0 and not data['canceled']:\n DeliveryItemModel.objects.create(\n delivery=delivery,\n item=data['id'],\n quantity=data['deliver_quantity'],\n )\n if not delivery.items.exists():\n # since no OrderItem was added to this delivery, discard it\n delivery.delete()","def __clean_orders(self):\n canceled_id = []\n for order_id, order in self.orders_dict.items():\n if order[\"status\"] == \"canceled\":\n canceled_id.append(order_id)\n for id in canceled_id:\n del self.orders_dict[id]","def is_approved(self) -> bool:\n return self.state == Order.OrderState.APPROVED.choice_value","def mark_completed(self, order_id=None):\n self.status = \"paid\"\n if order_id and not self.order_id:\n self.order_id = order_id\n print(\"Order completed\")\n self.save()","def approve(self, employee: Employee, comments: str = None) -> None:\n from .exceptions import OperationForbiddenError, OrderEmptyError\n\n # If order is not in the \"PENDING\" state, raise an\n # OperationForbiddenError\n if not self.is_pending:\n raise OperationForbiddenError(\n self.STATE_CHANGE_FORBIDDEN_ERROR_MSG % {\n 'current_state': Order.OrderState.get_choice_display(\n self.state\n ),\n 'new_state': Order.OrderState.APPROVED.choice_display\n }\n )\n\n # If the order's item list is empty, raise an OrderEmptyError\n if not self.orderitem_set.exists():\n raise OrderEmptyError(\n self,\n 'An order with no associated OrderItems cannot be '\n 'approved.'\n )\n\n # Perform db mutations in a transaction\n with transaction.atomic():\n # Adjust the stock of each item in the order's item list\n order_item: OrderItem\n for order_item in self.orderitem_set.all():\n item: Inventory = order_item.item\n item.deduct(employee.user, order_item.quantity)\n\n # Mark this order as approved\n self.update(\n employee.user,\n comments=comments,\n handler=employee,\n review_date=now(),\n state=Order.OrderState.APPROVED.choice_value\n )","def action_confirm(self):\n # context = self._context or {}\n inv_obj = self.env['account.invoice']\n\n brw = self.browse(self.ids[0])\n line_ids = brw.line_ids\n if not line_ids:\n raise exceptions.except_orm(\n _('Invalid Procedure!'), _(\"No retention lines\"))\n\n res = [True]\n res += [False for i in line_ids\n if (i.wh_amount <= 0.0 or\n i.base_amount <= 0.0 or\n i.wh_src_rate <= 0.0)]\n if not all(res):\n raise exceptions.except_orm(\n _('Invalid Procedure!'),\n _(\"Verify retention lines do not have Null values(0.00)\"))\n\n res = 0.0\n for i in line_ids:\n res += i.wh_amount\n if abs(res - brw.wh_amount) > 0.0001:\n raise exceptions.except_orm(\n _('Invalid Procedure!'),\n _(\"Check the amount of withholdings\"))\n\n inv_ids = [i.invoice_id.id for i in brw.line_ids]\n if inv_ids:\n inv_obj.write({'wh_src_id': self.ids[0]})\n\n return self.write({'state': 'confirmed'})","def test_success(self, order_placed):\n session, basket_id, order_number = self.prepare_basket()\n data = cs_factories.build_accepted_reply_data(order_number)\n data = cs_factories.sign_reply_data(data)\n url = reverse('cybersource-reply')\n self.assertEqual(order_placed.call_count, 0)\n resp = self.client.post(url, data)\n\n self.assertRedirects(resp, reverse('checkout:thank-you'))\n\n self.assertEqual(len(mail.outbox), 1, 'Should send email')\n self.assertEqual(order_placed.call_count, 1, 'Should trigger order_placed signal')\n\n order = order_placed.call_args[1]['order']\n self.assertEqual(order.status, 'Authorized', 'Should set order status')\n self.assertEqual(order.basket.id, basket_id, 'Should use basket from session')\n self.assertEqual(order.number, order_number, 'Should use order number from CS request')\n\n session = self.client.session\n self.assertEquals(session[CHECKOUT_ORDER_ID], order.id, 'Should save order_id in session')\n\n self.assertEqual(order.sources.count(), 1, 'Should save pPaymentSource')\n source = order.sources.first()\n self.assertEqual(source.currency, 'USD')\n self.assertEqual(source.amount_allocated, D('99.99'))\n self.assertEqual(source.amount_refunded, D('0.00'))\n self.assertEqual(source.amount_debited, D('0.00'))\n\n self.assertEqual(source.transactions.count(), 1, 'Should save Transaction')\n transaction = source.transactions.first()\n self.assertEqual(transaction.log.data, data)\n self.assertEqual(transaction.token.log, transaction.log)\n self.assertEqual(transaction.token.masked_card_number, 'xxxxxxxxxxxx1111')\n self.assertEqual(transaction.token.card_type, '001')\n self.assertEqual(transaction.txn_type, 'Authorise')\n self.assertEqual(transaction.amount, D('99.99'))\n self.assertEqual(transaction.reference, data['transaction_id'])\n self.assertEqual(transaction.status, 'ACCEPT')\n self.assertEqual(transaction.request_token, data['request_token'])\n\n self.assertEqual(order.payment_events.count(), 1, 'Should save PaymentEvent')\n event = order.payment_events.first()\n self.assertEqual(event.amount, D('99.99'))\n self.assertEqual(event.reference, data['transaction_id'])\n self.assertEqual(event.event_type.name, 'Authorise')\n\n self.assertEqual(event.line_quantities.count(), 1, 'Should save PaymentEventQuantity')\n lq = event.line_quantities.first()\n self.assertEqual(lq.line, order.lines.first())\n self.assertEqual(lq.quantity, 1)","def change_approval(self, status):\r\n if status == 'approve':\r\n return self.approve()\r\n elif status == 'disapprove':\r\n return self.disapprove()","def pending_confirm_parameter(request, action, rating_decision_pk):\n\n hash = None\n\n if action:\n\n rating_decision_obj = RatingDecision.objects.get(pk=rating_decision_pk)\n\n rating_process_obj, created = Process.objects.get_or_create(\n rating_decision=rating_decision_obj)\n\n rating_job_id = format_reference_number(number=rating_decision_obj.id,\n object_type='rating_decision',)\n\n if action == 'setup_done':\n hash = 'setup_step_2'\n\n rating_decision_obj.process_step = 2\n rating_process_obj.setup_done = timezone.now()\n\n to_list = [\n rating_decision_obj.chair.email\n ]\n\n # Notify analysts and relationship manager\n cc_list = [\n rating_decision_obj.issuer.analyst.\n primary_analyst.email,\n rating_decision_obj.issuer.analyst.\n secondary_analyst.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n header = SETUP_HEADER.format(\n rating_decision_obj.issuer,\n rating_job_id,\n )\n body = SETUP_BODY % rating_decision_obj.chair.first_name\n\n # Send notification to chair\n send_email.delay(\n header=header,\n body=body,\n to=to_list,\n from_sender=None,\n cc=cc_list)\n\n elif action == 'pre_committee_done':\n hash = 'setup_step_3'\n\n rating_decision_obj.process_step = 3\n rating_process_obj.pre_committee_done = timezone.now()\n rating_decision_obj.chair_confirmed = True\n rating_decision_obj.date_time_committee_confirmed = True\n\n to_list = [\n rating_decision_obj.issuer.analyst.\n primary_analyst.email,\n rating_decision_obj.issuer.analyst.\n secondary_analyst.email,\n ]\n cc_list = [\n rating_decision_obj.issuer.relationship_manager.email,\n rating_decision_obj.chair.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n header = PRE_COMMITTEE_HEADER.format(\n rating_decision_obj.issuer,\n rating_decision_obj.chair.get_full_name(),\n rating_job_id,\n )\n\n # Send notification\n send_email.delay(\n header=header,\n body=PRE_COMMITTEE_BODY % rating_decision_obj.issuer.analyst.\n primary_analyst.first_name,\n to=to_list,\n from_sender=None,\n cc=cc_list,)\n\n elif action == 'analytical_phase_done':\n hash = 'setup_step_4'\n\n rating_decision_obj.process_step = 4\n rating_process_obj.analytical_phase_done = timezone.now()\n\n # Send notification to chair and members of committee\n to_list = [\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n rating_decision_obj.chair.email,\n ]\n cc_list = [\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n # Get all members\n committee_members = list(\n JobMember.objects.confirmed_members().filter(\n rating_decision=rating_decision_obj,\n group_id=1))\n for item in committee_members:\n to_list.append(item.member.email)\n\n local_dt = timezone.localtime(\n rating_decision_obj.date_time_committee,\n timezone.get_fixed_timezone(60))\n\n header = ANALYTICAL_PHASE_HEADER.format(\n rating_decision_obj.issuer,\n local_dt.strftime('%Y-%m-%d %H:%M'),\n rating_job_id,\n )\n\n send_email.delay(\n header=header,\n body=ANALYTICAL_PHASE_BODY,\n to=to_list,\n cc=cc_list,\n from_sender=None)\n\n elif action == 'post_committee_done':\n hash = 'setup_step_5'\n\n rating_decision_obj.process_step = 5\n rating_process_obj.post_committee_done = timezone.now()\n\n # Contains the name and email of the editor\n editor_obj = JobMember.objects.get(\n rating_decision=rating_decision_obj,\n group=Group.objects.get(pk=2))\n\n to_list = [\n editor_obj.member.email,\n ]\n cc_list = [\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n rating_decision_obj.chair.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n header = EDITOR_HEADER.format(\n rating_decision_obj.issuer,\n rating_job_id,\n )\n\n # Send email with link to admin control to editor\n send_email.delay(header=header,\n body=EDITOR_EMAIL % (\n editor_obj.member.first_name,\n rating_decision_obj.issuer.analyst.\n primary_analyst.first_name),\n to=to_list,\n cc=cc_list,\n from_sender=None,)\n\n elif action == 'editor_phase_done':\n \"\"\"Here, we're sending the draft report to the issuer.\"\"\"\n\n hash = 'setup_step_6'\n\n rating_decision_obj.process_step = 6\n\n # External analysis\n send_public_report(rating_decision_obj)\n\n # Send notification to chair\n to_list = [\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n header = \"{} | the draft report has been sent to the \" \\\n \"issuer\".format(rating_decision_obj.issuer,)\n\n send_email.delay(\n header=header,\n body=' ',\n to=to_list,\n from_sender=None,)\n\n # Set a timestamp when se sent the report to the issuer\n rating_decision_obj.date_time_communicated_issuer = timezone.now()\n rating_process_obj.editor_review_done = timezone.now()\n\n elif action == 'issuer_confirmation_phase_done':\n hash = 'setup_step_7'\n\n rating_decision_obj.process_step = 7\n\n to_list = [\n rating_decision_obj.chair.email,\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n to_list.append('compliance@nordiccreditrating.com')\n\n header = \"{} | The issuer has confirmed the accuracy of the \" \\\n \"draft report for rating job {}\".format(\n rating_decision_obj.issuer,\n rating_job_id, )\n\n # Send notification\n send_email.delay(\n header=header,\n body='',\n to=to_list,\n from_sender=None,)\n\n rating_process_obj.issuer_confirmation_done = timezone.now()\n\n elif action == 'analyst_final_approval_phase_done':\n hash = 'setup_step_8'\n\n rating_process_obj.final_sign_off_analyst_done = timezone.now()\n rating_decision_obj.process_step = 8\n\n to_list = [\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n ]\n cc_list = [\n rating_decision_obj.chair.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n header = ANALYST_FINAL_APPROVAL_HEADER.format(\n rating_decision_obj.issuer\n )\n\n # Send notification to chair\n send_email.delay(\n header=header,\n body=ANALYST_FINAL_APPROVAL_BODY %\n rating_decision_obj.issuer.analyst.\n primary_analyst.first_name,\n to=to_list,\n from_sender=None,\n cc=cc_list)\n\n elif action == 'chair_final_approval_phase_done':\n hash = 'setup_step_9'\n\n rating_decision_obj.process_step = 9\n rating_process_obj.final_sign_off_chair_done = timezone.now()\n\n to_list = [\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n ]\n cc_list = [\n rating_decision_obj.chair.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n cc_list.append('compliance@nordiccreditrating.com')\n\n header = CHAIR_FINAL_APPROVAL_HEADER.format(\n rating_decision_obj.issuer\n )\n\n # Send notification to analyst\n send_email.delay(header=header,\n body=CHAIR_FINAL_APPROVAL_BODY %\n rating_decision_obj.issuer.analyst.\n primary_analyst.first_name,\n to=to_list,\n from_sender=None,\n cc=cc_list,)\n\n elif action == 'publishing_phase_done':\n hash = \"\"\n\n rating_decision_obj.date_time_published = timezone.now()\n\n to_list = [\n rating_decision_obj.issuer.analyst.primary_analyst.email,\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\n rating_decision_obj.chair.email,\n rating_decision_obj.issuer.relationship_manager.email,\n ]\n\n # We want to notify Compliance in production\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\n to_list.append('compliance@nordiccreditrating.com')\n\n header = \"{} | the rating job has been finalized\".format(\n rating_decision_obj.issuer\n )\n\n # Send notification\n send_email.delay(header=header,\n body='If this a public rating, the publishing '\n 'process will now commence.',\n to=to_list,\n from_sender=None,)\n\n \"\"\"If there is a previous decision, flag it as non-current.\"\"\"\n try:\n RatingDecision.objects.filter(\n pk=rating_decision_obj.previous_rating.pk).update(\n is_current=False,)\n except AttributeError:\n pass\n\n rating_decision_obj.is_current = True\n rating_process_obj.process_ended = timezone.now()\n\n # Create a DecisionAttribute object that\n # summarizes the rating decision\n refresh_decision_attributes(rating_decision_obj)\n\n # Create decisions on the issue level\n update_issue_rating(rating_decision_obj)\n\n # Create a draft campaign with MailChimp\n if rating_decision_obj.rating_type.id == 1:\n run_create_campaign.delay(rating_decision_obj.id)\n\n rating_decision_obj.process_step = 10\n\n # Save changes\n rating_process_obj.save()\n rating_decision_obj.save()\n\n redirect_url = request.META.get('HTTP_REFERER', '/') + '#' + hash\n\n return http.HttpResponseRedirect(redirect_url)","def approve(self, approver=None, parent_job=None):\r\n if self.status != 'PENDING':\r\n msg = _(\r\n \"Only orders that are in 'PENDING' state can be approved. \"\r\n \"Current state of order is '{status}'.\"\r\n ).format(status=self.status)\r\n raise CloudBoltException(msg)\r\n\r\n approve_this_order = False\r\n if self.is_multilevel_approval():\r\n logger.info('models.approve is multilevel!')\r\n self.approve_my_grms(approver)\r\n logger.info(f'models.approve after approve_my_grms ({approver})!')\r\n if self.is_multilevel_approval():\r\n logger.info('models.approve ml approval complete!')\r\n approve_this_order = True\r\n else:\r\n logger.info('models.approve is NOT multilevel!')\r\n #single-level approval\r\n approve_this_order = True\r\n\r\n if not approve_this_order:\r\n #should only kick off if multilevel approvals\r\n msg = _(\r\n \"Cannot fully approve this order. Multilevel approvals not complete. \"\r\n \"Current state of order is '{status}'.\"\r\n ).format(status=self.status)\r\n return [], msg\r\n\r\n try:\r\n # Raise an error to bubble up specific reason as part of the exception\r\n self.group.quota_set.can_use(raise_error=True, **self.net_usage())\r\n except QuotaSetError as quota_set_error:\r\n raise QuotaError(_(\r\n \"Cannot approve order #{order_id} because doing so would exceed the \"\r\n \"quota for group '{group}'. {error}\"\r\n ).format(order_id=self.id, group=self.group, error=quota_set_error))\r\n\r\n # Before we create job records, order the order items to make\r\n # sure decom jobs are queued before prov jobs. the job engine\r\n # may still parallelize them, that's something we can revisit\r\n # later. In the meantime, customers can set the concurrency\r\n # level to 1 to prevent this.\r\n # we're taking advantage of the fact that \"decom\" comes before\r\n # \"prov\" in the alphabet here.\r\n order_items = [oi.cast() for oi in self.top_level_items.order_by(\r\n \"real_type\", \"add_date\")]\r\n\r\n order_items, msg = self.__filter_illegal_order_items(order_items)\r\n if not order_items:\r\n msg = _(\"{message} There are no valid order items left. This order is \"\r\n \"being marked as complete.\").format(message=msg)\r\n self.complete(\"SUCCESS\")\r\n return [], msg\r\n\r\n self.status = \"ACTIVE\"\r\n self.approved_by = approver\r\n self.approve_date = get_current_time()\r\n self.save()\r\n\r\n history_msg = _(\"The '{order}' order has been approved.\").format(order=escape(self))\r\n self.add_event('APPROVED', history_msg, profile=self.owner)\r\n\r\n # run pre order execution hook\r\n try:\r\n cbhooks.run_hooks(\"pre_order_execution\", order=self)\r\n except cbhooks.exceptions.HookFailureException as e:\r\n self.status = \"FAILURE\"\r\n self.save()\r\n msg = _(\"Failed to run hook for order approval. Status: {status},\"\r\n \" Output: {output}, Errors: {errors}\").format(status=e.status, output=e.output, errors=e.errors)\r\n\r\n history_msg = _(\"The '{order}' order has failed.\").format(order=escape(self))\r\n self.add_event('FAILED', history_msg, profile=self.owner)\r\n raise CloudBoltException(msg)\r\n\r\n from jobs.models import Job\r\n # Saving job objects will cause them to be kicked off by the\r\n # job engine within a minute\r\n jobs = []\r\n\r\n for order_item in order_items:\r\n jobtype = getattr(order_item, 'job_type', None)\r\n if not jobtype:\r\n # the job type will default to the first word of the class type\r\n # ex. \"provision\", \"decom\"\r\n\r\n jobtype = str(order_item.real_type).split(\" \", 1)[0]\r\n quantity = 1\r\n # quantity is a special field on order_items. If an\r\n # order_item has the quantity field, kick off that many\r\n # jobs\r\n if hasattr(order_item, 'quantity') and \\\r\n order_item.quantity is not None and \\\r\n order_item.quantity != '':\r\n quantity = int(order_item.quantity)\r\n for i in range(quantity):\r\n job = Job(job_parameters=order_item,\r\n type=jobtype,\r\n owner=self.owner,\r\n parent_job=parent_job)\r\n job.save()\r\n\r\n # Associate the job with any server(s)\r\n # This may seem unnecessary because it's done when most jobs\r\n # run, but it's needed at the very least for scheduled server\r\n # modification jobs (for changing resources) so they show up on\r\n # the server as scheduled before they actually run\r\n servers = []\r\n if hasattr(order_item, \"server\"):\r\n servers = [order_item.server]\r\n elif hasattr(order_item, \"servers\"):\r\n servers = order_item.servers.all()\r\n for server in servers:\r\n server.jobs.add(job)\r\n\r\n jobs.append(job)\r\n\r\n # If it didn't make any jobs, just call it done\r\n if not jobs:\r\n self.complete(\"SUCCESS\")\r\n\r\n return jobs, msg"],"string":"[\n \"def test_update_order(self):\\n response = self.api_test_client.put('{}/orders/1'.format(\\n self.BASE_URL), json={'order_status': 'accepted'})\\n\\n self.assertEqual(response.status_code, 201)\\n self.assertTrue(\\n response_as_json(response)['order']['status_updated_on'])\\n self.assertEqual(\\n response_as_json(response)['order']['order_status'], 'accepted')\",\n \"def test_order_update_status_function(self):\\n order = OrderInfo.objects.create(user=self.create_test_user())\\n self.assertIsInstance(order.ordered, datetime)\\n self.assertIsNone(order.cooked)\\n self.assertIsNone(order.delivered)\\n\\n order.update_current_state()\\n self.assertIsInstance(order.cooked, datetime)\\n self.assertIsNone(order.delivered)\\n\\n order.update_current_state()\\n self.assertIsInstance(order.cooked, datetime)\\n self.assertIsInstance(order.delivered, datetime)\",\n \"def determine_order_status_change(order, decision):\\n if order.status == Order.FAILED and decision == CYBERSOURCE_DECISION_CANCEL:\\n # This is a duplicate message, ignore since it's already handled\\n return None\\n\\n if order.status != Order.CREATED:\\n raise EcommerceException(f\\\"{order} is expected to have status 'created'\\\")\\n\\n if decision != CYBERSOURCE_DECISION_ACCEPT:\\n log.warning(\\n \\\"Order fulfillment failed: received a decision that wasn't ACCEPT for order %s\\\",\\n order,\\n )\\n return Order.FAILED\\n\\n return Order.FULFILLED\",\n \"def test_manager_change_order_status(self):\\n self.client.force_authenticate(self.user)\\n cancel = \\\"CA\\\"\\n url = reverse('order-set_status', args=[self.order.id])\\n resp = self.client.patch(url, data={\\n \\\"status\\\": cancel\\n })\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n new_status = Order.objects.values(\\\"status\\\").get(pk=self.order.id)\\n self.assertEqual(new_status[\\\"status\\\"], cancel)\\n\\n with self.subTest('customer can not change order status'):\\n self.user.role = get_user_model().CUSTOMER\\n self.client.force_authenticate(self.user)\\n resp = self.client.patch(url, data={\\n \\\"status\\\": cancel\\n })\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def on_update(self):\\n if self.get('update_request') and not self.is_pending_approval():\\n if self.is_revert:\\n self.set_as_reverted()\\n else:\\n self.set_as_success()\",\n \"async def _check_order_update(self, *args, **kwargs):\\n order_nos = list(self._orders.keys())\\n if not order_nos:\\n return\\n for order_no in order_nos:\\n success, error = await self._rest_api.get_order_status(order_no)\\n if error:\\n return\\n await self._update_order(success[\\\"data\\\"][0])\",\n \"async def update_order_status():\\n symbol = App.config[\\\"symbol\\\"]\\n\\n # Get currently active order and id (if any)\\n order = App.order\\n order_id = order.get(\\\"orderId\\\", 0) if order else 0\\n if not order_id:\\n log.error(f\\\"Wrong state or use: check order status cannot find the order id.\\\")\\n return None\\n\\n # -----\\n # Retrieve order from the server\\n try:\\n new_order = App.client.get_order(symbol=symbol, orderId=order_id)\\n except Exception as e:\\n log.error(f\\\"Binance exception in 'get_order' {e}\\\")\\n return\\n\\n # Impose and overwrite the new order information\\n if new_order:\\n order.update(new_order)\\n else:\\n return None\\n\\n # Now order[\\\"status\\\"] contains the latest status of the order\\n return order[\\\"status\\\"]\",\n \"def update_order(self, order):\\n order.order_id = self.order_id\\n order.average_price = self.avg_execution_price\\n order.symbol = self.symbol\\n order.side = self.side\\n order.type = self.order_type\\n order.amount = self.original_amount\\n order.price = self.price\\n order.filled = self.executed_amount\\n order.remaining = self.remaining_amount\\n if self.is_cancelled:\\n order.status = exchanges.Order.Status.CANCELLED\\n elif self.is_live:\\n order.status = exchanges.Order.Status.OPEN\\n else:\\n order.status = exchanges.Order.Status.CLOSED\\n return order\",\n \"def notify_order(self, order):\\n if order.status in [order.Submitted, order.Accepted]:\\n return # active buy/sell order submitted/accepted - do nothing\\n\\n # check if order has been completed (could reject if not enough cash)\\n if order.status in [order.Completed]:\\n if order.isbuy():\\n self.log(f'BUY EXECUTED, {order.executed.price:.2f}')\\n elif order.issell():\\n self.log(f'SELL EXECUTED, {order.executed.price:.2f}')\\n elif order.status in [order.Canceled, order.Margin, order.Rejected]:\\n self.log('Order Canceled/Margin/Rejected')\\n\\n self.bar_executed = len(self)\\n\\n self.order = None # reset orders\",\n \"def order_update_status():\\n result = order_obj.order_update_status(request.forms) \\n return result\",\n \"def test_admin_change_order_status(self):\\n # Test unregistered id\\n # Correct format but not there\\n response = self.client.put(\\n 'api/v1/parcels/35420', headers=self.admin_token_dict)\\n data = json.loads(response.data)\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(\\n data, {'message': 'No Parcel delivery order with that id'})\\n # Test invalid format id\\n response = self.client.put(\\n 'api/v1/parcels/35uh420', headers=self.admin_token_dict) # Incorrect id format\\n data = json.loads(response.data)\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(data, {'message': 'Wrong id format'})\",\n \"def warehouse_officer_confirm_qty(self):\\n if (\\n self.approve_request_ids is None\\n or self.approve_request_ids is False\\n ):\\n raise UserError(\\\"No line(s) defined!\\\")\\n self._compute_confirm()\\n for line in self.approve_request_ids:\\n line._compute_state()\\n if any(line.state != \\\"available\\\" for line in self.approve_request_ids):\\n raise Warning(\\n \\\"Please procure the items that are short in stock or process pending purchase agreements and try again!\\\"\\n )\\n else:\\n self.state = 'transfer'\",\n \"def order_success(self, request):\\n order = self.order_from_request(request)\\n\\n if not order:\\n return self.order_new(request)\\n\\n if not order.balance_remaining:\\n self.set_order_on_request(request, order=None)\\n\\n\\n order_data = OrderData.objects.get(order=order)\\n o_data = simplejson.loads(order_data.data)\\n\\n paymentData = {}\\n paymentData['delivery_address2'] = o_data['delivery_address2']\\n paymentData['billing_address2'] = o_data['billing_address2']\\n paymentData['delivery_date'] = o_data['delivery_date']\\n paymentData['delivery_state'] = o_data['delivery_state']\\n paymentData['billing_state'] = o_data['billing_state']\\n paymentData['salutation'] = o_data['salutation']\\n paymentData['contact_number'] = o_data['billing_contact_number']\\n\\n #try:\\n oPayment = OrderPayment.objects.get(order=order)\\n oPayment.payment_method = o_data['order_payment_method']\\n oPayment.data = simplejson.dumps(paymentData)\\n oPayment.save()\\n #except:\\n # pass\\n\\n \\\"\\\"\\\"\\n order update note\\n \\\"\\\"\\\"\\n notes = o_data['order_notes']\\n order.notes = notes\\n order.save()\\n\\n # st_save_helper(request, order)\\n\\n \\\"\\\"\\\"\\n sbid = None\\n\\n if 'customer_styleboard' in request.session:\\n sbid = request.session.get('customer_styleboard').id\\n\\n if 'personalize_id' in request.session:\\n print \\\"There's a personalize_id\\\"\\n \\\"\\\"\\\"\\n\\n current_user = User.objects.get(id=int(request.user.id))\\n\\n if 'ipn_emailed' in o_data and o_data['ipn_emailed']:\\n\\n pass\\n \\n else:\\n\\n emailed = send_email_order(order, current_user, notes, paymentData['contact_number'], self)\\n\\n logr.info('emailed order confirmation to : %s from order success' % current_user.email)\\n\\n\\n order_data.delete() # not needed after saving to order payment\\\\\\n \\n clear_styleboard_session(request)\\n\\n try:\\n del request.session['customer_styleboard']\\n del request.session['personalize_id']\\n except:\\n pass\\n\\n return self.render(request, 'plata/shop_order_success.html',\\n self.get_context(request, {\\n 'order': order,\\n 'progress': 'success',\\n }))\",\n \"def update_or_create_delivery(self, orderitem_data):\",\n \"def _order_cannot_be_updated_if_not_pending(order_status):\\n pecan.abort(400, u._(\\\"Only PENDING orders can be updated. Order is in the\\\"\\n \\\"{0} state.\\\").format(order_status))\",\n \"def update_with_order_update(self, order_update: OrderUpdate) -> bool:\\n if (order_update.client_order_id != self.client_order_id\\n and order_update.exchange_order_id != self.exchange_order_id):\\n return False\\n\\n prev_data = (self.exchange_order_id, self.current_state)\\n\\n if self.exchange_order_id is None and order_update.exchange_order_id is not None:\\n self.update_exchange_order_id(order_update.exchange_order_id)\\n\\n self.current_state = order_update.new_state\\n self.check_processed_by_exchange_condition()\\n\\n updated: bool = prev_data != (self.exchange_order_id, self.current_state)\\n\\n if updated:\\n self.last_update_timestamp = order_update.update_timestamp\\n\\n return updated\",\n \"def test_set_assign_complete(self):\\n order_2 = StockOrderWrapper(self.order_2)\\n order_3 = StockOrderWrapper(self.order_3)\\n order_5 = StockOrderWrapper(self.order_5)\\n order_7 = StockOrderWrapper(self.order_7)\\n\\n order_2.set_assign_complete()\\n order_3.set_assign_complete()\\n order_5.set_assign_complete()\\n order_7.set_assign_complete()\\n\\n # =================================================================\\n # test: order_wrapper status is updated\\n # =================================================================\\n\\n self.assertEquals(order_2.order_status, PROCESSED)\\n self.assertEquals(order_3.order_status, PROCESSED)\\n self.assertEquals(order_5.order_status, PROCESSED)\\n self.assertEquals(order_7.order_status, PROCESSED)\\n\\n # =================================================================\\n # test: order is saved\\n # =================================================================\\n\\n real_order_2 = StockOrder.objects.get(order_id=1)\\n real_order_3 = StockOrder.objects.get(order_id=2)\\n real_order_5 = StockOrder.objects.get(order_id=3)\\n real_order_7 = StockOrder.objects.get(order_id=4)\\n\\n # =================================================================\\n # test: order_result is updated\\n # =================================================================\\n\\n self.assertEquals(real_order_2.order_result, ASSIGNED_COMPLETE)\\n self.assertEquals(real_order_3.order_result, ASSIGNED_COMPLETE)\\n self.assertEquals(real_order_5.order_result, ASSIGNED_COMPLETE)\\n self.assertEquals(real_order_7.order_result, ASSIGNED_COMPLETE)\\n\\n # =================================================================\\n # test: order_status is updated\\n # =================================================================\\n\\n self.assertEquals(real_order_2.order_status, PROCESSED)\\n self.assertEquals(real_order_3.order_status, PROCESSED)\\n self.assertEquals(real_order_5.order_status, PROCESSED)\\n self.assertEquals(real_order_7.order_status, PROCESSED)\",\n \"def mark_refunded(self):\\n order = self.clone()\\n order.status = Order.STATUS_REFUNDED\\n order.save()\\n return order\",\n \"def converge_orders(self, buy_orders, sell_orders, order_status):\\n\\n tickLog = self.exchange.get_instrument()['tickLog']\\n to_amend = []\\n to_create = []\\n to_cancel = []\\n buys_matched = 0\\n sells_matched = 0\\n existing_orders = self.exchange.get_orders()\\n\\n # Check all existing orders and match them up with what we want to place.\\n # If there's an open one, we might be able to amend it to fit what we want.\\n for order in existing_orders:\\n if order['ordType'] != 'Limit':\\n continue\\n try:\\n if (order['side'] == 'Buy' and (order_status == 0 or order_status == 4 or order_status == 3 or order_status == 1 or order_status == 7)):\\n desired_order = buy_orders[buys_matched]\\n buys_matched += 1\\n elif (order['side'] == 'Sell' and (order_status == 0 or order_status == 2 or order_status == 1 or order_status == 3 or order_status == 8)):\\n desired_order = sell_orders[sells_matched]\\n sells_matched += 1\\n elif (order['price'] == buy_orders[buys_matched]['price'] and order_status == 6):\\n to_cancel.append(order)\\n buys_matched += 1\\n continue\\n elif (order['price'] == sell_orders[sells_matched]['price'] and order_status == 6):\\n to_cancel.append(order)\\n sells_matched += 1\\n continue\\n else:\\n continue\\n\\n # Found an existing order. Do we need to amend it?\\n if desired_order['orderQty'] != order['leavesQty'] or (\\n # If price has changed, and the change is more than our RELIST_INTERVAL, amend.\\n desired_order['price'] != order['price'] and\\n abs((desired_order['price'] / order['price']) - 1) > 0):\\n to_amend.append({'orderID': order['orderID'], 'orderQty': order['cumQty'] + desired_order['orderQty'],\\n 'price': desired_order['price'], 'side': order['side']})\\n # Found an stop existing order. Do we need to amend it?\\n\\n except IndexError:\\n # Will throw if there isn't a desired order to match. In that case, cancel it.\\n if ((order_status == 2 and order['side'] == 'Sell') or (order_status == 1 and self.running_qty > 0) or (order_status == 4 and order['side'] == 'Buy') or (order_status == 3 and self.running_qty < 0) or (order_status == 7 and order['side'] == 'Buy') or (order_status == 8 and order['side'] == 'Sell')):\\n to_cancel.append(order)\\n\\n if (order_status == 0 or order_status == 4 or order_status == 3 or order_status == 1 or order_status == 5 or order_status == 7):\\n while buys_matched < len(buy_orders):\\n to_create.append(buy_orders[buys_matched])\\n buys_matched += 1\\n if (order_status == 0 or order_status == 2 or order_status == 1 or order_status == 3 or order_status == 5 or order_status == 8):\\n while sells_matched < len(sell_orders):\\n to_create.append(sell_orders[sells_matched])\\n sells_matched += 1\\n\\n if len(to_amend) > 0:\\n for amended_order in reversed(to_amend):\\n reference_order = [o for o in existing_orders if o['orderID'] == amended_order['orderID']][0]\\n logger.info(\\\"Amending %4s: %d @ %.*f to %d @ %.*f (%+.*f)\\\" % (\\n amended_order['side'],\\n reference_order['leavesQty'], tickLog, reference_order['price'],\\n (amended_order['orderQty'] - reference_order['cumQty']), tickLog, amended_order['price'],\\n tickLog, (amended_order['price'] - reference_order['price'])\\n ))\\n # This can fail if an order has closed in the time we were processing.\\n # The API will send us `invalid ordStatus`, which means that the order's status (Filled/Canceled)\\n # made it not amendable.\\n # If that happens, we need to catch it and re-tick.\\n try:\\n self.exchange.amend_bulk_orders(to_amend)\\n except requests.exceptions.HTTPError as e:\\n errorObj = e.response.json()\\n if errorObj['error']['message'] == 'Invalid ordStatus':\\n logger.warn(\\\"Amending failed. Waiting for order data to converge and retrying.\\\")\\n sleep(0.5)\\n return self.place_orders()\\n else:\\n logger.error(\\\"Unknown error on amend: %s. Exiting\\\" % errorObj)\\n sys.exit(1)\\n\\n if len(to_create) > 0:\\n logger.info(\\\"Creating %d orders:\\\" % (len(to_create)))\\n for order in reversed(to_create):\\n logger.info(\\\"%4s %d @ %.*f\\\" % (order['side'], order['orderQty'], tickLog, order['price']))\\n self.exchange.create_bulk_orders(to_create)\\n\\n # Could happen if we exceed a delta limit\\n if len(to_cancel) > 0:\\n logger.info(\\\"Canceling %d orders:\\\" % (len(to_cancel)))\\n for order in reversed(to_cancel):\\n logger.info(\\\"%4s %d @ %.*f\\\" % (order['side'], order['leavesQty'], tickLog, order['price']))\\n self.exchange.cancel_bulk_orders(to_cancel)\",\n \"def _handle_orders(self, response):\\n response_type = response['type']\\n state_updated = False\\n if response_type == \\\"subscription_ack\\\":\\n # Insure the subscription details are expected. Don't do anything.\\n account_id = response['accountId']\\n # TODO: should we do anything with the subscription id?\\n # subscription_id = response['subscriptionId']\\n symbol_filter = response['symbolFilter']\\n api_session_filter = response['apiSessionFilter']\\n event_type_filter = response['eventTypeFilter']\\n if len(symbol_filter) or len(event_type_filter):\\n raise Exception(\\\"No symbol or event type were specified, but \\\"\\n \\\"filters were registered.\\\")\\n if len(api_session_filter) != 1:\\n raise Exception(\\\"1 session filter should have been registered.\\\"\\n f\\\"{len(api_session_filter)} were registered.\\\")\\n accepted_key = api_session_filter[0]\\n if accepted_key != self._api_credentials\\\\\\n .api_key:\\n raise Exception(\\\"The whitelisted api session key does not \\\"\\n \\\"match our session key.\\\")\\n elif response_type == \\\"initial\\\":\\n # Create a new order record for the initial response.\\n order_response = OrderResponse.from_json_dict(response)\\n new_order = exchanges.Order()\\n order_response.update_order(new_order)\\n existing_order = self.exchange_state.order(new_order.order_id)\\n if existing_order:\\n raise Exception(\\\"An initial response was received for an \\\"\\n \\\"existing order (id: {new_order.order_id}).\\\")\\n self.exchange_state.set_order(new_order.order_id, new_order)\\n state_updated = True\\n elif response_type == \\\"accepted\\\":\\n # Create a new order. Mark the corresponding action as successful.\\n order_response = OrderResponse.from_json_dict(response)\\n new_order = exchanges.Order()\\n order_response.update_order(new_order)\\n self.exchange_state.set_order(new_order.order_id, new_order)\\n found_action = False\\n for a in self._create_actions:\\n if id(a) == int(order_response.client_order_id):\\n if a.order is not None:\\n raise Exception(\\\"An order accept message was received, \\\"\\n \\\"but its corresponding action already \\\"\\n \\\"has an order (id:{a.order.order_id}).\\\")\\n a.order = new_order\\n # I don't know if we need this status.\\n a.status = exchanges.Action.Status.SUCCESS\\n found_action = True\\n break\\n if not found_action:\\n raise Exception(\\\"Received an order accept message, but no \\\"\\n \\\"matching order action was found.\\\")\\n state_updated = True\\n elif response_type == \\\"rejected\\\":\\n order_response = OrderResponse.from_json_dict(response)\\n log.warning(f\\\"An order was rejected. Reason: \\\" + response['reason'])\\n new_order = exchanges.Order()\\n order_response.update_order(new_order)\\n self.exchange_state.set_order(new_order.order_id, new_order)\\n found_action = False\\n for a in self._create_actions:\\n if id(a) == int(order_response.client_order_id):\\n if a.order is not None:\\n raise Exception(\\\"An order reject message was received, \\\"\\n \\\"but its corresponding action already \\\"\\n \\\"has an order (id:{a.order.order_id}).\\\")\\n a.order = new_order\\n a.status = exchanges.Action.Status.FAILED\\n found_action = True\\n break\\n if not found_action:\\n raise Exception(\\\"Received an order reject message, but no \\\"\\n \\\"matching order action was found.\\\")\\n state_updated = True\\n elif response_type == \\\"booked\\\":\\n # I don't think we need to act on this.\\n log.info(\\\"Order booked. Order id:{response['order_id']}.\\\")\\n elif response_type == \\\"fill\\\":\\n order_response = OrderResponse.from_json_dict(response)\\n order = self.exchange_state.order(order_response.order_id)\\n if not order:\\n raise Exception(\\\"Received a fill response for an unknown order \\\"\\n f\\\"(id:{order_response.order_id}).\\\")\\n log.info(\\\"Order fill response received for order id: \\\"\\n f\\\"{order_response.order_id}.\\\")\\n order_response.update_order(order)\\n state_updated = True\\n # TODO: we could add some checks here to see if our fee calculation\\n # is correct.\\n elif response_type == \\\"cancelled\\\":\\n order_response = OrderResponse.from_json_dict(response)\\n order = self.exchange_state.order(order_response.order_id)\\n reason = response.get('reason', 'No reason provided.')\\n # Unused:\\n # cancel_command_id = response.get('cancel_command_id', None)\\n if not order:\\n raise Exception(\\\"Received a cancelled response for an unknown \\\"\\n f\\\"order (id:{order_response.order_id}). Reason:\\\"\\n f\\\"{reason}\\\")\\n log.info(\\\"Order fill response received for order id: \\\"\\n f\\\"{order_response.order_id}. Reason: {reason}\\\")\\n cancel_action = self._cancel_actions.get(order_response.order_id,\\n None)\\n if not cancel_action:\\n raise Exception(\\\"Received a cancel response but can't find a \\\"\\n \\\"matching cancel action.\\\")\\n cancel_action.status = exchanges.Action.Status.SUCCESS\\n state_updated = True\\n elif response_type == \\\"cancel_rejected\\\":\\n order_response = OrderResponse.from_json_dict(response)\\n reason = response.get('reason', 'No reason provided.')\\n log.warning(\\\"Failed to cancel order (id: \\\"\\n f\\\"{order_response.order_id}). Reason: {reason}\\\")\\n cancel_action = self._cancel_actions.get(order_response.order_id,\\n None)\\n if not cancel_action:\\n raise Exception(\\\"Received a cancel rejected response but can't \\\"\\n \\\"find a matching cancel action.\\\")\\n cancel_action.status = exchanges.Action.Status.FAILED\\n state_updated = True\\n elif response_type == \\\"closed\\\":\\n order_response = OrderResponse.from_json_dict(response)\\n order = self.exchange_state.order(order_response.order_id)\\n if not order:\\n raise Exception(\\\"Received a close response for an unknown order\\\"\\n f\\\" (id:{order_response.order_id}).\\\")\\n log.info(\\\"Order close response received for order id: \\\"\\n f\\\"{order_response.order_id}.\\\")\\n order_response.update_order(order)\\n state_updated = True\\n else:\\n raise Exception(f\\\"Unexpected response type: {response_type}.\\\")\\n return state_updated\",\n \"def SendOrderConfirmation(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def _update_time_delivered(self, time_delivered):\\n # Update db record's time_delivered field\\n update = {'time_delivered': time_delivered}\\n datebase.update_transaction_record(filter=self.filter, update=update)\\n \\n # Update db record's estimated_time field\\n datebase.update_transaction_record(filter=self.filter, {estimated_time:'0'})\\n \\n # Update db record's transaction status to delivered\\n self._update_transaction_status(transaction_status='delivered')\\n \\t\\t self.transaction_info.update(delivery_status='delivered')\\n \\n # Update object\\n \\t\\tself.transaction_info.update(time_delivered=time_delivered)\\n self.transaction_info.update(estimated_time=0)\\n self.transaction_info(transaction_status='delivered')\\n\\n \\tdef _update_transaction_status(self, transaction_status, photo=None):\\n \\\"\\\"\\\"\\n Update record's transaction_status and send sms msg to update seeker\\n \\\"\\\"\\\"\\n # Send text message when status changes \\n self.send_text(message_type=transaction_status)\\n\\n # Update db record's transaction status\\n update = {'transaction_status': transaction_status}\\n datebase.update_transaction_record(filter=self.filter, update=update)\\n\\n # Update object\\n self.transaction_info.update('transaction_seeker': transaction_status)\\n\\n # If delivered ... TODO: do we actually want to remove from db? \\n \\t\\t# if transaction_status == 'delivered':\\n # datebase.delete_transaction_record()\\n # return 1 \\n # arguments against: we wont be able to access delivered photo if we want to do that\",\n \"def set_order_valid():\\n data = select_data_source()\\n order_id = data['id']\\n valid = data['valid']\\n user = data['user']\\n \\n if check_user_permission(user) : return permission_denied_return\\n \\n db = database.getdb()\\n \\n ### Check if order exists.