(epoch) + '.' + str(iteration) + '.ckpt')\n if not os.path.isfile(path):\n raise Exception(\"Checkpoint in epoch %d doesn't exist :sob:\" % epoch)\n\n checkpoint = torch.load(path)\n start_epoch = checkpoint['epoch']\n state_dict = checkpoint['state_dict']\n start_iteration = checkpoint['iteration']\n\n assert iteration == start_iteration\n return start_epoch, start_iteration, state_dict","def loadCheckpoint(self, time_stamp, data_only=False, load_memory=True):\n state_filename = os.path.join(self.saving_dir, 'checkpoint.' + time_stamp + '.pth.tar')\n mem_filename = os.path.join(self.saving_dir, 'memory.' + time_stamp + '.pth.tar')\n\n print 'loading checkpoint: ', time_stamp\n checkpoint = torch.load(state_filename)\n if data_only:\n self.episode_rewards = checkpoint['episode_rewards']\n self.episode_lengths = checkpoint['episode_lengths']\n return\n\n self.episodes_done = checkpoint['episode']\n self.steps_done = checkpoint['steps']\n self.episode_rewards = checkpoint['episode_rewards']\n self.episode_lengths = checkpoint['episode_lengths']\n\n self.policy_net.load_state_dict(checkpoint['policy_state_dict'])\n self.policy_net = self.policy_net.to(self.device)\n self.policy_net.train()\n\n self.target_net.load_state_dict(checkpoint['policy_state_dict'])\n self.target_net = self.target_net.to(self.device)\n self.target_net.eval()\n\n self.optimizer = optim.Adam(self.policy_net.parameters())\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n if load_memory:\n memory = torch.load(mem_filename)\n self.memory = memory['memory']","def load_model(self, checkpoint):\n print(f'Load parameters from {checkpoint}')\n epoch = re.match(r\"[0-9]*\", os.path.basename(checkpoint)).group(0)\n self.epoch_i = int(epoch)\n self.model.load_state_dict(torch.load(checkpoint))","def load_model(model, checkpoint_path: str): \r\n checkpoint = torch.load(checkpoint_path)\r\n model.load_state_dict(checkpoint['model'])\r\n epoch = checkpoint['epoch']\r\n print('Loaded model from {}, epoch {}'.format(checkpoint_path, epoch))","def restore(self, checkpoint_path: str):\r\n raise NotImplementedError","def load_ckp(checkpoint_fpath, model, optimizer, device):\n\n checkpoint = torch.load(checkpoint_fpath,map_location=device)\n model.load_state_dict(checkpoint['state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer'])\n valid_acc = checkpoint['valid_acc'] \n return model, optimizer, checkpoint['epoch'], valid_acc","def LoadProgramState(self, restored_checkpoint_path=None, sess=None):\n pass","def load_checkpoint(checkpoint_path):\n flat_checkpoint_dict = flatten_checkpoint(\n parse_checkpoint(checkpoint_path), keep_empty_nodes=True)\n return flat_checkpoint_dict","def load_checkpoint(ckpt_path):\n checkpoint = None\n if ckpt_path:\n logger.info(\"Loading checkpoint from %s\" % ckpt_path)\n checkpoint = torch.load(ckpt_path, map_location=torch.device(\"cpu\"))\n\n if \"model\" in checkpoint.keys():\n # This preserves backward-compat for models using customed layernorm\n def fix_key(s):\n s = re.sub(\n r\"(.*)\\.layer_norm((_\\d+)?)\\.b_2\", r\"\\1.layer_norm\\2.bias\", s\n )\n s = re.sub(\n r\"(.*)\\.layer_norm((_\\d+)?)\\.a_2\", r\"\\1.layer_norm\\2.weight\", s\n )\n return s\n\n checkpoint[\"model\"] = {\n fix_key(k): v for k, v in checkpoint[\"model\"].items()\n }\n # Force add_ffnbias to True if bias found in model w_1 keys\n for key in checkpoint[\"model\"].keys():\n if \"w_1.bias\" in key:\n checkpoint[\"opt\"].add_ffnbias = True\n\n if not hasattr(checkpoint[\"opt\"], \"num_kv\"):\n checkpoint[\"opt\"].num_kv = 0\n if not hasattr(checkpoint[\"opt\"], \"add_ffnbias\"):\n checkpoint[\"opt\"].add_ffnbias = False\n if not hasattr(checkpoint[\"opt\"], \"parallel_residual\"):\n checkpoint[\"opt\"].parallel_residual = False\n if not hasattr(checkpoint[\"opt\"], \"shared_layer_norm\"):\n checkpoint[\"opt\"].shared_layer_norm = False\n if not hasattr(checkpoint[\"opt\"], \"use_ckpting\"):\n checkpoint[\"opt\"].use_ckpting = []\n if not hasattr(checkpoint[\"opt\"], \"relative_positions_buckets\"):\n checkpoint[\"opt\"].relative_positions_buckets = 0\n if not hasattr(checkpoint[\"opt\"], \"parallel_mode\"):\n checkpoint[\"opt\"].parallel_mode = \"data_parallel\"\n if not hasattr(checkpoint[\"opt\"], \"norm_eps\"):\n checkpoint[\"opt\"].norm_eps = 1e-6\n\n # fix v2 compatibility\n if \"generator\" in checkpoint.keys() and checkpoint[\"generator\"]:\n if \"0.weight\" in checkpoint[\"generator\"]:\n checkpoint[\"generator\"][\"weight\"] = checkpoint[\"generator\"].pop(\n \"0.weight\"\n )\n if \"0.bias\" in checkpoint[\"generator\"]:\n checkpoint[\"generator\"][\"bias\"] = checkpoint[\"generator\"].pop(\"0.bias\")\n # end of patch for backward compatibility\n\n return checkpoint","def load(cls, model_path: str, sample_shape: tuple = None,\n checkpoint: str = None, **kwargs):","def load_pretrained_model(self, load_from):\n print(\"loading model from %s\\n\" % (load_from))\n try:\n if self.use_cuda:\n pretrained_dict = torch.load(load_from)\n else:\n pretrained_dict = torch.load(load_from, map_location='cpu')\n\n model_dict = self.online_net.state_dict()\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}\n model_dict.update(pretrained_dict)\n self.online_net.load_state_dict(model_dict)\n print(\"The loaded parameters are:\")\n keys = [key for key in pretrained_dict]\n print(\", \".join(keys))\n print(\"--------------------------\")\n except Exception as e:\n print(\"Failed to load checkpoint...\")\n print(e)","def load_epoch_checkpoint(self, directory, epoch):\n chkpnt = torch.load(directory / f\"chkpnt_epoch{epoch:04d}.pth\")\n self.load_state_dict(chkpnt['model_state_dict'])","def load_checkpoint(self, checkpoint_filepath=None, verbose=True):\n if checkpoint_filepath:\n print('loading', checkpoint_filepath, flush=True)\n optimistic_restore(self._session, checkpoint_filepath, verbose=verbose)\n return True\n else:\n checkpoints = retrieve_all_checkpoints(self._checkpoints_path) + retrieve_all_checkpoints(self._recovery_checkpoints_path)\n\n if checkpoints:\n last_checkpoint = sorted(checkpoints)[-1][1]\n print('loading', last_checkpoint, flush=True)\n optimistic_restore(self._session, last_checkpoint, verbose=verbose)\n return True\n else:\n print('nothing to restore. no checkpoint found.', flush=True)\n return False","def load_checkpoint(self, filename):\n results = Future.gen_list([\n self.call_async(rank, '_async_load_checkpoint', filename=filename)\n for rank in range(self.num_replicas)\n ])\n extra_state = results[0]\n return extra_state","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.mnt_best = checkpoint['monitor_best']\n\n # load model params from checkpoint.\n if checkpoint['config']['name'] != self.config['name']:\n self.logger.warning(\n 'Warning: Architecture configuration given in config file is different from that of checkpoint. ' + \\\n 'This may yield an exception while state_dict is being loaded.')\n self.model.load_state_dict(checkpoint['model_state_dict'])\n\n # load optimizer state from checkpoint only when optimizer type is not changed. \n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\n self.logger.warning('Warning: Optimizer type given in config file is different from that of checkpoint. ' + \\\n 'Optimizer parameters not being resumed.')\n self.optimizer.load_state_dict(checkpoint['model_optimizer'])\n\n # load scheduler state from checkpoint only when scheduler type is not changed\n if checkpoint['config']['scheduler']['type'] != self.config['scheduler']['type']:\n self.logger.warning('Warning: Scheduler type given in config file is different from that of checkpoint. ' + \\\n 'Scheduler parameters not being resumed.')\n self.scheduler.load_state_dict(checkpoint['model_scheduler'])\n\n self.train_logger = checkpoint['logger']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.monitor_best = checkpoint['monitor_best']\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n self.train_logger = checkpoint['logger']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def load(self, from_path):\n with open(from_path, 'rb') as f:\n self.load_state_dict(torch.load(f))","def load(self, from_path):\n with open(from_path, 'rb') as f:\n self.load_state_dict(torch.load(f))","def restore_checkpoint(self, checkpoint_id, name, path=''):\n\n\t\tself.log.debug(\"restoring Notebook %s from checkpoint %s\", name, checkpoint_id)\n\t\tnb_path = self._get_os_path(name, path)\n\t\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\n\n\t\tif not key_exists(self.bucket, cp_path):\n\t\t\tself.log.debug(\"checkpoint file does not exist: %s\", cp_path)\n\t\t\traise web.HTTPError(404,\n\t\t\t\tu'Notebook checkpoint does not exist: %s-%s' % (name, checkpoint_id)\n\t\t\t)\n\t\t# ensure notebook is readable (never restore from an unreadable notebook)\n\t\tkey = self.bucket.get_key(cp_path)\n\t\tnb = current.reads(key.get_contents_as_string(), u'json')\n\t\tself._copy(cp_path, nb_path)\n\t\tself.log.debug(\"copying %s -> %s\", cp_path, nb_path)","def _resume_checkpoint(self, resume_path):\n resume_path = str(resume_path)\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n\n # load architecture params from checkpoint.\n if checkpoint['config']['model'] != self.config['model']:\n self.logger.warning(\"Warning: Architecture configuration given in config file is different from that of \"\n \"checkpoint. This may yield an exception while state_dict is being loaded.\")\n self.model.load_state_dict(checkpoint['state_dict'])\n\n # load optimizer state from checkpoint only when optimizer type is not changed.\n if checkpoint['config']['trainer']['optimizer']['type'] != self.config['trainer']['optimizer']['type']:\n self.logger.warning(\"Warning: Optimizer type given in config file is different from that of checkpoint. \"\n \"Optimizer parameters not being resumed.\")\n else:\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n\n self.logger.info(\"Checkpoint loaded. Resume from epoch {}\".format(self.start_epoch))","def try_and_init_from(self, path):\n log.info(\"Loading weights from foreign checkpoint {}\".format(path))\n if not os.path.exists(path):\n raise ValueError(\"Checkpoint {} does not exist\".format(path))\n\n chkpt = th.load(path, map_location=th.device(\"cpu\"))\n if \"model\" not in chkpt.keys() or chkpt[\"model\"] is None:\n raise ValueError(\"{} has no model saved\".format(path))\n\n mdl = chkpt[\"model\"]\n for n, p in self.model.named_parameters():\n if n in mdl:\n p2 = mdl[n]\n if p2.shape != p.shape:\n log.warning(\"Parameter {} ignored, checkpoint size does not match: {}, should be {}\".format(n, p2.shape, p.shape))\n continue\n log.debug(\"Parameter {} copied\".format(n))\n p.data.copy_(p2)\n else:\n log.warning(\"Parameter {} ignored, not found in source checkpoint.\".format(n))\n\n log.info(\"Weights loaded from foreign checkpoint {}\".format(path))","def restore_checkpoint(checkpoint_path: str,\n train_state: Optional[TrainState] = None,\n assert_exist: bool = False,\n step: Optional[int] = None) -> Tuple[TrainState, int]:\n if assert_exist:\n glob_path = os.path.join(checkpoint_path, 'checkpoint_*')\n if not gfile.glob(glob_path):\n raise ValueError('No checkpoint for the pretrained model is found in: '\n f'{checkpoint_path}')\n if train_state is None:\n raise ValueError('Please use `restore_pretrained_checkpoint` for loading'\n 'a checkpoint without providing a Scenic TrainState.')\n train_state = checkpoints.restore_checkpoint(checkpoint_path, train_state,\n step)\n return train_state, int(train_state.global_step)","def load_training_checkpoint(args, model, PATH, ckpt_id):\r\n logger = args.logger\r\n _, checkpoint_state_dict = model.network.load_checkpoint(PATH, ckpt_id)\r\n epoch = checkpoint_state_dict['epoch']\r\n last_global_step = checkpoint_state_dict['last_global_step']\r\n last_global_data_samples = checkpoint_state_dict[\r\n 'last_global_data_samples']\r\n del checkpoint_state_dict\r\n return (epoch, last_global_step, last_global_data_samples)","def checkpoint(state, file_name='./saves/checkpoint.pth.tar'):\n\n assert isinstance(state, dict)\n assert isinstance(file_name, str)\n\n torch.save(state, file_name)","def load_state(path: str, state: T) -> T:\n logging.info(\"Restoring state %s\", path)\n with file_open(path, \"rb\") as fp:\n state_new = serialization.from_bytes(state, fp.read())\n tree = jax.tree_util.tree_structure(state)\n leaves_new = jax.tree_util.tree_leaves(state_new)\n return jax.tree_util.tree_unflatten(tree, leaves_new)","def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:\n # TODO: move to CheckpointIO\n torch.cuda.empty_cache()\n checkpoint_path = inject_model_parallel_rank(checkpoint_path)\n return self.checkpoint_io.load_checkpoint(checkpoint_path)","def load_checkpoint(model,\n filename,\n map_location='cpu',\n strict=False,\n logger=None):\n checkpoint = _load_checkpoint(filename, map_location)\n # OrderedDict is a subclass of dict\n if not isinstance(checkpoint, dict):\n raise RuntimeError(\n f'No state_dict found in checkpoint file {filename}')\n # get state_dict from checkpoint\n if 'state_dict' in checkpoint:\n state_dict = checkpoint['state_dict']\n elif 'model' in checkpoint:\n state_dict = checkpoint['model']\n elif 'module' in checkpoint:\n state_dict = checkpoint['module']\n else:\n state_dict = checkpoint\n # strip prefix of state_dict\n if list(state_dict.keys())[0].startswith('module.'):\n state_dict = {k[7:]: v for k, v in state_dict.items()}\n\n # for MoBY, load model of online branch\n if sorted(list(state_dict.keys()))[0].startswith('encoder'):\n state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}\n\n # reshape absolute position embedding for Swin\n if state_dict.get('absolute_pos_embed') is not None:\n absolute_pos_embed = state_dict['absolute_pos_embed']\n N1, L, C1 = absolute_pos_embed.size()\n N2, C2, H, W = model.absolute_pos_embed.size()\n if N1 != N2 or C1 != C2 or L != H*W:\n logger.warning(\"Error in loading absolute_pos_embed, pass\")\n else:\n state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)\n\n rank, _ = get_dist_info()\n if \"rel_pos_bias.relative_position_bias_table\" in state_dict:\n if rank == 0:\n print(\"Expand the shared relative position embedding to each layers. \")\n num_layers = model.get_num_layers()\n rel_pos_bias = state_dict[\"rel_pos_bias.relative_position_bias_table\"]\n for i in range(num_layers):\n state_dict[\"blocks.%d.attn.relative_position_bias_table\" % i] = rel_pos_bias.clone()\n\n state_dict.pop(\"rel_pos_bias.relative_position_bias_table\")\n\n all_keys = list(state_dict.keys())\n for key in all_keys:\n if \"relative_position_index\" in key:\n state_dict.pop(key)\n\n if \"relative_position_bias_table\" in key:\n rel_pos_bias = state_dict[key]\n src_num_pos, num_attn_heads = rel_pos_bias.size()\n dst_num_pos, _ = model.state_dict()[key].size()\n dst_patch_shape = model.patch_embed.patch_shape\n if dst_patch_shape[0] != dst_patch_shape[1]:\n raise NotImplementedError()\n num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)\n src_size = int((src_num_pos - num_extra_tokens) ** 0.5)\n dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)\n if src_size != dst_size:\n if rank == 0:\n print(\"Position interpolate for %s from %dx%d to %dx%d\" % (\n key, src_size, src_size, dst_size, dst_size))\n extra_tokens = rel_pos_bias[-num_extra_tokens:, :]\n rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]\n\n def geometric_progression(a, r, n):\n return a * (1.0 - r ** n) / (1.0 - r)\n\n left, right = 1.01, 1.5\n while right - left > 1e-6:\n q = (left + right) / 2.0\n gp = geometric_progression(1, q, src_size // 2)\n if gp > dst_size // 2:\n right = q\n else:\n left = q\n\n # if q > 1.13492:\n # q = 1.13492\n\n dis = []\n cur = 1\n for i in range(src_size // 2):\n dis.append(cur)\n cur += q ** (i + 1)\n\n r_ids = [-_ for _ in reversed(dis)]\n\n x = r_ids + [0] + dis\n y = r_ids + [0] + dis\n\n t = dst_size // 2.0\n dx = np.arange(-t, t + 0.1, 1.0)\n dy = np.arange(-t, t + 0.1, 1.0)\n if rank == 0:\n print(\"x = {}\".format(x))\n print(\"dx = {}\".format(dx))\n\n all_rel_pos_bias = []\n\n for i in range(num_attn_heads):\n z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()\n f = interpolate.interp2d(x, y, z, kind='cubic')\n all_rel_pos_bias.append(\n torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))\n\n rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)\n new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)\n state_dict[key] = new_rel_pos_bias\n\n if 'pos_embed' in state_dict:\n pos_embed_checkpoint = state_dict['pos_embed']\n embedding_size = pos_embed_checkpoint.shape[-1]\n num_patches = model.patch_embed.num_patches\n num_extra_tokens = model.pos_embed.shape[-2] - num_patches\n # height (== width) for the checkpoint position embedding\n orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\n # height (== width) for the new position embedding\n new_size = int(num_patches ** 0.5)\n # class_token and dist_token are kept unchanged\n if orig_size != new_size:\n if rank == 0:\n print(\"Position interpolate from %dx%d to %dx%d\" % (orig_size, orig_size, new_size, new_size))\n extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\n # only the position tokens are interpolated\n pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\n pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)\n pos_tokens = torch.nn.functional.interpolate(\n pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\n state_dict['pos_embed'] = new_pos_embed\n\n # interpolate position bias table if needed\n relative_position_bias_table_keys = [k for k in state_dict.keys() if \"relative_position_bias_table\" in k]\n for table_key in relative_position_bias_table_keys:\n table_pretrained = state_dict[table_key]\n table_current = model.state_dict()[table_key]\n L1, nH1 = table_pretrained.size()\n L2, nH2 = table_current.size()\n if nH1 != nH2:\n logger.warning(f\"Error in loading {table_key}, pass\")\n else:\n if L1 != L2:\n S1 = int(L1 ** 0.5)\n S2 = int(L2 ** 0.5)\n table_pretrained_resized = F.interpolate(\n table_pretrained.permute(1, 0).view(1, nH1, S1, S1),\n size=(S2, S2), mode='bicubic')\n state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)\n\n # load state_dict\n load_state_dict(model, state_dict, strict, logger)\n return checkpoint","def load_state(self, state):\n raise NotImplemented","def load(self):\n logger.debug('Loading state from file %s', self.file_path)\n\n with open(self.file_path, 'rb') as f:\n self.data = pickle.load(f)","def load(self, path):\n checkpoint = torch.load(path, map_location=torch.device(\"cpu\"))\n self.load_state_dict(checkpoint[\"state_dict\"])\n self.on_epoch_start(checkpoint[\"epoch\"])\n self.logger.info(\"Loaded controller network from %s\", path)","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.monitor_best = checkpoint['monitor_best']\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n if self.with_cuda:\n for state in self.optimizer.state.values():\n for k, v in state.items():\n if isinstance(v, torch.Tensor):\n state[k] = v.cuda(torch.device('cuda'))\n self.train_logger = checkpoint['logger']\n #self.config = checkpoint['config']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def _restore_checkpoint(self, checkpoint_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(checkpoint_path))\n checkpoint = torch.load(checkpoint_path)\n pretrained_dict = checkpoint['state_dict'] # 预训练模型的state_dict\n model_dict = self.model.state_dict() # 当前用来训练的模型的state_dict\n \n if pretrained_dict.keys() != model_dict.keys(): # 需要进行参数的适配\n print('Parameters are inconsistant, adapting model parameters ...')\n # 在合并前(update),需要去除pretrained_dict一些不需要的参数\n # 只含有识别分支的预训练模型参数字典中键'0', '1'对应全模型参数字典中键'2', '3'\n pretrained_dict['2'] = transfer_state_dict(pretrained_dict['0'], model_dict['2'])\n pretrained_dict['3'] = transfer_state_dict(pretrained_dict['1'], model_dict['3'])\n del pretrained_dict['0'] # 把原本预训练模型中的键值对删掉，以免错误地更新当前模型中的键值对\n del pretrained_dict['1']\n model_dict.update(pretrained_dict) # 更新(合并)模型的参数\n self.model.load_state_dict(model_dict)\n else:\n print('Parameters are consistant, load state dict directly ...')\n self.model.load_state_dict(checkpoint['state_dict'])\n # self.optimizer.load_state_dict(checkpoint['optimizer'])\n # if self.with_cuda:\n # for state in self.optimizer.state.values():\n # for k, v in state.items():\n # if isinstance(v, torch.Tensor):\n # state[k] = v.cuda(self.device)","def restore(self, checkpoint):\n raise NotImplementedError","def load_checkpoint(self, session: tf.Session):\n if not _load_checkpoint(session, str(self.info.checkpoint_path)):\n tf_util.init_variables(session)\n return False\n else:\n return True","def restore(self,\n sess,\n ckpt_file,\n ckpt_type):\n if ckpt_file is None:\n raise FileNotFoundError(\"checkpoint file doesn't exist\")\n \n if ckpt_type == \"debug\":\n self.ckpt_debug_saver.restore(sess, ckpt_file)\n elif ckpt_type == \"epoch\":\n self.ckpt_epoch_saver.restore(sess, ckpt_file)\n else:\n raise ValueError(\"unsupported checkpoint type {0}\".format(ckpt_type))","def load(self, path):\n self.load_state_dict(torch.load(path))","def load(self, path):\n self.load_state_dict(torch.load(path))","def resume(self, checkpoint):\n model_dict = paddle.load(checkpoint)\n self.model.set_state_dict(model_dict)","def read_checkpoint(out_fp):\r\n pickle_fh = open(out_fp, \"r\")\r\n return pickle.load(pickle_fh)","def from_previous_ckpt(network, checkpoint):\n if os.path.exists(checkpoint):\n if os.path.isfile(checkpoint):\n try:\n network.load_state_dict(torch.load(checkpoint))\n print(f\"Loaded weights from {checkpoint}\")\n except RuntimeError:\n print(f\"{checkpoint} is a invalid checkpoint\")\n print(\"Neglecting this checkpoint.\")\n if os.path.isdir(checkpoint):\n epoch = 0\n file_name = None\n for ckpt in os.listdir(checkpoint):\n if ckpt[-4:] == '.pth':\n try:\n tmp_int_list = re.findall('[0-9]+', ckpt)\n ckpt_epoch = int(tmp_int_list[-1])\n except IndexError:\n ckpt_epoch = 0\n if ckpt_epoch >= epoch:\n epoch = ckpt_epoch\n file_name = os.path.join(checkpoint, ckpt)\n\n if file_name is None:\n print(f\"No checkpoint found in {checkpoint}\")\n print(\"Neglecting this checkpoint.\")\n else:\n try:\n network.load_state_dict(torch.load(file_name))\n print(f\"Loaded weights from {file_name}\")\n except (RuntimeError):\n print(f\"{file_name} is a invalid checkpoint\")\n print(\"Neglecting this checkpoint.\")\n\n else:\n print(f\"the checkpoint path: {checkpoint} doesn't exist.\")\n print(\"Neglecting this checkpoint.\")"],"string":"[\n \"def load_checkpoint(self, checkpoint: str, **kwargs) -> None:\\n with open(checkpoint, \\\"rb\\\") as f:\\n state = SafePickle.load(f)\\n\\n state_id = ray.put(state)\\n ray.get([worker.set_state.remote(state_id, **kwargs) for worker in self.remote_workers])\",\n \"def load_from_checkpoint(self, path):\\n print(f'# loading trainer state from {path}')\\n checkpoint = torch.load(path)\\n self.load(checkpoint)\",\n \"def load_checkpoint(self, checkpoint_path, continue_from_epoch=True):\\n print(\\\"Loading checkpoint: {}\\\".format(checkpoint_path))\\n state = torch.load(checkpoint_path)\\n self.model.load_state_dict(state['state_dict'])\\n self.optimizer.load_state_dict(state['optim_dict'])\\n\\n if continue_from_epoch:\\n self.epoch = state['epoch']\",\n \"def load_checkpoint(self, file):\\n \\\"\\\"\\\"Load \\\"\\\"\\\"\\n chkpnt = torch.load(file)\\n self.load_state_dict(chkpnt['model_state_dict'])\",\n \"def load_checkpoint(self, checkpoint_path=None):\\n if checkpoint_path is None:\\n checkpoint_path = self.get_latest_path()\\n\\n if os.path.isfile(checkpoint_path):\\n key = 'cuda' if torch.cuda.is_available() else 'cpu'\\n checkpoint = torch.load(checkpoint_path, map_location=key)\\n self.network.load_state_dict(checkpoint['network'])\\n self.network_target.load_state_dict(checkpoint['network_target'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n\\n print('checkpoint loaded at {}'.format(checkpoint_path))\\n else:\\n raise OSError(\\\"Checkpoint file not found.\\\")\",\n \"def load(self):\\r\\n checkpoint = torch.load(self.checkpoint_path,\\r\\n map_location=self.device)\\r\\n self.load_state_dict(checkpoint)\\r\\n del checkpoint\",\n \"def load(self):\\n checkpoint = torch.load(self.checkpoint_path,\\n map_location=self.net.device)\\n self.load_state_dict(checkpoint)\\n del checkpoint\",\n \"def load_checkpoint(self, session, model_dir):\\n assert self.params.cp_load == True, (\\\"cp_load must be set to true to load a checkpoint\\\")\\n self.loader.restore(session, model_dir)\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(f\\\"Checkpoint '{checkpoint_path}' does not exist\\\")\\n\\n state = torch.load(checkpoint_path)\\n try:\\n model.load_state_dict(state['model_state_dict'])\\n except BaseException as e:\\n print('Failed to do something: ' + str(e))\\n\\n if optimizer is not None:\\n try:\\n optimizer.load_state_dict(state['optimizer_state_dict'])\\n except Exception as e:\\n print(e)\\n\\n return state\",\n \"def load_checkpoint(fpath):\\n if fpath is None:\\n raise ValueError('File path is None')\\n if not osp.exists(fpath):\\n raise FileNotFoundError('File is not found at \\\"{}\\\"'.format(fpath))\\n map_location = None if torch.cuda.is_available() else 'cpu'\\n try:\\n checkpoint = torch.load(fpath, map_location=map_location)\\n except UnicodeDecodeError:\\n pickle.load = partial(pickle.load, encoding=\\\"latin1\\\")\\n pickle.Unpickler = partial(pickle.Unpickler, encoding=\\\"latin1\\\")\\n checkpoint = torch.load(\\n fpath, pickle_module=pickle, map_location=map_location\\n )\\n except Exception:\\n print('Unable to load checkpoint from \\\"{}\\\"'.format(fpath))\\n raise\\n return checkpoint\",\n \"def load_checkpoint(self):\\n if self.params.resume_from is not None and os.path.exists(self.params.resume_from):\\n try:\\n LOG('Loading Checkpoint at %s' % self.params.resume_from)\\n ckpt = torch.load(self.params.resume_from)\\n self.epoch = ckpt['epoch']\\n try:\\n self.train_loss = ckpt['train_loss']\\n self.val_loss = ckpt['val_loss']\\n except:\\n self.train_loss = []\\n self.val_loss = []\\n self.network.load_state_dict(ckpt['state_dict'])\\n self.opt.load_state_dict(ckpt['optimizer'])\\n LOG('Checkpoint Loaded!')\\n LOG('Current Epoch: %d' % self.epoch)\\n self.ckpt_flag = True\\n except:\\n WARNING('Cannot load checkpoint from %s. Start loading pre-trained model......' % self.params.resume_from)\\n else:\\n WARNING('Checkpoint do not exists. Start loading pre-trained model......')\",\n \"def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\\n return torch.load(checkpoint_path, *args, **kwargs)\",\n \"def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\\n return torch.load(checkpoint_path, *args, **kwargs)\",\n \"def load_checkpoint(self, checkpoint: Dict[str, OrderedDict]):\\n self.model.load_state_dict(checkpoint[\\\"model_state_dict\\\"])\\n self.optimizer.load_state_dict(checkpoint[\\\"optimizer_state_dict\\\"])\\n return self\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None,\\n model_key='model_state_dict', optimizer_key='optimizer_state_dict'):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(f\\\"Checkpoint '{checkpoint_path}' does not exist\\\")\\n\\n state = torch.load(checkpoint_path, map_location='cpu')\\n model.load_state_dict(state[model_key])\\n\\n if optimizer is not None:\\n optimizer.load_state_dict(state[optimizer_key])\\n\\n return state\",\n \"def load_model_from_checkpoint(self, path: str):\\n ckpt = torch.load(path, map_location='cpu')\\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\\n self.net_q.head.load_state_dict(ckpt['head'])\\n self.net_ps.load_state_dict(ckpt['net_ps'])\\n self.net_k.load_state_dict(ckpt['net_k'])\\n self.queue.load_state_dict(ckpt['queue'])\\n self.optimizer.load_state_dict(ckpt['optimizer'])\\n if 'scheduler' in ckpt:\\n self.scheduler.load_stae_dict(ckpt['scheduler'])\\n self.move_optimizer_states(self.optimizer, self.local_rank)\",\n \"def load_from_checkpoint(self, chkpt, section=None):\\n if section is None:\\n section = self.name\\n self.load_state_dict(chkpt[section])\",\n \"def load(self, checkpoint_dir):\\n print(\\\"\\\\nReading Checkpoints.....\\\\n\\\\n\\\")\\n model_dir = \\\"%s\\\" % (\\\"cnn\\\") # give the model name by label_size\\n checkpoint_dir = os.path.join(checkpoint_dir, model_dir)\\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\\n \\n # Check the checkpoint is exist\\n if ckpt and ckpt.model_checkpoint_path:\\n ckpt_path = str(ckpt.model_checkpoint_path) # convert the unicode to string\\n self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path))\\n print(\\\"\\\\n Checkpoint Loading Success! %s\\\\n\\\\n\\\"% ckpt_path)\\n else:\\n print(\\\"\\\\n! Checkpoint Loading Failed \\\\n\\\\n\\\")\",\n \"def load_checkpoint(self, label):\\n model_dir = os.path.join(\\n config.results_dir, config.experiment_name, 'checkpoints')\\n model_file = os.path.join(model_dir, '{}_net.pth.tar'.format(label))\\n\\n print(\\\"Loading model from {}\\\".format(model_file))\\n model_dict = torch.load(model_file)\\n\\n self.use_cuda = model_dict['use_cuda']\\n if self.use_cuda:\\n self.net.cuda()\\n\\n self.net.load_state_dict(model_dict['net_state_dict'])\",\n \"def load(loadname, checkpoint=None):\\n ckpt_dir = \\\"./models/tf_ckpt_\\\" + loadname + \\\"/\\\"\\n if checkpoint is not None:\\n status = checkpoint.restore(tf.train.latest_checkpoint(ckpt_dir))\\n status.assert_consumed()\\n print(\\\"Loaded checkpoint\\\")\\n else:\\n print(\\\"Not Loading any checkpoint\\\")\\n print(\\\"Starting training from initial configuration\\\")\",\n \"def _load_checkpoint(filename, map_location=None):\\n if filename.startswith('modelzoo://'):\\n warnings.warn('The URL scheme of \\\"modelzoo://\\\" is deprecated, please '\\n 'use \\\"torchvision://\\\" instead')\\n model_urls = get_torchvision_models()\\n model_name = filename[11:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n elif filename.startswith('torchvision://'):\\n model_urls = get_torchvision_models()\\n model_name = filename[14:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n elif filename.startswith('open-mmlab://'):\\n model_urls = get_external_models()\\n model_name = filename[13:]\\n deprecated_urls = get_deprecated_model_names()\\n if model_name in deprecated_urls:\\n warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '\\n f'of open-mmlab://{deprecated_urls[model_name]}')\\n model_name = deprecated_urls[model_name]\\n model_url = model_urls[model_name]\\n # check if is url\\n if model_url.startswith(('http://', 'https://')):\\n checkpoint = load_url_dist(model_url)\\n else:\\n filename = osp.join(_get_mmcv_home(), model_url)\\n if not osp.isfile(filename):\\n raise IOError(f'{filename} is not a checkpoint file')\\n checkpoint = torch.load(filename, map_location=map_location)\\n elif filename.startswith('mmcls://'):\\n model_urls = get_mmcls_models()\\n model_name = filename[8:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n checkpoint = _process_mmcls_checkpoint(checkpoint)\\n elif filename.startswith(('http://', 'https://')):\\n checkpoint = load_url_dist(filename)\\n elif filename.startswith('pavi://'):\\n model_path = filename[7:]\\n checkpoint = load_pavimodel_dist(model_path, map_location=map_location)\\n elif filename.startswith('s3://'):\\n checkpoint = load_fileclient_dist(\\n filename, backend='ceph', map_location=map_location)\\n else:\\n if not osp.isfile(filename):\\n raise IOError(f'{filename} is not a checkpoint file')\\n checkpoint = torch.load(filename, map_location=map_location)\\n return checkpoint\",\n \"def load_checkpoint(checkpoint, model, optimizer=None):\\n if not os.path.exists(checkpoint):\\n raise (\\\"File doesn't exist {}\\\".format(checkpoint))\\n checkpoint = torch.load(checkpoint)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if optimizer:\\n optimizer.load_state_dict(checkpoint['optim_dict'])\\n\\n return checkpoint\",\n \"def load(cls, path):\\n print(f'load checkpoint from {path}')\\n if torch.cuda.is_available():\\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))\\n model = torch.load(os.path.join(path, cls.MODEL_NAME))\\n else:\\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME), map_location=lambda storage, loc: storage)\\n model = torch.load(os.path.join(path, cls.MODEL_NAME), map_location=lambda storage, loc: storage)\\n \\n # model.flatten_parameters() # make RNN parameters contiguous\\n optimizer = resume_checkpoint['optimizer']\\n return Checkpoint(\\n model=model, \\n optimizer=optimizer,\\n epoch=resume_checkpoint['epoch'],\\n path=path\\n )\",\n \"def load_checkpoint(self, checkpoint_dir):\\r\\n\\r\\n if not os.path.exists(checkpoint_dir):\\r\\n raise Exception('No checkpoint directory <%s>' % checkpoint_dir)\\r\\n\\r\\n path = os.path.join(checkpoint_dir, 'model.pt')\\r\\n self.model.load_state_dict(torch.load(path, self.device))\\r\\n self.update()\",\n \"def load_from_path(self, checkpoint_dir):\\n\\n vars = self.save_var_names\\n saver = tf.train.Saver(vars)\\n\\n def load_aux(ckpt_path):\\n \\\"\\\"\\\"Helper function to not repeat the same code in the following lines.\\\"\\\"\\\"\\n\\n ckpt_name = os.path.basename(ckpt_path)\\n saver.restore(self.sess, ckpt_path)\\n counter = int(next(re.finditer(\\\"(\\\\d+)(?!.*\\\\d)\\\", ckpt_name)).group(0))\\n self.counter = counter\\n print(\\\" [*] Loaded {}\\\".format(ckpt_name))\\n return True, counter\\n\\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\\n try:\\n if ckpt and ckpt.model_checkpoint_path:\\n ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\\n return load_aux(os.path.join(checkpoint_dir, ckpt_name))\\n else:\\n print(\\n \\\" [!] Failed to find a checkpoint within directory {}\\\".format(\\n FLAGS.ckpt_path))\\n return False, 0\\n except:\\n print(\\\" [!] Failed to find a checkpoint, Exception!\\\")\\n return False, 0\",\n \"def load_states(self, checkpoint):\\n raise NotImplementedError()\",\n \"def load_checkpoint(model, filename, map_location='cpu', strict=False, logger=None):\\n checkpoint = _load_checkpoint(filename, map_location)\\n if not isinstance(checkpoint, dict):\\n raise RuntimeError(f'No state_dict found in checkpoint file {filename}')\\n if 'state_dict' in checkpoint:\\n state_dict_tmp = checkpoint['state_dict']\\n else:\\n state_dict_tmp = checkpoint\\n state_dict = OrderedDict()\\n for k, v in state_dict_tmp.items():\\n if k.startswith('module.backbone.'):\\n state_dict[k[16:]] = v\\n elif k.startswith('module.'):\\n state_dict[k[7:]] = v\\n elif k.startswith('backbone.'):\\n state_dict[k[9:]] = v\\n else:\\n state_dict[k] = v\\n load_state_dict(model, state_dict, strict, logger)\\n return checkpoint\",\n \"def load_ckpt(self, name=None):\\r\\n name = name if name == 'latest' else \\\"ckpt_epoch{}\\\".format(name)\\r\\n load_path = os.path.join(self.model_dir, \\\"{}.pth\\\".format(name))\\r\\n if not os.path.exists(load_path):\\r\\n raise ValueError(\\\"Checkpoint {} not exists.\\\".format(load_path))\\r\\n\\r\\n checkpoint = torch.load(load_path)\\r\\n print(\\\"Checkpoint loaded from {}\\\".format(load_path))\\r\\n if isinstance(self.net, nn.DataParallel):\\r\\n self.net.module.load_state_dict(checkpoint['model_state_dict'])\\r\\n else:\\r\\n self.net.load_state_dict(checkpoint['model_state_dict'])\\r\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\r\\n self.scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\\r\\n self.clock.restore_checkpoint(checkpoint['clock'])\",\n \"def load_checkpoint(tag, params, model):\\r\\n file_name = os.path.join(\\\"saved_models\\\", params.path, tag + \\\".pt\\\")\\r\\n logger.info(\\\"Load checkpoint from %s\\\" % file_name)\\r\\n if os.path.exists(file_name):\\r\\n checkpoint = torch.load(file_name, map_location='cpu')\\r\\n params.training_id = checkpoint['training_id']\\r\\n logger.info(checkpoint['training_id'])\\r\\n model.global_step = checkpoint['global_step']\\r\\n model.load_state_dict(checkpoint['model'])\\r\\n for i, optimizer in enumerate(model.optimizers):\\r\\n optimizer.load_state_dict(checkpoint['optimizers'][i])\\r\\n else:\\r\\n raise Exception(\\\"Checkpoint not found.\\\")\",\n \"def load_state(self, fname: str) -> _TrainingState:\\n training_state = None\\n with open(fname, \\\"rb\\\") as fp:\\n training_state = pickle.load(fp)\\n return training_state\",\n \"def load_checkpoint(filename: str) -> CheckpointData:\\n return torch.load(filename)\",\n \"def load_checkpoint(checkpoint, model, optimizer=None):\\n if not os.path.exists(checkpoint):\\n raise (\\\"File doesn't exist {}\\\".format(checkpoint))\\n checkpoint = torch.load(checkpoint, map_location=torch.device('cpu'))\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if optimizer:\\n optimizer.load_state_dict(checkpoint['optim_dict'])\\n\\n return checkpoint\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(\\\"Checkpoint '{}' does not exist\\\".format(checkpoint_path))\\n\\n device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else 'cpu')\\n state = torch.load(checkpoint_path, map_location=\\\"cuda:0\\\")\\n model.load_state_dict(state['model_state_dict'])\\n\\n if optimizer is not None:\\n optimizer.load_state_dict(state['optimizer_state_dict'])\\n\\n return state\",\n \"def load(self, filename):\\n\\n c = torch.load(filename)\\n\\n if type(c) is dict:\\n sd = c['state_dict']\\n self.net.load_state_dict(sd)\\n if 'monitors' in c: # Remove the branching eventually\\n self.monitors = c['monitors']\\n else:\\n self.monitors = {'loss_train': c['train_monitor'], 'loss_val': c['val_monitor'],\\n 'accu_train': MetricHistory(), 'accu_val': MetricHistory()}\\n if 'optimizer' in c: # Remove the branching eventually\\n self.optimizer.load_state_dict(c['optimizer'])\\n else:\\n raise RuntimeError('Unsupported checkpoint. (Not a dict)')\\n\\n self.parent = filename\\n self.last_checkpoint = filename\\n self.start_epoch = self.monitors['loss_train'].num_epochs\",\n \"def load_checkpoint(filename, from_gpu=True):\\r\\n assert os.path.exists(filename)\\r\\n if from_gpu:\\r\\n return torch.load(filename)\\r\\n else:\\r\\n return torch.load(filename, map_location=lambda storage, loc: storage)\",\n \"def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\\n print(\\\"Load checkpoint from: {}\\\".format(path))\\n state_dict, optimizer_state = _load(path)\\n\\n model.load_dict(state_dict)\\n if not reset_optimizer and optimizer is not None:\\n if optimizer_state is not None:\\n print(\\\"[loading] Load optimizer state from {}\\\".format(path))\\n optimizer.load(optimizer_state)\\n\\n return model\",\n \"def load_checkpoint(model, save_path):\\n model.load_state_dict(torch.load(save_path))\",\n \"def load_checkpoint(self):\\n checkpoin_path = self.get_checkpoint_path()\\n _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)\",\n \"def load(self, path):\\n\\n filename = self.__path(path, prefix=None)\\n chkpt = th.load(filename, map_location=\\\"cpu\\\") # TODO: check behavior\\n\\n if self.model is not None and chkpt[\\\"model\\\"] is not None:\\n log.debug(\\\"Loading model state dict\\\")\\n self.model.load_state_dict(chkpt[\\\"model\\\"])\\n\\n if \\\"optimizers\\\" in chkpt.keys():\\n if self.optimizers is not None and chkpt[\\\"optimizers\\\"] is not None:\\n try:\\n for opt, state in zip(self.optimizers,\\n chkpt[\\\"optimizers\\\"]):\\n log.debug(\\\"Loading optimizers state dict for %s\\\", opt)\\n opt.load_state_dict(state)\\n except:\\n # We do not raise an error here, e.g. in case the user simply\\n # changes optimizer\\n log.warning(\\\"Could not load optimizer state dicts, \\\"\\n \\\"starting from scratch\\\")\\n\\n if \\\"schedulers\\\" in chkpt.keys():\\n if self.schedulers is not None and chkpt[\\\"schedulers\\\"] is not None:\\n try:\\n for s, state in zip(self.schedulers,\\n chkpt[\\\"schedulers\\\"]):\\n log.debug(\\\"Loading scheduler state dict for %s\\\", s)\\n s.load_state_dict(state)\\n except:\\n log.warning(\\\"Could not load scheduler state dicts, \\\"\\n \\\"starting from scratch\\\")\\n\\n log.debug(\\\"Loaded checkpoint \\\\\\\"{}\\\\\\\"\\\".format(filename))\\n return tuple(chkpt[k] for k in [\\\"extras\\\", \\\"meta\\\"])\",\n \"def load_actor(self, checkpoint):\\n \\n model = torch.load(checkpoint)\\n self.actor_local.load_state_dict(model)\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def load_checkpoint(cfg, args):\\n checkpoint_iteration = args.checkpoint\\n bucket = connect_to_bucket(args.bucket)\\n # load actual checkpoint\\n if not os.path.isdir(cfg.OUTPUT_DIR):\\n os.mkdir(cfg.OUTPUT_DIR)\\n blob = bucket.blob(cfg.OUTPUT_DIR + \\\"/model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n blob.download_to_filename(cfg.OUTPUT_DIR + \\\"/model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n if args.resume:\\n # also write last checkpoint file for when --resume statement, model gets checkpoint name from this file\\n with open(cfg.OUTPUT_DIR + \\\"/last_checkpoint\\\", \\\"w\\\") as file:\\n file.write(\\\"model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n # return statement not clean, but useful for inference code\\n return checkpoint_iteration, bucket\",\n \"def loadCheckpoint(self, file):\\n if isinstance(file, str):\\n with open(file, 'rb') as f:\\n self.context.loadCheckpoint(f.read())\\n else:\\n self.context.loadCheckpoint(file.read())\",\n \"def load_model(self, ckpt_fn):\\n checkpoint = torch.load(ckpt_fn)\\n self.net_.load_state_dict(checkpoint[\\\"model\\\"])\\n self.optimizer_.load_state_dict(checkpoint[\\\"optimizer\\\"])\\n self.epoch_ = checkpoint[\\\"epoch\\\"]\\n self.global_step_ = checkpoint[\\\"global_step\\\"]\\n self.model_samples_ = deque(checkpoint[\\\"model_samples\\\"])\\n self.sampler.load_state_dict(checkpoint[\\\"sampler_state\\\"])\\n self.ais_loss.load_state_dict(checkpoint[\\\"ais_state\\\"])\\n self.replay_prob = checkpoint[\\\"replay_prob\\\"]\\n self.max_replay = checkpoint[\\\"max_replay\\\"]\",\n \"def load_checkpoint(checkpoint, model, optimizer=None):\\n model_state_dict, optimizer_state_dict = torch.load(checkpoint)\\n model.load_state_dict(model_state_dict)\\n\\n if optimizer is not None:\\n optimizer.load_state_dict(optimizer_state_dict)\",\n \"def load(self, checkpoint_dir=None):\\n\\n if checkpoint_dir is None:\\n checkpoint_dir = FLAGS.checkpoint_dir\\n\\n checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)\\n\\n return self.load_from_path(checkpoint_dir)\",\n \"def _load_checkpoint(cls, model: DeviceAwareModule, checkpoint_path: Path,\\n key_in_state_dict: str, use_gpu: bool) -> int:\\n logging.info(f\\\"Loading checkpoint {checkpoint_path}\\\")\\n checkpoint = ModelAndInfo.read_checkpoint(checkpoint_path, use_gpu)\\n\\n try:\\n state_dict = checkpoint[key_in_state_dict]\\n except KeyError:\\n logging.error(f\\\"Key {key_in_state_dict} not found in checkpoint\\\")\\n return False\\n\\n if isinstance(model, torch.nn.DataParallel):\\n result = model.module.load_state_dict(state_dict, strict=False)\\n else:\\n result = model.load_state_dict(state_dict, strict=False)\\n\\n if result.missing_keys:\\n logging.warning(f\\\"Missing keys in model checkpoint: {result.missing_keys}\\\")\\n if result.unexpected_keys:\\n logging.warning(f\\\"Unexpected keys in model checkpoint: {result.unexpected_keys}\\\")\\n\\n return checkpoint[ModelAndInfo.EPOCH_KEY]\",\n \"def load_checkpoint(checkpoint_file: pl.Path) -> Optional[Dict[str, Any]]:\\n if checkpoint_file.exists():\\n logger.info(f\\\"Loading checkpoint {checkpoint_file}.\\\")\\n checkpoint = torch.load(str(checkpoint_file))\\n logger.info(f\\\"Done loading checkpoint from epoch {checkpoint['epoch']}.\\\")\\n else:\\n logger.warning(f\\\"No {checkpoint_file} checkpoint file found. Starting normal.\\\")\\n return checkpoint\",\n \"def restore(self, checkpoint_path):\\n start_time = time.time()\\n latest_checkpoint = train_util.get_latest_chekpoint(checkpoint_path)\\n if latest_checkpoint is not None:\\n checkpoint = tf.train.Checkpoint(model=self)\\n checkpoint.restore(latest_checkpoint).expect_partial()\\n logging.info('Loaded checkpoint %s', latest_checkpoint)\\n logging.info('Loading model took %.1f seconds', time.time() - start_time)\\n else:\\n logging.info('Could not find checkpoint to load at %s, skipping.',\\n checkpoint_path)\",\n \"def load_checkpoint(path: str, use_cuda: bool = True) -> dict:\\n assert os.path.isfile(path), \\\"Checkpoint %s not found\\\" % path\\n checkpoint = torch.load(path, map_location=\\\"cuda\\\" if use_cuda else \\\"cpu\\\")\\n return checkpoint\",\n \"def load_checkpoint(self, model):\\n print(f\\\"load model {self.save_model_path}\\\")\\n model.load_state_dict(torch.load(self.save_model_path))\",\n \"def load_model_states_from_checkpoint(model, filename, tag, from_gpu=True):\\r\\n assert os.path.exists(filename)\\r\\n if from_gpu:\\r\\n checkpoint = torch.load(filename)\\r\\n else:\\r\\n checkpoint = torch.load(filename, map_location=lambda storage, loc: storage)\\r\\n model.load_state_dict(checkpoint[tag])\",\n \"def load_checkpoint(self, path: str = '', train: bool = True) -> int:\\n\\n if not path:\\n dir_ = os.path.dirname(os.path.realpath('__file__'))\\n path = os.path.join(dir_, 'model.pt')\\n\\n try:\\n ckpt = torch.load(path)\\n except FileNotFoundError:\\n return 0\\n else:\\n print('Loaded model at epoch: ', end='')\\n\\n self.load_state_dict(ckpt['model_state_dict'])\\n self.actor_optimizer.load_state_dict(ckpt['ac_optim_dict'])\\n self.critic_optimizer.load_state_dict(ckpt['critic_optim_dict'])\\n epoch = ckpt['epoch']\\n\\n print(epoch)\\n\\n if not train:\\n self.eval()\\n else:\\n self.train()\\n\\n return epoch\",\n \"def load_checkpoint(args, trainer, epoch_itr):\\n os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)\\n checkpoint_path = os.path.join(args.save_dir, 'checkpoints', args.restore_file)\\n if os.path.isfile(checkpoint_path):\\n extra_state = trainer.load_checkpoint(checkpoint_path)\\n if extra_state is not None:\\n # replay train iterator to match checkpoint\\n epoch_itr.load_state_dict(extra_state['train_iterator'])\\n\\n print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(\\n checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))\\n\\n trainer.lr_step(epoch_itr.epoch)\\n trainer.lr_step_update(trainer.get_num_updates())\\n if 'best' in extra_state:\\n save_checkpoint.best = extra_state['best']\",\n \"def _load(checkpoint_path):\\n state_dict, optimizer_state = dg.load_persistables(dirname=checkpoint_path)\\n return state_dict, optimizer_state\",\n \"def load_from_checkpoint(results_dir, load_fn, args):\\n ckpt_dir = os.path.join(results_dir, \\\"tb\\\", \\\"version_0\\\", \\\"checkpoints\\\")\\n files = os.listdir(ckpt_dir)\\n assert len(files) > 0, \\\"Checkpoint directory is empty\\\"\\n ckpt_path = os.path.join(ckpt_dir, files[-1])\\n model = load_fn(checkpoint_path=ckpt_path, args=args)\\n return model\",\n \"def load_checkpoint(filepath):\\n checkpoint = torch.load(filepath)\\n \\n arch = checkpoint['arch']\\n if arch == 'vgg':\\n model = models.vgg16(pretrained=True)\\n elif arch == 'densenet':\\n model = models.densenet121(pretrained=True) \\n \\n model.class_to_idx = checkpoint['class_to_idx']\\n model.classifier = checkpoint['classifier']\\n model.classifier.load_sate_dict = checkpoint['classifier_state_dict']\\n model.optimizer = checkpoint['optimizer_state_dict']\\n model.input_size = checkpoint['input_size']\\n model.output_size = checkpoint['output_size']\\n \\n return model\",\n \"def parse_checkpoint(checkpoint_path):\\n with gfile.Open(checkpoint_path, 'rb') as fp:\\n raw_contents = fp.read()\\n if raw_contents.startswith(b'model_checkpoint_path'):\\n raise ValueError(\\n 'Attempting to restore a TensorFlow checkpoint as a native T5X '\\n f'checkpoint. Path: {checkpoint_path}')\\n return serialization.msgpack_restore(raw_contents)\",\n \"def load_pretrained(model, fname, optimizer=None):\\n if os.path.isfile(fname):\\n print(\\\"=> loading checkpoint '{}'\\\".format(fname))\\n checkpoint = torch.load(fname)\\n model.load_state_dict(checkpoint['state_dict'])\\n if optimizer is not None:\\n optimizer.load_state_dict(checkpoint['optimizer'])\\n return model, optimizer, checkpoint['epoch']\\n else:\\n return model\\n else:\\n raise Exception(\\\"=> no checkpoint found at '{}'\\\".format(fname))\",\n \"def load_checkpoint(checkpoint_dir, epoch, iteration):\\n path = opj(checkpoint_dir, str(epoch) + '.' + str(iteration) + '.ckpt')\\n if not os.path.isfile(path):\\n raise Exception(\\\"Checkpoint in epoch %d doesn't exist :sob:\\\" % epoch)\\n\\n checkpoint = torch.load(path)\\n start_epoch = checkpoint['epoch']\\n state_dict = checkpoint['state_dict']\\n start_iteration = checkpoint['iteration']\\n\\n assert iteration == start_iteration\\n return start_epoch, start_iteration, state_dict\",\n \"def loadCheckpoint(self, time_stamp, data_only=False, load_memory=True):\\n state_filename = os.path.join(self.saving_dir, 'checkpoint.' + time_stamp + '.pth.tar')\\n mem_filename = os.path.join(self.saving_dir, 'memory.' + time_stamp + '.pth.tar')\\n\\n print 'loading checkpoint: ', time_stamp\\n checkpoint = torch.load(state_filename)\\n if data_only:\\n self.episode_rewards = checkpoint['episode_rewards']\\n self.episode_lengths = checkpoint['episode_lengths']\\n return\\n\\n self.episodes_done = checkpoint['episode']\\n self.steps_done = checkpoint['steps']\\n self.episode_rewards = checkpoint['episode_rewards']\\n self.episode_lengths = checkpoint['episode_lengths']\\n\\n self.policy_net.load_state_dict(checkpoint['policy_state_dict'])\\n self.policy_net = self.policy_net.to(self.device)\\n self.policy_net.train()\\n\\n self.target_net.load_state_dict(checkpoint['policy_state_dict'])\\n self.target_net = self.target_net.to(self.device)\\n self.target_net.eval()\\n\\n self.optimizer = optim.Adam(self.policy_net.parameters())\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\n\\n if load_memory:\\n memory = torch.load(mem_filename)\\n self.memory = memory['memory']\",\n \"def load_model(self, checkpoint):\\n print(f'Load parameters from {checkpoint}')\\n epoch = re.match(r\\\"[0-9]*\\\", os.path.basename(checkpoint)).group(0)\\n self.epoch_i = int(epoch)\\n self.model.load_state_dict(torch.load(checkpoint))\",\n \"def load_model(model, checkpoint_path: str): \\r\\n checkpoint = torch.load(checkpoint_path)\\r\\n model.load_state_dict(checkpoint['model'])\\r\\n epoch = checkpoint['epoch']\\r\\n print('Loaded model from {}, epoch {}'.format(checkpoint_path, epoch))\",\n \"def restore(self, checkpoint_path: str):\\r\\n raise NotImplementedError\",\n \"def load_ckp(checkpoint_fpath, model, optimizer, device):\\n\\n checkpoint = torch.load(checkpoint_fpath,map_location=device)\\n model.load_state_dict(checkpoint['state_dict'])\\n optimizer.load_state_dict(checkpoint['optimizer'])\\n valid_acc = checkpoint['valid_acc'] \\n return model, optimizer, checkpoint['epoch'], valid_acc\",\n \"def LoadProgramState(self, restored_checkpoint_path=None, sess=None):\\n pass\",\n \"def load_checkpoint(checkpoint_path):\\n flat_checkpoint_dict = flatten_checkpoint(\\n parse_checkpoint(checkpoint_path), keep_empty_nodes=True)\\n return flat_checkpoint_dict\",\n \"def load_checkpoint(ckpt_path):\\n checkpoint = None\\n if ckpt_path:\\n logger.info(\\\"Loading checkpoint from %s\\\" % ckpt_path)\\n checkpoint = torch.load(ckpt_path, map_location=torch.device(\\\"cpu\\\"))\\n\\n if \\\"model\\\" in checkpoint.keys():\\n # This preserves backward-compat for models using customed layernorm\\n def fix_key(s):\\n s = re.sub(\\n r\\\"(.*)\\\\.layer_norm((_\\\\d+)?)\\\\.b_2\\\", r\\\"\\\\1.layer_norm\\\\2.bias\\\", s\\n )\\n s = re.sub(\\n r\\\"(.*)\\\\.layer_norm((_\\\\d+)?)\\\\.a_2\\\", r\\\"\\\\1.layer_norm\\\\2.weight\\\", s\\n )\\n return s\\n\\n checkpoint[\\\"model\\\"] = {\\n fix_key(k): v for k, v in checkpoint[\\\"model\\\"].items()\\n }\\n # Force add_ffnbias to True if bias found in model w_1 keys\\n for key in checkpoint[\\\"model\\\"].keys():\\n if \\\"w_1.bias\\\" in key:\\n checkpoint[\\\"opt\\\"].add_ffnbias = True\\n\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"num_kv\\\"):\\n checkpoint[\\\"opt\\\"].num_kv = 0\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"add_ffnbias\\\"):\\n checkpoint[\\\"opt\\\"].add_ffnbias = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"parallel_residual\\\"):\\n checkpoint[\\\"opt\\\"].parallel_residual = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"shared_layer_norm\\\"):\\n checkpoint[\\\"opt\\\"].shared_layer_norm = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"use_ckpting\\\"):\\n checkpoint[\\\"opt\\\"].use_ckpting = []\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"relative_positions_buckets\\\"):\\n checkpoint[\\\"opt\\\"].relative_positions_buckets = 0\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"parallel_mode\\\"):\\n checkpoint[\\\"opt\\\"].parallel_mode = \\\"data_parallel\\\"\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"norm_eps\\\"):\\n checkpoint[\\\"opt\\\"].norm_eps = 1e-6\\n\\n # fix v2 compatibility\\n if \\\"generator\\\" in checkpoint.keys() and checkpoint[\\\"generator\\\"]:\\n if \\\"0.weight\\\" in checkpoint[\\\"generator\\\"]:\\n checkpoint[\\\"generator\\\"][\\\"weight\\\"] = checkpoint[\\\"generator\\\"].pop(\\n \\\"0.weight\\\"\\n )\\n if \\\"0.bias\\\" in checkpoint[\\\"generator\\\"]:\\n checkpoint[\\\"generator\\\"][\\\"bias\\\"] = checkpoint[\\\"generator\\\"].pop(\\\"0.bias\\\")\\n # end of patch for backward compatibility\\n\\n return checkpoint\",\n \"def load(cls, model_path: str, sample_shape: tuple = None,\\n checkpoint: str = None, **kwargs):\",\n \"def load_pretrained_model(self, load_from):\\n print(\\\"loading model from %s\\\\n\\\" % (load_from))\\n try:\\n if self.use_cuda:\\n pretrained_dict = torch.load(load_from)\\n else:\\n pretrained_dict = torch.load(load_from, map_location='cpu')\\n\\n model_dict = self.online_net.state_dict()\\n pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}\\n model_dict.update(pretrained_dict)\\n self.online_net.load_state_dict(model_dict)\\n print(\\\"The loaded parameters are:\\\")\\n keys = [key for key in pretrained_dict]\\n print(\\\", \\\".join(keys))\\n print(\\\"--------------------------\\\")\\n except Exception as e:\\n print(\\\"Failed to load checkpoint...\\\")\\n print(e)\",\n \"def load_epoch_checkpoint(self, directory, epoch):\\n chkpnt = torch.load(directory / f\\\"chkpnt_epoch{epoch:04d}.pth\\\")\\n self.load_state_dict(chkpnt['model_state_dict'])\",\n \"def load_checkpoint(self, checkpoint_filepath=None, verbose=True):\\n if checkpoint_filepath:\\n print('loading', checkpoint_filepath, flush=True)\\n optimistic_restore(self._session, checkpoint_filepath, verbose=verbose)\\n return True\\n else:\\n checkpoints = retrieve_all_checkpoints(self._checkpoints_path) + retrieve_all_checkpoints(self._recovery_checkpoints_path)\\n\\n if checkpoints:\\n last_checkpoint = sorted(checkpoints)[-1][1]\\n print('loading', last_checkpoint, flush=True)\\n optimistic_restore(self._session, last_checkpoint, verbose=verbose)\\n return True\\n else:\\n print('nothing to restore. no checkpoint found.', flush=True)\\n return False\",\n \"def load_checkpoint(self, filename):\\n results = Future.gen_list([\\n self.call_async(rank, '_async_load_checkpoint', filename=filename)\\n for rank in range(self.num_replicas)\\n ])\\n extra_state = results[0]\\n return extra_state\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.mnt_best = checkpoint['monitor_best']\\n\\n # load model params from checkpoint.\\n if checkpoint['config']['name'] != self.config['name']:\\n self.logger.warning(\\n 'Warning: Architecture configuration given in config file is different from that of checkpoint. ' + \\\\\\n 'This may yield an exception while state_dict is being loaded.')\\n self.model.load_state_dict(checkpoint['model_state_dict'])\\n\\n # load optimizer state from checkpoint only when optimizer type is not changed. \\n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\\n self.logger.warning('Warning: Optimizer type given in config file is different from that of checkpoint. ' + \\\\\\n 'Optimizer parameters not being resumed.')\\n self.optimizer.load_state_dict(checkpoint['model_optimizer'])\\n\\n # load scheduler state from checkpoint only when scheduler type is not changed\\n if checkpoint['config']['scheduler']['type'] != self.config['scheduler']['type']:\\n self.logger.warning('Warning: Scheduler type given in config file is different from that of checkpoint. ' + \\\\\\n 'Scheduler parameters not being resumed.')\\n self.scheduler.load_state_dict(checkpoint['model_scheduler'])\\n\\n self.train_logger = checkpoint['logger']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.monitor_best = checkpoint['monitor_best']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n self.train_logger = checkpoint['logger']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def load(self, from_path):\\n with open(from_path, 'rb') as f:\\n self.load_state_dict(torch.load(f))\",\n \"def load(self, from_path):\\n with open(from_path, 'rb') as f:\\n self.load_state_dict(torch.load(f))\",\n \"def restore_checkpoint(self, checkpoint_id, name, path=''):\\n\\n\\t\\tself.log.debug(\\\"restoring Notebook %s from checkpoint %s\\\", name, checkpoint_id)\\n\\t\\tnb_path = self._get_os_path(name, path)\\n\\t\\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\\n\\n\\t\\tif not key_exists(self.bucket, cp_path):\\n\\t\\t\\tself.log.debug(\\\"checkpoint file does not exist: %s\\\", cp_path)\\n\\t\\t\\traise web.HTTPError(404,\\n\\t\\t\\t\\tu'Notebook checkpoint does not exist: %s-%s' % (name, checkpoint_id)\\n\\t\\t\\t)\\n\\t\\t# ensure notebook is readable (never restore from an unreadable notebook)\\n\\t\\tkey = self.bucket.get_key(cp_path)\\n\\t\\tnb = current.reads(key.get_contents_as_string(), u'json')\\n\\t\\tself._copy(cp_path, nb_path)\\n\\t\\tself.log.debug(\\\"copying %s -> %s\\\", cp_path, nb_path)\",\n \"def _resume_checkpoint(self, resume_path):\\n resume_path = str(resume_path)\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n\\n # load architecture params from checkpoint.\\n if checkpoint['config']['model'] != self.config['model']:\\n self.logger.warning(\\\"Warning: Architecture configuration given in config file is different from that of \\\"\\n \\\"checkpoint. This may yield an exception while state_dict is being loaded.\\\")\\n self.model.load_state_dict(checkpoint['state_dict'])\\n\\n # load optimizer state from checkpoint only when optimizer type is not changed.\\n if checkpoint['config']['trainer']['optimizer']['type'] != self.config['trainer']['optimizer']['type']:\\n self.logger.warning(\\\"Warning: Optimizer type given in config file is different from that of checkpoint. \\\"\\n \\\"Optimizer parameters not being resumed.\\\")\\n else:\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n\\n self.logger.info(\\\"Checkpoint loaded. Resume from epoch {}\\\".format(self.start_epoch))\",\n \"def try_and_init_from(self, path):\\n log.info(\\\"Loading weights from foreign checkpoint {}\\\".format(path))\\n if not os.path.exists(path):\\n raise ValueError(\\\"Checkpoint {} does not exist\\\".format(path))\\n\\n chkpt = th.load(path, map_location=th.device(\\\"cpu\\\"))\\n if \\\"model\\\" not in chkpt.keys() or chkpt[\\\"model\\\"] is None:\\n raise ValueError(\\\"{} has no model saved\\\".format(path))\\n\\n mdl = chkpt[\\\"model\\\"]\\n for n, p in self.model.named_parameters():\\n if n in mdl:\\n p2 = mdl[n]\\n if p2.shape != p.shape:\\n log.warning(\\\"Parameter {} ignored, checkpoint size does not match: {}, should be {}\\\".format(n, p2.shape, p.shape))\\n continue\\n log.debug(\\\"Parameter {} copied\\\".format(n))\\n p.data.copy_(p2)\\n else:\\n log.warning(\\\"Parameter {} ignored, not found in source checkpoint.