\\n \\n cmd = 'select * from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_info = db.execute(cmd).fetchall()\\n if len(order_info) != 1 :\\n return validate_not_exist_return\\n \\n ### Check if this order belongs to the user.\\n \\n cmd = 'select * from orders where id==\\\"{0}\\\" AND owner==\\\"{1}\\\"'.format(order_id, user)\\n user_info = db.execute(cmd).fetchall()\\n if len(user_info) != 1 :\\n return validate_invalid_user_return\\n \\n ### Check if order is not valid recently.\\n \\n cmd = 'select * from orders where id==\\\"{0}\\\" AND passed!=0'.format(order_id)\\n order_valid = db.execute(cmd).fetchall()\\n if len(order_valid) != 0 :\\n return validate_is_valid_return\\n \\n ### Check if there is an order already valid at the same time.\\n cmd = 'select time from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_time = db.execute(cmd).fetchall()[0][0]\\n cmd = 'select * from orders where time==\\\"{0}\\\" AND passed!=0'.format(order_time)\\n conflict = db.execute(cmd).fetchall()\\n if len(conflict) != 0 :\\n return validate_conflict_return\\n \\n if str.lower(valid) == 'true' :\\n ### Set order valid.\\n cmd = 'update orders set passed=1 where id==\\\"{0}\\\"'.format(order_id)\\n db.execute(cmd)\\n db.commit()\\n print('set order {0} to valid.'.format(order_id))\\n return validate_complete_return\\n elif str.lower(valid) == 'false' :\\n ### Remove the order entry.\\n cmd = 'delete from orders where id=\\\"{0}\\\"'.format(order_id)\\n db.execute(cmd)\\n db.commit()\\n print('deny the order {0} and remove it from databse.'.format(order_id))\\n # TODO: email something to announce...\\n return validate_reject_return\\n else:\\n return validate_valid_parameter_error_return\",\n \"def test_acknowledge_orders(self):\\n pass\",\n \"async def process(self, msg):\\n logger.debug(\\\"msg:\\\", json.dumps(msg), caller=self)\\n e = msg.get(\\\"e\\\")\\n if e == \\\"executionReport\\\": # Order update.\\n if msg[\\\"s\\\"] != self._raw_symbol:\\n return\\n order_no = \\\"{}_{}\\\".format(msg[\\\"i\\\"], msg[\\\"c\\\"])\\n if msg[\\\"X\\\"] == \\\"NEW\\\":\\n status = ORDER_STATUS_SUBMITTED\\n elif msg[\\\"X\\\"] == \\\"PARTIALLY_FILLED\\\":\\n status = ORDER_STATUS_PARTIAL_FILLED\\n elif msg[\\\"X\\\"] == \\\"FILLED\\\":\\n status = ORDER_STATUS_FILLED\\n elif msg[\\\"X\\\"] == \\\"CANCELED\\\":\\n status = ORDER_STATUS_CANCELED\\n elif msg[\\\"X\\\"] == \\\"REJECTED\\\":\\n status = ORDER_STATUS_FAILED\\n elif msg[\\\"X\\\"] == \\\"EXPIRED\\\":\\n status = ORDER_STATUS_FAILED\\n else:\\n logger.warn(\\\"unknown status:\\\", msg, caller=self)\\n return\\n order = self._orders.get(order_no)\\n if not order:\\n info = {\\n \\\"platform\\\": self._platform,\\n \\\"account\\\": self._account,\\n \\\"strategy\\\": self._strategy,\\n \\\"order_no\\\": order_no,\\n \\\"action\\\": msg[\\\"S\\\"],\\n \\\"order_type\\\": msg[\\\"o\\\"],\\n \\\"symbol\\\": self._symbol,\\n \\\"price\\\": msg[\\\"p\\\"],\\n \\\"quantity\\\": msg[\\\"q\\\"],\\n \\\"ctime\\\": msg[\\\"O\\\"]\\n }\\n order = Order(**info)\\n self._orders[order_no] = order\\n order.remain = float(msg[\\\"q\\\"]) - float(msg[\\\"z\\\"])\\n order.status = status\\n order.utime = msg[\\\"T\\\"]\\n if self._order_update_callback:\\n SingleTask.run(self._order_update_callback, copy.copy(order))\",\n \"def fulfill_order(request_data):\\n # First, save this information in a receipt\\n receipt = Receipt.objects.create(data=request_data)\\n\\n # Link the order with the receipt if we can parse it\\n reference_number = request_data[\\\"req_reference_number\\\"]\\n req_bill_to_email = request_data.get(\\\"req_bill_to_email\\\")\\n order = Order.objects.get_by_reference_number(reference_number)\\n receipt.order = order\\n receipt.save()\\n\\n new_order_status = determine_order_status_change(order, request_data[\\\"decision\\\"])\\n if new_order_status is None:\\n # This is a duplicate message, ignore since it's already handled\\n return\\n\\n order.status = new_order_status\\n order.save()\\n sync_hubspot_deal(order)\\n\\n if order.status == Order.FULFILLED:\\n complete_order(order)\\n if settings.ENABLE_ORDER_RECEIPTS:\\n send_ecommerce_order_receipt(\\n order=order, cyber_source_provided_email=req_bill_to_email\\n )\\n\\n # Save to log everything to an audit table including enrollments created in complete_order\\n order.save_and_log(None)\",\n \"def test_change_order_status_when_order_does_not_exist(self):\\n response = self.api_test_client.put('{}/orders/1000'.format(\\n self.BASE_URL), json={'order_status': 'accepted'})\\n\\n self.assertEqual(response.status_code, 404)\\n self.assertEqual(response_as_json(\\n response)['message'],\\n 'Order with id 1000 not found')\",\n \"def on_update_after_submit(self):\\n if self.get('update_request') and not self.is_pending_approval():\\n if self.is_revert:\\n self.set_as_reverted()\\n else:\\n self.set_as_success()\",\n \"def _onSuccess(self, controller):\\r\\n if controller.order.paid_in_full:\\r\\n controller.cart.empty()\\r\\n for item in controller.order.orderitem_set.all():\\r\\n if item.product.is_subscription:\\r\\n item.completed = True\\r\\n item.save()\\r\\n try:\\r\\n curr_status = controller.order.orderstatus_set.latest() \\r\\n except OrderStatus.DoesNotExist:\\r\\n curr_status = None\\r\\n \\r\\n if (curr_status is None) or (curr_status.notes and curr_status.status == \\\"New\\\"):\\r\\n controller.order.add_status(status='New', notes = \\\"Order successfully submitted\\\")\\r\\n else:\\r\\n # otherwise just update and save\\r\\n if not curr_status.notes:\\r\\n curr_status.notes = _(\\\"Order successfully submitted\\\")\\r\\n curr_status.save() \\r\\n\\r\\n #Redirect to the success page\\r\\n url = controller.lookup_url('satchmo_checkout-success')\\r\\n return HttpResponseRedirect(url) \\r\\n\\r\\n else:\\r\\n log.debug('Order #%i not paid in full, sending to pay rest of balance', controller.order.id)\\r\\n #url = controller.order.get_balance_remaining_url()\\r\\n url = reverse('satchmo_balance_remaining')\\r\\n return HttpResponseRedirect(url)\",\n \"async def process(self, msg):\\n logger.debug(\\\"msg:\\\", json.dumps(msg), caller=self)\\n e = msg.get(\\\"e\\\")\\n if e == \\\"ORDER_TRADE_UPDATE\\\": # Order update.\\n self._update_order(msg[\\\"o\\\"])\",\n \"def test_confirm_add_wrong_flow_request_state(self):\\n # create fake flow request with active status\\n profile = Profile.objects.get(pk=1)\\n destination = Destination.objects.get(pk=1)\\n data = {\\n 'flow_id': '112233',\\n 'process_id': '332211',\\n 'status': FlowRequest.ACTIVE,\\n 'profile': profile,\\n 'destination': destination,\\n 'start_validity': '2017-10-23T10:00:00+02:00',\\n 'expire_validity': '2018-10-23T10:00:00+02:00'\\n }\\n flow_request = FlowRequest.objects.create(**data)\\n flow_request.save()\\n cc = ConfirmationCode.objects.create(flow_request=flow_request)\\n cc.save()\\n\\n callback_url = 'http://127.0.0.1/'\\n self.client.login(username='duck', password='duck')\\n for status in [s[0] for s in FlowRequest.STATUS_CHOICES if s[0] != FlowRequest.PENDING]:\\n flow_request.status = status\\n flow_request.save()\\n res = self.client.get('/v1/flow_requests/confirm/?confirm_id={}&callback_url={}&action=add'.format(\\n cc.code,\\n callback_url\\n ))\\n self.assertEqual(res.status_code, 400)\\n self.assertEqual(res.content.decode('utf-8'), ERRORS_MESSAGE['INVALID_FR_STATUS'])\",\n \"def _autoconfirm_production(self):\\n moves_to_confirm = self.env['stock.move']\\n for production in self:\\n if production.state in ('done', 'cancel'):\\n continue\\n additional_moves = production.move_raw_ids.filtered(\\n lambda move: move.state == 'draft' and move.additional\\n )\\n additional_moves.write({\\n 'group_id': production.procurement_group_id.id,\\n })\\n additional_moves._adjust_procure_method()\\n moves_to_confirm |= additional_moves\\n additional_byproducts = production.move_finished_ids.filtered(\\n lambda move: move.state == 'draft' and move.additional\\n )\\n moves_to_confirm |= additional_byproducts\\n\\n if moves_to_confirm:\\n moves_to_confirm._action_confirm()\\n # run scheduler for moves forecasted to not have enough in stock\\n moves_to_confirm._trigger_scheduler()\\n\\n self.workorder_ids.filtered(lambda w: w.state not in ['done', 'cancel'])._action_confirm()\",\n \"def checked_classified(self, order):\\n assert (order.get_status() is OrderStatus.Created)\\n assert (order.direction is not Direction.Cancel)\\n if order.exec_type not in [Exectype.Market, Exectype.Stop]:\\n assert (order.price is not None)\\n check_left = np.round(order.price / self.table.tick_size, self.tick_size_decimals)\\n check_right = np.round(order.price / self.table.tick_size, self.tick_size_decimals)\\n assert check_left == check_right\\n # Check expiration\\n order = self.checked_expired(order)\\n if order.get_status() is OrderStatus.Expired:\\n self.debug(\\\"Order expired: order.reject()\\\")\\n order.reject(self.table.get_current_time())\\n else:\\n if not self.table.allow_duplicated_ids and order.m_orderId in self.queue_observer.get_order_ids():\\n self.debug(\\\"Order implies duplicated id: order.reject()\\\")\\n order.reject(self.table.get_current_time())\\n else:\\n self.debug(\\\"Order can be accepted: order.accept()\\\")\\n order.accept(self.table.get_current_time())\\n return order\",\n \"def update_orders(comp, order, user_correct, payment_id):\\n users_orders = []\\n for item in order.items.all():\\n users_orders.append(item.id)\\n item.is_paid = True\\n item.save()\\n order.related_competition = comp\\n order.payment_id = payment_id\\n order.order_date = timezone.now()\\n order.answer_correct = user_correct\\n order.ordered = True\\n order.save()\\n return order\",\n \"def test_update_order_with_no_status(self):\\n response = self.api_test_client.put('{}/orders/1'.format(\\n self.BASE_URL), json={})\\n\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(response_as_json(\\n response)['message'], 'Bad request. Missing required param')\",\n \"def on_trade_order_check(self, orderid, envtype, status):\\n if is_cntrade_order_status_finish(status):\\n self._obj_order_sub.del_val(orderid=orderid, envtype=envtype)\\n elif (not self._obj_order_sub.has_val(orderid, envtype)) and self._obj_order_sub.has_val(u'', envtype):\\n self._obj_order_sub.add_val(orderid, envtype) # record info for subscribe order u''\",\n \"def set_order_done():\\n data = select_data_source()\\n user = data['user']\\n order_id = data['id']\\n \\n if check_user_permission(user) : return permission_denied_return\\n \\n db = database.getdb()\\n \\n ### Check if is valid.\\n \\n cmd = 'select passed from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_valid = db.execute(cmd).fetchall()[0][0]\\n if order_valid == 0 :\\n return finish_invalid_return\\n \\n ### Check if is done.\\n cmd = 'select done from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_done = db.execute(cmd).fetchall()[0][0]\\n if order_done != 0 :\\n return finish_done_return\\n \\n ### All check done.\\n ### Set it to done.\\n cmd = 'update orders set done=1 where id==\\\"{0}\\\"'.format(order_id)\\n db.execute(cmd)\\n db.commit()\\n print('user sets order {0} to be done.'.format(user))\\n \\n return finish_complete_return\",\n \"def reject_order(self, order: Order) -> None:\\n order = self.get_order_by_id(order.id)\\n order.status = OrderStatus.REJECT\",\n \"async def update_trade_status():\\n # GET /api/v3/openOrders - get current open orders\\n # GET /api/v3/allOrders - get all orders: active, canceled, or filled\\n\\n symbol = App.config[\\\"symbol\\\"]\\n\\n # -----\\n try:\\n open_orders = App.client.get_open_orders(symbol=symbol) # By \\\"open\\\" orders they probably mean \\\"NEW\\\" or \\\"PARTIALLY_FILLED\\\"\\n # orders = App.client.get_all_orders(symbol=symbol, limit=10)\\n except Exception as e:\\n log.error(f\\\"Binance exception in 'get_open_orders' {e}\\\")\\n return\\n\\n if not open_orders:\\n # -----\\n await update_account_balance()\\n\\n last_kline = App.analyzer.get_last_kline(symbol)\\n last_close_price = to_decimal(last_kline[4]) # Close price of kline has index 4 in the list\\n\\n base_quantity = App.base_quantity # BTC\\n btc_assets_in_usd = base_quantity * last_close_price # Cost of available BTC in USD\\n\\n usd_assets = App.quote_quantity # USD\\n\\n if usd_assets >= btc_assets_in_usd:\\n App.status = \\\"SOLD\\\"\\n else:\\n App.status = \\\"BOUGHT\\\"\\n\\n elif len(open_orders) == 1:\\n order = open_orders[0]\\n if order.get(\\\"side\\\") == SIDE_SELL:\\n App.status = \\\"SELLING\\\"\\n elif order.get(\\\"side\\\") == SIDE_BUY:\\n App.status = \\\"BUYING\\\"\\n else:\\n log.error(f\\\"Neither SELL nor BUY side of the order {order}.\\\")\\n return None\\n\\n else: # Many orders\\n log.error(f\\\"Wrong state. More than one open order. Fix manually.\\\")\\n return None\",\n \"def confirmed(self, cr, uid, ids, context=None):\\n\\tallow_archive_line_obj = self.pool.get('services.contracts.allowances.lines')\\n for record in self.browse(cr, uid, ids, context=context):\\n\\t\\tif not record.allowances_lines_before :\\n \\traise osv.except_osv(_('Partner Lines !'), _('Sorry no partner Lines!'))\\n\\n\\t \\tlines_ids = [line.id for line in record.allowances_lines_after]\\n \\tallow_archive_line_obj.unlink(cr,uid,lines_ids,context=context)\\n\\n\\t\\tfor lines in record.allowances_lines_before:\\n\\t\\t\\tif lines.percentage_rating < 0 or lines.percentage_rating > 100 :\\n \\t\\traise osv.except_osv(_('Rate Error !'), _('Sorry you insert wrong rate ... rate is between (0,100)!'))\\n \\t\\tamount_after_rate_id = allow_archive_line_obj.create(cr, uid, {\\n \\t\\t\\t\\t'cost_of_rent':lines.cost_of_rent,\\n \\t\\t\\t\\t'amount_untaxed':round (lines.amount_untaxed*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_tax':round(lines.amount_tax*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'amount_total':round(lines.amount_total*lines.percentage_rating/100,2),\\n \\t\\t\\t\\t'deduct_days':lines.deduct_days,\\n \\t\\t\\t\\t'deduct_amount':lines.deduct_amount,\\n \\t\\t\\t\\t'contract_id':lines.contract_id.id,\\n\\t\\t\\t\\t\\t'env_allow_id_after_rate':record.id,\\n\\t\\t\\t\\t\\t'type': 'after',\\n 'category_id':lines.category_id.id,\\n\\t\\t\\t\\t\\t'percentage_rating':lines.percentage_rating,\\n\\n })\\n\\t\\t\\n \\n self.write(cr, uid, ids, {'state':'confirmed'})\\n return True\",\n \"def test_updating_item_status(self):\\n #test original quantity\\n self.assertEqual(self.supply1.quantity, 10)\\n self.assertEqual(self.supply2.quantity, 10)\\n \\n modified_po = copy.deepcopy(base_purchase_order)\\n modified_po['status'] = 'Received'\\n modified_po['items'][0]['id'] = 1\\n modified_po['items'][0]['status'] = 'Receieved'\\n \\n resp = self.client.put('/api/v1/purchase-order/1/',\\n format='json',\\n data=modified_po)\\n \\n self.assertEqual(resp.status_code, 200, msg=resp)\\n \\n po = resp.data\\n \\n self.assertEqual(Supply.objects.get(pk=1).quantity, 20)\",\n \"def test_update_order_invalid_status(self):\\n response = self.api_test_client.put('{}/orders/1'.format(\\n self.BASE_URL), json={\\\"order_status\\\": \\\"brilliant\\\"}, headers={\\n 'Content-Type': 'application/json'\\n })\\n\\n response_json = response_as_json(response)\\n\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(response_json['message'], 'Bad request. Invalid order status')\",\n \"def test_confirm_delete_wrong_flow_request_state(self):\\n # create fake flow request with active status\\n profile = Profile.objects.get(pk=1)\\n destination = Destination.objects.get(pk=1)\\n\\n data = {\\n 'flow_id': '112233',\\n 'process_id': '332211',\\n 'status': FlowRequest.ACTIVE,\\n 'profile': profile,\\n 'destination': destination,\\n 'start_validity': '2017-10-23T10:00:00+02:00',\\n 'expire_validity': '2018-10-23T10:00:00+02:00'\\n }\\n flow_request = FlowRequest.objects.create(**data)\\n flow_request.save()\\n cc = ConfirmationCode.objects.create(flow_request=flow_request)\\n cc.save()\\n\\n callback_url = 'http://127.0.0.1/'\\n self.client.login(username='duck', password='duck')\\n for status in [s[0] for s in FlowRequest.STATUS_CHOICES if s[0] != FlowRequest.DELETE_REQUESTED]:\\n flow_request.status = status\\n flow_request.save()\\n res = self.client.get('/v1/flow_requests/confirm/?confirm_id={}&callback_url={}&action=delete'.format(\\n cc.code,\\n callback_url\\n ))\\n self.assertEqual(res.status_code, 400)\\n self.assertEqual(res.content.decode('utf-8'), ERRORS_MESSAGE['INVALID_FR_STATUS'])\",\n \"def test_decision_maker_handle_update_apply(self):\\n good_holdings = {\\\"good_id\\\": 2}\\n currency_holdings = {\\\"FET\\\": 100}\\n currency_deltas = {\\\"FET\\\": -10}\\n good_deltas = {\\\"good_id\\\": 1}\\n state_update_message = StateUpdateMessage(\\n performative=StateUpdateMessage.Performative.APPLY,\\n amount_by_currency_id=currency_deltas,\\n quantities_by_good_id=good_deltas,\\n )\\n self.decision_maker.handle(state_update_message)\\n expected_amount_by_currency_id = {\\n key: currency_holdings.get(key, 0) + currency_deltas.get(key, 0)\\n for key in set(currency_holdings) | set(currency_deltas)\\n }\\n expected_quantities_by_good_id = {\\n key: good_holdings.get(key, 0) + good_deltas.get(key, 0)\\n for key in set(good_holdings) | set(good_deltas)\\n }\\n assert (\\n self.decision_maker.ownership_state.amount_by_currency_id\\n == expected_amount_by_currency_id\\n ), \\\"The amount_by_currency_id must be equal with the expected amount.\\\"\\n assert (\\n self.decision_maker.ownership_state.quantities_by_good_id\\n == expected_quantities_by_good_id\\n )\",\n \"def update_request_status(self, requestID, ISBN, quantity, approved):\\n if approved:\\n self.cursor.execute(\\\"\\\"\\\"SELECT orderNumber FROM returnrequest WHERE requestID=%s\\\"\\\"\\\", (requestID,))\\n orderNumber = self.cursor.fetchone()[0]\\n self.cursor.execute(\\\"\\\"\\\"UPDATE returnrequest SET status='APPROVED' WHERE requestID=%s\\\"\\\"\\\", (requestID,))\\n self.cursor.execute(\\\"\\\"\\\"SELECT quantity FROM productof WHERE orderNumber=%s AND ISBN=%s\\\"\\\"\\\",\\n (orderNumber, ISBN))\\n remaining_books_ordered = self.cursor.fetchone()[0] - int(quantity)\\n if not int(remaining_books_ordered):\\n self.cursor.execute(\\\"\\\"\\\"DELETE FROM productof WHERE orderNumber=%s AND ISBN=%s\\\"\\\"\\\", (orderNumber, ISBN))\\n else:\\n self.cursor.execute(\\\"\\\"\\\"UPDATE productof SET quantity=quantity-%s WHERE orderNumber=%s AND ISBN=%s\\\"\\\"\\\",\\n (quantity, orderNumber, ISBN))\\n self.cursor.execute(\\\"\\\"\\\"UPDATE book SET stock=stock+%s WHERE ISBN=%s\\\"\\\"\\\", (quantity, ISBN))\\n self.db.commit()\\n if self.is_empty_order(orderNumber):\\n self.cursor.execute(\\\"\\\"\\\"DELETE FROM orderlog WHERE orderNumber=%s\\\"\\\"\\\", (orderNumber,))\\n else:\\n self.cursor.execute(\\\"\\\"\\\"UPDATE returnrequest SET status='DENIED' WHERE requestID=%s\\\"\\\"\\\", (requestID,))\\n self.db.commit()\",\n \"def confirmed(self, cr, uid, ids, context=None): \\n self.write(cr, uid, ids, {'state':'confirmed'})\\n return True\",\n \"def test_transaction_update_receive(self):\\n currency_endowment = {\\\"FET\\\": 75}\\n good_endowment = {\\\"good_id\\\": 30}\\n self.ownership_state.init(\\n amount_by_currency_id=currency_endowment,\\n quantities_by_good_id=good_endowment,\\n )\\n assert self.ownership_state.amount_by_currency_id == currency_endowment\\n assert self.ownership_state.quantities_by_good_id == good_endowment\\n tx_message = TransactionMessage(\\n performative=TransactionMessage.Performative.PROPOSE_FOR_SETTLEMENT,\\n skill_callback_ids=[PublicId(\\\"author\\\", \\\"a_skill\\\", \\\"0.1.0\\\")],\\n tx_id=\\\"transaction0\\\",\\n tx_sender_addr=\\\"agent_1\\\",\\n tx_counterparty_addr=\\\"pk\\\",\\n tx_amount_by_currency_id={\\\"FET\\\": 20},\\n tx_sender_fee=5,\\n tx_counterparty_fee=0,\\n tx_quantities_by_good_id={\\\"good_id\\\": -10},\\n info={\\\"some_info_key\\\": \\\"some_info_value\\\"},\\n ledger_id=\\\"fetchai\\\",\\n tx_nonce=\\\"transaction nonce\\\",\\n )\\n self.ownership_state._update(tx_message=tx_message)\\n expected_amount_by_currency_id = {\\\"FET\\\": 90}\\n expected_quantities_by_good_id = {\\\"good_id\\\": 20}\\n assert (\\n self.ownership_state.amount_by_currency_id == expected_amount_by_currency_id\\n )\\n assert (\\n self.ownership_state.quantities_by_good_id == expected_quantities_by_good_id\\n )\",\n \"def test_cancel_order(self):\\n # create a order to cancel\\n test_order = OrderFactory()\\n resp = self.app.post('/orders',\\n json=test_order.serialize(),\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\\n\\n # cancel the order\\n new_order = resp.get_json()\\n new_order['status'] = 'Cancelled'\\n resp = self.app.put('/orders/{}/cancel'.format(new_order['id']),\\n json=new_order,\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n cancelled_order = resp.get_json()\\n self.assertEqual(cancelled_order['status'], 'Cancelled')\",\n \"def can_update_order_items(self) -> bool:\\n return self.is_created or self.is_pending\",\n \"def test_cancel_order_failure(self):\\n # create a order to cancel\\n test_order = OrderFactory()\\n resp = self.app.post('/orders',\\n json=test_order.serialize(),\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\\n\\n # cancel the order\\n new_order = resp.get_json()\\n new_order['status'] = 'Cancelled'\\n resp = self.app.put('/orders/{}/cancel'.format(23),\\n json=new_order,\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_404_NOT_FOUND)\",\n \"def test_update_order_with_non_json_data(self):\\n response = self.api_test_client.put('{}/orders/1'.format(\\n self.BASE_URL), data='order_status=rejected')\\n\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(response_as_json(\\n response)['message'],\\n 'Bad request. Request data must be in json format')\",\n \"def slot_user_order(self, dummy_sender, data):\\r\\n (price, volume, typ, oid, status) = data\\r\\n found = False\\r\\n removed = False # was the order removed?\\r\\n opened = False # did the order change from 'post-pending' to 'open'\\\"?\\r\\n voldiff = 0 # did the order volume change (full or partial fill)\\r\\n if \\\"executing\\\" in status:\\r\\n # don't need this status at all\\r\\n return\\r\\n if \\\"post-pending\\\" in status:\\r\\n # don't need this status at all\\r\\n return\\r\\n if \\\"removed\\\" in status:\\r\\n for i in range(len(self.owns)):\\r\\n if self.owns[i].oid == oid:\\r\\n order = self.owns[i]\\r\\n\\r\\n # work around MtGox strangeness:\\r\\n # for some reason it will send a \\\"completed_passive\\\"\\r\\n # immediately followed by a \\\"completed_active\\\" when a\\r\\n # market order is filled and removed. Since \\\"completed_passive\\\"\\r\\n # is meant for limit orders only we will just completely\\r\\n # IGNORE all \\\"completed_passive\\\" if it affects a market order,\\r\\n # there WILL follow a \\\"completed_active\\\" immediately after.\\r\\n if order.price == 0:\\r\\n if \\\"passive\\\" in status:\\r\\n # ignore it, the correct one with\\r\\n # \\\"active\\\" will follow soon\\r\\n return\\r\\n\\r\\n self.debug(\\r\\n \\\"### removing order %s \\\" % oid,\\r\\n \\\"price:\\\", self.gox.quote2str(order.price),\\r\\n \\\"type:\\\", order.typ)\\r\\n\\r\\n # remove it from owns...\\r\\n self.owns.pop(i)\\r\\n\\r\\n # ...and update own volume cache in the bids or asks\\r\\n self._update_level_own_volume(\\r\\n order.typ,\\r\\n order.price,\\r\\n self.get_own_volume_at(order.price, order.typ)\\r\\n )\\r\\n removed = True\\r\\n break\\r\\n else:\\r\\n for order in self.owns:\\r\\n if order.oid == oid:\\r\\n found = True\\r\\n self.debug(\\r\\n \\\"### updating order %s \\\" % oid,\\r\\n \\\"volume:\\\", self.gox.base2str(volume),\\r\\n \\\"status:\\\", status)\\r\\n voldiff = volume - order.volume\\r\\n opened = (order.status != \\\"open\\\" and status == \\\"open\\\")\\r\\n order.volume = volume\\r\\n order.status = status\\r\\n break\\r\\n\\r\\n if not found:\\r\\n # This can happen if we added the order with a different\\r\\n # application or the gox server sent the user_order message\\r\\n # before the reply to \\\"order/add\\\" (this can happen because\\r\\n # actually there is no guarantee which one arrives first).\\r\\n # We will treat this like a reply to \\\"order/add\\\"\\r\\n self.add_own(Order(price, volume, typ, oid, status))\\r\\n\\r\\n # The add_own() method has handled everything that was needed\\r\\n # for new orders and also emitted all signals already, we\\r\\n # can immediately return here because the job is done.\\r\\n return\\r\\n\\r\\n # update level own volume cache\\r\\n self._update_level_own_volume(\\r\\n typ, price, self.get_own_volume_at(price, typ))\\r\\n\\r\\n # We try to help the strategy with tracking the orders as good\\r\\n # as we can by sending different signals for different events.\\r\\n if removed:\\r\\n reason = self.gox.msg[\\\"user_order\\\"][\\\"reason\\\"]\\r\\n self.signal_own_removed(self, (order, reason))\\r\\n if opened:\\r\\n self.signal_own_opened(self, (order))\\r\\n if voldiff:\\r\\n self.signal_own_volume(self, (order, voldiff))\\r\\n self.signal_changed(self, None)\\r\\n self.signal_owns_changed(self, None)\",\n \"def confirm_handle(self, update, context):\\n chat_id = update.effective_chat.id\\n response_code = update.callback_query[\\\"data\\\"] # caution_ok or caution_cancel\\n request_id = context.user_data[\\\"reviewed_request\\\"]\\n log.info(\\\"In progress req:%s %s\\\", request_id, response_code)\\n\\n if response_code == \\\"handle_onmyway\\\":\\n # they pressed \\\"I am 'on my way' in the GUI\\\"\\n self.updater.bot.send_message(\\n chat_id=chat_id,\\n text=f\\\"{c.MSG_SAFETY_INSTRUCTIONS} \\\\n\\\\n {c.MSG_LET_ME_KNOW_ARRIVE} \\\\n\\\\n p.s. {c.MSG_SAFETY_REMINDER}\\\",\\n parse_mode=ParseMode.MARKDOWN,\\n reply_markup=InlineKeyboardMarkup(k.inprogress_choices),\\n )\\n self.backend.update_request_status(request_id, \\\"onprogress\\\")\\n\\n elif response_code == \\\"handle_done\\\":\\n # they pressed 'Mission accomplished' in the GUI\\n self.send_message_ex(chat_id, c.MSG_THANKS_FEEDBACK)\\n self.updater.bot.send_message(\\n chat_id=chat_id,\\n text=c.MSG_FEEDBACK_EXPENSES,\\n parse_mode=ParseMode.MARKDOWN,\\n reply_markup=InlineKeyboardMarkup(k.endgame_choices),\\n )\\n context.user_data[\\\"state\\\"] = c.State.EXPECTING_AMOUNT\\n self.backend.update_request_status(request_id, \\\"done\\\")\\n\\n elif response_code == \\\"handle_no_expenses\\\":\\n # they indicated no compensation is required; proceed to the exit survey and ask some additional questions\\n # about this request\\n self.send_exit_survey(update, context)\\n context.user_data[\\\"state\\\"] = c.State.EXPECTING_EXIT_SURVEY\\n\\n elif response_code == \\\"handle_cancel\\\":\\n # they bailed out at some point while the request was in progress\\n self.send_message(chat_id, c.MSG_NO_WORRIES_LATER)\\n context.user_data[\\\"reviewed_request\\\"] = None\\n context.user_data[\\\"state\\\"] = c.State.AVAILABLE\\n self.backend.update_request_status(request_id, \\\"cancelled\\\")\",\n \"def confirm_state(self, approved=None, completed=None, cancelled=None):\\n if completed is not None:\\n if self.task.completed and not completed:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task has already been completed.\\\"\\n )\\n if not self.task.completed and completed:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task hasn't been completed.\\\"\\n )\\n\\n if cancelled is not None:\\n if self.task.cancelled and not cancelled:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task has been cancelled.\\\"\\n )\\n if not self.task.cancelled and cancelled:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task has not been cancelled.\\\"\\n )\\n if approved is not None:\\n if self.task.approved and not approved:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task has already been approved.\\\"\\n )\\n if not self.task.approved and approved:\\n raise exceptions.TaskStateInvalid(\\n self.task, \\\"This task has not been approved.\\\"\\n )\",\n \"def cancel_order(self):\\n self.withdraw_from_delivery()\\n if self.amount_paid:\\n self.refund_payment()\\n return 'refund_payment' if self.amount_paid else 'order_canceled'\",\n \"def test_cancel_shipment_old(self):\\n pass\",\n \"def confirmed(self):\",\n \"def post_save_order_receiver(sender, instance, created, *args, **kwargs):\\n sender_email = instance.cargo.sender.email\\n recepient_email = instance.cargo.recepient.email\\n\\n booking_agent = instance.cargo.booking_station.branch_agent.email\\n\\n if created:\\n instance._set_order_price()\\n instance._set_time_approximations()\\n price = instance.price\\n\\n subject = \\\"Order Finalized and ready to go.\\\"\\n message = f\\\"Your cargo has been booked and is ready for delivery. You will be notified whenever the status changes. It is currently {instance.get_status_display().title()}. It cost a total of ${price:.3f}. Your booking agent is {booking_agent}\\\"\\n\\n send_async_email(\\n subject=subject,\\n message=message,\\n sender=booking_agent,\\n recepients=[sender_email, recepient_email],\\n )\",\n \"def do_subscription_approval(sender, **kwargs):\\r\\n req_payment = sender.get_product_class().get_requires_payment_details()\\r\\n if not req_payment or has_valid_billing_details(sender.billing_account):\\r\\n status = 'approved'\\r\\n else:\\r\\n status = 'declined'\\r\\n sender.set_current_approval_status(status)\\r\\n return status\",\n \"def order_ready(request):\\n\\tcs , status = CookStatus.objects.get_or_create(cook_name=request.user)\\n\\tif cs.current_order is not None:\\n\\t\\tcs.current_order.status = 'ready-to-serve'\\n\\t\\tcs.current_order.save()\\n\\t\\tcs.current_order = None\\n\\t\\tcs.save()\\n\\n\\treturn HttpResponseRedirect(\\\"/staff/cook_order_list/\\\")\",\n \"def _handle_co_acquittal(self, message):\\n # TODO: validate the previous status is `accepted` or `sent`\\n country, org_id, cert_id = message['subject'].split('.')\\n cert = Certificate.objects.get(\\n id=cert_id,\\n org_id=org_id,\\n )\\n cert.acquitted_at = timezone.now()\\n if message not in cert.acquitted_details:\\n cert.acquitted_details.append(message)\\n cert.status = Certificate.STATUS_ACQUITTED\\n cert.save()\",\n \"def _order_update_not_supported():\\n pecan.abort(405, u._(\\\"Order update is not supported.\\\"))\",\n \"def test_accept_or_reject(self):\\n for decision, expected_status in [(1, Transfer.ACCEPTED), (0, Transfer.REJECTED)]:\\n transfer = random.choice(Transfer.objects.filter(process_status=Transfer.VALIDATED))\\n self.assert_status_code(\\n \\\"get\\\", reverse(\\\"appraise:list\\\"), 200,\\n data={\\n \\\"req_form\\\": \\\"appraise\\\",\\n \\\"req_type\\\": \\\"decision\\\",\\n \\\"upload_id\\\": transfer.pk,\\n \\\"appraisal_decision\\\": decision,\\n }, ajax=True)\\n transfer.refresh_from_db()\\n self.assertEqual(transfer.process_status, expected_status)\",\n \"def receive_confirmation(self):\\n #print(\\\"(%d) receive_confirmation:\\\" % int(time.time()))\\n #print(\\\" **> state:\\\", self.state)\\n if self.state != KeyExchangeManager.STATE_CONFIRMING:\\n return\\n rand_time = int(KeyExchangeManager.KEY_REFRESH_INTERVAL*random.uniform(0.9, 1.1))\\n self.set_invoke_timer(rand_time)\\n self._set_delete_timer(self.key_name, KeyExchangeManager.KEY_OBSOLETE_TIMER)\\n self.key_name = self.pending_key_name\\n self._set_state(KeyExchangeManager.STATE_ESTABLISHED)\\n #print(\\\"*STATE_ESTABLISHED\\\")\",\n \"def _transition(self, next_status: Status) -> None:\\n if next_status == Status.CLOSED:\\n self._status = next_status\\n elif (\\n self._status == Status.AWAITING_SERVER_MESSAGE\\n and next_status == Status.SERVER_MESSAGE_AVAILABLE\\n and self._server_message is not None\\n and self._client_message is None\\n ):\\n self._status = next_status\\n elif (\\n self._status == Status.SERVER_MESSAGE_AVAILABLE\\n and next_status == Status.AWAITING_CLIENT_MESSAGE\\n and self._server_message is None\\n and self._client_message is None\\n ):\\n self._status = next_status\\n elif (\\n self._status == Status.AWAITING_CLIENT_MESSAGE\\n and next_status == Status.CLIENT_MESSAGE_AVAILABLE\\n and self._server_message is None\\n and self._client_message is not None\\n ):\\n self._status = next_status\\n elif (\\n self._status == Status.CLIENT_MESSAGE_AVAILABLE\\n and next_status == Status.AWAITING_SERVER_MESSAGE\\n and self._server_message is None\\n and self._client_message is None\\n ):\\n self._status = next_status\\n else:\\n raise Exception(f\\\"Invalid transition: {self._status} to {next_status}\\\")\\n\\n self._cv.notify_all()\",\n \"def test_create_confirm_order_details(self):\\n pass\",\n \"def mark_ready_for_review(self, user: User) -> None:\\n from .exceptions import OperationForbiddenError, OrderEmptyError\\n\\n # If order is not in the \\\"CREATED\\\" state, raise an\\n # OperationForbiddenError\\n if not self.is_created:\\n raise OperationForbiddenError(\\n self.STATE_CHANGE_FORBIDDEN_ERROR_MSG % {\\n 'current_state': Order.OrderState.get_choice_display(\\n self.state\\n ),\\n 'new_state': Order.OrderState.PENDING.choice_display\\n }\\n )\\n\\n # If the order's item list is empty, raise an OrderEmptyError\\n if not self.orderitem_set.exists():\\n raise OrderEmptyError(\\n self,\\n 'An order should contain at least one Order item before it '\\n 'can be marked as \\\"PENDING\\\".'\\n )\\n\\n # Update the order to \\\"PENDING\\\" state\\n self.update(user, state=Order.OrderState.PENDING.choice_value)\",\n \"def test_confirm_add_flow_request_wrong_consent_status(self):\\n self.client.login(username='duck', password='duck')\\n res = self.client.get(\\n '/v1/flow_requests/consents_confirmed/?success=true&consent_confirm_id={}'.format(WRONG_CONFIRM_ID))\\n self.assertEqual(res.status_code, 302)\\n flow_request = FlowRequest.objects.get(flow_id='f_11111')\\n self.assertEqual(flow_request.status, FlowRequest.PENDING)\\n for channel in Channel.objects.filter(flow_request=flow_request):\\n channel.status = Channel.CONSENT_REQUESTED\",\n \"def confirm_further(self, update, context):\\n response_code = update.callback_query[\\\"data\\\"] # wouldyou_{yes|no}\\n request_id = context.user_data[\\\"current_request\\\"]\\n log.info(\\\"No further comments req:%s %s\\\", request_id, response_code)\\n self.finalize_request(update, context, request_id)\",\n \"def fulfill_order_online(self, **kwargs):\\n return self.client.execute(\\\"order/fulfill-online\\\", \\\"POST\\\", kwargs)\",\n \"def test_update_order_failure(self):\\n # create a order to update\\n test_order = OrderFactory()\\n resp = self.app.post('/orders',\\n json=test_order.serialize(),\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\\n\\n # update the order\\n new_order = resp.get_json()\\n new_order['product_id'] = 2\\n resp = self.app.put('/orders/{}'.format(5),\\n json=new_order,\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_404_NOT_FOUND)\",\n \"def cancel_for(self, other):\\n\\n if not self.event.settings.cancel_orderpositions:\\n raise Exception(\\\"Order position canceling is currently not allowed\\\")\\n\\n if (\\n self.position.subevent != other.subevent\\n or self.position.item != other.item\\n or self.position.variation != other.variation\\n ):\\n raise Exception(\\\"Cancelation failed, orders are not equal\\\")\\n if not can_be_canceled(self.event, self.position.item, self.position.subevent):\\n raise Exception(\\\"Cancelation failed, currently not allowed\\\")\\n\\n # Make sure AGAIN that the state is alright, because timings\\n self.refresh_from_db()\\n if not self.state == self.States.REQUESTED:\\n raise Exception(\\\"Not in 'requesting' state.\\\")\\n if self.position.price > other.price:\\n raise Exception(\\\"Cannot cancel for a cheaper product.\\\")\\n\\n try:\\n change_manager = OrderChangeManager(order=self.position.order)\\n change_manager.cancel(position=self.position)\\n change_manager.commit()\\n except OrderError: # Let's hope this order error is because we're trying to empty the order\\n cancel_order(\\n self.position.order.pk,\\n cancellation_fee=self.event.settings.swap_cancellation_fee,\\n try_auto_refund=True,\\n )\\n self.state = self.States.COMPLETED\\n self.target_order = other.order # Should be set already, let's just make sure\\n self.save()\\n self.position.order.log_action(\\n \\\"pretix_swap.cancelation.complete\\\",\\n data={\\n \\\"position\\\": self.position.pk,\\n \\\"positionid\\\": self.position.positionid,\\n \\\"other_position\\\": other.pk,\\n \\\"other_positionid\\\": other.positionid,\\n \\\"other_order\\\": other.order.code,\\n },\\n )\",\n \"def put(self):\\n request_data = json.loads(request.data)\\n print(request_data)\\n order_id = request_data['order_id']\\n status = request_data['status']\\n MM.update_order_status(ObjectId(order_id), status)\\n return {\\\"message\\\": \\\"Order Status Updated\\\"}, 200\",\n \"def awaiting_payment(self):\",\n \"def handle_payment_intent_succeeded(self, event):\\n intent = event.data.object\\n pid = intent.id\\n bag = intent.metadata.bag\\n\\n billing_details = intent.charges.data[0].billing_details\\n grand_total = round(intent.charges.data[0].amount / 100, 2)\\n\\n order_exists = False\\n attempt = 1\\n while attempt <= 5:\\n try:\\n order = Order.objects.get(\\n full_name__iexact=billing_details.name,\\n email__iexact=billing_details.email,\\n phone_number__iexact=billing_details.phone,\\n street_address1__iexact=(\\n billing_details.address.line1),\\n street_address2__iexact=(\\n billing_details.address.line2),\\n town_or_city__iexact=billing_details.address.city,\\n county__iexact=billing_details.address.state,\\n country__iexact=billing_details.address.country,\\n grand_total=grand_total,\\n original_bag=bag,\\n stripe_pid=pid,\\n )\\n order_exists = True\\n break\\n except Order.DoesNotExist:\\n attempt += 1\\n time.sleep(1)\\n\\n if order_exists:\\n return HttpResponse(\\n content=f'Webhook received: ({event[\\\"type\\\"]}'\\n '| SUCCESS: Verified order already in database',\\n status=200)\\n else:\\n order = None\\n try:\\n order = Order.objects.create(\\n full_name=billing_details.name,\\n email=billing_details.email,\\n phone_number=billing_details.phone,\\n street_address1=billing_details.address.line1,\\n street_address2=billing_details.address.line2,\\n town_or_city=billing_details.address.city,\\n county=billing_details.state,\\n country=billing_details.country,\\n original_bag=bag,\\n stripe_pid=pid,\\n )\\n for workshop_id, quantity in json.loads(bag).items():\\n workshop = Workshop.objects.get(id=workshop_id)\\n if isinstance(quantity, int):\\n order_line_item = OrderLineItem(\\n order=order,\\n workshop=workshop,\\n quantity=quantity,\\n )\\n order_line_item.save()\\n except Exception as e:\\n if order:\\n order.delete()\\n return HttpResponse(\\n content=f'Webhook received: {event[\\\"type\\\"]} | ERROR: {e}',\\n status=500)\\n return HttpResponse(\\n content=f'Webhook received: {event[\\\"type\\\"]}'\\n '| SUCCESS: Created order in webhook',\\n status=200)\",\n \"def create(self, validated_data):\\n\\n order_obj = Order.objects.get(order_id=validated_data['order_id'])\\n order_obj.complete_time = validated_data['complete_time']\\n order_obj.save()\\n\\n # if current delivery is over, add earnings\\n if not Order.objects.filter(courier_id=order_obj.courier_id, complete_time__isnull=True).exists():\\n order_obj.courier_id.earnings = F('earnings') + 500 * order_obj.courier_type.earnings_coef\\n order_obj.courier_id.save()\\n Order.objects.filter(assign_time=order_obj.assign_time).update(delivery_complete=True)\\n\\n response = {'order_id': validated_data['order_id']}\\n return response\",\n \"def test_update_order(self):\\n # create a order to update\\n test_order = OrderFactory()\\n resp = self.app.post('/orders',\\n json=test_order.serialize(),\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_201_CREATED)\\n\\n # update the order\\n new_order = resp.get_json()\\n new_order['product_id'] = 2\\n resp = self.app.put('/orders/{}'.format(new_order['id']),\\n json=new_order,\\n content_type='application/json')\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n updated_order = resp.get_json()\\n self.assertEqual(updated_order['product_id'], 2)\",\n \"def modify_expiration(self, new_expiration):\\n if self.is_cancellable:\\n if self.expiration!=new_expiration:\\n log.info(\\\"bo#%s: modify expiration of pending order\\\" % self.ticket)\\n dt = get_datetime()\\n new_expiration = dt_td_to_dt(new_expiration, dt)\\n not_implemented_error(\\\"Can't modify modify expiration of pending order\\\")\\n else:\\n return\",\n \"def m_ts_OrderRejected(self, sender, e):\\r\\n print(\\\"Order was rejected. {0}\\\".format(e.Message))\",\n \"def update_specific_order(self,status,order_id):\\n self.query = \\\"UPDATE orders SET order_status=%s WHERE order_id=%s\\\"\\n self.input = (status,order_id) #tuple to support indexing\\n self.query_1 = \\\"SELECT order_id FROM orders ORDER BY order_id DESC LIMIT 1.\\\"\\n self.query_2 = \\\"SELECT * FROM orders WHERE order_id=%s\\\"\\n self.input_2 = (order_id,) #tuple to support indexing\\n self.event = \\\"admin_update_specific_order\\\"\\n self.error = \\\"Invalid order id\\\"\\n self.message = \\\"Successfully updated the order.\\\"\\n self.order_id = order_id\\n self.db_error = None\",\n \"def update_order():\",\n \"def update_order():\",\n \"def m_ps_FieldsUpdated(self, sender, e):\\r\\n if e.Error == None:\\r\\n # Make sure that there is a valid bid\\r\\n if e.Fields.GetBestBidPriceField().HasValidValue:\\r\\n if self.m_orderKey == \\\"\\\":\\r\\n # If there is no order working, submit one through the first valid order feed.\\r\\n # You should use the order feed that is valid for your purposes.\\r\\n op = ttapi.OrderProfile(e.Fields.Instrument.GetValidOrderFeeds()[0], e.Fields.Instrument)\\r\\n op.BuySell = ttapi.BuySell.Buy\\r\\n op.AccountName = \\\"12345678\\\"\\r\\n op.AccountType = ttapi.AccountType.A1\\r\\n op.OrderQuantity = ttapi.Quantity.FromInt(e.Fields.Instrument, 1)\\r\\n op.OrderType = ttapi.OrderType.Limit\\r\\n op.LimitPrice = e.Fields.GetBestBidPriceField().Value\\r\\n if not self.m_ts.SendOrder(op):\\r\\n print(\\\"Send new order failed. {0}\\\".format(op.RoutingStatus.Message))\\r\\n self.Dispose()\\r\\n else:\\r\\n self.m_orderKey = op.SiteOrderKey\\r\\n print(\\\"Send new order succeeded.\\\")\\r\\n elif self.m_ts.Orders.ContainsKey(self.m_orderKey) and self.m_ts.Orders[self.m_orderKey].LimitPrice != e.Fields.GetBestBidPriceField().Value:\\r\\n # If there is a working order, reprice it if its price is not the same as the bid\\r\\n op = self.m_ts.Orders[self.m_orderKey].GetOrderProfile()\\r\\n op.LimitPrice = e.Fields.GetBestBidPriceField().Value\\r\\n op.Action = ttapi.OrderAction.Change\\r\\n if not self.m_ts.SendOrder(op):\\r\\n print(\\\"Send change order failed. {0}\\\".format(op.RoutingStatus.Message))\\r\\n else:\\r\\n print(\\\"Send change order succeeded.\\\")\\r\\n else:\\r\\n if e.Error.IsRecoverableError == False:\\r\\n print(\\\"Unrecoverable price subscription error: {0}\\\".format(e.Error.Message))\\r\\n self.Dispose()\",\n \"def _on_op_private_user_order(self, msg):\\r\\n order = msg[\\\"user_order\\\"]\\r\\n oid = order[\\\"oid\\\"]\\r\\n\\r\\n # there exist 3 fundamentally different types of user_order messages,\\r\\n # they differ in the presence or absence of certain parts of the message\\r\\n\\r\\n if \\\"status\\\" in order:\\r\\n # these are limit orders or market orders (new or updated).\\r\\n #\\r\\n # we also need to check whether they belong to our own gox instance,\\r\\n # since they contain currency this is easy, we compare the currency\\r\\n # and simply ignore mesages for all unrelated currencies.\\r\\n if order[\\\"currency\\\"] == self.curr_quote and order[\\\"item\\\"] == self.curr_base:\\r\\n volume = int(order[\\\"amount\\\"][\\\"value_int\\\"])\\r\\n typ = order[\\\"type\\\"]\\r\\n status = order[\\\"status\\\"]\\r\\n if \\\"price\\\" in order:\\r\\n # these are limit orders (new or updated)\\r\\n price = int(order[\\\"price\\\"][\\\"value_int\\\"])\\r\\n else:\\r\\n # these are market orders (new or updated)\\r\\n price = 0\\r\\n self.signal_userorder(self, (price, volume, typ, oid, status))\\r\\n\\r\\n else:\\r\\n # these are remove messages (cancel or fill)\\r\\n # here it is a bit more expensive to check whether they belong to\\r\\n # this gox instance, they don't carry any other useful data besides\\r\\n # the order id and the remove reason but since a remove message can\\r\\n # only affect us if the oid is in the owns list already we just\\r\\n # ask the orderbook instance whether it knows about this order\\r\\n # and ignore all the ones that have unknown oid\\r\\n if self.orderbook.have_own_oid(oid):\\r\\n # they don't contain a status field either, so we make up\\r\\n # our own status string to make it more useful. It will\\r\\n # be \\\"removed:\\\" followed by the reason. Possible reasons are:\\r\\n # \\\"requested\\\", \\\"completed_passive\\\", \\\"completed_active\\\"\\r\\n # so for example a cancel would be \\\"removed:requested\\\"\\r\\n # and a limit order fill would be \\\"removed:completed_passive\\\".\\r\\n status = \\\"removed:\\\" + order[\\\"reason\\\"]\\r\\n self.signal_userorder(self, (0, 0, \\\"\\\", oid, status))\",\n \"def process_order(self, order_event : event.EventOrder) :\\n pass\",\n \"def test_order_complete_order_completed(self):\\n u = User.objects.get(username=\\\"test1\\\")\\n u.userplan.expire = date.today() + timedelta(days=50)\\n u.userplan.active = False\\n u.userplan.save()\\n plan_pricing = PlanPricing.objects.get(plan=u.userplan.plan, pricing__period=30)\\n order = Order.objects.create(\\n user=u,\\n pricing=plan_pricing.pricing,\\n amount=100,\\n plan=plan_pricing.plan,\\n completed=date(2010, 10, 10),\\n )\\n self.assertFalse(order.complete_order())\",\n \"def test_payment_accepted_order(self):\\r\\n student1 = UserFactory()\\r\\n student1.save()\\r\\n\\r\\n order1 = Order.get_cart_for_user(student1)\\r\\n params = {\\r\\n 'card_accountNumber': '1234',\\r\\n 'card_cardType': '001',\\r\\n 'billTo_firstName': student1.first_name,\\r\\n 'billTo_lastName': u\\\"\\\\u2603\\\",\\r\\n 'orderNumber': str(order1.id),\\r\\n 'orderCurrency': 'usd',\\r\\n 'decision': 'ACCEPT',\\r\\n 'ccAuthReply_amount': '0.00'\\r\\n }\\r\\n\\r\\n # tests for an order number that doesn't match up\\r\\n params_bad_ordernum = params.copy()\\r\\n params_bad_ordernum['orderNumber'] = str(order1.id + 10)\\r\\n with self.assertRaises(CCProcessorDataException):\\r\\n payment_accepted(params_bad_ordernum)\\r\\n\\r\\n # tests for a reply amount of the wrong type\\r\\n params_wrong_type_amt = params.copy()\\r\\n params_wrong_type_amt['ccAuthReply_amount'] = 'ab'\\r\\n with self.assertRaises(CCProcessorDataException):\\r\\n payment_accepted(params_wrong_type_amt)\\r\\n\\r\\n # tests for a reply amount of the wrong type\\r\\n params_wrong_amt = params.copy()\\r\\n params_wrong_amt['ccAuthReply_amount'] = '1.00'\\r\\n with self.assertRaises(CCProcessorWrongAmountException):\\r\\n payment_accepted(params_wrong_amt)\\r\\n\\r\\n # tests for a not accepted order\\r\\n params_not_accepted = params.copy()\\r\\n params_not_accepted['decision'] = \\\"REJECT\\\"\\r\\n self.assertFalse(payment_accepted(params_not_accepted)['accepted'])\\r\\n\\r\\n # finally, tests an accepted order\\r\\n self.assertTrue(payment_accepted(params)['accepted'])\",\n \"def test_manually_confirm(self):\\n data = {}\\n response = self.client.post(self.url, data)\\n self.assertRedirects(response, reverse('reminders_dashboard'))\\n\\n reminder = reminders.SentNotification.objects.get(pk=self.unconfirmed.pk)\\n self.assertEqual(reminder.status, 'manual')\\n self.assertEqual(reminder.date_confirmed.date(), datetime.date.today())\",\n \"def test_manually_confirm(self):\\n data = {}\\n response = self.client.post(self.url, data)\\n self.assertRedirects(response, reverse('reminders_dashboard'))\\n\\n reminder = reminders.SentNotification.objects.get(pk=self.unconfirmed.pk)\\n self.assertEqual(reminder.status, 'manual')\\n self.assertEqual(reminder.date_confirmed.date(), datetime.date.today())\",\n \"def test_update_an_order(self):\\n order = PizzaOrder.objects.get(id=self.response.data['id'])\\n updated_order = {\\\"customer\\\": {\\n \\\"first_name\\\": \\\"Lara\\\",\\n \\\"last_name\\\": \\\"Tanbari\\\",\\n \\\"address\\\": \\\"Coppistr22, 10365 Berlin\\\"\\n },\\n \\\"size\\\": \\\"SMALL\\\"\\n }\\n res = self.client.put(\\n reverse('order_details',\\n kwargs={'order_id': order.id}),\\n updated_order,\\n format='json'\\n )\\n\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def update_or_create_delivery(self, orderitem_data):\\n delivery, _ = DeliveryModel.objects.get_or_create(\\n order=self,\\n shipping_id__isnull=True,\\n shipped_at__isnull=True,\\n shipping_method=self.extra.get('shipping_modifier'),\\n defaults={'fulfilled_at': timezone.now()}\\n )\\n\\n # create a DeliveryItem object for each ordered item to be shipped with this delivery\\n for data in orderitem_data:\\n if data['deliver_quantity'] > 0 and not data['canceled']:\\n DeliveryItemModel.objects.create(\\n delivery=delivery,\\n item=data['id'],\\n quantity=data['deliver_quantity'],\\n )\\n if not delivery.items.exists():\\n # since no OrderItem was added to this delivery, discard it\\n delivery.delete()\",\n \"def __clean_orders(self):\\n canceled_id = []\\n for order_id, order in self.orders_dict.items():\\n if order[\\\"status\\\"] == \\\"canceled\\\":\\n canceled_id.append(order_id)\\n for id in canceled_id:\\n del self.orders_dict[id]\",\n \"def is_approved(self) -> bool:\\n return self.state == Order.OrderState.APPROVED.choice_value\",\n \"def mark_completed(self, order_id=None):\\n self.status = \\\"paid\\\"\\n if order_id and not self.order_id:\\n self.order_id = order_id\\n print(\\\"Order completed\\\")\\n self.save()\",\n \"def approve(self, employee: Employee, comments: str = None) -> None:\\n from .exceptions import OperationForbiddenError, OrderEmptyError\\n\\n # If order is not in the \\\"PENDING\\\" state, raise an\\n # OperationForbiddenError\\n if not self.is_pending:\\n raise OperationForbiddenError(\\n self.STATE_CHANGE_FORBIDDEN_ERROR_MSG % {\\n 'current_state': Order.OrderState.get_choice_display(\\n self.state\\n ),\\n 'new_state': Order.OrderState.APPROVED.choice_display\\n }\\n )\\n\\n # If the order's item list is empty, raise an OrderEmptyError\\n if not self.orderitem_set.exists():\\n raise OrderEmptyError(\\n self,\\n 'An order with no associated OrderItems cannot be '\\n 'approved.'\\n )\\n\\n # Perform db mutations in a transaction\\n with transaction.atomic():\\n # Adjust the stock of each item in the order's item list\\n order_item: OrderItem\\n for order_item in self.orderitem_set.all():\\n item: Inventory = order_item.item\\n item.deduct(employee.user, order_item.quantity)\\n\\n # Mark this order as approved\\n self.update(\\n employee.user,\\n comments=comments,\\n handler=employee,\\n review_date=now(),\\n state=Order.OrderState.APPROVED.choice_value\\n )\",\n \"def action_confirm(self):\\n # context = self._context or {}\\n inv_obj = self.env['account.invoice']\\n\\n brw = self.browse(self.ids[0])\\n line_ids = brw.line_ids\\n if not line_ids:\\n raise exceptions.except_orm(\\n _('Invalid Procedure!'), _(\\\"No retention lines\\\"))\\n\\n res = [True]\\n res += [False for i in line_ids\\n if (i.wh_amount <= 0.0 or\\n i.base_amount <= 0.0 or\\n i.wh_src_rate <= 0.0)]\\n if not all(res):\\n raise exceptions.except_orm(\\n _('Invalid Procedure!'),\\n _(\\\"Verify retention lines do not have Null values(0.00)\\\"))\\n\\n res = 0.0\\n for i in line_ids:\\n res += i.wh_amount\\n if abs(res - brw.wh_amount) > 0.0001:\\n raise exceptions.except_orm(\\n _('Invalid Procedure!'),\\n _(\\\"Check the amount of withholdings\\\"))\\n\\n inv_ids = [i.invoice_id.id for i in brw.line_ids]\\n if inv_ids:\\n inv_obj.write({'wh_src_id': self.ids[0]})\\n\\n return self.write({'state': 'confirmed'})\",\n \"def test_success(self, order_placed):\\n session, basket_id, order_number = self.prepare_basket()\\n data = cs_factories.build_accepted_reply_data(order_number)\\n data = cs_factories.sign_reply_data(data)\\n url = reverse('cybersource-reply')\\n self.assertEqual(order_placed.call_count, 0)\\n resp = self.client.post(url, data)\\n\\n self.assertRedirects(resp, reverse('checkout:thank-you'))\\n\\n self.assertEqual(len(mail.outbox), 1, 'Should send email')\\n self.assertEqual(order_placed.call_count, 1, 'Should trigger order_placed signal')\\n\\n order = order_placed.call_args[1]['order']\\n self.assertEqual(order.status, 'Authorized', 'Should set order status')\\n self.assertEqual(order.basket.id, basket_id, 'Should use basket from session')\\n self.assertEqual(order.number, order_number, 'Should use order number from CS request')\\n\\n session = self.client.session\\n self.assertEquals(session[CHECKOUT_ORDER_ID], order.id, 'Should save order_id in session')\\n\\n self.assertEqual(order.sources.count(), 1, 'Should save pPaymentSource')\\n source = order.sources.first()\\n self.assertEqual(source.currency, 'USD')\\n self.assertEqual(source.amount_allocated, D('99.99'))\\n self.assertEqual(source.amount_refunded, D('0.00'))\\n self.assertEqual(source.amount_debited, D('0.00'))\\n\\n self.assertEqual(source.transactions.count(), 1, 'Should save Transaction')\\n transaction = source.transactions.first()\\n self.assertEqual(transaction.log.data, data)\\n self.assertEqual(transaction.token.log, transaction.log)\\n self.assertEqual(transaction.token.masked_card_number, 'xxxxxxxxxxxx1111')\\n self.assertEqual(transaction.token.card_type, '001')\\n self.assertEqual(transaction.txn_type, 'Authorise')\\n self.assertEqual(transaction.amount, D('99.99'))\\n self.assertEqual(transaction.reference, data['transaction_id'])\\n self.assertEqual(transaction.status, 'ACCEPT')\\n self.assertEqual(transaction.request_token, data['request_token'])\\n\\n self.assertEqual(order.payment_events.count(), 1, 'Should save PaymentEvent')\\n event = order.payment_events.first()\\n self.assertEqual(event.amount, D('99.99'))\\n self.assertEqual(event.reference, data['transaction_id'])\\n self.assertEqual(event.event_type.name, 'Authorise')\\n\\n self.assertEqual(event.line_quantities.count(), 1, 'Should save PaymentEventQuantity')\\n lq = event.line_quantities.first()\\n self.assertEqual(lq.line, order.lines.first())\\n self.assertEqual(lq.quantity, 1)\",\n \"def change_approval(self, status):\\r\\n if status == 'approve':\\r\\n return self.approve()\\r\\n elif status == 'disapprove':\\r\\n return self.disapprove()\",\n \"def pending_confirm_parameter(request, action, rating_decision_pk):\\n\\n hash = None\\n\\n if action:\\n\\n rating_decision_obj = RatingDecision.objects.get(pk=rating_decision_pk)\\n\\n rating_process_obj, created = Process.objects.get_or_create(\\n rating_decision=rating_decision_obj)\\n\\n rating_job_id = format_reference_number(number=rating_decision_obj.id,\\n object_type='rating_decision',)\\n\\n if action == 'setup_done':\\n hash = 'setup_step_2'\\n\\n rating_decision_obj.process_step = 2\\n rating_process_obj.setup_done = timezone.now()\\n\\n to_list = [\\n rating_decision_obj.chair.email\\n ]\\n\\n # Notify analysts and relationship manager\\n cc_list = [\\n rating_decision_obj.issuer.analyst.\\n primary_analyst.email,\\n rating_decision_obj.issuer.analyst.\\n secondary_analyst.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n header = SETUP_HEADER.format(\\n rating_decision_obj.issuer,\\n rating_job_id,\\n )\\n body = SETUP_BODY % rating_decision_obj.chair.first_name\\n\\n # Send notification to chair\\n send_email.delay(\\n header=header,\\n body=body,\\n to=to_list,\\n from_sender=None,\\n cc=cc_list)\\n\\n elif action == 'pre_committee_done':\\n hash = 'setup_step_3'\\n\\n rating_decision_obj.process_step = 3\\n rating_process_obj.pre_committee_done = timezone.now()\\n rating_decision_obj.chair_confirmed = True\\n rating_decision_obj.date_time_committee_confirmed = True\\n\\n to_list = [\\n rating_decision_obj.issuer.analyst.\\n primary_analyst.email,\\n rating_decision_obj.issuer.analyst.\\n secondary_analyst.email,\\n ]\\n cc_list = [\\n rating_decision_obj.issuer.relationship_manager.email,\\n rating_decision_obj.chair.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n header = PRE_COMMITTEE_HEADER.format(\\n rating_decision_obj.issuer,\\n rating_decision_obj.chair.get_full_name(),\\n rating_job_id,\\n )\\n\\n # Send notification\\n send_email.delay(\\n header=header,\\n body=PRE_COMMITTEE_BODY % rating_decision_obj.issuer.analyst.\\n primary_analyst.first_name,\\n to=to_list,\\n from_sender=None,\\n cc=cc_list,)\\n\\n elif action == 'analytical_phase_done':\\n hash = 'setup_step_4'\\n\\n rating_decision_obj.process_step = 4\\n rating_process_obj.analytical_phase_done = timezone.now()\\n\\n # Send notification to chair and members of committee\\n to_list = [\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n rating_decision_obj.chair.email,\\n ]\\n cc_list = [\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n # Get all members\\n committee_members = list(\\n JobMember.objects.confirmed_members().filter(\\n rating_decision=rating_decision_obj,\\n group_id=1))\\n for item in committee_members:\\n to_list.append(item.member.email)\\n\\n local_dt = timezone.localtime(\\n rating_decision_obj.date_time_committee,\\n timezone.get_fixed_timezone(60))\\n\\n header = ANALYTICAL_PHASE_HEADER.format(\\n rating_decision_obj.issuer,\\n local_dt.strftime('%Y-%m-%d %H:%M'),\\n rating_job_id,\\n )\\n\\n send_email.delay(\\n header=header,\\n body=ANALYTICAL_PHASE_BODY,\\n to=to_list,\\n cc=cc_list,\\n from_sender=None)\\n\\n elif action == 'post_committee_done':\\n hash = 'setup_step_5'\\n\\n rating_decision_obj.process_step = 5\\n rating_process_obj.post_committee_done = timezone.now()\\n\\n # Contains the name and email of the editor\\n editor_obj = JobMember.objects.get(\\n rating_decision=rating_decision_obj,\\n group=Group.objects.get(pk=2))\\n\\n to_list = [\\n editor_obj.member.email,\\n ]\\n cc_list = [\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n rating_decision_obj.chair.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n header = EDITOR_HEADER.format(\\n rating_decision_obj.issuer,\\n rating_job_id,\\n )\\n\\n # Send email with link to admin control to editor\\n send_email.delay(header=header,\\n body=EDITOR_EMAIL % (\\n editor_obj.member.first_name,\\n rating_decision_obj.issuer.analyst.\\n primary_analyst.first_name),\\n to=to_list,\\n cc=cc_list,\\n from_sender=None,)\\n\\n elif action == 'editor_phase_done':\\n \\\"\\\"\\\"Here, we're sending the draft report to the issuer.\\\"\\\"\\\"\\n\\n hash = 'setup_step_6'\\n\\n rating_decision_obj.process_step = 6\\n\\n # External analysis\\n send_public_report(rating_decision_obj)\\n\\n # Send notification to chair\\n to_list = [\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n header = \\\"{} | the draft report has been sent to the \\\" \\\\\\n \\\"issuer\\\".format(rating_decision_obj.issuer,)\\n\\n send_email.delay(\\n header=header,\\n body=' ',\\n to=to_list,\\n from_sender=None,)\\n\\n # Set a timestamp when se sent the report to the issuer\\n rating_decision_obj.date_time_communicated_issuer = timezone.now()\\n rating_process_obj.editor_review_done = timezone.now()\\n\\n elif action == 'issuer_confirmation_phase_done':\\n hash = 'setup_step_7'\\n\\n rating_decision_obj.process_step = 7\\n\\n to_list = [\\n rating_decision_obj.chair.email,\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n to_list.append('compliance@nordiccreditrating.com')\\n\\n header = \\\"{} | The issuer has confirmed the accuracy of the \\\" \\\\\\n \\\"draft report for rating job {}\\\".format(\\n rating_decision_obj.issuer,\\n rating_job_id, )\\n\\n # Send notification\\n send_email.delay(\\n header=header,\\n body='',\\n to=to_list,\\n from_sender=None,)\\n\\n rating_process_obj.issuer_confirmation_done = timezone.now()\\n\\n elif action == 'analyst_final_approval_phase_done':\\n hash = 'setup_step_8'\\n\\n rating_process_obj.final_sign_off_analyst_done = timezone.now()\\n rating_decision_obj.process_step = 8\\n\\n to_list = [\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n ]\\n cc_list = [\\n rating_decision_obj.chair.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n header = ANALYST_FINAL_APPROVAL_HEADER.format(\\n rating_decision_obj.issuer\\n )\\n\\n # Send notification to chair\\n send_email.delay(\\n header=header,\\n body=ANALYST_FINAL_APPROVAL_BODY %\\n rating_decision_obj.issuer.analyst.\\n primary_analyst.first_name,\\n to=to_list,\\n from_sender=None,\\n cc=cc_list)\\n\\n elif action == 'chair_final_approval_phase_done':\\n hash = 'setup_step_9'\\n\\n rating_decision_obj.process_step = 9\\n rating_process_obj.final_sign_off_chair_done = timezone.now()\\n\\n to_list = [\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n ]\\n cc_list = [\\n rating_decision_obj.chair.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n cc_list.append('compliance@nordiccreditrating.com')\\n\\n header = CHAIR_FINAL_APPROVAL_HEADER.format(\\n rating_decision_obj.issuer\\n )\\n\\n # Send notification to analyst\\n send_email.delay(header=header,\\n body=CHAIR_FINAL_APPROVAL_BODY %\\n rating_decision_obj.issuer.analyst.\\n primary_analyst.first_name,\\n to=to_list,\\n from_sender=None,\\n cc=cc_list,)\\n\\n elif action == 'publishing_phase_done':\\n hash = \\\"\\\"\\n\\n rating_decision_obj.date_time_published = timezone.now()\\n\\n to_list = [\\n rating_decision_obj.issuer.analyst.primary_analyst.email,\\n rating_decision_obj.issuer.analyst.secondary_analyst.email,\\n rating_decision_obj.chair.email,\\n rating_decision_obj.issuer.relationship_manager.email,\\n ]\\n\\n # We want to notify Compliance in production\\n if os.environ['ENVIRONMENT_MODE'] == 'PROD':\\n to_list.append('compliance@nordiccreditrating.com')\\n\\n header = \\\"{} | the rating job has been finalized\\\".format(\\n rating_decision_obj.issuer\\n )\\n\\n # Send notification\\n send_email.delay(header=header,\\n body='If this a public rating, the publishing '\\n 'process will now commence.',\\n to=to_list,\\n from_sender=None,)\\n\\n \\\"\\\"\\\"If there is a previous decision, flag it as non-current.\\\"\\\"\\\"\\n try:\\n RatingDecision.objects.filter(\\n pk=rating_decision_obj.previous_rating.pk).update(\\n is_current=False,)\\n except AttributeError:\\n pass\\n\\n rating_decision_obj.is_current = True\\n rating_process_obj.process_ended = timezone.now()\\n\\n # Create a DecisionAttribute object that\\n # summarizes the rating decision\\n refresh_decision_attributes(rating_decision_obj)\\n\\n # Create decisions on the issue level\\n update_issue_rating(rating_decision_obj)\\n\\n # Create a draft campaign with MailChimp\\n if rating_decision_obj.rating_type.id == 1:\\n run_create_campaign.delay(rating_decision_obj.id)\\n\\n rating_decision_obj.process_step = 10\\n\\n # Save changes\\n rating_process_obj.save()\\n rating_decision_obj.save()\\n\\n redirect_url = request.META.get('HTTP_REFERER', '/') + '#' + hash\\n\\n return http.HttpResponseRedirect(redirect_url)\",\n \"def approve(self, approver=None, parent_job=None):\\r\\n if self.status != 'PENDING':\\r\\n msg = _(\\r\\n \\\"Only orders that are in 'PENDING' state can be approved. \\\"\\r\\n \\\"Current state of order is '{status}'.\\\"\\r\\n ).format(status=self.status)\\r\\n raise CloudBoltException(msg)\\r\\n\\r\\n approve_this_order = False\\r\\n if self.is_multilevel_approval():\\r\\n logger.info('models.approve is multilevel!')\\r\\n self.approve_my_grms(approver)\\r\\n logger.info(f'models.approve after approve_my_grms ({approver})!')\\r\\n if self.is_multilevel_approval():\\r\\n logger.info('models.approve ml approval complete!')\\r\\n approve_this_order = True\\r\\n else:\\r\\n logger.info('models.approve is NOT multilevel!')\\r\\n #single-level approval\\r\\n approve_this_order = True\\r\\n\\r\\n if not approve_this_order:\\r\\n #should only kick off if multilevel approvals\\r\\n msg = _(\\r\\n \\\"Cannot fully approve this order. Multilevel approvals not complete. \\\"\\r\\n \\\"Current state of order is '{status}'.\\\"\\r\\n ).format(status=self.status)\\r\\n return [], msg\\r\\n\\r\\n try:\\r\\n # Raise an error to bubble up specific reason as part of the exception\\r\\n self.group.quota_set.can_use(raise_error=True, **self.net_usage())\\r\\n except QuotaSetError as quota_set_error:\\r\\n raise QuotaError(_(\\r\\n \\\"Cannot approve order #{order_id} because doing so would exceed the \\\"\\r\\n \\\"quota for group '{group}'. {error}\\\"\\r\\n ).format(order_id=self.id, group=self.group, error=quota_set_error))\\r\\n\\r\\n # Before we create job records, order the order items to make\\r\\n # sure decom jobs are queued before prov jobs. the job engine\\r\\n # may still parallelize them, that's something we can revisit\\r\\n # later. In the meantime, customers can set the concurrency\\r\\n # level to 1 to prevent this.\\r\\n # we're taking advantage of the fact that \\\"decom\\\" comes before\\r\\n # \\\"prov\\\" in the alphabet here.\\r\\n order_items = [oi.cast() for oi in self.top_level_items.order_by(\\r\\n \\\"real_type\\\", \\\"add_date\\\")]\\r\\n\\r\\n order_items, msg = self.__filter_illegal_order_items(order_items)\\r\\n if not order_items:\\r\\n msg = _(\\\"{message} There are no valid order items left. This order is \\\"\\r\\n \\\"being marked as complete.\\\").format(message=msg)\\r\\n self.complete(\\\"SUCCESS\\\")\\r\\n return [], msg\\r\\n\\r\\n self.status = \\\"ACTIVE\\\"\\r\\n self.approved_by = approver\\r\\n self.approve_date = get_current_time()\\r\\n self.save()\\r\\n\\r\\n history_msg = _(\\\"The '{order}' order has been approved.\\\").format(order=escape(self))\\r\\n self.add_event('APPROVED', history_msg, profile=self.owner)\\r\\n\\r\\n # run pre order execution hook\\r\\n try:\\r\\n cbhooks.run_hooks(\\\"pre_order_execution\\\", order=self)\\r\\n except cbhooks.exceptions.HookFailureException as e:\\r\\n self.status = \\\"FAILURE\\\"\\r\\n self.save()\\r\\n msg = _(\\\"Failed to run hook for order approval. Status: {status},\\\"\\r\\n \\\" Output: {output}, Errors: {errors}\\\").format(status=e.status, output=e.output, errors=e.errors)\\r\\n\\r\\n history_msg = _(\\\"The '{order}' order has failed.\\\").format(order=escape(self))\\r\\n self.add_event('FAILED', history_msg, profile=self.owner)\\r\\n raise CloudBoltException(msg)\\r\\n\\r\\n from jobs.models import Job\\r\\n # Saving job objects will cause them to be kicked off by the\\r\\n # job engine within a minute\\r\\n jobs = []\\r\\n\\r\\n for order_item in order_items:\\r\\n jobtype = getattr(order_item, 'job_type', None)\\r\\n if not jobtype:\\r\\n # the job type will default to the first word of the class type\\r\\n # ex. \\\"provision\\\", \\\"decom\\\"\\r\\n\\r\\n jobtype = str(order_item.real_type).split(\\\" \\\", 1)[0]\\r\\n quantity = 1\\r\\n # quantity is a special field on order_items. If an\\r\\n # order_item has the quantity field, kick off that many\\r\\n # jobs\\r\\n if hasattr(order_item, 'quantity') and \\\\\\r\\n order_item.quantity is not None and \\\\\\r\\n order_item.quantity != '':\\r\\n quantity = int(order_item.quantity)\\r\\n for i in range(quantity):\\r\\n job = Job(job_parameters=order_item,\\r\\n type=jobtype,\\r\\n owner=self.owner,\\r\\n parent_job=parent_job)\\r\\n job.save()\\r\\n\\r\\n # Associate the job with any server(s)\\r\\n # This may seem unnecessary because it's done when most jobs\\r\\n # run, but it's needed at the very least for scheduled server\\r\\n # modification jobs (for changing resources) so they show up on\\r\\n # the server as scheduled before they actually run\\r\\n servers = []\\r\\n if hasattr(order_item, \\\"server\\\"):\\r\\n servers = [order_item.server]\\r\\n elif hasattr(order_item, \\\"servers\\\"):\\r\\n servers = order_item.servers.all()\\r\\n for server in servers:\\r\\n server.jobs.add(job)\\r\\n\\r\\n jobs.append(job)\\r\\n\\r\\n # If it didn't make any jobs, just call it done\\r\\n if not jobs:\\r\\n self.complete(\\\"SUCCESS\\\")\\r\\n\\r\\n return jobs, msg\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.55200195\",\n \"0.5519232\",\n \"0.5518385\",\n \"0.55177206\",\n \"0.55134255\",\n \"0.5510958\",\n \"0.5509436\",\n \"0.55038536\",\n \"0.5503784\",\n \"0.5503709\",\n \"0.55008256\",\n \"0.54940575\",\n \"0.5491972\",\n \"0.5465499\",\n \"0.5459037\",\n \"0.54585105\",\n \"0.5440418\",\n \"0.54283565\",\n \"0.5426477\",\n \"0.542379\",\n \"0.54193985\",\n \"0.5416539\",\n \"0.5410222\",\n \"0.5410222\",\n \"0.5402036\",\n \"0.5397621\",\n \"0.53962547\",\n \"0.5393996\",\n \"0.538546\",\n \"0.5380925\",\n \"0.5380925\",\n \"0.5376458\",\n \"0.53749436\",\n \"0.5371855\",\n \"0.5371853\",\n \"0.53708756\",\n \"0.53672695\",\n \"0.5361653\",\n \"0.53590506\",\n \"0.53490907\",\n \"0.53464466\",\n \"0.53236663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76192147"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17391,"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(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n if attr in self.sensitive_list:\n result[attr] = \"****\"\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 ToDict(self):\n atributes_dictionary = {}\n for key, value in self.__dict__.iteritems():\n atributes_dictionary[key] = value\n return atributes_dictionary","def properties(self):","def properties(self):","def properties(self):","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 as_dict(self):\n return self.__dict__","def _get_model_state(self) -> dict:\n return dict(model=self.model, kwargs=self._model_kwargs)","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 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 get_attributes(self) -> Dict[str, str]:\n pass","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 asdict(self):\n return attr.asdict(self)","def to_dict(self) -> dict:","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 ToDict(self):\\n atributes_dictionary = {}\\n for key, value in self.__dict__.iteritems():\\n atributes_dictionary[key] = value\\n return atributes_dictionary\",\n \"def properties(self):\",\n \"def properties(self):\",\n \"def properties(self):\",\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 as_dict(self):\\n return self.__dict__\",\n \"def _get_model_state(self) -> dict:\\n return dict(model=self.model, kwargs=self._model_kwargs)\",\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 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 get_attributes(self) -> Dict[str, str]:\\n pass\",\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 asdict(self):\\n return attr.asdict(self)\",\n \"def to_dict(self) -> dict:\",\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":["0.7751993","0.7751993","0.73391134","0.7334895","0.7297356","0.727818","0.7159078","0.71578115","0.71494967","0.71494967","0.71283495","0.71275014","0.7122587","0.71079814","0.7060394","0.7043251","0.7034103","0.70233124","0.69635814","0.69586295","0.690053","0.6881568","0.6881568","0.6857664","0.68415916","0.68122137","0.680096","0.67914945","0.6757063","0.6753585","0.6741746","0.6741746","0.6741746","0.6735291","0.67126125","0.6697801","0.6695801","0.6689893","0.6680752","0.66802895","0.66802895","0.66802895","0.66547817","0.66495687","0.6633999","0.6619567","0.6619567","0.66156983","0.66049474","0.6590706","0.6590706","0.6590206","0.6587873","0.65861845","0.65822417","0.65794736","0.65792733","0.657747","0.6571183","0.65662557","0.65637356","0.6539919","0.65396816","0.65283066","0.65252614","0.6513477","0.65098846","0.65077883","0.65077883","0.65077883","0.65077883","0.65077883","0.65077883","0.6507418","0.6505772","0.65015876","0.64951885","0.64951885","0.64951885","0.64857763","0.6474329","0.6469453","0.64684683","0.6453606","0.6453024","0.6453024","0.6430734","0.6429058","0.6426903","0.64215595","0.64201874","0.6417152","0.6414739","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.64035517"],"string":"[\n \"0.7751993\",\n \"0.7751993\",\n \"0.73391134\",\n \"0.7334895\",\n \"0.7297356\",\n \"0.727818\",\n \"0.7159078\",\n \"0.71578115\",\n \"0.71494967\",\n \"0.71494967\",\n \"0.71283495\",\n \"0.71275014\",\n \"0.7122587\",\n \"0.71079814\",\n \"0.7060394\",\n \"0.7043251\",\n \"0.7034103\",\n \"0.70233124\",\n \"0.69635814\",\n \"0.69586295\",\n \"0.690053\",\n \"0.6881568\",\n \"0.6881568\",\n \"0.6857664\",\n \"0.68415916\",\n \"0.68122137\",\n \"0.680096\",\n \"0.67914945\",\n \"0.6757063\",\n \"0.6753585\",\n \"0.6741746\",\n \"0.6741746\",\n \"0.6741746\",\n \"0.6735291\",\n \"0.67126125\",\n \"0.6697801\",\n \"0.6695801\",\n \"0.6689893\",\n \"0.6680752\",\n \"0.66802895\",\n \"0.66802895\",\n \"0.66802895\",\n \"0.66547817\",\n \"0.66495687\",\n \"0.6633999\",\n \"0.6619567\",\n \"0.6619567\",\n \"0.66156983\",\n \"0.66049474\",\n \"0.6590706\",\n \"0.6590706\",\n \"0.6590206\",\n \"0.6587873\",\n \"0.65861845\",\n \"0.65822417\",\n \"0.65794736\",\n \"0.65792733\",\n \"0.657747\",\n \"0.6571183\",\n \"0.65662557\",\n \"0.65637356\",\n \"0.6539919\",\n \"0.65396816\",\n \"0.65283066\",\n \"0.65252614\",\n \"0.6513477\",\n \"0.65098846\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.6507418\",\n \"0.6505772\",\n \"0.65015876\",\n \"0.64951885\",\n \"0.64951885\",\n \"0.64951885\",\n \"0.64857763\",\n \"0.6474329\",\n \"0.6469453\",\n \"0.64684683\",\n \"0.6453606\",\n \"0.6453024\",\n \"0.6453024\",\n \"0.6430734\",\n \"0.6429058\",\n \"0.6426903\",\n \"0.64215595\",\n \"0.64201874\",\n \"0.6417152\",\n \"0.6414739\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.64035517\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17392,"cells":{"query":{"kind":"string","value":"Returns the string representation of the model"},"document":{"kind":"string","value":"def to_str(self):\n import simplejson as json\n if six.PY2:\n import sys\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\n return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)"},"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 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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 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\"0.8066026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17395,"cells":{"query":{"kind":"string","value":"Create lr scheduler based on config. note that lr_scheduler must accept a optimizer that has been restored."},"document":{"kind":"string","value":"def build(optimizer_config, optimizer, total_step):\n optimizer_type = optimizer_config.WhichOneof('optimizer')\n\n if optimizer_type == 'rms_prop_optimizer':\n config = optimizer_config.rms_prop_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n if optimizer_type == 'momentum_optimizer':\n config = optimizer_config.momentum_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n if optimizer_type == 'adam_optimizer':\n config = optimizer_config.adam_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n return lr_scheduler"},"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 build_lr_scheduler(\n cfg: CfgNode, optimizer: torch.optim.Optimizer\n) -> torch.optim.lr_scheduler._LRScheduler:\n name = cfg.SOLVER.LR_SCHEDULER_NAME\n if name == \"WarmupMultiStepLR\":\n return WarmupMultiStepLR(\n optimizer,\n cfg.SOLVER.STEPS,\n cfg.SOLVER.GAMMA,\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\n )\n elif name == \"WarmupCosineLR\":\n return WarmupCosineLR(\n optimizer,\n cfg.SOLVER.MAX_ITER,\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\n )\n else:\n raise ValueError(\"Unknown LR scheduler: {}\".format(name))","def create_lr_scheduler(\n self, optimizer: torch.optim.Optimizer # type: ignore\n ) -> Optional[LRScheduler]:\n pass","def scheduler_creator(optimizer, config):\n return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)","def _create_learning_rate_scheduler(learning_rate_config, optimizer, total_step):\n lr_scheduler = None\n learning_rate_type = learning_rate_config.WhichOneof('learning_rate')\n if learning_rate_type == 'multi_phase':\n config = learning_rate_config.multi_phase\n lr_phases = []\n mom_phases = []\n for phase_cfg in config.phases:\n lr_phases.append((phase_cfg.start, phase_cfg.lambda_func))\n mom_phases.append((phase_cfg.start, phase_cfg.momentum_lambda_func))\n lr_scheduler = lsf.LRSchedulerStep(\n optimizer,total_step, lr_phases, mom_phases)\n\n if learning_rate_type == 'one_cycle':\n config = learning_rate_config.one_cycle\n lr_scheduler = lsf.OneCycle(\n optimizer, total_step, config.lr_max, list(config.moms), config.div_factor, config.pct_start)\n\n if lr_scheduler is None:\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\n\n return lr_scheduler","def _create_learning_rate_scheduler(learning_rate_config, optimizer, last_step=-1):\n lr_scheduler = None\n learning_rate_type = learning_rate_config.name \n if learning_rate_type == 'constant_learning_rate': \n\n lr_scheduler = learning_schedules.Constant(\n optimizer, last_step=last_step)\n\n if learning_rate_type == 'exponential_decay_learning_rate':\n config = learning_rate_config \n lr_scheduler = learning_schedules.ExponentialDecay(\n optimizer, config.decay_steps, \n config.decay_factor, config.staircase, last_step=last_step)\n\n if learning_rate_type == 'manual_step_learning_rate':\n config = learning_rate_config\n if not config.schedule:\n raise ValueError('Empty learning rate schedule.')\n learning_rate_step_boundaries = [x.step for x in config.schedule]\n learning_rate_sequence = [config.initial_learning_rate]\n learning_rate_sequence += [x.learning_rate for x in config.schedule]\n lr_scheduler = learning_schedules.ManualStepping(\n optimizer, learning_rate_step_boundaries, learning_rate_sequence, \n last_step=last_step)\n\n if learning_rate_type == 'cosine_decay_learning_rate':\n config = learning_rate_config.cosine_decay_learning_rate\n lr_scheduler = learning_schedules.CosineDecayWithWarmup(\n optimizer, config.total_steps, \n config.warmup_learning_rate, config.warmup_steps, \n last_step=last_step)\n\n if lr_scheduler is None:\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\n\n return lr_scheduler","def build_lr_scheduler(optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1,\n frozen=20, warmup=10, warmup_factor_base=0.1):\n if lr_scheduler not in AVAI_SCH:\n raise ValueError('Unsupported scheduler: {}. Must be one of {}'.format(lr_scheduler, AVAI_SCH))\n\n if lr_scheduler == 'single_step':\n if isinstance(stepsize, list):\n stepsize = stepsize[-1]\n\n if not isinstance(stepsize, int):\n raise TypeError(\n 'For single_step lr_scheduler, stepsize must '\n 'be an integer, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer, step_size=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'multi_step':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\n optimizer, milestones=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'cosine':\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, float(max_epoch)\n )\n elif lr_scheduler == 'multi_step_warmup':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = MultiStepLRWithWarmUp(\n optimizer, milestones=stepsize, warmup_iters=warmup, frozen_iters=frozen, gamma=gamma,\n warmup_factor_base=warmup_factor_base\n )\n\n return scheduler","def build_lr_scheduler(\n optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1\n):\n if lr_scheduler not in AVAI_SCH:\n raise ValueError(\n 'Unsupported scheduler: {}. Must be one of {}'.format(\n lr_scheduler, AVAI_SCH\n )\n )\n\n if lr_scheduler == 'single_step':\n if isinstance(stepsize, list):\n stepsize = stepsize[-1]\n\n if not isinstance(stepsize, int):\n raise TypeError(\n 'For single_step lr_scheduler, stepsize must '\n 'be an integer, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer, step_size=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'multi_step':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\n optimizer, milestones=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'cosine':\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, float(max_epoch)\n )\n\n return scheduler","def _get_lr_scheduler(project_parameters, optimizer):\n if project_parameters.lr_scheduler == 'StepLR':\n lr_scheduler = StepLR(optimizer=optimizer,\n step_size=project_parameters.step_size, gamma=project_parameters.gamma)\n elif project_parameters.lr_scheduler == 'CosineAnnealingLR':\n lr_scheduler = CosineAnnealingLR(\n optimizer=optimizer, T_max=project_parameters.step_size)\n return lr_scheduler","def get_scheduler(optimizer, opt):\n \n epochs_no_decay = opt.epochs - opt.lr_linear\n lr_policy = opt.lr_policy\n \n if lr_policy == 'linear':\n def lr_lambda(epoch):\n return 1. - max(0, epoch - epochs_no_decay) / float(opt.lr_linear + 1)\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)\n elif lr_policy == 'step':\n # multiply by gamma every lr_decay_steps\n # for example lr_decay_steps=50 and initial learning = .5\n # then we have \n # lr = .5 for 0 <= epoch < 50;\n # lr = .05 for 50 <= epoch < 100;\n # lr = .005 for 100 <= epoch < 150;\n scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_step, gamma=.1)\n elif lr_policy == 'plateau':\n # Reduce learning rate when a metric has stopped improving. \n # Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. \n # This scheduler reads a metrics quantity and if no improvement \n # is seen for a ‘patience’ number of epochs, \n # the learning rate is reduced.\n # Parameters\n # - mode (str, default=min): In `min` mode, lr will be reduced when the quantity monitored has stopped decreasing; \n # in `max` mode, lr will be reduced when the quantity monitored has stopped increasing.\n # - factor (float, default=.1): Factor by which the learning rate will be reduced. new_lr = lr * factor.\n # - patience (int, default=10): Number of epochs with no improvement after which learning rate will be reduced. \n # - threshold (float): only decrease lr if the change in the quantitiy monitored is smaller than threshold. \n # Say we have threshold=0.001, if loss is $18.0$ on epoch $n$ and loss is $17.9999$ on epoch $n+1$,\n # then multiply current learning rate by the factor.\n # On the contrary, if the loss is 17.99, lr doesn't have to be changed.