\\\".format(n))\\n\\n log.info(\\\"Weights loaded from foreign checkpoint {}\\\".format(path))\",\n \"def restore_checkpoint(checkpoint_path: str,\\n train_state: Optional[TrainState] = None,\\n assert_exist: bool = False,\\n step: Optional[int] = None) -> Tuple[TrainState, int]:\\n if assert_exist:\\n glob_path = os.path.join(checkpoint_path, 'checkpoint_*')\\n if not gfile.glob(glob_path):\\n raise ValueError('No checkpoint for the pretrained model is found in: '\\n f'{checkpoint_path}')\\n if train_state is None:\\n raise ValueError('Please use `restore_pretrained_checkpoint` for loading'\\n 'a checkpoint without providing a Scenic TrainState.')\\n train_state = checkpoints.restore_checkpoint(checkpoint_path, train_state,\\n step)\\n return train_state, int(train_state.global_step)\",\n \"def load_training_checkpoint(args, model, PATH, ckpt_id):\\r\\n logger = args.logger\\r\\n _, checkpoint_state_dict = model.network.load_checkpoint(PATH, ckpt_id)\\r\\n epoch = checkpoint_state_dict['epoch']\\r\\n last_global_step = checkpoint_state_dict['last_global_step']\\r\\n last_global_data_samples = checkpoint_state_dict[\\r\\n 'last_global_data_samples']\\r\\n del checkpoint_state_dict\\r\\n return (epoch, last_global_step, last_global_data_samples)\",\n \"def checkpoint(state, file_name='./saves/checkpoint.pth.tar'):\\n\\n assert isinstance(state, dict)\\n assert isinstance(file_name, str)\\n\\n torch.save(state, file_name)\",\n \"def load_state(path: str, state: T) -> T:\\n logging.info(\\\"Restoring state %s\\\", path)\\n with file_open(path, \\\"rb\\\") as fp:\\n state_new = serialization.from_bytes(state, fp.read())\\n tree = jax.tree_util.tree_structure(state)\\n leaves_new = jax.tree_util.tree_leaves(state_new)\\n return jax.tree_util.tree_unflatten(tree, leaves_new)\",\n \"def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:\\n # TODO: move to CheckpointIO\\n torch.cuda.empty_cache()\\n checkpoint_path = inject_model_parallel_rank(checkpoint_path)\\n return self.checkpoint_io.load_checkpoint(checkpoint_path)\",\n \"def load_checkpoint(model,\\n filename,\\n map_location='cpu',\\n strict=False,\\n logger=None):\\n checkpoint = _load_checkpoint(filename, map_location)\\n # OrderedDict is a subclass of dict\\n if not isinstance(checkpoint, dict):\\n raise RuntimeError(\\n f'No state_dict found in checkpoint file {filename}')\\n # get state_dict from checkpoint\\n if 'state_dict' in checkpoint:\\n state_dict = checkpoint['state_dict']\\n elif 'model' in checkpoint:\\n state_dict = checkpoint['model']\\n elif 'module' in checkpoint:\\n state_dict = checkpoint['module']\\n else:\\n state_dict = checkpoint\\n # strip prefix of state_dict\\n if list(state_dict.keys())[0].startswith('module.'):\\n state_dict = {k[7:]: v for k, v in state_dict.items()}\\n\\n # for MoBY, load model of online branch\\n if sorted(list(state_dict.keys()))[0].startswith('encoder'):\\n state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}\\n\\n # reshape absolute position embedding for Swin\\n if state_dict.get('absolute_pos_embed') is not None:\\n absolute_pos_embed = state_dict['absolute_pos_embed']\\n N1, L, C1 = absolute_pos_embed.size()\\n N2, C2, H, W = model.absolute_pos_embed.size()\\n if N1 != N2 or C1 != C2 or L != H*W:\\n logger.warning(\\\"Error in loading absolute_pos_embed, pass\\\")\\n else:\\n state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)\\n\\n rank, _ = get_dist_info()\\n if \\\"rel_pos_bias.relative_position_bias_table\\\" in state_dict:\\n if rank == 0:\\n print(\\\"Expand the shared relative position embedding to each layers. \\\")\\n num_layers = model.get_num_layers()\\n rel_pos_bias = state_dict[\\\"rel_pos_bias.relative_position_bias_table\\\"]\\n for i in range(num_layers):\\n state_dict[\\\"blocks.%d.attn.relative_position_bias_table\\\" % i] = rel_pos_bias.clone()\\n\\n state_dict.pop(\\\"rel_pos_bias.relative_position_bias_table\\\")\\n\\n all_keys = list(state_dict.keys())\\n for key in all_keys:\\n if \\\"relative_position_index\\\" in key:\\n state_dict.pop(key)\\n\\n if \\\"relative_position_bias_table\\\" in key:\\n rel_pos_bias = state_dict[key]\\n src_num_pos, num_attn_heads = rel_pos_bias.size()\\n dst_num_pos, _ = model.state_dict()[key].size()\\n dst_patch_shape = model.patch_embed.patch_shape\\n if dst_patch_shape[0] != dst_patch_shape[1]:\\n raise NotImplementedError()\\n num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)\\n src_size = int((src_num_pos - num_extra_tokens) ** 0.5)\\n dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)\\n if src_size != dst_size:\\n if rank == 0:\\n print(\\\"Position interpolate for %s from %dx%d to %dx%d\\\" % (\\n key, src_size, src_size, dst_size, dst_size))\\n extra_tokens = rel_pos_bias[-num_extra_tokens:, :]\\n rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]\\n\\n def geometric_progression(a, r, n):\\n return a * (1.0 - r ** n) / (1.0 - r)\\n\\n left, right = 1.01, 1.5\\n while right - left > 1e-6:\\n q = (left + right) / 2.0\\n gp = geometric_progression(1, q, src_size // 2)\\n if gp > dst_size // 2:\\n right = q\\n else:\\n left = q\\n\\n # if q > 1.13492:\\n # q = 1.13492\\n\\n dis = []\\n cur = 1\\n for i in range(src_size // 2):\\n dis.append(cur)\\n cur += q ** (i + 1)\\n\\n r_ids = [-_ for _ in reversed(dis)]\\n\\n x = r_ids + [0] + dis\\n y = r_ids + [0] + dis\\n\\n t = dst_size // 2.0\\n dx = np.arange(-t, t + 0.1, 1.0)\\n dy = np.arange(-t, t + 0.1, 1.0)\\n if rank == 0:\\n print(\\\"x = {}\\\".format(x))\\n print(\\\"dx = {}\\\".format(dx))\\n\\n all_rel_pos_bias = []\\n\\n for i in range(num_attn_heads):\\n z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy()\\n f = interpolate.interp2d(x, y, z, kind='cubic')\\n all_rel_pos_bias.append(\\n torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device))\\n\\n rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)\\n new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)\\n state_dict[key] = new_rel_pos_bias\\n\\n if 'pos_embed' in state_dict:\\n pos_embed_checkpoint = state_dict['pos_embed']\\n embedding_size = pos_embed_checkpoint.shape[-1]\\n num_patches = model.patch_embed.num_patches\\n num_extra_tokens = model.pos_embed.shape[-2] - num_patches\\n # height (== width) for the checkpoint position embedding\\n orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)\\n # height (== width) for the new position embedding\\n new_size = int(num_patches ** 0.5)\\n # class_token and dist_token are kept unchanged\\n if orig_size != new_size:\\n if rank == 0:\\n print(\\\"Position interpolate from %dx%d to %dx%d\\\" % (orig_size, orig_size, new_size, new_size))\\n extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]\\n # only the position tokens are interpolated\\n pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]\\n pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)\\n pos_tokens = torch.nn.functional.interpolate(\\n pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)\\n pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)\\n new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)\\n state_dict['pos_embed'] = new_pos_embed\\n\\n # interpolate position bias table if needed\\n relative_position_bias_table_keys = [k for k in state_dict.keys() if \\\"relative_position_bias_table\\\" in k]\\n for table_key in relative_position_bias_table_keys:\\n table_pretrained = state_dict[table_key]\\n table_current = model.state_dict()[table_key]\\n L1, nH1 = table_pretrained.size()\\n L2, nH2 = table_current.size()\\n if nH1 != nH2:\\n logger.warning(f\\\"Error in loading {table_key}, pass\\\")\\n else:\\n if L1 != L2:\\n S1 = int(L1 ** 0.5)\\n S2 = int(L2 ** 0.5)\\n table_pretrained_resized = F.interpolate(\\n table_pretrained.permute(1, 0).view(1, nH1, S1, S1),\\n size=(S2, S2), mode='bicubic')\\n state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)\\n\\n # load state_dict\\n load_state_dict(model, state_dict, strict, logger)\\n return checkpoint\",\n \"def load_state(self, state):\\n raise NotImplemented\",\n \"def load(self):\\n logger.debug('Loading state from file %s', self.file_path)\\n\\n with open(self.file_path, 'rb') as f:\\n self.data = pickle.load(f)\",\n \"def load(self, path):\\n checkpoint = torch.load(path, map_location=torch.device(\\\"cpu\\\"))\\n self.load_state_dict(checkpoint[\\\"state_dict\\\"])\\n self.on_epoch_start(checkpoint[\\\"epoch\\\"])\\n self.logger.info(\\\"Loaded controller network from %s\\\", path)\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.monitor_best = checkpoint['monitor_best']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n if self.with_cuda:\\n for state in self.optimizer.state.values():\\n for k, v in state.items():\\n if isinstance(v, torch.Tensor):\\n state[k] = v.cuda(torch.device('cuda'))\\n self.train_logger = checkpoint['logger']\\n #self.config = checkpoint['config']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def _restore_checkpoint(self, checkpoint_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(checkpoint_path))\\n checkpoint = torch.load(checkpoint_path)\\n pretrained_dict = checkpoint['state_dict'] # 预训练模型的state_dict\\n model_dict = self.model.state_dict() # 当前用来训练的模型的state_dict\\n \\n if pretrained_dict.keys() != model_dict.keys(): # 需要进行参数的适配\\n print('Parameters are inconsistant, adapting model parameters ...')\\n # 在合并前(update),需要去除pretrained_dict一些不需要的参数\\n # 只含有识别分支的预训练模型参数字典中键'0', '1'对应全模型参数字典中键'2', '3'\\n pretrained_dict['2'] = transfer_state_dict(pretrained_dict['0'], model_dict['2'])\\n pretrained_dict['3'] = transfer_state_dict(pretrained_dict['1'], model_dict['3'])\\n del pretrained_dict['0'] # 把原本预训练模型中的键值对删掉，以免错误地更新当前模型中的键值对\\n del pretrained_dict['1']\\n model_dict.update(pretrained_dict) # 更新(合并)模型的参数\\n self.model.load_state_dict(model_dict)\\n else:\\n print('Parameters are consistant, load state dict directly ...')\\n self.model.load_state_dict(checkpoint['state_dict'])\\n # self.optimizer.load_state_dict(checkpoint['optimizer'])\\n # if self.with_cuda:\\n # for state in self.optimizer.state.values():\\n # for k, v in state.items():\\n # if isinstance(v, torch.Tensor):\\n # state[k] = v.cuda(self.device)\",\n \"def restore(self, checkpoint):\\n raise NotImplementedError\",\n \"def load_checkpoint(self, session: tf.Session):\\n if not _load_checkpoint(session, str(self.info.checkpoint_path)):\\n tf_util.init_variables(session)\\n return False\\n else:\\n return True\",\n \"def restore(self,\\n sess,\\n ckpt_file,\\n ckpt_type):\\n if ckpt_file is None:\\n raise FileNotFoundError(\\\"checkpoint file doesn't exist\\\")\\n \\n if ckpt_type == \\\"debug\\\":\\n self.ckpt_debug_saver.restore(sess, ckpt_file)\\n elif ckpt_type == \\\"epoch\\\":\\n self.ckpt_epoch_saver.restore(sess, ckpt_file)\\n else:\\n raise ValueError(\\\"unsupported checkpoint type {0}\\\".format(ckpt_type))\",\n \"def load(self, path):\\n self.load_state_dict(torch.load(path))\",\n \"def load(self, path):\\n self.load_state_dict(torch.load(path))\",\n \"def resume(self, checkpoint):\\n model_dict = paddle.load(checkpoint)\\n self.model.set_state_dict(model_dict)\",\n \"def read_checkpoint(out_fp):\\r\\n pickle_fh = open(out_fp, \\\"r\\\")\\r\\n return pickle.load(pickle_fh)\",\n \"def from_previous_ckpt(network, checkpoint):\\n if os.path.exists(checkpoint):\\n if os.path.isfile(checkpoint):\\n try:\\n network.load_state_dict(torch.load(checkpoint))\\n print(f\\\"Loaded weights from {checkpoint}\\\")\\n except RuntimeError:\\n print(f\\\"{checkpoint} is a invalid checkpoint\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n if os.path.isdir(checkpoint):\\n epoch = 0\\n file_name = None\\n for ckpt in os.listdir(checkpoint):\\n if ckpt[-4:] == '.pth':\\n try:\\n tmp_int_list = re.findall('[0-9]+', ckpt)\\n ckpt_epoch = int(tmp_int_list[-1])\\n except IndexError:\\n ckpt_epoch = 0\\n if ckpt_epoch >= epoch:\\n epoch = ckpt_epoch\\n file_name = os.path.join(checkpoint, ckpt)\\n\\n if file_name is None:\\n print(f\\\"No checkpoint found in {checkpoint}\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n else:\\n try:\\n network.load_state_dict(torch.load(file_name))\\n print(f\\\"Loaded weights from {file_name}\\\")\\n except (RuntimeError):\\n print(f\\\"{file_name} is a invalid checkpoint\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n\\n else:\\n print(f\\\"the checkpoint path: {checkpoint} doesn't exist.\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8046602","0.7789368","0.7547819","0.7534057","0.744159","0.74061614","0.73822767","0.7316122","0.72855556","0.7279566","0.7265061","0.7259501","0.7259501","0.71900815","0.71689534","0.7152443","0.7132168","0.712835","0.7123272","0.7122712","0.7074052","0.7061837","0.70576745","0.70559126","0.7051246","0.7043952","0.70264363","0.7019621","0.70146245","0.7013854","0.70118123","0.7009573","0.7007782","0.6997167","0.6989572","0.6974809","0.6944989","0.69356424","0.69196934","0.6919214","0.6899233","0.6899233","0.6891084","0.6836968","0.681112","0.68016994","0.6782328","0.6782285","0.67530876","0.6730656","0.67163175","0.67162925","0.67023665","0.66952664","0.66946495","0.6670329","0.6659752","0.66420823","0.66269624","0.66251886","0.66212565","0.6616782","0.65812784","0.6526284","0.6522306","0.6508409","0.6488819","0.6480939","0.6477932","0.6456474","0.6433545","0.6429915","0.6417688","0.6402892","0.63709396","0.63686365","0.6368611","0.6368611","0.6355399","0.63484776","0.6329884","0.63254994","0.6323414","0.62856716","0.6274334","0.6267266","0.62415326","0.6235121","0.6232828","0.6210601","0.62030447","0.62014383","0.6194311","0.6184886","0.6164353","0.61501455","0.61501455","0.61445326","0.61310285","0.61200935"],"string":"[\n \"0.8046602\",\n \"0.7789368\",\n \"0.7547819\",\n \"0.7534057\",\n \"0.744159\",\n \"0.74061614\",\n \"0.73822767\",\n \"0.7316122\",\n \"0.72855556\",\n \"0.7279566\",\n \"0.7265061\",\n \"0.7259501\",\n \"0.7259501\",\n \"0.71900815\",\n \"0.71689534\",\n \"0.7152443\",\n \"0.7132168\",\n \"0.712835\",\n \"0.7123272\",\n \"0.7122712\",\n \"0.7074052\",\n \"0.7061837\",\n \"0.70576745\",\n \"0.70559126\",\n \"0.7051246\",\n \"0.7043952\",\n \"0.70264363\",\n \"0.7019621\",\n \"0.70146245\",\n \"0.7013854\",\n \"0.70118123\",\n \"0.7009573\",\n \"0.7007782\",\n \"0.6997167\",\n \"0.6989572\",\n \"0.6974809\",\n \"0.6944989\",\n \"0.69356424\",\n \"0.69196934\",\n \"0.6919214\",\n \"0.6899233\",\n \"0.6899233\",\n \"0.6891084\",\n \"0.6836968\",\n \"0.681112\",\n \"0.68016994\",\n \"0.6782328\",\n \"0.6782285\",\n \"0.67530876\",\n \"0.6730656\",\n \"0.67163175\",\n \"0.67162925\",\n \"0.67023665\",\n \"0.66952664\",\n \"0.66946495\",\n \"0.6670329\",\n \"0.6659752\",\n \"0.66420823\",\n \"0.66269624\",\n \"0.66251886\",\n \"0.66212565\",\n \"0.6616782\",\n \"0.65812784\",\n \"0.6526284\",\n \"0.6522306\",\n \"0.6508409\",\n \"0.6488819\",\n \"0.6480939\",\n \"0.6477932\",\n \"0.6456474\",\n \"0.6433545\",\n \"0.6429915\",\n \"0.6417688\",\n \"0.6402892\",\n \"0.63709396\",\n \"0.63686365\",\n \"0.6368611\",\n \"0.6368611\",\n \"0.6355399\",\n \"0.63484776\",\n \"0.6329884\",\n \"0.63254994\",\n \"0.6323414\",\n \"0.62856716\",\n \"0.6274334\",\n \"0.6267266\",\n \"0.62415326\",\n \"0.6235121\",\n \"0.6232828\",\n \"0.6210601\",\n \"0.62030447\",\n \"0.62014383\",\n \"0.6194311\",\n \"0.6184886\",\n \"0.6164353\",\n \"0.61501455\",\n \"0.61501455\",\n \"0.61445326\",\n \"0.61310285\",\n \"0.61200935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67693573"},"document_rank":{"kind":"string","value":"48"}}},{"rowIdx":19697,"cells":{"query":{"kind":"string","value":"Broadcasts the ``path`` from the LOCAL rank zero to all LOCAL ranks."},"document":{"kind":"string","value":"def _get_local_rank_zero_path(path: Optional[str]) -> str:\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\n paths = dist.all_gather_object(path)\n local_rank_zero_path = paths[local_rank_zero]\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\n return local_rank_zero_path"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def send_path(self, path):\n self.clear_path()\n for coordinate in path:\n self.send_coordinate(coordinate)\n time.sleep(0.05)","def broadcast(value, root_rank, name=None):\n return _impl.broadcast(K, value, root_rank, name)","def extern_to_local_path(self, path: PurePath) -> Path:\n return self.path_supervisor / path.relative_to(self.path_extern_supervisor)","def local_to_extern_path(self, path: PurePath) -> PurePath:\n return self.path_extern_supervisor / path.relative_to(self.path_supervisor)","def Update(self, local_path_info):\n if not local_path_info:\n return\n for priority_group in local_path_info._path_priority_groups:\n group_list = []\n for path in priority_group:\n if not self.IsPathInLocalPaths(path):\n group_list.append(path)\n if group_list:\n self._path_priority_groups.append(group_list)","def local_rebroadcast_lift(node):\r\n op = node.op\r\n if not isinstance(op, T.Rebroadcast):\r\n return False\r\n\r\n input = node.inputs[0]\r\n inode = input.owner\r\n if inode and isinstance(inode.op, Elemwise) and len(inode.inputs) == 1:\r\n # It may happen that `input` has no client because this optimization\r\n # is called from `apply_rebroadcast_opt`, which in particular is used\r\n # by the `unbroadcast` function before we are in the actual function\r\n # compilation phase.\r\n if hasattr(input, 'clients') and len(input.clients) == 1:\r\n rval = inode.op.make_node(T.Rebroadcast(*op.axis.items())(\r\n inode.inputs[0])).outputs\r\n return rval\r\n if inode and isinstance(inode.op, T.Rebroadcast):\r\n # the \"axis\" specification in the outer Rebroadcast overrides\r\n # the axis of the inner one\r\n axis = inode.op.axis.copy()\r\n axis.update(op.axis)\r\n iinput = inode.inputs[0]\r\n rval = [T.Rebroadcast(*axis.items())(iinput)]\r\n return rval","def _format_path_with_rank_zero(path: str) -> str:\n return path.format(\n rank=0,\n local_rank=0,\n node_rank=0,\n )","def broadcast_global_variables(root_rank):\n return _impl.broadcast_global_variables(K, root_rank)","def broadcast(data: T, root: int) -> T:\n return collective.broadcast(data, root)","def _format_path_with_current_rank(path: str) -> str:\n return path.format(\n rank=dist.get_global_rank(),\n local_rank=dist.get_local_rank(),\n node_rank=dist.get_node_rank(),\n )","def broadcast_global_variables(root_rank):\n if _executing_eagerly():\n raise RuntimeError(\n \"bps.broadcast_global_variables() does not support eager execution. \"\n \"Please use `bps.broadcast_variables(<model/optimizer variables>)` instead.\"\n )\n\n return broadcast_variables(_global_variables(), root_rank)","def _exchange_ghosts_local(self):\n for d in xrange(self._dim):\n self._exchange_ghosts_local_d(d)","def comm_all_best_paths(self, peer):\n LOG.debug('Communicating current best path for all afi/safi except'\n ' 1/132')\n # We will enqueue best path from all global destination.\n for route_family, table in self._table_manager.iter:\n if route_family == RF_RTC_UC:\n continue\n if peer.is_mbgp_cap_valid(route_family):\n for dest in table.values():\n if dest.best_path:\n peer.communicate_path(dest.best_path)","def broadcast(self):\n comm.Barrier()\n\n if rank == 0:\n dim = np.array([self.nspin, self.nkpt, self.nband], dtype=np.int)\n else:\n dim = np.empty(3, dtype=np.int)\n\n comm.Bcast([dim, MPI.INT])\n\n if rank != 0:\n self.EIG = np.empty(dim, dtype=np.float64)\n self.Kptns = np.empty((dim[1],3), dtype=np.float64)\n\n comm.Bcast([self.EIG, MPI.DOUBLE])\n comm.Bcast([self.Kptns, MPI.DOUBLE])","def traceback_all(self, path, input_data):\r\n current = path[-1]\r\n neighbours = self._get_neighbours(current, input_data)\r\n\r\n for vec in neighbours:\r\n if (vec.x == 0 and vec.y == 0 and vec.z == 0):\r\n path.append(vec)\r\n self.paths.append(copy.deepcopy(path))\r\n path.pop()\r\n else:\r\n path.append(vec)\r\n self.traceback_all(path, input_data)\r\n path.pop()","def send_broadcast_packet(self, broadcast_packet: Packet) -> None:\n for neighbor_address in [*self.children_addresses, self.parent_address]:\n if neighbor_address:\n self.stream.add_message_to_out_buff(neighbor_address, broadcast_packet)\n log(f'Message packet added to out buff of Node({neighbor_address}).')","def walk(self, priv_path:list):\n # End conditions for recursive loop\n current_node = priv_path[-1]\n if current_node.location in self.destination and len(priv_path)>1:\n self.addItinerary(priv_path)\n self.n_routes+=1\n return\n if self.n_routes >= self.max_n_routes:\n return\n\n if len(priv_path)>1:\n # Get metadata of last edge type\n last_edge = self.EdgeType(priv_path[-2], priv_path[-1])\n else: # If it's start of itinerary, next edge would be travel edge\n # So, make last edge as stay\n last_edge = 'stay'\n if last_edge == 'stay': # next edge will be travel i.e., ship not None\n next_nodes = [node for node in self.G.neighbors(current_node) \n if self.G.edges[current_node, node]['ship'] is not None]\n else: # Next edge will be stay, i.e., ship = None\n next_nodes = [node for node in self.G.neighbors(current_node)\n if self.G.edges[current_node, node]['ship'] is None]\n \n for node in next_nodes:\n self.walk(priv_path+[node])","def zero_base_local(*args):\n robots = get_robot_roots()\n if not robots:\n pm.warning('Nothing Selected; Select a valid robot')\n return\n\n try:\n for robot in robots:\n local_ctrl_path = get_local_ctrl_path(robot)\n pm.setAttr(local_ctrl_path + '.translate', 0, 0, 0)\n pm.setAttr(local_ctrl_path + '.rotate', 0, 0, 0)\n except:\n pm.warning('Cannot zero base (local)')","def update_path(self):\r\n if len(self.queue) == 0:\r\n return\r\n self.path[:] = []\r\n current = self.peek_queue()[0]\r\n while current in self.previous:\r\n self.path.append(current)\r\n current = self.previous[current]","def local_path(G, grid, lcl, p1, p2):\n\n # create a local graph\n lcl_graph = local_graph(G)\n\n # plan over the local graph from p1 to p2\n path, _ = astar.a_star_graph(lcl_graph, utils.norm_distance, p1, p2)\n print(path)\n for p in path:\n lcl.append(p)\n return lcl","def broadcast(self, tx):\n\n for neighbor_id in self.adjacencies:\n self.sendMsg(neighbor_id, Message(self.id, Type.BLOCK, tx))","def _build_path(self):\n for point_3d in self.path_coordinates:\n self.connect_point_with_neighbors(point_3d)","def _get_local_dest(self, path: Path) -> Path:\n dest = \"\"\n\n if str(path).startswith(\"~\"):\n path = path.relative_to(\"~\")\n\n if self.category == \"global\":\n dest = f\"{self.local_base}/global/{path}\"\n elif self.category == \"local\":\n dest = f\"{self.local_base}/local/{path}\"\n else:\n dest = f\"{self.local_base}/custom/{path}\"\n\n return Path(dest)","def test_broadcast(self):\n if _debug: TestSimple._debug(\"test_broadcast\")\n\n # create a network\n tnet = TNetwork()\n\n # make a PDU from node 1 to node 2\n pdu_data = xtob('dead.beef')\n pdu = PDU(pdu_data, source=tnet.td.address, destination=LocalBroadcast())\n if _debug: TestSimple._debug(\" - pdu: %r\", pdu)\n\n # test device sends it, iut gets it\n tnet.td.start_state.send(pdu).success()\n tnet.iut.start_state.receive(PDU, pduSource=tnet.td.address).success()\n\n # sniffer sees message on the wire\n tnet.sniffer.start_state.receive(OriginalBroadcastNPDU,\n pduSource=tnet.td.address.addrTuple,\n# pduDestination=('192.168.4.255', 47808),\n pduData=pdu_data,\n ).timeout(1.0).success()\n\n # run the group\n tnet.run()","def localpath(self, *args):\n return os.path.join(os.path.expanduser(self.serverfiles_dir), *args)","def sync_model(model):\n size = float(dist.get_world_size())\n\n for param in model.parameters():\n dist.broadcast(param.data, 0)","def emit(self, *path):\n path = list(path)\n for module in self.modules.values():\n module.emit_local(*path)","def get_remote_path(self, local_path, mapped_paths):\n return self.get_local_path(local_path, mapped_paths, reverse=True)","def update_trip_path(trip_mpois, paths, graph):\n n_nodes = len(trip_mpois)\n # adjacency matrix\n new_paths = np.zeros(shape=(n_nodes, n_nodes))\n\n # iterate through all the nodes and create a list of nodes with sequential id\n for i, node1 in enumerate(trip_mpois):\n for j, node2 in enumerate(trip_mpois):\n new_paths[i, j] = paths[node1, node2]\n\n # new_paths = new_paths/np.max(new_paths[new_paths < _INF])\n # new_paths[np.isinf(new_paths)] = _INF\n\n # create a dummy edge between end and start node with weight 0\n new_paths[1,0] = -_INF\n # new_paths[0,1] = _INF\n\n shortest_path = None\n if n_nodes > 5:\n shortest_path, dist = tsp.solve(n_nodes, new_paths)\n # shortest_path = range(n_nodes)\n else:\n shortest_path = range(n_nodes)\n\n trip_path = np.array(trip_mpois)[shortest_path]\n\n if ___DEBUG:\n fname = 'dump/' + str(n_nodes) + '.dist'\n np.savetxt(fname, new_paths, fmt='%.6f')\n \n mpoi_pos = np.zeros(shape=(n_nodes,2))\n \n for i, node in enumerate(trip_mpois):\n pos_3d = graph.vs[node]['position']\n assert node == graph.vs[node].index\n mpoi_pos[i,:] = pos_3d[:2]\n\n fname = 'dump/' + str(n_nodes) + '.pos'\n np.savetxt(fname, mpoi_pos)\n \n # print trip_mpois, trip_path\n\n return trip_path","def appendleft(self, path):\n self.paths.appendleft(path)\n self.time += path.time","def rsync_local_files(dry_run=None):\n local_path = os.path.join(os.path.dirname(settings.PROJECT_PATH), 'local')\n cmd = 'rsync -av{dry_run} {src}/ {host}:{dst}'.format(\n src=local_path,\n host=env.host,\n dst=local_path,\n dry_run='n' if dry_run else '',\n )\n local(cmd)","def get_local_path(self, remote_path, mapped_paths, reverse=False):\n for remote_prefix, local_prefix in mapped_paths:\n # Reverse. Return mapped remote path for given local path.\n if reverse:\n remote_prefix, local_prefix = local_prefix, remote_prefix\n if remote_path.startswith(remote_prefix):\n local_path = remote_path.replace(\n remote_prefix, local_prefix)\n break\n else:\n local_path = remote_path\n return local_path","def optimize_path(path):\n rospy.loginfo(\"Optimizing path\")\n\n opt_path = []\n current_direction = (0, 0)\n last_direction = (0, 0)\n\n for i in range(len(path) -1):\n current_direction = (path[i+1][0] - path[i][0], path[i+1][1] - path[i][1])\n if current_direction != last_direction:\n opt_path.append(path[i])\n last_direction = current_direction\n \n opt_path.append(path[-1]) #add the last coordinate back\n\n return opt_path","def join(self, path, *paths):","def prepare_path(self,path):\n debug.info(4,\"Set path: \" + str(path))\n\n # This is marked for debug\n path.set_path()\n\n # For debugging... if the path failed to route.\n if False or path==None:\n self.write_debug_gds()\n\n # First, simplify the path for\n #debug.info(1,str(self.path)) \n contracted_path = self.contract_path(path)\n debug.info(3,\"Contracted path: \" + str(contracted_path))\n \n return contracted_path","def broadcast() -> BroadcastDistribute:\n return _broadcast","def joinPath(path, *args):","def _broadcast(self, msg: str) -> None:\n from jesse.routes import router\n\n for r in router.routes:\n # skip self\n if r.strategy.id == self.id:\n continue\n\n if msg == 'route-open-position':\n r.strategy.on_route_open_position(self)\n elif msg == 'route-close-position':\n r.strategy.on_route_close_position(self)\n elif msg == 'route-increased-position':\n r.strategy.on_route_increased_position(self)\n elif msg == 'route-reduced-position':\n r.strategy.on_route_reduced_position(self)\n elif msg == 'route-canceled':\n r.strategy.on_route_canceled(self)\n\n r.strategy._detect_and_handle_entry_and_exit_modifications()","def empty_paths(self):\n self.paths[:]","def set(self, new_path):\n\n for i in range(self.depth):\n self.path[i] = new_path[self.max_input*i:self.max_input*(i + 1)]","def set_local_path(self):\n return HERE","def flatten_single_directories(self, name, path):\n self.m.path.assert_absolute(path)\n self._run(name, ['flatten_single_directories', path])","def normalize_path_from_root_per_parent(self, path):\n found_successor = self.root\n normalized_path = []\n for searched_successor in path:\n counter = 0\n successors = self.tree.successors(found_successor)\n for successor in successors:\n counter += 1\n if searched_successor == successor:\n normalized_path.append(counter)\n found_successor = searched_successor\n break\n\n assert (len(path) == len(normalized_path))\n return normalized_path","def broadcast(cls, controller):\n b = cls()\n\n for path in controller.paths:\n b.add_path(path)\n\n for dev in controller.devices:\n b.add_device(dev)\n\n return b","def local_paths(self) -> List[Path]:\n return self._local_paths","def collect_local(self, path, req_tag=True):\n for f in [os.path.join(dp, f) for dp, dn, filenames in os.walk(path) for f in filenames]:\n if not os.path.isfile(f):\n continue\n self.collect_single(f, req_tag)","def rsync_from(self, remote, local, flags='-av', user='root'):\n if self._portoffset:\n tail = \"/\" if local[-1] == \"/\" else ''\n local = os.path.abspath(os.path.expanduser(local)) + tail\n return self._rsync(\n 'rsync {flags} rsync://{usr}'\n '@{target}:{port}{src} {dest}'.format(\n flags=flags, src=remote, usr=user, target=self._target,\n port=(self._portoffset + 873), dest=local))\n return False","def _exchange_ghosts_mpi(self):\n for d in xrange(self._dim):\n if d in self._cutdir_list:\n self._exchange_ghosts_mpi_d(d)\n else:\n self._exchange_ghosts_local_d(d)","def path_extern_mounts(self) -> PurePath:\n return self.path_extern_supervisor / MOUNTS_FOLDER","def _get_local_src(self, path: Path) -> Path:\n src = \"\"\n\n if str(path).startswith(\"~\"):\n path = Path(str(path).replace(\"~/\", \"\"))\n\n if self.category == \"global\":\n src = f\"{self.local_base}/global{path}\"\n elif self.category == \"local\":\n src = f\"{self.local_base}/local/{path}\"\n else:\n src = f\"{self.local_base}/custom/{path}\"\n\n return Path(src)","def push(self, path):\n if path not in self.paths:\n self.paths.add(path)\n heappush(self.priority_queue, (path.path_cost, path))","def get(self, remote_path, local_path='',\n recursive=False, preserve_times=False):\n if not isinstance(remote_path, (list, tuple)):\n remote_path = [remote_path]\n remote_path = [self.sanitize(asbytes(r)) for r in remote_path]\n self._recv_dir = local_path or os.getcwd()\n self._rename = (len(remote_path) == 1 and\n not os.path.isdir(os.path.abspath(local_path)))\n if len(remote_path) > 1:\n if not os.path.exists(self._recv_dir):\n raise SCPException(\"Local path '%s' does not exist\" %\n asunicode(self._recv_dir))\n elif not os.path.isdir(self._recv_dir):\n raise SCPException(\"Local path '%s' is not a directory\" %\n asunicode(self._recv_dir))\n rcsv = (b'', b' -r')[recursive]\n prsv = (b'', b' -p')[preserve_times]\n self.channel = self._open()\n self._pushed = 0\n self.channel.settimeout(self.socket_timeout)\n self.channel.exec_command(b\"scp\" +\n rcsv +\n prsv +\n b\" -f \" +\n b' '.join(remote_path))\n self._recv_all()\n self.close()","def loadPaths(self):\n for ij in self.link:\n self.link[ij].flow = 0\n for p in self.path:\n for ij in self.path[p].links:\n self.link[ij].flow += self.path[p].flow\n for ij in self.link:\n self.link[ij].updateCost()\n for p in self.path:\n self.path[p].updateCost()","def calculateFirstPath(self):\n rd.shuffle(self.goals)\n self.path = self.goals","def test_path7():\n path = [(0, 0, 1)]\n path += [\n [('A', 3, 0)],\n (0, 1, 1),\n [('A', 2, 0)],\n (np.pi/2, 1, 1),\n [('B',3,0)],\n (0, 1, 1),\n [('B',2,0)],\n (np.pi/2, 1, 1),\n [('C',3,0)],\n (0, 1, 1),\n [('C',2,0)],\n (np.pi/2, 1, 1),\n [('D', 3, 0)],\n (0, 1, 1),\n [('D', 2,0)],\n (np.pi/2, 1, 1),\n ] * 4\n execute_path(path,True)","def __init__(self, root_rank, device=''):\n super(BroadcastGlobalVariablesHook, self).__init__()\n self.root_rank = root_rank\n self.bcast_op = None\n self.device = device","def reset_path(self):\n for i in self.grid:\n for y in i:\n y.g = 0\n y.h = 0\n y.f = 0\n y.parent = None\n y.visited = False","def map_to_local(self, idxs):\n return F.zerocopy_from_dgl_ndarray(\n _CAPI_DGLNDArrayPartitionMapToLocal(\n self._partition, F.zerocopy_to_dgl_ndarray(idxs)\n )\n )","def sync_from_root(self):\n self.optimizer.sync_from_root(self.trainable_vars)","def syncFromClient(self):\n\n # Acquire the client thread semaphore\n S_SEM.acquire()\n self.updateIndex()\n try:\n # Wait for signal then sends server's directory\n print('Started sync from client...')\n self.wait('OK')\n self.send(LOCAL_DIR)\n\n # Encode, wait for signal then send index to client\n outpkg = json.dumps(self.serverindex)\n self.wait('OK')\n self.send(outpkg)\n\n # Receive requests and files from client\n Q_LOCK.acquire()\n while True:\n request = self.receive()\n if request:\n job = tuple(request.split(','))\n self.send('OK')\n\n # Atomically add a single batch of sync jobs\n # Wait and receive file for all copy jobs\n # Put job and file in queue\n if job[0] == 'CP':\n file = self.receive(isFile=True)\n self.send('OK')\n self.jobqueue.append((job, file))\n\n # Finish adding jobs to the client\n elif job[0] == 'DONE':\n self.jobqueue.append((job, None))\n print('Done syncing from client!')\n Q_LOCK.release()\n break\n\n # Put job into jobqueue if not copy job\n else:\n self.jobqueue.append((job, None))\n\n # Start worker thread that will write to the local directory\n # Release the semaphore for the worker thread\n workerthread = WorkerThread(self.jobqueue, self)\n workerthread.start()\n THREADS['WorkerThread[{}]'.format(self.threadID)] = workerthread\n W_SEM.release()\n workerthread.join()\n self.updateIndex()\n except:\n S_SEM.release()\n self.updateIndex()","def relax_path( self, initial_path=None, n_shooting_moves=10000 ):\n if ( initial_path is None ):\n raise ValueError( \"Filename containing the initial path was not given!\" )\n self.load_path( initial_path )\n self.nuc_mc.remove_network_observers()\n min_slice,max_slice = self.find_timeslices_in_transition_region()\n self.log ( \"First timeslice outside reaction region: {}\".format(min_slice))\n self.log ( \"First timeslice in product region: {}\".format(max_slice))\n\n direcions = [\"forward\",\"backward\"]\n min_slice = 0\n max_slice = len(self.init_path[\"energy\"])\n for move in range(n_shooting_moves):\n if move%10 == 0:\n self.center_barrier()\n self.path2trajectory()\n\n self.nuc_mc.reset()\n direct = direcions[np.random.randint(low=0,high=2)]\n self.log( \"Move {} of {}\".format(move,n_shooting_moves) )\n timeslice = np.random.randint(low=min_slice,high=max_slice )\n self.log(\"Starting from timeslice {} of {}\".format(timeslice,max_slice))\n self.shooting_move(timeslice)\n\n ofname = initial_path.rpartition(\".\")[0]\n ofname += \"_relaxed.json\"\n with open(ofname,'w') as outfile:\n json.dump(self.init_path,outfile)\n self.log( \"Relaxed path written to {}\".format(ofname) )\n new_peak_energy = np.max(self.init_path[\"energy\"])\n self.log( \"Maximum energy along path changed from {} eV to {} eV\".format(self.initial_peak_energy,new_peak_energy) )","def forward_kinematics(self):\n def product(L):\n if len(L) == 0:\n return sp.Matrix.eye(4)\n cum = L[0]\n for i in L[1:]:\n cum *= i\n return cum\n\n # routes to base link\n paths = self.paths()\n # symbolic matrices\n matrices = {name: j.matrix for name, j in self.joints.items()}\n\n #\n combined = {k: product([matrices[i] for i in path])\n for k, path in paths.items()}\n\n return combined","def add_path(self, path):\n\n for i in range(1, len(path)):\n self.add_edge(path[i], path[i - 1])","def test_path1():\n path = [(0,0,1)]\n path.append([('A',4,0)])\n path.append((0,1,1))\n path.append([('A',3,0)])\n path.append((0,1,1))\n path.append([('A',2,0)])\n path.append((0,1,1))\n path.append([('A',1,0)])\n\n execute_path(path, True)","def local_useless_rebroadcast(node):\r\n if isinstance(node.op, T.Rebroadcast):\r\n x = node.inputs[0]\r\n if numpy.all(x.broadcastable == node.outputs[0].broadcastable):\r\n # No broadcastable flag was modified\r\n return [x]\r\n else:\r\n # Keep the flags that modify something\r\n new_axis = {}\r\n for dim, bc in node.op.axis.items():\r\n if x.broadcastable[dim] != bc:\r\n new_axis[dim] = bc\r\n if new_axis == node.op.axis:\r\n # All flags are useful\r\n return\r\n else:\r\n return [T.Rebroadcast(*new_axis.items())(x)]","def tails_local_path(self, new_path: str):\n self._tails_local_path = new_path","def get_srv_paths(self, sw, graph=None, local=False):\n if graph == None:\n graph = self.graph\n\n paths = []\n\n if local:\n avail_srvs = self.localservers\n else:\n avail_srvs = self.servers\n\n assert graph != None\n assert len(sw) > 0\n assert len(avail_srvs)> 0\n\n for server in avail_srvs:\n paths.append(nx.shortest_path(graph, server, sw))\n\n return paths","def broadcast_variables(variables, root_rank, scope=''):\n if size() <= 1:\n return tf.group(*variables)\n _assign = tf.assign if hasattr(tf, 'assign') else tf.compat.v1.assign\n return tf.group(*[_assign(var, broadcast(var, root_rank, scope))\n for var in variables])","def normpath(path):\n normalized = os.path.join(*path.split(\"/\"))\n if os.path.isabs(path):\n return os.path.abspath(\"/\") + normalized\n else:\n return normalized","def broadcast(tensor, from_process: int = 0):\n if PartialState().distributed_type == DistributedType.TPU:\n return _tpu_broadcast(tensor, src=from_process, name=\"accelerate.utils.broadcast\")\n elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:\n return _gpu_broadcast(tensor, src=from_process)\n else:\n return tensor","def get_path(self, r, c, blocked):\n # Create grid\n grid = [[0]*c for _ in range(r)]\n for br, bc in blocked:\n grid[br][bc] = 1\n\n return self.get_path_helper(grid, 0, 0, \"\", set())","def _execute_and_merge(self, path):\n\n state = path.state\n se = state.se\n ip_int = path.addr\n\n # Remove path._run\n path._run = None\n\n # Build a CFG out of the current function\n\n cfg_key = (ip_int, path.jumpkind)\n if cfg_key in self.cfg_cache:\n cfg, cfg_graph_with_loops = self.cfg_cache[cfg_key]\n\n else:\n if self._enable_function_inlining:\n call_tracing_filter = CallTracingFilter(self.project, depth=0)\n filter = call_tracing_filter.filter\n else:\n filter = None\n # To better handle syscalls, we make a copy of all registers if they are not symbolic\n cfg_initial_state = self.project.factory.blank_state(mode='fastpath')\n # FIXME: This is very hackish\n # FIXME: And now only Linux-like syscalls are supported\n if self.project.arch.name == 'X86':\n if not state.se.symbolic(state.regs.eax):\n cfg_initial_state.regs.eax = state.regs.eax\n elif self.project.arch.name == 'AMD64':\n if not state.se.symbolic(state.regs.rax):\n cfg_initial_state.regs.rax = state.regs.rax\n\n cfg = self.project.analyses.CFG(starts=((ip_int, path.jumpkind),),\n context_sensitivity_level=0,\n call_depth=0,\n call_tracing_filter=filter,\n initial_state=cfg_initial_state\n )\n cfg.normalize()\n cfg_graph_with_loops = networkx.DiGraph(cfg.graph)\n cfg.unroll_loops(self._loop_unrolling_limit)\n\n self.cfg_cache[cfg_key] = (cfg, cfg_graph_with_loops)\n\n loop_backedges = cfg._loop_back_edges\n loop_heads = set([ dst.addr for _, dst in loop_backedges ])\n\n # Find all merge points\n merge_points = self._get_all_merge_points(cfg, cfg_graph_with_loops)\n l.debug('Merge points: %s', [ hex(i[0]) for i in merge_points ])\n\n #\n # Controlled symbolic exploration\n #\n\n # Initialize the beginning path\n initial_path = path\n initial_path.info['loop_ctrs'] = defaultdict(int)\n initial_path.info['actionqueue_list'] = [ self._new_actionqueue() ]\n\n # This is a special hack for CGC stuff, since the CGCAnalysis relies on correct conditions of file actions\n # Otherwise we may just save out those actions, and then copy them back when returning those paths\n initial_path.history._events = [ a for a in initial_path.actions if a.type.startswith('file') ]\n\n path_group = PathGroup(self.project,\n active_paths=[ initial_path ],\n immutable=False,\n resilience=o.BYPASS_VERITESTING_EXCEPTIONS in initial_path.state.options)\n # Initialize all stashes\n for stash in self.all_stashes:\n path_group.stashes[stash] = [ ]\n # immediate_dominators = cfg.immediate_dominators(cfg.get_any_node(ip_int))\n\n saved_paths = { }\n\n def is_path_errored(path):\n if path.errored:\n return True\n elif len(path.jumpkinds) > 0 and path.jumpkinds[-1] in Path._jk_all_bad:\n l.debug(\"Errored jumpkind %s\", path.jumpkinds[-1])\n path._error = AngrPathError('path has a failure jumpkind of %s' % path.jumpkinds[-1])\n else:\n try:\n if path._run is None:\n ip = path.addr\n # FIXME: cfg._nodes should also be updated when calling cfg.normalize()\n size_of_next_irsb = [n for n in cfg.graph.nodes() if n.addr == ip][0].size\n path.step(max_size=size_of_next_irsb)\n except (AngrError, SimError, ClaripyError) as ex:\n l.debug('is_path_errored(): caxtching exception %s', ex)\n path._error = ex\n except (TypeError, ValueError, ArithmeticError, MemoryError) as ex:\n l.debug(\"is_path_errored(): catching exception %s\", ex)\n path._error = ex\n\n return False\n\n def is_path_overbound(path):\n \"\"\"\n Filter out all paths that run out of boundaries or loop too many times\n \"\"\"\n\n ip = path.addr\n\n if ip in self._boundaries:\n l.debug(\"... terminating Veritesting due to overbound\")\n return True\n\n if (ip in loop_heads # This is the beginning of the loop\n or path.jumpkind == 'Ijk_Call' # We also wanna catch recursive function calls\n ):\n path.info['loop_ctrs'][ip] += 1\n\n if path.info['loop_ctrs'][ip] >= self._loop_unrolling_limit + 1:\n l.debug('... terminating Veritesting due to overlooping')\n return True\n\n l.debug('... accepted')\n return False\n\n\n def generate_successors(path):\n ip = path.addr\n\n l.debug(\"Pushing 0x%x one step forward...\", ip)\n\n # FIXME: cfg._nodes should also be updated when calling cfg.normalize()\n size_of_next_irsb = [ n for n in cfg.graph.nodes() if n.addr == ip ][0].size\n # It has been called by is_path_errored before, but I'm doing it here anyways. Who knows how the logic in\n # PathGroup will change in the future...\n path.step(max_size=size_of_next_irsb)\n\n # Now it's safe to call anything that may access Path.next_run\n if self._path_callback:\n copied_path = path.copy()\n self._unfuck(copied_path)\n self._path_callback(copied_path)\n\n successors = path.successors\n\n # Get all unconstrained successors, and save them out\n if path.next_run:\n for s in path.next_run.unconstrained_successors:\n u_path = Path(self.project, s, path=path)\n path_group.stashes['unconstrained'].append(u_path)\n\n # Record their guards :-)\n for successing_path in successors:\n if 'guards' not in successing_path.info:\n successing_path.info['guards'] = [ ]\n last_guard = successing_path.guards[-1]\n if not successing_path.state.se.is_true(last_guard, exact=False):\n successing_path.info['guards'].append(last_guard)\n\n # Fill the ActionQueue list\n if len(successors) == 1:\n # Expand the last ActionQueue\n if not successors[0].info['actionqueue_list']:\n successors[0].info['actionqueue_list'].append(self._new_actionqueue())\n self._get_last_actionqueue(successors[0]).actions.extend(successors[0].last_actions)\n\n elif len(successors) > 1:\n # Save this current path, since we might need it in the future\n path_key = (path.addr, self._get_last_actionqueue(path).id)\n saved_paths[path_key] = path\n\n # Generate a new ActionQueue for each successor\n for successing_path in successors:\n successing_path.info['actionqueue_list'].append(self._new_actionqueue(parent_key=path_key))\n self._get_last_actionqueue(successing_path).actions.extend(successing_path.last_actions)\n\n l.debug(\"... new successors: %s\", successors)\n return successors\n\n def _path_not_in_cfg(p):\n \"\"\"\n Returns if p.addr is not a proper node in our CFG\n\n :param p: The Path instance to test.\n :return: False if our CFG contains p.addr, True otherwise\n \"\"\"\n\n n = cfg.get_any_node(p.addr, is_syscall=p.jumpkinds[-1].startswith('Ijk_Sys'))\n if n is None:\n return True\n\n if n.simprocedure_name == 'PathTerminator':\n return True\n\n return False\n\n while path_group.active:\n # Step one step forward\n l.debug('Steps %s with %d active paths: [ %s ]',\n path_group,\n len(path_group.active),\n path_group.active)\n\n # Apply self.deviation_func on every single active path, and move them to deviated stash if needed\n if self._deviation_filter is not None:\n path_group.stash(filter_func=self._deviation_filter, from_stash='active', to_stash='deviated')\n\n # Mark all those paths that are out of boundaries as successful\n path_group.stash(filter_func=is_path_overbound, from_stash='active', to_stash='successful')\n\n path_group.step(successor_func=generate_successors, check_func=is_path_errored)\n if self._terminator is not None and self._terminator(path_group):\n for p in path_group.unfuck:\n self._unfuck(p)\n break\n\n # Stash all paths that we do not see in our CFG\n path_group.stash(filter_func=_path_not_in_cfg,\n to_stash=\"deviated\"\n )\n\n # Stash all paths that we do not care about\n path_group.stash(filter_func=\n lambda p: (p.state.scratch.jumpkind not in\n ('Ijk_Boring', 'Ijk_Call', 'Ijk_Ret', 'Ijk_NoHook')\n and not p.state.scratch.jumpkind.startswith('Ijk_Sys')\n ),\n to_stash=\"deadended\"\n )\n if path_group.deadended:\n l.debug('Now we have some deadended paths: %s', path_group.deadended)\n\n # Stash all possible paths that we should merge later\n for merge_point_addr, merge_point_looping_times in merge_points:\n path_group.stash_addr(merge_point_addr,\n to_stash=\"_merge_%x_%d\" % (merge_point_addr, merge_point_looping_times)\n )\n\n # Try to merge a set of previously stashed paths, and then unstash them\n if not path_group.active:\n merged_anything = False\n\n for merge_point_addr, merge_point_looping_times in merge_points:\n if merged_anything:\n break\n\n stash_name = \"_merge_%x_%d\" % (merge_point_addr, merge_point_looping_times)\n\n if stash_name in path_group.stashes:\n # Try to prune the stash, so unsatisfiable paths will be thrown away\n path_group.prune(from_stash=stash_name, to_stash='pruned')\n if 'pruned' in path_group.stashes and len(path_group.pruned):\n l.info('... pruned %d paths from stash %s', len(path_group.pruned), stash_name)\n # Remove the pruned stash to save memory\n path_group.drop(stash='pruned')\n\n stash = path_group.stashes[stash_name]\n if not len(stash):\n continue\n\n # Group all those paths based on their callstacks\n groups = defaultdict(list)\n for p in stash:\n groups[p.callstack].append(p)\n\n l.debug('Trying to merge and activate stash %s', stash_name)\n l.debug('%d paths are grouped into %d groups based on their callstacks',\n len(stash),\n len(groups)\n )\n\n for g in groups.itervalues():\n if len(g) == 1:\n # Just unstash it\n p = g[0]\n path_group.stashes[stash_name].remove(p)\n\n if any([loop_ctr >= self._loop_unrolling_limit + 1 for loop_ctr in p.info['loop_ctrs'].itervalues()]):\n l.debug(\"%s is overlooping\", p)\n path_group.deadended.append(p)\n else:\n l.debug('Put %s into active stash', p)\n path_group.active.append(p)\n merged_anything = True\n\n elif len(g) > 1:\n for p in g:\n path_group.stashes[stash_name].remove(p)\n\n # Merge them first\n\n # Find the previous dominator for all those\n # Determine their common ancestor\n ancestor_key = self._determine_ancestor(g)\n initial_path = saved_paths[ancestor_key]\n merged_path = self._merge_path_list(se, initial_path, g)\n\n if any([ loop_ctr >= self._loop_unrolling_limit + 1 for loop_ctr in merged_path.info['loop_ctrs'].itervalues() ]):\n l.debug(\"%s is overlooping\", merged_path)\n path_group.deadended.append(merged_path)\n else:\n l.debug('Put %s into active stash', p)\n path_group.active.append(merged_path)\n\n merged_anything = True\n\n if any([ len(path_group.stashes[stash_name]) for stash_name in self.all_stashes]):\n # Remove all stashes other than errored or deadended\n path_group.stashes = { name: stash for name, stash in path_group.stashes.items()\n if name in self.all_stashes }\n\n for stash in path_group.stashes:\n path_group.apply(lambda p: self._unfuck(p), stash=stash)\n\n return path_group","def localPython ( localPath ) :\r\n\r\n if not type( localPath ) == str : return\r\n\r\n if not localPath.endswith( os.sep ) : localPath = localPath + os.sep\r\n\r\n # reads the paths to add to sys.path\r\n \r\n try :\r\n\r\n handler = open( localPath + \"sysPath.txt\", \"r\" )\r\n\r\n text = handler.read()\r\n\r\n handler.close()\r\n\r\n items = text.splitlines()\r\n\r\n except Exception, exception :\r\n\r\n items = [ ]\r\n\r\n\r\n # places the local paths before the previous search paths. only those that exist\r\n\r\n sysPath = [ ]\r\n\r\n for item in items :\r\n\r\n item = item.strip().replace( \"\\\\\", os.sep ).replace( \"/\", os.sep )\r\n\r\n if len( item ) == 0 : continue\r\n\r\n item = item.strip( os.sep )\r\n\r\n item = localPath + item\r\n\r\n if item in sysPath : continue\r\n\r\n if not os.path.exists( item ) : continue\r\n\r\n sysPath.append( item )\r\n\r\n # places the previous paths. only those that exist\r\n\r\n\r\n for item in sys.path :\r\n\r\n if item in sysPath : continue\r\n\r\n if not os.path.exists( item ) : continue\r\n\r\n sysPath.append( item )\r\n\r\n sys.path = sysPath","def connecthostbroadcast(localpop,\n hwport_tosite,\n sitevlan,\n meter=3,\n broadcast_rewritemac = None):\n\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\n hwswitchname = hwswitch.resourceName\n swswitch = Container.fromAnchor(localpop.properties['SwSwitch'])\n swswitchname = swswitch.resourceName\n topology = Container.getContainer(localpop.properties['SwSwitch']['containerName'])\n\n # Find the port on the HwSwitch connected to the software switch\n links = getlinks2(topology, hwswitchname, swswitchname)\n if links == None or len(links) == 0:\n print \"No links from\", hwswitchname, \"to\", swswitchname\n return False\n hwport_tosw = None\n for link in links:\n (node, port) = linkednode2(link, swswitchname)\n if port != None:\n # Found the link we're looking for\n hwport_tosw = port\n break\n\n broadcast = \"FF:FF:FF:FF:FF:FF\"\n translated_broadcast = broadcast\n if broadcast_rewritemac != None:\n translated_broadcast = broadcast_rewritemac\n\n fh1 = SCC.SdnInstallForward1(javaByteArray2(hwswitch.properties['DPID']),\n 1,\n BigInteger.ZERO,\n str(hwport_tosw),\n int(sitevlan),\n \"00:00:00:00:00:00\",\n translated_broadcast,\n str(hwport_tosite),\n int(sitevlan),\n broadcast,\n 0,\n 0,\n meter)\n if fh1 == None:\n return None\n\n fh2 = SCC.SdnInstallForward1(javaByteArray2(hwswitch.properties['DPID']),\n 1,\n BigInteger.ZERO,\n str(hwport_tosite),\n int(sitevlan),\n \"00:00:00:00:00:00\",\n broadcast,\n str(hwport_tosw),\n int(sitevlan),\n translated_broadcast,\n 0,\n 0,\n meter)\n if fh2 == None:\n SCC.deleteforward(fh1)\n return None\n\n return (fh1, fh2)","def compute_path(self,\n path):\n self.subidx = ~np.isnan(self.y_data)\n leaf_id = 0\n for decision in path:\n node = self.tree[leaf_id]\n if decision == 0:\n leaf_id = node.id_null\n self.subidx = (self.subidx &\n np.isnan(self.split_data[node.label]))\n else:\n non_null = ~np.isnan(self.split_data[node.label])\n new_subidx = np.zeros_like(self.split_data[node.label],\n dtype=bool)\n if decision < 0:\n leaf_id = node.id_lower\n new_subidx[non_null] = \\\n self.split_data[node.label][non_null] < node.cutoff\n elif decision > 0:\n leaf_id = node.id_higher\n new_subidx[non_null] = \\\n self.split_data[node.label][non_null] > node.cutoff\n self.subidx = self.subidx & new_subidx\n\n self.sub_y_data = self.y_data[self.subidx]\n self.sub_split_data = self.split_data[:, self.subidx]\n self.sub_bin_data = self.bin_data[self.subidx]\n self.sub_y_ranks = rankdata(self.sub_y_data)\n self.sub_split_args = self.sub_split_data.argsort(axis=1)","def path(self, *path):\n path = list(filter(None, path))\n path = self.remove_prefix(path)\n items = [self.prefix_] + path\n return self.delimiter.join(items)","def make_local_path(self, *args):\n return os.path.normpath(os.path.join(\n os.path.dirname(api.env.real_fabfile), *args).rstrip(os.path.sep))","def add_weighted_path(self, path, weights, scale=1):\n mn, mx = float(min(weights)), max(weights)\n n = len(weights)\n weights_normed = np.array(weights)\n #weights_normed = (weights - mn) / (mx - mn)\n\n last_w = weights_normed[0]\n lat1 = (path[0][0] + path[1][0]) / 2.\n lon1 = (path[0][1] + path[1][1]) / 2.\n\n newpath = [path[0], [lat1, lon1]]\n\n for i in xrange(1, n - 1):\n lat1 = (path[i - 1][0] + path[i][0]) / 2.\n if np.sign(path[i - 1][1]) == np.sign(path[i][1]):\n lon1 = (path[i - 1][1] + path[i][1]) / 2.\n else:\n lat1, lon1 = path[i - 1]\n\n lat2 = (path[i][0] + path[i + 1][0]) / 2.\n\n if np.sign(path[i][1]) == np.sign(path[i + 1][1]):\n lon2 = (path[i][1] + path[i + 1][1]) / 2.\n else:\n lat2, lon2 = path[i + 1]\n\n if weights_normed[i] == last_w:\n newpath.append([lat1, lon1])\n newpath.append(path[i])\n newpath.append([lat2, lon2])\n else:\n col = val2hex(last_w, scale)\n self.add_path(newpath, \"#\" + col)\n newpath = []\n newpath.append([lat1, lon1])\n newpath.append(path[i])\n newpath.append([lat2, lon2])\n last_w = weights_normed[i]\n\n if weights_normed[- 1] != last_w:\n col = val2hex(last_w, scale)\n self.add_path(newpath, \"#\" + col)\n newpath = []\n\n lat1 = (path[- 2][0] + path[- 1][0]) / 2.\n lon1 = (path[- 2][1] + path[- 1][1]) / 2.\n newpath.append([lat1, lon1])\n newpath.append(path[-1])\n col = val2hex(weights_normed[-1], scale)\n self.add_path(newpath, \"#\" + col)","def optimal_path(self, mission, start, sp):\n mission.add(start)\n while mission.targets[0] and mission.is_active():\n ds = [(sp[start][t], t) for t in mission.targets[0] if t in sp[start]]\n if not ds:\n mission.add(u'-1') # target not connected --> fill with dummies\n continue\n target = min(ds)\n for i in range(target[0] - 1):\n mission.add(u'0')\n mission.add(target[1])\n start = target[1]","def _staf_dir_copy(self, local_path, remote_path):\n\n staf_request = ('COPY DIRECTORY \"{0}\" TODIRECTORY \"{1}\" TOMACHINE \"{2}\" RECURSE '\n 'KEEPEMPTYDIRECTORIES'.format(unix_style_path(local_path),\n unix_style_path(remote_path),\n self._sut.network_address))\n\n result = self._staf_handle.submit('local', 'fs', staf_request)\n\n if result.rc != result.Ok:\n raise CoreError(result.result)","def upload_cluster(self, localpath, remotepath):\n if isinstance(localpath, str):\n filename = os.path.split(localpath)[-1]\n self.upload(localpath, filename)\n self.execute_command(\n \"hdfs dfs -put {0} {1}\".format(filename, remotepath))\n self.execute_command(\"rm {0}\".format(filename))\n else:\n self.upload(localpath, \".\")\n for afile in localpath:\n filename = os.path.split(afile)[-1]\n self.execute_command(\n \"hdfs dfs -put {0} {1}\".format(filename, remotepath))\n self.execute_command(\"rm {0}\".format(filename))\n\n return remotepath","def broadcast():\n # global receiving_message\n # if not receiving_message:\n router.broadcast(clients.copy(), json.dumps(current_state))","def broadcast_parameter(self, value_broadcast, config_options, param_type=int):\n\n dtype = np.dtype(param_type)\n\n if self.rank == 0:\n param = np.asarray(value_broadcast, dtype=dtype)\n else:\n param = np.empty(dtype=dtype, shape=())\n\n try:\n self.comm.Bcast(param, root=0)\n except MPI.Exception:\n config_options.errMsg = \"Unable to broadcast single value from rank 0.\"\n err_handler.log_critical(config_options, self)\n return None\n return param.item(0)","def plan_path(self, msg):\n # Request the map\n # In case of error, return an empty path\n mapdata = PathPlanner.request_map()\n\n if mapdata is None:\n return Path()\n # Calculate the C-space and publish it\n cspacedata = self.calc_cspace(mapdata, 3)\n # Execute A*\n start = PathPlanner.world_to_grid(mapdata, msg.start.pose.position)\n goal = PathPlanner.world_to_grid(mapdata, msg.goal.pose.position)\n \n path = self.a_star(cspacedata, start, goal) #, self.c_space_array, self.frontier, self.expanded)\n \n # Optimize waypoints\n waypoints = PathPlanner.optimize_path(path)\n # print waypoints\n waypoints.remove(waypoints[0])\n # print waypoints\n\n self.path_pub.publish(self.path_to_message(cspacedata, waypoints))\n # Return a Path message\n return self.path_to_message(cspacedata, waypoints)","def rsync_to(self, local, remote, flags='-av', user='root'):\n if self._portoffset:\n tail = \"/\" if local[-1] == \"/\" else ''\n local = os.path.abspath(os.path.expanduser(local)) + tail\n return self._rsync(\n 'rsync {flags} {src} '\n 'rsync://{usr}@{target}:{port}{dest}'.format(\n flags=flags, src=local, usr=user, target=self._target,\n port=(self._portoffset + 873), dest=remote))\n return False","def set_local(self):\n self.write_to_serial(':SYST:LOC')","def broadcast(self, message):\n for s in self.connections:\n s.send(bytes(message, encoding='utf-8'))","def _write_back_to_local_shard(self, handles: List[FlatParamHandle]):\n for handle in handles:\n # For `NO_SHARD`, `_local_shard` is the unsharded flattened\n # parameter as well\n if not handle.uses_sharded_strategy:\n continue\n assert (\n handle.flat_param.ndim == 1\n ), f\"Expects `flat_param` to be flattened but got {handle.flat_param.shape}\"\n # Get the unpadded shard instead of the padded shard to persist\n # user changes to the padding (though FSDP does not explicitly\n # support this)\n shard, _ = FlatParamHandle._get_unpadded_shard(handle.flat_param, handle.rank, handle.world_size)\n handle.flat_param._local_shard[:shard.numel()].copy_(shard)","def local_action(self, *links, all_links):\n S = 0.0\n for link in links:\n site1 = link[:-1]\n u = link[-1]\n for v in range(self.dim):\n if v != u:\n site2 = np.array(site1) - self.bases[v]\n plaq1 = self.plaquette_operator(site1, u, v, all_links)\n plaq2 = self.plaquette_operator(site2, u, v, all_links)\n S += (plaq1 + plaq2)\n return S","def local_subdir_accessible(self, local_subdir_accessible):\n \n self._local_subdir_accessible = local_subdir_accessible","def local_subtensor_lift(node):\r\n if isinstance(node.op, Subtensor):\r\n u = node.inputs[0]\r\n if not u.owner or len(u.clients) > 1:\r\n return False\r\n\r\n if isinstance(u.owner.op, T.Elemwise) and len(u.owner.inputs) == 1:\r\n idx = node.inputs[1:]\r\n x_idx = node.op(u.owner.inputs[0], *idx)\r\n return [u.owner.op(x_idx)]\r\n\r\n if isinstance(u.owner.op, T.Elemwise):\r\n new_inputs = []\r\n if all([sum(i.type.broadcastable) == 0 for i in u.owner.inputs]):\r\n # There is no broadcastable in the inputs\r\n idx = node.inputs[1:]\r\n new_inputs = [node.op(i, *idx) for i in u.owner.inputs]\r\n return [u.owner.op(*new_inputs)]\r\n elif all([sum(i.type.broadcastable) in [i.ndim, 0]\r\n for i in u.owner.inputs]):\r\n # There is no broadcastable in the inputs or it is scalar\r\n idx = node.inputs[1:]\r\n new_inputs = []\r\n for i in u.owner.inputs:\r\n if sum(i.type.broadcastable) == 0:\r\n new_inputs.append(node.op(i, *idx))\r\n else:\r\n # If the subtensor remove some dims, we must\r\n # lower the number of dimensions of this scalar.