\n scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=.2, threshold=.01, patience=5)\n else:\n return NotImplementedError(f'learning rate policy {lr_policy} is not implemented')\n return scheduler","def create_lr_scheduler(lr_scheduler_name: str, optimizer: torch.optim.Optimizer,\n lr_gamma: float, max_epochs: int) -> torch.optim.lr_scheduler._LRScheduler:\n if lr_scheduler_name.lower() == \"exponential\":\n lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, lr_gamma)\n elif lr_scheduler_name.lower() == \"linear\":\n lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: 1 - epoch / max_epochs)\n else:\n raise ValueError(\"SystemLog: Unknown learning rate scheduler {}\".format(lr_scheduler_name))\n\n return lr_scheduler","def make_lr_scheduler(lr: float, final_lr: float, n_epochs: int,\n verbose: int = 1) -> keras.callbacks.LearningRateScheduler:\n schedule = build_schedule(lr, final_lr, n_epochs)\n return LearningRateScheduler(schedule=schedule, verbose=verbose)","def get_lr_scheduler(self):\n try:\n scheduler_name = self.configs.OPTIM.LR_SCHEDULER.SCHEDULER_NAME\n except AttributeError:\n scheduler_name = None\n\n if scheduler_name is None:\n lr_scheduler = None\n\n elif scheduler_name == 'plateau':\n factor = self.configs.OPTIM.LR_SCHEDULER.FACTOR\n patience = self.configs.OPTIM.LR_SCHEDULER.PATIENCE\n\n lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer=self.optimizer,\n mode='min',\n factor=factor,\n patience=patience,\n verbose=False,\n threshold=1e-4,\n threshold_mode='rel',\n cooldown=0,\n min_lr=1e-6, # default min_lr is 0\n eps=1e-8,\n )\n\n elif scheduler_name == 'cyclic':\n base_lr = self.configs.OPTIM.LR_SCHEDULER.BASE_LR\n max_lr = self.configs.OPTIM.LR_SCHEDULER.MAX_LR\n\n lr_scheduler = torch.optim.lr_scheduler.CyclicLR(\n optimizer=self.optimizer,\n base_lr=base_lr,\n max_lr=max_lr,\n step_size_up=2000,\n step_size_down=None,\n mode='triangular',\n gamma=1.0,\n scale_fn=None,\n scale_mode='cycle',\n cycle_momentum=True,\n base_momentum=0.8,\n max_momentum=0.9,\n last_epoch=-1,\n )\n\n else:\n LOGGER.error('Invalid learning rate scheduler: %s', scheduler_name)\n raise NotImplementedError\n\n return lr_scheduler","def build_lr_scheduler(optimizer: Optimizer, args: Namespace,\n total_epochs: List[int] = None) -> _LRScheduler:\n # Learning rate scheduler\n return NoamLR(\n optimizer=optimizer,\n warmup_epochs=[args.warmup_epochs],\n total_epochs=total_epochs or [args.epochs] * args.num_lrs,\n steps_per_epoch=args.train_data_size // args.batch_size,\n init_lr=[args.init_lr],\n max_lr=[args.max_lr],\n final_lr=[args.final_lr]\n )","def get_scheduler(optimizer, opt):\n def lambda_rule(epoch):\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)\n return lr_l\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\n return scheduler","def check_lr_schedulers(self) -> None:\n # set default scheduler\n if (\n \"LR_SCHEDULER\" not in self.config\n or self.config[\"LR_SCHEDULER\"] == \"Identity\"\n ):\n self.config[\"LR_SCHEDULER\"] = \"Identity\"\n self.config[\"LR_SCHEDULER_PARAMS\"] = dict()\n\n lr_scheduler_names = get_class_names_in_files(\n \"src\" + os.path.sep + \"lr_schedulers.py\"\n )\n lr_scheduler_names.remove(\"LrScheduler\")\n\n # Check config regularizer exists\n assert self.config[\"LR_SCHEDULER\"] in lr_scheduler_names\n assert \"LR_SCHEDULER_PARAMS\" in self.config\n assert isinstance(self.config[\"LR_SCHEDULER_PARAMS\"], dict)\n\n if self.config[\"LR_SCHEDULER\"] == \"MultiStepLR\":\n # milestones: list[int]\n assert \"milestones\" in self.config[\"LR_SCHEDULER_PARAMS\"]\n for n in self.config[\"LR_SCHEDULER_PARAMS\"][\"milestones\"]:\n assert isinstance(n, int)\n\n assert \"gamma\" in self.config[\"LR_SCHEDULER_PARAMS\"]\n assert 0 < self.config[\"LR_SCHEDULER_PARAMS\"][\"gamma\"] <= 1.0\n assert isinstance(self.config[\"LR_SCHEDULER_PARAMS\"][\"gamma\"], float)\n\n elif self.config[\"LR_SCHEDULER\"] == \"WarmupCosineLR\":\n # set epochs: int\n self.config[\"LR_SCHEDULER_PARAMS\"][\"epochs\"] = self.config[\"EPOCHS\"]\n\n # set target_lr: float\n self.config[\"LR_SCHEDULER_PARAMS\"][\"target_lr\"] = self.config[\"LR\"]\n\n # warmp_epochs\n assert \"warmup_epochs\" in self.config[\"LR_SCHEDULER_PARAMS\"]\n assert (\n 0\n <= self.config[\"LR_SCHEDULER_PARAMS\"][\"warmup_epochs\"]\n <= self.config[\"EPOCHS\"]\n )\n assert isinstance(self.config[\"LR_SCHEDULER_PARAMS\"][\"warmup_epochs\"], int)\n\n # start_lr\n if self.config[\"LR_SCHEDULER_PARAMS\"][\"warmup_epochs\"] != 0:\n assert \"start_lr\" in self.config[\"LR_SCHEDULER_PARAMS\"]\n assert (\n 0\n < self.config[\"LR_SCHEDULER_PARAMS\"][\"start_lr\"]\n <= self.config[\"LR\"]\n )\n assert isinstance(self.config[\"LR_SCHEDULER_PARAMS\"][\"start_lr\"], float)\n\n # n_rewinding\n if \"n_rewinding\" not in self.config[\"LR_SCHEDULER_PARAMS\"]:\n self.config[\"LR_SCHEDULER_PARAMS\"][\"n_rewinding\"] = 1\n else:\n assert type(self.config[\"LR_SCHEDULER_PARAMS\"][\"n_rewinding\"]) is int\n assert self.config[\"LR_SCHEDULER_PARAMS\"][\"n_rewinding\"] > 0\n assert (\n self.config[\"EPOCHS\"]\n % self.config[\"LR_SCHEDULER_PARAMS\"][\"n_rewinding\"]\n == 0\n )\n\n # Check zero division in lr scheduling\n assert (\n self.config[\"EPOCHS\"]\n // self.config[\"LR_SCHEDULER_PARAMS\"][\"n_rewinding\"]\n > self.config[\"LR_SCHEDULER_PARAMS\"][\"warmup_epochs\"]\n )\n\n # min_lr\n if \"min_lr\" not in self.config[\"LR_SCHEDULER_PARAMS\"]:\n self.config[\"LR_SCHEDULER_PARAMS\"][\"min_lr\"] = 0.0\n else:\n assert type(self.config[\"LR_SCHEDULER_PARAMS\"][\"min_lr\"]) is float\n assert self.config[\"LR_SCHEDULER_PARAMS\"][\"min_lr\"] >= 0.0\n\n # decay\n if \"decay\" not in self.config[\"LR_SCHEDULER_PARAMS\"]:\n self.config[\"LR_SCHEDULER_PARAMS\"][\"decay\"] = 0.0\n else:\n assert type(self.config[\"LR_SCHEDULER_PARAMS\"][\"decay\"]) is float\n assert 0.0 <= self.config[\"LR_SCHEDULER_PARAMS\"][\"decay\"] < 1.0","def _initScheduler(self) -> torch.optim.lr_scheduler.ReduceLROnPlateau:\n\n return torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer, \n mode=cfg.training.scheduler_mode,\n factor=cfg.training.scheduler_factor,\n patience=cfg.training.scheduler_patience,\n threshold=cfg.training.scheduler_threshold\n )","def lr_scheduler(self, lr_init, global_step):\n pass","def get_lr_scheduler(scheduler_type: str,\n updates_per_checkpoint: int,\n learning_rate_half_life: int,\n learning_rate_reduce_factor: float,\n learning_rate_reduce_num_not_improved: int) -> Optional[LearningRateScheduler]:\n if scheduler_type is None:\n return None\n if scheduler_type == \"fixed-rate-inv-sqrt-t\":\n return LearningRateSchedulerInvSqrtT(updates_per_checkpoint, learning_rate_half_life)\n elif scheduler_type == \"fixed-rate-inv-t\":\n return LearningRateSchedulerInvT(updates_per_checkpoint, learning_rate_half_life)\n elif scheduler_type == \"plateau-reduce\":\n check_condition(learning_rate_reduce_factor is not None,\n \"learning_rate_reduce_factor needed for plateau-reduce scheduler\")\n check_condition(learning_rate_reduce_num_not_improved is not None,\n \"learning_rate_reduce_num_not_improved needed for plateau-reduce scheduler\")\n if learning_rate_reduce_factor >= 1.0:\n logger.warning(\"Not using plateau-reduce learning rate scheduling: learning_rate_reduce_factor == 1.0\")\n return None\n return LearningRateSchedulerPlateauReduce(learning_rate_reduce_factor, learning_rate_reduce_num_not_improved)\n else:\n raise ValueError(\"Unknown learning rate scheduler type %s.\" % scheduler_type)","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\r\n\r\n if epoch % lr_decay_epoch == 0 and epoch > 1:\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = param_group['lr'] * 0.1\r\n\r\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\n\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n return optimizer","def get_scheduler(optimizer: Optimizer,\n hparams: Optional[Union[HParams, Dict[str, Any]]] = None) -> \\\n Optional[_LRScheduler]:\n if hparams is None or isinstance(hparams, dict):\n hparams = HParams(hparams, default_optimization_hparams())\n\n hparams_scheduler = hparams[\"learning_rate_decay\"]\n\n scheduler_type = hparams_scheduler[\"type\"]\n if scheduler_type == \"\" or scheduler_type is None:\n scheduler = None\n else:\n if isinstance(scheduler_type, _LRScheduler):\n scheduler_class = scheduler_type\n else:\n scheduler_modules = ['torch.optim.lr_scheduler',\n 'texar.torch.custom']\n try:\n scheduler_class = utils.check_or_get_class( # type: ignore\n scheduler_type, scheduler_modules, _LRScheduler)\n except TypeError:\n raise ValueError(\n \"Unrecognized lr_scheduler. Must be string name of the \"\n \"lr_scheduler class, or the class which is a subclass of \"\n \"torch.optim._LRScheduler.\")\n\n scheduler_kwargs = hparams_scheduler[\"kwargs\"].todict()\n scheduler_kwargs.update({\"optimizer\": optimizer})\n scheduler = scheduler_class(**scheduler_kwargs) # type: ignore\n\n return scheduler","def _get_scheduler(\r\n self, optimizer, scheduler: str, warmup_steps: int, t_total: int):\r\n scheduler = scheduler.lower()\r\n if scheduler == 'constantlr':\r\n return transformers.get_constant_schedule(optimizer)\r\n elif scheduler == 'warmupconstant':\r\n # this uses warmup\r\n return transformers.get_constant_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps)\r\n elif scheduler == 'warmuplinear':\r\n # This uses warmup + lr-decay with t_total lr-decays\r\n # only this wrapper accepts num_trianing_steps\r\n # if you open the function get_linear_schedule_with_warmup\r\n # you will find that return\r\n # LambdaLR(optimizer, lr_lambda, last_epoch)\r\n # and last_epoch=-1. So it will end till\r\n # num_training_steps consume up\r\n # and you will see that each .step() consumes 1 training\r\n # step. You can see from\r\n # https://pytorch.org/docs/stable/optim.html\r\n # 'How to adjust Learning Rate'\r\n # When you call the step, the counter will reduce by 1,\r\n # and the learning rate will be adjusted accordingly.\r\n # Initial learning rate is given in the Optimiser.\r\n # Initially, at each step, the learning rate will be\r\n # adjusted by param_group in optimizer.param_groups:\r\n # param_group['lr'] = lr\r\n # Now it is implicitly adjusted by the scheduler\r\n return transformers.get_linear_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n elif scheduler == 'warmupcosine':\r\n return transformers.get_cosine_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n elif scheduler == 'warmupcosinewithhardrestarts':\r\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(\r\n optimizer,\r\n num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n else:\r\n raise ValueError(\"Unknown scheduler {}\".format(scheduler))","def scheduler(epoch_idx, lr):\n new_lr = lr\n if (epoch_idx == 60 or epoch_idx == 120 or epoch_idx == 160\n or epoch_idx == 260 or epoch_idx == 320 or epoch_idx == 360):\n new_lr *= 0.2\n \"\"\"\n if epoch_idx == 200:\n new_lr = 0.1\n \"\"\"\n return new_lr","def init_scheduler(self):\n gamma = self.config_dict.get(\"gamma\")\n if gamma is None:\n return None\n else:\n return torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=gamma)","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=decay):\n lr = init_lr * (0.1**(epoch // lr_decay_epoch))\n\n if epoch % lr_decay_epoch == 0:\n print('LR is set to {}'.format(lr))\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def reschedule_learning_rate(model, epoch, scheduler):\n if epoch == 7:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 13:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 19:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 25:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n\n return model, scheduler","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\r\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\r\n print('LR is set to {}'.format(lr))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr\r\n\r\n return optimizer","def get_scheduler(optimizer, recipe):\n\n # <optimizer>, \"stages__*__lr_scheduler\"\n return get_instance(optimizer, **recipe)","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\n print('LR is set to {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def configure_schedulers(self, **kwargs):\n if self.scheduler_type == 'CosineAnnealingWarmRestarts':\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, **self.scheduler_params)\n elif self.scheduler_type == 'CosineAnnealingLR':\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, **self.scheduler_params)\n elif self.scheduler_type == 'CyclicLR':\n self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, **self.scheduler_params)\n else:\n self.scheduler = None","def set_optimizer(self, config):\r\n self.optimizer = optim.Adam(self.net.parameters(), config.lr)\r\n self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, config.lr_decay)","def configure_optimizers(self):\n self.optimizer = AdamW(self.parameters(), lr=self.args[\"lr\"])\n self.scheduler = {\n \"scheduler\": torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer,\n mode=\"min\",\n factor=0.2,\n patience=2,\n min_lr=1e-6,\n verbose=True,\n ),\n \"monitor\": \"val_loss\",\n }\n return [self.optimizer], [self.scheduler]","def lr_scheduler(optimizer, lr_decay=0.001, epoch=None, step=1):\n if epoch is None or step == 1 or (epoch+1) % step == 0:\n for param_group in optimizer.param_groups:\n param_group['lr'] *= (1 - lr_decay)\n return optimizer","def test_scheduler_correct_lr():\n params = list(TEST_MODEL.parameters())\n opt = torch.optim.SGD([dict(params=params[:2], lr=1e-3), dict(params=params[2:], lr=1e-2)])\n sch = pt_clb.PhasesScheduler([{\"start\": 0, \"end\": 1, \"lr\": [0, 5]}], change_every=1)\n # should be half of max LR after half epochs\n runner = Runner(model=TEST_MODEL, optimizer=opt, criterion=TEST_CRITERION, callbacks=sch)\n runner.fit(TEST_LOADER, epochs=1)\n expected = 5 * (LOADER_LEN - 1) / (2 * LOADER_LEN)\n assert [pg[\"lr\"] for pg in runner.state.optimizer.param_groups] == [expected * 1e-3, expected * 1e-2]\n # should be full LR after full epochs\n runner.state.optimizer = torch.optim.SGD([dict(params=params[:2], lr=1e-3), dict(params=params[2:], lr=1e-2)])\n runner.fit(TEST_LOADER, epochs=2)\n expected = 5 * (2 * LOADER_LEN - 1) / (2 * LOADER_LEN)\n assert [pg[\"lr\"] for pg in runner.state.optimizer.param_groups] == [expected * 1e-3, expected * 1e-2]","def build_trainer(restore_state=None, train_policies=None, config=None):\n \n print(\"Using config\")\n print(config)\n cls = PPOTrainer\n trainer = cls(config=config)\n env = trainer.workers.local_worker().env\n if restore_state is not None:\n trainer.restore_from_object(restore_state)\n return trainer","def build_optimizer(\n self, lr: Union[tf.keras.optimizers.schedules.LearningRateSchedule,\n float]):\n\n optimizer_dict = self._optimizer_config.as_dict()\n optimizer_dict['learning_rate'] = lr\n\n optimizer = OPTIMIZERS_CLS[self._optimizer_type](**optimizer_dict)\n return optimizer","def exp_lr_scheduler(optimizer, epoch, init_lr, lr_decay_epoch):\n\n lr = init_lr * (0.1**((epoch-1) // lr_decay_epoch))\n\n if (epoch-1) % lr_decay_epoch == 0:\n print('LR is set to {}'.format(lr))\n # log_value('lr',lr,epoch)\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def get_optimizer(params, custom_scheduler=None):\n other_args = {}\n if \"clipnorm\" in params:\n other_args[\"clipnorm\"] = params.clipnorm\n if \"clipvalue\" in params:\n other_args[\"clipvalue\"] = params.clipvalue\n\n if params.lr_decay_policy == \"exp\":\n lr = tf.keras.optimizers.schedules.ExponentialDecay(params.lr,\n params.decay_steps,\n params.decay_rate,\n staircase=params.staircase)\n else:\n lr = params.init_lr\n if params.optimizer == \"adam\":\n beta_1 = params.get(\"beta_1\", 0.9)\n beta_2 = params.get(\"beta_2\", 0.999)\n optimizer = tf.keras.optimizers.Adam(lr, beta_1=beta_1, beta_2=beta_2, **other_args)\n elif params.optimizer == \"sgd\":\n momentum = params.get(\"momentum\", 0.9)\n optimizer = tf.keras.optimizers.SGD(lr, momentum=momentum, **other_args)\n else:\n raise ValueError(\"unsupported optimizer: {}\".format(params.optimizer))\n return optimizer","def configure_optimizers(self):\n allowed = list(OPTIM_LOOKUP.keys())\n if self.optimizer not in allowed:\n raise ValueError(\n f\"Illegal optimizer given. Got {self.optimizer}. Allowed: {allowed}.\"\n )\n\n allowed = list(SCHED_LOOKUP.keys())\n if self.scheduler not in allowed:\n raise ValueError(\n f\"Illegal scheduler given. Got {self.scheduler}. Allowed: {allowed}.\"\n )\n\n if self.optim_params is None:\n self.optim_params = {\n \"encoder\": {\"lr\": 0.00005, \"weight_decay\": 0.00005},\n \"decoder\": {\"lr\": 0.0005, \"weight_decay\": 0.0005},\n }\n\n params = adjust_optim_params(self.model, self.optim_params)\n optimizer = OPTIM_LOOKUP[self.optimizer](params)\n\n if self.lookahead:\n optimizer = OPTIM_LOOKUP[\"lookahead\"](optimizer, k=5, alpha=0.5)\n\n if self.scheduler_params is None:\n self.scheduler_params = {}\n\n scheduler = {\n \"scheduler\": SCHED_LOOKUP[self.scheduler](\n optimizer, **self.scheduler_params\n ),\n \"monitor\": \"val_loss\",\n \"interval\": \"epoch\",\n \"frequency\": 1,\n }\n\n return [optimizer], [scheduler]","def configure_optimizers(self):\n self.optimizer = torch.optim.Adam(\n self.parameters(),\n lr=self.args.get(\"lr\", 0.001),\n weight_decay=self.args.get(\"weight_decay\", 0),\n eps=self.args.get(\"eps\", 1e-8)\n )\n self.scheduler = {\n \"scheduler\":\n torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer,\n mode=\"min\",\n factor=0.2,\n patience=3,\n min_lr=1e-6,\n verbose=True,\n ),\n \"monitor\":\n \"val_loss\",\n }\n return [self.optimizer], [self.scheduler]","def from_config(cls, config: Dict[str, Any]) -> \"LinearParamScheduler\":\n assert (\n \"start_value\" in config and \"end_value\" in config\n ), \"Linear scheduler requires a start and a end\"\n\n return cls(\n start_value=config[\"start_value\"],\n end_value=config[\"end_value\"],\n update_interval=UpdateInterval.from_config(config, UpdateInterval.STEP),\n )","def build_learning_rate(self):\n\n # TODO(arashwan): Explore if we want to only allow explicit const lr sched.\n if not self._lr_config:\n lr = self._optimizer_config.learning_rate\n else:\n lr = LR_CLS[self._lr_type](**self._lr_config.as_dict())\n\n if self._warmup_config:\n lr = WARMUP_CLS[self._warmup_type](lr, **self._warmup_config.as_dict())\n\n return lr","def _inst_optimizer(self):\n optimizer = Optimizers(self.m_cfg['configs']['lr_politics']['optimizer']).value\n lr_schedule = self.m_cfg['configs']['lr_politics']['lr']\n opt = optimizer(learning_rate=lr_schedule)\n return opt","def exp_lr_scheduler(optimizer, epoch, lr_decay_epoch=100,lr_decay_factor=0.5):\n init_lr = optimizer.param_groups[0]['lr']\n if epoch > 0 and (epoch % lr_decay_epoch == 0):\n lr = init_lr*lr_decay_factor\n print('\\n LR is set to {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def get_lr_schedule(hparams, examples_per_epoch, replicas_to_aggregate=1):\n global_step = tf.train.get_or_create_global_step()\n steps_per_epoch = float(examples_per_epoch) / float(hparams.batch_size)\n if replicas_to_aggregate > 0:\n steps_per_epoch /= replicas_to_aggregate\n\n if hparams.lr_schedule == 'exp_decay':\n decay_steps = long(steps_per_epoch * hparams.lr_num_epochs_per_decay)\n learning_rate = tf.train.exponential_decay(\n hparams.learning_rate,\n global_step,\n decay_steps,\n hparams.lr_decay_factor,\n staircase=True)\n elif hparams.lr_schedule == 'step':\n lr_decay_steps = [long(epoch * steps_per_epoch)\n for epoch in hparams.lr_decay_epochs]\n learning_rate = tf.train.piecewise_constant(\n global_step, lr_decay_steps, hparams.lr_list)\n elif hparams.lr_schedule == 'fixed':\n learning_rate = hparams.learning_rate\n else:\n raise ValueError('Invalid value of lr_schedule: %s' % hparams.lr_schedule)\n\n if replicas_to_aggregate > 0:\n learning_rate *= replicas_to_aggregate\n\n return learning_rate, steps_per_epoch","def get_scheduler(optimizer, scheduler: str, warmup_steps: int, t_total: int):\n scheduler = scheduler.lower()\n if scheduler == 'constantlr':\n return transformers.get_constant_schedule(optimizer)\n elif scheduler == 'warmupconstant':\n return transformers.get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps)\n elif scheduler == 'warmuplinear':\n return transformers.get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\n num_training_steps=t_total)\n elif scheduler == 'warmupcosine':\n return transformers.get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\n num_training_steps=t_total)\n elif scheduler == 'warmupcosinewithhardrestarts':\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\n num_training_steps=t_total)\n else:\n raise ValueError(\"Unknown scheduler {}\".format(scheduler))","def configure_optimizers(self) -> tuple[list[Optimizer], list[LRScheduler]]:\n # Original paper uses LARS optimizer, but this is not defined in PyTorch\n optimizer = Adam(\n self.parameters(),\n lr=self.hparams[\"lr\"],\n weight_decay=self.hparams[\"weight_decay\"],\n )\n max_epochs = 200\n if self.trainer and self.trainer.max_epochs:\n max_epochs = self.trainer.max_epochs\n if self.hparams[\"version\"] == 1:\n warmup_epochs = 10\n else:\n warmup_epochs = int(max_epochs * 0.05)\n lr_scheduler = SequentialLR(\n optimizer,\n schedulers=[\n LinearLR(optimizer, total_iters=warmup_epochs),\n CosineAnnealingLR(optimizer, T_max=max_epochs),\n ],\n milestones=[warmup_epochs],\n )\n return [optimizer], [lr_scheduler]","def configure_optimizers(self):\n no_decay = [\"bias\", \"LayerNorm.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\n \"weight_decay\": self.args.weight_decay,\n },\n {\n \"params\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\n \"weight_decay\": 0.0,\n },\n ]\n if self.optimizer == \"adamw\":\n optimizer = AdamW(optimizer_grouped_parameters,\n betas=(0.9, 0.98), # according to RoBERTa paper\n lr=self.args.lr,\n eps=self.args.adam_epsilon,)\n elif self.optimizer == \"torch.adam\":\n optimizer = torch.optim.AdamW(optimizer_grouped_parameters,\n lr=self.args.lr,\n eps=self.args.adam_epsilon,\n weight_decay=self.args.weight_decay)\n else:\n raise ValueError(\"Optimizer type does not exist.\")\n num_gpus = len([x for x in str(self.args.gpus).split(\",\") if x.strip()])\n t_total = (len(self.train_dataloader()) // (self.args.accumulate_grad_batches * num_gpus) + 1) * self.args.max_epochs\n warmup_steps = int(self.args.warmup_proportion * t_total)\n if self.args.lr_scheduler == \"onecycle\":\n scheduler = torch.optim.lr_scheduler.OneCycleLR(\n optimizer, max_lr=self.args.lr, pct_start=float(warmup_steps/t_total),\n final_div_factor=self.args.final_div_factor,\n total_steps=t_total, anneal_strategy='linear')\n elif self.args.lr_scheduler == \"linear\":\n scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total)\n elif self.args.lr_scheulder == \"polydecay\":\n if self.args.lr_mini == -1:\n lr_mini = self.args.lr / self.args.polydecay_ratio\n else:\n lr_mini = self.args.lr_mini\n scheduler = get_polynomial_decay_schedule_with_warmup(optimizer, warmup_steps, t_total, lr_end=lr_mini)\n else:\n raise ValueError\n return [optimizer], [{\"scheduler\": scheduler, \"interval\": \"step\"}]","def lr_schedule(epoch, current_lr):\n lr = current_lr\n if epoch == 23: lr *= 0.5\n elif epoch == 21: lr *= 1e-1\n elif epoch == 16: lr *= 1e-1\n elif epoch == 11: lr *= 1e-1\n return lr","def build_trainer_config(config):\r\n # determining environment parameters\r\n env_fcn = config['_env_fcn']\r\n env = env_fcn(config['_env'])\r\n obs_space, act_space, n_policies = env.observation_space, env.action_space, env.n_policies\r\n env.close()\r\n\r\n policies = config['_get_policies'](config=config, n_policies=n_policies, obs_space=obs_space, act_space=act_space)\r\n select_policy = config['_select_policy']\r\n\r\n config = deepcopy(config)\r\n config['_all_policies'] = sorted(policies.keys())\r\n\r\n if config['_update_withpolicies'] and '_iteration' in config:\r\n config = config['_update_withpolicies'](config, iteration=config['_iteration'])\r\n\r\n config1 = deepcopy(config)\r\n config1['multiagent'] = {}\r\n config1['multiagent']['policies'] = policies\r\n\r\n for k in config['_train_policies']:\r\n assert k in policies.keys(), f\"Unknown policy {k} [range {policies.keys()}]\"\r\n\r\n rl_config = {\r\n \"env\": config['_env_name_rllib'],\r\n \"env_config\": config['_env'],\r\n \"multiagent\": {\r\n \"policies_to_train\": config['_train_policies'],\r\n \"policies\": policies,\r\n \"policy_mapping_fn\": partial(select_policy, config=config1),\r\n },\r\n 'tf_session_args': {'intra_op_parallelism_threads': config['_num_workers_tf'],\r\n 'inter_op_parallelism_threads': config['_num_workers_tf'],\r\n 'gpu_options': {'allow_growth': True},\r\n 'log_device_placement': True,\r\n 'device_count': {'CPU': config['_num_workers_tf']},\r\n 'allow_soft_placement': True\r\n },\r\n \"local_tf_session_args\": {\r\n \"intra_op_parallelism_threads\": config['_num_workers_tf'],\r\n \"inter_op_parallelism_threads\": config['_num_workers_tf'],\r\n },\r\n }\r\n\r\n # filling in the rest of variables\r\n for k, v in config.items():\r\n if k.startswith('_'): continue\r\n rl_config[k] = v\r\n\r\n if config.get('_verbose', True):\r\n print(\"Config:\")\r\n print(pretty_print(rl_config))\r\n\r\n if config['_trainer'] == 'External' and '_tmp_dir' in config:\r\n rl_config['tmp_dir'] = config['_tmp_dir']\r\n \r\n for key, val in rl_config.items():\r\n if isinstance(val, Domain):\r\n sampled_val = val.sample()\r\n rl_config[key] = sampled_val\r\n logging.warning(f\"Trainer got a ray.tune.sample for parameter {key}: {type(val)}({val}). Replacing it with a sampled value {sampled_val}\")\r\n\r\n return rl_config","def get_lr_scheduler(self) -> Optional[pytorch.LRScheduler]:\n return self.lr_scheduler","def createScheduler_(self):\n klass_name = 'Scheduler' + string.capitalize(self.scheduler_name)\n file_name = klass_name\n try:\n klass = importName(file_name, klass_name)\n except KeyError:\n msg = 'No `class '+klass_name+'` found in file `'+file_name+'.py`'\n raise SkimException(msg)\n except ImportError, e:\n msg = 'Cannot create scheduler '+self.scheduler_name\n msg += ' (file: '+file_name+', class '+klass_name+'):\\n'\n msg += str(e)\n raise SkimException(msg)\n\n common.scheduler = klass()\n common.scheduler.configure(self.cfg_params)\n return","def build_schedule(lr: float, final_lr: float, n_epochs: int) -> Callable:\n delta = (lr - final_lr) / n_epochs\n\n def schedule(epoch: int) -> float:\n assert 0 <= epoch < n_epochs\n return lr - delta * epoch\n return schedule","def exp_lr_scheduler(optimizer, epoch, lr_decay=0.1, lr_decay_epoch=7):\n if epoch % lr_decay_epoch or epoch>35:\n return optimizer\n \n for param_group in optimizer.param_groups:\n param_group['lr'] *= lr_decay\n return optimizer","def build_trainer_config(restore_state=None, train_policies=None, config=None):\n obs_space = env_cls(env_config).observation_space\n act_space = env_cls(env_config).action_space\n\n agent_config = (PPOTFPolicy, obs_space, act_space, {\n \"model\": {\n \"use_lstm\": True,\n \"fcnet_hiddens\": [config['fc_units'], config['fc_units']],\n \"lstm_cell_size\": config['lstm_units'],\n },\n \"framework\": \"tfe\",\n })\n\n # N_POLICIES = 2\n policies_keys = ['victim', 'adversary']\n\n #policies = {policy_template % i: agent_config for i in range(N_POLICIES)}\n policies = {name: agent_config for name in policies_keys}\n\n def select_policy(agent_id):\n assert agent_id in [\"player1\", \"player2\"]\n agent_ids = [\"player1\", \"player2\"]\n \n # selecting the corresponding policy (only for 2 policies)\n return policies_keys[agent_ids.index(agent_id)]\n\n # randomly choosing an opponent\n # return np.random.choice(list(policies.keys()))\n \n if train_policies is None:\n train_policies = list(policies.keys())\n \n for k in train_policies:\n assert k in policies.keys()\n\n config = {\n \"env\": env_cls,\n # \"gamma\": 0.9,\n \"num_workers\": 0,\n # \"num_envs_per_worker\": 10,\n # \"rollout_fragment_length\": 10,\n \"train_batch_size\": config['train_batch_size'],\n \"multiagent\": {\n \"policies_to_train\": train_policies,\n \"policies\": policies,\n \"policy_mapping_fn\": select_policy,\n },\n \"framework\": \"tfe\",\n #\"train_batch_size\": 512\n #\"num_cpus_per_worker\": 2\n }\n return config","def configure_optimizers(\n self,\n ) -> Dict[str, Union[torch.optim.Optimizer, Dict[str, Union[str, _LRScheduler]]]]:\n optimizer = instantiate(self.optimizer_cfg, params=self.encoder.parameters())\n scheduler = {\n \"scheduler\": instantiate(self.scheduler_cfg, optimizer),\n \"monitor\": \"train_err1\",\n }\n\n return dict(optimizer=optimizer, lr_scheduler=scheduler)","def fetch_optimizer(args, model):\n optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)\n\n scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps + 100,\n pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')\n\n return optimizer, scheduler","def adjust_learning_rate(args,optimizer, epoch):\n lr = args.lr\n schedule = args.lr_schedule\n # schedule from TRADES repo (different from paper due to bug there)\n if schedule == 'trades':\n if epoch >= 0.75 * args.epochs:\n lr = args.lr * 0.1\n # schedule as in TRADES paper\n elif schedule == 'trades_fixed':\n if epoch >= 0.75 * args.epochs:\n lr = args.lr * 0.1\n if epoch >= 0.9 * args.epochs:\n lr = args.lr * 0.01\n if epoch >= args.epochs:\n lr = args.lr * 0.001\n # cosine schedule\n elif schedule == 'cosine':\n lr = args.lr * 0.5 * (1 + np.cos((epoch - 1) / args.epochs * np.pi))\n # schedule as in WRN paper\n elif schedule == 'wrn':\n if epoch >= 0.3 * args.epochs:\n lr = args.lr * 0.2\n if epoch >= 0.6 * args.epochs:\n lr = args.lr * 0.2 * 0.2\n if epoch >= 0.8 * args.epochs:\n lr = args.lr * 0.2 * 0.2 * 0.2\n else:\n raise ValueError('Unkown LR schedule %s' % schedule)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def configure_optimizers(self):\n optimizer = torch.optim.Adam(\n self.model.parameters(),\n lr=self.lr,\n weight_decay=self.weight_decay,\n )\n\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer=optimizer,\n mode=\"min\",\n factor=0.5,\n patience=self.patience,\n ),\n \"monitor\": \"train_loss\",\n },\n }","def configure_optimizers(self):\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return [optimizer], [scheduler]","def get_optimizer(model, lr_method, lr_rate):\n lr_method_name = lr_method\n\n # initialize optimizer function\n if lr_method_name == 'sgd':\n optimizer = optim.SGD(model.parameters(), lr=lr_rate, momentum=0.9)\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\n elif lr_method_name == 'adagrad':\n optimizer = optim.Adagrad(model.parameters(), lr=lr_rate)\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n elif lr_method_name == 'adam':\n optimizer = optim.Adam(model.parameters(), lr=lr_rate)\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.001)\n else:\n raise Exception('unknown optimization method.')\n\n return optimizer # , scheduler","def optimizer_creator(model, config):\n return torch.optim.SGD(model.parameters(), lr=config.get(\"lr\", 1e-4))","def configure_optimizers(self):\n optimizer = _get_optimizer(model_parameters=self.parameters(\n ), project_parameters=self.project_parameters)\n if self.project_parameters.step_size > 0:\n lr_scheduler = _get_lr_scheduler(\n project_parameters=self.project_parameters, optimizer=optimizer)\n return [optimizer], [lr_scheduler]\n else:\n return optimizer","def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print('learning rate is set to: ', lr)\n return optimizer","def configure_optimizers(self) -> dict[str, Any]:\n optimizer = torch.optim.Adam(\n self.model.parameters(), lr=self.hyperparams[\"learning_rate\"]\n )\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer,\n patience=self.hyperparams[\"learning_rate_schedule_patience\"],\n ),\n \"monitor\": \"val_loss\",\n },\n }","def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1,\r\n max_iter=300, power=0.9):\r\n # if iter % lr_decay_iter or iter > max_iter:\r\n # \treturn optimizer\r\n\r\n lr = init_lr * (1 - iter / max_iter) ** power\r\n optimizer.param_groups[0]['lr'] = lr\r\n return lr","def lr_schedule(num_epochs):\n return lambda epoch: 1.0","def optimizer_factory(config, model):\n params = model.parameters()\n\n optimizer = config[\"loss\"].get(\"optimizer\", \"Adam\")\n lr = config[\"loss\"].get(\"lr\", 1e-3)\n momentum = config[\"loss\"].get(\"momentum\", 0.9)\n\n if optimizer == \"SGD\":\n return optim.SGD(params, lr=lr, momentum=momentum)\n elif optimizer == \"Adam\":\n return optim.Adam(params, lr=lr)\n else:\n raise NotImplementedError()","def learning_rate_scheduler(self, lr_decay_schedule, lr_decay_parameters):\n #Available learning rate decay schedules\n self.lr_decay_schedules = ['constant', 'multiplication', 'exponential', 'power']\n \n #Checks if asked schedule is implemented\n assert lr_decay_schedule in self.lr_decay_schedules, \\\n \"Learning rate schedule {0!r} not implemented. Choose one of the following: {1}\".format(\n lr_decay_schedule, self.lr_decay_schedules)\n\n assert type(lr_decay_parameters) is ListType or TupleType,\\\n \"Learning rate decay parameters must be a list or a tuple. Provided value: {0!r} and \\\n type {1!r}\".format(lr_decay_parameters, type(lr_decay_parameters))\n\n #Initial learning rate value for epoch 0.\n init_lr = lr_decay_parameters[0]\n\n assert type(init_lr) is FloatType or IntType,\\\n \"Initial learning_rate value must be float or int: {0!r}\".format(init_lr)\n\n assert init_lr >= 0, \"Initial learning_rate must be >=0: {0!r}\".format(init_lr)\n\n #Constant learning rate\n if lr_decay_schedule == self.lr_decay_schedules[0]:\n return lambda epoch: init_lr\n\n assert len(lr_decay_parameters) >= 2,\\\n \"If decay not constant at least two parameters [init learning_rate, decay rate] are\\\n expected for learning_rate_decay_parameters. Provided values: {0!r}\".format(lr_decay_parameters)\n\n #Parameter r\n decay_rate = lr_decay_parameters[1]\n\n assert type(decay_rate) is FloatType or IntType,\\\n \"Decay rate for learning rate value must be float or int: {0!r}\".format(decay_rate)\n\n assert decay_rate >= 0, \"Decay rate for learning rate must be >=0: {0!r}\".format(decay_rate)\n\n if lr_decay_schedule == self.lr_decay_schedules[1]:\n return lambda epoch: init_lr*decay_rate**epoch\n\n if lr_decay_schedule == self.lr_decay_schedules[2]:\n return lambda epoch: init_lr*10**(-epoch*1./decay_rate)\n\n if lr_decay_schedule == self.lr_decay_schedules[3]:\n assert len(lr_decay_parameters) == 3, \"For Power Scheduling 3 parameters are needed \\\n [init learning rate, r, c]: {0!r}\".format(lr_decay_parameters)\n\n c = lr_decay_parameters[2]\n assert type(c) is FloatType or IntType, \\\n \"c parameter in Power Schedule for lr must be float or int: {0!r}\".format(type(c)) \n\n assert c >=0, \"c in Power Schedule for lr must be >=0: {0!r}\".format(c)\n return lambda epoch: init_lr*(1+epoch*1./r)**-c","def train_scheduler(self, tau, Tau):\n # SAC-U doesn't have a trained scheduler\n pass","def train(self, optimizer, args, scheduler=None, verbose=True,\n plt_traj=False, plt_loss=False):\n self.execution_arg_history.append(args)\n\n start_epoch = 1\n epoch_times = []\n\n if 'ckpt_int' in args and exists_checkpoint():\n ckpt = self.load_checkpoint()\n\n start_epoch = ckpt[0]\n epoch_times = ckpt[1]\n\n optimizer.load_state_dict(ckpt[2])\n if scheduler:\n scheduler.load_state_dict(ckpt[3])\n\n for epoch in range(start_epoch, args['max_epoch']+1):\n start_time = time.time()\n\n for b_data, b_time in self.train_loader:\n optimizer.zero_grad()\n\n out = self.model.forward(b_data, b_time[0], args)\n elbo = self.model.get_elbo(b_data, *out, args)\n\n self.train_loss_meter.update(elbo.item())\n\n elbo.backward()\n if 'clip_norm' in args:\n clip_grad_norm_(self.model.parameters(), args['clip_norm'])\n optimizer.step()\n if scheduler:\n scheduler.step()\n\n end_time = time.time()\n epoch_times.append(end_time - start_time)\n\n with torch.no_grad():\n self.update_val_loss(args)\n\n if self.plot_func and plt_traj:\n self.plot_val_traj(args['plt_args'])\n self.train_loss_hist.append(self.train_loss_meter.avg)\n self.val_loss_hist.append(self.val_loss_meter.val)\n\n if verbose:\n if scheduler:\n print(\"Current LR: {}\".format(scheduler.get_last_lr()),\n flush=True)\n if plt_loss:\n self.plot_loss()\n self.print_loss(epoch)\n\n if 'ckpt_int' in args and epoch % args['ckpt_int']:\n self.save_checkpoint(optimizer, scheduler, epoch, epoch_times)\n\n self.runtimes.append(epoch_times)","def get_optimizer(args, net):\n if args.backbone_lr > 0.0:\n base_params = []\n resnet_params = []\n resnet_name = []\n resnet_name.append('layer0')\n resnet_name.append('layer1')\n #resnet_name.append('layer2')\n #resnet_name.append('layer3')\n #resnet_name.append('layer4')\n len_resnet = len(resnet_name)\n else:\n param_groups = net.parameters()\n\n if args.backbone_lr > 0.0:\n for name, param in net.named_parameters():\n is_resnet = False\n for i in range(len_resnet):\n if resnet_name[i] in name:\n resnet_params.append(param)\n # param.requires_grad=False\n print(\"resnet_name\", name)\n is_resnet = True\n break\n if not is_resnet:\n base_params.append(param)\n\n if args.sgd:\n if args.backbone_lr > 0.0:\n optimizer = optim.SGD([\n {'params': base_params},\n {'params': resnet_params, 'lr':args.backbone_lr}\n ],\n lr=args.lr,\n weight_decay=5e-4, #args.weight_decay,\n momentum=args.momentum,\n nesterov=False)\n else:\n optimizer = optim.SGD(param_groups,\n lr=args.lr,\n weight_decay=5e-4, #args.weight_decay,\n momentum=args.momentum,\n nesterov=False)\n else:\n raise ValueError('Not a valid optimizer')\n\n if args.lr_schedule == 'scl-poly':\n if cfg.REDUCE_BORDER_ITER == -1:\n raise ValueError('ERROR Cannot Do Scale Poly')\n\n rescale_thresh = cfg.REDUCE_BORDER_ITER\n scale_value = args.rescale\n lambda1 = lambda iteration: \\\n math.pow(1 - iteration / args.max_iter,\n args.poly_exp) if iteration < rescale_thresh else scale_value * math.pow(\n 1 - (iteration - rescale_thresh) / (args.max_iter - rescale_thresh),\n args.repoly)\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n elif args.lr_schedule == 'poly':\n lambda1 = lambda iteration: math.pow(1 - iteration / args.max_iter, args.poly_exp)\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\n else:\n raise ValueError('unknown lr schedule {}'.format(args.lr_schedule))\n\n return optimizer, scheduler","def set_optimizer(self, optimizer, opti_scheduler=True, loss_scheduler_patience=7):\n self.optimizer = optimizer\n if opti_scheduler:\n self.opti_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,\n 'min', # overwritten by update_dev_scores\n patience=loss_scheduler_patience,\n verbose=True)","def configure_optimizers(self) -> Tuple[List[optim.Optimizer], List[optim.lr_scheduler._LRScheduler]]:\n if (\n (\"weight_decay\" in self.optimizer_params)\n and (self.optimizer_params[\"weight_decay\"] != 0)\n and self.exclude_bn_bias\n ):\n defaults = copy(self.optimizer_params)\n weight_decay = defaults.pop(\"weight_decay\")\n\n wd_group = []\n nowd_group = []\n for name, tensor in self.named_parameters():\n if not tensor.requires_grad:\n continue\n if (\"bias\" in name) or (\"bn\" in name):\n nowd_group.append(tensor)\n else:\n wd_group.append(tensor)\n\n params = [\n {\"params\": wd_group, \"weight_decay\": weight_decay},\n {\"params\": nowd_group, \"weight_decay\": 0.0},\n ]\n optimizer = self.optimizer_class(params, **defaults)\n else:\n optimizer = self.optimizer_class(self.parameters(), **self.optimizer_params)\n lr_scheduler = self.lr_scheduler_class(optimizer, **self.lr_scheduler_params)\n return [optimizer], [lr_scheduler]","def _prepare_scheduler(self, training_batches, scheduler=None):\n if scheduler.batch_based_schedulers:\n raise Exception(\"New tasks don't yet support batch-based scheduling\")\n return scheduler","def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr):\n lr = initial_lr - (initial_lr * (epoch / float(total_num_epochs)))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 30:\n lr *= 0.5e-3\n elif epoch > 25:\n lr *= 1e-3\n elif epoch > 20:\n lr *= 1e-2\n elif epoch > 10:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def configure_optimizers(self) -> Dict[str, Any]:\n optimizer = torch.optim.AdamW(\n self.model.parameters(), lr=self.hparams[\"learning_rate\"]\n )\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer, patience=self.hparams[\"learning_rate_schedule_patience\"]\n ),\n \"monitor\": \"val_loss\",\n },\n }","def lr_schedule(self, epoch):\n lr = self.params.learning_rate\n\n if epoch > int(self.params.num_epochs * 0.8):\n lr *= 1e-3\n elif epoch > int(self.params.num_epochs * 0.6):\n lr *= 1e-2\n elif epoch > int(self.params.num_epochs * 0.4):\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr):\n final_lr = initial_lr * 0.1\n init_lr = (initial_lr - final_lr)\n lr = final_lr + init_lr - (init_lr * (min(1., epoch / (float(total_num_epochs)))))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr","def configure_optimizers(self):\n optimizer = optim.Adam(\n self.parameters(), lr=self.hparams.learning_rate)\n # scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return optimizer","def _get_optimizer(config, network, lr):\n if config.optimizer.lower() == \"adam\":\n optimizer = Adam(network.trainable_params(), lr, beta1=0.9, beta2=0.98)\n elif config.optimizer.lower() == \"lamb\":\n optimizer = Lamb(network.trainable_params(), learning_rate=lr,\n eps=1e-6)\n elif config.optimizer.lower() == \"momentum\":\n optimizer = Momentum(network.trainable_params(), lr, momentum=0.9)\n else:\n raise ValueError(f\"optimizer only support `adam` and `momentum` now.\")\n\n return optimizer","def lr_schedule(epoch):\n lr = 1e-3\n return lr","def rnn_learning_rate_schedule(dataset):\n model, prefix = make_rnn(non_negative=True)\n # model.set_params(**{prefix + \"val\": 0.1})\n\n # base = no schedule\n print(\"Baseline RNN\")\n # model.set_params(**{prefix + \"history_file\": \"nn_default.csv\"})\n cross_validate(dataset, model)\n # model.fit(dataset.inputs, dataset.outputs)\n\n # higher init, decay set to reach the same at 40 epochs\n print(\"RNN with decay\")\n model.set_params(**{prefix + \"lr_decay\": \"DecreasingLearningRateScheduler\"})\n # model.fit(dataset.inputs, dataset.outputs)\n cross_validate(dataset, model)","def adjust_learning_rate(optimizer, epoch):\n lr = config.optimizer.lr\n schedule = config.lr_schedule if hasattr(config, 'lr_schedule') else 'fixed'\n if schedule == 'fixed':\n if epoch >= 0.75 * config.epochs:\n lr = config.optimizer.lr * 0.1\n if epoch >= 0.9 * config.epochs:\n lr = config.optimizer.lr * 0.01\n if epoch >= config.epochs:\n lr = config.optimizer.lr * 0.001\n # cosine schedule\n elif schedule == 'cosine':\n lr = config.optimizer.lr * 0.5 * (1 + np.cos((epoch - 1) / config.epochs * np.pi))\n elif schedule == 'search':\n if epoch >= 75:\n lr = 0.01\n if epoch >= 90:\n lr = 0.001\n else:\n raise ValueError('Unkown LR schedule %s' % schedule)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def create_optimizer(init_lr, num_train_steps, num_warmup_steps):\n # Implements linear decay of the learning rate.