\r\n if node.outputs[0].ndim == i.ndim:\r\n new_inputs.append(i)\r\n else:\r\n new_inputs.append(\r\n i.dimshuffle(['x'] * node.outputs[0].ndim))\r\n return [u.owner.op(*new_inputs)]\r\n\r\n if isinstance(u.owner.op, T.Rebroadcast):\r\n # make sure that Rebroadcast has only 1 input\r\n assert len(u.owner.inputs) == 1\r\n\r\n # Subtensor might reduce dim., adapt broadcast pattern accordingly\r\n new_axis = []\r\n\r\n # loop through indices being subtensor-ed\r\n # i indexes broadcastable pattern before subtensor\r\n # j indexes broadcastable pattern after subtensor\r\n j = 0\r\n for (i, x) in enumerate(node.op.idx_list):\r\n # if its not a slice, it will reduce the dimension, should\r\n # not appear in the broascastable dimensions\r\n if isinstance(x, slice):\r\n new_axis += [(j, u.broadcastable[i])]\r\n j += 1\r\n # now keep the broadcastable pattern of all\r\n # items not appearing in subtensor list\r\n for i in xrange(len(node.op.idx_list), len(u.broadcastable)):\r\n new_axis += [(j, u.broadcastable[i])]\r\n j += 1\r\n\r\n subt_x = node.op(u.owner.inputs[0], *node.inputs[1:])\r\n rbcast_subt_x = T.Rebroadcast(*new_axis)(subt_x)\r\n\r\n return [rbcast_subt_x]","def _get_reaction_path(self):\n ## check if the atoms are on the same side of the unit cell\n cell = self.atomsIS.get_cell() # same cell used in IS and FS hopefully\n # get the vector respresenting the difference of the two \n vector_all = self.atomsIS.get_positions() - self.atomsFS.get_positions()\n vectors = vector_all[self.indices]\n min_vec = []\n for v in vectors:\n vmin, vlen = geometry.find_mic(v, cell, pbc=True)\n min_vec.append(vmin)\n ravel_vec = np.ravel(min_vec)\n self.modes.append( ravel_vec / np.linalg.norm(ravel_vec) )","def local_root_accessible(self, local_root_accessible):\n \n self._local_root_accessible = local_root_accessible","def _send(x, dst=0):\n x = torch.tensor(x)\n x = to_device(x)\n dist.send(x, dst)\n del x \n torch.cuda.empty_cache()","def test_path5():\n path = [(0, 0, 1)]\n path += [\n [('A', 3, 0)],\n (0, 1, 1),\n [('A', 2, 0)],\n (np.pi/2, 1, 1),\n [('B',3,0)],\n (0, 1, 1),\n [('B',2,0)],\n (np.pi/2, 1, 1),\n [('C',3,0)],\n (0, 1, 1),\n [('C',2,0)],\n (np.pi/2, 1, 1),\n [('D', 3, 0)],\n (0, 1, 1),\n #[('D',2,0)],\n (np.pi/2, 1, 1),\n ] * 4\n execute_path(path,True)","def PushPath(self, request_iterator, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def sync(self):\n if (len(self._source_list) == 0) or (len(self._dest_list) == 0):\n return\n \n for src in self._source_list:\n self._sync('.', src, self._depth)","def process_broadcast(data):\n logger.info(f\"Broadcast: {data}\")","def optimized_path(coords, startid, mask):\n coords = np.column_stack((coords, mask))\n pass_by = np.asarray(coords)\n path = [coords[startid]]\n pass_by = np.delete(pass_by, startid, axis=0)\n while pass_by.any():\n nearest_id, nearest = min(\n enumerate(pass_by), key=lambda x: distance(path[-1][:2], x[1][:2]))\n path.append(nearest)\n pass_by = np.delete(pass_by, nearest_id, axis=0)\n\n return path","def deploy_from_local():\n # don't die if tests fail\n # with settings(warn_only=True):\n # run_tests()\n # defaults rsync options:\n # -pthrvz\n # -p preserve permissions\n # -t preserve times\n # -h output numbers in a human-readable format\n # -r recurse into directories\n # -v increase verbosity\n # -z compress file data during the transfer\n extra_opts = '--omit-dir-times -e \"ssh\"' # -p 80\"'\n project.rsync_project(\n env.code_root,\n local_dir=env.local_dir,\n exclude=RSYNC_EXCLUDE,\n delete=True,\n extra_opts=extra_opts,\n )\n touch()\n restart_route()"],"string":"[\n \"def send_path(self, path):\\n self.clear_path()\\n for coordinate in path:\\n self.send_coordinate(coordinate)\\n time.sleep(0.05)\",\n \"def broadcast(value, root_rank, name=None):\\n return _impl.broadcast(K, value, root_rank, name)\",\n \"def extern_to_local_path(self, path: PurePath) -> Path:\\n return self.path_supervisor / path.relative_to(self.path_extern_supervisor)\",\n \"def local_to_extern_path(self, path: PurePath) -> PurePath:\\n return self.path_extern_supervisor / path.relative_to(self.path_supervisor)\",\n \"def Update(self, local_path_info):\\n if not local_path_info:\\n return\\n for priority_group in local_path_info._path_priority_groups:\\n group_list = []\\n for path in priority_group:\\n if not self.IsPathInLocalPaths(path):\\n group_list.append(path)\\n if group_list:\\n self._path_priority_groups.append(group_list)\",\n \"def local_rebroadcast_lift(node):\\r\\n op = node.op\\r\\n if not isinstance(op, T.Rebroadcast):\\r\\n return False\\r\\n\\r\\n input = node.inputs[0]\\r\\n inode = input.owner\\r\\n if inode and isinstance(inode.op, Elemwise) and len(inode.inputs) == 1:\\r\\n # It may happen that `input` has no client because this optimization\\r\\n # is called from `apply_rebroadcast_opt`, which in particular is used\\r\\n # by the `unbroadcast` function before we are in the actual function\\r\\n # compilation phase.\\r\\n if hasattr(input, 'clients') and len(input.clients) == 1:\\r\\n rval = inode.op.make_node(T.Rebroadcast(*op.axis.items())(\\r\\n inode.inputs[0])).outputs\\r\\n return rval\\r\\n if inode and isinstance(inode.op, T.Rebroadcast):\\r\\n # the \\\"axis\\\" specification in the outer Rebroadcast overrides\\r\\n # the axis of the inner one\\r\\n axis = inode.op.axis.copy()\\r\\n axis.update(op.axis)\\r\\n iinput = inode.inputs[0]\\r\\n rval = [T.Rebroadcast(*axis.items())(iinput)]\\r\\n return rval\",\n \"def _format_path_with_rank_zero(path: str) -> str:\\n return path.format(\\n rank=0,\\n local_rank=0,\\n node_rank=0,\\n )\",\n \"def broadcast_global_variables(root_rank):\\n return _impl.broadcast_global_variables(K, root_rank)\",\n \"def broadcast(data: T, root: int) -> T:\\n return collective.broadcast(data, root)\",\n \"def _format_path_with_current_rank(path: str) -> str:\\n return path.format(\\n rank=dist.get_global_rank(),\\n local_rank=dist.get_local_rank(),\\n node_rank=dist.get_node_rank(),\\n )\",\n \"def broadcast_global_variables(root_rank):\\n if _executing_eagerly():\\n raise RuntimeError(\\n \\\"bps.broadcast_global_variables() does not support eager execution. \\\"\\n \\\"Please use `bps.broadcast_variables(<model/optimizer variables>)` instead.\\\"\\n )\\n\\n return broadcast_variables(_global_variables(), root_rank)\",\n \"def _exchange_ghosts_local(self):\\n for d in xrange(self._dim):\\n self._exchange_ghosts_local_d(d)\",\n \"def comm_all_best_paths(self, peer):\\n LOG.debug('Communicating current best path for all afi/safi except'\\n ' 1/132')\\n # We will enqueue best path from all global destination.\\n for route_family, table in self._table_manager.iter:\\n if route_family == RF_RTC_UC:\\n continue\\n if peer.is_mbgp_cap_valid(route_family):\\n for dest in table.values():\\n if dest.best_path:\\n peer.communicate_path(dest.best_path)\",\n \"def broadcast(self):\\n comm.Barrier()\\n\\n if rank == 0:\\n dim = np.array([self.nspin, self.nkpt, self.nband], dtype=np.int)\\n else:\\n dim = np.empty(3, dtype=np.int)\\n\\n comm.Bcast([dim, MPI.INT])\\n\\n if rank != 0:\\n self.EIG = np.empty(dim, dtype=np.float64)\\n self.Kptns = np.empty((dim[1],3), dtype=np.float64)\\n\\n comm.Bcast([self.EIG, MPI.DOUBLE])\\n comm.Bcast([self.Kptns, MPI.DOUBLE])\",\n \"def traceback_all(self, path, input_data):\\r\\n current = path[-1]\\r\\n neighbours = self._get_neighbours(current, input_data)\\r\\n\\r\\n for vec in neighbours:\\r\\n if (vec.x == 0 and vec.y == 0 and vec.z == 0):\\r\\n path.append(vec)\\r\\n self.paths.append(copy.deepcopy(path))\\r\\n path.pop()\\r\\n else:\\r\\n path.append(vec)\\r\\n self.traceback_all(path, input_data)\\r\\n path.pop()\",\n \"def send_broadcast_packet(self, broadcast_packet: Packet) -> None:\\n for neighbor_address in [*self.children_addresses, self.parent_address]:\\n if neighbor_address:\\n self.stream.add_message_to_out_buff(neighbor_address, broadcast_packet)\\n log(f'Message packet added to out buff of Node({neighbor_address}).')\",\n \"def walk(self, priv_path:list):\\n # End conditions for recursive loop\\n current_node = priv_path[-1]\\n if current_node.location in self.destination and len(priv_path)>1:\\n self.addItinerary(priv_path)\\n self.n_routes+=1\\n return\\n if self.n_routes >= self.max_n_routes:\\n return\\n\\n if len(priv_path)>1:\\n # Get metadata of last edge type\\n last_edge = self.EdgeType(priv_path[-2], priv_path[-1])\\n else: # If it's start of itinerary, next edge would be travel edge\\n # So, make last edge as stay\\n last_edge = 'stay'\\n if last_edge == 'stay': # next edge will be travel i.e., ship not None\\n next_nodes = [node for node in self.G.neighbors(current_node) \\n if self.G.edges[current_node, node]['ship'] is not None]\\n else: # Next edge will be stay, i.e., ship = None\\n next_nodes = [node for node in self.G.neighbors(current_node)\\n if self.G.edges[current_node, node]['ship'] is None]\\n \\n for node in next_nodes:\\n self.walk(priv_path+[node])\",\n \"def zero_base_local(*args):\\n robots = get_robot_roots()\\n if not robots:\\n pm.warning('Nothing Selected; Select a valid robot')\\n return\\n\\n try:\\n for robot in robots:\\n local_ctrl_path = get_local_ctrl_path(robot)\\n pm.setAttr(local_ctrl_path + '.translate', 0, 0, 0)\\n pm.setAttr(local_ctrl_path + '.rotate', 0, 0, 0)\\n except:\\n pm.warning('Cannot zero base (local)')\",\n \"def update_path(self):\\r\\n if len(self.queue) == 0:\\r\\n return\\r\\n self.path[:] = []\\r\\n current = self.peek_queue()[0]\\r\\n while current in self.previous:\\r\\n self.path.append(current)\\r\\n current = self.previous[current]\",\n \"def local_path(G, grid, lcl, p1, p2):\\n\\n # create a local graph\\n lcl_graph = local_graph(G)\\n\\n # plan over the local graph from p1 to p2\\n path, _ = astar.a_star_graph(lcl_graph, utils.norm_distance, p1, p2)\\n print(path)\\n for p in path:\\n lcl.append(p)\\n return lcl\",\n \"def broadcast(self, tx):\\n\\n for neighbor_id in self.adjacencies:\\n self.sendMsg(neighbor_id, Message(self.id, Type.BLOCK, tx))\",\n \"def _build_path(self):\\n for point_3d in self.path_coordinates:\\n self.connect_point_with_neighbors(point_3d)\",\n \"def _get_local_dest(self, path: Path) -> Path:\\n dest = \\\"\\\"\\n\\n if str(path).startswith(\\\"~\\\"):\\n path = path.relative_to(\\\"~\\\")\\n\\n if self.category == \\\"global\\\":\\n dest = f\\\"{self.local_base}/global/{path}\\\"\\n elif self.category == \\\"local\\\":\\n dest = f\\\"{self.local_base}/local/{path}\\\"\\n else:\\n dest = f\\\"{self.local_base}/custom/{path}\\\"\\n\\n return Path(dest)\",\n \"def test_broadcast(self):\\n if _debug: TestSimple._debug(\\\"test_broadcast\\\")\\n\\n # create a network\\n tnet = TNetwork()\\n\\n # make a PDU from node 1 to node 2\\n pdu_data = xtob('dead.beef')\\n pdu = PDU(pdu_data, source=tnet.td.address, destination=LocalBroadcast())\\n if _debug: TestSimple._debug(\\\" - pdu: %r\\\", pdu)\\n\\n # test device sends it, iut gets it\\n tnet.td.start_state.send(pdu).success()\\n tnet.iut.start_state.receive(PDU, pduSource=tnet.td.address).success()\\n\\n # sniffer sees message on the wire\\n tnet.sniffer.start_state.receive(OriginalBroadcastNPDU,\\n pduSource=tnet.td.address.addrTuple,\\n# pduDestination=('192.168.4.255', 47808),\\n pduData=pdu_data,\\n ).timeout(1.0).success()\\n\\n # run the group\\n tnet.run()\",\n \"def localpath(self, *args):\\n return os.path.join(os.path.expanduser(self.serverfiles_dir), *args)\",\n \"def sync_model(model):\\n size = float(dist.get_world_size())\\n\\n for param in model.parameters():\\n dist.broadcast(param.data, 0)\",\n \"def emit(self, *path):\\n path = list(path)\\n for module in self.modules.values():\\n module.emit_local(*path)\",\n \"def get_remote_path(self, local_path, mapped_paths):\\n return self.get_local_path(local_path, mapped_paths, reverse=True)\",\n \"def update_trip_path(trip_mpois, paths, graph):\\n n_nodes = len(trip_mpois)\\n # adjacency matrix\\n new_paths = np.zeros(shape=(n_nodes, n_nodes))\\n\\n # iterate through all the nodes and create a list of nodes with sequential id\\n for i, node1 in enumerate(trip_mpois):\\n for j, node2 in enumerate(trip_mpois):\\n new_paths[i, j] = paths[node1, node2]\\n\\n # new_paths = new_paths/np.max(new_paths[new_paths < _INF])\\n # new_paths[np.isinf(new_paths)] = _INF\\n\\n # create a dummy edge between end and start node with weight 0\\n new_paths[1,0] = -_INF\\n # new_paths[0,1] = _INF\\n\\n shortest_path = None\\n if n_nodes > 5:\\n shortest_path, dist = tsp.solve(n_nodes, new_paths)\\n # shortest_path = range(n_nodes)\\n else:\\n shortest_path = range(n_nodes)\\n\\n trip_path = np.array(trip_mpois)[shortest_path]\\n\\n if ___DEBUG:\\n fname = 'dump/' + str(n_nodes) + '.dist'\\n np.savetxt(fname, new_paths, fmt='%.6f')\\n \\n mpoi_pos = np.zeros(shape=(n_nodes,2))\\n \\n for i, node in enumerate(trip_mpois):\\n pos_3d = graph.vs[node]['position']\\n assert node == graph.vs[node].index\\n mpoi_pos[i,:] = pos_3d[:2]\\n\\n fname = 'dump/' + str(n_nodes) + '.pos'\\n np.savetxt(fname, mpoi_pos)\\n \\n # print trip_mpois, trip_path\\n\\n return trip_path\",\n \"def appendleft(self, path):\\n self.paths.appendleft(path)\\n self.time += path.time\",\n \"def rsync_local_files(dry_run=None):\\n local_path = os.path.join(os.path.dirname(settings.PROJECT_PATH), 'local')\\n cmd = 'rsync -av{dry_run} {src}/ {host}:{dst}'.format(\\n src=local_path,\\n host=env.host,\\n dst=local_path,\\n dry_run='n' if dry_run else '',\\n )\\n local(cmd)\",\n \"def get_local_path(self, remote_path, mapped_paths, reverse=False):\\n for remote_prefix, local_prefix in mapped_paths:\\n # Reverse. Return mapped remote path for given local path.\\n if reverse:\\n remote_prefix, local_prefix = local_prefix, remote_prefix\\n if remote_path.startswith(remote_prefix):\\n local_path = remote_path.replace(\\n remote_prefix, local_prefix)\\n break\\n else:\\n local_path = remote_path\\n return local_path\",\n \"def optimize_path(path):\\n rospy.loginfo(\\\"Optimizing path\\\")\\n\\n opt_path = []\\n current_direction = (0, 0)\\n last_direction = (0, 0)\\n\\n for i in range(len(path) -1):\\n current_direction = (path[i+1][0] - path[i][0], path[i+1][1] - path[i][1])\\n if current_direction != last_direction:\\n opt_path.append(path[i])\\n last_direction = current_direction\\n \\n opt_path.append(path[-1]) #add the last coordinate back\\n\\n return opt_path\",\n \"def join(self, path, *paths):\",\n \"def prepare_path(self,path):\\n debug.info(4,\\\"Set path: \\\" + str(path))\\n\\n # This is marked for debug\\n path.set_path()\\n\\n # For debugging... if the path failed to route.\\n if False or path==None:\\n self.write_debug_gds()\\n\\n # First, simplify the path for\\n #debug.info(1,str(self.path)) \\n contracted_path = self.contract_path(path)\\n debug.info(3,\\\"Contracted path: \\\" + str(contracted_path))\\n \\n return contracted_path\",\n \"def broadcast() -> BroadcastDistribute:\\n return _broadcast\",\n \"def joinPath(path, *args):\",\n \"def _broadcast(self, msg: str) -> None:\\n from jesse.routes import router\\n\\n for r in router.routes:\\n # skip self\\n if r.strategy.id == self.id:\\n continue\\n\\n if msg == 'route-open-position':\\n r.strategy.on_route_open_position(self)\\n elif msg == 'route-close-position':\\n r.strategy.on_route_close_position(self)\\n elif msg == 'route-increased-position':\\n r.strategy.on_route_increased_position(self)\\n elif msg == 'route-reduced-position':\\n r.strategy.on_route_reduced_position(self)\\n elif msg == 'route-canceled':\\n r.strategy.on_route_canceled(self)\\n\\n r.strategy._detect_and_handle_entry_and_exit_modifications()\",\n \"def empty_paths(self):\\n self.paths[:]\",\n \"def set(self, new_path):\\n\\n for i in range(self.depth):\\n self.path[i] = new_path[self.max_input*i:self.max_input*(i + 1)]\",\n \"def set_local_path(self):\\n return HERE\",\n \"def flatten_single_directories(self, name, path):\\n self.m.path.assert_absolute(path)\\n self._run(name, ['flatten_single_directories', path])\",\n \"def normalize_path_from_root_per_parent(self, path):\\n found_successor = self.root\\n normalized_path = []\\n for searched_successor in path:\\n counter = 0\\n successors = self.tree.successors(found_successor)\\n for successor in successors:\\n counter += 1\\n if searched_successor == successor:\\n normalized_path.append(counter)\\n found_successor = searched_successor\\n break\\n\\n assert (len(path) == len(normalized_path))\\n return normalized_path\",\n \"def broadcast(cls, controller):\\n b = cls()\\n\\n for path in controller.paths:\\n b.add_path(path)\\n\\n for dev in controller.devices:\\n b.add_device(dev)\\n\\n return b\",\n \"def local_paths(self) -> List[Path]:\\n return self._local_paths\",\n \"def collect_local(self, path, req_tag=True):\\n for f in [os.path.join(dp, f) for dp, dn, filenames in os.walk(path) for f in filenames]:\\n if not os.path.isfile(f):\\n continue\\n self.collect_single(f, req_tag)\",\n \"def rsync_from(self, remote, local, flags='-av', user='root'):\\n if self._portoffset:\\n tail = \\\"/\\\" if local[-1] == \\\"/\\\" else ''\\n local = os.path.abspath(os.path.expanduser(local)) + tail\\n return self._rsync(\\n 'rsync {flags} rsync://{usr}'\\n '@{target}:{port}{src} {dest}'.format(\\n flags=flags, src=remote, usr=user, target=self._target,\\n port=(self._portoffset + 873), dest=local))\\n return False\",\n \"def _exchange_ghosts_mpi(self):\\n for d in xrange(self._dim):\\n if d in self._cutdir_list:\\n self._exchange_ghosts_mpi_d(d)\\n else:\\n self._exchange_ghosts_local_d(d)\",\n \"def path_extern_mounts(self) -> PurePath:\\n return self.path_extern_supervisor / MOUNTS_FOLDER\",\n \"def _get_local_src(self, path: Path) -> Path:\\n src = \\\"\\\"\\n\\n if str(path).startswith(\\\"~\\\"):\\n path = Path(str(path).replace(\\\"~/\\\", \\\"\\\"))\\n\\n if self.category == \\\"global\\\":\\n src = f\\\"{self.local_base}/global{path}\\\"\\n elif self.category == \\\"local\\\":\\n src = f\\\"{self.local_base}/local/{path}\\\"\\n else:\\n src = f\\\"{self.local_base}/custom/{path}\\\"\\n\\n return Path(src)\",\n \"def push(self, path):\\n if path not in self.paths:\\n self.paths.add(path)\\n heappush(self.priority_queue, (path.path_cost, path))\",\n \"def get(self, remote_path, local_path='',\\n recursive=False, preserve_times=False):\\n if not isinstance(remote_path, (list, tuple)):\\n remote_path = [remote_path]\\n remote_path = [self.sanitize(asbytes(r)) for r in remote_path]\\n self._recv_dir = local_path or os.getcwd()\\n self._rename = (len(remote_path) == 1 and\\n not os.path.isdir(os.path.abspath(local_path)))\\n if len(remote_path) > 1:\\n if not os.path.exists(self._recv_dir):\\n raise SCPException(\\\"Local path '%s' does not exist\\\" %\\n asunicode(self._recv_dir))\\n elif not os.path.isdir(self._recv_dir):\\n raise SCPException(\\\"Local path '%s' is not a directory\\\" %\\n asunicode(self._recv_dir))\\n rcsv = (b'', b' -r')[recursive]\\n prsv = (b'', b' -p')[preserve_times]\\n self.channel = self._open()\\n self._pushed = 0\\n self.channel.settimeout(self.socket_timeout)\\n self.channel.exec_command(b\\\"scp\\\" +\\n rcsv +\\n prsv +\\n b\\\" -f \\\" +\\n b' '.join(remote_path))\\n self._recv_all()\\n self.close()\",\n \"def loadPaths(self):\\n for ij in self.link:\\n self.link[ij].flow = 0\\n for p in self.path:\\n for ij in self.path[p].links:\\n self.link[ij].flow += self.path[p].flow\\n for ij in self.link:\\n self.link[ij].updateCost()\\n for p in self.path:\\n self.path[p].updateCost()\",\n \"def calculateFirstPath(self):\\n rd.shuffle(self.goals)\\n self.path = self.goals\",\n \"def test_path7():\\n path = [(0, 0, 1)]\\n path += [\\n [('A', 3, 0)],\\n (0, 1, 1),\\n [('A', 2, 0)],\\n (np.pi/2, 1, 1),\\n [('B',3,0)],\\n (0, 1, 1),\\n [('B',2,0)],\\n (np.pi/2, 1, 1),\\n [('C',3,0)],\\n (0, 1, 1),\\n [('C',2,0)],\\n (np.pi/2, 1, 1),\\n [('D', 3, 0)],\\n (0, 1, 1),\\n [('D', 2,0)],\\n (np.pi/2, 1, 1),\\n ] * 4\\n execute_path(path,True)\",\n \"def __init__(self, root_rank, device=''):\\n super(BroadcastGlobalVariablesHook, self).__init__()\\n self.root_rank = root_rank\\n self.bcast_op = None\\n self.device = device\",\n \"def reset_path(self):\\n for i in self.grid:\\n for y in i:\\n y.g = 0\\n y.h = 0\\n y.f = 0\\n y.parent = None\\n y.visited = False\",\n \"def map_to_local(self, idxs):\\n return F.zerocopy_from_dgl_ndarray(\\n _CAPI_DGLNDArrayPartitionMapToLocal(\\n self._partition, F.zerocopy_to_dgl_ndarray(idxs)\\n )\\n )\",\n \"def sync_from_root(self):\\n self.optimizer.sync_from_root(self.trainable_vars)\",\n \"def syncFromClient(self):\\n\\n # Acquire the client thread semaphore\\n S_SEM.acquire()\\n self.updateIndex()\\n try:\\n # Wait for signal then sends server's directory\\n print('Started sync from client...')\\n self.wait('OK')\\n self.send(LOCAL_DIR)\\n\\n # Encode, wait for signal then send index to client\\n outpkg = json.dumps(self.serverindex)\\n self.wait('OK')\\n self.send(outpkg)\\n\\n # Receive requests and files from client\\n Q_LOCK.acquire()\\n while True:\\n request = self.receive()\\n if request:\\n job = tuple(request.split(','))\\n self.send('OK')\\n\\n # Atomically add a single batch of sync jobs\\n # Wait and receive file for all copy jobs\\n # Put job and file in queue\\n if job[0] == 'CP':\\n file = self.receive(isFile=True)\\n self.send('OK')\\n self.jobqueue.append((job, file))\\n\\n # Finish adding jobs to the client\\n elif job[0] == 'DONE':\\n self.jobqueue.append((job, None))\\n print('Done syncing from client!')\\n Q_LOCK.release()\\n break\\n\\n # Put job into jobqueue if not copy job\\n else:\\n self.jobqueue.append((job, None))\\n\\n # Start worker thread that will write to the local directory\\n # Release the semaphore for the worker thread\\n workerthread = WorkerThread(self.jobqueue, self)\\n workerthread.start()\\n THREADS['WorkerThread[{}]'.format(self.threadID)] = workerthread\\n W_SEM.release()\\n workerthread.join()\\n self.updateIndex()\\n except:\\n S_SEM.release()\\n self.updateIndex()\",\n \"def relax_path( self, initial_path=None, n_shooting_moves=10000 ):\\n if ( initial_path is None ):\\n raise ValueError( \\\"Filename containing the initial path was not given!\\\" )\\n self.load_path( initial_path )\\n self.nuc_mc.remove_network_observers()\\n min_slice,max_slice = self.find_timeslices_in_transition_region()\\n self.log ( \\\"First timeslice outside reaction region: {}\\\".format(min_slice))\\n self.log ( \\\"First timeslice in product region: {}\\\".format(max_slice))\\n\\n direcions = [\\\"forward\\\",\\\"backward\\\"]\\n min_slice = 0\\n max_slice = len(self.init_path[\\\"energy\\\"])\\n for move in range(n_shooting_moves):\\n if move%10 == 0:\\n self.center_barrier()\\n self.path2trajectory()\\n\\n self.nuc_mc.reset()\\n direct = direcions[np.random.randint(low=0,high=2)]\\n self.log( \\\"Move {} of {}\\\".format(move,n_shooting_moves) )\\n timeslice = np.random.randint(low=min_slice,high=max_slice )\\n self.log(\\\"Starting from timeslice {} of {}\\\".format(timeslice,max_slice))\\n self.shooting_move(timeslice)\\n\\n ofname = initial_path.rpartition(\\\".\\\")[0]\\n ofname += \\\"_relaxed.json\\\"\\n with open(ofname,'w') as outfile:\\n json.dump(self.init_path,outfile)\\n self.log( \\\"Relaxed path written to {}\\\".format(ofname) )\\n new_peak_energy = np.max(self.init_path[\\\"energy\\\"])\\n self.log( \\\"Maximum energy along path changed from {} eV to {} eV\\\".format(self.initial_peak_energy,new_peak_energy) )\",\n \"def forward_kinematics(self):\\n def product(L):\\n if len(L) == 0:\\n return sp.Matrix.eye(4)\\n cum = L[0]\\n for i in L[1:]:\\n cum *= i\\n return cum\\n\\n # routes to base link\\n paths = self.paths()\\n # symbolic matrices\\n matrices = {name: j.matrix for name, j in self.joints.items()}\\n\\n #\\n combined = {k: product([matrices[i] for i in path])\\n for k, path in paths.items()}\\n\\n return combined\",\n \"def add_path(self, path):\\n\\n for i in range(1, len(path)):\\n self.add_edge(path[i], path[i - 1])\",\n \"def test_path1():\\n path = [(0,0,1)]\\n path.append([('A',4,0)])\\n path.append((0,1,1))\\n path.append([('A',3,0)])\\n path.append((0,1,1))\\n path.append([('A',2,0)])\\n path.append((0,1,1))\\n path.append([('A',1,0)])\\n\\n execute_path(path, True)\",\n \"def local_useless_rebroadcast(node):\\r\\n if isinstance(node.op, T.Rebroadcast):\\r\\n x = node.inputs[0]\\r\\n if numpy.all(x.broadcastable == node.outputs[0].broadcastable):\\r\\n # No broadcastable flag was modified\\r\\n return [x]\\r\\n else:\\r\\n # Keep the flags that modify something\\r\\n new_axis = {}\\r\\n for dim, bc in node.op.axis.items():\\r\\n if x.broadcastable[dim] != bc:\\r\\n new_axis[dim] = bc\\r\\n if new_axis == node.op.axis:\\r\\n # All flags are useful\\r\\n return\\r\\n else:\\r\\n return [T.Rebroadcast(*new_axis.items())(x)]\",\n \"def tails_local_path(self, new_path: str):\\n self._tails_local_path = new_path\",\n \"def get_srv_paths(self, sw, graph=None, local=False):\\n if graph == None:\\n graph = self.graph\\n\\n paths = []\\n\\n if local:\\n avail_srvs = self.localservers\\n else:\\n avail_srvs = self.servers\\n\\n assert graph != None\\n assert len(sw) > 0\\n assert len(avail_srvs)> 0\\n\\n for server in avail_srvs:\\n paths.append(nx.shortest_path(graph, server, sw))\\n\\n return paths\",\n \"def broadcast_variables(variables, root_rank, scope=''):\\n if size() <= 1:\\n return tf.group(*variables)\\n _assign = tf.assign if hasattr(tf, 'assign') else tf.compat.v1.assign\\n return tf.group(*[_assign(var, broadcast(var, root_rank, scope))\\n for var in variables])\",\n \"def normpath(path):\\n normalized = os.path.join(*path.split(\\\"/\\\"))\\n if os.path.isabs(path):\\n return os.path.abspath(\\\"/\\\") + normalized\\n else:\\n return normalized\",\n \"def broadcast(tensor, from_process: int = 0):\\n if PartialState().distributed_type == DistributedType.TPU:\\n return _tpu_broadcast(tensor, src=from_process, name=\\\"accelerate.utils.broadcast\\\")\\n elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:\\n return _gpu_broadcast(tensor, src=from_process)\\n else:\\n return tensor\",\n \"def get_path(self, r, c, blocked):\\n # Create grid\\n grid = [[0]*c for _ in range(r)]\\n for br, bc in blocked:\\n grid[br][bc] = 1\\n\\n return self.get_path_helper(grid, 0, 0, \\\"\\\", set())\",\n \"def _execute_and_merge(self, path):\\n\\n state = path.state\\n se = state.se\\n ip_int = path.addr\\n\\n # Remove path._run\\n path._run = None\\n\\n # Build a CFG out of the current function\\n\\n cfg_key = (ip_int, path.jumpkind)\\n if cfg_key in self.cfg_cache:\\n cfg, cfg_graph_with_loops = self.cfg_cache[cfg_key]\\n\\n else:\\n if self._enable_function_inlining:\\n call_tracing_filter = CallTracingFilter(self.project, depth=0)\\n filter = call_tracing_filter.filter\\n else:\\n filter = None\\n # To better handle syscalls, we make a copy of all registers if they are not symbolic\\n cfg_initial_state = self.project.factory.blank_state(mode='fastpath')\\n # FIXME: This is very hackish\\n # FIXME: And now only Linux-like syscalls are supported\\n if self.project.arch.name == 'X86':\\n if not state.se.symbolic(state.regs.eax):\\n cfg_initial_state.regs.eax = state.regs.eax\\n elif self.project.arch.name == 'AMD64':\\n if not state.se.symbolic(state.regs.rax):\\n cfg_initial_state.regs.rax = state.regs.rax\\n\\n cfg = self.project.analyses.CFG(starts=((ip_int, path.jumpkind),),\\n context_sensitivity_level=0,\\n call_depth=0,\\n call_tracing_filter=filter,\\n initial_state=cfg_initial_state\\n )\\n cfg.normalize()\\n cfg_graph_with_loops = networkx.DiGraph(cfg.graph)\\n cfg.unroll_loops(self._loop_unrolling_limit)\\n\\n self.cfg_cache[cfg_key] = (cfg, cfg_graph_with_loops)\\n\\n loop_backedges = cfg._loop_back_edges\\n loop_heads = set([ dst.addr for _, dst in loop_backedges ])\\n\\n # Find all merge points\\n merge_points = self._get_all_merge_points(cfg, cfg_graph_with_loops)\\n l.debug('Merge points: %s', [ hex(i[0]) for i in merge_points ])\\n\\n #\\n # Controlled symbolic exploration\\n #\\n\\n # Initialize the beginning path\\n initial_path = path\\n initial_path.info['loop_ctrs'] = defaultdict(int)\\n initial_path.info['actionqueue_list'] = [ self._new_actionqueue() ]\\n\\n # This is a special hack for CGC stuff, since the CGCAnalysis relies on correct conditions of file actions\\n # Otherwise we may just save out those actions, and then copy them back when returning those paths\\n initial_path.history._events = [ a for a in initial_path.actions if a.type.startswith('file') ]\\n\\n path_group = PathGroup(self.project,\\n active_paths=[ initial_path ],\\n immutable=False,\\n resilience=o.BYPASS_VERITESTING_EXCEPTIONS in initial_path.state.options)\\n # Initialize all stashes\\n for stash in self.all_stashes:\\n path_group.stashes[stash] = [ ]\\n # immediate_dominators = cfg.immediate_dominators(cfg.get_any_node(ip_int))\\n\\n saved_paths = { }\\n\\n def is_path_errored(path):\\n if path.errored:\\n return True\\n elif len(path.jumpkinds) > 0 and path.jumpkinds[-1] in Path._jk_all_bad:\\n l.debug(\\\"Errored jumpkind %s\\\", path.jumpkinds[-1])\\n path._error = AngrPathError('path has a failure jumpkind of %s' % path.jumpkinds[-1])\\n else:\\n try:\\n if path._run is None:\\n ip = path.addr\\n # FIXME: cfg._nodes should also be updated when calling cfg.normalize()\\n size_of_next_irsb = [n for n in cfg.graph.nodes() if n.addr == ip][0].size\\n path.step(max_size=size_of_next_irsb)\\n except (AngrError, SimError, ClaripyError) as ex:\\n l.debug('is_path_errored(): caxtching exception %s', ex)\\n path._error = ex\\n except (TypeError, ValueError, ArithmeticError, MemoryError) as ex:\\n l.debug(\\\"is_path_errored(): catching exception %s\\\", ex)\\n path._error = ex\\n\\n return False\\n\\n def is_path_overbound(path):\\n \\\"\\\"\\\"\\n Filter out all paths that run out of boundaries or loop too many times\\n \\\"\\\"\\\"\\n\\n ip = path.addr\\n\\n if ip in self._boundaries:\\n l.debug(\\\"... terminating Veritesting due to overbound\\\")\\n return True\\n\\n if (ip in loop_heads # This is the beginning of the loop\\n or path.jumpkind == 'Ijk_Call' # We also wanna catch recursive function calls\\n ):\\n path.info['loop_ctrs'][ip] += 1\\n\\n if path.info['loop_ctrs'][ip] >= self._loop_unrolling_limit + 1:\\n l.debug('... terminating Veritesting due to overlooping')\\n return True\\n\\n l.debug('... accepted')\\n return False\\n\\n\\n def generate_successors(path):\\n ip = path.addr\\n\\n l.debug(\\\"Pushing 0x%x one step forward...\\\", ip)\\n\\n # FIXME: cfg._nodes should also be updated when calling cfg.normalize()\\n size_of_next_irsb = [ n for n in cfg.graph.nodes() if n.addr == ip ][0].size\\n # It has been called by is_path_errored before, but I'm doing it here anyways. Who knows how the logic in\\n # PathGroup will change in the future...\\n path.step(max_size=size_of_next_irsb)\\n\\n # Now it's safe to call anything that may access Path.next_run\\n if self._path_callback:\\n copied_path = path.copy()\\n self._unfuck(copied_path)\\n self._path_callback(copied_path)\\n\\n successors = path.successors\\n\\n # Get all unconstrained successors, and save them out\\n if path.next_run:\\n for s in path.next_run.unconstrained_successors:\\n u_path = Path(self.project, s, path=path)\\n path_group.stashes['unconstrained'].append(u_path)\\n\\n # Record their guards :-)\\n for successing_path in successors:\\n if 'guards' not in successing_path.info:\\n successing_path.info['guards'] = [ ]\\n last_guard = successing_path.guards[-1]\\n if not successing_path.state.se.is_true(last_guard, exact=False):\\n successing_path.info['guards'].append(last_guard)\\n\\n # Fill the ActionQueue list\\n if len(successors) == 1:\\n # Expand the last ActionQueue\\n if not successors[0].info['actionqueue_list']:\\n successors[0].info['actionqueue_list'].append(self._new_actionqueue())\\n self._get_last_actionqueue(successors[0]).actions.extend(successors[0].last_actions)\\n\\n elif len(successors) > 1:\\n # Save this current path, since we might need it in the future\\n path_key = (path.addr, self._get_last_actionqueue(path).id)\\n saved_paths[path_key] = path\\n\\n # Generate a new ActionQueue for each successor\\n for successing_path in successors:\\n successing_path.info['actionqueue_list'].append(self._new_actionqueue(parent_key=path_key))\\n self._get_last_actionqueue(successing_path).actions.extend(successing_path.last_actions)\\n\\n l.debug(\\\"... new successors: %s\\\", successors)\\n return successors\\n\\n def _path_not_in_cfg(p):\\n \\\"\\\"\\\"\\n Returns if p.addr is not a proper node in our CFG\\n\\n :param p: The Path instance to test.\\n :return: False if our CFG contains p.addr, True otherwise\\n \\\"\\\"\\\"\\n\\n n = cfg.get_any_node(p.addr, is_syscall=p.jumpkinds[-1].startswith('Ijk_Sys'))\\n if n is None:\\n return True\\n\\n if n.simprocedure_name == 'PathTerminator':\\n return True\\n\\n return False\\n\\n while path_group.active:\\n # Step one step forward\\n l.debug('Steps %s with %d active paths: [ %s ]',\\n path_group,\\n len(path_group.active),\\n path_group.active)\\n\\n # Apply self.deviation_func on every single active path, and move them to deviated stash if needed\\n if self._deviation_filter is not None:\\n path_group.stash(filter_func=self._deviation_filter, from_stash='active', to_stash='deviated')\\n\\n # Mark all those paths that are out of boundaries as successful\\n path_group.stash(filter_func=is_path_overbound, from_stash='active', to_stash='successful')\\n\\n path_group.step(successor_func=generate_successors, check_func=is_path_errored)\\n if self._terminator is not None and self._terminator(path_group):\\n for p in path_group.unfuck:\\n self._unfuck(p)\\n break\\n\\n # Stash all paths that we do not see in our CFG\\n path_group.stash(filter_func=_path_not_in_cfg,\\n to_stash=\\\"deviated\\\"\\n )\\n\\n # Stash all paths that we do not care about\\n path_group.stash(filter_func=\\n lambda p: (p.state.scratch.jumpkind not in\\n ('Ijk_Boring', 'Ijk_Call', 'Ijk_Ret', 'Ijk_NoHook')\\n and not p.state.scratch.jumpkind.startswith('Ijk_Sys')\\n ),\\n to_stash=\\\"deadended\\\"\\n )\\n if path_group.deadended:\\n l.debug('Now we have some deadended paths: %s', path_group.deadended)\\n\\n # Stash all possible paths that we should merge later\\n for merge_point_addr, merge_point_looping_times in merge_points:\\n path_group.stash_addr(merge_point_addr,\\n to_stash=\\\"_merge_%x_%d\\\" % (merge_point_addr, merge_point_looping_times)\\n )\\n\\n # Try to merge a set of previously stashed paths, and then unstash them\\n if not path_group.active:\\n merged_anything = False\\n\\n for merge_point_addr, merge_point_looping_times in merge_points:\\n if merged_anything:\\n break\\n\\n stash_name = \\\"_merge_%x_%d\\\" % (merge_point_addr, merge_point_looping_times)\\n\\n if stash_name in path_group.stashes:\\n # Try to prune the stash, so unsatisfiable paths will be thrown away\\n path_group.prune(from_stash=stash_name, to_stash='pruned')\\n if 'pruned' in path_group.stashes and len(path_group.pruned):\\n l.info('... pruned %d paths from stash %s', len(path_group.pruned), stash_name)\\n # Remove the pruned stash to save memory\\n path_group.drop(stash='pruned')\\n\\n stash = path_group.stashes[stash_name]\\n if not len(stash):\\n continue\\n\\n # Group all those paths based on their callstacks\\n groups = defaultdict(list)\\n for p in stash:\\n groups[p.callstack].append(p)\\n\\n l.debug('Trying to merge and activate stash %s', stash_name)\\n l.debug('%d paths are grouped into %d groups based on their callstacks',\\n len(stash),\\n len(groups)\\n )\\n\\n for g in groups.itervalues():\\n if len(g) == 1:\\n # Just unstash it\\n p = g[0]\\n path_group.stashes[stash_name].remove(p)\\n\\n if any([loop_ctr >= self._loop_unrolling_limit + 1 for loop_ctr in p.info['loop_ctrs'].itervalues()]):\\n l.debug(\\\"%s is overlooping\\\", p)\\n path_group.deadended.append(p)\\n else:\\n l.debug('Put %s into active stash', p)\\n path_group.active.append(p)\\n merged_anything = True\\n\\n elif len(g) > 1:\\n for p in g:\\n path_group.stashes[stash_name].remove(p)\\n\\n # Merge them first\\n\\n # Find the previous dominator for all those\\n # Determine their common ancestor\\n ancestor_key = self._determine_ancestor(g)\\n initial_path = saved_paths[ancestor_key]\\n merged_path = self._merge_path_list(se, initial_path, g)\\n\\n if any([ loop_ctr >= self._loop_unrolling_limit + 1 for loop_ctr in merged_path.info['loop_ctrs'].itervalues() ]):\\n l.debug(\\\"%s is overlooping\\\", merged_path)\\n path_group.deadended.append(merged_path)\\n else:\\n l.debug('Put %s into active stash', p)\\n path_group.active.append(merged_path)\\n\\n merged_anything = True\\n\\n if any([ len(path_group.stashes[stash_name]) for stash_name in self.all_stashes]):\\n # Remove all stashes other than errored or deadended\\n path_group.stashes = { name: stash for name, stash in path_group.stashes.items()\\n if name in self.all_stashes }\\n\\n for stash in path_group.stashes:\\n path_group.apply(lambda p: self._unfuck(p), stash=stash)\\n\\n return path_group\",\n \"def localPython ( localPath ) :\\r\\n\\r\\n if not type( localPath ) == str : return\\r\\n\\r\\n if not localPath.endswith( os.sep ) : localPath = localPath + os.sep\\r\\n\\r\\n # reads the paths to add to sys.path\\r\\n \\r\\n try :\\r\\n\\r\\n handler = open( localPath + \\\"sysPath.txt\\\", \\\"r\\\" )\\r\\n\\r\\n text = handler.read()\\r\\n\\r\\n handler.close()\\r\\n\\r\\n items = text.splitlines()\\r\\n\\r\\n except Exception, exception :\\r\\n\\r\\n items = [ ]\\r\\n\\r\\n\\r\\n # places the local paths before the previous search paths. only those that exist\\r\\n\\r\\n sysPath = [ ]\\r\\n\\r\\n for item in items :\\r\\n\\r\\n item = item.strip().replace( \\\"\\\\\\\\\\\", os.sep ).replace( \\\"/\\\", os.sep )\\r\\n\\r\\n if len( item ) == 0 : continue\\r\\n\\r\\n item = item.strip( os.sep )\\r\\n\\r\\n item = localPath + item\\r\\n\\r\\n if item in sysPath : continue\\r\\n\\r\\n if not os.path.exists( item ) : continue\\r\\n\\r\\n sysPath.append( item )\\r\\n\\r\\n # places the previous paths. only those that exist\\r\\n\\r\\n\\r\\n for item in sys.path :\\r\\n\\r\\n if item in sysPath : continue\\r\\n\\r\\n if not os.path.exists( item ) : continue\\r\\n\\r\\n sysPath.append( item )\\r\\n\\r\\n sys.path = sysPath\",\n \"def connecthostbroadcast(localpop,\\n hwport_tosite,\\n sitevlan,\\n meter=3,\\n broadcast_rewritemac = None):\\n\\n hwswitch = Container.fromAnchor(localpop.properties['HwSwitch'])\\n hwswitchname = hwswitch.resourceName\\n swswitch = Container.fromAnchor(localpop.properties['SwSwitch'])\\n swswitchname = swswitch.resourceName\\n topology = Container.getContainer(localpop.properties['SwSwitch']['containerName'])\\n\\n # Find the port on the HwSwitch connected to the software switch\\n links = getlinks2(topology, hwswitchname, swswitchname)\\n if links == None or len(links) == 0:\\n print \\\"No links from\\\", hwswitchname, \\\"to\\\", swswitchname\\n return False\\n hwport_tosw = None\\n for link in links:\\n (node, port) = linkednode2(link, swswitchname)\\n if port != None:\\n # Found the link we're looking for\\n hwport_tosw = port\\n break\\n\\n broadcast = \\\"FF:FF:FF:FF:FF:FF\\\"\\n translated_broadcast = broadcast\\n if broadcast_rewritemac != None:\\n translated_broadcast = broadcast_rewritemac\\n\\n fh1 = SCC.SdnInstallForward1(javaByteArray2(hwswitch.properties['DPID']),\\n 1,\\n BigInteger.ZERO,\\n str(hwport_tosw),\\n int(sitevlan),\\n \\\"00:00:00:00:00:00\\\",\\n translated_broadcast,\\n str(hwport_tosite),\\n int(sitevlan),\\n broadcast,\\n 0,\\n 0,\\n meter)\\n if fh1 == None:\\n return None\\n\\n fh2 = SCC.SdnInstallForward1(javaByteArray2(hwswitch.properties['DPID']),\\n 1,\\n BigInteger.ZERO,\\n str(hwport_tosite),\\n int(sitevlan),\\n \\\"00:00:00:00:00:00\\\",\\n broadcast,\\n str(hwport_tosw),\\n int(sitevlan),\\n translated_broadcast,\\n 0,\\n 0,\\n meter)\\n if fh2 == None:\\n SCC.deleteforward(fh1)\\n return None\\n\\n return (fh1, fh2)\",\n \"def compute_path(self,\\n path):\\n self.subidx = ~np.isnan(self.y_data)\\n leaf_id = 0\\n for decision in path:\\n node = self.tree[leaf_id]\\n if decision == 0:\\n leaf_id = node.id_null\\n self.subidx = (self.subidx &\\n np.isnan(self.split_data[node.label]))\\n else:\\n non_null = ~np.isnan(self.split_data[node.label])\\n new_subidx = np.zeros_like(self.split_data[node.label],\\n dtype=bool)\\n if decision < 0:\\n leaf_id = node.id_lower\\n new_subidx[non_null] = \\\\\\n self.split_data[node.label][non_null] < node.cutoff\\n elif decision > 0:\\n leaf_id = node.id_higher\\n new_subidx[non_null] = \\\\\\n self.split_data[node.label][non_null] > node.cutoff\\n self.subidx = self.subidx & new_subidx\\n\\n self.sub_y_data = self.y_data[self.subidx]\\n self.sub_split_data = self.split_data[:, self.subidx]\\n self.sub_bin_data = self.bin_data[self.subidx]\\n self.sub_y_ranks = rankdata(self.sub_y_data)\\n self.sub_split_args = self.sub_split_data.argsort(axis=1)\",\n \"def path(self, *path):\\n path = list(filter(None, path))\\n path = self.remove_prefix(path)\\n items = [self.prefix_] + path\\n return self.delimiter.join(items)\",\n \"def make_local_path(self, *args):\\n return os.path.normpath(os.path.join(\\n os.path.dirname(api.env.real_fabfile), *args).rstrip(os.path.sep))\",\n \"def add_weighted_path(self, path, weights, scale=1):\\n mn, mx = float(min(weights)), max(weights)\\n n = len(weights)\\n weights_normed = np.array(weights)\\n #weights_normed = (weights - mn) / (mx - mn)\\n\\n last_w = weights_normed[0]\\n lat1 = (path[0][0] + path[1][0]) / 2.\\n lon1 = (path[0][1] + path[1][1]) / 2.\\n\\n newpath = [path[0], [lat1, lon1]]\\n\\n for i in xrange(1, n - 1):\\n lat1 = (path[i - 1][0] + path[i][0]) / 2.\\n if np.sign(path[i - 1][1]) == np.sign(path[i][1]):\\n lon1 = (path[i - 1][1] + path[i][1]) / 2.\\n else:\\n lat1, lon1 = path[i - 1]\\n\\n lat2 = (path[i][0] + path[i + 1][0]) / 2.\\n\\n if np.sign(path[i][1]) == np.sign(path[i + 1][1]):\\n lon2 = (path[i][1] + path[i + 1][1]) / 2.\\n else:\\n lat2, lon2 = path[i + 1]\\n\\n if weights_normed[i] == last_w:\\n newpath.append([lat1, lon1])\\n newpath.append(path[i])\\n newpath.append([lat2, lon2])\\n else:\\n col = val2hex(last_w, scale)\\n self.add_path(newpath, \\\"#\\\" + col)\\n newpath = []\\n newpath.append([lat1, lon1])\\n newpath.append(path[i])\\n newpath.append([lat2, lon2])\\n last_w = weights_normed[i]\\n\\n if weights_normed[- 1] != last_w:\\n col = val2hex(last_w, scale)\\n self.add_path(newpath, \\\"#\\\" + col)\\n newpath = []\\n\\n lat1 = (path[- 2][0] + path[- 1][0]) / 2.\\n lon1 = (path[- 2][1] + path[- 1][1]) / 2.\\n newpath.append([lat1, lon1])\\n newpath.append(path[-1])\\n col = val2hex(weights_normed[-1], scale)\\n self.add_path(newpath, \\\"#\\\" + col)\",\n \"def optimal_path(self, mission, start, sp):\\n mission.add(start)\\n while mission.targets[0] and mission.is_active():\\n ds = [(sp[start][t], t) for t in mission.targets[0] if t in sp[start]]\\n if not ds:\\n mission.add(u'-1') # target not connected --> fill with dummies\\n continue\\n target = min(ds)\\n for i in range(target[0] - 1):\\n mission.add(u'0')\\n mission.add(target[1])\\n start = target[1]\",\n \"def _staf_dir_copy(self, local_path, remote_path):\\n\\n staf_request = ('COPY DIRECTORY \\\"{0}\\\" TODIRECTORY \\\"{1}\\\" TOMACHINE \\\"{2}\\\" RECURSE '\\n 'KEEPEMPTYDIRECTORIES'.format(unix_style_path(local_path),\\n unix_style_path(remote_path),\\n self._sut.network_address))\\n\\n result = self._staf_handle.submit('local', 'fs', staf_request)\\n\\n if result.rc != result.Ok:\\n raise CoreError(result.result)\",\n \"def upload_cluster(self, localpath, remotepath):\\n if isinstance(localpath, str):\\n filename = os.path.split(localpath)[-1]\\n self.upload(localpath, filename)\\n self.execute_command(\\n \\\"hdfs dfs -put {0} {1}\\\".format(filename, remotepath))\\n self.execute_command(\\\"rm {0}\\\".format(filename))\\n else:\\n self.upload(localpath, \\\".\\\")\\n for afile in localpath:\\n filename = os.path.split(afile)[-1]\\n self.execute_command(\\n \\\"hdfs dfs -put {0} {1}\\\".format(filename, remotepath))\\n self.execute_command(\\\"rm {0}\\\".format(filename))\\n\\n return remotepath\",\n \"def broadcast():\\n # global receiving_message\\n # if not receiving_message:\\n router.broadcast(clients.copy(), json.dumps(current_state))\",\n \"def broadcast_parameter(self, value_broadcast, config_options, param_type=int):\\n\\n dtype = np.dtype(param_type)\\n\\n if self.rank == 0:\\n param = np.asarray(value_broadcast, dtype=dtype)\\n else:\\n param = np.empty(dtype=dtype, shape=())\\n\\n try:\\n self.comm.Bcast(param, root=0)\\n except MPI.Exception:\\n config_options.errMsg = \\\"Unable to broadcast single value from rank 0.\\\"\\n err_handler.log_critical(config_options, self)\\n return None\\n return param.item(0)\",\n \"def plan_path(self, msg):\\n # Request the map\\n # In case of error, return an empty path\\n mapdata = PathPlanner.request_map()\\n\\n if mapdata is None:\\n return Path()\\n # Calculate the C-space and publish it\\n cspacedata = self.calc_cspace(mapdata, 3)\\n # Execute A*\\n start = PathPlanner.world_to_grid(mapdata, msg.start.pose.position)\\n goal = PathPlanner.world_to_grid(mapdata, msg.goal.pose.position)\\n \\n path = self.a_star(cspacedata, start, goal) #, self.c_space_array, self.frontier, self.expanded)\\n \\n # Optimize waypoints\\n waypoints = PathPlanner.optimize_path(path)\\n # print waypoints\\n waypoints.remove(waypoints[0])\\n # print waypoints\\n\\n self.path_pub.publish(self.path_to_message(cspacedata, waypoints))\\n # Return a Path message\\n return self.path_to_message(cspacedata, waypoints)\",\n \"def rsync_to(self, local, remote, flags='-av', user='root'):\\n if self._portoffset:\\n tail = \\\"/\\\" if local[-1] == \\\"/\\\" else ''\\n local = os.path.abspath(os.path.expanduser(local)) + tail\\n return self._rsync(\\n 'rsync {flags} {src} '\\n 'rsync://{usr}@{target}:{port}{dest}'.format(\\n flags=flags, src=local, usr=user, target=self._target,\\n port=(self._portoffset + 873), dest=remote))\\n return False\",\n \"def set_local(self):\\n self.write_to_serial(':SYST:LOC')\",\n \"def broadcast(self, message):\\n for s in self.connections:\\n s.send(bytes(message, encoding='utf-8'))\",\n \"def _write_back_to_local_shard(self, handles: List[FlatParamHandle]):\\n for handle in handles:\\n # For `NO_SHARD`, `_local_shard` is the unsharded flattened\\n # parameter as well\\n if not handle.uses_sharded_strategy:\\n continue\\n assert (\\n handle.flat_param.ndim == 1\\n ), f\\\"Expects `flat_param` to be flattened but got {handle.flat_param.shape}\\\"\\n # Get the unpadded shard instead of the padded shard to persist\\n # user changes to the padding (though FSDP does not explicitly\\n # support this)\\n shard, _ = FlatParamHandle._get_unpadded_shard(handle.flat_param, handle.rank, handle.world_size)\\n handle.flat_param._local_shard[:shard.numel()].copy_(shard)\",\n \"def local_action(self, *links, all_links):\\n S = 0.0\\n for link in links:\\n site1 = link[:-1]\\n u = link[-1]\\n for v in range(self.dim):\\n if v != u:\\n site2 = np.array(site1) - self.bases[v]\\n plaq1 = self.plaquette_operator(site1, u, v, all_links)\\n plaq2 = self.plaquette_operator(site2, u, v, all_links)\\n S += (plaq1 + plaq2)\\n return S\",\n \"def local_subdir_accessible(self, local_subdir_accessible):\\n \\n self._local_subdir_accessible = local_subdir_accessible\",\n \"def local_subtensor_lift(node):\\r\\n if isinstance(node.op, Subtensor):\\r\\n u = node.inputs[0]\\r\\n if not u.owner or len(u.clients) > 1:\\r\\n return False\\r\\n\\r\\n if isinstance(u.owner.op, T.Elemwise) and len(u.owner.inputs) == 1:\\r\\n idx = node.inputs[1:]\\r\\n x_idx = node.op(u.owner.inputs[0], *idx)\\r\\n return [u.owner.op(x_idx)]\\r\\n\\r\\n if isinstance(u.owner.op, T.Elemwise):\\r\\n new_inputs = []\\r\\n if all([sum(i.type.broadcastable) == 0 for i in u.owner.inputs]):\\r\\n # There is no broadcastable in the inputs\\r\\n idx = node.inputs[1:]\\r\\n new_inputs = [node.op(i, *idx) for i in u.owner.inputs]\\r\\n return [u.owner.op(*new_inputs)]\\r\\n elif all([sum(i.type.broadcastable) in [i.ndim, 0]\\r\\n for i in u.owner.inputs]):\\r\\n # There is no broadcastable in the inputs or it is scalar\\r\\n idx = node.inputs[1:]\\r\\n new_inputs = []\\r\\n for i in u.owner.inputs:\\r\\n if sum(i.type.broadcastable) == 0:\\r\\n new_inputs.append(node.op(i, *idx))\\r\\n else:\\r\\n # If the subtensor remove some dims, we must\\r\\n # lower the number of dimensions of this scalar.\\r\\n if node.outputs[0].ndim == i.ndim:\\r\\n new_inputs.append(i)\\r\\n else:\\r\\n new_inputs.append(\\r\\n i.dimshuffle(['x'] * node.outputs[0].ndim))\\r\\n return [u.owner.op(*new_inputs)]\\r\\n\\r\\n if isinstance(u.owner.op, T.Rebroadcast):\\r\\n # make sure that Rebroadcast has only 1 input\\r\\n assert len(u.owner.inputs) == 1\\r\\n\\r\\n # Subtensor might reduce dim., adapt broadcast pattern accordingly\\r\\n new_axis = []\\r\\n\\r\\n # loop through indices being subtensor-ed\\r\\n # i indexes broadcastable pattern before subtensor\\r\\n # j indexes broadcastable pattern after subtensor\\r\\n j = 0\\r\\n for (i, x) in enumerate(node.op.idx_list):\\r\\n # if its not a slice, it will reduce the dimension, should\\r\\n # not appear in the broascastable dimensions\\r\\n if isinstance(x, slice):\\r\\n new_axis += [(j, u.broadcastable[i])]\\r\\n j += 1\\r\\n # now keep the broadcastable pattern of all\\r\\n # items not appearing in subtensor list\\r\\n for i in xrange(len(node.op.idx_list), len(u.broadcastable)):\\r\\n new_axis += [(j, u.broadcastable[i])]\\r\\n j += 1\\r\\n\\r\\n subt_x = node.op(u.owner.inputs[0], *node.inputs[1:])\\r\\n rbcast_subt_x = T.Rebroadcast(*new_axis)(subt_x)\\r\\n\\r\\n return [rbcast_subt_x]\",\n \"def _get_reaction_path(self):\\n ## check if the atoms are on the same side of the unit cell\\n cell = self.atomsIS.get_cell() # same cell used in IS and FS hopefully\\n # get the vector respresenting the difference of the two \\n vector_all = self.atomsIS.get_positions() - self.atomsFS.get_positions()\\n vectors = vector_all[self.indices]\\n min_vec = []\\n for v in vectors:\\n vmin, vlen = geometry.find_mic(v, cell, pbc=True)\\n min_vec.append(vmin)\\n ravel_vec = np.ravel(min_vec)\\n self.modes.append( ravel_vec / np.linalg.norm(ravel_vec) )\",\n \"def local_root_accessible(self, local_root_accessible):\\n \\n self._local_root_accessible = local_root_accessible\",\n \"def _send(x, dst=0):\\n x = torch.tensor(x)\\n x = to_device(x)\\n dist.send(x, dst)\\n del x \\n torch.cuda.empty_cache()\",\n \"def test_path5():\\n path = [(0, 0, 1)]\\n path += [\\n [('A', 3, 0)],\\n (0, 1, 1),\\n [('A', 2, 0)],\\n (np.pi/2, 1, 1),\\n [('B',3,0)],\\n (0, 1, 1),\\n [('B',2,0)],\\n (np.pi/2, 1, 1),\\n [('C',3,0)],\\n (0, 1, 1),\\n [('C',2,0)],\\n (np.pi/2, 1, 1),\\n [('D', 3, 0)],\\n (0, 1, 1),\\n #[('D',2,0)],\\n (np.pi/2, 1, 1),\\n ] * 4\\n execute_path(path,True)\",\n \"def PushPath(self, request_iterator, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def sync(self):\\n if (len(self._source_list) == 0) or (len(self._dest_list) == 0):\\n return\\n \\n for src in self._source_list:\\n self._sync('.', src, self._depth)\",\n \"def process_broadcast(data):\\n logger.info(f\\\"Broadcast: {data}\\\")\",\n \"def optimized_path(coords, startid, mask):\\n coords = np.column_stack((coords, mask))\\n pass_by = np.asarray(coords)\\n path = [coords[startid]]\\n pass_by = np.delete(pass_by, startid, axis=0)\\n while pass_by.any():\\n nearest_id, nearest = min(\\n enumerate(pass_by), key=lambda x: distance(path[-1][:2], x[1][:2]))\\n path.append(nearest)\\n pass_by = np.delete(pass_by, nearest_id, axis=0)\\n\\n return path\",\n \"def deploy_from_local():\\n # don't die if tests fail\\n # with settings(warn_only=True):\\n # run_tests()\\n # defaults rsync options:\\n # -pthrvz\\n # -p preserve permissions\\n # -t preserve times\\n # -h output numbers in a human-readable format\\n # -r recurse into directories\\n # -v increase verbosity\\n # -z compress file data during the transfer\\n extra_opts = '--omit-dir-times -e \\\"ssh\\\"' # -p 80\\\"'\\n project.rsync_project(\\n env.code_root,\\n local_dir=env.local_dir,\\n exclude=RSYNC_EXCLUDE,\\n delete=True,\\n extra_opts=extra_opts,\\n )\\n touch()\\n restart_route()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.558918","0.5540478","0.5262298","0.5138672","0.51280296","0.5033147","0.4981683","0.4957514","0.4915474","0.4859186","0.4790848","0.4757363","0.47432727","0.4711225","0.47017384","0.4673872","0.46665725","0.46487427","0.4647647","0.46301493","0.46140268","0.4612716","0.45998842","0.4593032","0.45724565","0.4556833","0.45547992","0.4547696","0.4529297","0.4523274","0.45173416","0.45074978","0.44945428","0.449275","0.4483781","0.44815663","0.4480274","0.44641635","0.44431964","0.44391394","0.44282952","0.44180593","0.44032854","0.4400027","0.43865526","0.43695077","0.43638957","0.4359364","0.4358077","0.43553445","0.4355317","0.43509114","0.4344893","0.43304944","0.43258348","0.4325763","0.4324956","0.4319452","0.42791012","0.42781","0.42761624","0.42689568","0.42687783","0.42613724","0.4259252","0.42590693","0.4254443","0.42524606","0.42498645","0.42443022","0.42415333","0.4236564","0.42363054","0.42302805","0.42227805","0.4221307","0.4211952","0.42090112","0.42048824","0.4203467","0.42033905","0.41975558","0.41944328","0.41894567","0.41873506","0.41855463","0.41792062","0.4173634","0.41713893","0.41654846","0.4164969","0.4154844","0.41510436","0.4148618","0.41484457","0.41461286","0.41430846","0.41362268","0.41317648","0.4131465"],"string":"[\n \"0.558918\",\n \"0.5540478\",\n \"0.5262298\",\n \"0.5138672\",\n \"0.51280296\",\n \"0.5033147\",\n \"0.4981683\",\n \"0.4957514\",\n \"0.4915474\",\n \"0.4859186\",\n \"0.4790848\",\n \"0.4757363\",\n \"0.47432727\",\n \"0.4711225\",\n \"0.47017384\",\n \"0.4673872\",\n \"0.46665725\",\n \"0.46487427\",\n \"0.4647647\",\n \"0.46301493\",\n \"0.46140268\",\n \"0.4612716\",\n \"0.45998842\",\n \"0.4593032\",\n \"0.45724565\",\n \"0.4556833\",\n \"0.45547992\",\n \"0.4547696\",\n \"0.4529297\",\n \"0.4523274\",\n \"0.45173416\",\n \"0.45074978\",\n \"0.44945428\",\n \"0.449275\",\n \"0.4483781\",\n \"0.44815663\",\n \"0.4480274\",\n \"0.44641635\",\n \"0.44431964\",\n \"0.44391394\",\n \"0.44282952\",\n \"0.44180593\",\n \"0.44032854\",\n \"0.4400027\",\n \"0.43865526\",\n \"0.43695077\",\n \"0.43638957\",\n \"0.4359364\",\n \"0.4358077\",\n \"0.43553445\",\n \"0.4355317\",\n \"0.43509114\",\n \"0.4344893\",\n \"0.43304944\",\n \"0.43258348\",\n \"0.4325763\",\n \"0.4324956\",\n \"0.4319452\",\n \"0.42791012\",\n \"0.42781\",\n \"0.42761624\",\n \"0.42689568\",\n \"0.42687783\",\n \"0.42613724\",\n \"0.4259252\",\n \"0.42590693\",\n \"0.4254443\",\n \"0.42524606\",\n \"0.42498645\",\n \"0.42443022\",\n \"0.42415333\",\n \"0.4236564\",\n \"0.42363054\",\n \"0.42302805\",\n \"0.42227805\",\n \"0.4221307\",\n \"0.4211952\",\n \"0.42090112\",\n \"0.42048824\",\n \"0.4203467\",\n \"0.42033905\",\n \"0.41975558\",\n \"0.41944328\",\n \"0.41894567\",\n \"0.41873506\",\n \"0.41855463\",\n \"0.41792062\",\n \"0.4173634\",\n \"0.41713893\",\n \"0.41654846\",\n \"0.4164969\",\n \"0.4154844\",\n \"0.41510436\",\n \"0.4148618\",\n \"0.41484457\",\n \"0.41461286\",\n \"0.41430846\",\n \"0.41362268\",\n \"0.41317648\",\n \"0.4131465\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.639906"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19698,"cells":{"query":{"kind":"string","value":"Download the checkpoint stored at ``path``, potentially in ``object_store``, to ``node_checkpoint_folder``. Returns a tuple of (``composer_states_filepath``, ``extracted_checkpoint_folder``, ``extracted_rank_n``). The ``composer_states_filepath``, is the path to the composer states, which can be passed into"},"document":{"kind":"string","value":"def download_checkpoint(path: str,\n node_checkpoint_folder: str,\n object_store: Optional[Union[ObjectStore, LoggerDestination]],\n progress_bar: bool,\n fsdp_sharded_state_dict_enabled: bool = False,\n deepspeed_sharded_checkpoint: bool = False) -> Tuple[str, Optional[str], bool]:\n log.debug('Downloading checkpoint to folder %s', node_checkpoint_folder)\n rank_zero_checkpoint_filepath = os.path.join(node_checkpoint_folder, 'rank0_checkpoint')\n rank_n_checkpoint_filepath = os.path.join(node_checkpoint_folder, f'rank{dist.get_global_rank()}_checkpoint')\n extracted_checkpoint_folder = None\n extracted_rank_n = False\n if is_tar(path):\n extracted_checkpoint_folder = os.path.join(node_checkpoint_folder, 'checkpoint')\n composer_states_filepath = os.path.join(extracted_checkpoint_folder, _COMPOSER_STATES_FILENAME)\n else:\n # it's not an archive; it's just the composer state dict\n # and only rank zero has this file unless fsdp_sharded_state_dict_enabled then\n # every rank has it's own file.\n extracted_checkpoint_folder = None\n composer_states_filepath = (rank_n_checkpoint_filepath\n if fsdp_sharded_state_dict_enabled else rank_zero_checkpoint_filepath)\n\n checkpoint_is_sharded = fsdp_sharded_state_dict_enabled or deepspeed_sharded_checkpoint\n try:\n if not checkpoint_is_sharded and dist.get_local_rank() == 0:\n # if the checkpoint is not sharded, then local rank 0 on each node needs to download the\n # global rank 0 checkpoint\n path = _format_path_with_rank_zero(path)\n get_file(destination=rank_zero_checkpoint_filepath,\n path=path,\n object_store=object_store,\n progress_bar=progress_bar)\n if extracted_checkpoint_folder is not None:\n try:\n with tarfile.open(rank_zero_checkpoint_filepath) as tarball:\n tarball.extractall(extracted_checkpoint_folder)\n except FileNotFoundError:\n # Not re-raising the file-not-found error as that is irrelevant;\n # the underlying issue is that the checkpoint file does not exist on the disk\n # or could not be downloaded\n raise RuntimeError(f'Checkpoint {path} does not exist')\n elif checkpoint_is_sharded:\n # if the checkpoint is sharded, then every rank needs to download its own checkpoint\n try:\n get_file(destination=rank_n_checkpoint_filepath,\n path=_format_path_with_current_rank(path),\n object_store=object_store,\n progress_bar=progress_bar)\n except FileNotFoundError as e:\n raise FileNotFoundError(\n (f'Checkpoint {_format_path_with_current_rank(path)} does not exist, '\n f'but is required for sharded checkpointing on rank {dist.get_global_rank()}. '\n 'Please ensure that the checkpoint exists and your load_path was specified as a format string'\n 'with the {rank} argument.')) from e\n\n if extracted_checkpoint_folder is not None:\n with contextlib.suppress(FileNotFoundError):\n # it's an archive and needs to be extracted\n with tarfile.open(rank_n_checkpoint_filepath) as tarball:\n tarball.extractall(extracted_checkpoint_folder)\n extracted_rank_n = True\n finally:\n # Use busy wait to avoid timeouts on large downloads for non-sharded checkpoints\n if not checkpoint_is_sharded:\n signal_file_path = os.path.join(node_checkpoint_folder, '.local_rank0_completed')\n if dist.get_local_rank() == 0:\n with open(signal_file_path, 'wb') as f:\n f.write(b'local_rank0_completed')\n\n # Avoid the collective call until the local rank zero has finished trying to download the\n # checkpoint so that we don't timeout for large downloads. This syncs all processes on the\n # node\n with dist.local_rank_zero_download_and_wait(signal_file_path):\n # Then, wait to ensure every node has finished downloading the checkpoint\n dist.barrier()\n\n if dist.get_local_rank() == 0:\n os.remove(signal_file_path)\n dist.barrier()\n\n return composer_states_filepath, extracted_checkpoint_folder, extracted_rank_n"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load_checkpoint(\n path: str,\n state: State,\n logger: Logger,\n object_store: Optional[Union[ObjectStore, LoggerDestination]] = None,\n load_weights_only: bool = False,\n strict_model_weights: bool = False,\n progress_bar: bool = True,\n ignore_keys: Optional[Union[List[str], Callable[[Dict], None]]] = None,\n exclude_algorithms: Optional[List[str]] = None,\n algorithm_passes: Optional[List[AlgorithmPass]] = None,\n):\n # Download the checkpoint to the node-local folder\n log.debug('Loading checkpoint at %s', path)\n # Each node gets one unique folder to store checkpoints that is shared amongst all local ranks in that node.\n # If fsdp sharded state_dicts is enabled then EVERY rank gets a unique checkpoint folder.\n needs_unique_checkpoint_folder = state.fsdp_sharded_state_dict_enabled or dist.get_local_rank() == 0\n tempdir_ctx = tempfile.TemporaryDirectory() if needs_unique_checkpoint_folder else contextlib.nullcontext(None)\n with tempdir_ctx as tempdir:\n try:\n # Get the path to the proper checkpoint folder corresponding to the current rank's node.\n # If fsdp_sharded_state_dict_enabled then just use that rank's unique tempdir.\n node_checkpoint_folder = (tempdir\n if state.fsdp_sharded_state_dict_enabled else _get_local_rank_zero_path(tempdir))\n assert node_checkpoint_folder is not None\n\n composer_states_filepath, extracted_checkpoint_folder, extracted_rank_n = download_checkpoint(\n path=path,\n node_checkpoint_folder=node_checkpoint_folder,\n object_store=object_store,\n progress_bar=progress_bar,\n fsdp_sharded_state_dict_enabled=state.fsdp_sharded_state_dict_enabled,\n deepspeed_sharded_checkpoint=is_model_deepspeed(state.model),\n )\n rng_state_dicts = _restore_checkpoint(\n state,\n logger,\n composer_states_filepath,\n extracted_rank_n,\n extracted_checkpoint_folder,\n load_weights_only=load_weights_only,\n strict_model_weights=strict_model_weights,\n ignore_keys=ignore_keys,\n exclude_algorithms=exclude_algorithms,\n algorithm_passes=algorithm_passes,\n )\n finally:\n # Wait for all ranks to finish restoring the checkpoint before releasing the tempdir, since tempdir can\n # be a shared resource between nodes.