\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\n initial_learning_rate=init_lr,\n decay_steps=num_train_steps,\n end_learning_rate=0.0)\n if num_warmup_steps:\n learning_rate_fn = WarmUp(initial_learning_rate=init_lr,\n decay_schedule_fn=learning_rate_fn,\n warmup_steps=num_warmup_steps)\n optimizer = AdamWeightDecay(\n learning_rate=learning_rate_fn,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[LAYER_NORM_NAME, 'bias'])\n return optimizer","def lr_schedule(epoch,lr):\r\n learning_rate = lr\r\n if epoch > 10:\r\n learning_rate *= 0.1\r\n if epoch > 20:\r\n learning_rate *= 0.1\r\n if epoch > 50:\r\n learning_rate *= 0.01\r\n\r\n # tf.summary.scalar('learning rate', data=learning_rate, step=epoch)\r\n return learning_rate","def __init__(self, model, optimizer, scheduler, generator, train_loader, val_loader, device='cpu',\n cameras=None, lights=None, log_dir=None, vis_dir=None, debug_dir=None, val_dir=None,\n n_eval_points=8000,\n lambda_dr_rgb=1.0, lambda_dr_silhouette=1.0,\n lambda_dr_proj=0.0, lambda_dr_repel=0.0,\n overwrite_visualization=True,\n n_debug_points=4000, steps_dss_backward_radii=100,\n **kwargs):\n self.cfg = kwargs\n self.device = device\n self.model = model\n self.cameras = cameras\n self.lights = lights\n\n self.val_loader = val_loader\n self.train_loader = train_loader\n\n self.tb_logger = SummaryWriter(\n log_dir + datetime.datetime.now().strftime(\"-%Y%m%d-%H%M%S\"))\n\n # implicit function model\n self.vis_dir = vis_dir\n self.val_dir = val_dir\n\n self.lambda_dr_rgb = lambda_dr_rgb\n self.lambda_dr_silhouette = lambda_dr_silhouette\n self.lambda_dr_proj = lambda_dr_proj\n self.lambda_dr_repel = lambda_dr_repel\n\n self.generator = generator\n self.n_eval_points = n_eval_points\n self.overwrite_visualization = overwrite_visualization\n\n # tuple (score, mesh)\n init_dss_backward_radii = 0\n if isinstance(self.model, PointModel):\n init_dss_backward_radii = self.model.renderer.rasterizer.raster_settings.radii_backward_scaler\n\n self.training_scheduler = TrainerScheduler(init_dss_backward_radii=init_dss_backward_radii,\n steps_dss_backward_radii=steps_dss_backward_radii,\n limit_dss_backward_radii=1.0,\n steps_proj=self.cfg.get(\n 'steps_proj', -1),\n gamma_proj=self.cfg.get('gamma_proj', 5))\n\n self.debug_dir = debug_dir\n self.hooks = []\n self._mesh_cache = None\n\n self.n_debug_points = n_debug_points\n\n self.optimizer = optimizer\n self.scheduler = scheduler\n\n self.projection_loss = ProjectionLoss(\n reduction='mean', filter_scale=2.0, knn_k=12)\n self.repulsion_loss = RepulsionLoss(\n reduction='mean', filter_scale=2.0, knn_k=12)\n self.iou_loss = IouLoss(\n reduction='mean', channel_dim=None)\n self.l1_loss = L1Loss(reduction='mean')\n self.l2_loss = L2Loss(reduction='mean')\n self.smape_loss = SmapeLoss(reduction='mean')","def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=89280, power=0.9):\n curr_lr = init_lr\n if iter % lr_decay_iter or iter > max_iter:\n return curr_lr\n\n for param_group in optimizer.param_groups:\n curr_lr = init_lr * (1 - iter / max_iter) ** power\n param_group['lr'] = curr_lr\n\n return curr_lr","def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,\n num_example, batch_size, begin_epoch):\n assert lr_refactor_ratio > 0\n iter_refactor = [int(r) for r in lr_refactor_step.split(',') if r.strip()]\n if lr_refactor_ratio >= 1:\n return (learning_rate, None)\n else:\n lr = learning_rate\n epoch_size = num_example // batch_size\n for s in iter_refactor:\n if begin_epoch >= s:\n lr *= lr_refactor_ratio\n if lr != learning_rate:\n pass\n # logging.getLogger().info(\"Adjusted learning rate to {} for epoch {}\".format(lr, begin_epoch))\n steps = [epoch_size * (x - begin_epoch) for x in iter_refactor if x > begin_epoch]\n if not steps:\n return (lr, None)\n lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(step=steps, factor=lr_refactor_ratio)\n return (lr, lr_scheduler)","def build_optimizer(opt_config, learning_rate):\n if opt_config.opt_method == 'SGD':\n print('Using SGD as the optimizer', file=sys.stderr)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n elif opt_config.opt_method == 'Adam':\n print('Using Adam as the optimizer', file=sys.stderr)\n optimizer = tf.train.AdamOptimizer(\n learning_rate, beta1=opt_config.adam_beta1,\n beta2=opt_config.adam_beta2, epsilon=opt_config.adam_epsilon\n )\n else:\n raise ValueError(\n 'Unknown optimization method {0}!'.format(opt_config.opt_method))\n return optimizer","def learning_rate_schedule(epoch):\n lr = 1.0\n\n if config[\"distributed_lr_warmup_epoch\"] > 0:\n warmup_epochs = config[\"distributed_lr_warmup_epoch\"]\n max_factor = config[\"distributed_lr_warmup_factor\"]\n factor = 1.0 + (max_factor - 1.0) * min(epoch / warmup_epochs, 1.0)\n lr *= factor\n\n for (milestone, factor) in config[\"lr_schedule_milestones\"]:\n if epoch >= milestone:\n lr *= factor\n else:\n return lr\n return lr","def lr_schedule(epoch):\n lr = 0.0001\n if epoch > 40:\n lr *= 1e-2\n elif epoch > 30:\n lr *= 5e-2\n elif epoch > 20:\n lr *= 1e-1\n elif epoch > 10:\n lr *= 5e-1\n print ('Learning rate: ', lr)\n return lr","def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def lr_schedule(epoch):\n lr = 1e-3\n if epoch > 180:\n lr *= 0.5e-3\n elif epoch > 160:\n lr *= 1e-3\n elif epoch > 120:\n lr *= 1e-2\n elif epoch > 80:\n lr *= 1e-1\n print('Learning rate: ', lr)\n return lr","def initialize_optimizer(model, args):\n parameters = [p for p in model.parameters() if p.requires_grad]\n if args.optimizer == 'sgd':\n optimizer = optim.SGD(parameters, args.learning_rate,\n momentum=args.momentum,\n weight_decay=args.weight_decay)\n elif args.optimizer == 'adam':\n optimizer = optim.Adam(parameters, args.learning_rate,\n weight_decay=args.weight_decay)\n elif args.optimizer == 'adamax':\n optimizer = optim.Adamax(parameters, args.learning_rate,\n weight_decay=args.weight_decay)\n elif args.optimizer == 'adagrad':\n optimizer = optim.Adagrad(parameters, args.learning_rate,\n weight_decay=args.weight_decay)\n scheduler = ReduceLROnPlateau(optimizer, 'min', verbose=True)\n return optimizer, scheduler"],"string":"[\n \"def build_lr_scheduler(\\n cfg: CfgNode, optimizer: torch.optim.Optimizer\\n) -> torch.optim.lr_scheduler._LRScheduler:\\n name = cfg.SOLVER.LR_SCHEDULER_NAME\\n if name == \\\"WarmupMultiStepLR\\\":\\n return WarmupMultiStepLR(\\n optimizer,\\n cfg.SOLVER.STEPS,\\n cfg.SOLVER.GAMMA,\\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\\n )\\n elif name == \\\"WarmupCosineLR\\\":\\n return WarmupCosineLR(\\n optimizer,\\n cfg.SOLVER.MAX_ITER,\\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\\n )\\n else:\\n raise ValueError(\\\"Unknown LR scheduler: {}\\\".format(name))\",\n \"def create_lr_scheduler(\\n self, optimizer: torch.optim.Optimizer # type: ignore\\n ) -> Optional[LRScheduler]:\\n pass\",\n \"def scheduler_creator(optimizer, config):\\n return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)\",\n \"def _create_learning_rate_scheduler(learning_rate_config, optimizer, total_step):\\n lr_scheduler = None\\n learning_rate_type = learning_rate_config.WhichOneof('learning_rate')\\n if learning_rate_type == 'multi_phase':\\n config = learning_rate_config.multi_phase\\n lr_phases = []\\n mom_phases = []\\n for phase_cfg in config.phases:\\n lr_phases.append((phase_cfg.start, phase_cfg.lambda_func))\\n mom_phases.append((phase_cfg.start, phase_cfg.momentum_lambda_func))\\n lr_scheduler = lsf.LRSchedulerStep(\\n optimizer,total_step, lr_phases, mom_phases)\\n\\n if learning_rate_type == 'one_cycle':\\n config = learning_rate_config.one_cycle\\n lr_scheduler = lsf.OneCycle(\\n optimizer, total_step, config.lr_max, list(config.moms), config.div_factor, config.pct_start)\\n\\n if lr_scheduler is None:\\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\\n\\n return lr_scheduler\",\n \"def _create_learning_rate_scheduler(learning_rate_config, optimizer, last_step=-1):\\n lr_scheduler = None\\n learning_rate_type = learning_rate_config.name \\n if learning_rate_type == 'constant_learning_rate': \\n\\n lr_scheduler = learning_schedules.Constant(\\n optimizer, last_step=last_step)\\n\\n if learning_rate_type == 'exponential_decay_learning_rate':\\n config = learning_rate_config \\n lr_scheduler = learning_schedules.ExponentialDecay(\\n optimizer, config.decay_steps, \\n config.decay_factor, config.staircase, last_step=last_step)\\n\\n if learning_rate_type == 'manual_step_learning_rate':\\n config = learning_rate_config\\n if not config.schedule:\\n raise ValueError('Empty learning rate schedule.')\\n learning_rate_step_boundaries = [x.step for x in config.schedule]\\n learning_rate_sequence = [config.initial_learning_rate]\\n learning_rate_sequence += [x.learning_rate for x in config.schedule]\\n lr_scheduler = learning_schedules.ManualStepping(\\n optimizer, learning_rate_step_boundaries, learning_rate_sequence, \\n last_step=last_step)\\n\\n if learning_rate_type == 'cosine_decay_learning_rate':\\n config = learning_rate_config.cosine_decay_learning_rate\\n lr_scheduler = learning_schedules.CosineDecayWithWarmup(\\n optimizer, config.total_steps, \\n config.warmup_learning_rate, config.warmup_steps, \\n last_step=last_step)\\n\\n if lr_scheduler is None:\\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\\n\\n return lr_scheduler\",\n \"def build_lr_scheduler(optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1,\\n frozen=20, warmup=10, warmup_factor_base=0.1):\\n if lr_scheduler not in AVAI_SCH:\\n raise ValueError('Unsupported scheduler: {}. Must be one of {}'.format(lr_scheduler, AVAI_SCH))\\n\\n if lr_scheduler == 'single_step':\\n if isinstance(stepsize, list):\\n stepsize = stepsize[-1]\\n\\n if not isinstance(stepsize, int):\\n raise TypeError(\\n 'For single_step lr_scheduler, stepsize must '\\n 'be an integer, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.StepLR(\\n optimizer, step_size=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'multi_step':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\\n optimizer, milestones=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'cosine':\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, float(max_epoch)\\n )\\n elif lr_scheduler == 'multi_step_warmup':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = MultiStepLRWithWarmUp(\\n optimizer, milestones=stepsize, warmup_iters=warmup, frozen_iters=frozen, gamma=gamma,\\n warmup_factor_base=warmup_factor_base\\n )\\n\\n return scheduler\",\n \"def build_lr_scheduler(\\n optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1\\n):\\n if lr_scheduler not in AVAI_SCH:\\n raise ValueError(\\n 'Unsupported scheduler: {}. Must be one of {}'.format(\\n lr_scheduler, AVAI_SCH\\n )\\n )\\n\\n if lr_scheduler == 'single_step':\\n if isinstance(stepsize, list):\\n stepsize = stepsize[-1]\\n\\n if not isinstance(stepsize, int):\\n raise TypeError(\\n 'For single_step lr_scheduler, stepsize must '\\n 'be an integer, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.StepLR(\\n optimizer, step_size=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'multi_step':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\\n optimizer, milestones=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'cosine':\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, float(max_epoch)\\n )\\n\\n return scheduler\",\n \"def _get_lr_scheduler(project_parameters, optimizer):\\n if project_parameters.lr_scheduler == 'StepLR':\\n lr_scheduler = StepLR(optimizer=optimizer,\\n step_size=project_parameters.step_size, gamma=project_parameters.gamma)\\n elif project_parameters.lr_scheduler == 'CosineAnnealingLR':\\n lr_scheduler = CosineAnnealingLR(\\n optimizer=optimizer, T_max=project_parameters.step_size)\\n return lr_scheduler\",\n \"def get_scheduler(optimizer, opt):\\n \\n epochs_no_decay = opt.epochs - opt.lr_linear\\n lr_policy = opt.lr_policy\\n \\n if lr_policy == 'linear':\\n def lr_lambda(epoch):\\n return 1. - max(0, epoch - epochs_no_decay) / float(opt.lr_linear + 1)\\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)\\n elif lr_policy == 'step':\\n # multiply by gamma every lr_decay_steps\\n # for example lr_decay_steps=50 and initial learning = .5\\n # then we have \\n # lr = .5 for 0 <= epoch < 50;\\n # lr = .05 for 50 <= epoch < 100;\\n # lr = .005 for 100 <= epoch < 150;\\n scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_step, gamma=.1)\\n elif lr_policy == 'plateau':\\n # Reduce learning rate when a metric has stopped improving. \\n # Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. \\n # This scheduler reads a metrics quantity and if no improvement \\n # is seen for a ‘patience’ number of epochs, \\n # the learning rate is reduced.\\n # Parameters\\n # - mode (str, default=min): In `min` mode, lr will be reduced when the quantity monitored has stopped decreasing; \\n # in `max` mode, lr will be reduced when the quantity monitored has stopped increasing.\\n # - factor (float, default=.1): Factor by which the learning rate will be reduced. new_lr = lr * factor.\\n # - patience (int, default=10): Number of epochs with no improvement after which learning rate will be reduced. \\n # - threshold (float): only decrease lr if the change in the quantitiy monitored is smaller than threshold. \\n # Say we have threshold=0.001, if loss is $18.0$ on epoch $n$ and loss is $17.9999$ on epoch $n+1$,\\n # then multiply current learning rate by the factor.\\n # On the contrary, if the loss is 17.99, lr doesn't have to be changed.\\n scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=.2, threshold=.01, patience=5)\\n else:\\n return NotImplementedError(f'learning rate policy {lr_policy} is not implemented')\\n return scheduler\",\n \"def create_lr_scheduler(lr_scheduler_name: str, optimizer: torch.optim.Optimizer,\\n lr_gamma: float, max_epochs: int) -> torch.optim.lr_scheduler._LRScheduler:\\n if lr_scheduler_name.lower() == \\\"exponential\\\":\\n lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, lr_gamma)\\n elif lr_scheduler_name.lower() == \\\"linear\\\":\\n lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: 1 - epoch / max_epochs)\\n else:\\n raise ValueError(\\\"SystemLog: Unknown learning rate scheduler {}\\\".format(lr_scheduler_name))\\n\\n return lr_scheduler\",\n \"def make_lr_scheduler(lr: float, final_lr: float, n_epochs: int,\\n verbose: int = 1) -> keras.callbacks.LearningRateScheduler:\\n schedule = build_schedule(lr, final_lr, n_epochs)\\n return LearningRateScheduler(schedule=schedule, verbose=verbose)\",\n \"def get_lr_scheduler(self):\\n try:\\n scheduler_name = self.configs.OPTIM.LR_SCHEDULER.SCHEDULER_NAME\\n except AttributeError:\\n scheduler_name = None\\n\\n if scheduler_name is None:\\n lr_scheduler = None\\n\\n elif scheduler_name == 'plateau':\\n factor = self.configs.OPTIM.LR_SCHEDULER.FACTOR\\n patience = self.configs.OPTIM.LR_SCHEDULER.PATIENCE\\n\\n lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\\n optimizer=self.optimizer,\\n mode='min',\\n factor=factor,\\n patience=patience,\\n verbose=False,\\n threshold=1e-4,\\n threshold_mode='rel',\\n cooldown=0,\\n min_lr=1e-6, # default min_lr is 0\\n eps=1e-8,\\n )\\n\\n elif scheduler_name == 'cyclic':\\n base_lr = self.configs.OPTIM.LR_SCHEDULER.BASE_LR\\n max_lr = self.configs.OPTIM.LR_SCHEDULER.MAX_LR\\n\\n lr_scheduler = torch.optim.lr_scheduler.CyclicLR(\\n optimizer=self.optimizer,\\n base_lr=base_lr,\\n max_lr=max_lr,\\n step_size_up=2000,\\n step_size_down=None,\\n mode='triangular',\\n gamma=1.0,\\n scale_fn=None,\\n scale_mode='cycle',\\n cycle_momentum=True,\\n base_momentum=0.8,\\n max_momentum=0.9,\\n last_epoch=-1,\\n )\\n\\n else:\\n LOGGER.error('Invalid learning rate scheduler: %s', scheduler_name)\\n raise NotImplementedError\\n\\n return lr_scheduler\",\n \"def build_lr_scheduler(optimizer: Optimizer, args: Namespace,\\n total_epochs: List[int] = None) -> _LRScheduler:\\n # Learning rate scheduler\\n return NoamLR(\\n optimizer=optimizer,\\n warmup_epochs=[args.warmup_epochs],\\n total_epochs=total_epochs or [args.epochs] * args.num_lrs,\\n steps_per_epoch=args.train_data_size // args.batch_size,\\n init_lr=[args.init_lr],\\n max_lr=[args.max_lr],\\n final_lr=[args.final_lr]\\n )\",\n \"def get_scheduler(optimizer, opt):\\n def lambda_rule(epoch):\\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)\\n return lr_l\\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\\n return scheduler\",\n \"def check_lr_schedulers(self) -> None:\\n # set default scheduler\\n if (\\n \\\"LR_SCHEDULER\\\" not in self.config\\n or self.config[\\\"LR_SCHEDULER\\\"] == \\\"Identity\\\"\\n ):\\n self.config[\\\"LR_SCHEDULER\\\"] = \\\"Identity\\\"\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"] = dict()\\n\\n lr_scheduler_names = get_class_names_in_files(\\n \\\"src\\\" + os.path.sep + \\\"lr_schedulers.py\\\"\\n )\\n lr_scheduler_names.remove(\\\"LrScheduler\\\")\\n\\n # Check config regularizer exists\\n assert self.config[\\\"LR_SCHEDULER\\\"] in lr_scheduler_names\\n assert \\\"LR_SCHEDULER_PARAMS\\\" in self.config\\n assert isinstance(self.config[\\\"LR_SCHEDULER_PARAMS\\\"], dict)\\n\\n if self.config[\\\"LR_SCHEDULER\\\"] == \\\"MultiStepLR\\\":\\n # milestones: list[int]\\n assert \\\"milestones\\\" in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]\\n for n in self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"milestones\\\"]:\\n assert isinstance(n, int)\\n\\n assert \\\"gamma\\\" in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]\\n assert 0 < self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"gamma\\\"] <= 1.0\\n assert isinstance(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"gamma\\\"], float)\\n\\n elif self.config[\\\"LR_SCHEDULER\\\"] == \\\"WarmupCosineLR\\\":\\n # set epochs: int\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"epochs\\\"] = self.config[\\\"EPOCHS\\\"]\\n\\n # set target_lr: float\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"target_lr\\\"] = self.config[\\\"LR\\\"]\\n\\n # warmp_epochs\\n assert \\\"warmup_epochs\\\" in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]\\n assert (\\n 0\\n <= self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"warmup_epochs\\\"]\\n <= self.config[\\\"EPOCHS\\\"]\\n )\\n assert isinstance(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"warmup_epochs\\\"], int)\\n\\n # start_lr\\n if self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"warmup_epochs\\\"] != 0:\\n assert \\\"start_lr\\\" in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]\\n assert (\\n 0\\n < self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"start_lr\\\"]\\n <= self.config[\\\"LR\\\"]\\n )\\n assert isinstance(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"start_lr\\\"], float)\\n\\n # n_rewinding\\n if \\\"n_rewinding\\\" not in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]:\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"n_rewinding\\\"] = 1\\n else:\\n assert type(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"n_rewinding\\\"]) is int\\n assert self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"n_rewinding\\\"] > 0\\n assert (\\n self.config[\\\"EPOCHS\\\"]\\n % self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"n_rewinding\\\"]\\n == 0\\n )\\n\\n # Check zero division in lr scheduling\\n assert (\\n self.config[\\\"EPOCHS\\\"]\\n // self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"n_rewinding\\\"]\\n > self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"warmup_epochs\\\"]\\n )\\n\\n # min_lr\\n if \\\"min_lr\\\" not in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]:\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"min_lr\\\"] = 0.0\\n else:\\n assert type(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"min_lr\\\"]) is float\\n assert self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"min_lr\\\"] >= 0.0\\n\\n # decay\\n if \\\"decay\\\" not in self.config[\\\"LR_SCHEDULER_PARAMS\\\"]:\\n self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"decay\\\"] = 0.0\\n else:\\n assert type(self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"decay\\\"]) is float\\n assert 0.0 <= self.config[\\\"LR_SCHEDULER_PARAMS\\\"][\\\"decay\\\"] < 1.0\",\n \"def _initScheduler(self) -> torch.optim.lr_scheduler.ReduceLROnPlateau:\\n\\n return torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer, \\n mode=cfg.training.scheduler_mode,\\n factor=cfg.training.scheduler_factor,\\n patience=cfg.training.scheduler_patience,\\n threshold=cfg.training.scheduler_threshold\\n )\",\n \"def lr_scheduler(self, lr_init, global_step):\\n pass\",\n \"def get_lr_scheduler(scheduler_type: str,\\n updates_per_checkpoint: int,\\n learning_rate_half_life: int,\\n learning_rate_reduce_factor: float,\\n learning_rate_reduce_num_not_improved: int) -> Optional[LearningRateScheduler]:\\n if scheduler_type is None:\\n return None\\n if scheduler_type == \\\"fixed-rate-inv-sqrt-t\\\":\\n return LearningRateSchedulerInvSqrtT(updates_per_checkpoint, learning_rate_half_life)\\n elif scheduler_type == \\\"fixed-rate-inv-t\\\":\\n return LearningRateSchedulerInvT(updates_per_checkpoint, learning_rate_half_life)\\n elif scheduler_type == \\\"plateau-reduce\\\":\\n check_condition(learning_rate_reduce_factor is not None,\\n \\\"learning_rate_reduce_factor needed for plateau-reduce scheduler\\\")\\n check_condition(learning_rate_reduce_num_not_improved is not None,\\n \\\"learning_rate_reduce_num_not_improved needed for plateau-reduce scheduler\\\")\\n if learning_rate_reduce_factor >= 1.0:\\n logger.warning(\\\"Not using plateau-reduce learning rate scheduling: learning_rate_reduce_factor == 1.0\\\")\\n return None\\n return LearningRateSchedulerPlateauReduce(learning_rate_reduce_factor, learning_rate_reduce_num_not_improved)\\n else:\\n raise ValueError(\\\"Unknown learning rate scheduler type %s.\\\" % scheduler_type)\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\\r\\n\\r\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = param_group['lr'] * 0.1\\r\\n\\r\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\\n\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n return optimizer\",\n \"def get_scheduler(optimizer: Optimizer,\\n hparams: Optional[Union[HParams, Dict[str, Any]]] = None) -> \\\\\\n Optional[_LRScheduler]:\\n if hparams is None or isinstance(hparams, dict):\\n hparams = HParams(hparams, default_optimization_hparams())\\n\\n hparams_scheduler = hparams[\\\"learning_rate_decay\\\"]\\n\\n scheduler_type = hparams_scheduler[\\\"type\\\"]\\n if scheduler_type == \\\"\\\" or scheduler_type is None:\\n scheduler = None\\n else:\\n if isinstance(scheduler_type, _LRScheduler):\\n scheduler_class = scheduler_type\\n else:\\n scheduler_modules = ['torch.optim.lr_scheduler',\\n 'texar.torch.custom']\\n try:\\n scheduler_class = utils.check_or_get_class( # type: ignore\\n scheduler_type, scheduler_modules, _LRScheduler)\\n except TypeError:\\n raise ValueError(\\n \\\"Unrecognized lr_scheduler. Must be string name of the \\\"\\n \\\"lr_scheduler class, or the class which is a subclass of \\\"\\n \\\"torch.optim._LRScheduler.\\\")\\n\\n scheduler_kwargs = hparams_scheduler[\\\"kwargs\\\"].todict()\\n scheduler_kwargs.update({\\\"optimizer\\\": optimizer})\\n scheduler = scheduler_class(**scheduler_kwargs) # type: ignore\\n\\n return scheduler\",\n \"def _get_scheduler(\\r\\n self, optimizer, scheduler: str, warmup_steps: int, t_total: int):\\r\\n scheduler = scheduler.lower()\\r\\n if scheduler == 'constantlr':\\r\\n return transformers.get_constant_schedule(optimizer)\\r\\n elif scheduler == 'warmupconstant':\\r\\n # this uses warmup\\r\\n return transformers.get_constant_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps)\\r\\n elif scheduler == 'warmuplinear':\\r\\n # This uses warmup + lr-decay with t_total lr-decays\\r\\n # only this wrapper accepts num_trianing_steps\\r\\n # if you open the function get_linear_schedule_with_warmup\\r\\n # you will find that return\\r\\n # LambdaLR(optimizer, lr_lambda, last_epoch)\\r\\n # and last_epoch=-1. So it will end till\\r\\n # num_training_steps consume up\\r\\n # and you will see that each .step() consumes 1 training\\r\\n # step. You can see from\\r\\n # https://pytorch.org/docs/stable/optim.html\\r\\n # 'How to adjust Learning Rate'\\r\\n # When you call the step, the counter will reduce by 1,\\r\\n # and the learning rate will be adjusted accordingly.\\r\\n # Initial learning rate is given in the Optimiser.\\r\\n # Initially, at each step, the learning rate will be\\r\\n # adjusted by param_group in optimizer.param_groups:\\r\\n # param_group['lr'] = lr\\r\\n # Now it is implicitly adjusted by the scheduler\\r\\n return transformers.get_linear_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n elif scheduler == 'warmupcosine':\\r\\n return transformers.get_cosine_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n elif scheduler == 'warmupcosinewithhardrestarts':\\r\\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(\\r\\n optimizer,\\r\\n num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n else:\\r\\n raise ValueError(\\\"Unknown scheduler {}\\\".format(scheduler))\",\n \"def scheduler(epoch_idx, lr):\\n new_lr = lr\\n if (epoch_idx == 60 or epoch_idx == 120 or epoch_idx == 160\\n or epoch_idx == 260 or epoch_idx == 320 or epoch_idx == 360):\\n new_lr *= 0.2\\n \\\"\\\"\\\"\\n if epoch_idx == 200:\\n new_lr = 0.1\\n \\\"\\\"\\\"\\n return new_lr\",\n \"def init_scheduler(self):\\n gamma = self.config_dict.get(\\\"gamma\\\")\\n if gamma is None:\\n return None\\n else:\\n return torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=gamma)\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=decay):\\n lr = init_lr * (0.1**(epoch // lr_decay_epoch))\\n\\n if epoch % lr_decay_epoch == 0:\\n print('LR is set to {}'.format(lr))\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def reschedule_learning_rate(model, epoch, scheduler):\\n if epoch == 7:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 13:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 19:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 25:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n\\n return model, scheduler\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\\r\\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\\r\\n print('LR is set to {}'.format(lr))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\\r\\n\\r\\n return optimizer\",\n \"def get_scheduler(optimizer, recipe):\\n\\n # <optimizer>, \\\"stages__*__lr_scheduler\\\"\\n return get_instance(optimizer, **recipe)\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\\n print('LR is set to {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def configure_schedulers(self, **kwargs):\\n if self.scheduler_type == 'CosineAnnealingWarmRestarts':\\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, **self.scheduler_params)\\n elif self.scheduler_type == 'CosineAnnealingLR':\\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, **self.scheduler_params)\\n elif self.scheduler_type == 'CyclicLR':\\n self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, **self.scheduler_params)\\n else:\\n self.scheduler = None\",\n \"def set_optimizer(self, config):\\r\\n self.optimizer = optim.Adam(self.net.parameters(), config.lr)\\r\\n self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, config.lr_decay)\",\n \"def configure_optimizers(self):\\n self.optimizer = AdamW(self.parameters(), lr=self.args[\\\"lr\\\"])\\n self.scheduler = {\\n \\\"scheduler\\\": torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer,\\n mode=\\\"min\\\",\\n factor=0.2,\\n patience=2,\\n min_lr=1e-6,\\n verbose=True,\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n }\\n return [self.optimizer], [self.scheduler]\",\n \"def lr_scheduler(optimizer, lr_decay=0.001, epoch=None, step=1):\\n if epoch is None or step == 1 or (epoch+1) % step == 0:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] *= (1 - lr_decay)\\n return optimizer\",\n \"def test_scheduler_correct_lr():\\n params = list(TEST_MODEL.parameters())\\n opt = torch.optim.SGD([dict(params=params[:2], lr=1e-3), dict(params=params[2:], lr=1e-2)])\\n sch = pt_clb.PhasesScheduler([{\\\"start\\\": 0, \\\"end\\\": 1, \\\"lr\\\": [0, 5]}], change_every=1)\\n # should be half of max LR after half epochs\\n runner = Runner(model=TEST_MODEL, optimizer=opt, criterion=TEST_CRITERION, callbacks=sch)\\n runner.fit(TEST_LOADER, epochs=1)\\n expected = 5 * (LOADER_LEN - 1) / (2 * LOADER_LEN)\\n assert [pg[\\\"lr\\\"] for pg in runner.state.optimizer.param_groups] == [expected * 1e-3, expected * 1e-2]\\n # should be full LR after full epochs\\n runner.state.optimizer = torch.optim.SGD([dict(params=params[:2], lr=1e-3), dict(params=params[2:], lr=1e-2)])\\n runner.fit(TEST_LOADER, epochs=2)\\n expected = 5 * (2 * LOADER_LEN - 1) / (2 * LOADER_LEN)\\n assert [pg[\\\"lr\\\"] for pg in runner.state.optimizer.param_groups] == [expected * 1e-3, expected * 1e-2]\",\n \"def build_trainer(restore_state=None, train_policies=None, config=None):\\n \\n print(\\\"Using config\\\")\\n print(config)\\n cls = PPOTrainer\\n trainer = cls(config=config)\\n env = trainer.workers.local_worker().env\\n if restore_state is not None:\\n trainer.restore_from_object(restore_state)\\n return trainer\",\n \"def build_optimizer(\\n self, lr: Union[tf.keras.optimizers.schedules.LearningRateSchedule,\\n float]):\\n\\n optimizer_dict = self._optimizer_config.as_dict()\\n optimizer_dict['learning_rate'] = lr\\n\\n optimizer = OPTIMIZERS_CLS[self._optimizer_type](**optimizer_dict)\\n return optimizer\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr, lr_decay_epoch):\\n\\n lr = init_lr * (0.1**((epoch-1) // lr_decay_epoch))\\n\\n if (epoch-1) % lr_decay_epoch == 0:\\n print('LR is set to {}'.format(lr))\\n # log_value('lr',lr,epoch)\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def get_optimizer(params, custom_scheduler=None):\\n other_args = {}\\n if \\\"clipnorm\\\" in params:\\n other_args[\\\"clipnorm\\\"] = params.clipnorm\\n if \\\"clipvalue\\\" in params:\\n other_args[\\\"clipvalue\\\"] = params.clipvalue\\n\\n if params.lr_decay_policy == \\\"exp\\\":\\n lr = tf.keras.optimizers.schedules.ExponentialDecay(params.lr,\\n params.decay_steps,\\n params.decay_rate,\\n staircase=params.staircase)\\n else:\\n lr = params.init_lr\\n if params.optimizer == \\\"adam\\\":\\n beta_1 = params.get(\\\"beta_1\\\", 0.9)\\n beta_2 = params.get(\\\"beta_2\\\", 0.999)\\n optimizer = tf.keras.optimizers.Adam(lr, beta_1=beta_1, beta_2=beta_2, **other_args)\\n elif params.optimizer == \\\"sgd\\\":\\n momentum = params.get(\\\"momentum\\\", 0.9)\\n optimizer = tf.keras.optimizers.SGD(lr, momentum=momentum, **other_args)\\n else:\\n raise ValueError(\\\"unsupported optimizer: {}\\\".format(params.optimizer))\\n return optimizer\",\n \"def configure_optimizers(self):\\n allowed = list(OPTIM_LOOKUP.keys())\\n if self.optimizer not in allowed:\\n raise ValueError(\\n f\\\"Illegal optimizer given. Got {self.optimizer}. Allowed: {allowed}.\\\"\\n )\\n\\n allowed = list(SCHED_LOOKUP.keys())\\n if self.scheduler not in allowed:\\n raise ValueError(\\n f\\\"Illegal scheduler given. Got {self.scheduler}. Allowed: {allowed}.\\\"\\n )\\n\\n if self.optim_params is None:\\n self.optim_params = {\\n \\\"encoder\\\": {\\\"lr\\\": 0.00005, \\\"weight_decay\\\": 0.00005},\\n \\\"decoder\\\": {\\\"lr\\\": 0.0005, \\\"weight_decay\\\": 0.0005},\\n }\\n\\n params = adjust_optim_params(self.model, self.optim_params)\\n optimizer = OPTIM_LOOKUP[self.optimizer](params)\\n\\n if self.lookahead:\\n optimizer = OPTIM_LOOKUP[\\\"lookahead\\\"](optimizer, k=5, alpha=0.5)\\n\\n if self.scheduler_params is None:\\n self.scheduler_params = {}\\n\\n scheduler = {\\n \\\"scheduler\\\": SCHED_LOOKUP[self.scheduler](\\n optimizer, **self.scheduler_params\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n \\\"interval\\\": \\\"epoch\\\",\\n \\\"frequency\\\": 1,\\n }\\n\\n return [optimizer], [scheduler]\",\n \"def configure_optimizers(self):\\n self.optimizer = torch.optim.Adam(\\n self.parameters(),\\n lr=self.args.get(\\\"lr\\\", 0.001),\\n weight_decay=self.args.get(\\\"weight_decay\\\", 0),\\n eps=self.args.get(\\\"eps\\\", 1e-8)\\n )\\n self.scheduler = {\\n \\\"scheduler\\\":\\n torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer,\\n mode=\\\"min\\\",\\n factor=0.2,\\n patience=3,\\n min_lr=1e-6,\\n verbose=True,\\n ),\\n \\\"monitor\\\":\\n \\\"val_loss\\\",\\n }\\n return [self.optimizer], [self.scheduler]\",\n \"def from_config(cls, config: Dict[str, Any]) -> \\\"LinearParamScheduler\\\":\\n assert (\\n \\\"start_value\\\" in config and \\\"end_value\\\" in config\\n ), \\\"Linear scheduler requires a start and a end\\\"\\n\\n return cls(\\n start_value=config[\\\"start_value\\\"],\\n end_value=config[\\\"end_value\\\"],\\n update_interval=UpdateInterval.from_config(config, UpdateInterval.STEP),\\n )\",\n \"def build_learning_rate(self):\\n\\n # TODO(arashwan): Explore if we want to only allow explicit const lr sched.\\n if not self._lr_config:\\n lr = self._optimizer_config.learning_rate\\n else:\\n lr = LR_CLS[self._lr_type](**self._lr_config.as_dict())\\n\\n if self._warmup_config:\\n lr = WARMUP_CLS[self._warmup_type](lr, **self._warmup_config.as_dict())\\n\\n return lr\",\n \"def _inst_optimizer(self):\\n optimizer = Optimizers(self.m_cfg['configs']['lr_politics']['optimizer']).value\\n lr_schedule = self.m_cfg['configs']['lr_politics']['lr']\\n opt = optimizer(learning_rate=lr_schedule)\\n return opt\",\n \"def exp_lr_scheduler(optimizer, epoch, lr_decay_epoch=100,lr_decay_factor=0.5):\\n init_lr = optimizer.param_groups[0]['lr']\\n if epoch > 0 and (epoch % lr_decay_epoch == 0):\\n lr = init_lr*lr_decay_factor\\n print('\\\\n LR is set to {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def get_lr_schedule(hparams, examples_per_epoch, replicas_to_aggregate=1):\\n global_step = tf.train.get_or_create_global_step()\\n steps_per_epoch = float(examples_per_epoch) / float(hparams.batch_size)\\n if replicas_to_aggregate > 0:\\n steps_per_epoch /= replicas_to_aggregate\\n\\n if hparams.lr_schedule == 'exp_decay':\\n decay_steps = long(steps_per_epoch * hparams.lr_num_epochs_per_decay)\\n learning_rate = tf.train.exponential_decay(\\n hparams.learning_rate,\\n global_step,\\n decay_steps,\\n hparams.lr_decay_factor,\\n staircase=True)\\n elif hparams.lr_schedule == 'step':\\n lr_decay_steps = [long(epoch * steps_per_epoch)\\n for epoch in hparams.lr_decay_epochs]\\n learning_rate = tf.train.piecewise_constant(\\n global_step, lr_decay_steps, hparams.lr_list)\\n elif hparams.lr_schedule == 'fixed':\\n learning_rate = hparams.learning_rate\\n else:\\n raise ValueError('Invalid value of lr_schedule: %s' % hparams.lr_schedule)\\n\\n if replicas_to_aggregate > 0:\\n learning_rate *= replicas_to_aggregate\\n\\n return learning_rate, steps_per_epoch\",\n \"def get_scheduler(optimizer, scheduler: str, warmup_steps: int, t_total: int):\\n scheduler = scheduler.lower()\\n if scheduler == 'constantlr':\\n return transformers.get_constant_schedule(optimizer)\\n elif scheduler == 'warmupconstant':\\n return transformers.get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps)\\n elif scheduler == 'warmuplinear':\\n return transformers.get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\\n num_training_steps=t_total)\\n elif scheduler == 'warmupcosine':\\n return transformers.get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\\n num_training_steps=t_total)\\n elif scheduler == 'warmupcosinewithhardrestarts':\\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps,\\n num_training_steps=t_total)\\n else:\\n raise ValueError(\\\"Unknown scheduler {}\\\".format(scheduler))\",\n \"def configure_optimizers(self) -> tuple[list[Optimizer], list[LRScheduler]]:\\n # Original paper uses LARS optimizer, but this is not defined in PyTorch\\n optimizer = Adam(\\n self.parameters(),\\n lr=self.hparams[\\\"lr\\\"],\\n weight_decay=self.hparams[\\\"weight_decay\\\"],\\n )\\n max_epochs = 200\\n if self.trainer and self.trainer.max_epochs:\\n max_epochs = self.trainer.max_epochs\\n if self.hparams[\\\"version\\\"] == 1:\\n warmup_epochs = 10\\n else:\\n warmup_epochs = int(max_epochs * 0.05)\\n lr_scheduler = SequentialLR(\\n optimizer,\\n schedulers=[\\n LinearLR(optimizer, total_iters=warmup_epochs),\\n CosineAnnealingLR(optimizer, T_max=max_epochs),\\n ],\\n milestones=[warmup_epochs],\\n )\\n return [optimizer], [lr_scheduler]\",\n \"def configure_optimizers(self):\\n no_decay = [\\\"bias\\\", \\\"LayerNorm.weight\\\"]\\n optimizer_grouped_parameters = [\\n {\\n \\\"params\\\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": self.args.weight_decay,\\n },\\n {\\n \\\"params\\\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": 0.0,\\n },\\n ]\\n if self.optimizer == \\\"adamw\\\":\\n optimizer = AdamW(optimizer_grouped_parameters,\\n betas=(0.9, 0.98), # according to RoBERTa paper\\n lr=self.args.lr,\\n eps=self.args.adam_epsilon,)\\n elif self.optimizer == \\\"torch.adam\\\":\\n optimizer = torch.optim.AdamW(optimizer_grouped_parameters,\\n lr=self.args.lr,\\n eps=self.args.adam_epsilon,\\n weight_decay=self.args.weight_decay)\\n else:\\n raise ValueError(\\\"Optimizer type does not exist.\\\")\\n num_gpus = len([x for x in str(self.args.gpus).split(\\\",\\\") if x.strip()])\\n t_total = (len(self.train_dataloader()) // (self.args.accumulate_grad_batches * num_gpus) + 1) * self.args.max_epochs\\n warmup_steps = int(self.args.warmup_proportion * t_total)\\n if self.args.lr_scheduler == \\\"onecycle\\\":\\n scheduler = torch.optim.lr_scheduler.OneCycleLR(\\n optimizer, max_lr=self.args.lr, pct_start=float(warmup_steps/t_total),\\n final_div_factor=self.args.final_div_factor,\\n total_steps=t_total, anneal_strategy='linear')\\n elif self.args.lr_scheduler == \\\"linear\\\":\\n scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total)\\n elif self.args.lr_scheulder == \\\"polydecay\\\":\\n if self.args.lr_mini == -1:\\n lr_mini = self.args.lr / self.args.polydecay_ratio\\n else:\\n lr_mini = self.args.lr_mini\\n scheduler = get_polynomial_decay_schedule_with_warmup(optimizer, warmup_steps, t_total, lr_end=lr_mini)\\n else:\\n raise ValueError\\n return [optimizer], [{\\\"scheduler\\\": scheduler, \\\"interval\\\": \\\"step\\\"}]\",\n \"def lr_schedule(epoch, current_lr):\\n lr = current_lr\\n if epoch == 23: lr *= 0.5\\n elif epoch == 21: lr *= 1e-1\\n elif epoch == 16: lr *= 1e-1\\n elif epoch == 11: lr *= 1e-1\\n return lr\",\n \"def build_trainer_config(config):\\r\\n # determining environment parameters\\r\\n env_fcn = config['_env_fcn']\\r\\n env = env_fcn(config['_env'])\\r\\n obs_space, act_space, n_policies = env.observation_space, env.action_space, env.n_policies\\r\\n env.close()\\r\\n\\r\\n policies = config['_get_policies'](config=config, n_policies=n_policies, obs_space=obs_space, act_space=act_space)\\r\\n select_policy = config['_select_policy']\\r\\n\\r\\n config = deepcopy(config)\\r\\n config['_all_policies'] = sorted(policies.keys())\\r\\n\\r\\n if config['_update_withpolicies'] and '_iteration' in config:\\r\\n config = config['_update_withpolicies'](config, iteration=config['_iteration'])\\r\\n\\r\\n config1 = deepcopy(config)\\r\\n config1['multiagent'] = {}\\r\\n config1['multiagent']['policies'] = policies\\r\\n\\r\\n for k in config['_train_policies']:\\r\\n assert k in policies.keys(), f\\\"Unknown policy {k} [range {policies.keys()}]\\\"\\r\\n\\r\\n rl_config = {\\r\\n \\\"env\\\": config['_env_name_rllib'],\\r\\n \\\"env_config\\\": config['_env'],\\r\\n \\\"multiagent\\\": {\\r\\n \\\"policies_to_train\\\": config['_train_policies'],\\r\\n \\\"policies\\\": policies,\\r\\n \\\"policy_mapping_fn\\\": partial(select_policy, config=config1),\\r\\n },\\r\\n 'tf_session_args': {'intra_op_parallelism_threads': config['_num_workers_tf'],\\r\\n 'inter_op_parallelism_threads': config['_num_workers_tf'],\\r\\n 'gpu_options': {'allow_growth': True},\\r\\n 'log_device_placement': True,\\r\\n 'device_count': {'CPU': config['_num_workers_tf']},\\r\\n 'allow_soft_placement': True\\r\\n },\\r\\n \\\"local_tf_session_args\\\": {\\r\\n \\\"intra_op_parallelism_threads\\\": config['_num_workers_tf'],\\r\\n \\\"inter_op_parallelism_threads\\\": config['_num_workers_tf'],\\r\\n },\\r\\n }\\r\\n\\r\\n # filling in the rest of variables\\r\\n for k, v in config.items():\\r\\n if k.startswith('_'): continue\\r\\n rl_config[k] = v\\r\\n\\r\\n if config.get('_verbose', True):\\r\\n print(\\\"Config:\\\")\\r\\n print(pretty_print(rl_config))\\r\\n\\r\\n if config['_trainer'] == 'External' and '_tmp_dir' in config:\\r\\n rl_config['tmp_dir'] = config['_tmp_dir']\\r\\n \\r\\n for key, val in rl_config.items():\\r\\n if isinstance(val, Domain):\\r\\n sampled_val = val.sample()\\r\\n rl_config[key] = sampled_val\\r\\n logging.warning(f\\\"Trainer got a ray.tune.sample for parameter {key}: {type(val)}({val}). Replacing it with a sampled value {sampled_val}\\\")\\r\\n\\r\\n return rl_config\",\n \"def get_lr_scheduler(self) -> Optional[pytorch.LRScheduler]:\\n return self.lr_scheduler\",\n \"def createScheduler_(self):\\n klass_name = 'Scheduler' + string.capitalize(self.scheduler_name)\\n file_name = klass_name\\n try:\\n klass = importName(file_name, klass_name)\\n except KeyError:\\n msg = 'No `class '+klass_name+'` found in file `'+file_name+'.py`'\\n raise SkimException(msg)\\n except ImportError, e:\\n msg = 'Cannot create scheduler '+self.scheduler_name\\n msg += ' (file: '+file_name+', class '+klass_name+'):\\\\n'\\n msg += str(e)\\n raise SkimException(msg)\\n\\n common.scheduler = klass()\\n common.scheduler.configure(self.cfg_params)\\n return\",\n \"def build_schedule(lr: float, final_lr: float, n_epochs: int) -> Callable:\\n delta = (lr - final_lr) / n_epochs\\n\\n def schedule(epoch: int) -> float:\\n assert 0 <= epoch < n_epochs\\n return lr - delta * epoch\\n return schedule\",\n \"def exp_lr_scheduler(optimizer, epoch, lr_decay=0.1, lr_decay_epoch=7):\\n if epoch % lr_decay_epoch or epoch>35:\\n return optimizer\\n \\n for param_group in optimizer.param_groups:\\n param_group['lr'] *= lr_decay\\n return optimizer\",\n \"def build_trainer_config(restore_state=None, train_policies=None, config=None):\\n obs_space = env_cls(env_config).observation_space\\n act_space = env_cls(env_config).action_space\\n\\n agent_config = (PPOTFPolicy, obs_space, act_space, {\\n \\\"model\\\": {\\n \\\"use_lstm\\\": True,\\n \\\"fcnet_hiddens\\\": [config['fc_units'], config['fc_units']],\\n \\\"lstm_cell_size\\\": config['lstm_units'],\\n },\\n \\\"framework\\\": \\\"tfe\\\",\\n })\\n\\n # N_POLICIES = 2\\n policies_keys = ['victim', 'adversary']\\n\\n #policies = {policy_template % i: agent_config for i in range(N_POLICIES)}\\n policies = {name: agent_config for name in policies_keys}\\n\\n def select_policy(agent_id):\\n assert agent_id in [\\\"player1\\\", \\\"player2\\\"]\\n agent_ids = [\\\"player1\\\", \\\"player2\\\"]\\n \\n # selecting the corresponding policy (only for 2 policies)\\n return policies_keys[agent_ids.index(agent_id)]\\n\\n # randomly choosing an opponent\\n # return np.random.choice(list(policies.keys()))\\n \\n if train_policies is None:\\n train_policies = list(policies.keys())\\n \\n for k in train_policies:\\n assert k in policies.keys()\\n\\n config = {\\n \\\"env\\\": env_cls,\\n # \\\"gamma\\\": 0.9,\\n \\\"num_workers\\\": 0,\\n # \\\"num_envs_per_worker\\\": 10,\\n # \\\"rollout_fragment_length\\\": 10,\\n \\\"train_batch_size\\\": config['train_batch_size'],\\n \\\"multiagent\\\": {\\n \\\"policies_to_train\\\": train_policies,\\n \\\"policies\\\": policies,\\n \\\"policy_mapping_fn\\\": select_policy,\\n },\\n \\\"framework\\\": \\\"tfe\\\",\\n #\\\"train_batch_size\\\": 512\\n #\\\"num_cpus_per_worker\\\": 2\\n }\\n return config\",\n \"def configure_optimizers(\\n self,\\n ) -> Dict[str, Union[torch.optim.Optimizer, Dict[str, Union[str, _LRScheduler]]]]:\\n optimizer = instantiate(self.optimizer_cfg, params=self.encoder.parameters())\\n scheduler = {\\n \\\"scheduler\\\": instantiate(self.scheduler_cfg, optimizer),\\n \\\"monitor\\\": \\\"train_err1\\\",\\n }\\n\\n return dict(optimizer=optimizer, lr_scheduler=scheduler)\",\n \"def fetch_optimizer(args, model):\\n optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)\\n\\n scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps + 100,\\n pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')\\n\\n return optimizer, scheduler\",\n \"def adjust_learning_rate(args,optimizer, epoch):\\n lr = args.lr\\n schedule = args.lr_schedule\\n # schedule from TRADES repo (different from paper due to bug there)\\n if schedule == 'trades':\\n if epoch >= 0.75 * args.epochs:\\n lr = args.lr * 0.1\\n # schedule as in TRADES paper\\n elif schedule == 'trades_fixed':\\n if epoch >= 0.75 * args.epochs:\\n lr = args.lr * 0.1\\n if epoch >= 0.9 * args.epochs:\\n lr = args.lr * 0.01\\n if epoch >= args.epochs:\\n lr = args.lr * 0.001\\n # cosine schedule\\n elif schedule == 'cosine':\\n lr = args.lr * 0.5 * (1 + np.cos((epoch - 1) / args.epochs * np.pi))\\n # schedule as in WRN paper\\n elif schedule == 'wrn':\\n if epoch >= 0.3 * args.epochs:\\n lr = args.lr * 0.2\\n if epoch >= 0.6 * args.epochs:\\n lr = args.lr * 0.2 * 0.2\\n if epoch >= 0.8 * args.epochs:\\n lr = args.lr * 0.2 * 0.2 * 0.2\\n else:\\n raise ValueError('Unkown LR schedule %s' % schedule)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def configure_optimizers(self):\\n optimizer = torch.optim.Adam(\\n self.model.parameters(),\\n lr=self.lr,\\n weight_decay=self.weight_decay,\\n )\\n\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer=optimizer,\\n mode=\\\"min\\\",\\n factor=0.5,\\n patience=self.patience,\\n ),\\n \\\"monitor\\\": \\\"train_loss\\\",\\n },\\n }\",\n \"def configure_optimizers(self):\\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return [optimizer], [scheduler]\",\n \"def get_optimizer(model, lr_method, lr_rate):\\n lr_method_name = lr_method\\n\\n # initialize optimizer function\\n if lr_method_name == 'sgd':\\n optimizer = optim.SGD(model.parameters(), lr=lr_rate, momentum=0.9)\\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\\n elif lr_method_name == 'adagrad':\\n optimizer = optim.Adagrad(model.parameters(), lr=lr_rate)\\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\\n elif lr_method_name == 'adam':\\n optimizer = optim.Adam(model.parameters(), lr=lr_rate)\\n # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.001)\\n else:\\n raise Exception('unknown optimization method.')\\n\\n return optimizer # , scheduler\",\n \"def optimizer_creator(model, config):\\n return torch.optim.SGD(model.parameters(), lr=config.get(\\\"lr\\\", 1e-4))\",\n \"def configure_optimizers(self):\\n optimizer = _get_optimizer(model_parameters=self.parameters(\\n ), project_parameters=self.project_parameters)\\n if self.project_parameters.step_size > 0:\\n lr_scheduler = _get_lr_scheduler(\\n project_parameters=self.project_parameters, optimizer=optimizer)\\n return [optimizer], [lr_scheduler]\\n else:\\n return optimizer\",\n \"def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print('learning rate is set to: ', lr)\\n return optimizer\",\n \"def configure_optimizers(self) -> dict[str, Any]:\\n optimizer = torch.optim.Adam(\\n self.model.parameters(), lr=self.hyperparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer,\\n patience=self.hyperparams[\\\"learning_rate_schedule_patience\\\"],\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1,\\r\\n max_iter=300, power=0.9):\\r\\n # if iter % lr_decay_iter or iter > max_iter:\\r\\n # \\treturn optimizer\\r\\n\\r\\n lr = init_lr * (1 - iter / max_iter) ** power\\r\\n optimizer.param_groups[0]['lr'] = lr\\r\\n return lr\",\n \"def lr_schedule(num_epochs):\\n return lambda epoch: 1.0\",\n \"def optimizer_factory(config, model):\\n params = model.parameters()\\n\\n optimizer = config[\\\"loss\\\"].get(\\\"optimizer\\\", \\\"Adam\\\")\\n lr = config[\\\"loss\\\"].get(\\\"lr\\\", 1e-3)\\n momentum = config[\\\"loss\\\"].get(\\\"momentum\\\", 0.9)\\n\\n if optimizer == \\\"SGD\\\":\\n return optim.SGD(params, lr=lr, momentum=momentum)\\n elif optimizer == \\\"Adam\\\":\\n return optim.Adam(params, lr=lr)\\n else:\\n raise NotImplementedError()\",\n \"def learning_rate_scheduler(self, lr_decay_schedule, lr_decay_parameters):\\n #Available learning rate decay schedules\\n self.lr_decay_schedules = ['constant', 'multiplication', 'exponential', 'power']\\n \\n #Checks if asked schedule is implemented\\n assert lr_decay_schedule in self.lr_decay_schedules, \\\\\\n \\\"Learning rate schedule {0!r} not implemented. Choose one of the following: {1}\\\".format(\\n lr_decay_schedule, self.lr_decay_schedules)\\n\\n assert type(lr_decay_parameters) is ListType or TupleType,\\\\\\n \\\"Learning rate decay parameters must be a list or a tuple. Provided value: {0!r} and \\\\\\n type {1!r}\\\".format(lr_decay_parameters, type(lr_decay_parameters))\\n\\n #Initial learning rate value for epoch 0.\\n init_lr = lr_decay_parameters[0]\\n\\n assert type(init_lr) is FloatType or IntType,\\\\\\n \\\"Initial learning_rate value must be float or int: {0!r}\\\".format(init_lr)\\n\\n assert init_lr >= 0, \\\"Initial learning_rate must be >=0: {0!r}\\\".format(init_lr)\\n\\n #Constant learning rate\\n if lr_decay_schedule == self.lr_decay_schedules[0]:\\n return lambda epoch: init_lr\\n\\n assert len(lr_decay_parameters) >= 2,\\\\\\n \\\"If decay not constant at least two parameters [init learning_rate, decay rate] are\\\\\\n expected for learning_rate_decay_parameters. Provided values: {0!r}\\\".format(lr_decay_parameters)\\n\\n #Parameter r\\n decay_rate = lr_decay_parameters[1]\\n\\n assert type(decay_rate) is FloatType or IntType,\\\\\\n \\\"Decay rate for learning rate value must be float or int: {0!r}\\\".format(decay_rate)\\n\\n assert decay_rate >= 0, \\\"Decay rate for learning rate must be >=0: {0!r}\\\".format(decay_rate)\\n\\n if lr_decay_schedule == self.lr_decay_schedules[1]:\\n return lambda epoch: init_lr*decay_rate**epoch\\n\\n if lr_decay_schedule == self.lr_decay_schedules[2]:\\n return lambda epoch: init_lr*10**(-epoch*1./decay_rate)\\n\\n if lr_decay_schedule == self.lr_decay_schedules[3]:\\n assert len(lr_decay_parameters) == 3, \\\"For Power Scheduling 3 parameters are needed \\\\\\n [init learning rate, r, c]: {0!r}\\\".format(lr_decay_parameters)\\n\\n c = lr_decay_parameters[2]\\n assert type(c) is FloatType or IntType, \\\\\\n \\\"c parameter in Power Schedule for lr must be float or int: {0!r}\\\".format(type(c)) \\n\\n assert c >=0, \\\"c in Power Schedule for lr must be >=0: {0!r}\\\".format(c)\\n return lambda epoch: init_lr*(1+epoch*1./r)**-c\",\n \"def train_scheduler(self, tau, Tau):\\n # SAC-U doesn't have a trained scheduler\\n pass\",\n \"def train(self, optimizer, args, scheduler=None, verbose=True,\\n plt_traj=False, plt_loss=False):\\n self.execution_arg_history.append(args)\\n\\n start_epoch = 1\\n epoch_times = []\\n\\n if 'ckpt_int' in args and exists_checkpoint():\\n ckpt = self.load_checkpoint()\\n\\n start_epoch = ckpt[0]\\n epoch_times = ckpt[1]\\n\\n optimizer.load_state_dict(ckpt[2])\\n if scheduler:\\n scheduler.load_state_dict(ckpt[3])\\n\\n for epoch in range(start_epoch, args['max_epoch']+1):\\n start_time = time.time()\\n\\n for b_data, b_time in self.train_loader:\\n optimizer.zero_grad()\\n\\n out = self.model.forward(b_data, b_time[0], args)\\n elbo = self.model.get_elbo(b_data, *out, args)\\n\\n self.train_loss_meter.update(elbo.item())\\n\\n elbo.backward()\\n if 'clip_norm' in args:\\n clip_grad_norm_(self.model.parameters(), args['clip_norm'])\\n optimizer.step()\\n if scheduler:\\n scheduler.step()\\n\\n end_time = time.time()\\n epoch_times.append(end_time - start_time)\\n\\n with torch.no_grad():\\n self.update_val_loss(args)\\n\\n if self.plot_func and plt_traj:\\n self.plot_val_traj(args['plt_args'])\\n self.train_loss_hist.append(self.train_loss_meter.avg)\\n self.val_loss_hist.append(self.val_loss_meter.val)\\n\\n if verbose:\\n if scheduler:\\n print(\\\"Current LR: {}\\\".format(scheduler.get_last_lr()),\\n flush=True)\\n if plt_loss:\\n self.plot_loss()\\n self.print_loss(epoch)\\n\\n if 'ckpt_int' in args and epoch % args['ckpt_int']:\\n self.save_checkpoint(optimizer, scheduler, epoch, epoch_times)\\n\\n self.runtimes.append(epoch_times)\",\n \"def get_optimizer(args, net):\\n if args.backbone_lr > 0.0:\\n base_params = []\\n resnet_params = []\\n resnet_name = []\\n resnet_name.append('layer0')\\n resnet_name.append('layer1')\\n #resnet_name.append('layer2')\\n #resnet_name.append('layer3')\\n #resnet_name.append('layer4')\\n len_resnet = len(resnet_name)\\n else:\\n param_groups = net.parameters()\\n\\n if args.backbone_lr > 0.0:\\n for name, param in net.named_parameters():\\n is_resnet = False\\n for i in range(len_resnet):\\n if resnet_name[i] in name:\\n resnet_params.append(param)\\n # param.requires_grad=False\\n print(\\\"resnet_name\\\", name)\\n is_resnet = True\\n break\\n if not is_resnet:\\n base_params.append(param)\\n\\n if args.sgd:\\n if args.backbone_lr > 0.0:\\n optimizer = optim.SGD([\\n {'params': base_params},\\n {'params': resnet_params, 'lr':args.backbone_lr}\\n ],\\n lr=args.lr,\\n weight_decay=5e-4, #args.weight_decay,\\n momentum=args.momentum,\\n nesterov=False)\\n else:\\n optimizer = optim.SGD(param_groups,\\n lr=args.lr,\\n weight_decay=5e-4, #args.weight_decay,\\n momentum=args.momentum,\\n nesterov=False)\\n else:\\n raise ValueError('Not a valid optimizer')\\n\\n if args.lr_schedule == 'scl-poly':\\n if cfg.REDUCE_BORDER_ITER == -1:\\n raise ValueError('ERROR Cannot Do Scale Poly')\\n\\n rescale_thresh = cfg.REDUCE_BORDER_ITER\\n scale_value = args.rescale\\n lambda1 = lambda iteration: \\\\\\n math.pow(1 - iteration / args.max_iter,\\n args.poly_exp) if iteration < rescale_thresh else scale_value * math.pow(\\n 1 - (iteration - rescale_thresh) / (args.max_iter - rescale_thresh),\\n args.repoly)\\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\\n elif args.lr_schedule == 'poly':\\n lambda1 = lambda iteration: math.pow(1 - iteration / args.max_iter, args.poly_exp)\\n scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda1)\\n else:\\n raise ValueError('unknown lr schedule {}'.format(args.lr_schedule))\\n\\n return optimizer, scheduler\",\n \"def set_optimizer(self, optimizer, opti_scheduler=True, loss_scheduler_patience=7):\\n self.optimizer = optimizer\\n if opti_scheduler:\\n self.opti_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,\\n 'min', # overwritten by update_dev_scores\\n patience=loss_scheduler_patience,\\n verbose=True)\",\n \"def configure_optimizers(self) -> Tuple[List[optim.Optimizer], List[optim.lr_scheduler._LRScheduler]]:\\n if (\\n (\\\"weight_decay\\\" in self.optimizer_params)\\n and (self.optimizer_params[\\\"weight_decay\\\"] != 0)\\n and self.exclude_bn_bias\\n ):\\n defaults = copy(self.optimizer_params)\\n weight_decay = defaults.pop(\\\"weight_decay\\\")\\n\\n wd_group = []\\n nowd_group = []\\n for name, tensor in self.named_parameters():\\n if not tensor.requires_grad:\\n continue\\n if (\\\"bias\\\" in name) or (\\\"bn\\\" in name):\\n nowd_group.append(tensor)\\n else:\\n wd_group.append(tensor)\\n\\n params = [\\n {\\\"params\\\": wd_group, \\\"weight_decay\\\": weight_decay},\\n {\\\"params\\\": nowd_group, \\\"weight_decay\\\": 0.0},\\n ]\\n optimizer = self.optimizer_class(params, **defaults)\\n else:\\n optimizer = self.optimizer_class(self.parameters(), **self.optimizer_params)\\n lr_scheduler = self.lr_scheduler_class(optimizer, **self.lr_scheduler_params)\\n return [optimizer], [lr_scheduler]\",\n \"def _prepare_scheduler(self, training_batches, scheduler=None):\\n if scheduler.batch_based_schedulers:\\n raise Exception(\\\"New tasks don't yet support batch-based scheduling\\\")\\n return scheduler\",\n \"def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr):\\n lr = initial_lr - (initial_lr * (epoch / float(total_num_epochs)))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 30:\\n lr *= 0.5e-3\\n elif epoch > 25:\\n lr *= 1e-3\\n elif epoch > 20:\\n lr *= 1e-2\\n elif epoch > 10:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def configure_optimizers(self) -> Dict[str, Any]:\\n optimizer = torch.optim.AdamW(\\n self.model.parameters(), lr=self.hparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer, patience=self.hparams[\\\"learning_rate_schedule_patience\\\"]\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def lr_schedule(self, epoch):\\n lr = self.params.learning_rate\\n\\n if epoch > int(self.params.num_epochs * 0.8):\\n lr *= 1e-3\\n elif epoch > int(self.params.num_epochs * 0.6):\\n lr *= 1e-2\\n elif epoch > int(self.params.num_epochs * 0.4):\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def update_linear_schedule(optimizer, epoch, total_num_epochs, initial_lr):\\n final_lr = initial_lr * 0.1\\n init_lr = (initial_lr - final_lr)\\n lr = final_lr + init_lr - (init_lr * (min(1., epoch / (float(total_num_epochs)))))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\",\n \"def configure_optimizers(self):\\n optimizer = optim.Adam(\\n self.parameters(), lr=self.hparams.learning_rate)\\n # scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return optimizer\",\n \"def _get_optimizer(config, network, lr):\\n if config.optimizer.lower() == \\\"adam\\\":\\n optimizer = Adam(network.trainable_params(), lr, beta1=0.9, beta2=0.98)\\n elif config.optimizer.lower() == \\\"lamb\\\":\\n optimizer = Lamb(network.trainable_params(), learning_rate=lr,\\n eps=1e-6)\\n elif config.optimizer.lower() == \\\"momentum\\\":\\n optimizer = Momentum(network.trainable_params(), lr, momentum=0.9)\\n else:\\n raise ValueError(f\\\"optimizer only support `adam` and `momentum` now.\\\")\\n\\n return optimizer\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n return lr\",\n \"def rnn_learning_rate_schedule(dataset):\\n model, prefix = make_rnn(non_negative=True)\\n # model.set_params(**{prefix + \\\"val\\\": 0.1})\\n\\n # base = no schedule\\n print(\\\"Baseline RNN\\\")\\n # model.set_params(**{prefix + \\\"history_file\\\": \\\"nn_default.csv\\\"})\\n cross_validate(dataset, model)\\n # model.fit(dataset.inputs, dataset.outputs)\\n\\n # higher init, decay set to reach the same at 40 epochs\\n print(\\\"RNN with decay\\\")\\n model.set_params(**{prefix + \\\"lr_decay\\\": \\\"DecreasingLearningRateScheduler\\\"})\\n # model.fit(dataset.inputs, dataset.outputs)\\n cross_validate(dataset, model)\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = config.optimizer.lr\\n schedule = config.lr_schedule if hasattr(config, 'lr_schedule') else 'fixed'\\n if schedule == 'fixed':\\n if epoch >= 0.75 * config.epochs:\\n lr = config.optimizer.lr * 0.1\\n if epoch >= 0.9 * config.epochs:\\n lr = config.optimizer.lr * 0.01\\n if epoch >= config.epochs:\\n lr = config.optimizer.lr * 0.001\\n # cosine schedule\\n elif schedule == 'cosine':\\n lr = config.optimizer.lr * 0.5 * (1 + np.cos((epoch - 1) / config.epochs * np.pi))\\n elif schedule == 'search':\\n if epoch >= 75:\\n lr = 0.01\\n if epoch >= 90:\\n lr = 0.001\\n else:\\n raise ValueError('Unkown LR schedule %s' % schedule)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def create_optimizer(init_lr, num_train_steps, num_warmup_steps):\\n # Implements linear decay of the learning rate.\\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\\n initial_learning_rate=init_lr,\\n decay_steps=num_train_steps,\\n end_learning_rate=0.0)\\n if num_warmup_steps:\\n learning_rate_fn = WarmUp(initial_learning_rate=init_lr,\\n decay_schedule_fn=learning_rate_fn,\\n warmup_steps=num_warmup_steps)\\n optimizer = AdamWeightDecay(\\n learning_rate=learning_rate_fn,\\n weight_decay_rate=0.01,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=1e-6,\\n exclude_from_weight_decay=[LAYER_NORM_NAME, 'bias'])\\n return optimizer\",\n \"def lr_schedule(epoch,lr):\\r\\n learning_rate = lr\\r\\n if epoch > 10:\\r\\n learning_rate *= 0.1\\r\\n if epoch > 20:\\r\\n learning_rate *= 0.1\\r\\n if epoch > 50:\\r\\n learning_rate *= 0.01\\r\\n\\r\\n # tf.summary.scalar('learning rate', data=learning_rate, step=epoch)\\r\\n return learning_rate\",\n \"def __init__(self, model, optimizer, scheduler, generator, train_loader, val_loader, device='cpu',\\n cameras=None, lights=None, log_dir=None, vis_dir=None, debug_dir=None, val_dir=None,\\n n_eval_points=8000,\\n lambda_dr_rgb=1.0, lambda_dr_silhouette=1.0,\\n lambda_dr_proj=0.0, lambda_dr_repel=0.0,\\n overwrite_visualization=True,\\n n_debug_points=4000, steps_dss_backward_radii=100,\\n **kwargs):\\n self.cfg = kwargs\\n self.device = device\\n self.model = model\\n self.cameras = cameras\\n self.lights = lights\\n\\n self.val_loader = val_loader\\n self.train_loader = train_loader\\n\\n self.tb_logger = SummaryWriter(\\n log_dir + datetime.datetime.now().strftime(\\\"-%Y%m%d-%H%M%S\\\"))\\n\\n # implicit function model\\n self.vis_dir = vis_dir\\n self.val_dir = val_dir\\n\\n self.lambda_dr_rgb = lambda_dr_rgb\\n self.lambda_dr_silhouette = lambda_dr_silhouette\\n self.lambda_dr_proj = lambda_dr_proj\\n self.lambda_dr_repel = lambda_dr_repel\\n\\n self.generator = generator\\n self.n_eval_points = n_eval_points\\n self.overwrite_visualization = overwrite_visualization\\n\\n # tuple (score, mesh)\\n init_dss_backward_radii = 0\\n if isinstance(self.model, PointModel):\\n init_dss_backward_radii = self.model.renderer.rasterizer.raster_settings.radii_backward_scaler\\n\\n self.training_scheduler = TrainerScheduler(init_dss_backward_radii=init_dss_backward_radii,\\n steps_dss_backward_radii=steps_dss_backward_radii,\\n limit_dss_backward_radii=1.0,\\n steps_proj=self.cfg.get(\\n 'steps_proj', -1),\\n gamma_proj=self.cfg.get('gamma_proj', 5))\\n\\n self.debug_dir = debug_dir\\n self.hooks = []\\n self._mesh_cache = None\\n\\n self.n_debug_points = n_debug_points\\n\\n self.optimizer = optimizer\\n self.scheduler = scheduler\\n\\n self.projection_loss = ProjectionLoss(\\n reduction='mean', filter_scale=2.0, knn_k=12)\\n self.repulsion_loss = RepulsionLoss(\\n reduction='mean', filter_scale=2.0, knn_k=12)\\n self.iou_loss = IouLoss(\\n reduction='mean', channel_dim=None)\\n self.l1_loss = L1Loss(reduction='mean')\\n self.l2_loss = L2Loss(reduction='mean')\\n self.smape_loss = SmapeLoss(reduction='mean')\",\n \"def poly_lr_scheduler(optimizer, init_lr, iter, lr_decay_iter=1, max_iter=89280, power=0.9):\\n curr_lr = init_lr\\n if iter % lr_decay_iter or iter > max_iter:\\n return curr_lr\\n\\n for param_group in optimizer.param_groups:\\n curr_lr = init_lr * (1 - iter / max_iter) ** power\\n param_group['lr'] = curr_lr\\n\\n return curr_lr\",\n \"def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,\\n num_example, batch_size, begin_epoch):\\n assert lr_refactor_ratio > 0\\n iter_refactor = [int(r) for r in lr_refactor_step.split(',') if r.strip()]\\n if lr_refactor_ratio >= 1:\\n return (learning_rate, None)\\n else:\\n lr = learning_rate\\n epoch_size = num_example // batch_size\\n for s in iter_refactor:\\n if begin_epoch >= s:\\n lr *= lr_refactor_ratio\\n if lr != learning_rate:\\n pass\\n # logging.getLogger().info(\\\"Adjusted learning rate to {} for epoch {}\\\".format(lr, begin_epoch))\\n steps = [epoch_size * (x - begin_epoch) for x in iter_refactor if x > begin_epoch]\\n if not steps:\\n return (lr, None)\\n lr_scheduler = mx.lr_scheduler.MultiFactorScheduler(step=steps, factor=lr_refactor_ratio)\\n return (lr, lr_scheduler)\",\n \"def build_optimizer(opt_config, learning_rate):\\n if opt_config.opt_method == 'SGD':\\n print('Using SGD as the optimizer', file=sys.stderr)\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n elif opt_config.opt_method == 'Adam':\\n print('Using Adam as the optimizer', file=sys.stderr)\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate, beta1=opt_config.adam_beta1,\\n beta2=opt_config.adam_beta2, epsilon=opt_config.adam_epsilon\\n )\\n else:\\n raise ValueError(\\n 'Unknown optimization method {0}!'.format(opt_config.opt_method))\\n return optimizer\",\n \"def learning_rate_schedule(epoch):\\n lr = 1.0\\n\\n if config[\\\"distributed_lr_warmup_epoch\\\"] > 0:\\n warmup_epochs = config[\\\"distributed_lr_warmup_epoch\\\"]\\n max_factor = config[\\\"distributed_lr_warmup_factor\\\"]\\n factor = 1.0 + (max_factor - 1.0) * min(epoch / warmup_epochs, 1.0)\\n lr *= factor\\n\\n for (milestone, factor) in config[\\\"lr_schedule_milestones\\\"]:\\n if epoch >= milestone:\\n lr *= factor\\n else:\\n return lr\\n return lr\",\n \"def lr_schedule(epoch):\\n lr = 0.0001\\n if epoch > 40:\\n lr *= 1e-2\\n elif epoch > 30:\\n lr *= 5e-2\\n elif epoch > 20:\\n lr *= 1e-1\\n elif epoch > 10:\\n lr *= 5e-1\\n print ('Learning rate: ', lr)\\n return lr\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def lr_schedule(epoch):\\n lr = 1e-3\\n if epoch > 180:\\n lr *= 0.5e-3\\n elif epoch > 160:\\n lr *= 1e-3\\n elif epoch > 120:\\n lr *= 1e-2\\n elif epoch > 80:\\n lr *= 1e-1\\n print('Learning rate: ', lr)\\n return lr\",\n \"def initialize_optimizer(model, args):\\n parameters = [p for p in model.parameters() if p.requires_grad]\\n if args.optimizer == 'sgd':\\n optimizer = optim.SGD(parameters, args.learning_rate,\\n momentum=args.momentum,\\n weight_decay=args.weight_decay)\\n elif args.optimizer == 'adam':\\n optimizer = optim.Adam(parameters, args.learning_rate,\\n weight_decay=args.weight_decay)\\n elif args.optimizer == 'adamax':\\n optimizer = optim.Adamax(parameters, args.learning_rate,\\n weight_decay=args.weight_decay)\\n elif args.optimizer == 'adagrad':\\n optimizer = optim.Adagrad(parameters, args.learning_rate,\\n weight_decay=args.weight_decay)\\n scheduler = ReduceLROnPlateau(optimizer, 'min', verbose=True)\\n return optimizer, scheduler\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8214888","0.81373125","0.8030262","0.7877491","0.7863203","0.777814","0.7759245","0.76554245","0.7646608","0.7585435","0.748087","0.74216324","0.7285573","0.7153245","0.7007013","0.69302773","0.6923065","0.6863463","0.68190587","0.6761453","0.6728476","0.6728476","0.66816854","0.6656579","0.6535839","0.64993954","0.6497161","0.6482549","0.6437417","0.6433141","0.6432463","0.64285487","0.6409961","0.6402087","0.63876635","0.6372188","0.63714653","0.63669884","0.63174635","0.6285379","0.6252127","0.6230378","0.615347","0.6146415","0.61459607","0.6145735","0.61408156","0.6137489","0.6109074","0.6075884","0.6067272","0.6052574","0.60509074","0.60192436","0.6008727","0.59926647","0.5973457","0.5909972","0.5894347","0.5892111","0.586037","0.58428204","0.58233565","0.58116394","0.5797862","0.57721287","0.5751246","0.57473016","0.5746188","0.5744536","0.57381076","0.5727433","0.57063466","0.5680809","0.5677931","0.5675058","0.567248","0.567054","0.5668941","0.56389314","0.56389105","0.5637636","0.5635725","0.5617896","0.56129545","0.5609042","0.56001157","0.5594032","0.5584722","0.5582561","0.55823535","0.5549655","0.5544655","0.55325574","0.55317813","0.5511333","0.5511333","0.5511333","0.5511333","0.55099434"],"string":"[\n \"0.8214888\",\n \"0.81373125\",\n \"0.8030262\",\n \"0.7877491\",\n \"0.7863203\",\n \"0.777814\",\n \"0.7759245\",\n \"0.76554245\",\n \"0.7646608\",\n \"0.7585435\",\n \"0.748087\",\n \"0.74216324\",\n \"0.7285573\",\n \"0.7153245\",\n \"0.7007013\",\n \"0.69302773\",\n \"0.6923065\",\n \"0.6863463\",\n \"0.68190587\",\n \"0.6761453\",\n \"0.6728476\",\n \"0.6728476\",\n \"0.66816854\",\n \"0.6656579\",\n \"0.6535839\",\n \"0.64993954\",\n \"0.6497161\",\n \"0.6482549\",\n \"0.6437417\",\n \"0.6433141\",\n \"0.6432463\",\n \"0.64285487\",\n \"0.6409961\",\n \"0.6402087\",\n \"0.63876635\",\n \"0.6372188\",\n \"0.63714653\",\n \"0.63669884\",\n \"0.63174635\",\n \"0.6285379\",\n \"0.6252127\",\n \"0.6230378\",\n \"0.615347\",\n \"0.6146415\",\n \"0.61459607\",\n \"0.6145735\",\n \"0.61408156\",\n \"0.6137489\",\n \"0.6109074\",\n \"0.6075884\",\n \"0.6067272\",\n \"0.6052574\",\n \"0.60509074\",\n \"0.60192436\",\n \"0.6008727\",\n \"0.59926647\",\n \"0.5973457\",\n \"0.5909972\",\n \"0.5894347\",\n \"0.5892111\",\n \"0.586037\",\n \"0.58428204\",\n \"0.58233565\",\n \"0.58116394\",\n \"0.5797862\",\n \"0.57721287\",\n \"0.5751246\",\n \"0.57473016\",\n \"0.5746188\",\n \"0.5744536\",\n \"0.57381076\",\n \"0.5727433\",\n \"0.57063466\",\n \"0.5680809\",\n \"0.5677931\",\n \"0.5675058\",\n \"0.567248\",\n \"0.567054\",\n \"0.5668941\",\n \"0.56389314\",\n \"0.56389105\",\n \"0.5637636\",\n \"0.5635725\",\n \"0.5617896\",\n \"0.56129545\",\n \"0.5609042\",\n \"0.56001157\",\n \"0.5594032\",\n \"0.5584722\",\n \"0.5582561\",\n \"0.55823535\",\n \"0.5549655\",\n \"0.5544655\",\n \"0.55325574\",\n \"0.55317813\",\n \"0.5511333\",\n \"0.5511333\",\n \"0.5511333\",\n \"0.5511333\",\n \"0.55099434\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81571466"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17396,"cells":{"query":{"kind":"string","value":"Create optimizer learning rate scheduler based on config."},"document":{"kind":"string","value":"def _create_learning_rate_scheduler(learning_rate_config, optimizer, total_step):\n lr_scheduler = None\n learning_rate_type = learning_rate_config.WhichOneof('learning_rate')\n if learning_rate_type == 'multi_phase':\n config = learning_rate_config.multi_phase\n lr_phases = []\n mom_phases = []\n for phase_cfg in config.phases:\n lr_phases.append((phase_cfg.start, phase_cfg.lambda_func))\n mom_phases.append((phase_cfg.start, phase_cfg.momentum_lambda_func))\n lr_scheduler = lsf.LRSchedulerStep(\n optimizer,total_step, lr_phases, mom_phases)\n\n if learning_rate_type == 'one_cycle':\n config = learning_rate_config.one_cycle\n lr_scheduler = lsf.OneCycle(\n optimizer, total_step, config.lr_max, list(config.moms), config.div_factor, config.pct_start)\n\n if lr_scheduler is None:\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\n\n return lr_scheduler"},"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 scheduler_creator(optimizer, config):\n return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)","def build(optimizer_config, optimizer, total_step):\n optimizer_type = optimizer_config.WhichOneof('optimizer')\n\n if optimizer_type == 'rms_prop_optimizer':\n config = optimizer_config.rms_prop_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n if optimizer_type == 'momentum_optimizer':\n config = optimizer_config.momentum_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n if optimizer_type == 'adam_optimizer':\n config = optimizer_config.adam_optimizer\n lr_scheduler = _create_learning_rate_scheduler(\n config.learning_rate, optimizer, total_step=total_step)\n\n return lr_scheduler","def _create_learning_rate_scheduler(learning_rate_config, optimizer, last_step=-1):\n lr_scheduler = None\n learning_rate_type = learning_rate_config.name \n if learning_rate_type == 'constant_learning_rate': \n\n lr_scheduler = learning_schedules.Constant(\n optimizer, last_step=last_step)\n\n if learning_rate_type == 'exponential_decay_learning_rate':\n config = learning_rate_config \n lr_scheduler = learning_schedules.ExponentialDecay(\n optimizer, config.decay_steps, \n config.decay_factor, config.staircase, last_step=last_step)\n\n if learning_rate_type == 'manual_step_learning_rate':\n config = learning_rate_config\n if not config.schedule:\n raise ValueError('Empty learning rate schedule.')\n learning_rate_step_boundaries = [x.step for x in config.schedule]\n learning_rate_sequence = [config.initial_learning_rate]\n learning_rate_sequence += [x.learning_rate for x in config.schedule]\n lr_scheduler = learning_schedules.ManualStepping(\n optimizer, learning_rate_step_boundaries, learning_rate_sequence, \n last_step=last_step)\n\n if learning_rate_type == 'cosine_decay_learning_rate':\n config = learning_rate_config.cosine_decay_learning_rate\n lr_scheduler = learning_schedules.CosineDecayWithWarmup(\n optimizer, config.total_steps, \n config.warmup_learning_rate, config.warmup_steps, \n last_step=last_step)\n\n if lr_scheduler is None:\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\n\n return lr_scheduler","def get_scheduler(optimizer, opt):\n \n epochs_no_decay = opt.epochs - opt.lr_linear\n lr_policy = opt.lr_policy\n \n if lr_policy == 'linear':\n def lr_lambda(epoch):\n return 1. - max(0, epoch - epochs_no_decay) / float(opt.lr_linear + 1)\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)\n elif lr_policy == 'step':\n # multiply by gamma every lr_decay_steps\n # for example lr_decay_steps=50 and initial learning = .5\n # then we have \n # lr = .5 for 0 <= epoch < 50;\n # lr = .05 for 50 <= epoch < 100;\n # lr = .005 for 100 <= epoch < 150;\n scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_step, gamma=.1)\n elif lr_policy == 'plateau':\n # Reduce learning rate when a metric has stopped improving. \n # Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. \n # This scheduler reads a metrics quantity and if no improvement \n # is seen for a ‘patience’ number of epochs, \n # the learning rate is reduced.\n # Parameters\n # - mode (str, default=min): In `min` mode, lr will be reduced when the quantity monitored has stopped decreasing; \n # in `max` mode, lr will be reduced when the quantity monitored has stopped increasing.\n # - factor (float, default=.1): Factor by which the learning rate will be reduced. new_lr = lr * factor.\n # - patience (int, default=10): Number of epochs with no improvement after which learning rate will be reduced. \n # - threshold (float): only decrease lr if the change in the quantitiy monitored is smaller than threshold. \n # Say we have threshold=0.001, if loss is $18.0$ on epoch $n$ and loss is $17.9999$ on epoch $n+1$,\n # then multiply current learning rate by the factor.\n # On the contrary, if the loss is 17.99, lr doesn't have to be changed.\n scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=.2, threshold=.01, patience=5)\n else:\n return NotImplementedError(f'learning rate policy {lr_policy} is not implemented')\n return scheduler","def configure_optimizers(self):\n allowed = list(OPTIM_LOOKUP.keys())\n if self.optimizer not in allowed:\n raise ValueError(\n f\"Illegal optimizer given. Got {self.optimizer}. Allowed: {allowed}.\"\n )\n\n allowed = list(SCHED_LOOKUP.keys())\n if self.scheduler not in allowed:\n raise ValueError(\n f\"Illegal scheduler given. Got {self.scheduler}. Allowed: {allowed}.\"\n )\n\n if self.optim_params is None:\n self.optim_params = {\n \"encoder\": {\"lr\": 0.00005, \"weight_decay\": 0.00005},\n \"decoder\": {\"lr\": 0.0005, \"weight_decay\": 0.0005},\n }\n\n params = adjust_optim_params(self.model, self.optim_params)\n optimizer = OPTIM_LOOKUP[self.optimizer](params)\n\n if self.lookahead:\n optimizer = OPTIM_LOOKUP[\"lookahead\"](optimizer, k=5, alpha=0.5)\n\n if self.scheduler_params is None:\n self.scheduler_params = {}\n\n scheduler = {\n \"scheduler\": SCHED_LOOKUP[self.scheduler](\n optimizer, **self.scheduler_params\n ),\n \"monitor\": \"val_loss\",\n \"interval\": \"epoch\",\n \"frequency\": 1,\n }\n\n return [optimizer], [scheduler]","def configure_optimizers(self):\n self.optimizer = AdamW(self.parameters(), lr=self.args[\"lr\"])\n self.scheduler = {\n \"scheduler\": torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer,\n mode=\"min\",\n factor=0.2,\n patience=2,\n min_lr=1e-6,\n verbose=True,\n ),\n \"monitor\": \"val_loss\",\n }\n return [self.optimizer], [self.scheduler]","def create_lr_scheduler(\n self, optimizer: torch.optim.Optimizer # type: ignore\n ) -> Optional[LRScheduler]:\n pass","def configure_optimizers(self):\n no_decay = [\"bias\", \"LayerNorm.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\n \"weight_decay\": self.args.weight_decay,\n },\n {\n \"params\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\n \"weight_decay\": 0.0,\n },\n ]\n if self.optimizer == \"adamw\":\n optimizer = AdamW(optimizer_grouped_parameters,\n betas=(0.9, 0.98), # according to RoBERTa paper\n lr=self.args.lr,\n eps=self.args.adam_epsilon,)\n elif self.optimizer == \"torch.adam\":\n optimizer = torch.optim.AdamW(optimizer_grouped_parameters,\n lr=self.args.lr,\n eps=self.args.adam_epsilon,\n weight_decay=self.args.weight_decay)\n else:\n raise ValueError(\"Optimizer type does not exist.\")\n num_gpus = len([x for x in str(self.args.gpus).split(\",\") if x.strip()])\n t_total = (len(self.train_dataloader()) // (self.args.accumulate_grad_batches * num_gpus) + 1) * self.args.max_epochs\n warmup_steps = int(self.args.warmup_proportion * t_total)\n if self.args.lr_scheduler == \"onecycle\":\n scheduler = torch.optim.lr_scheduler.OneCycleLR(\n optimizer, max_lr=self.args.lr, pct_start=float(warmup_steps/t_total),\n final_div_factor=self.args.final_div_factor,\n total_steps=t_total, anneal_strategy='linear')\n elif self.args.lr_scheduler == \"linear\":\n scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total)\n elif self.args.lr_scheulder == \"polydecay\":\n if self.args.lr_mini == -1:\n lr_mini = self.args.lr / self.args.polydecay_ratio\n else:\n lr_mini = self.args.lr_mini\n scheduler = get_polynomial_decay_schedule_with_warmup(optimizer, warmup_steps, t_total, lr_end=lr_mini)\n else:\n raise ValueError\n return [optimizer], [{\"scheduler\": scheduler, \"interval\": \"step\"}]","def configure_optimizers(self):\n self.optimizer = torch.optim.Adam(\n self.parameters(),\n lr=self.args.get(\"lr\", 0.001),\n weight_decay=self.args.get(\"weight_decay\", 0),\n eps=self.args.get(\"eps\", 1e-8)\n )\n self.scheduler = {\n \"scheduler\":\n torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer,\n mode=\"min\",\n factor=0.2,\n patience=3,\n min_lr=1e-6,\n verbose=True,\n ),\n \"monitor\":\n \"val_loss\",\n }\n return [self.optimizer], [self.scheduler]","def set_optimizer(self, config):\r\n self.optimizer = optim.Adam(self.net.parameters(), config.lr)\r\n self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, config.lr_decay)","def get_scheduler(optimizer, opt):\n def lambda_rule(epoch):\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)\n return lr_l\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\n return scheduler","def build_lr_scheduler(\n cfg: CfgNode, optimizer: torch.optim.Optimizer\n) -> torch.optim.lr_scheduler._LRScheduler:\n name = cfg.SOLVER.LR_SCHEDULER_NAME\n if name == \"WarmupMultiStepLR\":\n return WarmupMultiStepLR(\n optimizer,\n cfg.SOLVER.STEPS,\n cfg.SOLVER.GAMMA,\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\n )\n elif name == \"WarmupCosineLR\":\n return WarmupCosineLR(\n optimizer,\n cfg.SOLVER.MAX_ITER,\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\n )\n else:\n raise ValueError(\"Unknown LR scheduler: {}\".format(name))","def _inst_optimizer(self):\n optimizer = Optimizers(self.m_cfg['configs']['lr_politics']['optimizer']).value\n lr_schedule = self.m_cfg['configs']['lr_politics']['lr']\n opt = optimizer(learning_rate=lr_schedule)\n return opt","def reschedule_learning_rate(model, epoch, scheduler):\n if epoch == 7:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 13:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 19:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n if epoch == 25:\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\n current_lr = next(iter(optimizer.param_groups))[\"lr\"]\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\n )\n\n return model, scheduler","def configure_optimizers(self):\n optimizer = torch.optim.Adam(\n self.model.parameters(),\n lr=self.lr,\n weight_decay=self.weight_decay,\n )\n\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer=optimizer,\n mode=\"min\",\n factor=0.5,\n patience=self.patience,\n ),\n \"monitor\": \"train_loss\",\n },\n }","def configure_optimizers(self):\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return [optimizer], [scheduler]","def configure_optimizers(self):\n optimizer = optim.Adam(\n self.parameters(), lr=self.hparams.learning_rate)\n # scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return optimizer","def configure_optimizers(self) -> dict[str, Any]:\n optimizer = torch.optim.Adam(\n self.model.parameters(), lr=self.hyperparams[\"learning_rate\"]\n )\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer,\n patience=self.hyperparams[\"learning_rate_schedule_patience\"],\n ),\n \"monitor\": \"val_loss\",\n },\n }","def build_lr_scheduler(optimizer: Optimizer, args: Namespace,\n total_epochs: List[int] = None) -> _LRScheduler:\n # Learning rate scheduler\n return NoamLR(\n optimizer=optimizer,\n warmup_epochs=[args.warmup_epochs],\n total_epochs=total_epochs or [args.epochs] * args.num_lrs,\n steps_per_epoch=args.train_data_size // args.batch_size,\n init_lr=[args.init_lr],\n max_lr=[args.max_lr],\n final_lr=[args.final_lr]\n )","def build_lr_scheduler(\n optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1\n):\n if lr_scheduler not in AVAI_SCH:\n raise ValueError(\n 'Unsupported scheduler: {}. Must be one of {}'.format(\n lr_scheduler, AVAI_SCH\n )\n )\n\n if lr_scheduler == 'single_step':\n if isinstance(stepsize, list):\n stepsize = stepsize[-1]\n\n if not isinstance(stepsize, int):\n raise TypeError(\n 'For single_step lr_scheduler, stepsize must '\n 'be an integer, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer, step_size=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'multi_step':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\n optimizer, milestones=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'cosine':\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, float(max_epoch)\n )\n\n return scheduler","def configure_optimizers(self) -> Dict[str, Any]:\n optimizer = torch.optim.AdamW(\n self.model.parameters(), lr=self.hparams[\"learning_rate\"]\n )\n return {\n \"optimizer\": optimizer,\n \"lr_scheduler\": {\n \"scheduler\": ReduceLROnPlateau(\n optimizer, patience=self.hparams[\"learning_rate_schedule_patience\"]\n ),\n \"monitor\": \"val_loss\",\n },\n }","def build_lr_scheduler(optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1,\n frozen=20, warmup=10, warmup_factor_base=0.1):\n if lr_scheduler not in AVAI_SCH:\n raise ValueError('Unsupported scheduler: {}. Must be one of {}'.format(lr_scheduler, AVAI_SCH))\n\n if lr_scheduler == 'single_step':\n if isinstance(stepsize, list):\n stepsize = stepsize[-1]\n\n if not isinstance(stepsize, int):\n raise TypeError(\n 'For single_step lr_scheduler, stepsize must '\n 'be an integer, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.StepLR(\n optimizer, step_size=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'multi_step':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\n optimizer, milestones=stepsize, gamma=gamma\n )\n\n elif lr_scheduler == 'cosine':\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n optimizer, float(max_epoch)\n )\n elif lr_scheduler == 'multi_step_warmup':\n if not isinstance(stepsize, list):\n raise TypeError(\n 'For multi_step lr_scheduler, stepsize must '\n 'be a list, but got {}'.format(type(stepsize))\n )\n\n scheduler = MultiStepLRWithWarmUp(\n optimizer, milestones=stepsize, warmup_iters=warmup, frozen_iters=frozen, gamma=gamma,\n warmup_factor_base=warmup_factor_base\n )\n\n return scheduler","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\n\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\n if epoch % lr_decay_epoch == 0 and epoch > 1:\n for param_group in optimizer.param_groups:\n param_group['lr'] = param_group['lr'] * 0.1\n return optimizer","def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\r\n\r\n if epoch % lr_decay_epoch == 0 and epoch > 1:\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = param_group['lr'] * 0.1\r\n\r\n return optimizer","def configure_optimizers(self) -> tuple[list[Optimizer], list[LRScheduler]]:\n # Original paper uses LARS optimizer, but this is not defined in PyTorch\n optimizer = Adam(\n self.parameters(),\n lr=self.hparams[\"lr\"],\n weight_decay=self.hparams[\"weight_decay\"],\n )\n max_epochs = 200\n if self.trainer and self.trainer.max_epochs:\n max_epochs = self.trainer.max_epochs\n if self.hparams[\"version\"] == 1:\n warmup_epochs = 10\n else:\n warmup_epochs = int(max_epochs * 0.05)\n lr_scheduler = SequentialLR(\n optimizer,\n schedulers=[\n LinearLR(optimizer, total_iters=warmup_epochs),\n CosineAnnealingLR(optimizer, T_max=max_epochs),\n ],\n milestones=[warmup_epochs],\n )\n return [optimizer], [lr_scheduler]","def _get_scheduler(\r\n self, optimizer, scheduler: str, warmup_steps: int, t_total: int):\r\n scheduler = scheduler.lower()\r\n if scheduler == 'constantlr':\r\n return transformers.get_constant_schedule(optimizer)\r\n elif scheduler == 'warmupconstant':\r\n # this uses warmup\r\n return transformers.get_constant_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps)\r\n elif scheduler == 'warmuplinear':\r\n # This uses warmup + lr-decay with t_total lr-decays\r\n # only this wrapper accepts num_trianing_steps\r\n # if you open the function get_linear_schedule_with_warmup\r\n # you will find that return\r\n # LambdaLR(optimizer, lr_lambda, last_epoch)\r\n # and last_epoch=-1. So it will end till\r\n # num_training_steps consume up\r\n # and you will see that each .step() consumes 1 training\r\n # step. You can see from\r\n # https://pytorch.org/docs/stable/optim.html\r\n # 'How to adjust Learning Rate'\r\n # When you call the step, the counter will reduce by 1,\r\n # and the learning rate will be adjusted accordingly.