\n dist.barrier()\n\n log.info('%s loaded from %s', 'Model weights' if load_weights_only else 'Trainer checkpoint', path)\n return rng_state_dicts","def download_checkpoint_ngc(checkpoint_url: str, checkpoint_path: pathlib.Path) -> None:\n with tqdm(unit=\"B\") as t:\n reporthook = download_progress(t)\n result = urllib.request.urlretrieve(checkpoint_url, reporthook=reporthook)\n\n filename = result[0]\n\n file_path = pathlib.Path(filename)\n assert file_path.is_file() or file_path.is_dir(), \"Checkpoint was not downloaded\"\n\n shutil.move(file_path, checkpoint_path.parent / file_path.name)\n\n archive_path = checkpoint_path.parent / file_path.name\n unzip(checkpoint_path, archive_path)","def parse_checkpoint(checkpoint_path):\n with gfile.Open(checkpoint_path, 'rb') as fp:\n raw_contents = fp.read()\n if raw_contents.startswith(b'model_checkpoint_path'):\n raise ValueError(\n 'Attempting to restore a TensorFlow checkpoint as a native T5X '\n f'checkpoint. Path: {checkpoint_path}')\n return serialization.msgpack_restore(raw_contents)","def _restore_checkpoint(\n state: State,\n logger: Logger,\n composer_states_filepath: str,\n extracted_rank_n: bool,\n extracted_checkpoint_folder: Optional[str],\n load_weights_only: bool,\n strict_model_weights: bool,\n ignore_keys: Optional[Union[List[str], Callable[[Dict], None]]],\n exclude_algorithms: Optional[List[str]],\n algorithm_passes: Optional[List[AlgorithmPass]],\n) -> Optional[List[Dict[str, Any]]]:\n # Now, all ranks load the checkpoint that local rank zero downloaded\n state_dict = safe_torch_load(\n composer_states_filepath=composer_states_filepath,\n load_fsdp_monolith_rank0_only=state.load_fsdp_monolith_rank0_only,\n )\n if ignore_keys:\n # Filter provided list of key paths\n if not callable(ignore_keys):\n ignore_keys = glob_filter(ignore_keys)\n # Call function to modify state_dict\n ignore_keys(state_dict)\n log.debug(f\"Loaded checkpoint with keys {state_dict.keys()} and state keys {state_dict['state'].keys()}\")\n\n if is_model_deepspeed(state.model):\n if extracted_checkpoint_folder is None:\n raise RuntimeError('Deepspeed checkpoints require a tarball, not a weights file.')\n\n global_rank = dist.get_global_rank()\n if global_rank > 0 and not extracted_rank_n:\n raise RuntimeError(f'Deepspeed checkpoint missing for rank {global_rank}')\n\n load_path, _ = state.deepspeed_model.load_checkpoint(\n extracted_checkpoint_folder,\n tag=_DEEPSPEED_TAG,\n load_module_only=load_weights_only,\n load_module_strict=strict_model_weights,\n )\n if load_path is None:\n raise RuntimeError('Failed to load DeepSpeed checkpoint')\n elif load_weights_only:\n state.load_model_state(\n state_dict['state'],\n logger,\n strict=strict_model_weights,\n exclude_algorithms=exclude_algorithms,\n algorithm_passes=algorithm_passes,\n )\n if not load_weights_only:\n state.load_state_dict(\n state_dict['state'],\n logger,\n exclude_algorithms=exclude_algorithms,\n algorithm_passes=algorithm_passes,\n )\n step_to_resume_from = state.timestamp.batch.value\n max_step_to_resume_from = state.device.tensor_to_device(\n torch.tensor(state.timestamp.batch.value, dtype=torch.int64))\n min_step_to_resume_from = state.device.tensor_to_device(\n torch.tensor(state.timestamp.batch.value, dtype=torch.int64))\n dist.all_reduce(max_step_to_resume_from, reduce_operation='MAX')\n dist.all_reduce(min_step_to_resume_from, reduce_operation='MIN')\n if max_step_to_resume_from.data != min_step_to_resume_from.data:\n raise RuntimeError(\n textwrap.dedent(\n f'Timestamp mismatch error: batch to resume from {step_to_resume_from} is not the same on all ranks. '\n 'This usually occurs when at least one rank fails to save the last checkpoint '\n 'while using sharded checkpointing + autoresume. '\n 'Please manually resume by disabling autoresume and explicitly setting load_path '\n 'to the most recent checkpoints that all ranks have saved. '\n 'E.g. for the 10th batch: trainer = Trainer(autoresume=False, load_path=\"/path/to/checkpoint/ba10-rank{rank}.pt\", ...). '\n 'Remember to keep the {rank} placeholder!'))\n return state_dict['rng']","def load_checkpoint(self, checkpoint_path=None):\n if checkpoint_path is None:\n checkpoint_path = self.get_latest_path()\n\n if os.path.isfile(checkpoint_path):\n key = 'cuda' if torch.cuda.is_available() else 'cpu'\n checkpoint = torch.load(checkpoint_path, map_location=key)\n self.network.load_state_dict(checkpoint['network'])\n self.network_target.load_state_dict(checkpoint['network_target'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n\n print('checkpoint loaded at {}'.format(checkpoint_path))\n else:\n raise OSError(\"Checkpoint file not found.\")","def load_checkpoint(checkpoint_path):\n flat_checkpoint_dict = flatten_checkpoint(\n parse_checkpoint(checkpoint_path), keep_empty_nodes=True)\n return flat_checkpoint_dict","def load_from_checkpoint(self, path):\n print(f'# loading trainer state from {path}')\n checkpoint = torch.load(path)\n self.load(checkpoint)","def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:\n # TODO: move to CheckpointIO\n torch.cuda.empty_cache()\n checkpoint_path = inject_model_parallel_rank(checkpoint_path)\n return self.checkpoint_io.load_checkpoint(checkpoint_path)","def get_checkpoint_file(path, checkpoint):\n if checkpoint is not None:\n return checkpoint\n else:\n file_list = glob.glob(path + \"/*.pt\")\n if not file_list:\n raise AssertionError(\"Please ensure a checkpoint file (.pt)\"\n f\" is present in folder {path}\")\n elif len(file_list) > 1:\n raise AssertionError(\n \"Multiple checkpoint files present. Either ensure only 1 \"\n \"is present or indicate which checkpoint to use \"\n \"via --checkpoint\")\n else:\n return split(file_list[0])[1]","def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\n return torch.load(checkpoint_path, *args, **kwargs)","def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\n return torch.load(checkpoint_path, *args, **kwargs)","def restore(self, checkpoint_path):\n start_time = time.time()\n latest_checkpoint = train_util.get_latest_chekpoint(checkpoint_path)\n if latest_checkpoint is not None:\n checkpoint = tf.train.Checkpoint(model=self)\n checkpoint.restore(latest_checkpoint).expect_partial()\n logging.info('Loaded checkpoint %s', latest_checkpoint)\n logging.info('Loading model took %.1f seconds', time.time() - start_time)\n else:\n logging.info('Could not find checkpoint to load at %s, skipping.',\n checkpoint_path)","def load_from_path(self, checkpoint_dir):\n\n vars = self.save_var_names\n saver = tf.train.Saver(vars)\n\n def load_aux(ckpt_path):\n \"\"\"Helper function to not repeat the same code in the following lines.\"\"\"\n\n ckpt_name = os.path.basename(ckpt_path)\n saver.restore(self.sess, ckpt_path)\n counter = int(next(re.finditer(\"(\\d+)(?!.*\\d)\", ckpt_name)).group(0))\n self.counter = counter\n print(\" [*] Loaded {}\".format(ckpt_name))\n return True, counter\n\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n try:\n if ckpt and ckpt.model_checkpoint_path:\n ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\n return load_aux(os.path.join(checkpoint_dir, ckpt_name))\n else:\n print(\n \" [!] Failed to find a checkpoint within directory {}\".format(\n FLAGS.ckpt_path))\n return False, 0\n except:\n print(\" [!] Failed to find a checkpoint, Exception!\")\n return False, 0","def _load(checkpoint_path):\n state_dict, optimizer_state = dg.load_persistables(dirname=checkpoint_path)\n return state_dict, optimizer_state","def load_checkpoint(checkpoint_dir, epoch, iteration):\n path = opj(checkpoint_dir, str(epoch) + '.' + str(iteration) + '.ckpt')\n if not os.path.isfile(path):\n raise Exception(\"Checkpoint in epoch %d doesn't exist :sob:\" % epoch)\n\n checkpoint = torch.load(path)\n start_epoch = checkpoint['epoch']\n state_dict = checkpoint['state_dict']\n start_iteration = checkpoint['iteration']\n\n assert iteration == start_iteration\n return start_epoch, start_iteration, state_dict","def load_checkpoint(cfg, args):\n checkpoint_iteration = args.checkpoint\n bucket = connect_to_bucket(args.bucket)\n # load actual checkpoint\n if not os.path.isdir(cfg.OUTPUT_DIR):\n os.mkdir(cfg.OUTPUT_DIR)\n blob = bucket.blob(cfg.OUTPUT_DIR + \"/model_\" + str(checkpoint_iteration) + \".pth\")\n blob.download_to_filename(cfg.OUTPUT_DIR + \"/model_\" + str(checkpoint_iteration) + \".pth\")\n if args.resume:\n # also write last checkpoint file for when --resume statement, model gets checkpoint name from this file\n with open(cfg.OUTPUT_DIR + \"/last_checkpoint\", \"w\") as file:\n file.write(\"model_\" + str(checkpoint_iteration) + \".pth\")\n # return statement not clean, but useful for inference code\n return checkpoint_iteration, bucket","def restore_checkpoint(checkpoint_path: str,\n train_state: Optional[TrainState] = None,\n assert_exist: bool = False,\n step: Optional[int] = None) -> Tuple[TrainState, int]:\n if assert_exist:\n glob_path = os.path.join(checkpoint_path, 'checkpoint_*')\n if not gfile.glob(glob_path):\n raise ValueError('No checkpoint for the pretrained model is found in: '\n f'{checkpoint_path}')\n if train_state is None:\n raise ValueError('Please use `restore_pretrained_checkpoint` for loading'\n 'a checkpoint without providing a Scenic TrainState.')\n train_state = checkpoints.restore_checkpoint(checkpoint_path, train_state,\n step)\n return train_state, int(train_state.global_step)","def load(cls, path):\n print(f'load checkpoint from {path}')\n if torch.cuda.is_available():\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))\n model = torch.load(os.path.join(path, cls.MODEL_NAME))\n else:\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME), map_location=lambda storage, loc: storage)\n model = torch.load(os.path.join(path, cls.MODEL_NAME), map_location=lambda storage, loc: storage)\n \n # model.flatten_parameters() # make RNN parameters contiguous\n optimizer = resume_checkpoint['optimizer']\n return Checkpoint(\n model=model, \n optimizer=optimizer,\n epoch=resume_checkpoint['epoch'],\n path=path\n )","def load_checkpoint(checkpoint_path, model, optimizer=None):\n if not os.path.exists(checkpoint_path):\n raise IOError(f\"Checkpoint '{checkpoint_path}' does not exist\")\n\n state = torch.load(checkpoint_path)\n try:\n model.load_state_dict(state['model_state_dict'])\n except BaseException as e:\n print('Failed to do something: ' + str(e))\n\n if optimizer is not None:\n try:\n optimizer.load_state_dict(state['optimizer_state_dict'])\n except Exception as e:\n print(e)\n\n return state","def load_checkpoint(self, checkpoint: str, **kwargs) -> None:\n with open(checkpoint, \"rb\") as f:\n state = SafePickle.load(f)\n\n state_id = ray.put(state)\n ray.get([worker.set_state.remote(state_id, **kwargs) for worker in self.remote_workers])","def get_latest_checkpoint(cls, experiment_path):\n checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)\n all_times = sorted(os.listdir(checkpoints_path), reverse=True)\n return os.path.join(checkpoints_path, all_times[0])","def restore_checkpoint(self, checkpoint_id, name, path=''):\n\n\t\tself.log.debug(\"restoring Notebook %s from checkpoint %s\", name, checkpoint_id)\n\t\tnb_path = self._get_os_path(name, path)\n\t\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\n\n\t\tif not key_exists(self.bucket, cp_path):\n\t\t\tself.log.debug(\"checkpoint file does not exist: %s\", cp_path)\n\t\t\traise web.HTTPError(404,\n\t\t\t\tu'Notebook checkpoint does not exist: %s-%s' % (name, checkpoint_id)\n\t\t\t)\n\t\t# ensure notebook is readable (never restore from an unreadable notebook)\n\t\tkey = self.bucket.get_key(cp_path)\n\t\tnb = current.reads(key.get_contents_as_string(), u'json')\n\t\tself._copy(cp_path, nb_path)\n\t\tself.log.debug(\"copying %s -> %s\", cp_path, nb_path)","def load_checkpoint(checkpoint_path, model, optimizer=None,\n model_key='model_state_dict', optimizer_key='optimizer_state_dict'):\n if not os.path.exists(checkpoint_path):\n raise IOError(f\"Checkpoint '{checkpoint_path}' does not exist\")\n\n state = torch.load(checkpoint_path, map_location='cpu')\n model.load_state_dict(state[model_key])\n\n if optimizer is not None:\n optimizer.load_state_dict(state[optimizer_key])\n\n return state","def load_model_from_checkpoint(self, path: str):\n ckpt = torch.load(path, map_location='cpu')\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\n self.net_q.head.load_state_dict(ckpt['head'])\n self.net_ps.load_state_dict(ckpt['net_ps'])\n self.net_k.load_state_dict(ckpt['net_k'])\n self.queue.load_state_dict(ckpt['queue'])\n self.optimizer.load_state_dict(ckpt['optimizer'])\n if 'scheduler' in ckpt:\n self.scheduler.load_stae_dict(ckpt['scheduler'])\n self.move_optimizer_states(self.optimizer, self.local_rank)","def load_checkpoint(checkpoint_path, model, optimizer=None):\n if not os.path.exists(checkpoint_path):\n raise IOError(\"Checkpoint '{}' does not exist\".format(checkpoint_path))\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else 'cpu')\n state = torch.load(checkpoint_path, map_location=\"cuda:0\")\n model.load_state_dict(state['model_state_dict'])\n\n if optimizer is not None:\n optimizer.load_state_dict(state['optimizer_state_dict'])\n\n return state","def load_checkpoint(path: str, use_cuda: bool = True) -> dict:\n assert os.path.isfile(path), \"Checkpoint %s not found\" % path\n checkpoint = torch.load(path, map_location=\"cuda\" if use_cuda else \"cpu\")\n return checkpoint","def restore(self, checkpoint_path: str):\r\n raise NotImplementedError","def load_checkpoint(checkpoint_directory,\n session):\n variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n # filter variables if needed.\n print(variables)\n saver_ob = tf.train.Saver(variables, max_to_keep=0)\n os.makedirs(checkpoint_directory, exist_ok=True)\n # verify if we don't have a checkpoint saved directly\n step = 0\n ckpt = tf.train.get_checkpoint_state(checkpoint_directory)\n if ckpt and ckpt.model_checkpoint_path:\n # Restores from checkpoint\n model_checkpoint_path = ckpt.model_checkpoint_path\n saver_ob.restore(session, model_checkpoint_path)\n step = int(model_checkpoint_path.rsplit('-', 1)[1])\n print('Model loaded = ', step)\n return saver_ob, step","def load_checkpoint(model, save_path):\n model.load_state_dict(torch.load(save_path))","def load_checkpoint(self, checkpoint_path, continue_from_epoch=True):\n print(\"Loading checkpoint: {}\".format(checkpoint_path))\n state = torch.load(checkpoint_path)\n self.model.load_state_dict(state['state_dict'])\n self.optimizer.load_state_dict(state['optim_dict'])\n\n if continue_from_epoch:\n self.epoch = state['epoch']","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.monitor_best = checkpoint['monitor_best']\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n self.train_logger = checkpoint['logger']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def _get_checkpoint(self):\n ckpt = tf.train.get_checkpoint_state(self.model)\n if ckpt and ckpt.model_checkpoint_path:\n ckpt_path = ckpt.model_checkpoint_path\n else:\n raise RuntimeError('No checkpoint file found')\n return ckpt_path","def _resume_checkpoint(self, resume_path):\n resume_path = str(resume_path)\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n\n # load architecture params from checkpoint.\n if checkpoint['config']['model'] != self.config['model']:\n self.logger.warning(\"Warning: Architecture configuration given in config file is different from that of \"\n \"checkpoint. This may yield an exception while state_dict is being loaded.\")\n self.model.load_state_dict(checkpoint['state_dict'])\n\n # load optimizer state from checkpoint only when optimizer type is not changed.\n if checkpoint['config']['trainer']['optimizer']['type'] != self.config['trainer']['optimizer']['type']:\n self.logger.warning(\"Warning: Optimizer type given in config file is different from that of checkpoint. \"\n \"Optimizer parameters not being resumed.\")\n else:\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n\n self.logger.info(\"Checkpoint loaded. Resume from epoch {}\".format(self.start_epoch))","def load_checkpoint(self, path: str = '', train: bool = True) -> int:\n\n if not path:\n dir_ = os.path.dirname(os.path.realpath('__file__'))\n path = os.path.join(dir_, 'model.pt')\n\n try:\n ckpt = torch.load(path)\n except FileNotFoundError:\n return 0\n else:\n print('Loaded model at epoch: ', end='')\n\n self.load_state_dict(ckpt['model_state_dict'])\n self.actor_optimizer.load_state_dict(ckpt['ac_optim_dict'])\n self.critic_optimizer.load_state_dict(ckpt['critic_optim_dict'])\n epoch = ckpt['epoch']\n\n print(epoch)\n\n if not train:\n self.eval()\n else:\n self.train()\n\n return epoch","def create_checkpoint(self, name, path=''):\n\n\t\tnb_path = self._get_os_path(name, path)\n\t\tself.log.debug('creating checkpoint \"%s\" \"%s\" \"%s\"' % (path, name, nb_path))\n\t\t# only the one checkpoint ID:\n\t\tcheckpoint_id = u\"checkpoint\"\n\t\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\n\t\tself.log.debug(\"creating checkpoint for notebook %s\", name)\n\t\tif not key_exists(self.bucket, self.checkpoint_dir):\n\t\t\tnew_key_from_string(self.bucket, self.checkpoint_dir, '')\n\t\tself._copy(nb_path, cp_path)\n\n\t\t# return the checkpoint info\n\t\treturn self.get_checkpoint_model(checkpoint_id, name, path)","def get_step_from_checkpoint_path(checkpoint_path: Text) -> Optional[int]:\n if not checkpoint_path:\n return None\n try:\n return int(checkpoint_path.strip().split(\"-\")[-1])\n except ValueError:\n return None","def find_checkpoint(load_dir, seen_step):\n ckpt = tf.train.get_checkpoint_state(load_dir)\n if ckpt and ckpt.model_checkpoint_path:\n global_step = extract_step(ckpt.model_checkpoint_path)\n if int(global_step) != seen_step:\n return int(global_step), ckpt.model_checkpoint_path\n return -1, None","def load(self, path):\n\n filename = self.__path(path, prefix=None)\n chkpt = th.load(filename, map_location=\"cpu\") # TODO: check behavior\n\n if self.model is not None and chkpt[\"model\"] is not None:\n log.debug(\"Loading model state dict\")\n self.model.load_state_dict(chkpt[\"model\"])\n\n if \"optimizers\" in chkpt.keys():\n if self.optimizers is not None and chkpt[\"optimizers\"] is not None:\n try:\n for opt, state in zip(self.optimizers,\n chkpt[\"optimizers\"]):\n log.debug(\"Loading optimizers state dict for %s\", opt)\n opt.load_state_dict(state)\n except:\n # We do not raise an error here, e.g. in case the user simply\n # changes optimizer\n log.warning(\"Could not load optimizer state dicts, \"\n \"starting from scratch\")\n\n if \"schedulers\" in chkpt.keys():\n if self.schedulers is not None and chkpt[\"schedulers\"] is not None:\n try:\n for s, state in zip(self.schedulers,\n chkpt[\"schedulers\"]):\n log.debug(\"Loading scheduler state dict for %s\", s)\n s.load_state_dict(state)\n except:\n log.warning(\"Could not load scheduler state dicts, \"\n \"starting from scratch\")\n\n log.debug(\"Loaded checkpoint \\\"{}\\\"\".format(filename))\n return tuple(chkpt[k] for k in [\"extras\", \"meta\"])","def get_checkpoint_path(self, checkpoint_id, name, path=''):\n\t\tself.log.debug(\"getting checkpoint path %s, %s\", name, path)\n\t\tbasename, _ = os.path.splitext(name)\n\t\tfilename = u\"{name}-{checkpoint_id}{ext}\".format(\n\t\t\tname=basename,\n\t\t\tcheckpoint_id=checkpoint_id,\n\t\t\text=self.filename_ext,\n\t\t)\n\n\t\t# Checkpoints are stored in relative directories\n\t\t# e.g. given:\n\t\t# folder1/notebook1.py\n\t\t# ... the checkpoint is at ...\n\t\t# folder1/<checkpoint directory>/notebook1.py\n\t\tbase_path = os.path.join(path, self.checkpoint_dir)\t\t \n\t\tfull_path = self._get_os_path( filename, path=base_path)\n\t\tself.log.debug(\"checkpoint path + filename %s\" % full_path)\n\t\treturn full_path","def read_checkpoint(out_fp):\r\n pickle_fh = open(out_fp, \"r\")\r\n return pickle.load(pickle_fh)","def load_checkpoint(self, session, model_dir):\n assert self.params.cp_load == True, (\"cp_load must be set to true to load a checkpoint\")\n self.loader.restore(session, model_dir)","def load_checkpoint(args, trainer, epoch_itr):\n os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)\n checkpoint_path = os.path.join(args.save_dir, 'checkpoints', args.restore_file)\n if os.path.isfile(checkpoint_path):\n extra_state = trainer.load_checkpoint(checkpoint_path)\n if extra_state is not None:\n # replay train iterator to match checkpoint\n epoch_itr.load_state_dict(extra_state['train_iterator'])\n\n print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(\n checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))\n\n trainer.lr_step(epoch_itr.epoch)\n trainer.lr_step_update(trainer.get_num_updates())\n if 'best' in extra_state:\n save_checkpoint.best = extra_state['best']","def get_checkpoint():\n if ((FLAGS.run_dir or FLAGS.checkpoint_file) and\n FLAGS.bundle_file and not should_save_generator_bundle()):\n raise sequence_generator.SequenceGeneratorException(\n 'Cannot specify both bundle_file and run_dir or checkpoint_file')\n if FLAGS.run_dir:\n train_dir = os.path.join(os.path.expanduser(FLAGS.run_dir), 'train')\n return train_dir\n elif FLAGS.checkpoint_file:\n return os.path.expanduser(FLAGS.checkpoint_file)\n else:\n return None","def get_oldest_checkpoint(cls, experiment_path):\n checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)\n all_times = sorted(os.listdir(checkpoints_path))\n return os.path.join(checkpoints_path, all_times[0])","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.mnt_best = checkpoint['monitor_best']\n\n # load model params from checkpoint.\n if checkpoint['config']['name'] != self.config['name']:\n self.logger.warning(\n 'Warning: Architecture configuration given in config file is different from that of checkpoint. ' + \\\n 'This may yield an exception while state_dict is being loaded.')\n self.model.load_state_dict(checkpoint['model_state_dict'])\n\n # load optimizer state from checkpoint only when optimizer type is not changed. \n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\n self.logger.warning('Warning: Optimizer type given in config file is different from that of checkpoint. ' + \\\n 'Optimizer parameters not being resumed.')\n self.optimizer.load_state_dict(checkpoint['model_optimizer'])\n\n # load scheduler state from checkpoint only when scheduler type is not changed\n if checkpoint['config']['scheduler']['type'] != self.config['scheduler']['type']:\n self.logger.warning('Warning: Scheduler type given in config file is different from that of checkpoint. ' + \\\n 'Scheduler parameters not being resumed.')\n self.scheduler.load_state_dict(checkpoint['model_scheduler'])\n\n self.train_logger = checkpoint['logger']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def get_checkpoint_model(self, checkpoint_id, name, path=''):\n\n\t\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\n\t\tkey = self.bucket.get_key(cp_path)\n\n\t\tlast_modified = key.last_modified\n\t\tinfo = dict(\n\t\t\tid=checkpoint_id,\n\t\t\tlast_modifie=last_modified,\n\t\t)\n\t\treturn info","def _get_checkpoint(\n user_checkpoint: Optional[Path], context: ContainerTaskContext\n ) -> Tuple[Optional[Path], Optional[Path]]:\n if user_checkpoint is None:\n host_checkpoint_dir = None\n container_checkpoint_dir = None\n else:\n host_checkpoint_dir = user_checkpoint\n container_checkpoint_dir = context.container_root_dir / \"checkpoint\"\n\n return host_checkpoint_dir, container_checkpoint_dir","def _get_checkpoint(\n user_checkpoint: Optional[Path], context: ContainerTaskContext\n ) -> Tuple[Optional[Path], Optional[Path]]:\n if user_checkpoint is None:\n host_checkpoint_dir = None\n container_checkpoint_dir = None\n else:\n host_checkpoint_dir = user_checkpoint\n container_checkpoint_dir = context.container_root_dir / \"checkpoint\"\n\n return host_checkpoint_dir, container_checkpoint_dir","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.monitor_best = checkpoint['monitor_best']\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n if self.with_cuda:\n for state in self.optimizer.state.values():\n for k, v in state.items():\n if isinstance(v, torch.Tensor):\n state[k] = v.cuda(torch.device('cuda'))\n self.train_logger = checkpoint['logger']\n #self.config = checkpoint['config']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def get_checkpoint_path(self) -> str:\n return self._j_checkpoint_storage.getCheckpointPath().toString()","def load_checkpoint(ckpt_path):\n checkpoint = None\n if ckpt_path:\n logger.info(\"Loading checkpoint from %s\" % ckpt_path)\n checkpoint = torch.load(ckpt_path, map_location=torch.device(\"cpu\"))\n\n if \"model\" in checkpoint.keys():\n # This preserves backward-compat for models using customed layernorm\n def fix_key(s):\n s = re.sub(\n r\"(.*)\\.layer_norm((_\\d+)?)\\.b_2\", r\"\\1.layer_norm\\2.bias\", s\n )\n s = re.sub(\n r\"(.*)\\.layer_norm((_\\d+)?)\\.a_2\", r\"\\1.layer_norm\\2.weight\", s\n )\n return s\n\n checkpoint[\"model\"] = {\n fix_key(k): v for k, v in checkpoint[\"model\"].items()\n }\n # Force add_ffnbias to True if bias found in model w_1 keys\n for key in checkpoint[\"model\"].keys():\n if \"w_1.bias\" in key:\n checkpoint[\"opt\"].add_ffnbias = True\n\n if not hasattr(checkpoint[\"opt\"], \"num_kv\"):\n checkpoint[\"opt\"].num_kv = 0\n if not hasattr(checkpoint[\"opt\"], \"add_ffnbias\"):\n checkpoint[\"opt\"].add_ffnbias = False\n if not hasattr(checkpoint[\"opt\"], \"parallel_residual\"):\n checkpoint[\"opt\"].parallel_residual = False\n if not hasattr(checkpoint[\"opt\"], \"shared_layer_norm\"):\n checkpoint[\"opt\"].shared_layer_norm = False\n if not hasattr(checkpoint[\"opt\"], \"use_ckpting\"):\n checkpoint[\"opt\"].use_ckpting = []\n if not hasattr(checkpoint[\"opt\"], \"relative_positions_buckets\"):\n checkpoint[\"opt\"].relative_positions_buckets = 0\n if not hasattr(checkpoint[\"opt\"], \"parallel_mode\"):\n checkpoint[\"opt\"].parallel_mode = \"data_parallel\"\n if not hasattr(checkpoint[\"opt\"], \"norm_eps\"):\n checkpoint[\"opt\"].norm_eps = 1e-6\n\n # fix v2 compatibility\n if \"generator\" in checkpoint.keys() and checkpoint[\"generator\"]:\n if \"0.weight\" in checkpoint[\"generator\"]:\n checkpoint[\"generator\"][\"weight\"] = checkpoint[\"generator\"].pop(\n \"0.weight\"\n )\n if \"0.bias\" in checkpoint[\"generator\"]:\n checkpoint[\"generator\"][\"bias\"] = checkpoint[\"generator\"].pop(\"0.bias\")\n # end of patch for backward compatibility\n\n return checkpoint","def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\n print(\"Load checkpoint from: {}\".format(path))\n state_dict, optimizer_state = _load(path)\n\n model.load_dict(state_dict)\n if not reset_optimizer and optimizer is not None:\n if optimizer_state is not None:\n print(\"[loading] Load optimizer state from {}\".format(path))\n optimizer.load(optimizer_state)\n\n return model","def _init_checkpoint_and_variables(pretrain_checkpoint_path,\n pretrain_checkpoint_exclude_scopes):\n checkpoint_reader = tf.contrib.framework.load_checkpoint(\n pretrain_checkpoint_path)\n return get_variables_to_restore_from_pretrain_checkpoint(\n pretrain_checkpoint_exclude_scopes,\n checkpoint_reader.get_variable_to_shape_map())","def load_training_checkpoint(args, model, PATH, ckpt_id):\r\n logger = args.logger\r\n _, checkpoint_state_dict = model.network.load_checkpoint(PATH, ckpt_id)\r\n epoch = checkpoint_state_dict['epoch']\r\n last_global_step = checkpoint_state_dict['last_global_step']\r\n last_global_data_samples = checkpoint_state_dict[\r\n 'last_global_data_samples']\r\n del checkpoint_state_dict\r\n return (epoch, last_global_step, last_global_data_samples)","def load_checkpoint(path):\n\n # Get the model name\n model_name = path.split('-')[0]\n assert (model_name in ['vgg16', 'resnet50'\n ]), \"Path must have the correct model name\"\n\n # Load in checkpoint\n checkpoint = torch.load(path)\n\n if model_name == 'vgg16':\n model = models.vgg16(pretrained=True)\n # Make sure to set parameters as not trainable\n for param in model.parameters():\n param.requires_grad = False\n model.classifier = checkpoint['classifier']\n\n elif model_name == 'resnet50':\n model = models.resnet50(pretrained=True)\n # Make sure to set parameters as not trainable\n for param in model.parameters():\n param.requires_grad = False\n model.fc = checkpoint['fc']\n\n # Load in the state dict\n model.load_state_dict(checkpoint['state_dict'])\n\n total_params = sum(p.numel() for p in model.parameters())\n print(f'{total_params:,} total parameters.')\n total_trainable_params = sum(\n p.numel() for p in model.parameters() if p.requires_grad)\n print(f'{total_trainable_params:,} total gradient parameters.')\n\n # Move to gpu\n if multi_gpu:\n model = nn.DataParallel(model)\n\n if train_on_gpu:\n model = model.to('cuda')\n\n # Model basics\n model.class_to_idx = checkpoint['class_to_idx']\n model.idx_to_class = checkpoint['idx_to_class']\n model.epochs = checkpoint['epochs']\n\n # Optimizer\n optimizer = checkpoint['optimizer']\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n return model, optimizer","def get_checkpoint_data(self) -> Dict[str, Any]:\n # get ckpt file path from config.trainer.params.resume_from_checkpoint\n path = self.config.trainer.params.get(\"resume_from_checkpoint\", None)\n if path is not None:\n is_zoo = self.is_zoo_path(path)\n ckpt_filepath = path\n if is_zoo:\n folder = download_pretrained_model(path)\n ckpt_filepath = get_ckpt_path_from_folder(folder)\n ckpt = get_ckpt_from_path(ckpt_filepath)\n config = get_config_from_folder_or_ckpt(folder, ckpt)\n else:\n ckpt = get_ckpt_from_path(ckpt_filepath)\n config = None\n\n return {\n \"ckpt\": ckpt,\n \"checkpoint_path\": ckpt_filepath,\n \"is_zoo\": is_zoo,\n \"config\": config,\n }\n\n is_zoo = False\n config = None\n ckpt = None\n # get ckpt file path from config.checkpoint\n ckpt_config = self.config.checkpoint\n suffix = \"best.ckpt\" if ckpt_config.resume_best else \"current.ckpt\"\n path = os.path.join(get_mmf_env(key=\"save_dir\"), suffix)\n ckpt_filepath = None\n resume_from_specified_path = (\n ckpt_config.resume_file is not None or ckpt_config.resume_zoo is not None\n ) and (not ckpt_config.resume or not PathManager.exists(path))\n if resume_from_specified_path:\n if ckpt_config.resume_file and PathManager.exists(ckpt_config.resume_file):\n ckpt_filepath = ckpt_config.resume_file\n elif ckpt_config.resume_zoo is not None:\n is_zoo = True\n folder = download_pretrained_model(ckpt_config.resume_zoo)\n ckpt_filepath = get_ckpt_path_from_folder(folder)\n ckpt = get_ckpt_from_path(ckpt_filepath)\n config = get_config_from_folder_or_ckpt(folder, ckpt)\n else:\n raise RuntimeError(f\"{ckpt_config.resume_file} doesn't exist\")\n\n if ckpt_config.resume and PathManager.exists(path):\n ckpt_filepath = path\n\n if ckpt_filepath is not None:\n ckpt = get_ckpt_from_path(ckpt_filepath)\n\n return {\n \"ckpt\": ckpt,\n \"checkpoint_path\": ckpt_filepath,\n \"is_zoo\": is_zoo,\n \"config\": config,\n }","def _load_checkpoint(filename, map_location=None):\n if filename.startswith('modelzoo://'):\n warnings.warn('The URL scheme of \"modelzoo://\" is deprecated, please '\n 'use \"torchvision://\" instead')\n model_urls = get_torchvision_models()\n model_name = filename[11:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('torchvision://'):\n model_urls = get_torchvision_models()\n model_name = filename[14:]\n checkpoint = load_url_dist(model_urls[model_name])\n elif filename.startswith('open-mmlab://'):\n model_urls = get_external_models()\n model_name = filename[13:]\n deprecated_urls = get_deprecated_model_names()\n if model_name in deprecated_urls:\n warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '\n f'of open-mmlab://{deprecated_urls[model_name]}')\n model_name = deprecated_urls[model_name]\n model_url = model_urls[model_name]\n # check if is url\n if model_url.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(model_url)\n else:\n filename = osp.join(_get_mmcv_home(), model_url)\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n elif filename.startswith('mmcls://'):\n model_urls = get_mmcls_models()\n model_name = filename[8:]\n checkpoint = load_url_dist(model_urls[model_name])\n checkpoint = _process_mmcls_checkpoint(checkpoint)\n elif filename.startswith(('http://', 'https://')):\n checkpoint = load_url_dist(filename)\n elif filename.startswith('pavi://'):\n model_path = filename[7:]\n checkpoint = load_pavimodel_dist(model_path, map_location=map_location)\n elif filename.startswith('s3://'):\n checkpoint = load_fileclient_dist(\n filename, backend='ceph', map_location=map_location)\n else:\n if not osp.isfile(filename):\n raise IOError(f'{filename} is not a checkpoint file')\n checkpoint = torch.load(filename, map_location=map_location)\n return checkpoint","def checkpoint(self, epoch, losses, path):\n dct = {'epoch': epoch, \n 'losses': losses, \n 'model_state_dict': self.TrajectoryAutoencoder.state_dict()}\n torch.save(dct, path)","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def load(self, path):\n checkpoint = torch.load(path)\n self.load_state_dict(checkpoint['model_state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])","def load_ckp(checkpoint_fpath, model, optimizer, device):\n\n checkpoint = torch.load(checkpoint_fpath,map_location=device)\n model.load_state_dict(checkpoint['state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer'])\n valid_acc = checkpoint['valid_acc'] \n return model, optimizer, checkpoint['epoch'], valid_acc","def _resume_checkpoint(self, resume_path):\n self.logger.info(\"Loading checkpoint: {} ...\".format(resume_path))\n checkpoint = torch.load(resume_path)\n self.start_epoch = checkpoint['epoch'] + 1\n self.monitor_best = checkpoint['monitor_best']\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n # 将参数全部放入GPU\n if self.with_cuda:\n for state in self.optimizer.state.values():\n for k, v in state.items():\n if isinstance(v, torch.Tensor):\n state[k] = v.cuda(self.device)\n self.train_logger = checkpoint['logger']\n self.config = checkpoint['config']\n self.logger.info(\"Checkpoint '{}' (epoch {}) loaded\".format(resume_path, self.start_epoch))","def _load_checkpoint(cls, model: DeviceAwareModule, checkpoint_path: Path,\n key_in_state_dict: str, use_gpu: bool) -> int:\n logging.info(f\"Loading checkpoint {checkpoint_path}\")\n checkpoint = ModelAndInfo.read_checkpoint(checkpoint_path, use_gpu)\n\n try:\n state_dict = checkpoint[key_in_state_dict]\n except KeyError:\n logging.error(f\"Key {key_in_state_dict} not found in checkpoint\")\n return False\n\n if isinstance(model, torch.nn.DataParallel):\n result = model.module.load_state_dict(state_dict, strict=False)\n else:\n result = model.load_state_dict(state_dict, strict=False)\n\n if result.missing_keys:\n logging.warning(f\"Missing keys in model checkpoint: {result.missing_keys}\")\n if result.unexpected_keys:\n logging.warning(f\"Unexpected keys in model checkpoint: {result.unexpected_keys}\")\n\n return checkpoint[ModelAndInfo.EPOCH_KEY]","def load_checkpoint(self):\n if self.params.resume_from is not None and os.path.exists(self.params.resume_from):\n try:\n LOG('Loading Checkpoint at %s' % self.params.resume_from)\n ckpt = torch.load(self.params.resume_from)\n self.epoch = ckpt['epoch']\n try:\n self.train_loss = ckpt['train_loss']\n self.val_loss = ckpt['val_loss']\n except:\n self.train_loss = []\n self.val_loss = []\n self.network.load_state_dict(ckpt['state_dict'])\n self.opt.load_state_dict(ckpt['optimizer'])\n LOG('Checkpoint Loaded!')\n LOG('Current Epoch: %d' % self.epoch)\n self.ckpt_flag = True\n except:\n WARNING('Cannot load checkpoint from %s. Start loading pre-trained model......' % self.params.resume_from)\n else:\n WARNING('Checkpoint do not exists. Start loading pre-trained model......')","def load_checkpoint(self, checkpoint_filepath=None, verbose=True):\n if checkpoint_filepath:\n print('loading', checkpoint_filepath, flush=True)\n optimistic_restore(self._session, checkpoint_filepath, verbose=verbose)\n return True\n else:\n checkpoints = retrieve_all_checkpoints(self._checkpoints_path) + retrieve_all_checkpoints(self._recovery_checkpoints_path)\n\n if checkpoints:\n last_checkpoint = sorted(checkpoints)[-1][1]\n print('loading', last_checkpoint, flush=True)\n optimistic_restore(self._session, last_checkpoint, verbose=verbose)\n return True\n else:\n print('nothing to restore. no checkpoint found.', flush=True)\n return False","def get_latest_checkpoint_path(dirpath: str) -> Optional[str]:\n\n ret = None\n rank = get_global_rank()\n # Do all filesystem reads from rank 0 only\n if rank == 0:\n ret = _latest_checkpoint_path(dirpath)\n\n # If not running in a distributed setting, return as is\n if not (dist.is_available() and dist.is_initialized()):\n return ret\n\n # Otherwise, broadcast result from rank 0 to all ranks\n pg = PGWrapper(dist.group.WORLD)\n path_container = [ret] if rank == 0 else [None]\n pg.broadcast_object_list(path_container, 0)\n val = path_container[0]\n return val","def load_checkpoint(fpath):\n if fpath is None:\n raise ValueError('File path is None')\n if not osp.exists(fpath):\n raise FileNotFoundError('File is not found at \"{}\"'.format(fpath))\n map_location = None if torch.cuda.is_available() else 'cpu'\n try:\n checkpoint = torch.load(fpath, map_location=map_location)\n except UnicodeDecodeError:\n pickle.load = partial(pickle.load, encoding=\"latin1\")\n pickle.Unpickler = partial(pickle.Unpickler, encoding=\"latin1\")\n checkpoint = torch.load(\n fpath, pickle_module=pickle, map_location=map_location\n )\n except Exception:\n print('Unable to load checkpoint from \"{}\"'.format(fpath))\n raise\n return checkpoint","def load_checkpoint(self):\n checkpoin_path = self.get_checkpoint_path()\n _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)","def get_checkpoint_path(self) -> Optional[str]:\n j_path = self._j_checkpoint_storage.getCheckpointPath()\n if j_path is None:\n return None\n else:\n return j_path.toString()","def unzip(checkpoint_path: pathlib.Path, archive_path: pathlib.Path) -> None:\n checkpoint_path.mkdir(parents=True, exist_ok=True)\n with ZipFile(archive_path, \"r\") as zf:\n zf.extractall(path=checkpoint_path)\n archive_path.unlink()","def _resume_checkpoint(self, resume_path, model, optimizer):\n if not resume_path:\n return model, optimizer\n\n self.logger.info(f'Loading checkpoint: {resume_path}')\n checkpoint = torch.load(resume_path)\n model.load_state_dict(checkpoint['state_dict'])\n\n # load optimizer state from checkpoint only when optimizer type is not changed.\n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\n self.logger.warning(\"Warning: Optimizer type given in config file is different from \"\n \"that of checkpoint. Optimizer parameters not being resumed.\")\n else:\n optimizer.load_state_dict(checkpoint['optimizer'])\n\n self.logger.info(f'Checkpoint \"{resume_path}\" loaded')\n return model, optimizer","def load(self, checkpoint_dir):\n print(\"\\nReading Checkpoints.....\\n\\n\")\n model_dir = \"%s\" % (\"cnn\") # give the model name by label_size\n checkpoint_dir = os.path.join(checkpoint_dir, model_dir)\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\n \n # Check the checkpoint is exist\n if ckpt and ckpt.model_checkpoint_path:\n ckpt_path = str(ckpt.model_checkpoint_path) # convert the unicode to string\n self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path))\n print(\"\\n Checkpoint Loading Success! %s\\n\\n\"% ckpt_path)\n else:\n print(\"\\n! Checkpoint Loading Failed \\n\\n\")","def load_checkpoint(checkpoint, model, optimizer=None):\n if not os.path.exists(checkpoint):\n raise (\"File doesn't exist {}\".format(checkpoint))\n checkpoint = torch.load(checkpoint)\n model.load_state_dict(checkpoint['state_dict'])\n\n if optimizer:\n optimizer.load_state_dict(checkpoint['optim_dict'])\n\n return checkpoint","def from_previous_ckpt(network, checkpoint):\n if os.path.exists(checkpoint):\n if os.path.isfile(checkpoint):\n try:\n network.load_state_dict(torch.load(checkpoint))\n print(f\"Loaded weights from {checkpoint}\")\n except RuntimeError:\n print(f\"{checkpoint} is a invalid checkpoint\")\n print(\"Neglecting this checkpoint.\")\n if os.path.isdir(checkpoint):\n epoch = 0\n file_name = None\n for ckpt in os.listdir(checkpoint):\n if ckpt[-4:] == '.pth':\n try:\n tmp_int_list = re.findall('[0-9]+', ckpt)\n ckpt_epoch = int(tmp_int_list[-1])\n except IndexError:\n ckpt_epoch = 0\n if ckpt_epoch >= epoch:\n epoch = ckpt_epoch\n file_name = os.path.join(checkpoint, ckpt)\n\n if file_name is None:\n print(f\"No checkpoint found in {checkpoint}\")\n print(\"Neglecting this checkpoint.\")\n else:\n try:\n network.load_state_dict(torch.load(file_name))\n print(f\"Loaded weights from {file_name}\")\n except (RuntimeError):\n print(f\"{file_name} is a invalid checkpoint\")\n print(\"Neglecting this checkpoint.\")\n\n else:\n print(f\"the checkpoint path: {checkpoint} doesn't exist.\")\n print(\"Neglecting this checkpoint.\")","def load_graph_from_ckpt(checkpoint_path, meta_graph_path=None, graph=None, session=None):\n from diplomacy_research.utils.tensorflow import tf\n\n dir_path, filename = os.path.split(checkpoint_path)\n\n # checkpoint_path is a directory - Loading latest checkpoint in directory\n if os.path.isdir(checkpoint_path):\n checkpoint = tf.train.latest_checkpoint(checkpoint_path)\n if meta_graph_path is None:\n meta_graph_path = max(glob.iglob(os.path.join(checkpoint_path, '*.meta')), key=os.path.getctime)\n\n # checkpoint_path is a checkpoint file - Loading latest checkpoint in directory\n elif filename == 'checkpoint':\n checkpoint = tf.train.latest_checkpoint(dir_path, 'checkpoint')\n if meta_graph_path is None:\n meta_graph_path = max(glob.iglob(os.path.join(dir_path, '*.meta')), key=os.path.getctime)\n\n # Loading a specific checkpoint\n else:\n # Removing extension\n if len(filename.split('.')) > 2:\n checkpoint_path = os.path.join(dir_path, '.'.join(filename.split('.')[:2]))\n checkpoint = checkpoint_path\n if meta_graph_path is None:\n if os.path.exists('{}.meta'.format(checkpoint_path)):\n meta_graph_path = '{}.meta'.format(checkpoint_path)\n else:\n meta_graph_path = max(glob.iglob(os.path.join(dir_path, '*.meta')), key=os.path.getctime)\n\n # Loading the checkpoint in the graph\n graph = tf.Graph() if graph is None else graph\n with graph.as_default():\n session = tf.Session(graph=graph) if session is None else session\n saver = tf.train.import_meta_graph(meta_graph_path)\n saver.restore(session, checkpoint)\n\n # Returning graph and session\n return graph, session","def resume_model(self, resume_path):\n if resume_path:\n resume_path = f'{self.model_dir}/{resume_path}.pth.tar'\n if os.path.isfile(resume_path):\n print(f\"=> loading checkpoint '{resume_path}'\")\n checkpoint = torch.load(resume_path, map_location=self.device)\n self.best_acc = checkpoint['best_acc']\n self.model.load_state_dict(checkpoint['state_dict'])\n print(f\"=> loaded checkpoint '{resume_path}' (Round {checkpoint['rnd']})\")\n del checkpoint\n else:\n print(f\"=> no checkpoint found at '{resume_path}'\")","def load_checkpoint(checkpoint, model, optimizer=None):\n if not os.path.exists(checkpoint):\n raise (\"File doesn't exist {}\".format(checkpoint))\n checkpoint = torch.load(checkpoint, map_location=torch.device('cpu'))\n model.load_state_dict(checkpoint['state_dict'])\n\n if optimizer:\n optimizer.load_state_dict(checkpoint['optim_dict'])\n\n return checkpoint","def loadCheckpoint(self, time_stamp, data_only=False, load_memory=True):\n state_filename = os.path.join(self.saving_dir, 'checkpoint.' + time_stamp + '.pth.tar')\n mem_filename = os.path.join(self.saving_dir, 'memory.' + time_stamp + '.pth.tar')\n\n print 'loading checkpoint: ', time_stamp\n checkpoint = torch.load(state_filename)\n if data_only:\n self.episode_rewards = checkpoint['episode_rewards']\n self.episode_lengths = checkpoint['episode_lengths']\n return\n\n self.episodes_done = checkpoint['episode']\n self.steps_done = checkpoint['steps']\n self.episode_rewards = checkpoint['episode_rewards']\n self.episode_lengths = checkpoint['episode_lengths']\n\n self.policy_net.load_state_dict(checkpoint['policy_state_dict'])\n self.policy_net = self.policy_net.to(self.device)\n self.policy_net.train()\n\n self.target_net.load_state_dict(checkpoint['policy_state_dict'])\n self.target_net = self.target_net.to(self.device)\n self.target_net.eval()\n\n self.optimizer = optim.Adam(self.policy_net.parameters())\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n\n if load_memory:\n memory = torch.load(mem_filename)\n self.memory = memory['memory']","def checkpoint(request, tmp_path):\n checkpoint_fs_type = request.param\n\n checkpoint_path = tmp_path / \"ckpt_dir\"\n checkpoint_path.mkdir(exist_ok=True)\n (checkpoint_path / _CHECKPOINT_CONTENT_FILE).write_text(\"dummy\")\n\n if checkpoint_fs_type == \"local\":\n yield Checkpoint.from_directory(str(checkpoint_path))\n elif checkpoint_fs_type == \"mock\":\n _checkpoint = Checkpoint(path=\"mock:///mock_bucket/ckpt_dir\")\n _upload_to_fs_path(\n local_path=str(checkpoint_path),\n fs=_checkpoint.filesystem,\n fs_path=_checkpoint.path,\n )\n # The \"mock://\" URI doesn't persist across different instances of\n # the pyarrow.fs.MockFileSystem, so we must make sure to return\n # the checkpoint with the same filesystem instance that we uploaded\n # some mock content to.\n yield _checkpoint\n elif checkpoint_fs_type == \"custom_fs\":\n custom_storage_fs = _create_mock_custom_fs(tmp_path / \"custom_fs\")\n _upload_to_fs_path(\n local_path=str(checkpoint_path),\n fs=custom_storage_fs,\n fs_path=\"mock_bucket/ckpt_dir\",\n )\n yield Checkpoint(path=\"mock_bucket/ckpt_dir\", filesystem=custom_storage_fs)","async def get_checkpoint_async(self, partition_id):","def _get_file(self, path: str) -> Tuple[str, bytes]:\n self._trace(\"fetching: %s\" % path)\n meta, resp = self._connection.files_download(path)\n return (meta.rev, resp.content)","def load_checkpoint(self, filename):\n results = Future.gen_list([\n self.call_async(rank, '_async_load_checkpoint', filename=filename)\n for rank in range(self.num_replicas)\n ])\n extra_state = results[0]\n return extra_state","def save_checkpoint(state, is_best, file_path, file_name='checkpoint.pth.tar'):\n\n save_path = file_path + '/' + file_name\n torch.save(state, save_path)\n if is_best:\n shutil.copyfile(save_path, file_path + '/model_best.pth.tar')","def LoadProgramState(self, restored_checkpoint_path=None, sess=None):\n pass","def save_checkpoint(state, filename='checkpoint.pth.tar'):\n torch.save(state, filename)","def get_latest_ckpt(self,\n ckpt_type):\n if ckpt_type == \"debug\":\n ckpt_file = tf.train.latest_checkpoint(self.ckpt_debug_dir)\n if ckpt_file is None:\n raise FileNotFoundError(\"latest checkpoint file doesn't exist\")\n \n return ckpt_file\n elif ckpt_type == \"epoch\":\n ckpt_file = tf.train.latest_checkpoint(self.ckpt_epoch_dir)\n if ckpt_file is None:\n raise FileNotFoundError(\"latest checkpoint file doesn't exist\")\n \n return ckpt_file\n else:\n raise ValueError(\"unsupported checkpoint type {0}\".format(ckpt_type))","def save_checkpoint(self, path: str, **kwargs):\n if self.distributed:\n encoder = self.net_q.module.encoder\n head = self.net_q.module.head\n else:\n encoder = self.net_q.encoder\n head = self.net_q.head\n\n ckpt = {\n 'encoder': encoder.state_dict(),\n 'head': head.state_dict(),\n 'net_ps': self.net_ps.state_dict(),\n 'net_k': self.net_k.state_dict(),\n 'queue': self.queue.state_dict(),\n 'optimizer': self.optimizer.state_dict(),\n 'scheduler': self.scheduler.state_dict(),\n }\n if kwargs:\n ckpt.update(kwargs)\n torch.save(ckpt, path)","def load_epoch_checkpoint(self, directory, epoch):\n chkpnt = torch.load(directory / f\"chkpnt_epoch{epoch:04d}.pth\")\n self.load_state_dict(chkpnt['model_state_dict'])","def pickle_load(path):\n data = pickle.load(open(os.path.join(os.getcwd(), path), 'rb'))\n return data","def load_pickle(path):\n assert osp.exists(path)\n with open(path, 'r') as f:\n ret = pickle.load(f)\n return ret","def load_checkpoint(checkpoint_file: pl.Path) -> Optional[Dict[str, Any]]:\n if checkpoint_file.exists():\n logger.info(f\"Loading checkpoint {checkpoint_file}.\")\n checkpoint = torch.load(str(checkpoint_file))\n logger.info(f\"Done loading checkpoint from epoch {checkpoint['epoch']}.\")\n else:\n logger.warning(f\"No {checkpoint_file} checkpoint file found. Starting normal.\")\n return checkpoint","def _get_global_step_for_checkpoint(checkpoint_path: Text) -> int:\n re_match = re.search(r'ckpt-(\\d+)$', checkpoint_path)\n return -1 if re_match is None else int(re_match.group(1))","def load_model(path, model, optimizer):\n print(\"LOADING MODEL...\")\n ckpt = tf.train.Checkpoint(model=model, optimizer=optimizer)\n status = ckpt.restore(tf.train.latest_checkpoint(path))\n ckpt_manager = tf.train.CheckpointManager(\n checkpoint=ckpt, \n directory=FLAGS.model_dir, \n max_to_keep=3 \n )\n return model, optimizer, ckpt, ckpt_manager","def load_checkpoint(filepath):\n checkpoint = torch.load(filepath)\n \n arch = checkpoint['arch']\n if arch == 'vgg':\n model = models.vgg16(pretrained=True)\n elif arch == 'densenet':\n model = models.densenet121(pretrained=True) \n \n model.class_to_idx = checkpoint['class_to_idx']\n model.classifier = checkpoint['classifier']\n model.classifier.load_sate_dict = checkpoint['classifier_state_dict']\n model.optimizer = checkpoint['optimizer_state_dict']\n model.input_size = checkpoint['input_size']\n model.output_size = checkpoint['output_size']\n \n return model","def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n directory = \"checkoutpoint/%s/\" % args.name\n if not os.path.exists(directory):\n os.makedirs(directory)\n filename = directory + filename\n torch.save(state, filename)\n if is_best:\n shutil.copyfile(filename, 'checkoutpoint/%s/' % args.name + 'model_best.pth.tar')","def pickle_path(self) -> pathlib.Path:\n return self.path / 'pickle.pkl'","def load_ckpt(saver, sess, ckpt_dir=\"train\"):\n while True:\n try:\n latest_filename = \"checkpoint_best\" if ckpt_dir == \"eval\" else None\n ckpt_dir = os.path.join(FLAGS.log_root, ckpt_dir)\n ckpt_state = tf.train.get_checkpoint_state(ckpt_dir, latest_filename=latest_filename)\n tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)\n saver.restore(sess, ckpt_state.model_checkpoint_path)\n return ckpt_state.model_checkpoint_path\n except:\n tf.logging.info(\"Failed to load checkpoint from %s. Sleeping for %i secs...\", ckpt_dir, 10)\n time.sleep(10)","def checkpoint(state, file_name='./saves/checkpoint.pth.tar'):\n\n assert isinstance(state, dict)\n assert isinstance(file_name, str)\n\n torch.save(state, file_name)","def load_ckpt(saver, sess, ckpt_dir=\"train\"):\n while True:\n try:\n latest_filename = \"checkpoint_best\" if ckpt_dir==\"eval\" else None\n ckpt_dir = os.path.join(FLAGS.log_root, ckpt_dir)\n ckpt_state = tf.train.get_checkpoint_state(ckpt_dir, latest_filename=latest_filename)\n tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)\n saver.restore(sess, ckpt_state.model_checkpoint_path)\n return ckpt_state.model_checkpoint_path\n except:\n tf.logging.info(\"Failed to load checkpoint from %s. Sleeping for %i secs...\", ckpt_dir, 10)\n time.sleep(10)","def maybe_restore_train_and_indicator_state(\n train_state,\n indicator_state,\n *,\n ckpt_manager,\n mesh,\n):\n latest_step = ckpt_manager.latest_step()\n if latest_step is None:\n return train_state, indicator_state\n logging.info('Restoring from step %d', latest_step)\n\n # Check if the directory is empty, Orbax could have failed to save\n # the checkpoint alltogether\n save_dir = checkpoint.utils.get_save_directory(\n latest_step,\n ckpt_manager.directory,\n )\n # If there's no files in the directory we should remove it and try\n # again with the checkpoint before that.\n if not any(save_dir.iterdir()):\n logging.info(\n 'Save directory %s is empty, removing and recursing restore',\n save_dir,\n )\n save_dir.rmdir()\n return maybe_restore_train_and_indicator_state(\n train_state,\n indicator_state,\n ckpt_manager=ckpt_manager,\n mesh=mesh,\n )\n\n def restore_arguments_with_mesh_axes(\n mesh_axes):\n if not mesh:\n mesh_axes = None\n\n def closure(_):\n return checkpoint.ArrayRestoreArgs(\n lazy=True,\n restore_type=jax.Array,\n mesh=mesh,\n mesh_axes=mesh_axes,\n )\n\n return closure\n\n # Evaluate the shape and filter empty nodes\n # We save the entire PyTree so there's no need to further filter\n train_state_shape = jax.eval_shape(lambda x: x, train_state)\n train_state_shape = tree_utils.filter_empty_nodes(train_state_shape,\n train_state_shape)\n train_state_pspec = create_train_state_partition_spec_from_shape(\n train_state_shape)\n train_state_restore_args = jax.tree_util.tree_map(\n restore_arguments_with_mesh_axes(train_state_pspec), train_state_shape)\n\n indicator_state_shape = jax.eval_shape(lambda x: x, indicator_state)\n indicator_state_shape = tree_utils.tree_map_with_regex(\n lambda _: None, indicator_state_shape, [(r'.*params/encoder/.*',)],\n lambda leaf: leaf)\n indicator_state_shape = tree_utils.filter_empty_nodes(indicator_state_shape,\n indicator_state_shape)\n indicator_state_pspec = create_indicator_state_partition_spec_from_shape(\n indicator_state_shape)\n indicator_state_restore_args = jax.tree_util.tree_map(\n restore_arguments_with_mesh_axes(indicator_state_pspec),\n indicator_state_shape)\n\n restored_state = ckpt_manager.restore(\n latest_step,\n items={\n 'train': train_state_shape,\n 'indicator': indicator_state_shape\n },\n restore_kwargs={\n 'train': {\n 'restore_args': train_state_restore_args\n },\n 'indicator': {\n 'restore_args': indicator_state_restore_args\n }\n })\n\n restored_state = checkpoint.apply_transformations(\n original_tree=restored_state,\n transformations=dict(),\n new_tree={\n 'train': train_state,\n 'indicator': indicator_state\n },\n default_to_original=False)\n restored_state = checkpoint.lazy_utils.maybe_get_tree(restored_state)\n logging.info('Restore finished')\n\n return operator.itemgetter('train', 'indicator')(restored_state)"],"string":"[\n \"def load_checkpoint(\\n path: str,\\n state: State,\\n logger: Logger,\\n object_store: Optional[Union[ObjectStore, LoggerDestination]] = None,\\n load_weights_only: bool = False,\\n strict_model_weights: bool = False,\\n progress_bar: bool = True,\\n ignore_keys: Optional[Union[List[str], Callable[[Dict], None]]] = None,\\n exclude_algorithms: Optional[List[str]] = None,\\n algorithm_passes: Optional[List[AlgorithmPass]] = None,\\n):\\n # Download the checkpoint to the node-local folder\\n log.debug('Loading checkpoint at %s', path)\\n # Each node gets one unique folder to store checkpoints that is shared amongst all local ranks in that node.\\n # If fsdp sharded state_dicts is enabled then EVERY rank gets a unique checkpoint folder.\\n needs_unique_checkpoint_folder = state.fsdp_sharded_state_dict_enabled or dist.get_local_rank() == 0\\n tempdir_ctx = tempfile.TemporaryDirectory() if needs_unique_checkpoint_folder else contextlib.nullcontext(None)\\n with tempdir_ctx as tempdir:\\n try:\\n # Get the path to the proper checkpoint folder corresponding to the current rank's node.\\n # If fsdp_sharded_state_dict_enabled then just use that rank's unique tempdir.\\n node_checkpoint_folder = (tempdir\\n if state.fsdp_sharded_state_dict_enabled else _get_local_rank_zero_path(tempdir))\\n assert node_checkpoint_folder is not None\\n\\n composer_states_filepath, extracted_checkpoint_folder, extracted_rank_n = download_checkpoint(\\n path=path,\\n node_checkpoint_folder=node_checkpoint_folder,\\n object_store=object_store,\\n progress_bar=progress_bar,\\n fsdp_sharded_state_dict_enabled=state.fsdp_sharded_state_dict_enabled,\\n deepspeed_sharded_checkpoint=is_model_deepspeed(state.model),\\n )\\n rng_state_dicts = _restore_checkpoint(\\n state,\\n logger,\\n composer_states_filepath,\\n extracted_rank_n,\\n extracted_checkpoint_folder,\\n load_weights_only=load_weights_only,\\n strict_model_weights=strict_model_weights,\\n ignore_keys=ignore_keys,\\n exclude_algorithms=exclude_algorithms,\\n algorithm_passes=algorithm_passes,\\n )\\n finally:\\n # Wait for all ranks to finish restoring the checkpoint before releasing the tempdir, since tempdir can\\n # be a shared resource between nodes.\\n dist.barrier()\\n\\n log.info('%s loaded from %s', 'Model weights' if load_weights_only else 'Trainer checkpoint', path)\\n return rng_state_dicts\",\n \"def download_checkpoint_ngc(checkpoint_url: str, checkpoint_path: pathlib.Path) -> None:\\n with tqdm(unit=\\\"B\\\") as t:\\n reporthook = download_progress(t)\\n result = urllib.request.urlretrieve(checkpoint_url, reporthook=reporthook)\\n\\n filename = result[0]\\n\\n file_path = pathlib.Path(filename)\\n assert file_path.is_file() or file_path.is_dir(), \\\"Checkpoint was not downloaded\\\"\\n\\n shutil.move(file_path, checkpoint_path.parent / file_path.name)\\n\\n archive_path = checkpoint_path.parent / file_path.name\\n unzip(checkpoint_path, archive_path)\",\n \"def parse_checkpoint(checkpoint_path):\\n with gfile.Open(checkpoint_path, 'rb') as fp:\\n raw_contents = fp.read()\\n if raw_contents.startswith(b'model_checkpoint_path'):\\n raise ValueError(\\n 'Attempting to restore a TensorFlow checkpoint as a native T5X '\\n f'checkpoint. Path: {checkpoint_path}')\\n return serialization.msgpack_restore(raw_contents)\",\n \"def _restore_checkpoint(\\n state: State,\\n logger: Logger,\\n composer_states_filepath: str,\\n extracted_rank_n: bool,\\n extracted_checkpoint_folder: Optional[str],\\n load_weights_only: bool,\\n strict_model_weights: bool,\\n ignore_keys: Optional[Union[List[str], Callable[[Dict], None]]],\\n exclude_algorithms: Optional[List[str]],\\n algorithm_passes: Optional[List[AlgorithmPass]],\\n) -> Optional[List[Dict[str, Any]]]:\\n # Now, all ranks load the checkpoint that local rank zero downloaded\\n state_dict = safe_torch_load(\\n composer_states_filepath=composer_states_filepath,\\n load_fsdp_monolith_rank0_only=state.load_fsdp_monolith_rank0_only,\\n )\\n if ignore_keys:\\n # Filter provided list of key paths\\n if not callable(ignore_keys):\\n ignore_keys = glob_filter(ignore_keys)\\n # Call function to modify state_dict\\n ignore_keys(state_dict)\\n log.debug(f\\\"Loaded checkpoint with keys {state_dict.keys()} and state keys {state_dict['state'].keys()}\\\")\\n\\n if is_model_deepspeed(state.model):\\n if extracted_checkpoint_folder is None:\\n raise RuntimeError('Deepspeed checkpoints require a tarball, not a weights file.')\\n\\n global_rank = dist.get_global_rank()\\n if global_rank > 0 and not extracted_rank_n:\\n raise RuntimeError(f'Deepspeed checkpoint missing for rank {global_rank}')\\n\\n load_path, _ = state.deepspeed_model.load_checkpoint(\\n extracted_checkpoint_folder,\\n tag=_DEEPSPEED_TAG,\\n load_module_only=load_weights_only,\\n load_module_strict=strict_model_weights,\\n )\\n if load_path is None:\\n raise RuntimeError('Failed to load DeepSpeed checkpoint')\\n elif load_weights_only:\\n state.load_model_state(\\n state_dict['state'],\\n logger,\\n strict=strict_model_weights,\\n exclude_algorithms=exclude_algorithms,\\n algorithm_passes=algorithm_passes,\\n )\\n if not load_weights_only:\\n state.load_state_dict(\\n state_dict['state'],\\n logger,\\n exclude_algorithms=exclude_algorithms,\\n algorithm_passes=algorithm_passes,\\n )\\n step_to_resume_from = state.timestamp.batch.value\\n max_step_to_resume_from = state.device.tensor_to_device(\\n torch.tensor(state.timestamp.batch.value, dtype=torch.int64))\\n min_step_to_resume_from = state.device.tensor_to_device(\\n torch.tensor(state.timestamp.batch.value, dtype=torch.int64))\\n dist.all_reduce(max_step_to_resume_from, reduce_operation='MAX')\\n dist.all_reduce(min_step_to_resume_from, reduce_operation='MIN')\\n if max_step_to_resume_from.data != min_step_to_resume_from.data:\\n raise RuntimeError(\\n textwrap.dedent(\\n f'Timestamp mismatch error: batch to resume from {step_to_resume_from} is not the same on all ranks. '\\n 'This usually occurs when at least one rank fails to save the last checkpoint '\\n 'while using sharded checkpointing + autoresume. '\\n 'Please manually resume by disabling autoresume and explicitly setting load_path '\\n 'to the most recent checkpoints that all ranks have saved. '\\n 'E.g. for the 10th batch: trainer = Trainer(autoresume=False, load_path=\\\"/path/to/checkpoint/ba10-rank{rank}.pt\\\", ...). '\\n 'Remember to keep the {rank} placeholder!'))\\n return state_dict['rng']\",\n \"def load_checkpoint(self, checkpoint_path=None):\\n if checkpoint_path is None:\\n checkpoint_path = self.get_latest_path()\\n\\n if os.path.isfile(checkpoint_path):\\n key = 'cuda' if torch.cuda.is_available() else 'cpu'\\n checkpoint = torch.load(checkpoint_path, map_location=key)\\n self.network.load_state_dict(checkpoint['network'])\\n self.network_target.load_state_dict(checkpoint['network_target'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n\\n print('checkpoint loaded at {}'.format(checkpoint_path))\\n else:\\n raise OSError(\\\"Checkpoint file not found.\\\")\",\n \"def load_checkpoint(checkpoint_path):\\n flat_checkpoint_dict = flatten_checkpoint(\\n parse_checkpoint(checkpoint_path), keep_empty_nodes=True)\\n return flat_checkpoint_dict\",\n \"def load_from_checkpoint(self, path):\\n print(f'# loading trainer state from {path}')\\n checkpoint = torch.load(path)\\n self.load(checkpoint)\",\n \"def load_checkpoint(self, checkpoint_path: Union[str, Path]) -> Dict[str, Any]:\\n # TODO: move to CheckpointIO\\n torch.cuda.empty_cache()\\n checkpoint_path = inject_model_parallel_rank(checkpoint_path)\\n return self.checkpoint_io.load_checkpoint(checkpoint_path)\",\n \"def get_checkpoint_file(path, checkpoint):\\n if checkpoint is not None:\\n return checkpoint\\n else:\\n file_list = glob.glob(path + \\\"/*.pt\\\")\\n if not file_list:\\n raise AssertionError(\\\"Please ensure a checkpoint file (.pt)\\\"\\n f\\\" is present in folder {path}\\\")\\n elif len(file_list) > 1:\\n raise AssertionError(\\n \\\"Multiple checkpoint files present. Either ensure only 1 \\\"\\n \\\"is present or indicate which checkpoint to use \\\"\\n \\\"via --checkpoint\\\")\\n else:\\n return split(file_list[0])[1]\",\n \"def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\\n return torch.load(checkpoint_path, *args, **kwargs)\",\n \"def load_checkpoint(self, checkpoint_path: str, *args, **kwargs) -> Dict:\\n return torch.load(checkpoint_path, *args, **kwargs)\",\n \"def restore(self, checkpoint_path):\\n start_time = time.time()\\n latest_checkpoint = train_util.get_latest_chekpoint(checkpoint_path)\\n if latest_checkpoint is not None:\\n checkpoint = tf.train.Checkpoint(model=self)\\n checkpoint.restore(latest_checkpoint).expect_partial()\\n logging.info('Loaded checkpoint %s', latest_checkpoint)\\n logging.info('Loading model took %.1f seconds', time.time() - start_time)\\n else:\\n logging.info('Could not find checkpoint to load at %s, skipping.',\\n checkpoint_path)\",\n \"def load_from_path(self, checkpoint_dir):\\n\\n vars = self.save_var_names\\n saver = tf.train.Saver(vars)\\n\\n def load_aux(ckpt_path):\\n \\\"\\\"\\\"Helper function to not repeat the same code in the following lines.\\\"\\\"\\\"\\n\\n ckpt_name = os.path.basename(ckpt_path)\\n saver.restore(self.sess, ckpt_path)\\n counter = int(next(re.finditer(\\\"(\\\\d+)(?!.*\\\\d)\\\", ckpt_name)).group(0))\\n self.counter = counter\\n print(\\\" [*] Loaded {}\\\".format(ckpt_name))\\n return True, counter\\n\\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\\n try:\\n if ckpt and ckpt.model_checkpoint_path:\\n ckpt_name = os.path.basename(ckpt.model_checkpoint_path)\\n return load_aux(os.path.join(checkpoint_dir, ckpt_name))\\n else:\\n print(\\n \\\" [!] Failed to find a checkpoint within directory {}\\\".format(\\n FLAGS.ckpt_path))\\n return False, 0\\n except:\\n print(\\\" [!] Failed to find a checkpoint, Exception!\\\")\\n return False, 0\",\n \"def _load(checkpoint_path):\\n state_dict, optimizer_state = dg.load_persistables(dirname=checkpoint_path)\\n return state_dict, optimizer_state\",\n \"def load_checkpoint(checkpoint_dir, epoch, iteration):\\n path = opj(checkpoint_dir, str(epoch) + '.' + str(iteration) + '.ckpt')\\n if not os.path.isfile(path):\\n raise Exception(\\\"Checkpoint in epoch %d doesn't exist :sob:\\\" % epoch)\\n\\n checkpoint = torch.load(path)\\n start_epoch = checkpoint['epoch']\\n state_dict = checkpoint['state_dict']\\n start_iteration = checkpoint['iteration']\\n\\n assert iteration == start_iteration\\n return start_epoch, start_iteration, state_dict\",\n \"def load_checkpoint(cfg, args):\\n checkpoint_iteration = args.checkpoint\\n bucket = connect_to_bucket(args.bucket)\\n # load actual checkpoint\\n if not os.path.isdir(cfg.OUTPUT_DIR):\\n os.mkdir(cfg.OUTPUT_DIR)\\n blob = bucket.blob(cfg.OUTPUT_DIR + \\\"/model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n blob.download_to_filename(cfg.OUTPUT_DIR + \\\"/model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n if args.resume:\\n # also write last checkpoint file for when --resume statement, model gets checkpoint name from this file\\n with open(cfg.OUTPUT_DIR + \\\"/last_checkpoint\\\", \\\"w\\\") as file:\\n file.write(\\\"model_\\\" + str(checkpoint_iteration) + \\\".pth\\\")\\n # return statement not clean, but useful for inference code\\n return checkpoint_iteration, bucket\",\n \"def restore_checkpoint(checkpoint_path: str,\\n train_state: Optional[TrainState] = None,\\n assert_exist: bool = False,\\n step: Optional[int] = None) -> Tuple[TrainState, int]:\\n if assert_exist:\\n glob_path = os.path.join(checkpoint_path, 'checkpoint_*')\\n if not gfile.glob(glob_path):\\n raise ValueError('No checkpoint for the pretrained model is found in: '\\n f'{checkpoint_path}')\\n if train_state is None:\\n raise ValueError('Please use `restore_pretrained_checkpoint` for loading'\\n 'a checkpoint without providing a Scenic TrainState.')\\n train_state = checkpoints.restore_checkpoint(checkpoint_path, train_state,\\n step)\\n return train_state, int(train_state.global_step)\",\n \"def load(cls, path):\\n print(f'load checkpoint from {path}')\\n if torch.cuda.is_available():\\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME))\\n model = torch.load(os.path.join(path, cls.MODEL_NAME))\\n else:\\n resume_checkpoint = torch.load(os.path.join(path, cls.TRAINER_STATE_NAME), map_location=lambda storage, loc: storage)\\n model = torch.load(os.path.join(path, cls.MODEL_NAME), map_location=lambda storage, loc: storage)\\n \\n # model.flatten_parameters() # make RNN parameters contiguous\\n optimizer = resume_checkpoint['optimizer']\\n return Checkpoint(\\n model=model, \\n optimizer=optimizer,\\n epoch=resume_checkpoint['epoch'],\\n path=path\\n )\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(f\\\"Checkpoint '{checkpoint_path}' does not exist\\\")\\n\\n state = torch.load(checkpoint_path)\\n try:\\n model.load_state_dict(state['model_state_dict'])\\n except BaseException as e:\\n print('Failed to do something: ' + str(e))\\n\\n if optimizer is not None:\\n try:\\n optimizer.load_state_dict(state['optimizer_state_dict'])\\n except Exception as e:\\n print(e)\\n\\n return state\",\n \"def load_checkpoint(self, checkpoint: str, **kwargs) -> None:\\n with open(checkpoint, \\\"rb\\\") as f:\\n state = SafePickle.load(f)\\n\\n state_id = ray.put(state)\\n ray.get([worker.set_state.remote(state_id, **kwargs) for worker in self.remote_workers])\",\n \"def get_latest_checkpoint(cls, experiment_path):\\n checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)\\n all_times = sorted(os.listdir(checkpoints_path), reverse=True)\\n return os.path.join(checkpoints_path, all_times[0])\",\n \"def restore_checkpoint(self, checkpoint_id, name, path=''):\\n\\n\\t\\tself.log.debug(\\\"restoring Notebook %s from checkpoint %s\\\", name, checkpoint_id)\\n\\t\\tnb_path = self._get_os_path(name, path)\\n\\t\\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\\n\\n\\t\\tif not key_exists(self.bucket, cp_path):\\n\\t\\t\\tself.log.debug(\\\"checkpoint file does not exist: %s\\\", cp_path)\\n\\t\\t\\traise web.HTTPError(404,\\n\\t\\t\\t\\tu'Notebook checkpoint does not exist: %s-%s' % (name, checkpoint_id)\\n\\t\\t\\t)\\n\\t\\t# ensure notebook is readable (never restore from an unreadable notebook)\\n\\t\\tkey = self.bucket.get_key(cp_path)\\n\\t\\tnb = current.reads(key.get_contents_as_string(), u'json')\\n\\t\\tself._copy(cp_path, nb_path)\\n\\t\\tself.log.debug(\\\"copying %s -> %s\\\", cp_path, nb_path)\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None,\\n model_key='model_state_dict', optimizer_key='optimizer_state_dict'):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(f\\\"Checkpoint '{checkpoint_path}' does not exist\\\")\\n\\n state = torch.load(checkpoint_path, map_location='cpu')\\n model.load_state_dict(state[model_key])\\n\\n if optimizer is not None:\\n optimizer.load_state_dict(state[optimizer_key])\\n\\n return state\",\n \"def load_model_from_checkpoint(self, path: str):\\n ckpt = torch.load(path, map_location='cpu')\\n self.net_q.encoder.load_state_dict(ckpt['encoder'])\\n self.net_q.head.load_state_dict(ckpt['head'])\\n self.net_ps.load_state_dict(ckpt['net_ps'])\\n self.net_k.load_state_dict(ckpt['net_k'])\\n self.queue.load_state_dict(ckpt['queue'])\\n self.optimizer.load_state_dict(ckpt['optimizer'])\\n if 'scheduler' in ckpt:\\n self.scheduler.load_stae_dict(ckpt['scheduler'])\\n self.move_optimizer_states(self.optimizer, self.local_rank)\",\n \"def load_checkpoint(checkpoint_path, model, optimizer=None):\\n if not os.path.exists(checkpoint_path):\\n raise IOError(\\\"Checkpoint '{}' does not exist\\\".format(checkpoint_path))\\n\\n device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else 'cpu')\\n state = torch.load(checkpoint_path, map_location=\\\"cuda:0\\\")\\n model.load_state_dict(state['model_state_dict'])\\n\\n if optimizer is not None:\\n optimizer.load_state_dict(state['optimizer_state_dict'])\\n\\n return state\",\n \"def load_checkpoint(path: str, use_cuda: bool = True) -> dict:\\n assert os.path.isfile(path), \\\"Checkpoint %s not found\\\" % path\\n checkpoint = torch.load(path, map_location=\\\"cuda\\\" if use_cuda else \\\"cpu\\\")\\n return checkpoint\",\n \"def restore(self, checkpoint_path: str):\\r\\n raise NotImplementedError\",\n \"def load_checkpoint(checkpoint_directory,\\n session):\\n variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\\n # filter variables if needed.\\n print(variables)\\n saver_ob = tf.train.Saver(variables, max_to_keep=0)\\n os.makedirs(checkpoint_directory, exist_ok=True)\\n # verify if we don't have a checkpoint saved directly\\n step = 0\\n ckpt = tf.train.get_checkpoint_state(checkpoint_directory)\\n if ckpt and ckpt.model_checkpoint_path:\\n # Restores from checkpoint\\n model_checkpoint_path = ckpt.model_checkpoint_path\\n saver_ob.restore(session, model_checkpoint_path)\\n step = int(model_checkpoint_path.rsplit('-', 1)[1])\\n print('Model loaded = ', step)\\n return saver_ob, step\",\n \"def load_checkpoint(model, save_path):\\n model.load_state_dict(torch.load(save_path))\",\n \"def load_checkpoint(self, checkpoint_path, continue_from_epoch=True):\\n print(\\\"Loading checkpoint: {}\\\".format(checkpoint_path))\\n state = torch.load(checkpoint_path)\\n self.model.load_state_dict(state['state_dict'])\\n self.optimizer.load_state_dict(state['optim_dict'])\\n\\n if continue_from_epoch:\\n self.epoch = state['epoch']\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.monitor_best = checkpoint['monitor_best']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n self.train_logger = checkpoint['logger']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def _get_checkpoint(self):\\n ckpt = tf.train.get_checkpoint_state(self.model)\\n if ckpt and ckpt.model_checkpoint_path:\\n ckpt_path = ckpt.model_checkpoint_path\\n else:\\n raise RuntimeError('No checkpoint file found')\\n return ckpt_path\",\n \"def _resume_checkpoint(self, resume_path):\\n resume_path = str(resume_path)\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n\\n # load architecture params from checkpoint.\\n if checkpoint['config']['model'] != self.config['model']:\\n self.logger.warning(\\\"Warning: Architecture configuration given in config file is different from that of \\\"\\n \\\"checkpoint. This may yield an exception while state_dict is being loaded.\\\")\\n self.model.load_state_dict(checkpoint['state_dict'])\\n\\n # load optimizer state from checkpoint only when optimizer type is not changed.\\n if checkpoint['config']['trainer']['optimizer']['type'] != self.config['trainer']['optimizer']['type']:\\n self.logger.warning(\\\"Warning: Optimizer type given in config file is different from that of checkpoint. \\\"\\n \\\"Optimizer parameters not being resumed.\\\")\\n else:\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n\\n self.logger.info(\\\"Checkpoint loaded. Resume from epoch {}\\\".format(self.start_epoch))\",\n \"def load_checkpoint(self, path: str = '', train: bool = True) -> int:\\n\\n if not path:\\n dir_ = os.path.dirname(os.path.realpath('__file__'))\\n path = os.path.join(dir_, 'model.pt')\\n\\n try:\\n ckpt = torch.load(path)\\n except FileNotFoundError:\\n return 0\\n else:\\n print('Loaded model at epoch: ', end='')\\n\\n self.load_state_dict(ckpt['model_state_dict'])\\n self.actor_optimizer.load_state_dict(ckpt['ac_optim_dict'])\\n self.critic_optimizer.load_state_dict(ckpt['critic_optim_dict'])\\n epoch = ckpt['epoch']\\n\\n print(epoch)\\n\\n if not train:\\n self.eval()\\n else:\\n self.train()\\n\\n return epoch\",\n \"def create_checkpoint(self, name, path=''):\\n\\n\\t\\tnb_path = self._get_os_path(name, path)\\n\\t\\tself.log.debug('creating checkpoint \\\"%s\\\" \\\"%s\\\" \\\"%s\\\"' % (path, name, nb_path))\\n\\t\\t# only the one checkpoint ID:\\n\\t\\tcheckpoint_id = u\\\"checkpoint\\\"\\n\\t\\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\\n\\t\\tself.log.debug(\\\"creating checkpoint for notebook %s\\\", name)\\n\\t\\tif not key_exists(self.bucket, self.checkpoint_dir):\\n\\t\\t\\tnew_key_from_string(self.bucket, self.checkpoint_dir, '')\\n\\t\\tself._copy(nb_path, cp_path)\\n\\n\\t\\t# return the checkpoint info\\n\\t\\treturn self.get_checkpoint_model(checkpoint_id, name, path)\",\n \"def get_step_from_checkpoint_path(checkpoint_path: Text) -> Optional[int]:\\n if not checkpoint_path:\\n return None\\n try:\\n return int(checkpoint_path.strip().split(\\\"-\\\")[-1])\\n except ValueError:\\n return None\",\n \"def find_checkpoint(load_dir, seen_step):\\n ckpt = tf.train.get_checkpoint_state(load_dir)\\n if ckpt and ckpt.model_checkpoint_path:\\n global_step = extract_step(ckpt.model_checkpoint_path)\\n if int(global_step) != seen_step:\\n return int(global_step), ckpt.model_checkpoint_path\\n return -1, None\",\n \"def load(self, path):\\n\\n filename = self.__path(path, prefix=None)\\n chkpt = th.load(filename, map_location=\\\"cpu\\\") # TODO: check behavior\\n\\n if self.model is not None and chkpt[\\\"model\\\"] is not None:\\n log.debug(\\\"Loading model state dict\\\")\\n self.model.load_state_dict(chkpt[\\\"model\\\"])\\n\\n if \\\"optimizers\\\" in chkpt.keys():\\n if self.optimizers is not None and chkpt[\\\"optimizers\\\"] is not None:\\n try:\\n for opt, state in zip(self.optimizers,\\n chkpt[\\\"optimizers\\\"]):\\n log.debug(\\\"Loading optimizers state dict for %s\\\", opt)\\n opt.load_state_dict(state)\\n except:\\n # We do not raise an error here, e.g. in case the user simply\\n # changes optimizer\\n log.warning(\\\"Could not load optimizer state dicts, \\\"\\n \\\"starting from scratch\\\")\\n\\n if \\\"schedulers\\\" in chkpt.keys():\\n if self.schedulers is not None and chkpt[\\\"schedulers\\\"] is not None:\\n try:\\n for s, state in zip(self.schedulers,\\n chkpt[\\\"schedulers\\\"]):\\n log.debug(\\\"Loading scheduler state dict for %s\\\", s)\\n s.load_state_dict(state)\\n except:\\n log.warning(\\\"Could not load scheduler state dicts, \\\"\\n \\\"starting from scratch\\\")\\n\\n log.debug(\\\"Loaded checkpoint \\\\\\\"{}\\\\\\\"\\\".format(filename))\\n return tuple(chkpt[k] for k in [\\\"extras\\\", \\\"meta\\\"])\",\n \"def get_checkpoint_path(self, checkpoint_id, name, path=''):\\n\\t\\tself.log.debug(\\\"getting checkpoint path %s, %s\\\", name, path)\\n\\t\\tbasename, _ = os.path.splitext(name)\\n\\t\\tfilename = u\\\"{name}-{checkpoint_id}{ext}\\\".format(\\n\\t\\t\\tname=basename,\\n\\t\\t\\tcheckpoint_id=checkpoint_id,\\n\\t\\t\\text=self.filename_ext,\\n\\t\\t)\\n\\n\\t\\t# Checkpoints are stored in relative directories\\n\\t\\t# e.g. given:\\n\\t\\t# folder1/notebook1.py\\n\\t\\t# ... the checkpoint is at ...\\n\\t\\t# folder1/<checkpoint directory>/notebook1.py\\n\\t\\tbase_path = os.path.join(path, self.checkpoint_dir)\\t\\t \\n\\t\\tfull_path = self._get_os_path( filename, path=base_path)\\n\\t\\tself.log.debug(\\\"checkpoint path + filename %s\\\" % full_path)\\n\\t\\treturn full_path\",\n \"def read_checkpoint(out_fp):\\r\\n pickle_fh = open(out_fp, \\\"r\\\")\\r\\n return pickle.load(pickle_fh)\",\n \"def load_checkpoint(self, session, model_dir):\\n assert self.params.cp_load == True, (\\\"cp_load must be set to true to load a checkpoint\\\")\\n self.loader.restore(session, model_dir)\",\n \"def load_checkpoint(args, trainer, epoch_itr):\\n os.makedirs(os.path.join(args.save_dir, 'checkpoints'), exist_ok=True)\\n checkpoint_path = os.path.join(args.save_dir, 'checkpoints', args.restore_file)\\n if os.path.isfile(checkpoint_path):\\n extra_state = trainer.load_checkpoint(checkpoint_path)\\n if extra_state is not None:\\n # replay train iterator to match checkpoint\\n epoch_itr.load_state_dict(extra_state['train_iterator'])\\n\\n print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(\\n checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))\\n\\n trainer.lr_step(epoch_itr.epoch)\\n trainer.lr_step_update(trainer.get_num_updates())\\n if 'best' in extra_state:\\n save_checkpoint.best = extra_state['best']\",\n \"def get_checkpoint():\\n if ((FLAGS.run_dir or FLAGS.checkpoint_file) and\\n FLAGS.bundle_file and not should_save_generator_bundle()):\\n raise sequence_generator.SequenceGeneratorException(\\n 'Cannot specify both bundle_file and run_dir or checkpoint_file')\\n if FLAGS.run_dir:\\n train_dir = os.path.join(os.path.expanduser(FLAGS.run_dir), 'train')\\n return train_dir\\n elif FLAGS.checkpoint_file:\\n return os.path.expanduser(FLAGS.checkpoint_file)\\n else:\\n return None\",\n \"def get_oldest_checkpoint(cls, experiment_path):\\n checkpoints_path = os.path.join(experiment_path, cls.CHECKPOINT_DIR_NAME)\\n all_times = sorted(os.listdir(checkpoints_path))\\n return os.path.join(checkpoints_path, all_times[0])\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.mnt_best = checkpoint['monitor_best']\\n\\n # load model params from checkpoint.\\n if checkpoint['config']['name'] != self.config['name']:\\n self.logger.warning(\\n 'Warning: Architecture configuration given in config file is different from that of checkpoint. ' + \\\\\\n 'This may yield an exception while state_dict is being loaded.')\\n self.model.load_state_dict(checkpoint['model_state_dict'])\\n\\n # load optimizer state from checkpoint only when optimizer type is not changed. \\n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\\n self.logger.warning('Warning: Optimizer type given in config file is different from that of checkpoint. ' + \\\\\\n 'Optimizer parameters not being resumed.')\\n self.optimizer.load_state_dict(checkpoint['model_optimizer'])\\n\\n # load scheduler state from checkpoint only when scheduler type is not changed\\n if checkpoint['config']['scheduler']['type'] != self.config['scheduler']['type']:\\n self.logger.warning('Warning: Scheduler type given in config file is different from that of checkpoint. ' + \\\\\\n 'Scheduler parameters not being resumed.')\\n self.scheduler.load_state_dict(checkpoint['model_scheduler'])\\n\\n self.train_logger = checkpoint['logger']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def get_checkpoint_model(self, checkpoint_id, name, path=''):\\n\\n\\t\\tcp_path = self.get_checkpoint_path(checkpoint_id, name, path)\\n\\t\\tkey = self.bucket.get_key(cp_path)\\n\\n\\t\\tlast_modified = key.last_modified\\n\\t\\tinfo = dict(\\n\\t\\t\\tid=checkpoint_id,\\n\\t\\t\\tlast_modifie=last_modified,\\n\\t\\t)\\n\\t\\treturn info\",\n \"def _get_checkpoint(\\n user_checkpoint: Optional[Path], context: ContainerTaskContext\\n ) -> Tuple[Optional[Path], Optional[Path]]:\\n if user_checkpoint is None:\\n host_checkpoint_dir = None\\n container_checkpoint_dir = None\\n else:\\n host_checkpoint_dir = user_checkpoint\\n container_checkpoint_dir = context.container_root_dir / \\\"checkpoint\\\"\\n\\n return host_checkpoint_dir, container_checkpoint_dir\",\n \"def _get_checkpoint(\\n user_checkpoint: Optional[Path], context: ContainerTaskContext\\n ) -> Tuple[Optional[Path], Optional[Path]]:\\n if user_checkpoint is None:\\n host_checkpoint_dir = None\\n container_checkpoint_dir = None\\n else:\\n host_checkpoint_dir = user_checkpoint\\n container_checkpoint_dir = context.container_root_dir / \\\"checkpoint\\\"\\n\\n return host_checkpoint_dir, container_checkpoint_dir\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.monitor_best = checkpoint['monitor_best']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n if self.with_cuda:\\n for state in self.optimizer.state.values():\\n for k, v in state.items():\\n if isinstance(v, torch.Tensor):\\n state[k] = v.cuda(torch.device('cuda'))\\n self.train_logger = checkpoint['logger']\\n #self.config = checkpoint['config']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def get_checkpoint_path(self) -> str:\\n return self._j_checkpoint_storage.getCheckpointPath().toString()\",\n \"def load_checkpoint(ckpt_path):\\n checkpoint = None\\n if ckpt_path:\\n logger.info(\\\"Loading checkpoint from %s\\\" % ckpt_path)\\n checkpoint = torch.load(ckpt_path, map_location=torch.device(\\\"cpu\\\"))\\n\\n if \\\"model\\\" in checkpoint.keys():\\n # This preserves backward-compat for models using customed layernorm\\n def fix_key(s):\\n s = re.sub(\\n r\\\"(.*)\\\\.layer_norm((_\\\\d+)?)\\\\.b_2\\\", r\\\"\\\\1.layer_norm\\\\2.bias\\\", s\\n )\\n s = re.sub(\\n r\\\"(.*)\\\\.layer_norm((_\\\\d+)?)\\\\.a_2\\\", r\\\"\\\\1.layer_norm\\\\2.weight\\\", s\\n )\\n return s\\n\\n checkpoint[\\\"model\\\"] = {\\n fix_key(k): v for k, v in checkpoint[\\\"model\\\"].items()\\n }\\n # Force add_ffnbias to True if bias found in model w_1 keys\\n for key in checkpoint[\\\"model\\\"].keys():\\n if \\\"w_1.bias\\\" in key:\\n checkpoint[\\\"opt\\\"].add_ffnbias = True\\n\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"num_kv\\\"):\\n checkpoint[\\\"opt\\\"].num_kv = 0\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"add_ffnbias\\\"):\\n checkpoint[\\\"opt\\\"].add_ffnbias = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"parallel_residual\\\"):\\n checkpoint[\\\"opt\\\"].parallel_residual = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"shared_layer_norm\\\"):\\n checkpoint[\\\"opt\\\"].shared_layer_norm = False\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"use_ckpting\\\"):\\n checkpoint[\\\"opt\\\"].use_ckpting = []\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"relative_positions_buckets\\\"):\\n checkpoint[\\\"opt\\\"].relative_positions_buckets = 0\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"parallel_mode\\\"):\\n checkpoint[\\\"opt\\\"].parallel_mode = \\\"data_parallel\\\"\\n if not hasattr(checkpoint[\\\"opt\\\"], \\\"norm_eps\\\"):\\n checkpoint[\\\"opt\\\"].norm_eps = 1e-6\\n\\n # fix v2 compatibility\\n if \\\"generator\\\" in checkpoint.keys() and checkpoint[\\\"generator\\\"]:\\n if \\\"0.weight\\\" in checkpoint[\\\"generator\\\"]:\\n checkpoint[\\\"generator\\\"][\\\"weight\\\"] = checkpoint[\\\"generator\\\"].pop(\\n \\\"0.weight\\\"\\n )\\n if \\\"0.bias\\\" in checkpoint[\\\"generator\\\"]:\\n checkpoint[\\\"generator\\\"][\\\"bias\\\"] = checkpoint[\\\"generator\\\"].pop(\\\"0.bias\\\")\\n # end of patch for backward compatibility\\n\\n return checkpoint\",\n \"def load_checkpoint(path, model, optimizer=None, reset_optimizer=True):\\n print(\\\"Load checkpoint from: {}\\\".format(path))\\n state_dict, optimizer_state = _load(path)\\n\\n model.load_dict(state_dict)\\n if not reset_optimizer and optimizer is not None:\\n if optimizer_state is not None:\\n print(\\\"[loading] Load optimizer state from {}\\\".format(path))\\n optimizer.load(optimizer_state)\\n\\n return model\",\n \"def _init_checkpoint_and_variables(pretrain_checkpoint_path,\\n pretrain_checkpoint_exclude_scopes):\\n checkpoint_reader = tf.contrib.framework.load_checkpoint(\\n pretrain_checkpoint_path)\\n return get_variables_to_restore_from_pretrain_checkpoint(\\n pretrain_checkpoint_exclude_scopes,\\n checkpoint_reader.get_variable_to_shape_map())\",\n \"def load_training_checkpoint(args, model, PATH, ckpt_id):\\r\\n logger = args.logger\\r\\n _, checkpoint_state_dict = model.network.load_checkpoint(PATH, ckpt_id)\\r\\n epoch = checkpoint_state_dict['epoch']\\r\\n last_global_step = checkpoint_state_dict['last_global_step']\\r\\n last_global_data_samples = checkpoint_state_dict[\\r\\n 'last_global_data_samples']\\r\\n del checkpoint_state_dict\\r\\n return (epoch, last_global_step, last_global_data_samples)\",\n \"def load_checkpoint(path):\\n\\n # Get the model name\\n model_name = path.split('-')[0]\\n assert (model_name in ['vgg16', 'resnet50'\\n ]), \\\"Path must have the correct model name\\\"\\n\\n # Load in checkpoint\\n checkpoint = torch.load(path)\\n\\n if model_name == 'vgg16':\\n model = models.vgg16(pretrained=True)\\n # Make sure to set parameters as not trainable\\n for param in model.parameters():\\n param.requires_grad = False\\n model.classifier = checkpoint['classifier']\\n\\n elif model_name == 'resnet50':\\n model = models.resnet50(pretrained=True)\\n # Make sure to set parameters as not trainable\\n for param in model.parameters():\\n param.requires_grad = False\\n model.fc = checkpoint['fc']\\n\\n # Load in the state dict\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n total_params = sum(p.numel() for p in model.parameters())\\n print(f'{total_params:,} total parameters.')\\n total_trainable_params = sum(\\n p.numel() for p in model.parameters() if p.requires_grad)\\n print(f'{total_trainable_params:,} total gradient parameters.')\\n\\n # Move to gpu\\n if multi_gpu:\\n model = nn.DataParallel(model)\\n\\n if train_on_gpu:\\n model = model.to('cuda')\\n\\n # Model basics\\n model.class_to_idx = checkpoint['class_to_idx']\\n model.idx_to_class = checkpoint['idx_to_class']\\n model.epochs = checkpoint['epochs']\\n\\n # Optimizer\\n optimizer = checkpoint['optimizer']\\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\n\\n return model, optimizer\",\n \"def get_checkpoint_data(self) -> Dict[str, Any]:\\n # get ckpt file path from config.trainer.params.resume_from_checkpoint\\n path = self.config.trainer.params.get(\\\"resume_from_checkpoint\\\", None)\\n if path is not None:\\n is_zoo = self.is_zoo_path(path)\\n ckpt_filepath = path\\n if is_zoo:\\n folder = download_pretrained_model(path)\\n ckpt_filepath = get_ckpt_path_from_folder(folder)\\n ckpt = get_ckpt_from_path(ckpt_filepath)\\n config = get_config_from_folder_or_ckpt(folder, ckpt)\\n else:\\n ckpt = get_ckpt_from_path(ckpt_filepath)\\n config = None\\n\\n return {\\n \\\"ckpt\\\": ckpt,\\n \\\"checkpoint_path\\\": ckpt_filepath,\\n \\\"is_zoo\\\": is_zoo,\\n \\\"config\\\": config,\\n }\\n\\n is_zoo = False\\n config = None\\n ckpt = None\\n # get ckpt file path from config.checkpoint\\n ckpt_config = self.config.checkpoint\\n suffix = \\\"best.ckpt\\\" if ckpt_config.resume_best else \\\"current.ckpt\\\"\\n path = os.path.join(get_mmf_env(key=\\\"save_dir\\\"), suffix)\\n ckpt_filepath = None\\n resume_from_specified_path = (\\n ckpt_config.resume_file is not None or ckpt_config.resume_zoo is not None\\n ) and (not ckpt_config.resume or not PathManager.exists(path))\\n if resume_from_specified_path:\\n if ckpt_config.resume_file and PathManager.exists(ckpt_config.resume_file):\\n ckpt_filepath = ckpt_config.resume_file\\n elif ckpt_config.resume_zoo is not None:\\n is_zoo = True\\n folder = download_pretrained_model(ckpt_config.resume_zoo)\\n ckpt_filepath = get_ckpt_path_from_folder(folder)\\n ckpt = get_ckpt_from_path(ckpt_filepath)\\n config = get_config_from_folder_or_ckpt(folder, ckpt)\\n else:\\n raise RuntimeError(f\\\"{ckpt_config.resume_file} doesn't exist\\\")\\n\\n if ckpt_config.resume and PathManager.exists(path):\\n ckpt_filepath = path\\n\\n if ckpt_filepath is not None:\\n ckpt = get_ckpt_from_path(ckpt_filepath)\\n\\n return {\\n \\\"ckpt\\\": ckpt,\\n \\\"checkpoint_path\\\": ckpt_filepath,\\n \\\"is_zoo\\\": is_zoo,\\n \\\"config\\\": config,\\n }\",\n \"def _load_checkpoint(filename, map_location=None):\\n if filename.startswith('modelzoo://'):\\n warnings.warn('The URL scheme of \\\"modelzoo://\\\" is deprecated, please '\\n 'use \\\"torchvision://\\\" instead')\\n model_urls = get_torchvision_models()\\n model_name = filename[11:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n elif filename.startswith('torchvision://'):\\n model_urls = get_torchvision_models()\\n model_name = filename[14:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n elif filename.startswith('open-mmlab://'):\\n model_urls = get_external_models()\\n model_name = filename[13:]\\n deprecated_urls = get_deprecated_model_names()\\n if model_name in deprecated_urls:\\n warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '\\n f'of open-mmlab://{deprecated_urls[model_name]}')\\n model_name = deprecated_urls[model_name]\\n model_url = model_urls[model_name]\\n # check if is url\\n if model_url.startswith(('http://', 'https://')):\\n checkpoint = load_url_dist(model_url)\\n else:\\n filename = osp.join(_get_mmcv_home(), model_url)\\n if not osp.isfile(filename):\\n raise IOError(f'{filename} is not a checkpoint file')\\n checkpoint = torch.load(filename, map_location=map_location)\\n elif filename.startswith('mmcls://'):\\n model_urls = get_mmcls_models()\\n model_name = filename[8:]\\n checkpoint = load_url_dist(model_urls[model_name])\\n checkpoint = _process_mmcls_checkpoint(checkpoint)\\n elif filename.startswith(('http://', 'https://')):\\n checkpoint = load_url_dist(filename)\\n elif filename.startswith('pavi://'):\\n model_path = filename[7:]\\n checkpoint = load_pavimodel_dist(model_path, map_location=map_location)\\n elif filename.startswith('s3://'):\\n checkpoint = load_fileclient_dist(\\n filename, backend='ceph', map_location=map_location)\\n else:\\n if not osp.isfile(filename):\\n raise IOError(f'{filename} is not a checkpoint file')\\n checkpoint = torch.load(filename, map_location=map_location)\\n return checkpoint\",\n \"def checkpoint(self, epoch, losses, path):\\n dct = {'epoch': epoch, \\n 'losses': losses, \\n 'model_state_dict': self.TrajectoryAutoencoder.state_dict()}\\n torch.save(dct, path)\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def load(self, path):\\n checkpoint = torch.load(path)\\n self.load_state_dict(checkpoint['model_state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\",\n \"def load_ckp(checkpoint_fpath, model, optimizer, device):\\n\\n checkpoint = torch.load(checkpoint_fpath,map_location=device)\\n model.load_state_dict(checkpoint['state_dict'])\\n optimizer.load_state_dict(checkpoint['optimizer'])\\n valid_acc = checkpoint['valid_acc'] \\n return model, optimizer, checkpoint['epoch'], valid_acc\",\n \"def _resume_checkpoint(self, resume_path):\\n self.logger.info(\\\"Loading checkpoint: {} ...\\\".format(resume_path))\\n checkpoint = torch.load(resume_path)\\n self.start_epoch = checkpoint['epoch'] + 1\\n self.monitor_best = checkpoint['monitor_best']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n # 将参数全部放入GPU\\n if self.with_cuda:\\n for state in self.optimizer.state.values():\\n for k, v in state.items():\\n if isinstance(v, torch.Tensor):\\n state[k] = v.cuda(self.device)\\n self.train_logger = checkpoint['logger']\\n self.config = checkpoint['config']\\n self.logger.info(\\\"Checkpoint '{}' (epoch {}) loaded\\\".format(resume_path, self.start_epoch))\",\n \"def _load_checkpoint(cls, model: DeviceAwareModule, checkpoint_path: Path,\\n key_in_state_dict: str, use_gpu: bool) -> int:\\n logging.info(f\\\"Loading checkpoint {checkpoint_path}\\\")\\n checkpoint = ModelAndInfo.read_checkpoint(checkpoint_path, use_gpu)\\n\\n try:\\n state_dict = checkpoint[key_in_state_dict]\\n except KeyError:\\n logging.error(f\\\"Key {key_in_state_dict} not found in checkpoint\\\")\\n return False\\n\\n if isinstance(model, torch.nn.DataParallel):\\n result = model.module.load_state_dict(state_dict, strict=False)\\n else:\\n result = model.load_state_dict(state_dict, strict=False)\\n\\n if result.missing_keys:\\n logging.warning(f\\\"Missing keys in model checkpoint: {result.missing_keys}\\\")\\n if result.unexpected_keys:\\n logging.warning(f\\\"Unexpected keys in model checkpoint: {result.unexpected_keys}\\\")\\n\\n return checkpoint[ModelAndInfo.EPOCH_KEY]\",\n \"def load_checkpoint(self):\\n if self.params.resume_from is not None and os.path.exists(self.params.resume_from):\\n try:\\n LOG('Loading Checkpoint at %s' % self.params.resume_from)\\n ckpt = torch.load(self.params.resume_from)\\n self.epoch = ckpt['epoch']\\n try:\\n self.train_loss = ckpt['train_loss']\\n self.val_loss = ckpt['val_loss']\\n except:\\n self.train_loss = []\\n self.val_loss = []\\n self.network.load_state_dict(ckpt['state_dict'])\\n self.opt.load_state_dict(ckpt['optimizer'])\\n LOG('Checkpoint Loaded!')\\n LOG('Current Epoch: %d' % self.epoch)\\n self.ckpt_flag = True\\n except:\\n WARNING('Cannot load checkpoint from %s. Start loading pre-trained model......' % self.params.resume_from)\\n else:\\n WARNING('Checkpoint do not exists. Start loading pre-trained model......')\",\n \"def load_checkpoint(self, checkpoint_filepath=None, verbose=True):\\n if checkpoint_filepath:\\n print('loading', checkpoint_filepath, flush=True)\\n optimistic_restore(self._session, checkpoint_filepath, verbose=verbose)\\n return True\\n else:\\n checkpoints = retrieve_all_checkpoints(self._checkpoints_path) + retrieve_all_checkpoints(self._recovery_checkpoints_path)\\n\\n if checkpoints:\\n last_checkpoint = sorted(checkpoints)[-1][1]\\n print('loading', last_checkpoint, flush=True)\\n optimistic_restore(self._session, last_checkpoint, verbose=verbose)\\n return True\\n else:\\n print('nothing to restore. no checkpoint found.', flush=True)\\n return False\",\n \"def get_latest_checkpoint_path(dirpath: str) -> Optional[str]:\\n\\n ret = None\\n rank = get_global_rank()\\n # Do all filesystem reads from rank 0 only\\n if rank == 0:\\n ret = _latest_checkpoint_path(dirpath)\\n\\n # If not running in a distributed setting, return as is\\n if not (dist.is_available() and dist.is_initialized()):\\n return ret\\n\\n # Otherwise, broadcast result from rank 0 to all ranks\\n pg = PGWrapper(dist.group.WORLD)\\n path_container = [ret] if rank == 0 else [None]\\n pg.broadcast_object_list(path_container, 0)\\n val = path_container[0]\\n return val\",\n \"def load_checkpoint(fpath):\\n if fpath is None:\\n raise ValueError('File path is None')\\n if not osp.exists(fpath):\\n raise FileNotFoundError('File is not found at \\\"{}\\\"'.format(fpath))\\n map_location = None if torch.cuda.is_available() else 'cpu'\\n try:\\n checkpoint = torch.load(fpath, map_location=map_location)\\n except UnicodeDecodeError:\\n pickle.load = partial(pickle.load, encoding=\\\"latin1\\\")\\n pickle.Unpickler = partial(pickle.Unpickler, encoding=\\\"latin1\\\")\\n checkpoint = torch.load(\\n fpath, pickle_module=pickle, map_location=map_location\\n )\\n except Exception:\\n print('Unable to load checkpoint from \\\"{}\\\"'.format(fpath))\\n raise\\n return checkpoint\",\n \"def load_checkpoint(self):\\n checkpoin_path = self.get_checkpoint_path()\\n _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)\",\n \"def get_checkpoint_path(self) -> Optional[str]:\\n j_path = self._j_checkpoint_storage.getCheckpointPath()\\n if j_path is None:\\n return None\\n else:\\n return j_path.toString()\",\n \"def unzip(checkpoint_path: pathlib.Path, archive_path: pathlib.Path) -> None:\\n checkpoint_path.mkdir(parents=True, exist_ok=True)\\n with ZipFile(archive_path, \\\"r\\\") as zf:\\n zf.extractall(path=checkpoint_path)\\n archive_path.unlink()\",\n \"def _resume_checkpoint(self, resume_path, model, optimizer):\\n if not resume_path:\\n return model, optimizer\\n\\n self.logger.info(f'Loading checkpoint: {resume_path}')\\n checkpoint = torch.load(resume_path)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # load optimizer state from checkpoint only when optimizer type is not changed.\\n if checkpoint['config']['optimizer']['type'] != self.config['optimizer']['type']:\\n self.logger.warning(\\\"Warning: Optimizer type given in config file is different from \\\"\\n \\\"that of checkpoint. Optimizer parameters not being resumed.\\\")\\n else:\\n optimizer.load_state_dict(checkpoint['optimizer'])\\n\\n self.logger.info(f'Checkpoint \\\"{resume_path}\\\" loaded')\\n return model, optimizer\",\n \"def load(self, checkpoint_dir):\\n print(\\\"\\\\nReading Checkpoints.....\\\\n\\\\n\\\")\\n model_dir = \\\"%s\\\" % (\\\"cnn\\\") # give the model name by label_size\\n checkpoint_dir = os.path.join(checkpoint_dir, model_dir)\\n ckpt = tf.train.get_checkpoint_state(checkpoint_dir)\\n \\n # Check the checkpoint is exist\\n if ckpt and ckpt.model_checkpoint_path:\\n ckpt_path = str(ckpt.model_checkpoint_path) # convert the unicode to string\\n self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path))\\n print(\\\"\\\\n Checkpoint Loading Success! %s\\\\n\\\\n\\\"% ckpt_path)\\n else:\\n print(\\\"\\\\n! Checkpoint Loading Failed \\\\n\\\\n\\\")\",\n \"def load_checkpoint(checkpoint, model, optimizer=None):\\n if not os.path.exists(checkpoint):\\n raise (\\\"File doesn't exist {}\\\".format(checkpoint))\\n checkpoint = torch.load(checkpoint)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if optimizer:\\n optimizer.load_state_dict(checkpoint['optim_dict'])\\n\\n return checkpoint\",\n \"def from_previous_ckpt(network, checkpoint):\\n if os.path.exists(checkpoint):\\n if os.path.isfile(checkpoint):\\n try:\\n network.load_state_dict(torch.load(checkpoint))\\n print(f\\\"Loaded weights from {checkpoint}\\\")\\n except RuntimeError:\\n print(f\\\"{checkpoint} is a invalid checkpoint\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n if os.path.isdir(checkpoint):\\n epoch = 0\\n file_name = None\\n for ckpt in os.listdir(checkpoint):\\n if ckpt[-4:] == '.pth':\\n try:\\n tmp_int_list = re.findall('[0-9]+', ckpt)\\n ckpt_epoch = int(tmp_int_list[-1])\\n except IndexError:\\n ckpt_epoch = 0\\n if ckpt_epoch >= epoch:\\n epoch = ckpt_epoch\\n file_name = os.path.join(checkpoint, ckpt)\\n\\n if file_name is None:\\n print(f\\\"No checkpoint found in {checkpoint}\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n else:\\n try:\\n network.load_state_dict(torch.load(file_name))\\n print(f\\\"Loaded weights from {file_name}\\\")\\n except (RuntimeError):\\n print(f\\\"{file_name} is a invalid checkpoint\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\\n\\n else:\\n print(f\\\"the checkpoint path: {checkpoint} doesn't exist.\\\")\\n print(\\\"Neglecting this checkpoint.\\\")\",\n \"def load_graph_from_ckpt(checkpoint_path, meta_graph_path=None, graph=None, session=None):\\n from diplomacy_research.utils.tensorflow import tf\\n\\n dir_path, filename = os.path.split(checkpoint_path)\\n\\n # checkpoint_path is a directory - Loading latest checkpoint in directory\\n if os.path.isdir(checkpoint_path):\\n checkpoint = tf.train.latest_checkpoint(checkpoint_path)\\n if meta_graph_path is None:\\n meta_graph_path = max(glob.iglob(os.path.join(checkpoint_path, '*.meta')), key=os.path.getctime)\\n\\n # checkpoint_path is a checkpoint file - Loading latest checkpoint in directory\\n elif filename == 'checkpoint':\\n checkpoint = tf.train.latest_checkpoint(dir_path, 'checkpoint')\\n if meta_graph_path is None:\\n meta_graph_path = max(glob.iglob(os.path.join(dir_path, '*.meta')), key=os.path.getctime)\\n\\n # Loading a specific checkpoint\\n else:\\n # Removing extension\\n if len(filename.split('.')) > 2:\\n checkpoint_path = os.path.join(dir_path, '.'.join(filename.split('.')[:2]))\\n checkpoint = checkpoint_path\\n if meta_graph_path is None:\\n if os.path.exists('{}.meta'.format(checkpoint_path)):\\n meta_graph_path = '{}.meta'.format(checkpoint_path)\\n else:\\n meta_graph_path = max(glob.iglob(os.path.join(dir_path, '*.meta')), key=os.path.getctime)\\n\\n # Loading the checkpoint in the graph\\n graph = tf.Graph() if graph is None else graph\\n with graph.as_default():\\n session = tf.Session(graph=graph) if session is None else session\\n saver = tf.train.import_meta_graph(meta_graph_path)\\n saver.restore(session, checkpoint)\\n\\n # Returning graph and session\\n return graph, session\",\n \"def resume_model(self, resume_path):\\n if resume_path:\\n resume_path = f'{self.model_dir}/{resume_path}.pth.tar'\\n if os.path.isfile(resume_path):\\n print(f\\\"=> loading checkpoint '{resume_path}'\\\")\\n checkpoint = torch.load(resume_path, map_location=self.device)\\n self.best_acc = checkpoint['best_acc']\\n self.model.load_state_dict(checkpoint['state_dict'])\\n print(f\\\"=> loaded checkpoint '{resume_path}' (Round {checkpoint['rnd']})\\\")\\n del checkpoint\\n else:\\n print(f\\\"=> no checkpoint found at '{resume_path}'\\\")\",\n \"def load_checkpoint(checkpoint, model, optimizer=None):\\n if not os.path.exists(checkpoint):\\n raise (\\\"File doesn't exist {}\\\".format(checkpoint))\\n checkpoint = torch.load(checkpoint, map_location=torch.device('cpu'))\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if optimizer:\\n optimizer.load_state_dict(checkpoint['optim_dict'])\\n\\n return checkpoint\",\n \"def loadCheckpoint(self, time_stamp, data_only=False, load_memory=True):\\n state_filename = os.path.join(self.saving_dir, 'checkpoint.' + time_stamp + '.pth.tar')\\n mem_filename = os.path.join(self.saving_dir, 'memory.' + time_stamp + '.pth.tar')\\n\\n print 'loading checkpoint: ', time_stamp\\n checkpoint = torch.load(state_filename)\\n if data_only:\\n self.episode_rewards = checkpoint['episode_rewards']\\n self.episode_lengths = checkpoint['episode_lengths']\\n return\\n\\n self.episodes_done = checkpoint['episode']\\n self.steps_done = checkpoint['steps']\\n self.episode_rewards = checkpoint['episode_rewards']\\n self.episode_lengths = checkpoint['episode_lengths']\\n\\n self.policy_net.load_state_dict(checkpoint['policy_state_dict'])\\n self.policy_net = self.policy_net.to(self.device)\\n self.policy_net.train()\\n\\n self.target_net.load_state_dict(checkpoint['policy_state_dict'])\\n self.target_net = self.target_net.to(self.device)\\n self.target_net.eval()\\n\\n self.optimizer = optim.Adam(self.policy_net.parameters())\\n self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\\n\\n if load_memory:\\n memory = torch.load(mem_filename)\\n self.memory = memory['memory']\",\n \"def checkpoint(request, tmp_path):\\n checkpoint_fs_type = request.param\\n\\n checkpoint_path = tmp_path / \\\"ckpt_dir\\\"\\n checkpoint_path.mkdir(exist_ok=True)\\n (checkpoint_path / _CHECKPOINT_CONTENT_FILE).write_text(\\\"dummy\\\")\\n\\n if checkpoint_fs_type == \\\"local\\\":\\n yield Checkpoint.from_directory(str(checkpoint_path))\\n elif checkpoint_fs_type == \\\"mock\\\":\\n _checkpoint = Checkpoint(path=\\\"mock:///mock_bucket/ckpt_dir\\\")\\n _upload_to_fs_path(\\n local_path=str(checkpoint_path),\\n fs=_checkpoint.filesystem,\\n fs_path=_checkpoint.path,\\n )\\n # The \\\"mock://\\\" URI doesn't persist across different instances of\\n # the pyarrow.fs.MockFileSystem, so we must make sure to return\\n # the checkpoint with the same filesystem instance that we uploaded\\n # some mock content to.\\n yield _checkpoint\\n elif checkpoint_fs_type == \\\"custom_fs\\\":\\n custom_storage_fs = _create_mock_custom_fs(tmp_path / \\\"custom_fs\\\")\\n _upload_to_fs_path(\\n local_path=str(checkpoint_path),\\n fs=custom_storage_fs,\\n fs_path=\\\"mock_bucket/ckpt_dir\\\",\\n )\\n yield Checkpoint(path=\\\"mock_bucket/ckpt_dir\\\", filesystem=custom_storage_fs)\",\n \"async def get_checkpoint_async(self, partition_id):\",\n \"def _get_file(self, path: str) -> Tuple[str, bytes]:\\n self._trace(\\\"fetching: %s\\\" % path)\\n meta, resp = self._connection.files_download(path)\\n return (meta.rev, resp.content)\",\n \"def load_checkpoint(self, filename):\\n results = Future.gen_list([\\n self.call_async(rank, '_async_load_checkpoint', filename=filename)\\n for rank in range(self.num_replicas)\\n ])\\n extra_state = results[0]\\n return extra_state\",\n \"def save_checkpoint(state, is_best, file_path, file_name='checkpoint.pth.tar'):\\n\\n save_path = file_path + '/' + file_name\\n torch.save(state, save_path)\\n if is_best:\\n shutil.copyfile(save_path, file_path + '/model_best.pth.tar')\",\n \"def LoadProgramState(self, restored_checkpoint_path=None, sess=None):\\n pass\",\n \"def save_checkpoint(state, filename='checkpoint.pth.tar'):\\n torch.save(state, filename)\",\n \"def get_latest_ckpt(self,\\n ckpt_type):\\n if ckpt_type == \\\"debug\\\":\\n ckpt_file = tf.train.latest_checkpoint(self.ckpt_debug_dir)\\n if ckpt_file is None:\\n raise FileNotFoundError(\\\"latest checkpoint file doesn't exist\\\")\\n \\n return ckpt_file\\n elif ckpt_type == \\\"epoch\\\":\\n ckpt_file = tf.train.latest_checkpoint(self.ckpt_epoch_dir)\\n if ckpt_file is None:\\n raise FileNotFoundError(\\\"latest checkpoint file doesn't exist\\\")\\n \\n return ckpt_file\\n else:\\n raise ValueError(\\\"unsupported checkpoint type {0}\\\".format(ckpt_type))\",\n \"def save_checkpoint(self, path: str, **kwargs):\\n if self.distributed:\\n encoder = self.net_q.module.encoder\\n head = self.net_q.module.head\\n else:\\n encoder = self.net_q.encoder\\n head = self.net_q.head\\n\\n ckpt = {\\n 'encoder': encoder.state_dict(),\\n 'head': head.state_dict(),\\n 'net_ps': self.net_ps.state_dict(),\\n 'net_k': self.net_k.state_dict(),\\n 'queue': self.queue.state_dict(),\\n 'optimizer': self.optimizer.state_dict(),\\n 'scheduler': self.scheduler.state_dict(),\\n }\\n if kwargs:\\n ckpt.update(kwargs)\\n torch.save(ckpt, path)\",\n \"def load_epoch_checkpoint(self, directory, epoch):\\n chkpnt = torch.load(directory / f\\\"chkpnt_epoch{epoch:04d}.pth\\\")\\n self.load_state_dict(chkpnt['model_state_dict'])\",\n \"def pickle_load(path):\\n data = pickle.load(open(os.path.join(os.getcwd(), path), 'rb'))\\n return data\",\n \"def load_pickle(path):\\n assert osp.exists(path)\\n with open(path, 'r') as f:\\n ret = pickle.load(f)\\n return ret\",\n \"def load_checkpoint(checkpoint_file: pl.Path) -> Optional[Dict[str, Any]]:\\n if checkpoint_file.exists():\\n logger.info(f\\\"Loading checkpoint {checkpoint_file}.\\\")\\n checkpoint = torch.load(str(checkpoint_file))\\n logger.info(f\\\"Done loading checkpoint from epoch {checkpoint['epoch']}.\\\")\\n else:\\n logger.warning(f\\\"No {checkpoint_file} checkpoint file found. Starting normal.\\\")\\n return checkpoint\",\n \"def _get_global_step_for_checkpoint(checkpoint_path: Text) -> int:\\n re_match = re.search(r'ckpt-(\\\\d+)$', checkpoint_path)\\n return -1 if re_match is None else int(re_match.group(1))\",\n \"def load_model(path, model, optimizer):\\n print(\\\"LOADING MODEL...\\\")\\n ckpt = tf.train.Checkpoint(model=model, optimizer=optimizer)\\n status = ckpt.restore(tf.train.latest_checkpoint(path))\\n ckpt_manager = tf.train.CheckpointManager(\\n checkpoint=ckpt, \\n directory=FLAGS.model_dir, \\n max_to_keep=3 \\n )\\n return model, optimizer, ckpt, ckpt_manager\",\n \"def load_checkpoint(filepath):\\n checkpoint = torch.load(filepath)\\n \\n arch = checkpoint['arch']\\n if arch == 'vgg':\\n model = models.vgg16(pretrained=True)\\n elif arch == 'densenet':\\n model = models.densenet121(pretrained=True) \\n \\n model.class_to_idx = checkpoint['class_to_idx']\\n model.classifier = checkpoint['classifier']\\n model.classifier.load_sate_dict = checkpoint['classifier_state_dict']\\n model.optimizer = checkpoint['optimizer_state_dict']\\n model.input_size = checkpoint['input_size']\\n model.output_size = checkpoint['output_size']\\n \\n return model\",\n \"def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\\n directory = \\\"checkoutpoint/%s/\\\" % args.name\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n filename = directory + filename\\n torch.save(state, filename)\\n if is_best:\\n shutil.copyfile(filename, 'checkoutpoint/%s/' % args.name + 'model_best.pth.tar')\",\n \"def pickle_path(self) -> pathlib.Path:\\n return self.path / 'pickle.pkl'\",\n \"def load_ckpt(saver, sess, ckpt_dir=\\\"train\\\"):\\n while True:\\n try:\\n latest_filename = \\\"checkpoint_best\\\" if ckpt_dir == \\\"eval\\\" else None\\n ckpt_dir = os.path.join(FLAGS.log_root, ckpt_dir)\\n ckpt_state = tf.train.get_checkpoint_state(ckpt_dir, latest_filename=latest_filename)\\n tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)\\n saver.restore(sess, ckpt_state.model_checkpoint_path)\\n return ckpt_state.model_checkpoint_path\\n except:\\n tf.logging.info(\\\"Failed to load checkpoint from %s. Sleeping for %i secs...\\\", ckpt_dir, 10)\\n time.sleep(10)\",\n \"def checkpoint(state, file_name='./saves/checkpoint.pth.tar'):\\n\\n assert isinstance(state, dict)\\n assert isinstance(file_name, str)\\n\\n torch.save(state, file_name)\",\n \"def load_ckpt(saver, sess, ckpt_dir=\\\"train\\\"):\\n while True:\\n try:\\n latest_filename = \\\"checkpoint_best\\\" if ckpt_dir==\\\"eval\\\" else None\\n ckpt_dir = os.path.join(FLAGS.log_root, ckpt_dir)\\n ckpt_state = tf.train.get_checkpoint_state(ckpt_dir, latest_filename=latest_filename)\\n tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)\\n saver.restore(sess, ckpt_state.model_checkpoint_path)\\n return ckpt_state.model_checkpoint_path\\n except:\\n tf.logging.info(\\\"Failed to load checkpoint from %s. Sleeping for %i secs...\\\", ckpt_dir, 10)\\n time.sleep(10)\",\n \"def maybe_restore_train_and_indicator_state(\\n train_state,\\n indicator_state,\\n *,\\n ckpt_manager,\\n mesh,\\n):\\n latest_step = ckpt_manager.latest_step()\\n if latest_step is None:\\n return train_state, indicator_state\\n logging.info('Restoring from step %d', latest_step)\\n\\n # Check if the directory is empty, Orbax could have failed to save\\n # the checkpoint alltogether\\n save_dir = checkpoint.utils.get_save_directory(\\n latest_step,\\n ckpt_manager.directory,\\n )\\n # If there's no files in the directory we should remove it and try\\n # again with the checkpoint before that.\\n if not any(save_dir.iterdir()):\\n logging.info(\\n 'Save directory %s is empty, removing and recursing restore',\\n save_dir,\\n )\\n save_dir.rmdir()\\n return maybe_restore_train_and_indicator_state(\\n train_state,\\n indicator_state,\\n ckpt_manager=ckpt_manager,\\n mesh=mesh,\\n )\\n\\n def restore_arguments_with_mesh_axes(\\n mesh_axes):\\n if not mesh:\\n mesh_axes = None\\n\\n def closure(_):\\n return checkpoint.ArrayRestoreArgs(\\n lazy=True,\\n restore_type=jax.Array,\\n mesh=mesh,\\n mesh_axes=mesh_axes,\\n )\\n\\n return closure\\n\\n # Evaluate the shape and filter empty nodes\\n # We save the entire PyTree so there's no need to further filter\\n train_state_shape = jax.eval_shape(lambda x: x, train_state)\\n train_state_shape = tree_utils.filter_empty_nodes(train_state_shape,\\n train_state_shape)\\n train_state_pspec = create_train_state_partition_spec_from_shape(\\n train_state_shape)\\n train_state_restore_args = jax.tree_util.tree_map(\\n restore_arguments_with_mesh_axes(train_state_pspec), train_state_shape)\\n\\n indicator_state_shape = jax.eval_shape(lambda x: x, indicator_state)\\n indicator_state_shape = tree_utils.tree_map_with_regex(\\n lambda _: None, indicator_state_shape, [(r'.*params/encoder/.*',)],\\n lambda leaf: leaf)\\n indicator_state_shape = tree_utils.filter_empty_nodes(indicator_state_shape,\\n indicator_state_shape)\\n indicator_state_pspec = create_indicator_state_partition_spec_from_shape(\\n indicator_state_shape)\\n indicator_state_restore_args = jax.tree_util.tree_map(\\n restore_arguments_with_mesh_axes(indicator_state_pspec),\\n indicator_state_shape)\\n\\n restored_state = ckpt_manager.restore(\\n latest_step,\\n items={\\n 'train': train_state_shape,\\n 'indicator': indicator_state_shape\\n },\\n restore_kwargs={\\n 'train': {\\n 'restore_args': train_state_restore_args\\n },\\n 'indicator': {\\n 'restore_args': indicator_state_restore_args\\n }\\n })\\n\\n restored_state = checkpoint.apply_transformations(\\n original_tree=restored_state,\\n transformations=dict(),\\n new_tree={\\n 'train': train_state,\\n 'indicator': indicator_state\\n },\\n default_to_original=False)\\n restored_state = checkpoint.lazy_utils.maybe_get_tree(restored_state)\\n logging.info('Restore finished')\\n\\n return operator.itemgetter('train', 'indicator')(restored_state)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62694466","0.61993414","0.6029626","0.5934021","0.5839948","0.5779024","0.5675783","0.5657235","0.55893075","0.5551518","0.5551518","0.55440056","0.55313367","0.5514979","0.5417088","0.54159427","0.5401518","0.5353508","0.53150314","0.529472","0.5274455","0.5255847","0.52468973","0.5233274","0.5200701","0.51954675","0.5182463","0.5162873","0.51401293","0.5114447","0.51097894","0.5107589","0.509768","0.5091877","0.50726575","0.5065291","0.50631195","0.50589293","0.5052109","0.50485903","0.5027895","0.50268424","0.502192","0.50137293","0.50099146","0.500295","0.5002643","0.5002643","0.5002393","0.49912864","0.49808422","0.4962525","0.49321222","0.4922471","0.49002975","0.48956385","0.48948878","0.48816213","0.48752436","0.48752436","0.48735258","0.4869342","0.48613054","0.48607022","0.4860127","0.48519614","0.4843924","0.4836012","0.48284405","0.4820705","0.48029995","0.48024604","0.47922355","0.4789803","0.4785961","0.47810075","0.47795194","0.4777053","0.47715795","0.47544295","0.4749165","0.47307974","0.47228828","0.47138757","0.47016093","0.46985063","0.46963185","0.46892652","0.468502","0.4683979","0.46784225","0.46750888","0.46691263","0.46617872","0.4659222","0.46439832","0.46434453","0.46409237","0.46402255","0.4620124"],"string":"[\n \"0.62694466\",\n \"0.61993414\",\n \"0.6029626\",\n \"0.5934021\",\n \"0.5839948\",\n \"0.5779024\",\n \"0.5675783\",\n \"0.5657235\",\n \"0.55893075\",\n \"0.5551518\",\n \"0.5551518\",\n \"0.55440056\",\n \"0.55313367\",\n \"0.5514979\",\n \"0.5417088\",\n \"0.54159427\",\n \"0.5401518\",\n \"0.5353508\",\n \"0.53150314\",\n \"0.529472\",\n \"0.5274455\",\n \"0.5255847\",\n \"0.52468973\",\n \"0.5233274\",\n \"0.5200701\",\n \"0.51954675\",\n \"0.5182463\",\n \"0.5162873\",\n \"0.51401293\",\n \"0.5114447\",\n \"0.51097894\",\n \"0.5107589\",\n \"0.509768\",\n \"0.5091877\",\n \"0.50726575\",\n \"0.5065291\",\n \"0.50631195\",\n \"0.50589293\",\n \"0.5052109\",\n \"0.50485903\",\n \"0.5027895\",\n \"0.50268424\",\n \"0.502192\",\n \"0.50137293\",\n \"0.50099146\",\n \"0.500295\",\n \"0.5002643\",\n \"0.5002643\",\n \"0.5002393\",\n \"0.49912864\",\n \"0.49808422\",\n \"0.4962525\",\n \"0.49321222\",\n \"0.4922471\",\n \"0.49002975\",\n \"0.48956385\",\n \"0.48948878\",\n \"0.48816213\",\n \"0.48752436\",\n \"0.48752436\",\n \"0.48735258\",\n \"0.4869342\",\n \"0.48613054\",\n \"0.48607022\",\n \"0.4860127\",\n \"0.48519614\",\n \"0.4843924\",\n \"0.4836012\",\n \"0.48284405\",\n \"0.4820705\",\n \"0.48029995\",\n \"0.48024604\",\n \"0.47922355\",\n \"0.4789803\",\n \"0.4785961\",\n \"0.47810075\",\n \"0.47795194\",\n \"0.4777053\",\n \"0.47715795\",\n \"0.47544295\",\n \"0.4749165\",\n \"0.47307974\",\n \"0.47228828\",\n \"0.47138757\",\n \"0.47016093\",\n \"0.46985063\",\n \"0.46963185\",\n \"0.46892652\",\n \"0.468502\",\n \"0.4683979\",\n \"0.46784225\",\n \"0.46750888\",\n \"0.46691263\",\n \"0.46617872\",\n \"0.4659222\",\n \"0.46439832\",\n \"0.46434453\",\n \"0.46409237\",\n \"0.46402255\",\n \"0.4620124\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7012607"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":19699,"cells":{"query":{"kind":"string","value":"Recursively flatten the keys of a dictionary or list into a set of paths."},"document":{"kind":"string","value":"def _flatten_keys(obj: Any, paths: List[str], existing_path: str):\n # Store path when we reach end, which is either non-Dict or empty Dict\n if isinstance(obj, list) and len(obj) > 0:\n for i, elm in enumerate(obj):\n _flatten_keys(elm, paths, f'{existing_path}/{i}')\n elif isinstance(obj, dict) and len(obj) > 0:\n for k, v in obj.items():\n _flatten_keys(v, paths, f'{existing_path}/{k}')\n # Remove leading /\n paths.append(existing_path.lstrip('/'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def flatten_keys(in_keys):\n return_keys = []\n if isinstance(in_keys, str):\n return [in_keys]\n if isinstance(in_keys, Iterable):\n for key in in_keys:\n if isinstance(key, Iterable):\n return_keys += flatten_keys(key)\n else:\n return_keys.append(key)\n else:\n return_keys.append(in_keys)\n return return_keys","def flatten(d, path):\n\n if isinstance(d, dict):\n for k, v in d.items():\n yield from flatten(v, path + [k])\n else:\n yield (\".\".join(path), d)","def flatten_dict_string_keys(x):\n return {'/'.join(k): v for k, v in flatten_dict(unfreeze(x)).items()}","def _flatten(object_, key):\n # Empty object can't be iterated, take as is\n if not object_:\n flattened_dict[key] = object_\n # These object types support iteration\n elif isinstance(object_, dict):\n for object_key in object_:\n if not (not key and object_key in root_keys_to_ignore):\n _flatten(object_[object_key], _construct_key(key,\n separator,\n object_key))\n elif isinstance(object_, (list, set, tuple)):\n for index, item in enumerate(object_):\n _flatten(item, _construct_key(key, separator, index))\n # Anything left take as is\n else:\n flattened_dict[key] = object_","def dict_path(my_dict, path=None):\n if path is None:\n path = \"\"\n for k, v in my_dict.items():\n newpath = path + (\".