\r\n # Initial learning rate is given in the Optimiser.\r\n # Initially, at each step, the learning rate will be\r\n # adjusted by param_group in optimizer.param_groups:\r\n # param_group['lr'] = lr\r\n # Now it is implicitly adjusted by the scheduler\r\n return transformers.get_linear_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n elif scheduler == 'warmupcosine':\r\n return transformers.get_cosine_schedule_with_warmup(\r\n optimizer, num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n elif scheduler == 'warmupcosinewithhardrestarts':\r\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(\r\n optimizer,\r\n num_warmup_steps=warmup_steps,\r\n num_training_steps=t_total)\r\n else:\r\n raise ValueError(\"Unknown scheduler {}\".format(scheduler))","def configure_optimizers(self):\n model = self.model\n no_decay = [\"bias\", \"LayerNorm.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n \"weight_decay\": self.hparams.weight_decay,\n },\n {\n \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n \"weight_decay\": 0.0,\n },\n ]\n if self.hparams.lamb:\n optimizer = FusedLAMB(\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\n\n elif self.hparams.adafactor:\n optimizer = Adafactor(\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False\n )\n else:\n optimizer = FusedAdam(\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\n self.opt = optimizer\n\n scheduler = self.get_lr_scheduler()\n\n return [optimizer], [scheduler]","def make_lr_scheduler(lr: float, final_lr: float, n_epochs: int,\n verbose: int = 1) -> keras.callbacks.LearningRateScheduler:\n schedule = build_schedule(lr, final_lr, n_epochs)\n return LearningRateScheduler(schedule=schedule, verbose=verbose)","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\n print('LR is set to {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def configure_schedulers(self, **kwargs):\n if self.scheduler_type == 'CosineAnnealingWarmRestarts':\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, **self.scheduler_params)\n elif self.scheduler_type == 'CosineAnnealingLR':\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, **self.scheduler_params)\n elif self.scheduler_type == 'CyclicLR':\n self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, **self.scheduler_params)\n else:\n self.scheduler = None","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\r\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\r\n print('LR is set to {}'.format(lr))\r\n for param_group in optimizer.param_groups:\r\n param_group['lr'] = lr\r\n\r\n return optimizer","def create_optimizer(init_lr, num_train_steps, num_warmup_steps):\n # Implements linear decay of the learning rate.\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\n initial_learning_rate=init_lr,\n decay_steps=num_train_steps,\n end_learning_rate=0.0)\n if num_warmup_steps:\n learning_rate_fn = WarmUp(initial_learning_rate=init_lr,\n decay_schedule_fn=learning_rate_fn,\n warmup_steps=num_warmup_steps)\n optimizer = AdamWeightDecay(\n learning_rate=learning_rate_fn,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[LAYER_NORM_NAME, 'bias'])\n return optimizer","def configure_optimizers(\n self,\n ) -> Dict[str, Union[torch.optim.Optimizer, Dict[str, Union[str, _LRScheduler]]]]:\n optimizer = instantiate(self.optimizer_cfg, params=self.encoder.parameters())\n scheduler = {\n \"scheduler\": instantiate(self.scheduler_cfg, optimizer),\n \"monitor\": \"train_err1\",\n }\n\n return dict(optimizer=optimizer, lr_scheduler=scheduler)","def configure_optimizers(self):\n opt = torch.optim.AdamW(\n self.parameters(), lr=self.lr, weight_decay=self.weight_decay\n )\n return {\n \"optimizer\": opt,\n \"lr_scheduler\": {\n \"scheduler\": torch.optim.lr_scheduler.ReduceLROnPlateau(opt),\n \"monitor\": \"train_loss\",\n \"frequency\": 1,\n },\n }","def _get_lr_scheduler(project_parameters, optimizer):\n if project_parameters.lr_scheduler == 'StepLR':\n lr_scheduler = StepLR(optimizer=optimizer,\n step_size=project_parameters.step_size, gamma=project_parameters.gamma)\n elif project_parameters.lr_scheduler == 'CosineAnnealingLR':\n lr_scheduler = CosineAnnealingLR(\n optimizer=optimizer, T_max=project_parameters.step_size)\n return lr_scheduler","def optimizer_config(self):\r\n return {\r\n \"lr\": self.args.lr[0],\r\n \"momentum\": self.args.momentum,\r\n \"weight_decay\": self.args.weight_decay,\r\n }","def _create_train_op(self):\n self.lr = self.learning_rate\n # global_step = tf.train.get_or_create_global_step()\n learning_rate = tf.constant(value=self.learning_rate, shape=[], dtype=tf.float32)\n learning_rate =tf.train.exponential_decay(learning_rate,self.global_step,2*self.num_warm_up,0.96,staircase=True,name=\"exponential_decay\")\n\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\n if self.num_warm_up:\n global_steps_int = tf.cast(self.global_step, tf.int32)\n warmup_steps_int = tf.constant(self.num_warm_up, dtype=tf.int32)\n\n global_steps_float = tf.cast(global_steps_int, tf.float32)\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\n\n warmup_percent_done = global_steps_float / warmup_steps_float\n warmup_learning_rate = self.learning_rate * warmup_percent_done\n\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\n learning_rate = (\n (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)\n self.current_learning_rate = learning_rate\n if self.optim_type == 'adagrad':\n self.optimizer = tf.train.AdagradOptimizer(self.lr)\n elif self.optim_type == 'adam':\n self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)\n elif self.optim_type == 'rprop':\n self.optimizer = tf.train.RMSPropOptimizer(self.lr)\n elif self.optim_type == 'sgd':\n self.optimizer = tf.train.GradientDescentOptimizer(self.lr)\n elif self.optim_type == \"bert\":\n self.optimizer = AdamWeightDecayOptimizer(learning_rate=learning_rate, weight_decay_rate=0.01, beta_1=0.9,\n beta_2=0.999, epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n else:\n raise NotImplementedError('Unsupported optimizer: {}'.format(self.optim_type))\n\n self.logger.info(\"applying optimize %s\" % self.optim_type)\n if self.clip_weight:\n # clip_weight\n tvars = tf.trainable_variables()\n grads = tf.gradients(self.loss, tvars)\n grads, _ = tf.clip_by_global_norm(grads, clip_norm=self.max_norm_grad)\n grad_var_pairs = zip(grads, tvars)\n train_op = self.optimizer.apply_gradients(grad_var_pairs, name='apply_grad', global_step=self.global_step)\n new_global_step = self.global_step + 1\n train_op = tf.group(train_op, [self.global_step.assign(new_global_step)])\n self.train_op = train_op\n else:\n self.train_op = self.optimizer.minimize(self.loss, global_step=self.global_step)","def get_optimizers(\n self, num_training_steps\n ):\n if self.optimizers is not None:\n return self.optimizers\n # Prepare optimizer and schedule (linear warmup and decay)\n no_decay = [\"bias\", \"LayerNorm.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [\n p for n, p in self.model.named_parameters()\n if not any(nd in n for nd in no_decay)\n ],\n \"weight_decay\": self.args.weight_decay,\n },\n {\n \"params\": [\n p for n, p in self.model.named_parameters()\n if any(nd in n for nd in no_decay)\n ],\n \"weight_decay\": 0.0,\n },\n ]\n if self.args.optimizer == \"sgd\":\n optimizer = SGD(optimizer_grouped_parameters, lr=self.args.learning_rate, momentum=self.args.sgd_momentum, \\\n weight_decay=self.args.weight_decay)\n elif self.args.optimizer == \"adam\":\n optimizer = AdamW(\n optimizer_grouped_parameters,\n lr=self.args.learning_rate,\n eps=self.args.adam_epsilon)\n if self.args.lr_schedule == \"constant\":\n scheduler = get_constant_schedule_with_warmup(\n optimizer, num_warmup_steps=self.args.warmup_steps)\n elif self.args.lr_schedule == \"linear\":\n scheduler = get_linear_schedule_with_warmup(\n optimizer,\n num_warmup_steps=self.args.warmup_steps,\n num_training_steps=num_training_steps)\n elif self.args.lr_schedule == \"invsqrt\":\n scheduler = get_invsqrt_schedule_with_warmup(\n optimizer, num_warmup_steps=self.args.warmup_steps)\n return optimizer, scheduler","def build_learning_rate(self):\n\n # TODO(arashwan): Explore if we want to only allow explicit const lr sched.\n if not self._lr_config:\n lr = self._optimizer_config.learning_rate\n else:\n lr = LR_CLS[self._lr_type](**self._lr_config.as_dict())\n\n if self._warmup_config:\n lr = WARMUP_CLS[self._warmup_type](lr, **self._warmup_config.as_dict())\n\n return lr","def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=decay):\n lr = init_lr * (0.1**(epoch // lr_decay_epoch))\n\n if epoch % lr_decay_epoch == 0:\n print('LR is set to {}'.format(lr))\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def init_scheduler(self):\n gamma = self.config_dict.get(\"gamma\")\n if gamma is None:\n return None\n else:\n return torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=gamma)","def scheduler(epoch_idx, lr):\n new_lr = lr\n if (epoch_idx == 60 or epoch_idx == 120 or epoch_idx == 160\n or epoch_idx == 260 or epoch_idx == 320 or epoch_idx == 360):\n new_lr *= 0.2\n \"\"\"\n if epoch_idx == 200:\n new_lr = 0.1\n \"\"\"\n return new_lr","def configure_optimizers(self):\n optimizer = _get_optimizer(model_parameters=self.parameters(\n ), project_parameters=self.project_parameters)\n if self.project_parameters.step_size > 0:\n lr_scheduler = _get_lr_scheduler(\n project_parameters=self.project_parameters, optimizer=optimizer)\n return [optimizer], [lr_scheduler]\n else:\n return optimizer","def lr_scheduler(optimizer, lr_decay=0.001, epoch=None, step=1):\n if epoch is None or step == 1 or (epoch+1) % step == 0:\n for param_group in optimizer.param_groups:\n param_group['lr'] *= (1 - lr_decay)\n return optimizer","def create_lr_scheduler(lr_scheduler_name: str, optimizer: torch.optim.Optimizer,\n lr_gamma: float, max_epochs: int) -> torch.optim.lr_scheduler._LRScheduler:\n if lr_scheduler_name.lower() == \"exponential\":\n lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, lr_gamma)\n elif lr_scheduler_name.lower() == \"linear\":\n lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: 1 - epoch / max_epochs)\n else:\n raise ValueError(\"SystemLog: Unknown learning rate scheduler {}\".format(lr_scheduler_name))\n\n return lr_scheduler","def _initScheduler(self) -> torch.optim.lr_scheduler.ReduceLROnPlateau:\n\n return torch.optim.lr_scheduler.ReduceLROnPlateau(\n self.optimizer, \n mode=cfg.training.scheduler_mode,\n factor=cfg.training.scheduler_factor,\n patience=cfg.training.scheduler_patience,\n threshold=cfg.training.scheduler_threshold\n )","def configure_optimizers(self) -> Tuple[List[optim.Optimizer], List[optim.lr_scheduler._LRScheduler]]:\n if (\n (\"weight_decay\" in self.optimizer_params)\n and (self.optimizer_params[\"weight_decay\"] != 0)\n and self.exclude_bn_bias\n ):\n defaults = copy(self.optimizer_params)\n weight_decay = defaults.pop(\"weight_decay\")\n\n wd_group = []\n nowd_group = []\n for name, tensor in self.named_parameters():\n if not tensor.requires_grad:\n continue\n if (\"bias\" in name) or (\"bn\" in name):\n nowd_group.append(tensor)\n else:\n wd_group.append(tensor)\n\n params = [\n {\"params\": wd_group, \"weight_decay\": weight_decay},\n {\"params\": nowd_group, \"weight_decay\": 0.0},\n ]\n optimizer = self.optimizer_class(params, **defaults)\n else:\n optimizer = self.optimizer_class(self.parameters(), **self.optimizer_params)\n lr_scheduler = self.lr_scheduler_class(optimizer, **self.lr_scheduler_params)\n return [optimizer], [lr_scheduler]","def configure_optimizer(learning_rate):\n\tif train_config['optimizer'] == 'adadelta':\n\t\toptimizer = tf.train.AdadeltaOptimizer(learning_rate,\n\t\t rho=train_config['adadelta_rho'],\n\t\t epsilon=train_config['opt_epsilon'])\n\telif train_config['optimizer'] == 'dadgrad':\n\t\toptimizer = tf.train.AdagradDAOptimizer(\n\t\t\tlearning_rate,\n\t\t\tinitial_gradient_squared_accumulator_value=train_config['adagrad_initial_accumulator_value'])\n\telif train_config['optimizer'] == 'adam':\n\t\toptimizer = tf.train.AdamOptimizer(\n\t\t\tlearning_rate,\n\t\t\tbeta1=train_config['adam_beta1'],\n\t\t\tbeta2=train_config['adam_beta2'],\n\t\t\tepsilon=train_config['opt_epsilon'])\n\telif train_config['optimizer'] == 'ftrl':\n\t\toptimizer = tf.train.FtrlOptimizer(\n\t\t\tlearning_rate,\n\t\t\tlearning_rate_power=train_config['ftrl_learning_rate_power'],\n\t\t\tinitial_accumulator_value=train_config['ftrl_initial_accumulator_value'],\n\t\t\tl1_regularization_strength=train_config['ftrl_l1'],\n\t\t\tl2_regularization_strength=train_config['ftrl_l2'])\n\telif train_config['optimizer'] == 'momentum':\n\t\toptimizer = tf.train.MomentumOptimizer(\n\t\t\tlearning_rate,\n\t\t\tmomentum=train_config['momentum'],\n\t\t\tname='Momentum')\n\telif train_config['optimizer'] == 'rmsprop':\n\t\toptimizer = tf.train.RMSPropOptimizer(\n\t\t\tlearning_rate,\n\t\t\tdecay=train_config['rmsprop_decay'],\n\t\t\tmomentum=train_config['rmsprop_momentum'],\n\t\t\tepsilon=train_config['opt_epsilon'])\n\telif train_config['optimizer'] == 'sgd':\n\t\toptimizer = tf.train.GradientDescentOptimizer(learning_rate)\n\telse:\n\t\traise ValueError('Optimizer [%s] was not recognized' % train_config['optimizer'])\n\treturn optimizer","def optimizer_factory(config, model):\n params = model.parameters()\n\n optimizer = config[\"loss\"].get(\"optimizer\", \"Adam\")\n lr = config[\"loss\"].get(\"lr\", 1e-3)\n momentum = config[\"loss\"].get(\"momentum\", 0.9)\n\n if optimizer == \"SGD\":\n return optim.SGD(params, lr=lr, momentum=momentum)\n elif optimizer == \"Adam\":\n return optim.Adam(params, lr=lr)\n else:\n raise NotImplementedError()","def get_optimizer(params, custom_scheduler=None):\n other_args = {}\n if \"clipnorm\" in params:\n other_args[\"clipnorm\"] = params.clipnorm\n if \"clipvalue\" in params:\n other_args[\"clipvalue\"] = params.clipvalue\n\n if params.lr_decay_policy == \"exp\":\n lr = tf.keras.optimizers.schedules.ExponentialDecay(params.lr,\n params.decay_steps,\n params.decay_rate,\n staircase=params.staircase)\n else:\n lr = params.init_lr\n if params.optimizer == \"adam\":\n beta_1 = params.get(\"beta_1\", 0.9)\n beta_2 = params.get(\"beta_2\", 0.999)\n optimizer = tf.keras.optimizers.Adam(lr, beta_1=beta_1, beta_2=beta_2, **other_args)\n elif params.optimizer == \"sgd\":\n momentum = params.get(\"momentum\", 0.9)\n optimizer = tf.keras.optimizers.SGD(lr, momentum=momentum, **other_args)\n else:\n raise ValueError(\"unsupported optimizer: {}\".format(params.optimizer))\n return optimizer","def exp_lr_scheduler(optimizer, epoch, init_lr, lr_decay_epoch):\n\n lr = init_lr * (0.1**((epoch-1) // lr_decay_epoch))\n\n if (epoch-1) % lr_decay_epoch == 0:\n print('LR is set to {}'.format(lr))\n # log_value('lr',lr,epoch)\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def add_optimizers_to_graph(self):\n with tf.device(self.params.device):\n with self.graph.as_default():\n with tf.compat.v1.variable_scope(\"optimizers\") as scope:\n self.grads_and_vars = list() # [sch_idx][weight_idx]\n self.apply_grads = list() # [sch_idx][weight_idx]\n self.learning_rates = list() # [sch_idx][weight_idx]\n if self.params.optimizer == \"lbfgsb\":\n self.minimizer = None\n #self.minimizer = tfp.optimizer.lbfgs_minimize(\n # value_and_gradients_function=self.loss_value_and_grad,#self.total_loss,\n # initial_position=self.w_init,#self.trainable_variables,\n # max_iterations=self.params.maxiter)\n #self.minimizer = tf.contrib.opt.ScipyOptimizerInterface(self.total_loss,\n # options={\"maxiter\":self.params.maxiter}) # Default method is L-BFGSB\n for schedule_idx, sch in enumerate(self.params.schedule):\n sch_grads_and_vars = list() # [weight_idx]\n sch_apply_grads = list() # [weight_idx]\n sch_lrs = list() # [weight_idx]\n #Construct weight ops\n weight_ops = [self.trainable_variables[weight] for weight in sch[\"weights\"]]\n for w_idx, weight in enumerate(sch[\"weights\"]):\n weight_name = weight.split(\"/\")[-1].split(\":\")[0]\n learning_rates = tf.compat.v1.train.exponential_decay(\n learning_rate=sch[\"weight_lr\"][w_idx],\n global_step=self.global_step,\n decay_steps=sch[\"decay_steps\"][w_idx],\n decay_rate=sch[\"decay_rate\"][w_idx],\n staircase=sch[\"staircase\"][w_idx],\n name=\"annealing_schedule_\"+weight_name)\n sch_lrs.append(learning_rates)\n if self.params.optimizer == \"sgd\":\n optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rates,\n name=\"grad_optimizer_\"+weight_name)\n elif self.params.optimizer == \"adam\":\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rates, beta1=0.9, beta2=0.99,\n epsilon=1e-07, name=\"adam_optimizer_\"+weight_name)\n elif self.params.optimizer == \"adadelta\":\n optimizer = tf.compat.v1.train.AdadeltaOptimizer(learning_rates, epsilon=1e-07,\n name=\"adadelta_optimizer_\"+weight_name)\n elif self.params.optimizer == \"lbfgsb\":\n optimizer = None\n else:\n assert False, (\"Optimizer \"+self.params.optimizer+\" is not supported.\")\n weight_op = self.trainable_variables[weight]\n sch_grads_and_vars.append(self.compute_weight_gradients(optimizer, weight_op))\n gstep = self.global_step if w_idx == 0 else None # Only increment once\n if self.params.optimizer == \"lbfgsb\": # BFGS doesn't actually need the update op\n if w_idx == 0:\n sch_apply_grads.append(tf.compat.v1.assign_add(self.global_step, 1))\n else:\n sch_apply_grads.append(None)\n else:\n sch_apply_grads.append(optimizer.apply_gradients(sch_grads_and_vars[w_idx],\n global_step=gstep))\n self.learning_rates.append(sch_lrs)\n self.grads_and_vars.append(sch_grads_and_vars)\n self.apply_grads.append(sch_apply_grads)\n self.optimizers_added = True","def __configure_optimizer(self, learning_rate):\n if self.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=self.adadelta_rho,\n epsilon=self.opt_epsilon)\n elif self.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=self.adagrad_initial_accumulator_value)\n elif self.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=self.adam_beta1,\n beta2=self.adam_beta2,\n epsilon=self.opt_epsilon)\n elif self.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=self.ftrl_learning_rate_power,\n initial_accumulator_value=self.ftrl_initial_accumulator_value,\n l1_regularization_strength=self.ftrl_l1,\n l2_regularization_strength=self.ftrl_l2)\n elif self.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=self.momentum,\n name='Momentum')\n elif self.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=self.rmsprop_decay,\n momentum=self.rmsprop_momentum,\n epsilon=self.opt_epsilon)\n elif self.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n else:\n raise ValueError('Optimizer [%s] was not recognized', self.optimizer)\n return optimizer","def get_lr_scheduler(self):\n try:\n scheduler_name = self.configs.OPTIM.LR_SCHEDULER.SCHEDULER_NAME\n except AttributeError:\n scheduler_name = None\n\n if scheduler_name is None:\n lr_scheduler = None\n\n elif scheduler_name == 'plateau':\n factor = self.configs.OPTIM.LR_SCHEDULER.FACTOR\n patience = self.configs.OPTIM.LR_SCHEDULER.PATIENCE\n\n lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer=self.optimizer,\n mode='min',\n factor=factor,\n patience=patience,\n verbose=False,\n threshold=1e-4,\n threshold_mode='rel',\n cooldown=0,\n min_lr=1e-6, # default min_lr is 0\n eps=1e-8,\n )\n\n elif scheduler_name == 'cyclic':\n base_lr = self.configs.OPTIM.LR_SCHEDULER.BASE_LR\n max_lr = self.configs.OPTIM.LR_SCHEDULER.MAX_LR\n\n lr_scheduler = torch.optim.lr_scheduler.CyclicLR(\n optimizer=self.optimizer,\n base_lr=base_lr,\n max_lr=max_lr,\n step_size_up=2000,\n step_size_down=None,\n mode='triangular',\n gamma=1.0,\n scale_fn=None,\n scale_mode='cycle',\n cycle_momentum=True,\n base_momentum=0.8,\n max_momentum=0.9,\n last_epoch=-1,\n )\n\n else:\n LOGGER.error('Invalid learning rate scheduler: %s', scheduler_name)\n raise NotImplementedError\n\n return lr_scheduler","def configure_optimizers(self):\n model = self.model\n no_decay = [\"bias\", \"LayerNorm.weight\"]\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n \"weight_decay\": self.hparams.weight_decay,\n },\n {\n \"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\n \"weight_decay\": 0.0,\n },\n ]\n if self.hparams.lamb:\n optimizer_reduced_precision_type = self.config.dtype if self.hparams.allreduce_post_accumulation_half_precision else None\n optimizer = FusedMixedPrecisionLamb(\n optimizer_grouped_parameters,\n lr=self.hparams.learning_rate,\n eps=self.hparams.adam_epsilon,\n max_grad_norm=self.hparams.gradient_clip_val,\n reduced_precision_dtype=optimizer_reduced_precision_type)\n elif self.hparams.allreduce_post_accumulation_half_precision:\n raise ValueError(\"--allreduce_post_accumulation_half_precision is only supported on LAMB optimizer\")\n elif self.hparams.adafactor:\n optimizer = Adafactor(\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False\n )\n else:\n optimizer = FusedAdam(\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\n self.opt = optimizer\n\n scheduler = self.get_lr_scheduler()\n\n return [optimizer], [scheduler]","def build_optimizer(\n self, lr: Union[tf.keras.optimizers.schedules.LearningRateSchedule,\n float]):\n\n optimizer_dict = self._optimizer_config.as_dict()\n optimizer_dict['learning_rate'] = lr\n\n optimizer = OPTIMIZERS_CLS[self._optimizer_type](**optimizer_dict)\n return optimizer","def fetch_optimizer(args, model):\n optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)\n\n scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps + 100,\n pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')\n\n return optimizer, scheduler","def build_optimizer(opt_config, learning_rate):\n if opt_config.opt_method == 'SGD':\n print('Using SGD as the optimizer', file=sys.stderr)\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n elif opt_config.opt_method == 'Adam':\n print('Using Adam as the optimizer', file=sys.stderr)\n optimizer = tf.train.AdamOptimizer(\n learning_rate, beta1=opt_config.adam_beta1,\n beta2=opt_config.adam_beta2, epsilon=opt_config.adam_epsilon\n )\n else:\n raise ValueError(\n 'Unknown optimization method {0}!'.format(opt_config.opt_method))\n return optimizer","def _build_optimizers(self):\r\n self._optimize_ops = []\r\n all_trainable_variables = tf.trainable_variables()\r\n all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\r\n all_reg_losses = tf.losses.get_regularization_losses()\r\n for spec in self._learning_schedule:\r\n optimize_ops = []\r\n update_ops = []\r\n loss_terms = spec['loss_terms_to_optimize']\r\n reg_losses = []\r\n assert isinstance(loss_terms, dict)\r\n for loss_term_key, prefixes in loss_terms.items():\r\n assert loss_term_key in self.loss_terms['train'].keys()\r\n variables_to_train = []\r\n for prefix in prefixes:\r\n variables_to_train += [\r\n v for v in all_trainable_variables\r\n if v.name.startswith(prefix)\r\n ]\r\n update_ops += [\r\n o for o in all_update_ops\r\n if o.name.startswith(prefix)\r\n ]\r\n reg_losses += [\r\n l for l in all_reg_losses\r\n if l.name.startswith(prefix)\r\n ]\r\n\r\n optimizer_class = tf.train.AdamOptimizer\r\n optimizer = optimizer_class(\r\n learning_rate=self.learning_rate_multiplier * spec['learning_rate'],\r\n # beta1=0.9,\r\n # beta2=0.999,\r\n )\r\n final_loss = self.loss_terms['train'][loss_term_key]\r\n if len(reg_losses) > 0:\r\n final_loss += tf.reduce_sum(reg_losses)\r\n with tf.control_dependencies(update_ops):\r\n gradients, variables = zip(*optimizer.compute_gradients(\r\n loss=final_loss,\r\n var_list=variables_to_train,\r\n aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N,\r\n ))\r\n # gradients, _ = tf.clip_by_global_norm(gradients, 5.0) # TODO: generalize\r\n optimize_op = optimizer.apply_gradients(zip(gradients, variables))\r\n optimize_ops.append(optimize_op)\r\n self._optimize_ops.append(optimize_ops)\r\n logger.info('Built optimizer for: %s' % ', '.join(loss_terms.keys()))","def adjust_learning_rate_by_schedule(config, optimizer, epoch, decrease_rate=0.1):\n if not isinstance(optimizer, torch.optim.SGD):\n return\n #lr = config.lr * (0.1 ** (epoch // 10))\n if epoch and epoch % 10 == 0:\n for i, param_group in enumerate(optimizer.param_groups):\n param_group['lr'] *= decrease_rate\n logger.info('Setting learning layer=i, rate=%.6f', i, param_group['lr'])","def optimizer_creator(model, config):\n return torch.optim.SGD(model.parameters(), lr=config.get(\"lr\", 1e-4))","def lr_scheduler(self, lr_init, global_step):\n pass","def create_learning_rate_scheduler(\n factors: str = 'constant * linear_warmup * rsqrt_decay',\n base_learning_rate: float = 0.5,\n warmup_steps: int = 1000,\n decay_factor: float = 0.5,\n steps_per_decay: int = 20000,\n steps_per_cycle: int = 100000,\n step_offset: int = 0,\n min_learning_rate: float = 1e-8) -> LearningRateCallable:\n factors = [n.strip() for n in factors.split('*')]\n\n def step_fn(step: jnp.ndarray) -> jnp.ndarray:\n \"\"\"Step to learning rate function.\"\"\"\n step = jnp.maximum(0, step - step_offset)\n ret = 1.0\n for name in factors:\n if name == 'constant':\n ret *= base_learning_rate\n elif name == 'linear_warmup':\n ret *= jnp.minimum(1.0, step / warmup_steps)\n elif name == 'linear_decay':\n ret *= base_learning_rate * jnp.minimum(\n step / warmup_steps, 1.0 + decay_factor * (warmup_steps - step))\n elif name == 'rsqrt_decay':\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\n elif name == 'rsqrt_normalized_decay':\n ret *= jnp.sqrt(warmup_steps)\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\n elif name == 'decay_every':\n ret *= (decay_factor**(step // steps_per_decay))\n elif name == 'cosine_decay':\n progress = jnp.maximum(0.0,\n (step - warmup_steps) / float(steps_per_cycle))\n ret *= jnp.maximum(0.0,\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\n else:\n raise ValueError('Unknown factor %s.' % name)\n ret = jnp.maximum(ret, min_learning_rate)\n return jnp.asarray(ret, dtype=jnp.float32)\n\n return step_fn","def exp_lr_scheduler(optimizer, epoch, lr_decay_epoch=100,lr_decay_factor=0.5):\n init_lr = optimizer.param_groups[0]['lr']\n if epoch > 0 and (epoch % lr_decay_epoch == 0):\n lr = init_lr*lr_decay_factor\n print('\\n LR is set to {}'.format(lr))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n\n return optimizer","def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):\n global_step = tf.compat.v1.train.get_or_create_global_step()\n\n learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)\n\n # Implements linear decay of the learning rate.\n learning_rate = tf.compat.v1.train.polynomial_decay(\n learning_rate,\n global_step,\n num_train_steps,\n end_learning_rate=0.0,\n power=1.0,\n cycle=False)\n\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\n if num_warmup_steps:\n global_steps_int = tf.cast(global_step, tf.int32)\n warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)\n\n global_steps_float = tf.cast(global_steps_int, tf.float32)\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\n\n warmup_percent_done = global_steps_float / warmup_steps_float\n warmup_learning_rate = init_lr * warmup_percent_done\n\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\n learning_rate = (\n (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)\n\n # It is recommended that you use this optimizer for fine tuning, since this\n # is how the model was trained (note that the Adam m/v variables are NOT\n # loaded from init_checkpoint.)\n optimizer = AdamWeightDecayOptimizer(\n learning_rate=learning_rate,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n\n if use_tpu:\n optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\n\n tvars = tf.compat.v1.trainable_variables()\n grads = tf.gradients(loss, tvars)\n\n # This is how the model was pre-trained.\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)\n\n train_op = optimizer.apply_gradients(\n zip(grads, tvars), global_step=global_step)\n\n # Normally the global step update is done inside of `apply_gradients`.\n # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use\n # a different optimizer, you should probably take this line out.\n new_global_step = global_step + 1\n train_op = tf.group(train_op, [global_step.assign(new_global_step)])\n return train_op","def create_custom_optimizer(tvars, loss, bert_init_lr, task_init_lr, num_train_steps, num_warmup_steps, use_tpu, global_step=None, freeze=-1, task_opt='adam', eps=1e-6):\n if global_step is None:\n global_step = tf.train.get_or_create_global_step()\n\n bert_learning_rate = tf.constant(value=bert_init_lr, shape=[], dtype=tf.float32)\n task_learning_rate = tf.constant(value=task_init_lr, shape=[], dtype=tf.float32)\n\n # Implements linear decay of the learning rate.\n bert_learning_rate = tf.train.polynomial_decay(\n bert_learning_rate,\n global_step,\n num_train_steps,\n end_learning_rate=0.0,\n power=1.0,\n cycle=False)\n task_learning_rate = tf.train.polynomial_decay(\n task_learning_rate,\n global_step,\n num_train_steps,\n end_learning_rate=0.0,\n power=1.0,\n cycle=False)\n\n ## TODO DEBUG\n print(f'type task learning rate: {type(task_learning_rate)} ##################')\n\n\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\n if num_warmup_steps:\n global_steps_int = tf.cast(global_step, tf.int32)\n warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)\n\n global_steps_float = tf.cast(global_steps_int, tf.float32)\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\n\n warmup_percent_done = global_steps_float / warmup_steps_float\n bert_warmup_learning_rate = bert_init_lr * warmup_percent_done\n task_warmup_learning_rate = task_init_lr * warmup_percent_done\n\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\n bert_learning_rate = (\n (1.0 - is_warmup) * bert_learning_rate + is_warmup * bert_warmup_learning_rate)\n\n # It is recommended that you use this optimizer for fine tuning, since this\n # is how the model was trained (note that the Adam m/v variables are NOT\n # loaded from init_checkpoint.)\n bert_optimizer = AdamWeightDecayOptimizer(\n learning_rate=bert_learning_rate,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=eps,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n if task_opt == 'adam_weight_decay':\n task_optimizer = AdamWeightDecayOptimizer(\n learning_rate=task_learning_rate,\n weight_decay_rate=0.01,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=eps\n )\n elif task_opt == 'adam':\n task_optimizer = tf.train.AdamOptimizer(\n learning_rate=task_learning_rate)\n else:\n raise NotImplementedError('Check optimizer. {} is invalid.'.format(task_opt))\n\n # tvars = tf.trainable_variables()\n bert_vars, task_vars = [], []\n for var in tvars:\n if var.name.startswith('bert'):\n can_optimize = False\n if var.name.startswith('bert/encoder/layer_') and int(var.name.split('/')[2][len('layer_'):]) >= freeze:\n can_optimize = True\n if freeze == -1 or can_optimize:\n bert_vars.append(var)\n else:\n task_vars.append(var)\n print('bert:task', len(bert_vars), len(task_vars))\n grads = tf.gradients(loss, bert_vars + task_vars)\n bert_grads = grads[:len(bert_vars)]\n task_grads = grads[len(bert_vars):]\n\n # This is how the model was pre-trained.\n (bert_grads, _) = tf.clip_by_global_norm(bert_grads, clip_norm=1.0)\n (task_grads, _) = tf.clip_by_global_norm(task_grads, clip_norm=1.0)\n\n # global_step1 = tf.Print(global_step, [global_step], 'before')\n bert_train_op = bert_optimizer.apply_gradients(\n zip(bert_grads, bert_vars), global_step=global_step)\n task_train_op = task_optimizer.apply_gradients(\n zip(task_grads, task_vars), global_step=global_step)\n if task_opt == 'adam_weight_decay':\n new_global_step = global_step + 1\n train_op = tf.group(bert_train_op, task_train_op, [global_step.assign(new_global_step)])\n else:\n train_op = tf.group(bert_train_op, task_train_op)\n return train_op","def setup_optimiser(self):\n self.optimiser = ScheduledOptim(\n optim.Adam(\n filter(lambda x: x.requires_grad, self.model.parameters()),\n betas=(0.9, 0.98), eps=1e-09, lr=self.opt.learning_rate),\n self.opt.d_model, self.opt.n_warmup_steps)\n if self.opt.verbose:\n print(\"[Info] optimiser configured.\")","def adjust_learning_rate(args,optimizer, epoch):\n lr = args.lr\n schedule = args.lr_schedule\n # schedule from TRADES repo (different from paper due to bug there)\n if schedule == 'trades':\n if epoch >= 0.75 * args.epochs:\n lr = args.lr * 0.1\n # schedule as in TRADES paper\n elif schedule == 'trades_fixed':\n if epoch >= 0.75 * args.epochs:\n lr = args.lr * 0.1\n if epoch >= 0.9 * args.epochs:\n lr = args.lr * 0.01\n if epoch >= args.epochs:\n lr = args.lr * 0.001\n # cosine schedule\n elif schedule == 'cosine':\n lr = args.lr * 0.5 * (1 + np.cos((epoch - 1) / args.epochs * np.pi))\n # schedule as in WRN paper\n elif schedule == 'wrn':\n if epoch >= 0.3 * args.epochs:\n lr = args.lr * 0.2\n if epoch >= 0.6 * args.epochs:\n lr = args.lr * 0.2 * 0.2\n if epoch >= 0.8 * args.epochs:\n lr = args.lr * 0.2 * 0.2 * 0.2\n else:\n raise ValueError('Unkown LR schedule %s' % schedule)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def _create_optimizer(self):\n\n with tf.name_scope(\"optimizer\"):\n self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)","def get_scheduler(optimizer, recipe):\n\n # <optimizer>, \"stages__*__lr_scheduler\"\n return get_instance(optimizer, **recipe)","def exp_lr_scheduler(optimizer, epoch, lr_decay=0.1, lr_decay_epoch=7):\n if epoch % lr_decay_epoch or epoch>35:\n return optimizer\n \n for param_group in optimizer.param_groups:\n param_group['lr'] *= lr_decay\n return optimizer","def configure_optimizers(self):\n\n optimizer = self.optimizer(self.parameters(), lr=self.lr)\n\n return optimizer","def adam_optim(config = None, global_step = None):\n learning_rate = config[\"learning_rate\"]\n\n beta1 = config.get('beta1', 0.9)\n beta2 = config.get('beta2', 0.999)\n epsilon = config.get('epsilon', 1e-8)\n \n train_step = tf.train.AdamOptimizer(learning_rate, beta1, beta2, epsilon)\n\n return train_step","def configure_optimizers(self):\n optimizer = torch.optim.Adam(\n self.parameters(), lr=self.hparams[\"learning_rate\"]\n )\n return optimizer","def rnn_learning_rate_schedule(dataset):\n model, prefix = make_rnn(non_negative=True)\n # model.set_params(**{prefix + \"val\": 0.1})\n\n # base = no schedule\n print(\"Baseline RNN\")\n # model.set_params(**{prefix + \"history_file\": \"nn_default.csv\"})\n cross_validate(dataset, model)\n # model.fit(dataset.inputs, dataset.outputs)\n\n # higher init, decay set to reach the same at 40 epochs\n print(\"RNN with decay\")\n model.set_params(**{prefix + \"lr_decay\": \"DecreasingLearningRateScheduler\"})\n # model.fit(dataset.inputs, dataset.outputs)\n cross_validate(dataset, model)","def create_learning_rate_scheduler(\n base_learning_rate=0.5,\n factors='constant * linear_warmup * rsqrt_normalized_decay',\n warmup_steps=16000,\n decay_factor=0.5,\n steps_per_decay=50000,\n steps_per_cycle=100000):\n factors = [n.strip() for n in factors.split('*')]\n\n def step_fn(step):\n \"\"\"Step to learning rate function.\"\"\"\n ret = 1.0\n for name in factors:\n if name == 'constant':\n ret *= base_learning_rate\n elif name == 'linear_warmup':\n ret *= jnp.minimum(1.0, step / warmup_steps)\n elif name == 'rsqrt_decay':\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\n elif name == 'rsqrt_normalized_decay':\n ret *= jnp.sqrt(warmup_steps)\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\n elif name == 'decay_every':\n ret *= (decay_factor**(step // steps_per_decay))\n elif name == 'cosine_decay':\n progress = jnp.maximum(0.0,\n (step - warmup_steps) / float(steps_per_cycle))\n ret *= jnp.maximum(0.0,\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\n else:\n raise ValueError('Unknown factor %s.' % name)\n return jnp.asarray(ret, dtype=jnp.float32)\n\n return step_fn","def update_learning_rate(self) -> None:\n optimizer = list(self.optimizers.values())[0]\n old_lr = optimizer.param_groups[0]['lr']\n for name, scheduler in self.schedulers.items():\n if name == 'generator' and self.opt.generator_scheduler_name == 'plateau':\n scheduler.step(self.metric)\n elif name == 'discriminator' and self.opt.discriminator_scheduler_name == 'plateau':\n scheduler.step(self.metric)\n else:\n scheduler.step()\n\n lr = optimizer.param_groups[0]['lr']\n print('learning rate %.7f -> %.7f' % (old_lr, lr))\n return","def _adjust_learning_rate_resnet(optimizer, epoch):\n\n if epoch == 90:\n return lr_scheduler.MultiStepLR(optimizer, [30, 60, 80])\n elif epoch == 270: # autoaugment\n return lr_scheduler.MultiStepLR(optimizer, [90, 180, 240])\n else:\n raise ValueError('invalid epoch=%d for resnet scheduler' % epoch)","def configure_optimizer(learning_rate):\n if FLAGS.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=FLAGS.adadelta_rho,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\n elif FLAGS.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=FLAGS.adam_beta1,\n beta2=FLAGS.adam_beta2,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\n l1_regularization_strength=FLAGS.ftrl_l1,\n l2_regularization_strength=FLAGS.ftrl_l2)\n elif FLAGS.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=FLAGS.momentum,\n name='Momentum')\n elif FLAGS.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=FLAGS.rmsprop_decay,\n momentum=FLAGS.rmsprop_momentum,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n elif FLAGS.optimizer == \"adamweightdecay\":\n optimizer = AdamWeightDecayOptimizer(\n learning_rate=learning_rate,\n weight_decay_rate=0.01,\n beta_1=FLAGS.adam_beta1,\n beta_2=FLAGS.adam_beta2,\n epsilon=FLAGS.opt_epsilon,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n else:\n raise ValueError('Optimizer [%s] was not recognized' % FLAGS.optimizer)\n return optimizer","def configure_optimizers(self):\n optimizer = torch.optim.Adam(\n self.parameters(),\n lr=self.hparams.learning_rate,\n weight_decay=self.hparams.weight_decay,\n )\n return optimizer","def configure_optimizer(learning_rate):\r\n if FLAGS.optimizer == 'adadelta':\r\n optimizer = tf.train.AdadeltaOptimizer(learning_rate, \r\n rho=FLAGS.adadelta_rho,epsilon=FLAGS.opt_epsilon)\r\n elif FLAGS.optimizer == 'adagrad':\r\n optimizer = tf.train.AdagradOptimizer(learning_rate,\r\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\r\n elif FLAGS.optimizer == 'adam':\r\n optimizer = tf.train.AdamOptimizer(learning_rate,\r\n beta1=FLAGS.adam_beta1,beta2=FLAGS.adam_beta2,epsilon=FLAGS.opt_epsilon)\r\n elif FLAGS.optimizer == 'ftrl':\r\n optimizer = tf.train.FtrlOptimizer(learning_rate,learning_rate_power=FLAGS.ftrl_learning_rate_power,\r\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\r\n l1_regularization_strength=FLAGS.ftrl_l1,l2_regularization_strength=FLAGS.ftrl_l2)\r\n elif FLAGS.optimizer == 'momentum':\r\n optimizer = tf.train.MomentumOptimizer(learning_rate,\r\n momentum=FLAGS.momentum,name='Momentum')\r\n elif FLAGS.optimizer == 'rmsprop':\r\n optimizer = tf.train.RMSPropOptimizer(learning_rate,decay=FLAGS.rmsprop_decay,\r\n momentum=FLAGS.rmsprop_momentum,epsilon=FLAGS.opt_epsilon)\r\n elif FLAGS.optimizer == 'sgd':\r\n optimizer = tf.train.GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)\r\n else:\r\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\r\n return optimizer","def update_learning_rate(self):\n for scheduler in self.schedulers:\n if self.opt.lr_policy == 'plateau':\n scheduler.step(self.metric)\n else:\n scheduler.step()\n\n lr = self.optimizers[0].param_groups[0]['lr']\n print('learning rate = %.7f' % lr)","def configure_optimizers(self):\n optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n return optimizer","def configure_optimizer(learning_rate):\n if hp.