\" if path != \"\" else \"\") + k\n if isinstance(v, dict):\n for u in dict_path(v, newpath):\n yield u\n else:\n yield newpath, v","def graph_walk_collection_flat(indict, pre=None):\n pre = pre[:] if pre else []\n \n # if isinstance(indict, dict):\n if type(indict) in [dict, OrderedDict]:\n for key, value in indict.items():\n # if isinstance(value, dict):\n if type(value) in [dict, OrderedDict]:\n for d in graph_walk_collection_flat(value, [key] + pre):\n yield d\n # elif isinstance(value, list) or isinstance(value, tuple):\n elif type(value) in [list, tuple]:\n for v in value:\n for d in graph_walk_collection_flat(v, [key] + pre):\n yield d\n else:\n yield pre + [key, value]\n else:\n yield indict","def flat (root, ignore_access_errors=False):\n for key, subkeys, values in walk (root, ignore_access_errors):\n yield key\n for value in values:\n yield value","def _recursiveURISearch(self, multidict):\r\n valueList = []\r\n keys = []\r\n\r\n for k, v in multidict.iteritems():\r\n if isinstance(v, dict):\r\n valueList += self._recursiveURISearch(v)\r\n elif k[-1] == '*':\r\n keys.append(k)\r\n\r\n for k in keys:\r\n ele = multidict.pop(k)\r\n\r\n if isinstance(ele, list):\r\n lst = [None] * len(ele)\r\n multidict[k[:-1]] = lst\r\n\r\n for i, uri in enumerate(ele):\r\n valueList.append((uri, lst, i))\r\n else:\r\n valueList.append((ele, multidict, k[:-1]))\r\n\r\n return valueList","def flatten(d, parent_key='', sep='_'):\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, collections.MutableMapping):\n items.extend(flatten(v, new_key, sep=sep).items())\n else:\n items.append((new_key, v))\n items = dict(items)\n # remove info like PCA primitive ID\n items_not_strings = {k: v for k, v in items.items() if type(v) != str}\n return dict(items_not_strings)","def flatten_object(obj):\n\tpaths = []\n\n\tif isinstance(obj, (dict,)):\n\t\tfor f in obj:\n\t\t\tsub_paths = flatten_object(obj[f])\n\t\t\tfor p in sub_paths:\n\t\t\t\tpaths.append((\"/{}{}\".format(f, p[0]), p[1]))\n\telif isinstance(obj, (list,)):\n\t\tfor i, x in enumerate(obj):\n\t\t\tsub_paths = flatten_object(x)\n\t\t\tfor p in sub_paths:\n\t\t\t\tpaths.append((\"/{}{}\".format(i, p[0]), p[1]))\n\telse:\n\t\tpaths = [(\"\", obj)]\n\n\treturn paths","def get_by_list_of_keys(dictionary: Dict, key_path: List[Any]) -> Dict:\n if len(key_path) == 1:\n return dictionary[key_path[0]]\n else:\n return get_by_list_of_keys(dictionary[key_path[0]], key_path[1:])","def dict_get_nodekeys_recursive(d):\n nodekeys = set(d.keys())\n for nk in nodekeys:\n # print \"nodekey\", nk\n # print \"graphkeys\", d[nk]['params'].keys()\n if 'graph' in d[nk]['params']:\n # print \"graphkeys\", d[nk]['params']['graph'].keys()\n nodekeys = nodekeys.union(dict_get_nodekeys_recursive(d[nk]['params']['graph']))\n return nodekeys","def flatten(cmd, path=\"\", fc={}, sep=\".\"):\n fcmd = fc.copy()\n if isinstance(cmd, dict):\n for k, v in cmd.items():\n k = k.split(\":\")[1] if \":\" in k else k\n fcmd = flatten(v, sep.join((path, k)) if path else k, fcmd)\n elif isinstance(cmd, list):\n for n, v in enumerate(cmd):\n fcmd.update(flatten(v, sep.join([path, str(n)])))\n else:\n fcmd[path] = cmd\n return fcmd","def _Flatten(obj):\n\n def Flatten(obj, name, res):\n \"\"\"Recursively appends keys in path from obj into res.\n\n Args:\n obj: The object to flatten.\n name: The key name of the current obj.\n res: The ordered result value list.\n \"\"\"\n if isinstance(obj, list):\n if obj:\n for i, item in enumerate(obj):\n Flatten(item, '{name}[{index}]'.format(name=name, index=i), res)\n else:\n res.append((name, []))\n elif isinstance(obj, dict):\n if obj:\n for k, v in sorted(obj.iteritems()):\n Flatten(v, '{name}{dot}{key}'.format(\n name=name, dot='.' if name else '', key=k), res)\n else:\n res.append((name, {}))\n elif isinstance(obj, float):\n res.append((name, resource_transform.TransformFloat(obj)))\n else:\n res.append((name, obj))\n\n res = []\n Flatten(obj, '', res)\n return res","def RecurseKeys(self):\n yield self\n for subkey in self.GetSubkeys():\n for key in subkey.RecurseKeys():\n yield key","def _resolve_paths(d, path):\n try:\n if len(path) == 0:\n yield (), d\n elif len(path) == 1:\n yield (path[0],), d[path[0]]\n else:\n if path[0] == '*':\n keys = d.keys()\n else:\n keys = [path[0]]\n for key in keys:\n for p, v in CombinatorialTree._resolve_paths(d[key], path[1:]):\n if v is not None:\n yield (key,) + p, v\n except KeyError:\n yield None, None","def flatten(d, parent_key='', sep='_'):\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, collections.MutableMapping):\n items.extend(flatten(v, new_key, sep=sep).items())\n else:\n items.append((new_key, v))\n return dict(items)","def expand_flattened_dict(flattened, separator='.'):\n merged = {}\n for key, value in flattened.items():\n expanded = expand_flattened_path(key, value=value, separator=separator)\n merged = merge_dicts(merged, expanded)\n return merged","def flatten(items):\n for k, v in items:\n if isinstance(k, (list, tuple)):\n for key in k:\n yield key, v\n else:\n yield k, v","def flatten_paths(*items):\r\n\r\n flat = []\r\n\r\n def flatmap(item):\r\n if isinstance(item, Compatibility.string):\r\n flat.append(item)\r\n else:\r\n try:\r\n for i in iter(item):\r\n flatmap(i)\r\n except TypeError:\r\n if callable(item):\r\n flatmap(item())\r\n else:\r\n flat.append(item)\r\n\r\n for item in items:\r\n flatmap(item)\r\n\r\n return flat","def get_final_key_paths(\n obj: Union[dict, list, tuple], cur_path: str = '',\n append_values: bool = False,\n paths: list = None, black_list: list = None,\n final_keys_only: bool = False):\n if paths is None:\n paths = []\n\n if isinstance(obj, (dict, list, tuple)):\n if isinstance(obj, dict):\n for key in obj:\n new_path = cur_path + f'[\\'{key}\\']'\n if isinstance(obj[key], dict):\n if black_list is not None and key in black_list:\n continue\n get_final_key_paths(\n obj[key], new_path, append_values, paths, black_list,\n final_keys_only)\n elif isinstance(obj[key], (list, tuple)):\n get_final_key_paths(\n obj[key], new_path, append_values, paths, black_list,\n final_keys_only)\n else:\n if final_keys_only:\n last_bracket = new_path.rfind('[\\'')\n new_path = new_path[\n last_bracket+2:new_path.rfind('\\'')]\n if append_values:\n to_append = [new_path, obj[key]]\n else:\n to_append = new_path\n paths.append(to_append)\n else:\n key_added = False\n for i in range(len(obj)):\n if isinstance(obj[i], (dict, tuple, list)):\n get_final_key_paths(\n obj[i], cur_path + f'[{i}]', append_values,\n paths, black_list, final_keys_only)\n else:\n if not key_added:\n if final_keys_only:\n last_bracket = cur_path.rfind('[\\'')\n cur_path = cur_path[\n last_bracket+2:cur_path.rfind('\\'')]\n if append_values:\n to_append = [cur_path, obj]\n else:\n to_append = cur_path\n paths.append(to_append)\n key_added = True\n\n return paths","def dict_flatten(*args):\n hold = []\n for a in args:\n hold.append([i for s in a.values() for i in s])\n return hold","def all_key_seqs(template):\n result = []\n for k, v in template.iteritems():\n if isinstance(v, dict):\n for suffix in all_key_seqs(v):\n result.append([k] + suffix)\n else:\n result.append([k])\n return result","def unflatten(\n d: Dict[str, Any],\n base: Dict[str, Any] = None,\n) -> Dict[str, Any]:\n if base is None:\n base = {}\n\n for key, value in d.items():\n root = base\n\n ###\n # If a dotted path is encountered, create nested dicts for all but\n # the last level, then change root to that last level, and key to\n # the final key in the path. This allows one final setitem at the bottom\n # of the loop.\n if '.' in key:\n *parts, key = key.split('.')\n\n for part in parts:\n root.setdefault(part, {})\n root = root[part]\n\n if isinstance(value, dict):\n value = unflatten(value, root.get(key, {}))\n\n root[key] = value\n\n return base","def serialize_dict_keys(d, prefix=\"\"):\n keys = []\n for k, v in d.items():\n fqk = \"{}{}\".format(prefix, k)\n keys.append(fqk)\n if isinstance(v, dict):\n keys.extend(serialize_dict_keys(v, prefix=\"{}.\".format(fqk)))\n\n return keys","def unflatten(arg):\n if hasattr(arg, \"iteritems\"):\n items = arg.iteritems()\n elif hasattr(arg, \"items\"):\n items = arg.items()\n else:\n items = arg\n\n data = {}\n holders = []\n for flat_key, val in items:\n parsed_key = _parse_key(flat_key)\n obj = data\n for depth, (key, next_key) in enumerate(zip(parsed_key, parsed_key[1:]), 1):\n if isinstance(next_key, string_type):\n holder_type = _dict_holder\n else:\n holder_type = _list_holder\n\n if key not in obj:\n obj[key] = holder_type(_unparse_key(parsed_key[:depth]))\n holders.append((obj, key))\n elif not isinstance(obj[key], holder_type):\n raise ValueError(\n \"conflicting types %s and %s for key %r\"\n % (\n _node_type(obj[key]),\n holder_type.node_type,\n _unparse_key(parsed_key[:depth]),\n )\n )\n obj = obj[key]\n\n last_key = parsed_key[-1]\n if isinstance(obj.get(last_key), _holder):\n raise ValueError(\n \"conflicting types %s and terminal for key %r\"\n % (_node_type(obj[last_key]), flat_key)\n )\n obj[last_key] = val\n\n for obj, key in reversed(holders):\n obj[key] = obj[key].getvalue()\n\n return data","def allkeys(self, as_str=False):\n for key in self.__allkeys((\"__ROOT__\",), {\"__ROOT__\": self}):\n yield \".\".join(key) if as_str else key","def graph_walk_dict_flat(indict, pre=None):\n pre = pre[:] if pre else []\n if isinstance(indict, dict):\n for key, value in indict.items():\n if isinstance(value, dict):\n for d in dict_generator(value, [key] + pre):\n yield d\n elif isinstance(value, list) or isinstance(value, tuple):\n for v in value:\n for d in dict_generator(v, [key] + pre):\n yield d\n else:\n yield pre + [key, value]\n else:\n yield indict","def nested_set(dictionary: dict, keys: list, value):\n nested_dict = dictionary\n for key in keys[:-1]:\n nested_dict = nested_dict[key]\n nested_dict[keys[-1]] = value\n return dictionary","def flatten(d: Union[dict, list], parent_key: str = \"\", sep: str = \".\") -> dict:\n items = []\n if isinstance(d, dict):\n for k, v in d.items():\n new_key = f\"{parent_key}{sep}{k}\" if parent_key else str(k)\n items.extend(flatten(v, new_key, sep=sep).items())\n elif isinstance(d, list):\n for i, elem in enumerate(d):\n new_key = f\"{parent_key}{sep}{i}\" if parent_key else str(i)\n items.extend(flatten(elem, new_key, sep).items())\n else:\n items.append((parent_key, d))\n return dict(items)","def list_flatten(list_in: list):\n for k in list_in:\n if not isinstance(k, (list, tuple)):\n yield k\n else:\n yield from list_flatten(k)","def get_all_paths(dmt, directory_path=''):\n # Base case.\n if not dmt.children:\n return set()\n \n filesystem_items = set()\n for item in dmt.children.keys():\n filesystem_items.add(directory_path+item)\n # Also get the paths of subdirectory contents.\n if item[-1] == '/':\n subdir_name = item\n subdir_path = directory_path + subdir_name\n \n filesystem_items.add(subdir_path)\n filesystem_items.update(get_all_paths(dmt.children[subdir_name], subdir_path))\n \n return filesystem_items","def get_all_leaf_paths(coll):\n if isinstance(coll, dict) or isinstance(coll, Munch):\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\n get_all_leaf_paths(t[1])\n )),\n iteritems(coll)))\n )\n\n elif isinstance(coll, list):\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\n get_all_leaf_paths(t[1])\n )),\n enumerate(coll)))\n )\n else:\n return [[]]","def get_all_keys(\n cls, hierarchical_dict: dict, include_values: bool = False\n ) -> Union[List[str], dict]:\n all_keys = {}\n for key in hierarchical_dict:\n if isinstance(hierarchical_dict[key], dict):\n all_sub_keys = FuseUtilsHierarchicalDict.get_all_keys(\n hierarchical_dict[key], include_values=True\n )\n keys_to_add = {\n f\"{key}.{sub_key}\": all_sub_keys[sub_key]\n for sub_key in all_sub_keys\n }\n all_keys.update(keys_to_add)\n else:\n all_keys[key] = hierarchical_dict[key]\n if include_values:\n return all_keys\n else:\n return list(all_keys.keys())","def prepend_all_keys(d, char=\"@\", ignore_keys=frozenset()):\n if isinstance(d, dict):\n for key in list(d.keys()):\n if key in ignore_keys:\n continue\n if isinstance(d[key], list) or isinstance(d[key], dict):\n prepend_all_keys(d[key], char=char, ignore_keys=ignore_keys)\n else:\n new_key = char + key\n d[new_key] = d[key]\n del d[key]\n prepend_all_keys(d[new_key], char=char, ignore_keys=ignore_keys)\n elif isinstance(d, list):\n for item in d:\n prepend_all_keys(item, char=char, ignore_keys=ignore_keys)","def nestedDictKeys(d):\n for key in sorted(d.keys()):\n if isinstance(d[key], dict):\n yield from nestedDictKeys(d[key])\n else:\n yield key","def flatten(pathways):\n return tuple(it.chain(*[p[1] for p in sorted(pathways.items())]))","def flatten(d):\n\n c = {}\n\n def _flatten(parents, items):\n for k, v in items:\n cur = parents + [k]\n if isinstance(v, list):\n _flatten(cur, enumerate(v))\n elif isinstance(v, dict):\n _flatten(cur, v.items())\n else:\n if v is None:\n cur.append('$NULL')\n v = ''\n name = str(cur[0]) + ''.join(['['+str(x)+']' for x in cur[1:]])\n c[name] = v\n \n _flatten([], d.items())\n\n return c","def flatten_entrypoints(ep):\n def _flatten(d, prefix):\n d1 = {}\n for k, v in d.items():\n if isinstance(v, dict):\n yield from _flatten(v, prefix+'.'+k)\n else:\n d1[k] = v\n\n if d1:\n yield prefix, d1\n\n res = {}\n for k, v in ep.items():\n res.update(_flatten(v, k))\n return res","def _parse_directories(d):\n for k, v in d.items():\n if isinstance(v, abc.Mapping):\n _parse_directories(v)\n else:\n d[k] = os.path.expandvars(v)\n return d","def RecurseKeys(self):\n root_key = self.GetRootKey()\n if root_key:\n for registry_key in root_key.RecurseKeys():\n yield registry_key","def __iter__(self):\n\n result = []\n\n # d - dict, p - path (keys sequence)\n def recurs_iter(d, p=None):\n p = p or []\n\n # k - key, v - value\n for k, v in iteritems(d):\n next_p = p + [k]\n if isinstance(v, dict):\n recurs_iter(v, next_p)\n else:\n result.append(tuple(next_p))\n\n recurs_iter(self.__dict__)\n\n return iter(result)","def get_all_paths(coll, prefix_path=(), stop_at=None, stop_below=None):\n assert stop_at is None or stop_below is None, 'Only one of stop_at or stop_below can be used.'\n if stop_below is not None and stop_below in str(last(butlast(prefix_path))):\n return [[]]\n if stop_at is not None and stop_at in str(last(prefix_path)):\n return [[]]\n if isinstance(coll, dict) or isinstance(coll, Munch) or isinstance(coll, list):\n if isinstance(coll, dict) or isinstance(coll, Munch):\n items = iteritems(coll)\n else:\n items = enumerate(coll)\n\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\n get_all_paths(t[1],\n prefix_path=list(prefix_path) + [t[0]],\n stop_at=stop_at,\n stop_below=stop_below)\n )),\n items))\n )\n else:\n return [[]]","def flatten_dict(\n d, parent_key=\"\", sep=\".\", ignore_under_prefixed=True, mark_value=True\n):\n items = {}\n for k in d:\n if ignore_under_prefixed and k.startswith(\"__\"):\n continue\n v = d[k]\n if mark_value and k.startswith(\"_\") and not k.startswith(\"__\"):\n v = MarkValue(repr(v))\n\n new_key = sep.join((parent_key, k)) if parent_key else k\n if isinstance(v, collections.MutableMapping):\n items.update(\n flatten_dict(\n v, new_key, sep=sep, ignore_under_prefixed=True, mark_value=True\n )\n )\n else:\n items[new_key] = v\n\n return items","def flatten(nested_dict, separator=\"_\", root_keys_to_ignore=set()):\n assert isinstance(nested_dict, dict), \"flatten requires a dictionary input\"\n assert isinstance(separator, six.string_types), \"separator must be string\"\n\n # This global dictionary stores the flattened keys and values and is\n # ultimately returned\n flattened_dict = dict()\n\n def _flatten(object_, key):\n \"\"\"\n For dict, list and set objects_ calls itself on the elements and for\n other types assigns the object_ to\n the corresponding key in the global flattened_dict\n :param object_: object to flatten\n :param key: carries the concatenated key for the object_\n :return: None\n \"\"\"\n # Empty object can't be iterated, take as is\n if not object_:\n flattened_dict[key] = object_\n # These object types support iteration\n elif isinstance(object_, dict):\n for object_key in object_:\n if not (not key and object_key in root_keys_to_ignore):\n _flatten(object_[object_key], _construct_key(key,\n separator,\n object_key))\n elif isinstance(object_, (list, set, tuple)):\n for index, item in enumerate(object_):\n _flatten(item, _construct_key(key, separator, index))\n # Anything left take as is\n else:\n flattened_dict[key] = object_\n\n _flatten(nested_dict, None)\n return flattened_dict\n\n ###############################################################################################","def list_keys_to_expand(config, root=True, pre_keys=()):\n if isinstance(config, dict):\n keys = ()\n for k, v in sorted(config.items()):\n keys += list_keys_to_expand(v, root=False, pre_keys=pre_keys + (k,))\n return keys\n elif (not root and isinstance(config, list)) or callable(config):\n assert pre_keys\n return (pre_keys,)\n elif root and isinstance(config, list):\n return tuple(\n list_keys_to_expand(v, root=False, pre_keys=pre_keys) for v in config)\n else:\n return ()","def flatten_dict(base, v, d):\n if base != '':\n base = base + \".\"\n for k in d:\n if type(d[k]) in (type('a'), type(u'a'), type(1), type(1.1), type(False), type(None)):\n v[base + k] = d[k]\n elif type(d[k]) in (type([]), type((1,2))):\n v[base + k] = \", \".join(d[k])\n elif type(d[k]) == type({}):\n flatten_dict(base + k, v, d[k])\n else:\n print \"huh,\", type(d[k])","def _dict_keys(typingctx, d):\n resty = types.DictKeysIterableType(d)\n sig = resty(d)\n codegen = _iterator_codegen(resty)\n return sig, codegen","def dict_or_list(key, dictionary):\n if type(dictionary) != 'str':\n for k, v in dictionary.items():\n if k == key:\n yield v\n elif isinstance(v, dict):\n for result in dict_or_list(key, v):\n yield result\n elif isinstance(v, list):\n for d in v:\n if isinstance(d, dict):\n for result in dict_or_list(key, d):\n yield result","def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict:\n if not keys:\n return d\n\n if len(keys) == 1:\n d[keys[0]] = value\n return d\n\n subd = d\n for key in keys[:-1]:\n if key not in subd:\n subd = subd.setdefault(key, {})\n else:\n subd = subd[key]\n\n subd[keys[-1]] = value\n return d","def flatten(parsed):\r\n parsedList = [parsed] if isinstance(parsed, dict) else parsed\r\n result = []\r\n for dico in parsedList:\r\n #Sort keys between actual values and nested dicts\r\n listKeys = []\r\n standardKeys = []\r\n for key in dico:\r\n if isinstance(dico[key], list):\r\n listKeys.append(key)\r\n else:\r\n standardKeys.append(key)\r\n if not listKeys:\r\n #Terminal condition: no nested dict\r\n result.append(dico)\r\n else:\r\n partialResult = [{x:dico[x] for x in standardKeys}]\r\n for key in listKeys:\r\n #Create a dict with the keys from partialResult and\r\n #from the nested dicts\r\n recurs = Bricks.flatten(dico[key])\r\n partialResult = [{**x, **y} for x in partialResult for y in recurs]\r\n result.extend(partialResult)\r\n return result","def FlattenDictionary(value, keys=[]):\n result = {}\n if type(value) is dict:\n for key in value:\n result.update(FlattenDictionary(value[key], keys + [key]))\n return result\n else:\n key = '.'.join(keys)\n return {key: value}","def setKeyPath(*args, **kwargs)->List[AnyStr]:\n pass","def _object_requires_hdf5_recurse(curr: NDict, str_base: str = \"\") -> List[str]:\n keys = curr.keypaths()\n ans = []\n for k in keys:\n data = curr[k]\n if _object_requires_hdf5_single(data):\n ans.append(k)\n return ans","def find_keys(obj, key):\n arr = []\n\n def extract(obj, arr, key):\n \"\"\"Recursively search for values of key in JSON tree.\"\"\"\n if isinstance(obj, dict):\n for k, v in obj.items():\n if isinstance(v, (dict, list)):\n extract(v, arr, key)\n elif k == key:\n arr.append(v)\n elif isinstance(obj, list):\n for item in obj:\n extract(item, arr, key)\n return arr\n\n results = extract(obj, arr, key)\n return results","def _flatten_metadata(metadata):\r\n if metadata:\r\n # After changing recursive_keypairs` output we need to keep\r\n # flattening output unchanged.\r\n # Example: recursive_keypairs({'a': {'b':{'c':'d'}}}, '.')\r\n # output before: a.b:c=d\r\n # output now: a.b.c=d\r\n # So to keep the first variant just replace all dots except the first\r\n return dict((k.replace('.', ':').replace(':', '.', 1),\r\n six.text_type(v))\r\n for k, v in utils.recursive_keypairs(metadata,\r\n separator='.')\r\n if type(v) is not set)\r\n return {}","def _clean_paths(paths):\n\n\tclean_paths = {key: np.concatenate([path[key] for path in paths]) for key in paths[0].keys()}\n\n\treturn clean_paths","def flatten_values(d):\n if isinstance(d, dict):\n for v in d.values():\n if isinstance(v, dict):\n yield from flatten_values(v)\n else:\n yield v\n else:\n yield d","def flatten(dictionary, sep=\".\"):\n\n def _flatten(dictionary):\n if dictionary == {}:\n return dictionary\n\n key, value = dictionary.popitem()\n if not isinstance(value, dict) or not value:\n new_dictionary = {key: value}\n new_dictionary.update(flatten(dictionary, sep=sep))\n return new_dictionary\n\n flat_sub_dictionary = flatten(value, sep=sep)\n for flat_sub_key in list(flat_sub_dictionary.keys()):\n flat_key = key + sep + flat_sub_key\n flat_sub_dictionary[flat_key] = flat_sub_dictionary.pop(flat_sub_key)\n\n new_dictionary = flat_sub_dictionary\n new_dictionary.update(flatten(dictionary, sep=sep))\n return new_dictionary\n\n return _flatten(copy.deepcopy(dictionary))","def load_flattened_documents(data_dir: str, docids: Set[str]) -> Dict[str, List[str]]:\n unflattened_docs = load_documents(data_dir, docids)\n flattened_docs = dict()\n for doc, unflattened in unflattened_docs.items():\n flattened_docs[doc] = list(chain.from_iterable(unflattened))\n return flattened_docs","def flatten(data, delim='_'):\n result = {}\n\n def flatten_dict(keys, name=''):\n if isinstance(keys, collections.MutableMapping):\n for value in keys:\n flatten_dict(keys[value], \"{}{}{}\".format(name, value, delim))\n elif isinstance(keys, list):\n count = 0\n for value in keys:\n if isinstance(value, collections.MutableMapping):\n flatten_dict(value, \"{}{}{}\".format(name, count, delim))\n else:\n result[name[:-1]] = keys\n count += 1\n else:\n result[name[:-1]] = keys\n\n flatten_dict(data)\n return result","def filter_paths(paths : dict, spec : str) -> dict:\n all_paths = defaultdict(dict)\n for mag in paths.keys():\n specs = get_specs(mag, spec)\n paths_restructured = defaultdict(list)\n for path in paths[mag]:\n for s in specs:\n if s in path:\n paths_restructured[s].append(path)\n all_paths[mag] = paths_restructured\n return all_paths","def copy_dictionary_without_paths(dictionary: Dict, key_sequence: List[List[str]]):\n ret = {}\n possibles = [ks for ks in key_sequence if len(ks) == 1]\n possibles = set(reduce(lambda x, y: x + y, possibles, []))\n for k, v in dictionary.items():\n if k in possibles:\n continue\n if type(v) == dict:\n ret[k] = copy_dictionary_without_paths(v, [ks[1:] for ks in key_sequence if len(ks) > 1])\n else:\n ret[k] = v\n return ret","def path_it(d):\n for p in _path_walk([], d):\n yield p","def _get_keys(self, ckey):\n if self.has_key(ckey):\n doc = self[ckey]\n else:\n doc = [o for o in self.get_values(ckey)]\n if isinstance(doc, dict):\n for key in doc.keys():\n if ckey.rfind('%s.' % key) == -1:\n combo = '%s.%s' % (ckey, key)\n yield combo\n vals = [v for v in self.get_values(combo)]\n for kkk in helper_loop(combo, vals):\n yield kkk\n else:\n yield ckey\n elif isinstance(doc, list):\n for item in doc:\n if isinstance(item, dict):\n for key in item.keys():\n if ckey.rfind('%s.' % key) == -1:\n combo = '%s.%s' % (ckey, key)\n yield combo\n vals = [v for v in self.get_values(combo)]\n for kkk in helper_loop(combo, vals):\n yield kkk\n elif isinstance(item, list):\n for elem in item:\n if isinstance(elem, dict):\n for kkk in elem.keys():\n yield '%s.%s' % (ckey, kkk)\n else:\n yield ckey\n else: # basic type, so we reach the end\n yield ckey\n else: # basic type, so we reach the end\n yield ckey","def expand_flattened_path(flattened_path, value=None, separator='.'):\n split_list = flattened_path.split(separator)\n return add_keys({}, split_list, value)","def deep_flatten(iterable):\n pass","def flatten_dict(d, parent_key=\"\", sep=\"_\"):\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, MutableMapping):\n items.extend(flatten_dict(v, new_key, sep=sep).items())\n else:\n items.append((new_key, v))\n return dict(items)","def flatten(params, key=None):\n flat = {}\n for name, val in list(params.items()):\n if key is not None and not isinstance(key, int):\n name = \"%s[%s]\" % (key, name)\n if isinstance(val, dict):\n flat.update(flatten(val, name))\n elif isinstance(val, list):\n flat.update(flatten(dict(enumerate(val)), name))\n elif val is not None:\n flat[name] = val\n return flat","def clean_keys_of_slashes(record):\n for key in list(record):\n value = record[key]\n if \"/\" in key:\n # replace with _\n record[key.replace(\"/\", \"_\")] = record.pop(key)\n # Check if the value is a list containing nested dict and apply same\n if value:\n if isinstance(value, list) and isinstance(value[0], dict):\n for v in value:\n clean_keys_of_slashes(v)\n\n return record","def _expand_keys(entities):\n keys = list(entities.keys())\n values = list(product(*[entities[k] for k in keys]))\n return [{k: v for k, v in zip(keys, combs)} for combs in values]","def walk (root, ignore_access_errors=False, _want_types=False):\n root = registry (root, accept_value=False)\n yield (\n root,\n root.iterkeys (ignore_access_errors=ignore_access_errors),\n root.itervalues (ignore_access_errors=ignore_access_errors, _want_types=_want_types)\n )\n for subkey in root.iterkeys (ignore_access_errors=ignore_access_errors):\n for result in walk (subkey, ignore_access_errors=ignore_access_errors, _want_types=_want_types):\n yield result","def _flatten(flat_json: FlatJSON, keys: FlatKey, obj: JSONType) -> FlatJSON:\n jtype = typeof(obj)\n if jtype is JSONValue:\n flat_json[keys] = obj\n elif jtype is JSONArray:\n for index, value in enumerate(obj):\n _flatten(flat_json, (*keys, index), value)\n elif jtype is JSONObject:\n for key, value in obj.items():\n _flatten(flat_json, (*keys, key), value)\n else:\n raise PheresInternalError(f\"Unhandled json type {jtype} in flatten()\")\n return flat_json","def recursive_key_lookup(keys, obj):\n if not isinstance(keys, list):\n return obj.get(keys)\n\n for key in keys:\n if obj is not None:\n obj = obj.get(key)\n\n return obj","def add_by_list_of_keys(dictionary: Dict, key_path: List[Any], value: Any) -> Dict:\n key = key_path[0]\n dictionary[key] = (\n value\n if len(key_path) == 1\n else add_by_list_of_keys(\n dictionary[key] if key in dictionary else dict(),\n key_path[1:],\n value,\n )\n )\n return dictionary","def _try_set(set_list, index, nested_dict, dict_keys=[]):\n try:\n for dict_key in dict_keys:\n nested_dict = nested_dict.__getitem__(dict_key)\n set_list[index] = str(nested_dict)\n return nested_dict\n except:\n return ''","def find_key(key, var):\n if hasattr(var, 'items'):\n for k, v in var.items():\n if k == key:\n yield v\n if isinstance(v, dict):\n for result in find_key(key, v):\n yield result\n elif isinstance(v, list):\n for d in v:\n for result in find_key(key, d):\n yield result","def join_path(d, root):\n if isinstance(d, dict):\n if 'path' in d:\n d['path'] = os.path.join(root, d['path'])\n root = d['path']\n for item in d:\n d[item] = join_path(d[item], root)\n\n elif isinstance(d, list):\n d = [join_path(item, root) for item in d]\n\n return d","def _find_all_paths(sample, previous_path=None):\n paths = []\n for key in sample:\n current_path = []\n if previous_path:\n current_path.extend(previous_path)\n current_path.append(key)\n #If the current value ist a mapping, search in this mapping for more paths\n if isinstance(sample[key], abc.Mapping):\n paths.extend(MappingValidator._find_all_paths(sample[key],\n previous_path=current_path))\n paths.append(current_path)\n return sorted(paths, key=lambda k: len(k))","def iterkeys(d):\n return iter(getattr(d, _iterkeys)())","def flatten_state_dict(state_dict, keep_empty_nodes = False):\n return traverse_util.flatten_dict(\n state_dict,\n is_leaf=tensorstore_leaf,\n keep_empty_nodes=keep_empty_nodes,\n sep='/')","def iterkeys(d):\r\n return iter(getattr(d, _iterkeys)())","def search_deep_keys(search_text, cfn_dict, path):\n keys = []\n if isinstance(cfn_dict, dict):\n for key in cfn_dict:\n pathprop = path[:]\n pathprop.append(key)\n if key == search_text:\n pathprop.append(cfn_dict[key])\n keys.append(pathprop)\n # pop the last element off for nesting of found elements for\n # dict and list checks\n pathprop = pathprop[:-1]\n if isinstance(cfn_dict[key], dict):\n keys.extend(ContextParser.search_deep_keys(search_text, cfn_dict[key], pathprop))\n elif isinstance(cfn_dict[key], list):\n for index, item in enumerate(cfn_dict[key]):\n pathproparr = pathprop[:]\n pathproparr.append(index)\n keys.extend(ContextParser.search_deep_keys(search_text, item, pathproparr))\n elif isinstance(cfn_dict, list):\n for index, item in enumerate(cfn_dict):\n pathprop = path[:]\n pathprop.append(index)\n keys.extend(ContextParser.search_deep_keys(search_text, item, pathprop))\n\n return keys","def flatten(d: MutableMapping, sep: str = \".\", parent_key: str = \"\") -> dict:\n items = []\n for k, v in d.items():\n new_key = parent_key + sep + k if parent_key else k\n if isinstance(v, MutableMapping):\n items.extend(flatten(v, sep=sep, parent_key=new_key).items())\n else:\n items.append((new_key, v))\n return dict(items)","def keys_nested(self, subkeys_as='list'):\n keys = []\n for k, v in sorted(self.items()):\n if isinstance(v, AttrDict) or isinstance(v, dict):\n if subkeys_as == 'list':\n keys.extend([k + '.' + kk for kk in v.keys_nested()])\n elif subkeys_as == 'dict':\n keys.append({k: v.keys_nested(subkeys_as=subkeys_as)})\n else:\n keys.append(k)\n return keys","def flatten(c):\n for x in c:\n if isinstance(x, str) or not isinstance(x, Iterable):\n yield x\n else:\n yield from flatten(x)","def all_keys(blueprint: Union[dict, list]) -> list:\n\n keys = list()\n\n if isinstance(blueprint, list):\n for item in blueprint:\n keys.extend(all_keys(item))\n elif isinstance(blueprint, dict):\n for key, value in blueprint.items():\n keys.append(key)\n keys.extend(all_keys(value))\n\n return keys","def flatten_dict(d, target='key' or 'value'):\n def format_nested(nested, _key=None):\n if _key is None:\n _key = key\n return '{}_{}'.format(_key, nested)\n\n flattened = []\n target_is_key = target == 'key'\n for key, value in OrderedDict(sorted(d.items())).items():\n\n # Simple case: recursively flatten the dictionary.\n if isinstance(value, dict):\n flattened += map(\n format_nested if target_is_key else lambda x: x,\n flatten_dict(value, target=target)\n )\n\n # We are suddenly in muddy waters, because lists can have multiple types within them in JSON.\n elif isinstance(value, list):\n items_are_dict = [isinstance(item, dict) for item in value]\n items_are_list = [isinstance(item, list) for item in value]\n\n # To help reduce the complexity here, let's not support this case.\n # Besides, most sensible APIs won't bump into this case.\n if any(items_are_dict) and not all(items_are_dict):\n raise NotImplementedError(\"Ability to flatten dict with list of mixed dict and non-dict types \"\n \"is not currently supported\")\n\n # Same here, this is just weird.\n if any(items_are_list):\n raise NotImplementedError(\"Ability to flatten a dict with lists within lists \"\n \"is not currently supported. And we'd like to ask you to take it easy.\")\n\n # This case is common, but a little complex.\n elif all(items_are_dict):\n for index, item in enumerate(value):\n _flattened_dict = flatten_dict(item, target=target)\n\n # In this case we actually want to prepend the dict's index in the list to each flattened dict.\n if target_is_key:\n _flattened_dict = [format_nested(flattened_item, _key=index)\n for flattened_item in _flattened_dict]\n\n flattened += map(format_nested if target_is_key else lambda x: x, _flattened_dict)\n\n # All items are non-dict, so just directly add either the index or the value.\n else:\n flattened += map(format_nested, range(len(value))) if target_is_key else value\n\n # Kindergarten -- just add to the list.\n else:\n flattened.append(key if target_is_key else value)\n return flattened","def drop_keys(d):\n if isinstance(d, dict):\n return {\n k: drop_keys(v)\n for k, v in d.items()\n if k not in [\"propNames\", \"package\"]\n and v is not None\n and not (k == \"children\" and v == \"\")\n }\n elif isinstance(d, list):\n return [drop_keys(x) for x in d]\n return d","def flattenDict(inputDict: dict) -> list:\n return sum([[key] if type(value) is bool else [key, value] for key, value in inputDict.items() if value], list())","def expand(d):\n # make sure everything is a list\n for k, v in d.iteritems():\n if type(v) is not list:\n d[k] = [v]\n\n # take cross product\n product = [x for x in apply(itertools.product, d.values())]\n return flatten([dict(zip(d.keys(), p)) for p in product])","def flatten_dict(d):\n\n def expand(key, value):\n if isinstance(value, dict):\n return [(key + '.' + k, v) for k, v in flatten_dict(value).items()]\n else:\n return [(key, value)]\n\n items = [item for k, v in d.items() for item in expand(k, v)]\n return dict(items)","def find_key(self, key, dictionary):\n if isinstance(dictionary, list):\n for d in dictionary:\n for result in self.find_key(key, d):\n yield result\n else:\n for k, v in dictionary.items():\n if k == key:\n yield v\n elif isinstance(v, dict):\n for result in self.find_key(key, v):\n yield result\n elif isinstance(v, list):\n for d in v:\n for result in self.find_key(key, d):\n yield result","def get_special_paths(set_path: str, set_value, sp_to_label: Dict, label_to_ship: Dict):\n additions = set()\n if type(set_value) is not dict:\n # If this path is already labelled as special, then check that the ship matched with it does fit\n if set_path in sp_to_label:\n if label_to_ship[sp_to_label[set_path]].check_fit(set_value):\n return additions\n\n # If this is something a ship covers, add it as a special path\n for ship in label_to_ship.values():\n if ship.check_fit(set_value):\n additions.add( (set_path, ship.get_label()) )\n\n # If this is a dict, recursively build the set of additional paths\n else:\n for k, v in set_value.items():\n assert check_valid_key_name(k), \"Invalid Key: {}\".format(k)\n if set_path != Path.rootPath():\n child_path = \"{}.{}\".format(set_path, k)\n else:\n child_path = \".{}\".format(k)\n child_add = get_special_paths(child_path, v, sp_to_label, label_to_ship)\n additions = additions.union(child_add)\n\n return additions","def find_and_normalize(value):\n if isinstance(value, dict):\n # if value is a dictionary, try to normalize the values\n return {k: normalize_dict_value(v) for k, v in value.items()}\n return normalize_path(value)","def iterkeys(d, **kw):\r\n return iter(getattr(d, _iterkeys)(**kw))","def iterkeys(d, **kw):\r\n return iter(getattr(d, _iterkeys)(**kw))","def replace_paths(value):\n return {\n str: lambda: value.replace('__FILE__', sys.path[0]),\n list: lambda: [replace_paths(elt) for elt in value],\n dict: lambda: {key: replace_paths(val) for key, val in value.items()},\n OrderedDict: (lambda:\n OrderedDict((key, replace_paths(val)) for key, val in value.items()))\n }.get(type(value), lambda: value)()","def flatten_dict(dictionary,keys=None):\n flat_dict = {}\n for k,v in dictionary.items():\n if not keys or k in keys:\n if isinstance(v,dict):\n r = flatten_dict(v)\n flat_dict.update(r)\n else:\n flat_dict[k] = v\n return flat_dict","def nested_set(data, keys, value):\n for key in keys[:-1]:\n data = data.setdefault(key, {})\n data[keys[-1]] = value"],"string":"[\n \"def flatten_keys(in_keys):\\n return_keys = []\\n if isinstance(in_keys, str):\\n return [in_keys]\\n if isinstance(in_keys, Iterable):\\n for key in in_keys:\\n if isinstance(key, Iterable):\\n return_keys += flatten_keys(key)\\n else:\\n return_keys.append(key)\\n else:\\n return_keys.append(in_keys)\\n return return_keys\",\n \"def flatten(d, path):\\n\\n if isinstance(d, dict):\\n for k, v in d.items():\\n yield from flatten(v, path + [k])\\n else:\\n yield (\\\".\\\".join(path), d)\",\n \"def flatten_dict_string_keys(x):\\n return {'/'.join(k): v for k, v in flatten_dict(unfreeze(x)).items()}\",\n \"def _flatten(object_, key):\\n # Empty object can't be iterated, take as is\\n if not object_:\\n flattened_dict[key] = object_\\n # These object types support iteration\\n elif isinstance(object_, dict):\\n for object_key in object_:\\n if not (not key and object_key in root_keys_to_ignore):\\n _flatten(object_[object_key], _construct_key(key,\\n separator,\\n object_key))\\n elif isinstance(object_, (list, set, tuple)):\\n for index, item in enumerate(object_):\\n _flatten(item, _construct_key(key, separator, index))\\n # Anything left take as is\\n else:\\n flattened_dict[key] = object_\",\n \"def dict_path(my_dict, path=None):\\n if path is None:\\n path = \\\"\\\"\\n for k, v in my_dict.items():\\n newpath = path + (\\\".\\\" if path != \\\"\\\" else \\\"\\\") + k\\n if isinstance(v, dict):\\n for u in dict_path(v, newpath):\\n yield u\\n else:\\n yield newpath, v\",\n \"def graph_walk_collection_flat(indict, pre=None):\\n pre = pre[:] if pre else []\\n \\n # if isinstance(indict, dict):\\n if type(indict) in [dict, OrderedDict]:\\n for key, value in indict.items():\\n # if isinstance(value, dict):\\n if type(value) in [dict, OrderedDict]:\\n for d in graph_walk_collection_flat(value, [key] + pre):\\n yield d\\n # elif isinstance(value, list) or isinstance(value, tuple):\\n elif type(value) in [list, tuple]:\\n for v in value:\\n for d in graph_walk_collection_flat(v, [key] + pre):\\n yield d\\n else:\\n yield pre + [key, value]\\n else:\\n yield indict\",\n \"def flat (root, ignore_access_errors=False):\\n for key, subkeys, values in walk (root, ignore_access_errors):\\n yield key\\n for value in values:\\n yield value\",\n \"def _recursiveURISearch(self, multidict):\\r\\n valueList = []\\r\\n keys = []\\r\\n\\r\\n for k, v in multidict.iteritems():\\r\\n if isinstance(v, dict):\\r\\n valueList += self._recursiveURISearch(v)\\r\\n elif k[-1] == '*':\\r\\n keys.append(k)\\r\\n\\r\\n for k in keys:\\r\\n ele = multidict.pop(k)\\r\\n\\r\\n if isinstance(ele, list):\\r\\n lst = [None] * len(ele)\\r\\n multidict[k[:-1]] = lst\\r\\n\\r\\n for i, uri in enumerate(ele):\\r\\n valueList.append((uri, lst, i))\\r\\n else:\\r\\n valueList.append((ele, multidict, k[:-1]))\\r\\n\\r\\n return valueList\",\n \"def flatten(d, parent_key='', sep='_'):\\n items = []\\n for k, v in d.items():\\n new_key = parent_key + sep + k if parent_key else k\\n if isinstance(v, collections.MutableMapping):\\n items.extend(flatten(v, new_key, sep=sep).items())\\n else:\\n items.append((new_key, v))\\n items = dict(items)\\n # remove info like PCA primitive ID\\n items_not_strings = {k: v for k, v in items.items() if type(v) != str}\\n return dict(items_not_strings)\",\n \"def flatten_object(obj):\\n\\tpaths = []\\n\\n\\tif isinstance(obj, (dict,)):\\n\\t\\tfor f in obj:\\n\\t\\t\\tsub_paths = flatten_object(obj[f])\\n\\t\\t\\tfor p in sub_paths:\\n\\t\\t\\t\\tpaths.append((\\\"/{}{}\\\".format(f, p[0]), p[1]))\\n\\telif isinstance(obj, (list,)):\\n\\t\\tfor i, x in enumerate(obj):\\n\\t\\t\\tsub_paths = flatten_object(x)\\n\\t\\t\\tfor p in sub_paths:\\n\\t\\t\\t\\tpaths.append((\\\"/{}{}\\\".format(i, p[0]), p[1]))\\n\\telse:\\n\\t\\tpaths = [(\\\"\\\", obj)]\\n\\n\\treturn paths\",\n \"def get_by_list_of_keys(dictionary: Dict, key_path: List[Any]) -> Dict:\\n if len(key_path) == 1:\\n return dictionary[key_path[0]]\\n else:\\n return get_by_list_of_keys(dictionary[key_path[0]], key_path[1:])\",\n \"def dict_get_nodekeys_recursive(d):\\n nodekeys = set(d.keys())\\n for nk in nodekeys:\\n # print \\\"nodekey\\\", nk\\n # print \\\"graphkeys\\\", d[nk]['params'].keys()\\n if 'graph' in d[nk]['params']:\\n # print \\\"graphkeys\\\", d[nk]['params']['graph'].keys()\\n nodekeys = nodekeys.union(dict_get_nodekeys_recursive(d[nk]['params']['graph']))\\n return nodekeys\",\n \"def flatten(cmd, path=\\\"\\\", fc={}, sep=\\\".\\\"):\\n fcmd = fc.copy()\\n if isinstance(cmd, dict):\\n for k, v in cmd.items():\\n k = k.split(\\\":\\\")[1] if \\\":\\\" in k else k\\n fcmd = flatten(v, sep.join((path, k)) if path else k, fcmd)\\n elif isinstance(cmd, list):\\n for n, v in enumerate(cmd):\\n fcmd.update(flatten(v, sep.join([path, str(n)])))\\n else:\\n fcmd[path] = cmd\\n return fcmd\",\n \"def _Flatten(obj):\\n\\n def Flatten(obj, name, res):\\n \\\"\\\"\\\"Recursively appends keys in path from obj into res.\\n\\n Args:\\n obj: The object to flatten.\\n name: The key name of the current obj.\\n res: The ordered result value list.\\n \\\"\\\"\\\"\\n if isinstance(obj, list):\\n if obj:\\n for i, item in enumerate(obj):\\n Flatten(item, '{name}[{index}]'.format(name=name, index=i), res)\\n else:\\n res.append((name, []))\\n elif isinstance(obj, dict):\\n if obj:\\n for k, v in sorted(obj.iteritems()):\\n Flatten(v, '{name}{dot}{key}'.format(\\n name=name, dot='.' if name else '', key=k), res)\\n else:\\n res.append((name, {}))\\n elif isinstance(obj, float):\\n res.append((name, resource_transform.TransformFloat(obj)))\\n else:\\n res.append((name, obj))\\n\\n res = []\\n Flatten(obj, '', res)\\n return res\",\n \"def RecurseKeys(self):\\n yield self\\n for subkey in self.GetSubkeys():\\n for key in subkey.RecurseKeys():\\n yield key\",\n \"def _resolve_paths(d, path):\\n try:\\n if len(path) == 0:\\n yield (), d\\n elif len(path) == 1:\\n yield (path[0],), d[path[0]]\\n else:\\n if path[0] == '*':\\n keys = d.keys()\\n else:\\n keys = [path[0]]\\n for key in keys:\\n for p, v in CombinatorialTree._resolve_paths(d[key], path[1:]):\\n if v is not None:\\n yield (key,) + p, v\\n except KeyError:\\n yield None, None\",\n \"def flatten(d, parent_key='', sep='_'):\\n items = []\\n for k, v in d.items():\\n new_key = parent_key + sep + k if parent_key else k\\n if isinstance(v, collections.MutableMapping):\\n items.extend(flatten(v, new_key, sep=sep).items())\\n else:\\n items.append((new_key, v))\\n return dict(items)\",\n \"def expand_flattened_dict(flattened, separator='.'):\\n merged = {}\\n for key, value in flattened.items():\\n expanded = expand_flattened_path(key, value=value, separator=separator)\\n merged = merge_dicts(merged, expanded)\\n return merged\",\n \"def flatten(items):\\n for k, v in items:\\n if isinstance(k, (list, tuple)):\\n for key in k:\\n yield key, v\\n else:\\n yield k, v\",\n \"def flatten_paths(*items):\\r\\n\\r\\n flat = []\\r\\n\\r\\n def flatmap(item):\\r\\n if isinstance(item, Compatibility.string):\\r\\n flat.append(item)\\r\\n else:\\r\\n try:\\r\\n for i in iter(item):\\r\\n flatmap(i)\\r\\n except TypeError:\\r\\n if callable(item):\\r\\n flatmap(item())\\r\\n else:\\r\\n flat.append(item)\\r\\n\\r\\n for item in items:\\r\\n flatmap(item)\\r\\n\\r\\n return flat\",\n \"def get_final_key_paths(\\n obj: Union[dict, list, tuple], cur_path: str = '',\\n append_values: bool = False,\\n paths: list = None, black_list: list = None,\\n final_keys_only: bool = False):\\n if paths is None:\\n paths = []\\n\\n if isinstance(obj, (dict, list, tuple)):\\n if isinstance(obj, dict):\\n for key in obj:\\n new_path = cur_path + f'[\\\\'{key}\\\\']'\\n if isinstance(obj[key], dict):\\n if black_list is not None and key in black_list:\\n continue\\n get_final_key_paths(\\n obj[key], new_path, append_values, paths, black_list,\\n final_keys_only)\\n elif isinstance(obj[key], (list, tuple)):\\n get_final_key_paths(\\n obj[key], new_path, append_values, paths, black_list,\\n final_keys_only)\\n else:\\n if final_keys_only:\\n last_bracket = new_path.rfind('[\\\\'')\\n new_path = new_path[\\n last_bracket+2:new_path.rfind('\\\\'')]\\n if append_values:\\n to_append = [new_path, obj[key]]\\n else:\\n to_append = new_path\\n paths.append(to_append)\\n else:\\n key_added = False\\n for i in range(len(obj)):\\n if isinstance(obj[i], (dict, tuple, list)):\\n get_final_key_paths(\\n obj[i], cur_path + f'[{i}]', append_values,\\n paths, black_list, final_keys_only)\\n else:\\n if not key_added:\\n if final_keys_only:\\n last_bracket = cur_path.rfind('[\\\\'')\\n cur_path = cur_path[\\n last_bracket+2:cur_path.rfind('\\\\'')]\\n if append_values:\\n to_append = [cur_path, obj]\\n else:\\n to_append = cur_path\\n paths.append(to_append)\\n key_added = True\\n\\n return paths\",\n \"def dict_flatten(*args):\\n hold = []\\n for a in args:\\n hold.append([i for s in a.values() for i in s])\\n return hold\",\n \"def all_key_seqs(template):\\n result = []\\n for k, v in template.iteritems():\\n if isinstance(v, dict):\\n for suffix in all_key_seqs(v):\\n result.append([k] + suffix)\\n else:\\n result.append([k])\\n return result\",\n \"def unflatten(\\n d: Dict[str, Any],\\n base: Dict[str, Any] = None,\\n) -> Dict[str, Any]:\\n if base is None:\\n base = {}\\n\\n for key, value in d.items():\\n root = base\\n\\n ###\\n # If a dotted path is encountered, create nested dicts for all but\\n # the last level, then change root to that last level, and key to\\n # the final key in the path. This allows one final setitem at the bottom\\n # of the loop.\\n if '.' in key:\\n *parts, key = key.split('.')\\n\\n for part in parts:\\n root.setdefault(part, {})\\n root = root[part]\\n\\n if isinstance(value, dict):\\n value = unflatten(value, root.get(key, {}))\\n\\n root[key] = value\\n\\n return base\",\n \"def serialize_dict_keys(d, prefix=\\\"\\\"):\\n keys = []\\n for k, v in d.items():\\n fqk = \\\"{}{}\\\".format(prefix, k)\\n keys.append(fqk)\\n if isinstance(v, dict):\\n keys.extend(serialize_dict_keys(v, prefix=\\\"{}.\\\".format(fqk)))\\n\\n return keys\",\n \"def unflatten(arg):\\n if hasattr(arg, \\\"iteritems\\\"):\\n items = arg.iteritems()\\n elif hasattr(arg, \\\"items\\\"):\\n items = arg.items()\\n else:\\n items = arg\\n\\n data = {}\\n holders = []\\n for flat_key, val in items:\\n parsed_key = _parse_key(flat_key)\\n obj = data\\n for depth, (key, next_key) in enumerate(zip(parsed_key, parsed_key[1:]), 1):\\n if isinstance(next_key, string_type):\\n holder_type = _dict_holder\\n else:\\n holder_type = _list_holder\\n\\n if key not in obj:\\n obj[key] = holder_type(_unparse_key(parsed_key[:depth]))\\n holders.append((obj, key))\\n elif not isinstance(obj[key], holder_type):\\n raise ValueError(\\n \\\"conflicting types %s and %s for key %r\\\"\\n % (\\n _node_type(obj[key]),\\n holder_type.node_type,\\n _unparse_key(parsed_key[:depth]),\\n )\\n )\\n obj = obj[key]\\n\\n last_key = parsed_key[-1]\\n if isinstance(obj.get(last_key), _holder):\\n raise ValueError(\\n \\\"conflicting types %s and terminal for key %r\\\"\\n % (_node_type(obj[last_key]), flat_key)\\n )\\n obj[last_key] = val\\n\\n for obj, key in reversed(holders):\\n obj[key] = obj[key].getvalue()\\n\\n return data\",\n \"def allkeys(self, as_str=False):\\n for key in self.__allkeys((\\\"__ROOT__\\\",), {\\\"__ROOT__\\\": self}):\\n yield \\\".\\\".join(key) if as_str else key\",\n \"def graph_walk_dict_flat(indict, pre=None):\\n pre = pre[:] if pre else []\\n if isinstance(indict, dict):\\n for key, value in indict.items():\\n if isinstance(value, dict):\\n for d in dict_generator(value, [key] + pre):\\n yield d\\n elif isinstance(value, list) or isinstance(value, tuple):\\n for v in value:\\n for d in dict_generator(v, [key] + pre):\\n yield d\\n else:\\n yield pre + [key, value]\\n else:\\n yield indict\",\n \"def nested_set(dictionary: dict, keys: list, value):\\n nested_dict = dictionary\\n for key in keys[:-1]:\\n nested_dict = nested_dict[key]\\n nested_dict[keys[-1]] = value\\n return dictionary\",\n \"def flatten(d: Union[dict, list], parent_key: str = \\\"\\\", sep: str = \\\".\\\") -> dict:\\n items = []\\n if isinstance(d, dict):\\n for k, v in d.items():\\n new_key = f\\\"{parent_key}{sep}{k}\\\" if parent_key else str(k)\\n items.extend(flatten(v, new_key, sep=sep).items())\\n elif isinstance(d, list):\\n for i, elem in enumerate(d):\\n new_key = f\\\"{parent_key}{sep}{i}\\\" if parent_key else str(i)\\n items.extend(flatten(elem, new_key, sep).items())\\n else:\\n items.append((parent_key, d))\\n return dict(items)\",\n \"def list_flatten(list_in: list):\\n for k in list_in:\\n if not isinstance(k, (list, tuple)):\\n yield k\\n else:\\n yield from list_flatten(k)\",\n \"def get_all_paths(dmt, directory_path=''):\\n # Base case.\\n if not dmt.children:\\n return set()\\n \\n filesystem_items = set()\\n for item in dmt.children.keys():\\n filesystem_items.add(directory_path+item)\\n # Also get the paths of subdirectory contents.\\n if item[-1] == '/':\\n subdir_name = item\\n subdir_path = directory_path + subdir_name\\n \\n filesystem_items.add(subdir_path)\\n filesystem_items.update(get_all_paths(dmt.children[subdir_name], subdir_path))\\n \\n return filesystem_items\",\n \"def get_all_leaf_paths(coll):\\n if isinstance(coll, dict) or isinstance(coll, Munch):\\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\\n get_all_leaf_paths(t[1])\\n )),\\n iteritems(coll)))\\n )\\n\\n elif isinstance(coll, list):\\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\\n get_all_leaf_paths(t[1])\\n )),\\n enumerate(coll)))\\n )\\n else:\\n return [[]]\",\n \"def get_all_keys(\\n cls, hierarchical_dict: dict, include_values: bool = False\\n ) -> Union[List[str], dict]:\\n all_keys = {}\\n for key in hierarchical_dict:\\n if isinstance(hierarchical_dict[key], dict):\\n all_sub_keys = FuseUtilsHierarchicalDict.get_all_keys(\\n hierarchical_dict[key], include_values=True\\n )\\n keys_to_add = {\\n f\\\"{key}.{sub_key}\\\": all_sub_keys[sub_key]\\n for sub_key in all_sub_keys\\n }\\n all_keys.update(keys_to_add)\\n else:\\n all_keys[key] = hierarchical_dict[key]\\n if include_values:\\n return all_keys\\n else:\\n return list(all_keys.keys())\",\n \"def prepend_all_keys(d, char=\\\"@\\\", ignore_keys=frozenset()):\\n if isinstance(d, dict):\\n for key in list(d.keys()):\\n if key in ignore_keys:\\n continue\\n if isinstance(d[key], list) or isinstance(d[key], dict):\\n prepend_all_keys(d[key], char=char, ignore_keys=ignore_keys)\\n else:\\n new_key = char + key\\n d[new_key] = d[key]\\n del d[key]\\n prepend_all_keys(d[new_key], char=char, ignore_keys=ignore_keys)\\n elif isinstance(d, list):\\n for item in d:\\n prepend_all_keys(item, char=char, ignore_keys=ignore_keys)\",\n \"def nestedDictKeys(d):\\n for key in sorted(d.keys()):\\n if isinstance(d[key], dict):\\n yield from nestedDictKeys(d[key])\\n else:\\n yield key\",\n \"def flatten(pathways):\\n return tuple(it.chain(*[p[1] for p in sorted(pathways.items())]))\",\n \"def flatten(d):\\n\\n c = {}\\n\\n def _flatten(parents, items):\\n for k, v in items:\\n cur = parents + [k]\\n if isinstance(v, list):\\n _flatten(cur, enumerate(v))\\n elif isinstance(v, dict):\\n _flatten(cur, v.items())\\n else:\\n if v is None:\\n cur.append('$NULL')\\n v = ''\\n name = str(cur[0]) + ''.join(['['+str(x)+']' for x in cur[1:]])\\n c[name] = v\\n \\n _flatten([], d.items())\\n\\n return c\",\n \"def flatten_entrypoints(ep):\\n def _flatten(d, prefix):\\n d1 = {}\\n for k, v in d.items():\\n if isinstance(v, dict):\\n yield from _flatten(v, prefix+'.'+k)\\n else:\\n d1[k] = v\\n\\n if d1:\\n yield prefix, d1\\n\\n res = {}\\n for k, v in ep.items():\\n res.update(_flatten(v, k))\\n return res\",\n \"def _parse_directories(d):\\n for k, v in d.items():\\n if isinstance(v, abc.Mapping):\\n _parse_directories(v)\\n else:\\n d[k] = os.path.expandvars(v)\\n return d\",\n \"def RecurseKeys(self):\\n root_key = self.GetRootKey()\\n if root_key:\\n for registry_key in root_key.RecurseKeys():\\n yield registry_key\",\n \"def __iter__(self):\\n\\n result = []\\n\\n # d - dict, p - path (keys sequence)\\n def recurs_iter(d, p=None):\\n p = p or []\\n\\n # k - key, v - value\\n for k, v in iteritems(d):\\n next_p = p + [k]\\n if isinstance(v, dict):\\n recurs_iter(v, next_p)\\n else:\\n result.append(tuple(next_p))\\n\\n recurs_iter(self.__dict__)\\n\\n return iter(result)\",\n \"def get_all_paths(coll, prefix_path=(), stop_at=None, stop_below=None):\\n assert stop_at is None or stop_below is None, 'Only one of stop_at or stop_below can be used.'\\n if stop_below is not None and stop_below in str(last(butlast(prefix_path))):\\n return [[]]\\n if stop_at is not None and stop_at in str(last(prefix_path)):\\n return [[]]\\n if isinstance(coll, dict) or isinstance(coll, Munch) or isinstance(coll, list):\\n if isinstance(coll, dict) or isinstance(coll, Munch):\\n items = iteritems(coll)\\n else:\\n items = enumerate(coll)\\n\\n return list(cat(map(lambda t: list(map(lambda p: [t[0]] + p,\\n get_all_paths(t[1],\\n prefix_path=list(prefix_path) + [t[0]],\\n stop_at=stop_at,\\n stop_below=stop_below)\\n )),\\n items))\\n )\\n else:\\n return [[]]\",\n \"def flatten_dict(\\n d, parent_key=\\\"\\\", sep=\\\".\\\", ignore_under_prefixed=True, mark_value=True\\n):\\n items = {}\\n for k in d:\\n if ignore_under_prefixed and k.startswith(\\\"__\\\"):\\n continue\\n v = d[k]\\n if mark_value and k.startswith(\\\"_\\\") and not k.startswith(\\\"__\\\"):\\n v = MarkValue(repr(v))\\n\\n new_key = sep.join((parent_key, k)) if parent_key else k\\n if isinstance(v, collections.MutableMapping):\\n items.update(\\n flatten_dict(\\n v, new_key, sep=sep, ignore_under_prefixed=True, mark_value=True\\n )\\n )\\n else:\\n items[new_key] = v\\n\\n return items\",\n \"def flatten(nested_dict, separator=\\\"_\\\", root_keys_to_ignore=set()):\\n assert isinstance(nested_dict, dict), \\\"flatten requires a dictionary input\\\"\\n assert isinstance(separator, six.string_types), \\\"separator must be string\\\"\\n\\n # This global dictionary stores the flattened keys and values and is\\n # ultimately returned\\n flattened_dict = dict()\\n\\n def _flatten(object_, key):\\n \\\"\\\"\\\"\\n For dict, list and set objects_ calls itself on the elements and for\\n other types assigns the object_ to\\n the corresponding key in the global flattened_dict\\n :param object_: object to flatten\\n :param key: carries the concatenated key for the object_\\n :return: None\\n \\\"\\\"\\\"\\n # Empty object can't be iterated, take as is\\n if not object_:\\n flattened_dict[key] = object_\\n # These object types support iteration\\n elif isinstance(object_, dict):\\n for object_key in object_:\\n if not (not key and object_key in root_keys_to_ignore):\\n _flatten(object_[object_key], _construct_key(key,\\n separator,\\n object_key))\\n elif isinstance(object_, (list, set, tuple)):\\n for index, item in enumerate(object_):\\n _flatten(item, _construct_key(key, separator, index))\\n # Anything left take as is\\n else:\\n flattened_dict[key] = object_\\n\\n _flatten(nested_dict, None)\\n return flattened_dict\\n\\n ###############################################################################################\",\n \"def list_keys_to_expand(config, root=True, pre_keys=()):\\n if isinstance(config, dict):\\n keys = ()\\n for k, v in sorted(config.items()):\\n keys += list_keys_to_expand(v, root=False, pre_keys=pre_keys + (k,))\\n return keys\\n elif (not root and isinstance(config, list)) or callable(config):\\n assert pre_keys\\n return (pre_keys,)\\n elif root and isinstance(config, list):\\n return tuple(\\n list_keys_to_expand(v, root=False, pre_keys=pre_keys) for v in config)\\n else:\\n return ()\",\n \"def flatten_dict(base, v, d):\\n if base != '':\\n base = base + \\\".\\\"\\n for k in d:\\n if type(d[k]) in (type('a'), type(u'a'), type(1), type(1.1), type(False), type(None)):\\n v[base + k] = d[k]\\n elif type(d[k]) in (type([]), type((1,2))):\\n v[base + k] = \\\", \\\".join(d[k])\\n elif type(d[k]) == type({}):\\n flatten_dict(base + k, v, d[k])\\n else:\\n print \\\"huh,\\\", type(d[k])\",\n \"def _dict_keys(typingctx, d):\\n resty = types.DictKeysIterableType(d)\\n sig = resty(d)\\n codegen = _iterator_codegen(resty)\\n return sig, codegen\",\n \"def dict_or_list(key, dictionary):\\n if type(dictionary) != 'str':\\n for k, v in dictionary.items():\\n if k == key:\\n yield v\\n elif isinstance(v, dict):\\n for result in dict_or_list(key, v):\\n yield result\\n elif isinstance(v, list):\\n for d in v:\\n if isinstance(d, dict):\\n for result in dict_or_list(key, d):\\n yield result\",\n \"def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict:\\n if not keys:\\n return d\\n\\n if len(keys) == 1:\\n d[keys[0]] = value\\n return d\\n\\n subd = d\\n for key in keys[:-1]:\\n if key not in subd:\\n subd = subd.setdefault(key, {})\\n else:\\n subd = subd[key]\\n\\n subd[keys[-1]] = value\\n return d\",\n \"def flatten(parsed):\\r\\n parsedList = [parsed] if isinstance(parsed, dict) else parsed\\r\\n result = []\\r\\n for dico in parsedList:\\r\\n #Sort keys between actual values and nested dicts\\r\\n listKeys = []\\r\\n standardKeys = []\\r\\n for key in dico:\\r\\n if isinstance(dico[key], list):\\r\\n listKeys.append(key)\\r\\n else:\\r\\n standardKeys.append(key)\\r\\n if not listKeys:\\r\\n #Terminal condition: no nested dict\\r\\n result.append(dico)\\r\\n else:\\r\\n partialResult = [{x:dico[x] for x in standardKeys}]\\r\\n for key in listKeys:\\r\\n #Create a dict with the keys from partialResult and\\r\\n #from the nested dicts\\r\\n recurs = Bricks.flatten(dico[key])\\r\\n partialResult = [{**x, **y} for x in partialResult for y in recurs]\\r\\n result.extend(partialResult)\\r\\n return result\",\n \"def FlattenDictionary(value, keys=[]):\\n result = {}\\n if type(value) is dict:\\n for key in value:\\n result.update(FlattenDictionary(value[key], keys + [key]))\\n return result\\n else:\\n key = '.'.join(keys)\\n return {key: value}\",\n \"def setKeyPath(*args, **kwargs)->List[AnyStr]:\\n pass\",\n \"def _object_requires_hdf5_recurse(curr: NDict, str_base: str = \\\"\\\") -> List[str]:\\n keys = curr.keypaths()\\n ans = []\\n for k in keys:\\n data = curr[k]\\n if _object_requires_hdf5_single(data):\\n ans.append(k)\\n return ans\",\n \"def find_keys(obj, key):\\n arr = []\\n\\n def extract(obj, arr, key):\\n \\\"\\\"\\\"Recursively search for values of key in JSON tree.\\\"\\\"\\\"\\n if isinstance(obj, dict):\\n for k, v in obj.items():\\n if isinstance(v, (dict, list)):\\n extract(v, arr, key)\\n elif k == key:\\n arr.append(v)\\n elif isinstance(obj, list):\\n for item in obj:\\n extract(item, arr, key)\\n return arr\\n\\n results = extract(obj, arr, key)\\n return results\",\n \"def _flatten_metadata(metadata):\\r\\n if metadata:\\r\\n # After changing recursive_keypairs` output we need to keep\\r\\n # flattening output unchanged.\\r\\n # Example: recursive_keypairs({'a': {'b':{'c':'d'}}}, '.')\\r\\n # output before: a.b:c=d\\r\\n # output now: a.b.c=d\\r\\n # So to keep the first variant just replace all dots except the first\\r\\n return dict((k.replace('.', ':').replace(':', '.', 1),\\r\\n six.text_type(v))\\r\\n for k, v in utils.recursive_keypairs(metadata,\\r\\n separator='.')\\r\\n if type(v) is not set)\\r\\n return {}\",\n \"def _clean_paths(paths):\\n\\n\\tclean_paths = {key: np.concatenate([path[key] for path in paths]) for key in paths[0].keys()}\\n\\n\\treturn clean_paths\",\n \"def flatten_values(d):\\n if isinstance(d, dict):\\n for v in d.values():\\n if isinstance(v, dict):\\n yield from flatten_values(v)\\n else:\\n yield v\\n else:\\n yield d\",\n \"def flatten(dictionary, sep=\\\".\\\"):\\n\\n def _flatten(dictionary):\\n if dictionary == {}:\\n return dictionary\\n\\n key, value = dictionary.popitem()\\n if not isinstance(value, dict) or not value:\\n new_dictionary = {key: value}\\n new_dictionary.update(flatten(dictionary, sep=sep))\\n return new_dictionary\\n\\n flat_sub_dictionary = flatten(value, sep=sep)\\n for flat_sub_key in list(flat_sub_dictionary.keys()):\\n flat_key = key + sep + flat_sub_key\\n flat_sub_dictionary[flat_key] = flat_sub_dictionary.pop(flat_sub_key)\\n\\n new_dictionary = flat_sub_dictionary\\n new_dictionary.update(flatten(dictionary, sep=sep))\\n return new_dictionary\\n\\n return _flatten(copy.deepcopy(dictionary))\",\n \"def load_flattened_documents(data_dir: str, docids: Set[str]) -> Dict[str, List[str]]:\\n unflattened_docs = load_documents(data_dir, docids)\\n flattened_docs = dict()\\n for doc, unflattened in unflattened_docs.items():\\n flattened_docs[doc] = list(chain.from_iterable(unflattened))\\n return flattened_docs\",\n \"def flatten(data, delim='_'):\\n result = {}\\n\\n def flatten_dict(keys, name=''):\\n if isinstance(keys, collections.MutableMapping):\\n for value in keys:\\n flatten_dict(keys[value], \\\"{}{}{}\\\".format(name, value, delim))\\n elif isinstance(keys, list):\\n count = 0\\n for value in keys:\\n if isinstance(value, collections.MutableMapping):\\n flatten_dict(value, \\\"{}{}{}\\\".format(name, count, delim))\\n else:\\n result[name[:-1]] = keys\\n count += 1\\n else:\\n result[name[:-1]] = keys\\n\\n flatten_dict(data)\\n return result\",\n \"def filter_paths(paths : dict, spec : str) -> dict:\\n all_paths = defaultdict(dict)\\n for mag in paths.keys():\\n specs = get_specs(mag, spec)\\n paths_restructured = defaultdict(list)\\n for path in paths[mag]:\\n for s in specs:\\n if s in path:\\n paths_restructured[s].append(path)\\n all_paths[mag] = paths_restructured\\n return all_paths\",\n \"def copy_dictionary_without_paths(dictionary: Dict, key_sequence: List[List[str]]):\\n ret = {}\\n possibles = [ks for ks in key_sequence if len(ks) == 1]\\n possibles = set(reduce(lambda x, y: x + y, possibles, []))\\n for k, v in dictionary.items():\\n if k in possibles:\\n continue\\n if type(v) == dict:\\n ret[k] = copy_dictionary_without_paths(v, [ks[1:] for ks in key_sequence if len(ks) > 1])\\n else:\\n ret[k] = v\\n return ret\",\n \"def path_it(d):\\n for p in _path_walk([], d):\\n yield p\",\n \"def _get_keys(self, ckey):\\n if self.has_key(ckey):\\n doc = self[ckey]\\n else:\\n doc = [o for o in self.get_values(ckey)]\\n if isinstance(doc, dict):\\n for key in doc.keys():\\n if ckey.rfind('%s.' % key) == -1:\\n combo = '%s.%s' % (ckey, key)\\n yield combo\\n vals = [v for v in self.get_values(combo)]\\n for kkk in helper_loop(combo, vals):\\n yield kkk\\n else:\\n yield ckey\\n elif isinstance(doc, list):\\n for item in doc:\\n if isinstance(item, dict):\\n for key in item.keys():\\n if ckey.rfind('%s.' % key) == -1:\\n combo = '%s.%s' % (ckey, key)\\n yield combo\\n vals = [v for v in self.get_values(combo)]\\n for kkk in helper_loop(combo, vals):\\n yield kkk\\n elif isinstance(item, list):\\n for elem in item:\\n if isinstance(elem, dict):\\n for kkk in elem.keys():\\n yield '%s.%s' % (ckey, kkk)\\n else:\\n yield ckey\\n else: # basic type, so we reach the end\\n yield ckey\\n else: # basic type, so we reach the end\\n yield ckey\",\n \"def expand_flattened_path(flattened_path, value=None, separator='.'):\\n split_list = flattened_path.split(separator)\\n return add_keys({}, split_list, value)\",\n \"def deep_flatten(iterable):\\n pass\",\n \"def flatten_dict(d, parent_key=\\\"\\\", sep=\\\"_\\\"):\\n items = []\\n for k, v in d.items():\\n new_key = parent_key + sep + k if parent_key else k\\n if isinstance(v, MutableMapping):\\n items.extend(flatten_dict(v, new_key, sep=sep).items())\\n else:\\n items.append((new_key, v))\\n return dict(items)\",\n \"def flatten(params, key=None):\\n flat = {}\\n for name, val in list(params.items()):\\n if key is not None and not isinstance(key, int):\\n name = \\\"%s[%s]\\\" % (key, name)\\n if isinstance(val, dict):\\n flat.update(flatten(val, name))\\n elif isinstance(val, list):\\n flat.update(flatten(dict(enumerate(val)), name))\\n elif val is not None:\\n flat[name] = val\\n return flat\",\n \"def clean_keys_of_slashes(record):\\n for key in list(record):\\n value = record[key]\\n if \\\"/\\\" in key:\\n # replace with _\\n record[key.replace(\\\"/\\\", \\\"_\\\")] = record.pop(key)\\n # Check if the value is a list containing nested dict and apply same\\n if value:\\n if isinstance(value, list) and isinstance(value[0], dict):\\n for v in value:\\n clean_keys_of_slashes(v)\\n\\n return record\",\n \"def _expand_keys(entities):\\n keys = list(entities.keys())\\n values = list(product(*[entities[k] for k in keys]))\\n return [{k: v for k, v in zip(keys, combs)} for combs in values]\",\n \"def walk (root, ignore_access_errors=False, _want_types=False):\\n root = registry (root, accept_value=False)\\n yield (\\n root,\\n root.iterkeys (ignore_access_errors=ignore_access_errors),\\n root.itervalues (ignore_access_errors=ignore_access_errors, _want_types=_want_types)\\n )\\n for subkey in root.iterkeys (ignore_access_errors=ignore_access_errors):\\n for result in walk (subkey, ignore_access_errors=ignore_access_errors, _want_types=_want_types):\\n yield result\",\n \"def _flatten(flat_json: FlatJSON, keys: FlatKey, obj: JSONType) -> FlatJSON:\\n jtype = typeof(obj)\\n if jtype is JSONValue:\\n flat_json[keys] = obj\\n elif jtype is JSONArray:\\n for index, value in enumerate(obj):\\n _flatten(flat_json, (*keys, index), value)\\n elif jtype is JSONObject:\\n for key, value in obj.items():\\n _flatten(flat_json, (*keys, key), value)\\n else:\\n raise PheresInternalError(f\\\"Unhandled json type {jtype} in flatten()\\\")\\n return flat_json\",\n \"def recursive_key_lookup(keys, obj):\\n if not isinstance(keys, list):\\n return obj.get(keys)\\n\\n for key in keys:\\n if obj is not None:\\n obj = obj.get(key)\\n\\n return obj\",\n \"def add_by_list_of_keys(dictionary: Dict, key_path: List[Any], value: Any) -> Dict:\\n key = key_path[0]\\n dictionary[key] = (\\n value\\n if len(key_path) == 1\\n else add_by_list_of_keys(\\n dictionary[key] if key in dictionary else dict(),\\n key_path[1:],\\n value,\\n )\\n )\\n return dictionary\",\n \"def _try_set(set_list, index, nested_dict, dict_keys=[]):\\n try:\\n for dict_key in dict_keys:\\n nested_dict = nested_dict.__getitem__(dict_key)\\n set_list[index] = str(nested_dict)\\n return nested_dict\\n except:\\n return ''\",\n \"def find_key(key, var):\\n if hasattr(var, 'items'):\\n for k, v in var.items():\\n if k == key:\\n yield v\\n if isinstance(v, dict):\\n for result in find_key(key, v):\\n yield result\\n elif isinstance(v, list):\\n for d in v:\\n for result in find_key(key, d):\\n yield result\",\n \"def join_path(d, root):\\n if isinstance(d, dict):\\n if 'path' in d:\\n d['path'] = os.path.join(root, d['path'])\\n root = d['path']\\n for item in d:\\n d[item] = join_path(d[item], root)\\n\\n elif isinstance(d, list):\\n d = [join_path(item, root) for item in d]\\n\\n return d\",\n \"def _find_all_paths(sample, previous_path=None):\\n paths = []\\n for key in sample:\\n current_path = []\\n if previous_path:\\n current_path.extend(previous_path)\\n current_path.append(key)\\n #If the current value ist a mapping, search in this mapping for more paths\\n if isinstance(sample[key], abc.Mapping):\\n paths.extend(MappingValidator._find_all_paths(sample[key],\\n previous_path=current_path))\\n paths.append(current_path)\\n return sorted(paths, key=lambda k: len(k))\",\n \"def iterkeys(d):\\n return iter(getattr(d, _iterkeys)())\",\n \"def flatten_state_dict(state_dict, keep_empty_nodes = False):\\n return traverse_util.flatten_dict(\\n state_dict,\\n is_leaf=tensorstore_leaf,\\n keep_empty_nodes=keep_empty_nodes,\\n sep='/')\",\n \"def iterkeys(d):\\r\\n return iter(getattr(d, _iterkeys)())\",\n \"def search_deep_keys(search_text, cfn_dict, path):\\n keys = []\\n if isinstance(cfn_dict, dict):\\n for key in cfn_dict:\\n pathprop = path[:]\\n pathprop.append(key)\\n if key == search_text:\\n pathprop.append(cfn_dict[key])\\n keys.append(pathprop)\\n # pop the last element off for nesting of found elements for\\n # dict and list checks\\n pathprop = pathprop[:-1]\\n if isinstance(cfn_dict[key], dict):\\n keys.extend(ContextParser.search_deep_keys(search_text, cfn_dict[key], pathprop))\\n elif isinstance(cfn_dict[key], list):\\n for index, item in enumerate(cfn_dict[key]):\\n pathproparr = pathprop[:]\\n pathproparr.append(index)\\n keys.extend(ContextParser.search_deep_keys(search_text, item, pathproparr))\\n elif isinstance(cfn_dict, list):\\n for index, item in enumerate(cfn_dict):\\n pathprop = path[:]\\n pathprop.append(index)\\n keys.extend(ContextParser.search_deep_keys(search_text, item, pathprop))\\n\\n return keys\",\n \"def flatten(d: MutableMapping, sep: str = \\\".\\\", parent_key: str = \\\"\\\") -> dict:\\n items = []\\n for k, v in d.items():\\n new_key = parent_key + sep + k if parent_key else k\\n if isinstance(v, MutableMapping):\\n items.extend(flatten(v, sep=sep, parent_key=new_key).items())\\n else:\\n items.append((new_key, v))\\n return dict(items)\",\n \"def keys_nested(self, subkeys_as='list'):\\n keys = []\\n for k, v in sorted(self.items()):\\n if isinstance(v, AttrDict) or isinstance(v, dict):\\n if subkeys_as == 'list':\\n keys.extend([k + '.' + kk for kk in v.keys_nested()])\\n elif subkeys_as == 'dict':\\n keys.append({k: v.keys_nested(subkeys_as=subkeys_as)})\\n else:\\n keys.append(k)\\n return keys\",\n \"def flatten(c):\\n for x in c:\\n if isinstance(x, str) or not isinstance(x, Iterable):\\n yield x\\n else:\\n yield from flatten(x)\",\n \"def all_keys(blueprint: Union[dict, list]) -> list:\\n\\n keys = list()\\n\\n if isinstance(blueprint, list):\\n for item in blueprint:\\n keys.extend(all_keys(item))\\n elif isinstance(blueprint, dict):\\n for key, value in blueprint.items():\\n keys.append(key)\\n keys.extend(all_keys(value))\\n\\n return keys\",\n \"def flatten_dict(d, target='key' or 'value'):\\n def format_nested(nested, _key=None):\\n if _key is None:\\n _key = key\\n return '{}_{}'.format(_key, nested)\\n\\n flattened = []\\n target_is_key = target == 'key'\\n for key, value in OrderedDict(sorted(d.items())).items():\\n\\n # Simple case: recursively flatten the dictionary.\\n if isinstance(value, dict):\\n flattened += map(\\n format_nested if target_is_key else lambda x: x,\\n flatten_dict(value, target=target)\\n )\\n\\n # We are suddenly in muddy waters, because lists can have multiple types within them in JSON.\\n elif isinstance(value, list):\\n items_are_dict = [isinstance(item, dict) for item in value]\\n items_are_list = [isinstance(item, list) for item in value]\\n\\n # To help reduce the complexity here, let's not support this case.\\n # Besides, most sensible APIs won't bump into this case.\\n if any(items_are_dict) and not all(items_are_dict):\\n raise NotImplementedError(\\\"Ability to flatten dict with list of mixed dict and non-dict types \\\"\\n \\\"is not currently supported\\\")\\n\\n # Same here, this is just weird.\\n if any(items_are_list):\\n raise NotImplementedError(\\\"Ability to flatten a dict with lists within lists \\\"\\n \\\"is not currently supported. And we'd like to ask you to take it easy.\\\")\\n\\n # This case is common, but a little complex.\\n elif all(items_are_dict):\\n for index, item in enumerate(value):\\n _flattened_dict = flatten_dict(item, target=target)\\n\\n # In this case we actually want to prepend the dict's index in the list to each flattened dict.\\n if target_is_key:\\n _flattened_dict = [format_nested(flattened_item, _key=index)\\n for flattened_item in _flattened_dict]\\n\\n flattened += map(format_nested if target_is_key else lambda x: x, _flattened_dict)\\n\\n # All items are non-dict, so just directly add either the index or the value.\\n else:\\n flattened += map(format_nested, range(len(value))) if target_is_key else value\\n\\n # Kindergarten -- just add to the list.\\n else:\\n flattened.append(key if target_is_key else value)\\n return flattened\",\n \"def drop_keys(d):\\n if isinstance(d, dict):\\n return {\\n k: drop_keys(v)\\n for k, v in d.items()\\n if k not in [\\\"propNames\\\", \\\"package\\\"]\\n and v is not None\\n and not (k == \\\"children\\\" and v == \\\"\\\")\\n }\\n elif isinstance(d, list):\\n return [drop_keys(x) for x in d]\\n return d\",\n \"def flattenDict(inputDict: dict) -> list:\\n return sum([[key] if type(value) is bool else [key, value] for key, value in inputDict.items() if value], list())\",\n \"def expand(d):\\n # make sure everything is a list\\n for k, v in d.iteritems():\\n if type(v) is not list:\\n d[k] = [v]\\n\\n # take cross product\\n product = [x for x in apply(itertools.product, d.values())]\\n return flatten([dict(zip(d.keys(), p)) for p in product])\",\n \"def flatten_dict(d):\\n\\n def expand(key, value):\\n if isinstance(value, dict):\\n return [(key + '.' + k, v) for k, v in flatten_dict(value).items()]\\n else:\\n return [(key, value)]\\n\\n items = [item for k, v in d.items() for item in expand(k, v)]\\n return dict(items)\",\n \"def find_key(self, key, dictionary):\\n if isinstance(dictionary, list):\\n for d in dictionary:\\n for result in self.find_key(key, d):\\n yield result\\n else:\\n for k, v in dictionary.items():\\n if k == key:\\n yield v\\n elif isinstance(v, dict):\\n for result in self.find_key(key, v):\\n yield result\\n elif isinstance(v, list):\\n for d in v:\\n for result in self.find_key(key, d):\\n yield result\",\n \"def get_special_paths(set_path: str, set_value, sp_to_label: Dict, label_to_ship: Dict):\\n additions = set()\\n if type(set_value) is not dict:\\n # If this path is already labelled as special, then check that the ship matched with it does fit\\n if set_path in sp_to_label:\\n if label_to_ship[sp_to_label[set_path]].check_fit(set_value):\\n return additions\\n\\n # If this is something a ship covers, add it as a special path\\n for ship in label_to_ship.values():\\n if ship.check_fit(set_value):\\n additions.add( (set_path, ship.get_label()) )\\n\\n # If this is a dict, recursively build the set of additional paths\\n else:\\n for k, v in set_value.items():\\n assert check_valid_key_name(k), \\\"Invalid Key: {}\\\".format(k)\\n if set_path != Path.rootPath():\\n child_path = \\\"{}.{}\\\".format(set_path, k)\\n else:\\n child_path = \\\".{}\\\".format(k)\\n child_add = get_special_paths(child_path, v, sp_to_label, label_to_ship)\\n additions = additions.union(child_add)\\n\\n return additions\",\n \"def find_and_normalize(value):\\n if isinstance(value, dict):\\n # if value is a dictionary, try to normalize the values\\n return {k: normalize_dict_value(v) for k, v in value.items()}\\n return normalize_path(value)\",\n \"def iterkeys(d, **kw):\\r\\n return iter(getattr(d, _iterkeys)(**kw))\",\n \"def iterkeys(d, **kw):\\r\\n return iter(getattr(d, _iterkeys)(**kw))\",\n \"def replace_paths(value):\\n return {\\n str: lambda: value.replace('__FILE__', sys.path[0]),\\n list: lambda: [replace_paths(elt) for elt in value],\\n dict: lambda: {key: replace_paths(val) for key, val in value.items()},\\n OrderedDict: (lambda:\\n OrderedDict((key, replace_paths(val)) for key, val in value.items()))\\n }.get(type(value), lambda: value)()\",\n \"def flatten_dict(dictionary,keys=None):\\n flat_dict = {}\\n for k,v in dictionary.items():\\n if not keys or k in keys:\\n if isinstance(v,dict):\\n r = flatten_dict(v)\\n flat_dict.update(r)\\n else:\\n flat_dict[k] = v\\n return flat_dict\",\n \"def nested_set(data, keys, value):\\n for key in keys[:-1]:\\n data = data.setdefault(key, {})\\n data[keys[-1]] = value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6875952","0.6714829","0.6393634","0.6268066","0.6147617","0.60040855","0.5961839","0.58822143","0.5856093","0.58354765","0.5823535","0.58075947","0.58066595","0.5770507","0.57541543","0.5746323","0.574392","0.57279015","0.5708329","0.5703219","0.5697986","0.56909585","0.568402","0.5648774","0.56477576","0.5638721","0.5638624","0.56355137","0.5568469","0.555309","0.55472654","0.5543829","0.55413896","0.55318964","0.55300117","0.5524569","0.5513866","0.5510929","0.5508168","0.5491657","0.5488483","0.54850584","0.5477335","0.5465682","0.54629","0.545977","0.5455381","0.54465216","0.544498","0.5437352","0.54158866","0.5407119","0.5393639","0.53820825","0.5380408","0.53652054","0.536349","0.53627026","0.53602594","0.53586864","0.53529024","0.5342747","0.53425","0.5341569","0.5338541","0.5320216","0.5318997","0.53171396","0.5309713","0.5305009","0.5302064","0.52821","0.52546406","0.5253185","0.5245019","0.5242531","0.52340543","0.5233094","0.5232991","0.52242076","0.5216765","0.5207554","0.5198868","0.51968837","0.5188826","0.5178134","0.5167767","0.516704","0.51583916","0.51552397","0.51357514","0.5116817","0.51155925","0.5114337","0.5112052","0.5103075","0.5103075","0.50916123","0.50851953","0.50835407"],"string":"[\n \"0.6875952\",\n \"0.6714829\",\n \"0.6393634\",\n \"0.6268066\",\n \"0.6147617\",\n \"0.60040855\",\n \"0.5961839\",\n \"0.58822143\",\n \"0.5856093\",\n \"0.58354765\",\n \"0.5823535\",\n \"0.58075947\",\n \"0.58066595\",\n \"0.5770507\",\n \"0.57541543\",\n \"0.5746323\",\n \"0.574392\",\n \"0.57279015\",\n \"0.5708329\",\n \"0.5703219\",\n \"0.5697986\",\n \"0.56909585\",\n \"0.568402\",\n \"0.5648774\",\n \"0.56477576\",\n \"0.5638721\",\n \"0.5638624\",\n \"0.56355137\",\n \"0.5568469\",\n \"0.555309\",\n \"0.55472654\",\n \"0.5543829\",\n \"0.55413896\",\n \"0.55318964\",\n \"0.55300117\",\n \"0.5524569\",\n \"0.5513866\",\n \"0.5510929\",\n \"0.5508168\",\n \"0.5491657\",\n \"0.5488483\",\n \"0.54850584\",\n \"0.5477335\",\n \"0.5465682\",\n \"0.54629\",\n \"0.545977\",\n \"0.5455381\",\n \"0.54465216\",\n \"0.544498\",\n \"0.5437352\",\n \"0.54158866\",\n \"0.5407119\",\n \"0.5393639\",\n \"0.53820825\",\n \"0.5380408\",\n \"0.53652054\",\n \"0.536349\",\n \"0.53627026\",\n \"0.53602594\",\n \"0.53586864\",\n \"0.53529024\",\n \"0.5342747\",\n \"0.53425\",\n \"0.5341569\",\n \"0.5338541\",\n \"0.5320216\",\n \"0.5318997\",\n \"0.53171396\",\n \"0.5309713\",\n \"0.5305009\",\n \"0.5302064\",\n \"0.52821\",\n \"0.52546406\",\n \"0.5253185\",\n \"0.5245019\",\n \"0.5242531\",\n \"0.52340543\",\n \"0.5233094\",\n \"0.5232991\",\n \"0.52242076\",\n \"0.5216765\",\n \"0.5207554\",\n \"0.5198868\",\n \"0.51968837\",\n \"0.5188826\",\n \"0.5178134\",\n \"0.5167767\",\n \"0.516704\",\n \"0.51583916\",\n \"0.51552397\",\n \"0.51357514\",\n \"0.5116817\",\n \"0.51155925\",\n \"0.5114337\",\n \"0.5112052\",\n \"0.5103075\",\n \"0.5103075\",\n \"0.50916123\",\n \"0.50851953\",\n \"0.50835407\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7069572"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":196,"numItemsPerPage":100,"numTotalItems":95772,"offset":19600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjMxNTM4OCwianRpIjoiN2U4YTBhNmQtMDI3ZC00ZjJlLThmNzctYTY2NDNmNGU0NGY4Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzMTg5ODgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.kT_hJXscb0tPUp93bkSJa3ogX3dwHlJMCFqcnrmii3kEIlFVNoWhIFO8b96_451rApybR84WDzAlbsoBGB4DAA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Path to the main executable. For an app, this is app itself. For a Framework, this is the main framework | def get_executable_path(self):
executable_name = None
if 'CFBundleExecutable' in self.info:
executable_name = self.info['CFBundleExecutable']
else:
executable_name, _ = splitext(basename(self.path))
executable_name = utils.remove_control_char(executable_name)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_executable(self) -> str:\n ...",
"def GetLauncherPath(self):\n return os.path.dirname(__file__)",
"def GetLauncherPath(self):\n return os.path.dirname(__file__)",
"def this_folder():\n if getattr(sys, 'frozen', False):\n # The application is frozen\n return os.path.dirna... | [
"0.69457704",
"0.6923044",
"0.6923044",
"0.6894284",
"0.6816904",
"0.67749614",
"0.6739322",
"0.67294186",
"0.6697168",
"0.66547674",
"0.6633224",
"0.6616876",
"0.6615852",
"0.6609995",
"0.6549488",
"0.65441966",
"0.6451003",
"0.6433944",
"0.6424938",
"0.6412598",
"0.63580215... | 0.653631 | 16 |
Sign all the dylibs in this directory | def sign_dylibs(self, signer, path):
for dylib_path in glob.glob(join(path, '*.dylib')):
dylib = signable.Dylib(self, dylib_path, signer)
dylib.sign(self, signer) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sign_dylibs(self, cms_signer, path):\n for dylib_path in glob.glob(join(path, '*.dylib')):\n dylib = signable.Dylib(self, dylib_path, cms_signer)\n dylib.sign(self, cms_signer)",
"def sign(self, signer):\n # log.debug(\"SIGNING: %s\" % self.path)\n frameworks_path =... | [
"0.8432315",
"0.6325134",
"0.59998757",
"0.5821335",
"0.5536926",
"0.5455998",
"0.53087306",
"0.51840645",
"0.5171187",
"0.5158678",
"0.5043617",
"0.4983382",
"0.49256852",
"0.4905008",
"0.48366326",
"0.48080102",
"0.47965342",
"0.47516686",
"0.47421157",
"0.47248307",
"0.470... | 0.8295412 | 1 |
Sign everything in this bundle, recursively with subbundles | def sign(self, signer):
# log.debug("SIGNING: %s" % self.path)
frameworks_path = join(self.path, 'Frameworks')
if exists(frameworks_path):
# log.debug("SIGNING FRAMEWORKS: %s" % frameworks_path)
# sign all the frameworks
for framework_name in os.listdir(framew... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resign(self, deep, cms_signer, provisioner):\n # log.debug(\"SIGNING: %s\" % self.path)\n if deep:\n plugins_path = join(self.path, 'PlugIns')\n if exists(plugins_path):\n # sign the appex executables\n appex_paths = glob.glob(join(plugins_path,... | [
"0.5995381",
"0.5584379",
"0.55598193",
"0.55531466",
"0.55146366",
"0.54876906",
"0.52974087",
"0.52845377",
"0.5166314",
"0.51473546",
"0.5102368",
"0.50621337",
"0.50598043",
"0.5002848",
"0.49882516",
"0.49192393",
"0.4897663",
"0.48194253",
"0.48167092",
"0.48128116",
"0... | 0.59542173 | 1 |
signs bundle, modifies in place | def resign(self, signer):
self.sign(signer)
log.debug("Resigned bundle at <%s>", self.path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sign(self, object):\n pass",
"def sign(self, payload):\n raise NotImplementedError",
"def _re_codesign(app_path, signing_identity, provision_path=None):\n bundle_type = PackageType.get_type(app_path)\n logger.debug('Re-codesigning %s...' % (bundle_type,))\n if bundle_type == PackageT... | [
"0.6849241",
"0.65445966",
"0.6377147",
"0.6371257",
"0.63314426",
"0.6310475",
"0.62609345",
"0.61872184",
"0.61623085",
"0.6129731",
"0.61247724",
"0.607729",
"0.6056486",
"0.60021913",
"0.59386194",
"0.59081775",
"0.58716357",
"0.5862046",
"0.5852787",
"0.58021504",
"0.577... | 0.66369 | 1 |
Given a path to a provisioning profile, return the entitlements encoded therein | def extract_entitlements(provision_path):
cmd = [
'smime',
'-inform', 'der',
'-verify', # verifies content, prints verification status to STDERR,
# outputs content to STDOUT. In our case, will be an XML plist
'-noverify', # accept se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resign(self, signer, provisioning_profile, alternate_entitlements_path=None):\n\n # TODO all this mucking about with entitlements feels wrong. The entitlements_path is\n # not actually functional, it's just a way of passing it to later stages of signing.\n # Maybe we should determine entit... | [
"0.5083228",
"0.49126115",
"0.49077302",
"0.47404766",
"0.4691661",
"0.4553458",
"0.454329",
"0.4531003",
"0.4473628",
"0.44085193",
"0.4406375",
"0.44050503",
"0.439604",
"0.43790016",
"0.43628758",
"0.4326153",
"0.43213052",
"0.43070796",
"0.43030575",
"0.42926168",
"0.4262... | 0.7646078 | 0 |
Write entitlements to self.entitlements_path. This actually doesn't matter to the app, it's just used later on by other parts of the signing process. | def write_entitlements(self, entitlements):
biplist.writePlist(entitlements, self.entitlements_path, binary=False)
log.debug("wrote Entitlements to {0}".format(self.entitlements_path)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entitlements(self) -> Entitlements:\n return self.__entitlements",
"def resign(self, signer, provisioning_profile, alternate_entitlements_path=None):\n\n # TODO all this mucking about with entitlements feels wrong. The entitlements_path is\n # not actually functional, it's just a way of ... | [
"0.54211116",
"0.5367477",
"0.51338154",
"0.51240337",
"0.50888306",
"0.508606",
"0.4937517",
"0.48715833",
"0.48602337",
"0.48598403",
"0.48598403",
"0.4844882",
"0.48447084",
"0.4842885",
"0.48324612",
"0.48164335",
"0.47696745",
"0.4747149",
"0.4736882",
"0.46891227",
"0.4... | 0.7737914 | 0 |
signs app in place | def resign(self, signer, provisioning_profile, alternate_entitlements_path=None):
# TODO all this mucking about with entitlements feels wrong. The entitlements_path is
# not actually functional, it's just a way of passing it to later stages of signing.
# Maybe we should determine entitlements d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def package_app(\n self, app: BaseConfig, sign_app=True, identity=None, adhoc_sign=False, **kwargs\n ):\n if sign_app:\n if adhoc_sign:\n identity = \"-\"\n\n print()\n print(\"[{app.app_name}] Signing app with adhoc identity...\".format(app=... | [
"0.6645783",
"0.57181567",
"0.57020706",
"0.56410366",
"0.5603273",
"0.5575135",
"0.5537196",
"0.550395",
"0.5460537",
"0.5450264",
"0.54468817",
"0.54375774",
"0.5419112",
"0.5402969",
"0.5399384",
"0.5386594",
"0.5349695",
"0.53493875",
"0.5327894",
"0.5327894",
"0.5280954"... | 0.0 | -1 |
creates the Tk object, the socket and starts running the entire code. | def __init__(self, host_ip="127.0.0.1", dst_port=2000):
self.my_socket = socket.socket()
try:
self.my_socket.connect((host_ip, dst_port))
print("Connected to server successfully")
except socket.error:
print("no server is waiting...")
sys.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.root.mainloop()",
"def gui(host, port):\n class GUI:\n def __init__(self, window, host, port):\n self.window = window\n self.server = None\n self.scanner = None\n\n try:\n import Tkinter\n except Import... | [
"0.6866942",
"0.6787806",
"0.670932",
"0.66793466",
"0.6656448",
"0.6599677",
"0.6566132",
"0.6566132",
"0.650566",
"0.6502823",
"0.6465216",
"0.6464862",
"0.64515907",
"0.6450801",
"0.6447126",
"0.64432055",
"0.64035016",
"0.63944876",
"0.63612664",
"0.6329764",
"0.63289315"... | 0.0 | -1 |
this is the closing protocol. this function closes all Tk objects that might exist in order to close the program entirely. | def close_root(self):
if self.messages_window is not None:
self.messages_window.destroy()
self.root.destroy() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exit(self):\n \t\troot.destroy()\n \t\tpass",
"def close(self):\n self.master.destroy()\n root.quit()",
"def close_window(self):\n # Window - END\n self.root.destroy()",
"def quit(self) -> None:\n global tcl_interp\n\n for child in tuple(self._children.values... | [
"0.76227576",
"0.7615387",
"0.74897474",
"0.7447964",
"0.7378788",
"0.7371572",
"0.7316284",
"0.7227022",
"0.7165061",
"0.7121428",
"0.7119809",
"0.7099502",
"0.7061996",
"0.70610315",
"0.70588726",
"0.7033408",
"0.7003686",
"0.699336",
"0.6988916",
"0.6986488",
"0.69839627",... | 0.66862065 | 53 |
after 3 wrong attempts, this function will block the user from signing/logging in for 60 seconds. | def after_3_wrong_attempts(self, str_from_where):
self.clear_screen()
def timer_tick(seconds):
"""
this is an inner-function that is responsible for the
timer after3 failed attempts at logging/signing in.
:param seconds:
:return:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login():\n tries = 0\n username = ''\n while True:\n try:\n username = input(f'{Fore.YELLOW}please enter your username:{Fore.RESET}')\n check = check_lock(username)\n if not check:\n print(Fore.LIGHTRED_EX, 'your account is lock try later', Fore.R... | [
"0.6183937",
"0.61305475",
"0.60963434",
"0.59451866",
"0.59349215",
"0.5923862",
"0.5923862",
"0.5903164",
"0.58763486",
"0.58410287",
"0.5828673",
"0.5819729",
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"0.57935447",
"0.5790253",
"0.5775508",
"0.5775508",
"0.5775508",
"0.5775508",
"0.5775508",
"0.577550... | 0.5937697 | 4 |
this is an innerfunction that is responsible for the timer after3 failed attempts at logging/signing in. | def timer_tick(seconds):
if seconds > 0:
timer_label['text'] = "You had 3 wrong attempts.\nTry again in " + \
str(seconds) + " seconds."
self.root.after(1000, lambda: timer_tick(seconds - 1))
else:
if str... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def after_3_wrong_attempts(self, str_from_where):\r\n self.clear_screen()\r\n\r\n def timer_tick(seconds):\r\n \"\"\"\r\n this is an inner-function that is responsible for the\r\n timer after3 failed attempts at logging/signing in.\r\n :param seconds:\r\n ... | [
"0.7018955",
"0.6663694",
"0.65989476",
"0.63709235",
"0.6331907",
"0.58595014",
"0.5848615",
"0.57283294",
"0.5696457",
"0.56867135",
"0.568213",
"0.5636375",
"0.5482261",
"0.54760987",
"0.54586434",
"0.5451943",
"0.5442569",
"0.5427316",
"0.5427316",
"0.54147637",
"0.539198... | 0.64727724 | 3 |
this function shows the log in window | def log_in(self):
self.clear_screen()
lbl_log_in = Label(self.root, text="Welcome. Please log in to the system.",
font=self.title_font,
bg=self.bg_color)
lbl_log_in.pack(pady=5, padx=10)
user_name = Label(self.root, text="ente... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_in(self):\n\t\tpass",
"def show(self, window):\r\n\r\n return",
"def iniciaUI(self):\n\n self.setGeometry(100,100, 250, 250)\n self.setWindowTitle(\"Login\")\n self.displayWidgets()\n\n self.show()",
"def OnShowLog(self, event):\n dlg = LogViewer(self)\n ... | [
"0.6801101",
"0.6755017",
"0.6737144",
"0.66676944",
"0.65847325",
"0.6565406",
"0.655964",
"0.64743286",
"0.6425789",
"0.64248353",
"0.6400713",
"0.63787353",
"0.6243237",
"0.624312",
"0.62406",
"0.62182665",
"0.62177074",
"0.6210246",
"0.61971325",
"0.61968756",
"0.61666393... | 0.685493 | 0 |
this function sends to the server the data and returns whether the client logged in successfully or not. | def submit_log_in(self, user_name, password, passcode):
username_txt = user_name.get()
password_txt = password.get()
passcode_txt = passcode.get()
self.my_socket.send(dumps("log in"))
now = datetime.now()
current_time = now.strftime("%H:%M:%S").split(":")
t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login(self, input_user, input_pass):\n\n self.failed = False\n try:\n self.send_message('/login [' + input_user + '] [' + input_pass + ']\\r')\n\n except:\n sys.stderr.write('failed to login to server. \\n') \n Self.failed = True\n return False\n... | [
"0.6768166",
"0.6648105",
"0.6625738",
"0.6543316",
"0.6504854",
"0.64551735",
"0.6399411",
"0.6391859",
"0.6326033",
"0.62819505",
"0.6268547",
"0.62323344",
"0.62271833",
"0.62131685",
"0.61791146",
"0.61692303",
"0.6164457",
"0.6153566",
"0.6137831",
"0.6118876",
"0.611581... | 0.6190278 | 14 |
this function shows the sign in window | def sign_in(self):
self.clear_screen()
lbl_sign_in = Label(self.root, text="Welcome. Please sign in to the system.",
font=self.title_font, bg=self.bg_color)
lbl_sign_in.pack(pady=5, padx=10)
user_name = Label(self.root, text="enter user name", font=self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display():\n return render_template(\"signin.html\")",
"def signin():\n return render_template(\n 'signin.html',\n title='Signin',\n time=datetime.now(),\n message='only for administrator'\n )",
"def OnButtonSignInButton(self, event):\r\n\t\tself.staticTextSignedIn.SetL... | [
"0.715081",
"0.6884875",
"0.66977865",
"0.6528256",
"0.648015",
"0.6424218",
"0.6366826",
"0.63019854",
"0.6266997",
"0.6252133",
"0.6251155",
"0.62202835",
"0.61962193",
"0.61757565",
"0.6158013",
"0.61518556",
"0.6116523",
"0.61122763",
"0.6092992",
"0.6079997",
"0.6074448"... | 0.67017686 | 2 |
this function sends to the server the data and returns whether the user was created successfully or not. | def submit_sign_in(self, user_name, id_widget, password1, password2, passcode):
username_txt = user_name.get()
id_txt = id_widget.get()
password1_txt = password1.get()
password2_txt = password2.get()
passcode_txt = passcode.get()
self.my_socket.send(dumps("sign in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post(self):\n data = UserRegister.parser.parse_args()\n\n if UserModel.find_by_id(data['username']):\n print(\"Failed\", file=sys.stderr)\n return {\n 'message':\n \"A user with name '{}' already exists.\"\n .format(data['username... | [
"0.7700298",
"0.76356184",
"0.7605254",
"0.7489048",
"0.74458635",
"0.7436668",
"0.7430833",
"0.742777",
"0.73581654",
"0.7321634",
"0.7161986",
"0.71573776",
"0.7126827",
"0.71160984",
"0.7112433",
"0.7072901",
"0.7072577",
"0.70628417",
"0.70024604",
"0.6998687",
"0.6992374... | 0.0 | -1 |
this function shows the paths window. the client may choose what to do. | def choose_path(self):
if not self.receive_thread.is_alive():
self.receive_thread.start()
self.clear_screen()
self.refresh_button = None
user_label = Label(self.root, text="Hello " + self.username, font=self.title_font,
bg=self.bg_color, heigh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_paths(self):\r\n print(\"------------------------\")\r\n print(\"######### ALL PATHS #########\")\r\n\r\n if self.size == 0:\r\n print(\"Empty tree!\")\r\n else:\r\n for i in range(1, self.root.size_tree + 1):\r\n node = self.select(i)\r\n ... | [
"0.6825162",
"0.6483774",
"0.63352376",
"0.6284325",
"0.6282731",
"0.62729084",
"0.6136906",
"0.6114865",
"0.6085493",
"0.6074004",
"0.6042653",
"0.60341257",
"0.59863263",
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"0.5983212",
"0.5978311",
"0.5958783",
"0.593991",
"0.5929046",
"0.5928629",
"0.592335",
... | 0.5589428 | 48 |
this function shows the read window. it allows the client to read all the messages. both received and sent. | def read_messages(self, msg_num):
self.clear_screen()
user_label = Label(self.root, text="Hello " + self.username, font=self.title_font,
bg=self.bg_color, height=2)
user_label.pack(pady=5, padx=50)
lbl_msg = Label(self.root, text="Message " + str(msg_num),... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_messages(self):\n for msg in self.messages:\n print msg['text']",
"def show(self):\n self.set_text(self.read())",
"def show_messages(self):\n self.masterlog.revealme()",
"def read(self):\n from x84.bbs import getch\n from x84.bbs.session import getsession\... | [
"0.67919517",
"0.6346908",
"0.6337899",
"0.6265047",
"0.6182557",
"0.618034",
"0.6140841",
"0.6108053",
"0.6107602",
"0.60914344",
"0.6021179",
"0.5947622",
"0.58789885",
"0.5855277",
"0.58248854",
"0.5822408",
"0.5808994",
"0.5804789",
"0.58033186",
"0.57892084",
"0.5787142"... | 0.6980507 | 0 |
this function refreshes the read messages page. | def refresh(self, msg_num):
if self.messages_window is not None:
self.messages_window.destroy()
self.messages_window = None
self.read_messages(msg_num) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def chat_refresh(self, label):\n room = await self.get_room(label)\n messages = await self.fetch_all_message(room)\n await self.send_json(\n return_value(ACTION_REFRESH_CHAT, label, self.user.username, MSG_MESSAGE, messages)\n )",
"def read_messages(self, msg_num):\r... | [
"0.6961772",
"0.67089885",
"0.66341877",
"0.65815735",
"0.6418059",
"0.6338058",
"0.6332131",
"0.63038486",
"0.6238911",
"0.6234152",
"0.62187123",
"0.621134",
"0.621134",
"0.6167453",
"0.6129225",
"0.5976966",
"0.5967419",
"0.5957173",
"0.59484446",
"0.5933708",
"0.5933708",... | 0.76593846 | 0 |
this function makes sure that when going back from the read window, all windows work properly. | def go_back_read(self):
if self.messages_window is not None:
self.messages_window.destroy()
self.messages_window = None
self.choose_path() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __window_back(self):\n pass",
"def ev_windowrestored(self, event: WindowEvent) -> None:",
"def __window_forward(self):\n pass",
"def TransferToWindow(self):\n return True",
"def TransferFromWindow(self):\n return True",
"def ev_windowrestored(self, event: tcod.event.WindowEvent) -... | [
"0.66543084",
"0.64756507",
"0.6218632",
"0.61270016",
"0.6059469",
"0.5967816",
"0.59462374",
"0.5911877",
"0.5886296",
"0.5857155",
"0.5838113",
"0.5830953",
"0.5825556",
"0.58215696",
"0.5817999",
"0.5753222",
"0.5739507",
"0.5733871",
"0.57087755",
"0.5703051",
"0.5699966... | 0.68879884 | 0 |
opens a new window that contains all the messages. | def new_window_messages(self, button_see_all_msgs):
# changing the button command to closing the window
button_see_all_msgs.config(command=lambda: self.close_window(button_see_all_msgs))
# creating the chat Tk object
self.messages_window = Tk()
self.messages_window.resizab... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _open_window(self):\r\n\t\t# Creating the window\r\n\t\tself._window = Window(self, Locations.RESTAL)",
"def open_main_window(self):\r\n track_terms_dic = ''\r\n sg.theme(self.look)\r\n\r\n layout = [[sg.Text('Welcome to tweeet monitor ')],\r\n [sg.Text('Please enter Det... | [
"0.71605724",
"0.6652545",
"0.6622106",
"0.6552646",
"0.65350586",
"0.6522257",
"0.6512286",
"0.64399976",
"0.6398846",
"0.6395453",
"0.63476604",
"0.6302807",
"0.6253829",
"0.6239314",
"0.6238414",
"0.6192727",
"0.6176429",
"0.6140522",
"0.6134743",
"0.61296725",
"0.60993296... | 0.7269994 | 0 |
closing the second Tk object | def close_window(self, button_msgs):
if self.messages_window is not None:
self.messages_window.destroy()
self.messages_window = None
button_msgs.config(command=lambda: self.new_window_messages(button_msgs)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exit(self):\n \t\troot.destroy()\n \t\tpass",
"def destroy(self):\n tk.Frame.destroy(self)",
"def close_and_run(self, master, window):\r\n self.main_gui(master)\r\n window.destroy()",
"def close(event):\n event.widget.destroy()",
"def close_window(self):\n # Window - EN... | [
"0.70907074",
"0.69646",
"0.696005",
"0.6930957",
"0.69069535",
"0.6878592",
"0.6849327",
"0.68154705",
"0.675188",
"0.6751682",
"0.6737973",
"0.66876215",
"0.66687185",
"0.6583281",
"0.65789247",
"0.65704244",
"0.65456504",
"0.6526659",
"0.6495618",
"0.64723223",
"0.6447673"... | 0.0 | -1 |
this function is the send window. it allows the client to send a message. | def send_messages(self):
self.clear_screen()
user_label = Label(self.root, text="Hello " + self.username,
font=self.title_font, bg=self.bg_color, height=2)
user_label.pack(pady=10, padx=50)
messages_frame = Frame(self.root)
messages_frame.pack(pad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def send(self, msg):\n pass",
"def send(self, msg):\n self.message('Me', msg)",
"def send(self, message):\n pass",
"def send_message(self, message):\n pass",
"def transmit(self, msg):\r\n # send o... | [
"0.76155233",
"0.76155233",
"0.76155233",
"0.7408198",
"0.7336569",
"0.7297874",
"0.72387856",
"0.72170043",
"0.7180501",
"0.7176217",
"0.71481776",
"0.7098309",
"0.70943516",
"0.70592934",
"0.69874233",
"0.6976361",
"0.69505566",
"0.6949229",
"0.6911619",
"0.6911581",
"0.690... | 0.6597856 | 62 |
this function creates a thread that will listen to users input in microphone | def create_speech_thread(self, text_widget):
if not self.thread_speech_is_running:
thread_speech = Thread(target=self.speech_recognizer_function,
args=(text_widget,), daemon=True)
thread_speech.start()
self.thread_speech_is_running = Tr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listen(self):\n self.processor_thread = Thread(target = self.event_loop, name=\"InputThread-\"+str(self.thread_index), args=(self.thread_index, ))\n self.thread_index += 1\n self.processor_thread.daemon = True\n self.processor_thread.start()",
"def start(self):\n self.has_e... | [
"0.7250806",
"0.6790421",
"0.64903635",
"0.6438565",
"0.64301026",
"0.632874",
"0.6247852",
"0.61793077",
"0.6145515",
"0.61154944",
"0.61059767",
"0.6097376",
"0.6059375",
"0.6045819",
"0.6040814",
"0.60348105",
"0.58494747",
"0.5843194",
"0.5823038",
"0.58070415",
"0.577889... | 0.60714114 | 12 |
this function recognizes the input of the microphone and turns it into text. the text is inserted to the text widget and then the user will be able to send it as a message | def speech_recognizer_function(self, text_widget):
label_listening = Label(self.root, text="listening to input...",
font=self.text_font, bg=self.bg_color)
label_listening.pack(pady=10)
recognizer = speech_recognition.Recognizer()
microphone = speech_r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mic_input():\n try:\n r = sr.Recognizer()\n with sr.Microphone() as source:\n print('Say something...')\n r.pause_threshold = 1\n r.adjust_for_ambient_noise(source, duration=1)\n audio = r.listen(source)\n try:\n command = r.recogni... | [
"0.63976574",
"0.6331272",
"0.6316408",
"0.62616754",
"0.6221507",
"0.6133672",
"0.60445386",
"0.6034115",
"0.599703",
"0.5995486",
"0.59736764",
"0.5960848",
"0.5958265",
"0.59164345",
"0.58853114",
"0.5819938",
"0.58131707",
"0.5773462",
"0.57678306",
"0.57649124",
"0.57376... | 0.72579855 | 0 |
this function colors the encrypted letter label in the simulator for 300 milliseconds | def color_letter(self, letter, lst_labels, plain_text_widget, encrypted_text_widget):
new_letter, txt_encryption = self.simulator_enigma.encrypt_letter(letter)
lst_encryption_letter_stages = [i[-1] for i in txt_encryption.split("\n")]
lst_encryption_letter_stages.remove(')')
self.sim... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ColorizeDNA(self, text):\n if (text == 'A'):\n escape = '\\033[92m' # Green\n elif (text == 'G'):\n escape = '\\033[93m' # Yellow\n elif (text == 'T'):\n escape = '\\033[91m' # Red\n elif (text == 'C'):\n escape = '\\033[96m' # Blue\n ... | [
"0.6488497",
"0.64416146",
"0.6429084",
"0.6220818",
"0.62159586",
"0.61817086",
"0.6160907",
"0.61200315",
"0.61137754",
"0.6078523",
"0.60778445",
"0.6075475",
"0.6072182",
"0.6067036",
"0.606216",
"0.60522145",
"0.602722",
"0.6022543",
"0.60124403",
"0.59898627",
"0.597070... | 0.801215 | 0 |
displays the enigma simulator | def simulator(self, rotors_settings=(1, 2, 3, 'A', 'A', 'A'),
plugboard_settings=None, plain_text=""):
self.clear_screen()
user_label = Label(self.root, text="Hello " + self.username,
font=self.title_font, bg=self.bg_color, height=2)
user_label.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_eng(self):\n self.clear_terminal()\n self.menu_eng()\n self.handle_selection_eng()",
"def display(self):\n viewer = SpectraViewer(spectrometer=self)\n viewer.display()",
"def display(self):\n viewer = SpectraViewer(spectrometer=self)\n viewer.display... | [
"0.6631773",
"0.6175526",
"0.6175526",
"0.61313206",
"0.6080019",
"0.5969126",
"0.5952151",
"0.5921271",
"0.5917744",
"0.59115",
"0.59061027",
"0.5887677",
"0.58850664",
"0.5868575",
"0.583884",
"0.5790793",
"0.5734754",
"0.5731992",
"0.5710725",
"0.57077515",
"0.5699957",
... | 0.6890839 | 0 |
this function lets the user change the settings of the simulator | def change_settings(self):
self.clear_screen()
# making sure the screen grid will be organized
label_line = Label(self.root, text=" ", font=self.text_font, bg=self.bg_color)
label_line.grid(row=0, column=0)
label_line = Label(self.root, text=" ", font=self.text_font, b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_settings_devices(self):\n self.set_thermostat, self.set_humidifier, self.set_sprinklers, self.set_ventilation = self.settings[3:]",
"def test_change_config(self):\n browser = Browser(self.app)\n portalURL = self.portal.absolute_url()\n browser.addHeader('Authorization', 'Basic... | [
"0.67250514",
"0.6616514",
"0.6587167",
"0.6572284",
"0.65574336",
"0.65571165",
"0.65545833",
"0.6387912",
"0.6380834",
"0.6380834",
"0.6371281",
"0.63633394",
"0.63439715",
"0.61811125",
"0.6178413",
"0.61066014",
"0.60833853",
"0.6053914",
"0.6019179",
"0.60047317",
"0.599... | 0.7095382 | 0 |
this function saves the changes in the simulator settings made by the user. | def save_settings(self, rotor_num1, rotor_num2, rotor_num3, rotor_l1, rotor_l2, rotor_l3):
dict_rotor_num = {"I": 1, "II": 2, "III": 3, "IV": 4, "V": 5}
label_txt = ""
rotor_num1 = dict_rotor_num[rotor_num1]
rotor_num2 = dict_rotor_num[rotor_num2]
rotor_num3 = dict_rotor_num... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def saveSettings(self):\n self.userFiles.applyData()\n self.userPersonal.applyData()",
"def save_settings(self):\n settings = {'camera': self.comboCamera.currentIndex(),\n 'rotation': self.comboRotation.currentIndex(),\n 'colors': {\n ... | [
"0.76047224",
"0.7509991",
"0.7410456",
"0.7324456",
"0.7254209",
"0.72053295",
"0.71797514",
"0.7078135",
"0.703043",
"0.70278335",
"0.7012316",
"0.7006509",
"0.6991482",
"0.69491667",
"0.69386387",
"0.6928301",
"0.690888",
"0.6904757",
"0.6899911",
"0.6898468",
"0.68815815"... | 0.0 | -1 |
this function adds a letter to the plugboard | def add_letter_in_plugboard(self, letter, lst_buttons):
self.simulator_enigma.plugboard.add_letter(letter)
self.set_plugboard(lst_buttons) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_letter(self, letter):\r\n if (len(self.plugboard1) < 10 or\r\n (len(self.plugboard1) == 10 and self.plugboard2[-1] is None)) and \\\r\n letter not in self.plugboard1 and letter not in self.plugboard2:\r\n if len(self.plugboard1) == 0 or (len(self.plugboard1) < 10... | [
"0.74521935",
"0.6864215",
"0.6653494",
"0.6578328",
"0.6481865",
"0.64462173",
"0.64435834",
"0.6416639",
"0.6378046",
"0.61352384",
"0.61273557",
"0.60847396",
"0.60462296",
"0.60263854",
"0.60161275",
"0.597719",
"0.5976378",
"0.5974929",
"0.596187",
"0.59597677",
"0.59399... | 0.84619296 | 0 |
this function sets the plugboard in the simulator settings and lets the user edit it. | def set_plugboard(self, lst_buttons):
colors = ['purple', 'yellow', 'blue', 'orange', 'coral4', 'pink', 'cyan',
'SpringGreen2', 'red', 'green']
used_colors = list(filter(lambda button_bg: button_bg != "khaki",
[i['bg'] for i in lst_buttons]))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateSettingsUI(self):\n\n pass",
"def change_settings(self):\r\n self.clear_screen()\r\n # making sure the screen grid will be organized\r\n label_line = Label(self.root, text=\" \", font=self.text_font, bg=self.bg_color)\r\n label_line.grid(row=0, column=0)\r\n ... | [
"0.60254747",
"0.6003152",
"0.5903891",
"0.58452857",
"0.5839012",
"0.5837211",
"0.5816409",
"0.57903934",
"0.57348806",
"0.5674967",
"0.5640835",
"0.56229097",
"0.55756176",
"0.555146",
"0.55273783",
"0.5479405",
"0.54654926",
"0.54645574",
"0.5461459",
"0.5450382",
"0.54115... | 0.5971404 | 2 |
this function shows the encryption process in the enigma. | def show_simulator_encryption(self, rotors_settings, plugboard_settings, plain_text,
letter_number=1):
self.clear_screen()
if len(self.simulator_encryption) > 0:
user_label = Label(self.root, text="Hello " + self.username,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encrypt(event=None): # event is passed by binders.\n msg = inputText.get(\"1.0\",tkinter.END)\n outText.delete('1.0', tkinter.END)\n\n f = open(myTmpDir + 'pt' + str(identity) + '.bin','wb')\n f.write(msg)\n f.close()\n\n os.popen(\"rsa.exe e \" + myTmpDir + \"pt\" + str(identity) + \".bin \... | [
"0.61503357",
"0.60890895",
"0.6056018",
"0.60102695",
"0.5848196",
"0.5827546",
"0.58185095",
"0.581306",
"0.58001035",
"0.5777466",
"0.5757728",
"0.5731634",
"0.5707361",
"0.57026505",
"0.56965256",
"0.5668564",
"0.5661669",
"0.5643876",
"0.5641259",
"0.56192094",
"0.560030... | 0.5880569 | 4 |
this function shows the About the Project window. it shows information regarding the project. | def about_screen(self):
self.clear_screen()
user_label = Label(self.root, text="Hello " + self.username,
font=self.title_font, bg=self.bg_color, height=2)
user_label.pack(pady=10, padx=50)
about_text = """My name is Jasmin, I am 17 years old and this is my... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show():\n info(str(Project))",
"def aboutmenu(self):\n tkMessageBox.showinfo(\"About This Program\", \"The project of PSIT subject in 2014.\\nThis program is unit converter program.\")",
"def about_developer(self):\r\n self.pop_window(title=\"About\", \r\n msg=\"ChikonEye Version: 2... | [
"0.7788291",
"0.7652768",
"0.7533267",
"0.75056034",
"0.74691767",
"0.74680126",
"0.7466024",
"0.7465043",
"0.74397117",
"0.7409726",
"0.74092597",
"0.73757994",
"0.7321502",
"0.73042005",
"0.7288934",
"0.72532016",
"0.723343",
"0.722521",
"0.7187642",
"0.7187137",
"0.7175867... | 0.0 | -1 |
clears the screen from widgets. | def clear_screen(self):
lst_grid = self.root.grid_slaves()
for widget in lst_grid:
widget.destroy()
lst_pack = self.root.pack_slaves()
for widget in lst_pack:
widget.destroy() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear(self) -> None:\n self.screen.clear()",
"def clear(screen):\n screen.clear()\n screen.refresh()",
"def clear_widgets(self):\n self.json_progress = None\n self.progress_message_bar = None\n self.json_progress_message_bar = None\n if self.progress_message_bar_wid... | [
"0.8092894",
"0.803967",
"0.78183275",
"0.7779582",
"0.77743113",
"0.7751474",
"0.7615676",
"0.76115173",
"0.7582601",
"0.75689197",
"0.74467134",
"0.7411865",
"0.74054694",
"0.7385278",
"0.7318546",
"0.7276925",
"0.72769",
"0.72769",
"0.72497535",
"0.7242884",
"0.7230591",
... | 0.86054236 | 0 |
this is a thread method. it receives messages from sever and then decrypts them. | def receive(self):
print("waiting for messages")
finish = False
morse_object = Morse()
while not finish:
enigma_sim = Enigma()
try:
chunks = []
bytes_recd = 0
msg_length = loads(self.my_socket.recv(8000))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(self):\n buff = self.conn.recv(4096)\n if (self.algo == \"rsa\"):\n buff = self.rsa_decrypt(buff)\n if (self.algo == \"des\"):\n buff = self.des_decrypt(buff)\n if (self.algo == \"3des\"):\n buff = self.triple_des_decrypt(buff)\n if (self... | [
"0.744655",
"0.6937561",
"0.68522006",
"0.67841697",
"0.66552895",
"0.66094726",
"0.6527624",
"0.6526651",
"0.6401564",
"0.63021964",
"0.6296313",
"0.62897265",
"0.62491965",
"0.62269497",
"0.6191575",
"0.6179099",
"0.613436",
"0.60896724",
"0.60787946",
"0.6019799",
"0.60061... | 0.6854434 | 2 |
send a message to the rest of the clients. if from the send box encrypts according to the time. otherwise, from the simulator encrypts according to its settings. | def send(self, text_box, rotors_settings=None, plugboard_settings=None):
morse_instance = Morse()
enigma_sim = Enigma()
if plugboard_settings is None and rotors_settings is None:
enigma_sim.set_random_settings()
else:
enigma_sim.rotors.set_rotors(rotors_sett... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sendToClient(plaintext):\n signature = userKeys.signUsingPrivateKey(plaintext)\n encryptedText = userKeys.encrypt(plaintext, contactKey)\n s.send(encryptedText)\n time.sleep(1)\n s.send(signature)",
"def send(event=None): # event is passed by binders.\n msg = my_msg.get... | [
"0.72709787",
"0.6771924",
"0.65045714",
"0.64544195",
"0.63171625",
"0.63171625",
"0.63171625",
"0.630371",
"0.6284336",
"0.62738705",
"0.6248401",
"0.6222988",
"0.62097204",
"0.6184484",
"0.6150045",
"0.61194193",
"0.60920644",
"0.60865146",
"0.607641",
"0.6057104",
"0.6055... | 0.66178226 | 2 |
this function customizes the text so it can be encrypted. | def manage_text(msg):
msg = msg.upper()
msg_final = ""
for i in msg:
if i.isalpha():
msg_final += i
return msg_final | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _encrypt(self, text, **options):\n\n raise CoreNotImplementedError()",
"def __encrypt_text_aes__(self, text, password):\n BLOCK_SIZE = 32\n PADDING_CHAR = b'^'\n iv = Random.new().read(16)\n # key must be 32 bytes for AES-256, so the password is hashed with md5 first\n ... | [
"0.6969536",
"0.69532967",
"0.6782029",
"0.6768376",
"0.6672109",
"0.656223",
"0.6561474",
"0.65578926",
"0.6497807",
"0.6438681",
"0.6425754",
"0.6420916",
"0.6402289",
"0.630223",
"0.63020754",
"0.63016236",
"0.6272338",
"0.6262189",
"0.6205668",
"0.6191833",
"0.6173925",
... | 0.0 | -1 |
Retrieve fastq files for the given lane, ready to process. | def get_fastq_files(directory, work_dir, item, fc_name, bc_name=None, glob_ext="_fastq.txt",
config=None, unpack=True):
if "files" in item and bc_name is None:
names = item["files"]
if isinstance(names, basestring):
names = [names]
files = [x if os.path.isabs(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_fastqs(location, project_id, sample_id, lane=None):\n basename = '*.fastq.gz'\n if lane:\n basename = '*L00' + str(lane) + basename\n\n pattern = os.path.join(location, project_id, sample_id, basename)\n fastqs = find_files(pattern)\n app_logger.debug('Found %s fastq files for %s', l... | [
"0.74992424",
"0.5997929",
"0.5949587",
"0.59173447",
"0.5860292",
"0.5804238",
"0.55708313",
"0.55603546",
"0.54513943",
"0.5397355",
"0.53798234",
"0.53523076",
"0.5351622",
"0.534139",
"0.5237773",
"0.52329355",
"0.5212102",
"0.5193668",
"0.5143916",
"0.5136838",
"0.511812... | 0.61918277 | 1 |
Convert BAM input file into FASTQ files. | def convert_bam_to_fastq(in_file, work_dir, config):
out_dir = safe_makedir(os.path.join(work_dir, "fastq_convert"))
out_files = [os.path.join(out_dir, "{0}_{1}.fastq".format(
os.path.splitext(os.path.basename(in_file))[0], x))
for x in ["1", "2"]]
if _is_paired(in_file):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_fastq(self, prefix='', threads=1):\n # Write to uncompressed FASTQ for speed\n fastqs = [\n f'{prefix}_{i+1}.fastq.gz' if prefix else f'{i+1}.fastq.gz'\n for i in range(self.technology.n_files)\n ]\n logger.info(f'Splitting BAM file into FASTQs {\", \".join(... | [
"0.70499676",
"0.6740654",
"0.672978",
"0.64483637",
"0.63435805",
"0.60342306",
"0.5887938",
"0.58241993",
"0.58233976",
"0.5711803",
"0.56742835",
"0.5673183",
"0.5609216",
"0.5567203",
"0.54960185",
"0.54638755",
"0.54510033",
"0.5442137",
"0.5425194",
"0.5418631",
"0.5411... | 0.64614826 | 3 |
Convert barcode id to sample description, changing extension from _fastq.txt to .fastq in the process | def convert_barcode_id_to_name(multiplex, fc_name, fq):
fqout = list([None, None])
if multiplex is None:
fqout[0] = fq[0]
if not fq[1] == None:
fqout[1] = fq[1]
else:
bcid2name = dict([(mp['barcode_id'], mp['name']) for mp in multiplex])
for bcid in bcid2name.keys... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_name_to_barcode_id(multiplex, fc_name, fq):\n fqout = list([None, None])\n name2bcid = dict([(mp['name'], mp['barcode_id']) for mp in multiplex])\n for name in name2bcid.keys():\n mstr = \"%s_%s_\" % (fc_name, name) \n if fq[0].find(mstr) != -1:\n from_str = \"%s_%s_\"... | [
"0.6623599",
"0.63540995",
"0.59808934",
"0.5929442",
"0.58410364",
"0.57554716",
"0.5716192",
"0.5710112",
"0.5637982",
"0.5631498",
"0.5599545",
"0.5590032",
"0.5577469",
"0.55256295",
"0.5518841",
"0.5507794",
"0.5507233",
"0.5487758",
"0.5482285",
"0.54775095",
"0.5447416... | 0.6429612 | 1 |
Convert sample description to barcode id, changing extension from .fastq to _fastq.txt in the process | def convert_name_to_barcode_id(multiplex, fc_name, fq):
fqout = list([None, None])
name2bcid = dict([(mp['name'], mp['barcode_id']) for mp in multiplex])
for name in name2bcid.keys():
mstr = "%s_%s_" % (fc_name, name)
if fq[0].find(mstr) != -1:
from_str = "%s_%s_" %(fc_name, nam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_barcode_id_to_name(multiplex, fc_name, fq):\n fqout = list([None, None])\n if multiplex is None:\n fqout[0] = fq[0]\n if not fq[1] == None:\n fqout[1] = fq[1]\n else:\n bcid2name = dict([(mp['barcode_id'], mp['name']) for mp in multiplex])\n for bcid in b... | [
"0.635295",
"0.6289378",
"0.6153358",
"0.59830755",
"0.5973969",
"0.59590584",
"0.59040374",
"0.58626246",
"0.5838718",
"0.57817864",
"0.5680377",
"0.5662274",
"0.5659372",
"0.56566316",
"0.5644152",
"0.5641196",
"0.5615736",
"0.5564444",
"0.5530782",
"0.5522661",
"0.54676783... | 0.6725366 | 0 |
Create a schema from a commaseparated list of field specifications. | def ReadSchema(schema, bigquery_messages):
return bigquery_messages.TableSchema(
fields=[
_TableFieldSchemaForEntry(entry, bigquery_messages)
for entry in schema.split(',')]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_field_list(field_desc_list, names):\n for index, field_desc in enumerate(field_desc_list):\n yield RecordSchema._make_field(index, field_desc, names)",
"def from_fields(cls, field_list):\n field_dict = dict(field_list)\n return cls(**field_dict)",
"def build_schema(self... | [
"0.64310926",
"0.63509125",
"0.6165725",
"0.60274005",
"0.586239",
"0.57277334",
"0.56566375",
"0.5651166",
"0.5536092",
"0.5491589",
"0.5431351",
"0.5414425",
"0.53576344",
"0.5345713",
"0.532309",
"0.5318973",
"0.5298094",
"0.5284337",
"0.5270332",
"0.52690184",
"0.5243194"... | 0.0 | -1 |
Create a schema message from the name of a file containing a JSON schema. | def ReadSchemaFile(schema_file, bigquery_messages):
if os.path.exists(schema_file):
with open(schema_file, mode='r') as f:
try:
def UpperOrNone(string):
return string and string.upper()
field_schemas = [
bigquery_messages.TableFieldSchema(
name=json_obj... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_schema(filename):\n with open(filename) as f:\n schema = json.load(f)\n\n return schema",
"def _load_json_schema(filename: str):\n relative_path = path.join('schemas', filename)\n absolute_path = path.join(path.dirname(__file__), relative_path)\n\n with open(absolute_path, 'r', enc... | [
"0.7130944",
"0.7071065",
"0.7037193",
"0.6999802",
"0.67850494",
"0.6669903",
"0.6631411",
"0.66249794",
"0.6621027",
"0.6499103",
"0.64794594",
"0.64401793",
"0.64254344",
"0.6370778",
"0.6337134",
"0.6103374",
"0.60779864",
"0.60144544",
"0.59893954",
"0.5973443",
"0.59529... | 0.67639804 | 5 |
Drop alembic table for gdrive migrations. For db_reset, this table isn't supposed to exist since the migration chain is disabled. For existing databases, this table exists but won't be used since the migration chain is disabled. | def upgrade():
try:
op.drop_table("ggrc_gdrive_integration_alembic_version")
except sa.exc.OperationalError as e:
code, _ = e.orig.args
if code == 1051: # doesn't exist
# we're in a new DB with no trace of the removed chain
pass
else:
raise
# The following duplicates a part of ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop():\n if prompt_bool(\"Are you sure you want to lose all your data\"):\n db.drop_all()\n db.engine.execute(\"drop table if exists alembic_version\")",
"def drop_table(cls)->None:\n database.cursor.execute(\n \"DROP TABLE IF EXISTS {}\".format(cls.table_name))\n d... | [
"0.72148544",
"0.71591735",
"0.71193475",
"0.6952644",
"0.6881675",
"0.6875262",
"0.68424946",
"0.68139595",
"0.6792357",
"0.67086965",
"0.6690821",
"0.66735405",
"0.66718906",
"0.6636215",
"0.6631251",
"0.66307104",
"0.66115475",
"0.66045314",
"0.660408",
"0.6603744",
"0.659... | 0.6836839 | 7 |
Restore alembic table for gdrive migrations with latest correct content. Since the migration chain is disabled, this table won't be used. If the migration chain gets enabled, this table will contain correct tag for downgrades. | def downgrade():
op.execute("""
CREATE TABLE ggrc_gdrive_integration_alembic_version (
version_num varchar(32) NOT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8
""")
op.execute("""
INSERT INTO ggrc_gdrive_integration_alembic_version (version_num)
VALUES ('3f64d03c6c01')
""") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downgrade():\n # # commands auto generated by Alembic - please adjust! ###\n op.drop_table('downstream_map')\n # # end Alembic commands ###",
"def upgrade():\n try:\n op.drop_table(\"ggrc_gdrive_integration_alembic_version\")\n except sa.exc.OperationalError as e:\n code, _ = e.orig.args\n ... | [
"0.6851007",
"0.68070155",
"0.6506316",
"0.64786386",
"0.64288044",
"0.6319381",
"0.61630434",
"0.59586895",
"0.5892953",
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"0.57475394",
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"0.566395",
"0.5661779",
"0.56194675",
"0.5548934",
"0.5502293",
"0.549745",
"0.548365... | 0.70814365 | 0 |
call the registered function and return its results | def get(name,**kwargs):
try:
f = MyRegister._REGISTERED[name]
except KeyError:
raise KeyError("Dataset:'{} is registered Available dataset are:{}".format(name,",".join(MyRegister._REGISTERED.keys())) )
return f(**kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\n return self.fn()",
"def test_runs_given_function(self):\n from furious.processors import _handle_results\n\n processor = Mock()\n\n _handle_results({'_process_results': processor})\n\n processor.assert_called_once_with()",
"def call(self, data):\n\t\tret... | [
"0.675645",
"0.6693326",
"0.6510391",
"0.6497139",
"0.6425742",
"0.6425742",
"0.64201176",
"0.63799745",
"0.6349983",
"0.63358104",
"0.6335209",
"0.6309102",
"0.6287538",
"0.62626725",
"0.62462807",
"0.6244996",
"0.62310064",
"0.62300724",
"0.61896855",
"0.6153306",
"0.614721... | 0.0 | -1 |
Get HartreeFock values for H2 | def get_hf_value_by_dist(basis_set, distance):
try:
# Round to one because the actual distances might vary a little because of the psi4 issue.
distance = round(distance, 1)
if basis_set == "sto-3g":
if distance in sto_3g.keys():
return sto_3g[distance]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_h(f1):\n h_5_in = f1.Get(\"neutron_spectrum_tof_in4.69keVnr\")\n h_5_out = f1.Get(\"neutron_spectrum_tof_out4.69keVnr\")\n h_8_in = f1.Get(\"neutron_spectrum_tof_in8.33keVnr\")\n h_8_out = f1.Get(\"neutron_spectrum_tof_out8.33keVnr\")\n h_15_in = f1.Get(\"neutron_spectrum_tof_in14.75keVn... | [
"0.6044786",
"0.5959901",
"0.55953676",
"0.5572713",
"0.55250454",
"0.5487814",
"0.54236287",
"0.5314009",
"0.53044504",
"0.5291509",
"0.5269112",
"0.524244",
"0.51941276",
"0.5174624",
"0.5120342",
"0.5103384",
"0.507804",
"0.5052936",
"0.5041796",
"0.5024648",
"0.5020925",
... | 0.0 | -1 |
Browse the folder to locate the json file | def browse_folder(self):
# Get the file name from the user selection
filename = tkinter.filedialog.askopenfilename(initialdir=".", title="Select JSON file",
filetypes=(("json files", "*.json"), ("txt files", "*.txt"),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH",
"def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH",
"def test_files(self, location):\n for filename in os.listdir(location):\n with open(location + '/' + filename) as json_file:\n data = json.l... | [
"0.62802076",
"0.62802076",
"0.6238737",
"0.61273605",
"0.60416484",
"0.5966435",
"0.59093004",
"0.59044904",
"0.5819813",
"0.5813722",
"0.57989556",
"0.5781541",
"0.56752443",
"0.5675066",
"0.5660146",
"0.5640253",
"0.56322503",
"0.5631106",
"0.5621598",
"0.56130177",
"0.560... | 0.7507324 | 0 |
Build/load the barcode use the json file | def build_barcode(self):
# Get the file name/path to the json file
filename = self.filename_entry.get()
# Check if the filename is given
if not os.path.exists(filename):
showerror("JSON File Not Exists", "JSON file not exists.\n"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(self):\n # open json, len 161,260\n at_json = open_json(self.json_names[0])\n link_json = open_json(self.json_names[1])\n # if need preprocessing, do it\n if self.args.img_preprocessing:\n print(\"resize imgs\")\n for i in tqdm(range(len(link_json)... | [
"0.58827484",
"0.578684",
"0.5714043",
"0.56993884",
"0.5639737",
"0.5583572",
"0.54636484",
"0.5450821",
"0.54405653",
"0.54265416",
"0.5424942",
"0.5409185",
"0.53689647",
"0.53638726",
"0.5357695",
"0.5356618",
"0.53302157",
"0.5321161",
"0.53171873",
"0.53061134",
"0.5295... | 0.78892285 | 0 |
this function create folder for the captured frames and videos | def dirCapture(parameter, path):
# N should be argument of instances NO.
N = parameter
# set path for the captured frames
cpath = path + '%d' % N + '/'
# create directory if not exist
while (os.path.exists(cpath)):
# print('instance N%d' % N + ' exists')
N = N + 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_video(image_folder, video_name, video_frames_path):\n \n try:\n os.stat(video_frames_path)\n except:\n os.makedirs(video_frames_path)\n \n images = [img for img in os.listdir(image_folder)\n if img.endswith(\".jpg\") or\n img.endswith(\".jpeg\") o... | [
"0.701783",
"0.70178145",
"0.70023805",
"0.66897815",
"0.6628432",
"0.6599192",
"0.6571847",
"0.6534987",
"0.64688194",
"0.64438266",
"0.64366823",
"0.63567317",
"0.6308261",
"0.62857354",
"0.6236522",
"0.62322396",
"0.62297314",
"0.62270474",
"0.62222314",
"0.62166524",
"0.6... | 0.66204894 | 5 |
this function save resizer performence info to .txt file | def printInfo(totaltime, vid, cpath):
infotxt = open(cpath + 'Resize Info' + '.txt', 'a')
info = str('executeTime: %f' % totaltime + '\n')
converageRate = totaltime / (vid.get(7))
info += str('average converage rate is: %f' % converageRate + 'f/s' + '\n')
frameNum = vid.get(7)
info += str(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_results(self, name):\n # metrics in npz format\n # filename = join(self.out_dir, '%s_metrics.npz' % name)\n # np.savez(filename, tel_r=self.tel_r, cable_length=self.cable_length,\n # cable_length_2=self.cable_length_2, uv_hist=self.uv_hist)\n\n if self.cable_len... | [
"0.6311785",
"0.61142427",
"0.5883345",
"0.5869845",
"0.5838297",
"0.58195186",
"0.5771335",
"0.57199216",
"0.5712982",
"0.57044137",
"0.55961275",
"0.5577182",
"0.55757207",
"0.5559564",
"0.554992",
"0.5543646",
"0.5524419",
"0.5517075",
"0.55147904",
"0.55009633",
"0.549900... | 0.0 | -1 |
this function resize the recived video to size(480,480) and captured resized frames during the process | def resizeVideo(n, format, vpath, cpath):
start_time = time.time()
t = time.process_time()
vidcap = cv2.VideoCapture(vpath)
success, image = vidcap.read()
cv2.namedWindow('image')
cv2.imshow('image', image)
cv2.waitKey(1)
count = 0
CODE = 'XVID'
# default save to avi
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resize(frame: Frame):\n\n frame.img = cv2.resize(frame.img, (720, 480))\n frame.orginal_img = cv2.resize(frame.orginal_img, (720, 480))\n\n return frame",
"def resize_video(frames, width: int = UNIVERSAL_RESIZE):\n if frames.size < 1:\n raise ValueError('Must provide at least o... | [
"0.6975515",
"0.6965948",
"0.6840254",
"0.66904473",
"0.6561962",
"0.65187913",
"0.64801097",
"0.6479887",
"0.6456022",
"0.64197224",
"0.64138234",
"0.63360685",
"0.6334037",
"0.6292876",
"0.62918407",
"0.62625307",
"0.62448573",
"0.61914134",
"0.61655945",
"0.6139185",
"0.61... | 0.74632716 | 0 |
this function is action for the resize buttons to use, mainly call the resizeVideo methods | def resizeButton(format,vpath,cpath):
if os.path.exists(cpath):
cPath=cpath+'/vid-instance'
if os.path.exists(vpath):
vPath=vpath
N, cPath = dirCapture(1, cPath)
resizeVideo(N, format, vPath, cPath) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handleResize(self):\n pass",
"def resize(self):\n pass",
"def resize(self, event=None):\n #self.render()\n self.__resize_background(event)\n #self.__delete_background()\n #self.__create_background(self._imfname)\n for sym in self.itersymbols():\n ... | [
"0.7466857",
"0.67581195",
"0.6693336",
"0.6686181",
"0.6664426",
"0.66365945",
"0.6623954",
"0.6581972",
"0.65713274",
"0.6529807",
"0.6507639",
"0.6472563",
"0.6404809",
"0.6373768",
"0.63407046",
"0.632196",
"0.63161606",
"0.62875193",
"0.62567973",
"0.624347",
"0.62312657... | 0.74494356 | 1 |
this function manage a multiprocessing resize through keeping all the process in the pool | def multicore(format, filenames, cpath):
start = time.time()
po = multiprocessing.Pool(P)
file = str(filenames).split(',')
for file in filenames:
print(file)
po.apply_async(func=resizeButton, args=(format, file, cpath))
time.sleep(M)
print("Done")
po.close()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resize_all_images(image_dir, width, height, resize_type):\n if width == \"\" and height == \"\":\n return 0\n print(\"Enter resizing image.\")\n print(\"Enter resizing.\", width)\n pool = Pool(1)\n pool.starmap(resize_image, zip(\n image_dir, itertools.repeat(width), itertools.repe... | [
"0.63549966",
"0.6220944",
"0.59649426",
"0.59593916",
"0.59510225",
"0.58933437",
"0.58215255",
"0.5820519",
"0.58105135",
"0.57659066",
"0.5631996",
"0.5616365",
"0.5570653",
"0.5546573",
"0.5539349",
"0.55334586",
"0.5523554",
"0.5502881",
"0.5492138",
"0.5492138",
"0.5480... | 0.61451405 | 2 |
this function is called when the saveButton is clicked | def getCapturePath(self):
global cpath
cpath = askdirectory() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save():\n pass",
"def save():",
"def save (self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n pass",
"def save(self):\n # TODO (Pierre): code",
"def ... | [
"0.8244069",
"0.82181466",
"0.808178",
"0.8036641",
"0.8036641",
"0.8036641",
"0.8036641",
"0.8036641",
"0.8025759",
"0.79084253",
"0.790276",
"0.78900355",
"0.78506833",
"0.77407193",
"0.7713274",
"0.769706",
"0.769706",
"0.76262254",
"0.76262254",
"0.76262254",
"0.74757344"... | 0.0 | -1 |
this function is called when the openButton is clicked | def getVideosPath(self):
videoTypes = [
('MP4 files', '*.mp4'),
('3GP files', '*.3gp'),
('WMV files', '*.wmv'),
('FLV files', '*.flv'),
('AVI files', '*.avi'),
]
global filenames
filenames = askopenfilenames(title="Sele... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onOpen(self):",
"def open(self):\n self.state = True\n self.mainwindow.sendMessage('a')\n print(\"opening \" + self.name)",
"def on_lookpushButton_clicked(self):\n # TODO: not implemented yet\n self.openFile()",
"def trigger_open(self):\n self.get_selected()\n ... | [
"0.7722792",
"0.7232599",
"0.70331657",
"0.69601965",
"0.69194776",
"0.68946415",
"0.6837131",
"0.68361264",
"0.6835772",
"0.67767406",
"0.6758912",
"0.6709013",
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"0.6662738",
"0.66621774",
"0.66406375",
"0.66406375",
"0.66406375",
"0.66406375",
"0.6637556",
"0.6... | 0.0 | -1 |
this function is called when startButton is called and will use resizeButton() function to resize videos sequentially | def startResizing(self):
# total running times
global totaltime
start = time.time()
try:
str(cpath)
try:
file = str(filenames).split(',')[0].strip("('")
if os.path.exists(file):
print(file)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resizeButton(format,vpath,cpath):\r\n if os.path.exists(cpath):\r\n cPath=cpath+'/vid-instance'\r\n if os.path.exists(vpath):\r\n vPath=vpath\r\n N, cPath = dirCapture(1, cPath)\r\n resizeVideo(N, format, vPath, cPath)",
"def init_gui(self):\r\n self.root.title('Video... | [
"0.6814723",
"0.6706321",
"0.6082444",
"0.60067904",
"0.6005091",
"0.59289396",
"0.59223634",
"0.5910596",
"0.58606327",
"0.5837265",
"0.5814798",
"0.58082414",
"0.5801105",
"0.57729465",
"0.57504654",
"0.571494",
"0.57109207",
"0.5694669",
"0.5657087",
"0.5645747",
"0.562265... | 0.6654774 | 2 |
this function is called when multiStartButton is called and will use multicore() function to resize videos in parallel | def startMultiResizing(self):
global totaltime
try:
str(cpath)
try:
str(filenames)
try:
print(filenames)
totaltime = multicore(Format, filenames, cpath)
self.resultLabel['text'] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multicore(format, filenames, cpath):\r\n start = time.time()\r\n po = multiprocessing.Pool(P)\r\n file = str(filenames).split(',')\r\n for file in filenames:\r\n print(file)\r\n po.apply_async(func=resizeButton, args=(format, file, cpath))\r\n time.sleep(M)\r\n print(\"Done\... | [
"0.6563744",
"0.64110225",
"0.5975478",
"0.59674823",
"0.59062296",
"0.5873052",
"0.58316374",
"0.58308446",
"0.578684",
"0.5743147",
"0.5654799",
"0.56200457",
"0.5559133",
"0.55516684",
"0.5550606",
"0.5521735",
"0.546656",
"0.54456455",
"0.5401045",
"0.5391771",
"0.5387132... | 0.69108474 | 0 |
this function will create information string used to display in the resultLabel | def Results(self):
try:
numOfFiles = 0
file = str(filenames).split(',')
for file in filenames:
if os.path.exists(file):
numOfFiles += 1
print('%d' % numOfFiles + ' videos resized!')
info = 'totaltime:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n if len(self.label) > 0:\n descr = [\"'%s', target='%s' [%s]\" % (self.label, self.target.name, self.target.body_type)]\n else:\n descr = [\"target='%s' [%s]\" % (self.target.name, self.target.body_type)]\n if self.baseline:\n descr[0] += ',... | [
"0.6842208",
"0.670873",
"0.67032593",
"0.670306",
"0.6628362",
"0.654801",
"0.65101",
"0.6500394",
"0.6484612",
"0.6476954",
"0.64659363",
"0.6436427",
"0.6426024",
"0.64189446",
"0.6418149",
"0.64093786",
"0.64064646",
"0.64030755",
"0.63778704",
"0.63757837",
"0.63677",
... | 0.0 | -1 |
this function build the GUI | def init_gui(self):
self.root.title('Video Resizer')
self.root.option_add('*tearOff', 'FALSE')
self.grid(column=0, row=0, sticky='nsew')
# Buttons getvideos, save videos, start resize
self.openButton = ttk.Button(self, width=8, text="Browse", command=self.getVideosPath)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_UI(self):\n\n #Common local coordinates to change the UI positions\n common_x = 0\n common_y = 5\n\n #Create the Main Title\n self.titleFont = font.Font(family = FONTS[\"lucida grande\"], size = 30)\n self.title = cGUIf.get_TextLabel(self,\n ... | [
"0.7662315",
"0.76499087",
"0.7620427",
"0.749656",
"0.7488436",
"0.7481345",
"0.74784184",
"0.74288124",
"0.73963094",
"0.7382726",
"0.7380994",
"0.7349861",
"0.73217744",
"0.7301817",
"0.7283657",
"0.7255085",
"0.7252039",
"0.72487587",
"0.72324187",
"0.72228116",
"0.719720... | 0.66845316 | 68 |
button clicked to select video type called when action is clicked | def clickMe():
global Format
Format = typeToChoose.get()
print(Format)
action.configure(text='selected ' + Format) # show the selected item after clicked
action.configure(state='disabled') # button disabled after clicked
| {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def onclick_load_video(self):\n video_source = select_file(\n \"Select Video Files\",\n \"../\",\n \"Video Files (*.mp4 *.avi *.mpg *.gif *.mov)\")\n if video_source:\n param_name = select_file(\n \"Select Parameter\", \"../\", \"Parameter Fi... | [
"0.6792233",
"0.64212275",
"0.6258068",
"0.6204305",
"0.60913473",
"0.59596056",
"0.5902013",
"0.58849704",
"0.5827223",
"0.5816501",
"0.58017844",
"0.57247883",
"0.5711773",
"0.5698065",
"0.5668826",
"0.5657517",
"0.5654947",
"0.5625984",
"0.56113243",
"0.5544811",
"0.552534... | 0.55848324 | 19 |
Add metadata info to the header. | def add_metadata(self, column_name, data_type=None, version=None, description=None, dbname=None, delimiter='\t'):
data_line = '##COLUMNNAME='+'"'+ column_name +'"'
if column_name not in self.metadata:
if data_type:
if data_type not in ['Float', 'String', 'Integer']:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_metadata(self, metadata: dict) -> None:",
"def add_metadata(self, key, value):\n self._h5.attrs[key] = value",
"def add_file_metadata(self):\n metadata = self.__file.require_group(METADATA)\n self.__write_value(metadata, DATE_CREATED, date.today().strftime(\"%Y-%m-%d\"))\n s... | [
"0.7814327",
"0.7194901",
"0.70095164",
"0.6962156",
"0.69360846",
"0.68795687",
"0.68465805",
"0.6778648",
"0.6778648",
"0.6666871",
"0.6661003",
"0.65671706",
"0.6526206",
"0.6526206",
"0.6526206",
"0.6526206",
"0.6526206",
"0.6526206",
"0.6526206",
"0.6518479",
"0.65116614... | 0.6448522 | 23 |
Add an entry to the header line. The entry must be specified among the metadata lines first. | def add_header(self, header_name):
self.check_header(header_name)
if header_name not in self.header:
self.header.append(header_name)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_header(self, delta, header):\n\n if not delta or not header:\n return\n\n header_line = f\"{header}\\n\"\n\n delta.insert(0, header_line)",
"def add_header( name, value ):",
"def add_new_header(self, hdr):\n # if there are any lines in the unchanged-lines buff... | [
"0.7299191",
"0.6941185",
"0.6682752",
"0.66754097",
"0.66357225",
"0.64103943",
"0.64103943",
"0.6305915",
"0.6265915",
"0.6251994",
"0.6190674",
"0.61357397",
"0.6116551",
"0.6010695",
"0.59882915",
"0.598272",
"0.59807855",
"0.59691834",
"0.59167445",
"0.5910145",
"0.59081... | 0.625466 | 9 |
Returns a list with the metadata lines on correct format. | def get_headers_for_print(self):
lines_for_print = []
for header in self.metadata:
lines_for_print.append(self.metadata[header])
lines_for_print.append('\t'.join(self.header))
lines_for_print[-1] = '#' + lines_for_print[-1]
return lines_for_print | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMetadataList(self):\n return [ x.toTuple() for x in self.metadata ]",
"def get_metadata_header_lines(input_file):\n # type: (str) -> List[str]\n if not FileSystems.exists(input_file):\n raise ValueError('{} does not exist'.format(input_file))\n return[line for line in _header_line_generator(input... | [
"0.6990839",
"0.6778577",
"0.67761546",
"0.6596873",
"0.6596873",
"0.64930004",
"0.63260543",
"0.6162002",
"0.61361414",
"0.61300284",
"0.61048824",
"0.60900474",
"0.6013875",
"0.60055214",
"0.59965605",
"0.59938395",
"0.5990799",
"0.5955255",
"0.5949237",
"0.59370655",
"0.59... | 0.6469205 | 6 |
All entrys in the header must be specified in the metadata lines. | def check_header(self, entry):
if entry not in self.metadata:
raise SyntaxError("Header entry must be described in the metadata lines. Entry: %s is not in metadata." % entry) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_header_missing_fields(self):\r\n\r\n # Default header, should not generate any errors/warnings\r\n header = ['AAA', 'XXX', 'YYY',\r\n 'ZZZ']\r\n errors = []\r\n warnings = []\r\n\r\n errors, warnings = check_header(header,\r\n ... | [
"0.6268195",
"0.6264674",
"0.62105215",
"0.6196427",
"0.6144928",
"0.6113776",
"0.60642403",
"0.60047483",
"0.5985811",
"0.5964642",
"0.59487873",
"0.5914528",
"0.5880523",
"0.58377224",
"0.58223",
"0.58066964",
"0.5804777",
"0.5768354",
"0.5765713",
"0.57347786",
"0.5703136"... | 0.7916447 | 0 |
Test selection of a leaf node | def testSelectLeaf(self):
menu = self.menu
items = self.items
assertTrue = self.assertTrue
assertIsNone = self.assertIsNone
menu.select(tag="a11")
assertTrue(menu.selected)
assertTrue(items["a1"].selected)
assertTrue(items["a11"].selected)
asse... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IsLeaf(self):\n return not self.subtests",
"def is_leaf(node):\n return node.children == {}",
"def check_leaf_node(df):\r\n \r\n return len(set(df['class'])) == 1",
"def is_leaf(self, p):\n return self.num_children(p) == 0",
"def test_is_leaf(self):\n self.assertEqual(True, co... | [
"0.7235153",
"0.6678625",
"0.66372824",
"0.66100496",
"0.6575179",
"0.65703756",
"0.6556593",
"0.6548734",
"0.6526366",
"0.65042156",
"0.6485087",
"0.6446794",
"0.6434655",
"0.6423185",
"0.63771605",
"0.6371146",
"0.63690525",
"0.6355469",
"0.63342595",
"0.63308424",
"0.63199... | 0.7547813 | 0 |
Test selection of a branch | def testSelectBranch(self):
menu = self.menu
items = self.items
assertTrue = self.assertTrue
assertIsNone = self.assertIsNone
menu.select(tag="a2")
assertTrue(menu.selected)
assertIsNone(items["a1"].selected)
assertIsNone(items["a11"].selected)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_branch(opt, params):\n\n # Check the current branch and hash\n _get_branch(opt)\n\n if params.git_branch != opt.git_branch or params.git_hash != opt.git_hash:\n msg = 'You are not on the right branch or commit. Please run the following in the repository: \\n'\n msg += f'git checko... | [
"0.648244",
"0.6446408",
"0.64247346",
"0.63390535",
"0.62524986",
"0.6216893",
"0.6167209",
"0.61396754",
"0.6128586",
"0.60267806",
"0.60020113",
"0.59735584",
"0.5965118",
"0.5926897",
"0.58735794",
"0.58632785",
"0.5858319",
"0.5858319",
"0.58455837",
"0.58444524",
"0.584... | 0.7612395 | 0 |
Test selection of specific nodes | def testSelectSpecificNode(self):
menu = self.menu
items = self.items
assertTrue = self.assertTrue
assertIsNone = self.assertIsNone
items["a2"].select()
assertTrue(menu.selected)
assertIsNone(items["a1"].selected)
assertIsNone(items["a11"].selected)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_node_selection(self, node: dict, selection_type: SelectionType):\n assert ListSelectedExecutor.node_selection(node) == selection_type",
"def test_selecting_nodes_clicking_them_discovered(self):\n with Nodes()as n:\n for node in n.nodes_discovered:\n node.parent.cl... | [
"0.70753044",
"0.69574356",
"0.66272986",
"0.65160197",
"0.63747287",
"0.62622404",
"0.6255423",
"0.6250882",
"0.61456066",
"0.6128836",
"0.61121875",
"0.60613096",
"0.6057898",
"0.5994398",
"0.5972796",
"0.5919818",
"0.5912785",
"0.5878819",
"0.58584577",
"0.57744914",
"0.57... | 0.72523385 | 0 |
Test selection with nonexistent tag | def testSelectNonexistentTag(self):
menu = self.menu
items = self.items
assertTrue = self.assertTrue
assertIsNone = self.assertIsNone
# Make a selection
menu.select(tag="a21")
assertTrue(menu.selected)
assertIsNone(items["a1"].selected)
assertI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_tags_tag_search_invalid_tag(self):\n\n po = self.catalog.load_pageobject('TagsPage')\n po.goto_page()\n\n global NON_EXISTENT_TAG\n\n # perform the search\n self.browser.proxy_client.new_har(\"page\")\n po.search_for_tags(NON_EXISTENT_TAG)\n har_entry = sel... | [
"0.6625541",
"0.6273861",
"0.62554324",
"0.60222036",
"0.58781713",
"0.584287",
"0.5800551",
"0.5729907",
"0.5698181",
"0.5698148",
"0.5659998",
"0.56520003",
"0.5631423",
"0.5631034",
"0.55683744",
"0.55287963",
"0.550021",
"0.5499857",
"0.5495857",
"0.5495131",
"0.5466072",... | 0.7399087 | 0 |
Test consecutive manual selects | def testSwitchSelection(self):
menu = self.menu
items = self.items
assertTrue = self.assertTrue
assertIsNone = self.assertIsNone
# First selection
menu.select(tag="a11")
assertTrue(menu.selected)
assertTrue(items["a1"].selected)
assertTrue(item... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_boolean_and_selection(self):\n\n # The selection loop:\n sel = list(mol_res_spin.residue_loop(\"#Ap4Aase:4 & :Pro\"))\n\n # Test:\n self.assertEqual(len(sel), 1)\n for res in sel:\n self.assert_(res.name == \"Pro\" and res.num == 4)",
"def test_select_same_e... | [
"0.6418896",
"0.61642396",
"0.61210763",
"0.6017917",
"0.5952653",
"0.58946407",
"0.5882359",
"0.58499354",
"0.5843929",
"0.58132553",
"0.58132553",
"0.5807956",
"0.56424063",
"0.56390774",
"0.56188494",
"0.55462873",
"0.54957587",
"0.5488348",
"0.54776096",
"0.54506606",
"0.... | 0.5476562 | 19 |
Attach a text label above each bar displaying its height | def autolabel(rects): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def autolabel(rects, ax):\n global BAR_NUMBER_SIZE\n for rect in rects:\n height = rect.get_height()\n ax.text(rect.get_x() + rect.get_width()/2.,\n height,\n str(round(height, 1)),\n ha ='center',\n va ='bottom',\n size... | [
"0.738665",
"0.7127214",
"0.706221",
"0.70598906",
"0.6995252",
"0.6801717",
"0.6792009",
"0.6772195",
"0.6765225",
"0.676024",
"0.67559797",
"0.6748383",
"0.6728958",
"0.67258143",
"0.67216814",
"0.6714292",
"0.6711156",
"0.6692474",
"0.6655147",
"0.66464627",
"0.6645916",
... | 0.6127119 | 73 |
Read and parse NEXUS input (a filename, filehandle, or string). | def read(self, input):
# 1. Assume we have the name of a file in the execution dir or a
# file-like object.
# Note we need to add parsing of the path to dir/filename
try:
with File.as_handle(input, 'rU') as fp:
file_contents = fp.r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_nexus(filename):\n f = open(filename)\n return parse_nexus(f)",
"def parse(self, infile):\r\n raise NotImplementedError()",
"def _parse(self, infile):\n raise NotImplementedError()",
"def read_input(infile):\n #Some utility functions to read in particular types of input\n def r... | [
"0.5987714",
"0.59453547",
"0.58234787",
"0.58072245",
"0.57432646",
"0.5646696",
"0.5598178",
"0.557389",
"0.5563821",
"0.5482843",
"0.5413918",
"0.5413375",
"0.53326714",
"0.53081083",
"0.53040534",
"0.5293794",
"0.52868974",
"0.5284766",
"0.526946",
"0.52602595",
"0.523994... | 0.7064549 | 0 |
Run the solution in the exact specified directory | def _run_exact_solution(solution_dir, test=False):
sys.path.append(solution_dir)
# add to allow util.py import from day's directory
sys.path.append(os.path.dirname(solution_dir))
import solution
if test:
try:
solution.run
except AttributeError:
solution_file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_workdir(self, path):\n pass",
"def _run_simulator(self):\n os.chdir(self.test_cases_path)\n\n simulator_config_filename = self.simulator_config_filename\n script, options = runner.parse_commands(simulator_config_filename)\n\n if sys.platform.startswith('win'):\n ... | [
"0.66220284",
"0.64217085",
"0.63024724",
"0.61351603",
"0.61159885",
"0.6102285",
"0.6092397",
"0.6064275",
"0.6035573",
"0.6030033",
"0.60200626",
"0.59267926",
"0.5919935",
"0.59048945",
"0.5890944",
"0.58701456",
"0.58694714",
"0.58693445",
"0.58487374",
"0.57848525",
"0.... | 0.59686 | 11 |
Perform singular values decomposisiton on a document term matrix A. A = T S D^T A ~ T'S'D'^T Where T', S', and D' have a fewer columns than T, S, and D. | def computeTruncatedSVD(docTermMatrix, dim=500):
T, S, D = np.linalg.svd(np.transpose(docTermMatrix), full_matrices=False)
diagS = np.diag(S)
shape = np.shape(diagS)
if dim <= shape[0] and dim <= shape[1]:
subT = T[:,:dim]
subS = diagS[:dim,:dim]
subD = np.transpose(D)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def singular_decomp(A):\n # Initialization\n n, m = A.shape\n U = np.zeros((n, m), dtype='float64')\n\n # Diagonalization of A^T * A\n rot, e, V = eigen.diag(np.dot(np.transpose(A), A))\n\n # Calculate U\n U = np.dot(A, V)\n for i in range(m):\n e[i] = np.sqrt(e[i])\n U[:, i] ... | [
"0.6640744",
"0.58657926",
"0.58088976",
"0.57761854",
"0.57290536",
"0.5726822",
"0.5711986",
"0.5658731",
"0.5601522",
"0.55621266",
"0.5544685",
"0.5520135",
"0.54970425",
"0.5486736",
"0.5445198",
"0.54431707",
"0.5406663",
"0.53682363",
"0.53435516",
"0.529848",
"0.52955... | 0.6557088 | 1 |
Writes to db_filename somehow. | def __init__(self, db_filename):
self._conn = sqlite3.connect(db_filename)
self._conn.text_factory = str
self._cursor = self._conn.cursor() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_db(db):\n\n # Look for database in the same folder as this script\n script_dir = os.path.dirname(os.path.realpath(__file__))\n db_filepath = os.path.join(script_dir, 'cn_loads_database.dat')\n\n with open(db_filepath, 'w') as f:\n f.write(yaml.dump(db, default_flow_style=False))",
"d... | [
"0.7135077",
"0.7029683",
"0.70190406",
"0.67046505",
"0.66948396",
"0.668093",
"0.6621175",
"0.6610708",
"0.6508839",
"0.6508839",
"0.6502612",
"0.6467026",
"0.64418215",
"0.64383954",
"0.6423064",
"0.6422576",
"0.636483",
"0.6363369",
"0.6310691",
"0.62984604",
"0.6294062",... | 0.0 | -1 |
Selects all entries such that entry.timestamp >= timestamps[entry.client] window_size | def select(self, timestamps):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_search_coincidences(timestamps, window=200000):\n coincidences = []\n\n # traverse all timestamps\n for i in xrange(len(timestamps)):\n\n # build coincidence, starting with the current timestamp\n c = [i]\n t0 = timestamps[i][0]\n\n # traverse the rest of the timestamps\... | [
"0.54318",
"0.5304614",
"0.5213484",
"0.50306123",
"0.5017064",
"0.50053144",
"0.4959315",
"0.49128497",
"0.48927337",
"0.48854557",
"0.48687822",
"0.48336253",
"0.48201647",
"0.47550848",
"0.47432086",
"0.47310567",
"0.4719693",
"0.4709526",
"0.46631616",
"0.4658335",
"0.465... | 0.45346963 | 33 |
Trigger sai ptf test here. | def test_sai(sai_testbed,
sai_test_env_check,
creds,
duthost,
ptfhost,
ptf_sai_test_case,
request,
create_sai_test_interface_param):
dut_ip = duthost.host.options['inventory_manager'].get_host(
duthost.hostname).vars[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runtest(self):",
"def test():\n pass",
"def testing(self):\n print('test successful')",
"def test(self,*args,**kwargs):\n print(\"[TEMPLATE ENGINE] 'testing' function not implemented\")\n pass",
"def _test(self):\n pass",
"def _test(self):\n pass",
"def _te... | [
"0.65121084",
"0.64988273",
"0.647771",
"0.6437519",
"0.63721097",
"0.63721097",
"0.63721097",
"0.6358594",
"0.63234115",
"0.62675816",
"0.62112737",
"0.62007904",
"0.6182577",
"0.6182577",
"0.6182577",
"0.6182577",
"0.6182577",
"0.61620486",
"0.61444914",
"0.6125484",
"0.612... | 0.0 | -1 |
IaasUcsdManagedInfraAllOf a model defined in OpenAPI | def __init__(self,
advanced_catalog_count=None,
bm_catalog_count=None,
container_catalog_count=None,
esxi_host_count=None,
external_group_count=None,
hyperv_host_count=None,
local_group_count=None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def openapi_types():\n return {\n \"account\": (str,), # noqa: E501\n \"address_city\": (str,), # noqa: E501\n \"address_country\": (str,), # noqa: E501\n \"address_line1\": (str,), # noqa: E501\n \"address_line1_check\": (str,), # noqa: E501\n ... | [
"0.5080883",
"0.50748086",
"0.49696618",
"0.4908364",
"0.48975128",
"0.48663056",
"0.48609036",
"0.4858573",
"0.48343837",
"0.47895467",
"0.47518623",
"0.47351536",
"0.47230732",
"0.4704432",
"0.46940196",
"0.46839136",
"0.4650198",
"0.46466818",
"0.46428332",
"0.46342972",
"... | 0.0 | -1 |
Sets the advanced_catalog_count of this IaasUcsdManagedInfraAllOf. | def advanced_catalog_count(self, advanced_catalog_count):
self._advanced_catalog_count = advanced_catalog_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count",
"def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count",
"def standard_catalog_count(self, standard_catalog_count):\n\n self._stan... | [
"0.57526886",
"0.5751853",
"0.56568986",
"0.46540743",
"0.46511573",
"0.45186806",
"0.4483399",
"0.43547434",
"0.43004668",
"0.4271701",
"0.4244447",
"0.42245948",
"0.42140615",
"0.41743332",
"0.4168656",
"0.41071948",
"0.41035506",
"0.40634376",
"0.4060891",
"0.4047798",
"0.... | 0.8002607 | 0 |
Sets the bm_catalog_count of this IaasUcsdManagedInfraAllOf. | def bm_catalog_count(self, bm_catalog_count):
self._bm_catalog_count = bm_catalog_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count",
"def standard_catalog_count(self, standard_catalog_count):\n\n self._standard_catalog_count = standard_catalog_count",
"def advanced_catalog_count(self, advanced_catalog_count... | [
"0.67917866",
"0.62580836",
"0.60013944",
"0.5060284",
"0.4933995",
"0.4909605",
"0.48784313",
"0.48781607",
"0.47947842",
"0.4782925",
"0.47474197",
"0.47474197",
"0.46623227",
"0.46587622",
"0.4638956",
"0.46326226",
"0.46316606",
"0.46220344",
"0.4605496",
"0.45846856",
"0... | 0.7878276 | 0 |
Sets the container_catalog_count of this IaasUcsdManagedInfraAllOf. | def container_catalog_count(self, container_catalog_count):
self._container_catalog_count = container_catalog_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count",
"def standard_catalog_count(self, standard_catalog_count):\n\n self._standard_catalog_count = standard_catalog_count",
"def advanced_catalog_count(self, advanced_catalog_count):\n\n self._advanced... | [
"0.6810308",
"0.6344454",
"0.59025335",
"0.5486347",
"0.51642656",
"0.5138865",
"0.50294644",
"0.5021692",
"0.50136554",
"0.49914697",
"0.48896855",
"0.4718432",
"0.46324363",
"0.45579195",
"0.45579195",
"0.44740677",
"0.4469012",
"0.44313428",
"0.44271842",
"0.44019943",
"0.... | 0.8005635 | 0 |
Sets the esxi_host_count of this IaasUcsdManagedInfraAllOf. | def esxi_host_count(self, esxi_host_count):
self._esxi_host_count = esxi_host_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hyperv_host_count(self, hyperv_host_count):\n\n self._hyperv_host_count = hyperv_host_count",
"def hosts_every(self, hosts_every):\n\n self._hosts_every = hosts_every",
"def vcpu_set(vm_hostname, count, offline=False):\n with ExitStack() as es:\n vm = es.enter_context(_get_vm(vm_hos... | [
"0.59950227",
"0.5260858",
"0.50562745",
"0.50244135",
"0.49948582",
"0.48857233",
"0.48497176",
"0.48385507",
"0.482034",
"0.4762812",
"0.47252673",
"0.4718971",
"0.47022638",
"0.46596396",
"0.46280846",
"0.45461932",
"0.45392084",
"0.45244592",
"0.45231992",
"0.45225677",
"... | 0.8037844 | 0 |
Sets the external_group_count of this IaasUcsdManagedInfraAllOf. | def external_group_count(self, external_group_count):
self._external_group_count = external_group_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def local_group_count(self, local_group_count):\n\n self._local_group_count = local_group_count",
"def external_ids(self, external_ids):\n\n self._external_ids = external_ids",
"def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count",
"def set_all_data_ex... | [
"0.53656894",
"0.4734468",
"0.4715213",
"0.4630498",
"0.46285418",
"0.4532851",
"0.45295992",
"0.44841346",
"0.44807005",
"0.44654495",
"0.44654495",
"0.44654495",
"0.44536784",
"0.4435368",
"0.43804",
"0.42757764",
"0.42720485",
"0.42300162",
"0.42260072",
"0.4207612",
"0.41... | 0.75556606 | 0 |
Sets the hyperv_host_count of this IaasUcsdManagedInfraAllOf. | def hyperv_host_count(self, hyperv_host_count):
self._hyperv_host_count = hyperv_host_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def esxi_host_count(self, esxi_host_count):\n\n self._esxi_host_count = esxi_host_count",
"def vcpu_set(vm_hostname, count, offline=False):\n with ExitStack() as es:\n vm = es.enter_context(_get_vm(vm_hostname))\n\n if vm.dataset_obj['datacenter_type'] != 'kvm.dct':\n raise Not... | [
"0.66588247",
"0.5818077",
"0.5640094",
"0.5640094",
"0.5394924",
"0.5279623",
"0.5277185",
"0.52716357",
"0.5202823",
"0.50858754",
"0.5027976",
"0.4993971",
"0.4947955",
"0.4938431",
"0.48995036",
"0.4849338",
"0.4816406",
"0.4814211",
"0.48059392",
"0.47629017",
"0.4759356... | 0.7839512 | 0 |
Sets the local_group_count of this IaasUcsdManagedInfraAllOf. | def local_group_count(self, local_group_count):
self._local_group_count = local_group_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def external_group_count(self, external_group_count):\n\n self._external_group_count = external_group_count",
"def _set_group_resource(self, _g):\n\n if isinstance(_g, Server):\n return\n\n for _, sg in _g.subgroups.items():\n self._set_group_resource(sg)\n _... | [
"0.5947293",
"0.509726",
"0.4911288",
"0.4911288",
"0.4911288",
"0.4881376",
"0.4801572",
"0.4801572",
"0.4801572",
"0.4801572",
"0.46374658",
"0.46280244",
"0.46024618",
"0.46024618",
"0.46024618",
"0.46024618",
"0.45948556",
"0.45816755",
"0.45816755",
"0.45816755",
"0.4581... | 0.76894385 | 0 |
Sets the standard_catalog_count of this IaasUcsdManagedInfraAllOf. | def standard_catalog_count(self, standard_catalog_count):
self._standard_catalog_count = standard_catalog_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def container_catalog_count(self, container_catalog_count):\n\n self._container_catalog_count = container_catalog_count",
"def bm_catalog_count(self, bm_catalog_count):\n\n self._bm_catalog_count = bm_catalog_count",
"def advanced_catalog_count(self, advanced_catalog_count):\n\n self._adva... | [
"0.57423514",
"0.5616781",
"0.50827426",
"0.4542607",
"0.44935066",
"0.44344246",
"0.43874955",
"0.43789217",
"0.4358185",
"0.43167907",
"0.43059853",
"0.4270202",
"0.42586276",
"0.4214198",
"0.42093727",
"0.4138831",
"0.41344634",
"0.41310257",
"0.41012546",
"0.40627837",
"0... | 0.81387305 | 0 |
Sets the user_count of this IaasUcsdManagedInfraAllOf. | def user_count(self, user_count):
self._user_count = user_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_usr_ping_count(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name=\"usr-ping-count\", rest_name=\"usr-ping-count\", pa... | [
"0.61215466",
"0.5793492",
"0.54216063",
"0.54033273",
"0.532203",
"0.5165332",
"0.5099306",
"0.5092787",
"0.5068327",
"0.5068107",
"0.5022238",
"0.5022238",
"0.49769667",
"0.49539578",
"0.49485537",
"0.4900824",
"0.48729518",
"0.48636237",
"0.48355436",
"0.4805139",
"0.48023... | 0.72169346 | 1 |
Sets the vdc_count of this IaasUcsdManagedInfraAllOf. | def vdc_count(self, vdc_count):
self._vdc_count = vdc_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vm_count(self, vm_count):\n\n self._vm_count = vm_count",
"def vm_count(self, vm_count):\n\n self._vm_count = vm_count",
"def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)",
"def set_count(self, count, asset=None):\n self._... | [
"0.5437181",
"0.5437181",
"0.531096",
"0.5136232",
"0.50647324",
"0.49089286",
"0.47968695",
"0.4710327",
"0.46992522",
"0.46300593",
"0.46219018",
"0.46113285",
"0.46049786",
"0.4604565",
"0.45997623",
"0.45524865",
"0.45503348",
"0.45477548",
"0.45165822",
"0.45023248",
"0.... | 0.7397312 | 0 |
Sets the vm_count of this IaasUcsdManagedInfraAllOf. | def vm_count(self, vm_count):
self._vm_count = vm_count | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_cpu_count(self, nVmCpuCount):\n\t\tcall_sdk_function('PrlVmCfg_SetCpuCount', self.handle, nVmCpuCount)",
"def vdc_count(self, vdc_count):\n\n self._vdc_count = vdc_count",
"def lun_count(self, lun_count):\n\n self._lun_count = lun_count",
"def vcpus(self, vcpus):\n self._vcpus = ... | [
"0.60274035",
"0.5922383",
"0.5739843",
"0.5628235",
"0.56280166",
"0.5574185",
"0.55255973",
"0.53965175",
"0.5285271",
"0.5197171",
"0.5193007",
"0.5076765",
"0.5026433",
"0.5026433",
"0.50207126",
"0.49862435",
"0.4984859",
"0.49740753",
"0.4924906",
"0.49171144",
"0.49162... | 0.7385598 | 1 |
Sets the guid of this IaasUcsdManagedInfraAllOf. | def guid(self, guid):
self._guid = guid | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def guid(self, guid):\n self._guid = guid",
"def cmd_set_guid(argv,o):\n LibIBOpts.setup(o);\n (args,values) = o.parse_args(argv);\n lib = LibIBOpts(o,args,values,3,(tmpl_target,tmpl_int,tmpl_port_guid));\n\n if len(values) != 3:\n raise CmdError(\"Not enough arguments\");\n\n with l... | [
"0.5373949",
"0.5280875",
"0.46020597",
"0.45552626",
"0.4523249",
"0.45016184",
"0.43972918",
"0.4326833",
"0.42316926",
"0.42313373",
"0.4229026",
"0.42265818",
"0.4208345",
"0.4155647",
"0.41291952",
"0.41291952",
"0.41068876",
"0.4092342",
"0.40804192",
"0.40481693",
"0.4... | 0.5306522 | 3 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(
map(lambda x: x.to_dict()
if hasattr(x, "to_dict") else x, va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.775081",
"0.775081",
"0.7338297",
"0.73333067",
"0.7296288",
"0.7277521",
"0.7159291",
"0.71573424",
"0.714855",
"0.714855",
"0.71273863",
"0.71261555",
"0.7122034",
"0.71070296",
"0.7060047",
"0.7043975",
"0.7032398",
"0.7023222",
"0.6964265",
"0.6958815",
"0.68987364",
... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.85856134",
"0.7814518",
"0.77898884",
"0.7751367",
"0.7751367",
"0.7712228",
"0.76981676",
"0.76700574",
"0.7651133",
"0.7597206",
"0.75800353",
"0.7568254",
"0.7538184",
"0.75228703",
"0.7515832",
"0.7498764",
"0.74850684",
"0.74850684",
"0.7467648",
"0.74488163",
"0.7442... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.75577617",
"0.73375154",
"0.6986672",
"0.698475",
"0.6944995",
"0.692333",
"0.6899106",
"0.6898902",
"0.68146646",
"0.6806209",
"0.6753795",
"0.67497987",
"0.6744008",
"0.6700308",
"0.6691256",
"0.6674591",
"0.6658083",
"0.66091245",
"0.6606931",
"0.6601862",
"0.6563738",
... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, IaasUcsdManagedInfraAllOf):
return False
return self.to_dict() == other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
if not isinstance(other, IaasUcsdManagedInfraAllOf):
return True
return self.to_dict() != other.to_dict() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.84560555",
"0.83913696",
"0.8143685",
"0.81403786",
"0.8131871",
"0.8093231",
"0.80915105",
"0.80915105",
"0.80915105",
"0.8084564",
"0.8084564",
"0.8075547",
"0.8075547",
"0.8065006",
"0.80417657",
"0.80417657",
"0.80417657",
"0.80417657",
"0.80417657",
"0.80417657",
"0.8... | 0.0 | -1 |
Formats ``path`` with the rank zero values. | def _format_path_with_rank_zero(path: str) -> str:
return path.format(
rank=0,
local_rank=0,
node_rank=0,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_local_rank_zero_path(path: Optional[str]) -> str:\n local_rank_zero = dist.get_global_rank() - dist.get_local_rank()\n paths = dist.all_gather_object(path)\n local_rank_zero_path = paths[local_rank_zero]\n assert local_rank_zero_path is not None, 'local rank zero provides the path'\n return... | [
"0.6743314",
"0.6682493",
"0.5183608",
"0.51139396",
"0.51139396",
"0.51139396",
"0.50459886",
"0.49452367",
"0.49336353",
"0.49094725",
"0.4861403",
"0.48274943",
"0.48119223",
"0.48062816",
"0.47794765",
"0.47726187",
"0.4767327",
"0.47632933",
"0.47430956",
"0.4704121",
"0... | 0.890424 | 0 |
Formats ``path`` formatted with the current rank values. | def _format_path_with_current_rank(path: str) -> str:
return path.format(
rank=dist.get_global_rank(),
local_rank=dist.get_local_rank(),
node_rank=dist.get_node_rank(),
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _format_path_with_rank_zero(path: str) -> str:\n return path.format(\n rank=0,\n local_rank=0,\n node_rank=0,\n )",
"def format_path(path):\n if len(path) > 1:\n result = [crayons.yellow(path[0].name)]\n\n previous = path[0]\n for item in path[1:]:\n ... | [
"0.7308434",
"0.6294355",
"0.6030438",
"0.58486265",
"0.58486265",
"0.58486265",
"0.5666408",
"0.5501491",
"0.54912573",
"0.5491005",
"0.5487094",
"0.5456914",
"0.5407116",
"0.53897357",
"0.5379467",
"0.5378536",
"0.53413504",
"0.5290244",
"0.5282369",
"0.5271615",
"0.5261161... | 0.8522862 | 0 |
Load a checkpoint from a local file, URI, or cloud object store into ``state``. | def load_checkpoint(
path: str,
state: State,
logger: Logger,
object_store: Optional[Union[ObjectStore, LoggerDestination]] = None,
load_weights_only: bool = False,
strict_model_weights: bool = False,
progress_bar: bool = True,
ignore_keys: Optional[Union[List[str], Callable[[Dict], None... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_checkpoint(self, checkpoint: str, **kwargs) -> None:\n with open(checkpoint, \"rb\") as f:\n state = SafePickle.load(f)\n\n state_id = ray.put(state)\n ray.get([worker.set_state.remote(state_id, **kwargs) for worker in self.remote_workers])",
"def load_from_checkpoint(sel... | [
"0.8046602",
"0.7789368",
"0.7547819",
"0.7534057",
"0.744159",
"0.74061614",
"0.73822767",
"0.7316122",
"0.72855556",
"0.7279566",
"0.7265061",
"0.7259501",
"0.7259501",
"0.71900815",
"0.71689534",
"0.7152443",
"0.7132168",
"0.712835",
"0.7123272",
"0.7122712",
"0.7074052",
... | 0.67693573 | 48 |
Broadcasts the ``path`` from the LOCAL rank zero to all LOCAL ranks. | def _get_local_rank_zero_path(path: Optional[str]) -> str:
local_rank_zero = dist.get_global_rank() - dist.get_local_rank()
paths = dist.all_gather_object(path)
local_rank_zero_path = paths[local_rank_zero]
assert local_rank_zero_path is not None, 'local rank zero provides the path'
return local_ran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_path(self, path):\n self.clear_path()\n for coordinate in path:\n self.send_coordinate(coordinate)\n time.sleep(0.05)",
"def broadcast(value, root_rank, name=None):\n return _impl.broadcast(K, value, root_rank, name)",
"def extern_to_local_path(self, path: PurePa... | [
"0.558918",
"0.5540478",
"0.5262298",
"0.5138672",
"0.51280296",
"0.5033147",
"0.4981683",
"0.4957514",
"0.4915474",
"0.4859186",
"0.4790848",
"0.4757363",
"0.47432727",
"0.4711225",
"0.47017384",
"0.4673872",
"0.46665725",
"0.46487427",
"0.4647647",
"0.46301493",
"0.46140268... | 0.639906 | 0 |
Download the checkpoint stored at ``path``, potentially in ``object_store``, to ``node_checkpoint_folder``. Returns a tuple of (``composer_states_filepath``, ``extracted_checkpoint_folder``, ``extracted_rank_n``). The ``composer_states_filepath``, is the path to the composer states, which can be passed into | def download_checkpoint(path: str,
node_checkpoint_folder: str,
object_store: Optional[Union[ObjectStore, LoggerDestination]],
progress_bar: bool,
fsdp_sharded_state_dict_enabled: bool = False,
deepsp... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_checkpoint(\n path: str,\n state: State,\n logger: Logger,\n object_store: Optional[Union[ObjectStore, LoggerDestination]] = None,\n load_weights_only: bool = False,\n strict_model_weights: bool = False,\n progress_bar: bool = True,\n ignore_keys: Optional[Union[List[str], Callable... | [
"0.62694466",
"0.61993414",
"0.6029626",
"0.5934021",
"0.5839948",
"0.5779024",
"0.5675783",
"0.5657235",
"0.55893075",
"0.5551518",
"0.5551518",
"0.55440056",
"0.55313367",
"0.5514979",
"0.5417088",
"0.54159427",
"0.5401518",
"0.5353508",
"0.53150314",
"0.529472",
"0.5274455... | 0.7012607 | 0 |
Recursively flatten the keys of a dictionary or list into a set of paths. | def _flatten_keys(obj: Any, paths: List[str], existing_path: str):
# Store path when we reach end, which is either non-Dict or empty Dict
if isinstance(obj, list) and len(obj) > 0:
for i, elm in enumerate(obj):
_flatten_keys(elm, paths, f'{existing_path}/{i}')
elif isinstance(obj, dict) ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flatten_keys(in_keys):\n return_keys = []\n if isinstance(in_keys, str):\n return [in_keys]\n if isinstance(in_keys, Iterable):\n for key in in_keys:\n if isinstance(key, Iterable):\n return_keys += flatten_keys(key)\n else:\n return_ke... | [
"0.6875952",
"0.6714829",
"0.6393634",
"0.6268066",
"0.6147617",
"0.60040855",
"0.5961839",
"0.58822143",
"0.5856093",
"0.58354765",
"0.5823535",
"0.58075947",
"0.58066595",
"0.5770507",
"0.57541543",
"0.5746323",
"0.574392",
"0.57279015",
"0.5708329",
"0.5703219",
"0.5697986... | 0.7069572 | 0 |
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