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=hp.adadelta_rho,\n epsilon=hp.opt_epsilon)\n elif hp.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=hp.adagrad_initial_accumulator_value)\n elif hp.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=hp.adam_beta1,\n beta2=hp.adam_beta2,\n epsilon=hp.opt_epsilon)\n elif hp.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=hp.ftrl_learning_rate_power,\n initial_accumulator_value=hp.ftrl_initial_accumulator_value,\n l1_regularization_strength=hp.ftrl_l1,\n l2_regularization_strength=hp.ftrl_l2)\n elif hp.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=hp.momentum,\n name='Momentum')\n elif hp.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=hp.rmsprop_decay,\n momentum=hp.rmsprop_momentum,\n epsilon=hp.opt_epsilon)\n elif hp.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n else:\n raise ValueError('Optimizer [%s] was not recognized', hp.optimizer)\n return optimizer","def _update_initial_learning_rate(configs, learning_rate):\n\n optimizer_type = get_optimizer_type(configs[\"train_config\"])\n if optimizer_type == \"rms_prop_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.rms_prop_optimizer\n elif optimizer_type == \"momentum_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.momentum_optimizer\n elif optimizer_type == \"adam_optimizer\":\n optimizer_config = configs[\"train_config\"].optimizer.adam_optimizer\n else:\n raise TypeError(\"Optimizer %s is not supported.\" % optimizer_type)\n\n learning_rate_type = get_learning_rate_type(optimizer_config)\n if learning_rate_type == \"constant_learning_rate\":\n constant_lr = optimizer_config.learning_rate.constant_learning_rate\n constant_lr.learning_rate = learning_rate\n elif learning_rate_type == \"exponential_decay_learning_rate\":\n exponential_lr = (\n optimizer_config.learning_rate.exponential_decay_learning_rate)\n exponential_lr.initial_learning_rate = learning_rate\n elif learning_rate_type == \"manual_step_learning_rate\":\n manual_lr = optimizer_config.learning_rate.manual_step_learning_rate\n original_learning_rate = manual_lr.initial_learning_rate\n learning_rate_scaling = float(learning_rate) / original_learning_rate\n manual_lr.initial_learning_rate = learning_rate\n for schedule in manual_lr.schedule:\n schedule.learning_rate *= learning_rate_scaling\n elif learning_rate_type == \"cosine_decay_learning_rate\":\n cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate\n learning_rate_base = cosine_lr.learning_rate_base\n warmup_learning_rate = cosine_lr.warmup_learning_rate\n warmup_scale_factor = warmup_learning_rate / learning_rate_base\n cosine_lr.learning_rate_base = learning_rate\n cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate\n else:\n raise TypeError(\"Learning rate %s is not supported.\" % learning_rate_type)","def _configure_optimizer(learning_rate):\n if FLAGS.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=FLAGS.adadelta_rho,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\n elif FLAGS.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=FLAGS.adam_beta1,\n beta2=FLAGS.adam_beta2,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\n l1_regularization_strength=FLAGS.ftrl_l1,\n l2_regularization_strength=FLAGS.ftrl_l2)\n elif FLAGS.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=FLAGS.momentum,\n name='Momentum')\n elif FLAGS.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=FLAGS.rmsprop_decay,\n momentum=FLAGS.rmsprop_momentum,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n else:\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\n return optimizer","def adjust_learning_rate(opt, optimizer, epoch):\n epoch = copy.deepcopy(epoch)\n lr = opt.maxlr\n wd = opt.weightDecay\n if opt.learningratescheduler == 'imagenetscheduler':\n if epoch >= 1 and epoch <= 18:\n lr = 1e-3\n wd = 5e-5\n elif epoch >= 19 and epoch <= 29:\n lr = 5e-4\n wd = 5e-5\n elif epoch >= 30 and epoch <= 43:\n lr = 1e-4\n wd = 0\n elif epoch >= 44 and epoch <= 52:\n lr = 5e-5\n wd = 0\n elif epoch >= 53:\n lr = 2e-5\n wd = 0\n if opt.optimType=='sgd':\n lr *= 10\n opt.lr = lr\n opt.weightDecay = wd\n if opt.learningratescheduler == 'decayscheduler':\n while epoch >= opt.decayinterval:\n lr = lr/opt.decaylevel\n epoch = epoch - opt.decayinterval\n lr = max(lr,opt.minlr)\n opt.lr = lr\n\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n param_group['weight_decay'] = wd","def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n print('learning rate is set to: ', lr)\n return optimizer","def adjust_learning_rate(cfg, optimizer):\n for idx, group in enumerate(optimizer.param_groups):\n init_lr = cfg.TRAINING.LR\n if 'step' not in group:\n group['step'] = 0.\n else:\n group['step'] += 1.\n\n group['lr'] = init_lr * (\n 1.0 - float(group['step']) * float(cfg.TRAINING.BATCH_SIZE) /\n (cfg.TRAINING.N_TRIPLETS * float(cfg.TRAINING.EPOCHS)))\n return","def train_scheduler(self, tau, Tau):\n # SAC-U doesn't have a trained scheduler\n pass","def get_optimizer(start_epoch, factor, opt_config, model):\n optimizer = __optimizers[opt_config['optimizer']]\n opt_params = {'parameter_list': model.parameters()}\n for key in opt_config.keys():\n if key == 'learning_rate':\n lr = opt_config[key]\n bo = [val*factor for val in lr['bound']]\n decay_lr = fluid.dygraph.PiecewiseDecay(bo, lr['value'], start_epoch*factor)\n opt_params['learning_rate'] = decay_lr\n elif key == 'weight_decay':\n regularization = fluid.regularizer.L2Decay(regularization_coeff=opt_config[key])\n opt_params['regularization'] = regularization\n elif key == 'momentum':\n opt_params['momentum'] = opt_config[key]\n\n return optimizer(**opt_params)","def get_lr_scheduler(scheduler_type: str,\n updates_per_checkpoint: int,\n learning_rate_half_life: int,\n learning_rate_reduce_factor: float,\n learning_rate_reduce_num_not_improved: int) -> Optional[LearningRateScheduler]:\n if scheduler_type is None:\n return None\n if scheduler_type == \"fixed-rate-inv-sqrt-t\":\n return LearningRateSchedulerInvSqrtT(updates_per_checkpoint, learning_rate_half_life)\n elif scheduler_type == \"fixed-rate-inv-t\":\n return LearningRateSchedulerInvT(updates_per_checkpoint, learning_rate_half_life)\n elif scheduler_type == \"plateau-reduce\":\n check_condition(learning_rate_reduce_factor is not None,\n \"learning_rate_reduce_factor needed for plateau-reduce scheduler\")\n check_condition(learning_rate_reduce_num_not_improved is not None,\n \"learning_rate_reduce_num_not_improved needed for plateau-reduce scheduler\")\n if learning_rate_reduce_factor >= 1.0:\n logger.warning(\"Not using plateau-reduce learning rate scheduling: learning_rate_reduce_factor == 1.0\")\n return None\n return LearningRateSchedulerPlateauReduce(learning_rate_reduce_factor, learning_rate_reduce_num_not_improved)\n else:\n raise ValueError(\"Unknown learning rate scheduler type %s.\" % scheduler_type)","def _configure_optimizer(learning_rate):\n if FLAGS.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=FLAGS.adadelta_rho,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\n elif FLAGS.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=FLAGS.adam_beta1,\n beta2=FLAGS.adam_beta2,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\n l1_regularization_strength=FLAGS.ftrl_l1,\n l2_regularization_strength=FLAGS.ftrl_l2)\n elif FLAGS.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=FLAGS.momentum,\n name='Momentum')\n elif FLAGS.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=FLAGS.rmsprop_decay,\n momentum=FLAGS.rmsprop_momentum,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n else:\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\n return optimizer","def _configure_optimizer(learning_rate):\n if FLAGS.optimizer == 'adadelta':\n optimizer = tf.train.AdadeltaOptimizer(\n learning_rate,\n rho=FLAGS.adadelta_rho,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(\n learning_rate,\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\n elif FLAGS.optimizer == 'adam':\n optimizer = tf.train.AdamOptimizer(\n learning_rate,\n beta1=FLAGS.adam_beta1,\n beta2=FLAGS.adam_beta2,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'ftrl':\n optimizer = tf.train.FtrlOptimizer(\n learning_rate,\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\n l1_regularization_strength=FLAGS.ftrl_l1,\n l2_regularization_strength=FLAGS.ftrl_l2)\n elif FLAGS.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(\n learning_rate,\n momentum=FLAGS.momentum,\n use_nesterov=True,\n name='Momentum')\n elif FLAGS.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(\n learning_rate,\n decay=FLAGS.rmsprop_decay,\n momentum=FLAGS.momentum,\n epsilon=FLAGS.opt_epsilon)\n elif FLAGS.optimizer == 'sgd':\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n else:\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\n return optimizer","def init_optimizer(network, config):\n # define optimizer and loss\n if config.optimizer == 'adadelta':\n opt = torch.optim.Adadelta(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\n elif config.optimizer == 'adam':\n opt = torch.optim.Adam(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\n elif config.optimizer == 'rmsprop':\n opt = torch.optim.RMSprop(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\n return opt","def sgd_optim(config = None, global_step = None):\n learning_rate = config[\"learning_rate\"]\n \n train_step = tf.train.GradientDescentOptimizer(learning_rate)\n #train_step = tf.train.GradientDescentOptimizer(learning_rate)\n return train_step","def adjust_learning_rate(optimizer, epoch):\n lr = config.optimizer.lr\n schedule = config.lr_schedule if hasattr(config, 'lr_schedule') else 'fixed'\n if schedule == 'fixed':\n if epoch >= 0.75 * config.epochs:\n lr = config.optimizer.lr * 0.1\n if epoch >= 0.9 * config.epochs:\n lr = config.optimizer.lr * 0.01\n if epoch >= config.epochs:\n lr = config.optimizer.lr * 0.001\n # cosine schedule\n elif schedule == 'cosine':\n lr = config.optimizer.lr * 0.5 * (1 + np.cos((epoch - 1) / config.epochs * np.pi))\n elif schedule == 'search':\n if epoch >= 75:\n lr = 0.01\n if epoch >= 90:\n lr = 0.001\n else:\n raise ValueError('Unkown LR schedule %s' % schedule)\n for param_group in optimizer.param_groups:\n param_group['lr'] = lr\n return lr","def create_optimizer(\n loss, learning_rate, num_train_steps, weight_decay_rate=0.0, use_tpu=False,\n warmup_steps=0, warmup_proportion=0, lr_decay_power=1.0,\n layerwise_lr_decay_power=-1, n_transformer_layers=None, name=\"adamw\", var_map=None):\n global_step = tf.train.get_or_create_global_step()\n learning_rate = tf.train.polynomial_decay(\n learning_rate,\n global_step,\n num_train_steps,\n end_learning_rate=0.0,\n power=lr_decay_power,\n cycle=False)\n warmup_steps = max(num_train_steps * warmup_proportion, warmup_steps)\n learning_rate *= tf.minimum(\n 1.0, tf.cast(global_step, tf.float32) / tf.cast(warmup_steps, tf.float32))\n\n if layerwise_lr_decay_power > 0:\n learning_rate = _get_layer_lrs(learning_rate, layerwise_lr_decay_power,\n n_transformer_layers)\n if name == \"recadam\":\n optimizer = RecAdamOptimizer(\n learning_rate=learning_rate,\n weight_decay_rate=weight_decay_rate,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"],\n anneal_k=0.5,\n anneal_t0=500,\n anneal_w=1.0,\n pretrain_cof=5000.0,\n pretrain_params=var_map\n )\n else:\n optimizer = AdamWeightDecayOptimizer(\n learning_rate=learning_rate,\n weight_decay_rate=weight_decay_rate,\n beta_1=0.9,\n beta_2=0.999,\n epsilon=1e-6,\n exclude_from_weight_decay=[\"LayerNorm\", \"layer_norm\", \"bias\"])\n if use_tpu:\n optimizer = tf.tpu.CrossShardOptimizer(optimizer)\n\n tvars = tf.trainable_variables()\n grads = tf.gradients(loss, tvars)\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)\n train_op = optimizer.apply_gradients(\n zip(grads, tvars), global_step=global_step)\n new_global_step = global_step + 1\n train_op = tf.group(train_op, [global_step.assign(new_global_step)])\n return train_op"],"string":"[\n \"def scheduler_creator(optimizer, config):\\n return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)\",\n \"def build(optimizer_config, optimizer, total_step):\\n optimizer_type = optimizer_config.WhichOneof('optimizer')\\n\\n if optimizer_type == 'rms_prop_optimizer':\\n config = optimizer_config.rms_prop_optimizer\\n lr_scheduler = _create_learning_rate_scheduler(\\n config.learning_rate, optimizer, total_step=total_step)\\n\\n if optimizer_type == 'momentum_optimizer':\\n config = optimizer_config.momentum_optimizer\\n lr_scheduler = _create_learning_rate_scheduler(\\n config.learning_rate, optimizer, total_step=total_step)\\n\\n if optimizer_type == 'adam_optimizer':\\n config = optimizer_config.adam_optimizer\\n lr_scheduler = _create_learning_rate_scheduler(\\n config.learning_rate, optimizer, total_step=total_step)\\n\\n return lr_scheduler\",\n \"def _create_learning_rate_scheduler(learning_rate_config, optimizer, last_step=-1):\\n lr_scheduler = None\\n learning_rate_type = learning_rate_config.name \\n if learning_rate_type == 'constant_learning_rate': \\n\\n lr_scheduler = learning_schedules.Constant(\\n optimizer, last_step=last_step)\\n\\n if learning_rate_type == 'exponential_decay_learning_rate':\\n config = learning_rate_config \\n lr_scheduler = learning_schedules.ExponentialDecay(\\n optimizer, config.decay_steps, \\n config.decay_factor, config.staircase, last_step=last_step)\\n\\n if learning_rate_type == 'manual_step_learning_rate':\\n config = learning_rate_config\\n if not config.schedule:\\n raise ValueError('Empty learning rate schedule.')\\n learning_rate_step_boundaries = [x.step for x in config.schedule]\\n learning_rate_sequence = [config.initial_learning_rate]\\n learning_rate_sequence += [x.learning_rate for x in config.schedule]\\n lr_scheduler = learning_schedules.ManualStepping(\\n optimizer, learning_rate_step_boundaries, learning_rate_sequence, \\n last_step=last_step)\\n\\n if learning_rate_type == 'cosine_decay_learning_rate':\\n config = learning_rate_config.cosine_decay_learning_rate\\n lr_scheduler = learning_schedules.CosineDecayWithWarmup(\\n optimizer, config.total_steps, \\n config.warmup_learning_rate, config.warmup_steps, \\n last_step=last_step)\\n\\n if lr_scheduler is None:\\n raise ValueError('Learning_rate %s not supported.' % learning_rate_type)\\n\\n return lr_scheduler\",\n \"def get_scheduler(optimizer, opt):\\n \\n epochs_no_decay = opt.epochs - opt.lr_linear\\n lr_policy = opt.lr_policy\\n \\n if lr_policy == 'linear':\\n def lr_lambda(epoch):\\n return 1. - max(0, epoch - epochs_no_decay) / float(opt.lr_linear + 1)\\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)\\n elif lr_policy == 'step':\\n # multiply by gamma every lr_decay_steps\\n # for example lr_decay_steps=50 and initial learning = .5\\n # then we have \\n # lr = .5 for 0 <= epoch < 50;\\n # lr = .05 for 50 <= epoch < 100;\\n # lr = .005 for 100 <= epoch < 150;\\n scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_step, gamma=.1)\\n elif lr_policy == 'plateau':\\n # Reduce learning rate when a metric has stopped improving. \\n # Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. \\n # This scheduler reads a metrics quantity and if no improvement \\n # is seen for a ‘patience’ number of epochs, \\n # the learning rate is reduced.\\n # Parameters\\n # - mode (str, default=min): In `min` mode, lr will be reduced when the quantity monitored has stopped decreasing; \\n # in `max` mode, lr will be reduced when the quantity monitored has stopped increasing.\\n # - factor (float, default=.1): Factor by which the learning rate will be reduced. new_lr = lr * factor.\\n # - patience (int, default=10): Number of epochs with no improvement after which learning rate will be reduced. \\n # - threshold (float): only decrease lr if the change in the quantitiy monitored is smaller than threshold. \\n # Say we have threshold=0.001, if loss is $18.0$ on epoch $n$ and loss is $17.9999$ on epoch $n+1$,\\n # then multiply current learning rate by the factor.\\n # On the contrary, if the loss is 17.99, lr doesn't have to be changed.\\n scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=.2, threshold=.01, patience=5)\\n else:\\n return NotImplementedError(f'learning rate policy {lr_policy} is not implemented')\\n return scheduler\",\n \"def configure_optimizers(self):\\n allowed = list(OPTIM_LOOKUP.keys())\\n if self.optimizer not in allowed:\\n raise ValueError(\\n f\\\"Illegal optimizer given. Got {self.optimizer}. Allowed: {allowed}.\\\"\\n )\\n\\n allowed = list(SCHED_LOOKUP.keys())\\n if self.scheduler not in allowed:\\n raise ValueError(\\n f\\\"Illegal scheduler given. Got {self.scheduler}. Allowed: {allowed}.\\\"\\n )\\n\\n if self.optim_params is None:\\n self.optim_params = {\\n \\\"encoder\\\": {\\\"lr\\\": 0.00005, \\\"weight_decay\\\": 0.00005},\\n \\\"decoder\\\": {\\\"lr\\\": 0.0005, \\\"weight_decay\\\": 0.0005},\\n }\\n\\n params = adjust_optim_params(self.model, self.optim_params)\\n optimizer = OPTIM_LOOKUP[self.optimizer](params)\\n\\n if self.lookahead:\\n optimizer = OPTIM_LOOKUP[\\\"lookahead\\\"](optimizer, k=5, alpha=0.5)\\n\\n if self.scheduler_params is None:\\n self.scheduler_params = {}\\n\\n scheduler = {\\n \\\"scheduler\\\": SCHED_LOOKUP[self.scheduler](\\n optimizer, **self.scheduler_params\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n \\\"interval\\\": \\\"epoch\\\",\\n \\\"frequency\\\": 1,\\n }\\n\\n return [optimizer], [scheduler]\",\n \"def configure_optimizers(self):\\n self.optimizer = AdamW(self.parameters(), lr=self.args[\\\"lr\\\"])\\n self.scheduler = {\\n \\\"scheduler\\\": torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer,\\n mode=\\\"min\\\",\\n factor=0.2,\\n patience=2,\\n min_lr=1e-6,\\n verbose=True,\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n }\\n return [self.optimizer], [self.scheduler]\",\n \"def create_lr_scheduler(\\n self, optimizer: torch.optim.Optimizer # type: ignore\\n ) -> Optional[LRScheduler]:\\n pass\",\n \"def configure_optimizers(self):\\n no_decay = [\\\"bias\\\", \\\"LayerNorm.weight\\\"]\\n optimizer_grouped_parameters = [\\n {\\n \\\"params\\\": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": self.args.weight_decay,\\n },\\n {\\n \\\"params\\\": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": 0.0,\\n },\\n ]\\n if self.optimizer == \\\"adamw\\\":\\n optimizer = AdamW(optimizer_grouped_parameters,\\n betas=(0.9, 0.98), # according to RoBERTa paper\\n lr=self.args.lr,\\n eps=self.args.adam_epsilon,)\\n elif self.optimizer == \\\"torch.adam\\\":\\n optimizer = torch.optim.AdamW(optimizer_grouped_parameters,\\n lr=self.args.lr,\\n eps=self.args.adam_epsilon,\\n weight_decay=self.args.weight_decay)\\n else:\\n raise ValueError(\\\"Optimizer type does not exist.\\\")\\n num_gpus = len([x for x in str(self.args.gpus).split(\\\",\\\") if x.strip()])\\n t_total = (len(self.train_dataloader()) // (self.args.accumulate_grad_batches * num_gpus) + 1) * self.args.max_epochs\\n warmup_steps = int(self.args.warmup_proportion * t_total)\\n if self.args.lr_scheduler == \\\"onecycle\\\":\\n scheduler = torch.optim.lr_scheduler.OneCycleLR(\\n optimizer, max_lr=self.args.lr, pct_start=float(warmup_steps/t_total),\\n final_div_factor=self.args.final_div_factor,\\n total_steps=t_total, anneal_strategy='linear')\\n elif self.args.lr_scheduler == \\\"linear\\\":\\n scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total)\\n elif self.args.lr_scheulder == \\\"polydecay\\\":\\n if self.args.lr_mini == -1:\\n lr_mini = self.args.lr / self.args.polydecay_ratio\\n else:\\n lr_mini = self.args.lr_mini\\n scheduler = get_polynomial_decay_schedule_with_warmup(optimizer, warmup_steps, t_total, lr_end=lr_mini)\\n else:\\n raise ValueError\\n return [optimizer], [{\\\"scheduler\\\": scheduler, \\\"interval\\\": \\\"step\\\"}]\",\n \"def configure_optimizers(self):\\n self.optimizer = torch.optim.Adam(\\n self.parameters(),\\n lr=self.args.get(\\\"lr\\\", 0.001),\\n weight_decay=self.args.get(\\\"weight_decay\\\", 0),\\n eps=self.args.get(\\\"eps\\\", 1e-8)\\n )\\n self.scheduler = {\\n \\\"scheduler\\\":\\n torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer,\\n mode=\\\"min\\\",\\n factor=0.2,\\n patience=3,\\n min_lr=1e-6,\\n verbose=True,\\n ),\\n \\\"monitor\\\":\\n \\\"val_loss\\\",\\n }\\n return [self.optimizer], [self.scheduler]\",\n \"def set_optimizer(self, config):\\r\\n self.optimizer = optim.Adam(self.net.parameters(), config.lr)\\r\\n self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer, config.lr_decay)\",\n \"def get_scheduler(optimizer, opt):\\n def lambda_rule(epoch):\\n lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)\\n return lr_l\\n scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)\\n return scheduler\",\n \"def build_lr_scheduler(\\n cfg: CfgNode, optimizer: torch.optim.Optimizer\\n) -> torch.optim.lr_scheduler._LRScheduler:\\n name = cfg.SOLVER.LR_SCHEDULER_NAME\\n if name == \\\"WarmupMultiStepLR\\\":\\n return WarmupMultiStepLR(\\n optimizer,\\n cfg.SOLVER.STEPS,\\n cfg.SOLVER.GAMMA,\\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\\n )\\n elif name == \\\"WarmupCosineLR\\\":\\n return WarmupCosineLR(\\n optimizer,\\n cfg.SOLVER.MAX_ITER,\\n warmup_factor=cfg.SOLVER.WARMUP_FACTOR,\\n warmup_iters=cfg.SOLVER.WARMUP_ITERS,\\n warmup_method=cfg.SOLVER.WARMUP_METHOD,\\n )\\n else:\\n raise ValueError(\\\"Unknown LR scheduler: {}\\\".format(name))\",\n \"def _inst_optimizer(self):\\n optimizer = Optimizers(self.m_cfg['configs']['lr_politics']['optimizer']).value\\n lr_schedule = self.m_cfg['configs']['lr_politics']['lr']\\n opt = optimizer(learning_rate=lr_schedule)\\n return opt\",\n \"def reschedule_learning_rate(model, epoch, scheduler):\\n if epoch == 7:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 13:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.005)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 19:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n if epoch == 25:\\n optimizer = torch.optim.SGD(model.parameters(), lr=0.002)\\n current_lr = next(iter(optimizer.param_groups))[\\\"lr\\\"]\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, 6, eta_min=current_lr / 100, last_epoch=-1\\n )\\n\\n return model, scheduler\",\n \"def configure_optimizers(self):\\n optimizer = torch.optim.Adam(\\n self.model.parameters(),\\n lr=self.lr,\\n weight_decay=self.weight_decay,\\n )\\n\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer=optimizer,\\n mode=\\\"min\\\",\\n factor=0.5,\\n patience=self.patience,\\n ),\\n \\\"monitor\\\": \\\"train_loss\\\",\\n },\\n }\",\n \"def configure_optimizers(self):\\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return [optimizer], [scheduler]\",\n \"def configure_optimizers(self):\\n optimizer = optim.Adam(\\n self.parameters(), lr=self.hparams.learning_rate)\\n # scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return optimizer\",\n \"def configure_optimizers(self) -> dict[str, Any]:\\n optimizer = torch.optim.Adam(\\n self.model.parameters(), lr=self.hyperparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer,\\n patience=self.hyperparams[\\\"learning_rate_schedule_patience\\\"],\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def build_lr_scheduler(optimizer: Optimizer, args: Namespace,\\n total_epochs: List[int] = None) -> _LRScheduler:\\n # Learning rate scheduler\\n return NoamLR(\\n optimizer=optimizer,\\n warmup_epochs=[args.warmup_epochs],\\n total_epochs=total_epochs or [args.epochs] * args.num_lrs,\\n steps_per_epoch=args.train_data_size // args.batch_size,\\n init_lr=[args.init_lr],\\n max_lr=[args.max_lr],\\n final_lr=[args.final_lr]\\n )\",\n \"def build_lr_scheduler(\\n optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1\\n):\\n if lr_scheduler not in AVAI_SCH:\\n raise ValueError(\\n 'Unsupported scheduler: {}. Must be one of {}'.format(\\n lr_scheduler, AVAI_SCH\\n )\\n )\\n\\n if lr_scheduler == 'single_step':\\n if isinstance(stepsize, list):\\n stepsize = stepsize[-1]\\n\\n if not isinstance(stepsize, int):\\n raise TypeError(\\n 'For single_step lr_scheduler, stepsize must '\\n 'be an integer, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.StepLR(\\n optimizer, step_size=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'multi_step':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\\n optimizer, milestones=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'cosine':\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, float(max_epoch)\\n )\\n\\n return scheduler\",\n \"def configure_optimizers(self) -> Dict[str, Any]:\\n optimizer = torch.optim.AdamW(\\n self.model.parameters(), lr=self.hparams[\\\"learning_rate\\\"]\\n )\\n return {\\n \\\"optimizer\\\": optimizer,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": ReduceLROnPlateau(\\n optimizer, patience=self.hparams[\\\"learning_rate_schedule_patience\\\"]\\n ),\\n \\\"monitor\\\": \\\"val_loss\\\",\\n },\\n }\",\n \"def build_lr_scheduler(optimizer, lr_scheduler='single_step', stepsize=1, gamma=0.1, max_epoch=1,\\n frozen=20, warmup=10, warmup_factor_base=0.1):\\n if lr_scheduler not in AVAI_SCH:\\n raise ValueError('Unsupported scheduler: {}. Must be one of {}'.format(lr_scheduler, AVAI_SCH))\\n\\n if lr_scheduler == 'single_step':\\n if isinstance(stepsize, list):\\n stepsize = stepsize[-1]\\n\\n if not isinstance(stepsize, int):\\n raise TypeError(\\n 'For single_step lr_scheduler, stepsize must '\\n 'be an integer, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.StepLR(\\n optimizer, step_size=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'multi_step':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = torch.optim.lr_scheduler.MultiStepLR(\\n optimizer, milestones=stepsize, gamma=gamma\\n )\\n\\n elif lr_scheduler == 'cosine':\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\\n optimizer, float(max_epoch)\\n )\\n elif lr_scheduler == 'multi_step_warmup':\\n if not isinstance(stepsize, list):\\n raise TypeError(\\n 'For multi_step lr_scheduler, stepsize must '\\n 'be a list, but got {}'.format(type(stepsize))\\n )\\n\\n scheduler = MultiStepLRWithWarmUp(\\n optimizer, milestones=stepsize, warmup_iters=warmup, frozen_iters=frozen, gamma=gamma,\\n warmup_factor_base=warmup_factor_base\\n )\\n\\n return scheduler\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\\n\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=50):\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = param_group['lr'] * 0.1\\n return optimizer\",\n \"def lr_scheduler(optimizer, epoch, init_lr=0.1, lr_decay_epoch=100):\\r\\n\\r\\n if epoch % lr_decay_epoch == 0 and epoch > 1:\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = param_group['lr'] * 0.1\\r\\n\\r\\n return optimizer\",\n \"def configure_optimizers(self) -> tuple[list[Optimizer], list[LRScheduler]]:\\n # Original paper uses LARS optimizer, but this is not defined in PyTorch\\n optimizer = Adam(\\n self.parameters(),\\n lr=self.hparams[\\\"lr\\\"],\\n weight_decay=self.hparams[\\\"weight_decay\\\"],\\n )\\n max_epochs = 200\\n if self.trainer and self.trainer.max_epochs:\\n max_epochs = self.trainer.max_epochs\\n if self.hparams[\\\"version\\\"] == 1:\\n warmup_epochs = 10\\n else:\\n warmup_epochs = int(max_epochs * 0.05)\\n lr_scheduler = SequentialLR(\\n optimizer,\\n schedulers=[\\n LinearLR(optimizer, total_iters=warmup_epochs),\\n CosineAnnealingLR(optimizer, T_max=max_epochs),\\n ],\\n milestones=[warmup_epochs],\\n )\\n return [optimizer], [lr_scheduler]\",\n \"def _get_scheduler(\\r\\n self, optimizer, scheduler: str, warmup_steps: int, t_total: int):\\r\\n scheduler = scheduler.lower()\\r\\n if scheduler == 'constantlr':\\r\\n return transformers.get_constant_schedule(optimizer)\\r\\n elif scheduler == 'warmupconstant':\\r\\n # this uses warmup\\r\\n return transformers.get_constant_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps)\\r\\n elif scheduler == 'warmuplinear':\\r\\n # This uses warmup + lr-decay with t_total lr-decays\\r\\n # only this wrapper accepts num_trianing_steps\\r\\n # if you open the function get_linear_schedule_with_warmup\\r\\n # you will find that return\\r\\n # LambdaLR(optimizer, lr_lambda, last_epoch)\\r\\n # and last_epoch=-1. So it will end till\\r\\n # num_training_steps consume up\\r\\n # and you will see that each .step() consumes 1 training\\r\\n # step. You can see from\\r\\n # https://pytorch.org/docs/stable/optim.html\\r\\n # 'How to adjust Learning Rate'\\r\\n # When you call the step, the counter will reduce by 1,\\r\\n # and the learning rate will be adjusted accordingly.\\r\\n # Initial learning rate is given in the Optimiser.\\r\\n # Initially, at each step, the learning rate will be\\r\\n # adjusted by param_group in optimizer.param_groups:\\r\\n # param_group['lr'] = lr\\r\\n # Now it is implicitly adjusted by the scheduler\\r\\n return transformers.get_linear_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n elif scheduler == 'warmupcosine':\\r\\n return transformers.get_cosine_schedule_with_warmup(\\r\\n optimizer, num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n elif scheduler == 'warmupcosinewithhardrestarts':\\r\\n return transformers.get_cosine_with_hard_restarts_schedule_with_warmup(\\r\\n optimizer,\\r\\n num_warmup_steps=warmup_steps,\\r\\n num_training_steps=t_total)\\r\\n else:\\r\\n raise ValueError(\\\"Unknown scheduler {}\\\".format(scheduler))\",\n \"def configure_optimizers(self):\\n model = self.model\\n no_decay = [\\\"bias\\\", \\\"LayerNorm.weight\\\"]\\n optimizer_grouped_parameters = [\\n {\\n \\\"params\\\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": self.hparams.weight_decay,\\n },\\n {\\n \\\"params\\\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": 0.0,\\n },\\n ]\\n if self.hparams.lamb:\\n optimizer = FusedLAMB(\\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\\n\\n elif self.hparams.adafactor:\\n optimizer = Adafactor(\\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False\\n )\\n else:\\n optimizer = FusedAdam(\\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\\n self.opt = optimizer\\n\\n scheduler = self.get_lr_scheduler()\\n\\n return [optimizer], [scheduler]\",\n \"def make_lr_scheduler(lr: float, final_lr: float, n_epochs: int,\\n verbose: int = 1) -> keras.callbacks.LearningRateScheduler:\\n schedule = build_schedule(lr, final_lr, n_epochs)\\n return LearningRateScheduler(schedule=schedule, verbose=verbose)\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\\n print('LR is set to {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def configure_schedulers(self, **kwargs):\\n if self.scheduler_type == 'CosineAnnealingWarmRestarts':\\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(self.optimizer, **self.scheduler_params)\\n elif self.scheduler_type == 'CosineAnnealingLR':\\n self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, **self.scheduler_params)\\n elif self.scheduler_type == 'CyclicLR':\\n self.scheduler = torch.optim.lr_scheduler.CyclicLR(self.optimizer, **self.scheduler_params)\\n else:\\n self.scheduler = None\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=10):\\r\\n lr = init_lr * (0.8**(epoch // lr_decay_epoch))\\r\\n print('LR is set to {}'.format(lr))\\r\\n for param_group in optimizer.param_groups:\\r\\n param_group['lr'] = lr\\r\\n\\r\\n return optimizer\",\n \"def create_optimizer(init_lr, num_train_steps, num_warmup_steps):\\n # Implements linear decay of the learning rate.\\n learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(\\n initial_learning_rate=init_lr,\\n decay_steps=num_train_steps,\\n end_learning_rate=0.0)\\n if num_warmup_steps:\\n learning_rate_fn = WarmUp(initial_learning_rate=init_lr,\\n decay_schedule_fn=learning_rate_fn,\\n warmup_steps=num_warmup_steps)\\n optimizer = AdamWeightDecay(\\n learning_rate=learning_rate_fn,\\n weight_decay_rate=0.01,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=1e-6,\\n exclude_from_weight_decay=[LAYER_NORM_NAME, 'bias'])\\n return optimizer\",\n \"def configure_optimizers(\\n self,\\n ) -> Dict[str, Union[torch.optim.Optimizer, Dict[str, Union[str, _LRScheduler]]]]:\\n optimizer = instantiate(self.optimizer_cfg, params=self.encoder.parameters())\\n scheduler = {\\n \\\"scheduler\\\": instantiate(self.scheduler_cfg, optimizer),\\n \\\"monitor\\\": \\\"train_err1\\\",\\n }\\n\\n return dict(optimizer=optimizer, lr_scheduler=scheduler)\",\n \"def configure_optimizers(self):\\n opt = torch.optim.AdamW(\\n self.parameters(), lr=self.lr, weight_decay=self.weight_decay\\n )\\n return {\\n \\\"optimizer\\\": opt,\\n \\\"lr_scheduler\\\": {\\n \\\"scheduler\\\": torch.optim.lr_scheduler.ReduceLROnPlateau(opt),\\n \\\"monitor\\\": \\\"train_loss\\\",\\n \\\"frequency\\\": 1,\\n },\\n }\",\n \"def _get_lr_scheduler(project_parameters, optimizer):\\n if project_parameters.lr_scheduler == 'StepLR':\\n lr_scheduler = StepLR(optimizer=optimizer,\\n step_size=project_parameters.step_size, gamma=project_parameters.gamma)\\n elif project_parameters.lr_scheduler == 'CosineAnnealingLR':\\n lr_scheduler = CosineAnnealingLR(\\n optimizer=optimizer, T_max=project_parameters.step_size)\\n return lr_scheduler\",\n \"def optimizer_config(self):\\r\\n return {\\r\\n \\\"lr\\\": self.args.lr[0],\\r\\n \\\"momentum\\\": self.args.momentum,\\r\\n \\\"weight_decay\\\": self.args.weight_decay,\\r\\n }\",\n \"def _create_train_op(self):\\n self.lr = self.learning_rate\\n # global_step = tf.train.get_or_create_global_step()\\n learning_rate = tf.constant(value=self.learning_rate, shape=[], dtype=tf.float32)\\n learning_rate =tf.train.exponential_decay(learning_rate,self.global_step,2*self.num_warm_up,0.96,staircase=True,name=\\\"exponential_decay\\\")\\n\\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\\n if self.num_warm_up:\\n global_steps_int = tf.cast(self.global_step, tf.int32)\\n warmup_steps_int = tf.constant(self.num_warm_up, dtype=tf.int32)\\n\\n global_steps_float = tf.cast(global_steps_int, tf.float32)\\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\\n\\n warmup_percent_done = global_steps_float / warmup_steps_float\\n warmup_learning_rate = self.learning_rate * warmup_percent_done\\n\\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\\n learning_rate = (\\n (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)\\n self.current_learning_rate = learning_rate\\n if self.optim_type == 'adagrad':\\n self.optimizer = tf.train.AdagradOptimizer(self.lr)\\n elif self.optim_type == 'adam':\\n self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)\\n elif self.optim_type == 'rprop':\\n self.optimizer = tf.train.RMSPropOptimizer(self.lr)\\n elif self.optim_type == 'sgd':\\n self.optimizer = tf.train.GradientDescentOptimizer(self.lr)\\n elif self.optim_type == \\\"bert\\\":\\n self.optimizer = AdamWeightDecayOptimizer(learning_rate=learning_rate, weight_decay_rate=0.01, beta_1=0.9,\\n beta_2=0.999, epsilon=1e-6,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"])\\n else:\\n raise NotImplementedError('Unsupported optimizer: {}'.format(self.optim_type))\\n\\n self.logger.info(\\\"applying optimize %s\\\" % self.optim_type)\\n if self.clip_weight:\\n # clip_weight\\n tvars = tf.trainable_variables()\\n grads = tf.gradients(self.loss, tvars)\\n grads, _ = tf.clip_by_global_norm(grads, clip_norm=self.max_norm_grad)\\n grad_var_pairs = zip(grads, tvars)\\n train_op = self.optimizer.apply_gradients(grad_var_pairs, name='apply_grad', global_step=self.global_step)\\n new_global_step = self.global_step + 1\\n train_op = tf.group(train_op, [self.global_step.assign(new_global_step)])\\n self.train_op = train_op\\n else:\\n self.train_op = self.optimizer.minimize(self.loss, global_step=self.global_step)\",\n \"def get_optimizers(\\n self, num_training_steps\\n ):\\n if self.optimizers is not None:\\n return self.optimizers\\n # Prepare optimizer and schedule (linear warmup and decay)\\n no_decay = [\\\"bias\\\", \\\"LayerNorm.weight\\\"]\\n optimizer_grouped_parameters = [\\n {\\n \\\"params\\\": [\\n p for n, p in self.model.named_parameters()\\n if not any(nd in n for nd in no_decay)\\n ],\\n \\\"weight_decay\\\": self.args.weight_decay,\\n },\\n {\\n \\\"params\\\": [\\n p for n, p in self.model.named_parameters()\\n if any(nd in n for nd in no_decay)\\n ],\\n \\\"weight_decay\\\": 0.0,\\n },\\n ]\\n if self.args.optimizer == \\\"sgd\\\":\\n optimizer = SGD(optimizer_grouped_parameters, lr=self.args.learning_rate, momentum=self.args.sgd_momentum, \\\\\\n weight_decay=self.args.weight_decay)\\n elif self.args.optimizer == \\\"adam\\\":\\n optimizer = AdamW(\\n optimizer_grouped_parameters,\\n lr=self.args.learning_rate,\\n eps=self.args.adam_epsilon)\\n if self.args.lr_schedule == \\\"constant\\\":\\n scheduler = get_constant_schedule_with_warmup(\\n optimizer, num_warmup_steps=self.args.warmup_steps)\\n elif self.args.lr_schedule == \\\"linear\\\":\\n scheduler = get_linear_schedule_with_warmup(\\n optimizer,\\n num_warmup_steps=self.args.warmup_steps,\\n num_training_steps=num_training_steps)\\n elif self.args.lr_schedule == \\\"invsqrt\\\":\\n scheduler = get_invsqrt_schedule_with_warmup(\\n optimizer, num_warmup_steps=self.args.warmup_steps)\\n return optimizer, scheduler\",\n \"def build_learning_rate(self):\\n\\n # TODO(arashwan): Explore if we want to only allow explicit const lr sched.\\n if not self._lr_config:\\n lr = self._optimizer_config.learning_rate\\n else:\\n lr = LR_CLS[self._lr_type](**self._lr_config.as_dict())\\n\\n if self._warmup_config:\\n lr = WARMUP_CLS[self._warmup_type](lr, **self._warmup_config.as_dict())\\n\\n return lr\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr=0.01, lr_decay_epoch=decay):\\n lr = init_lr * (0.1**(epoch // lr_decay_epoch))\\n\\n if epoch % lr_decay_epoch == 0:\\n print('LR is set to {}'.format(lr))\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def init_scheduler(self):\\n gamma = self.config_dict.get(\\\"gamma\\\")\\n if gamma is None:\\n return None\\n else:\\n return torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=gamma)\",\n \"def scheduler(epoch_idx, lr):\\n new_lr = lr\\n if (epoch_idx == 60 or epoch_idx == 120 or epoch_idx == 160\\n or epoch_idx == 260 or epoch_idx == 320 or epoch_idx == 360):\\n new_lr *= 0.2\\n \\\"\\\"\\\"\\n if epoch_idx == 200:\\n new_lr = 0.1\\n \\\"\\\"\\\"\\n return new_lr\",\n \"def configure_optimizers(self):\\n optimizer = _get_optimizer(model_parameters=self.parameters(\\n ), project_parameters=self.project_parameters)\\n if self.project_parameters.step_size > 0:\\n lr_scheduler = _get_lr_scheduler(\\n project_parameters=self.project_parameters, optimizer=optimizer)\\n return [optimizer], [lr_scheduler]\\n else:\\n return optimizer\",\n \"def lr_scheduler(optimizer, lr_decay=0.001, epoch=None, step=1):\\n if epoch is None or step == 1 or (epoch+1) % step == 0:\\n for param_group in optimizer.param_groups:\\n param_group['lr'] *= (1 - lr_decay)\\n return optimizer\",\n \"def create_lr_scheduler(lr_scheduler_name: str, optimizer: torch.optim.Optimizer,\\n lr_gamma: float, max_epochs: int) -> torch.optim.lr_scheduler._LRScheduler:\\n if lr_scheduler_name.lower() == \\\"exponential\\\":\\n lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, lr_gamma)\\n elif lr_scheduler_name.lower() == \\\"linear\\\":\\n lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: 1 - epoch / max_epochs)\\n else:\\n raise ValueError(\\\"SystemLog: Unknown learning rate scheduler {}\\\".format(lr_scheduler_name))\\n\\n return lr_scheduler\",\n \"def _initScheduler(self) -> torch.optim.lr_scheduler.ReduceLROnPlateau:\\n\\n return torch.optim.lr_scheduler.ReduceLROnPlateau(\\n self.optimizer, \\n mode=cfg.training.scheduler_mode,\\n factor=cfg.training.scheduler_factor,\\n patience=cfg.training.scheduler_patience,\\n threshold=cfg.training.scheduler_threshold\\n )\",\n \"def configure_optimizers(self) -> Tuple[List[optim.Optimizer], List[optim.lr_scheduler._LRScheduler]]:\\n if (\\n (\\\"weight_decay\\\" in self.optimizer_params)\\n and (self.optimizer_params[\\\"weight_decay\\\"] != 0)\\n and self.exclude_bn_bias\\n ):\\n defaults = copy(self.optimizer_params)\\n weight_decay = defaults.pop(\\\"weight_decay\\\")\\n\\n wd_group = []\\n nowd_group = []\\n for name, tensor in self.named_parameters():\\n if not tensor.requires_grad:\\n continue\\n if (\\\"bias\\\" in name) or (\\\"bn\\\" in name):\\n nowd_group.append(tensor)\\n else:\\n wd_group.append(tensor)\\n\\n params = [\\n {\\\"params\\\": wd_group, \\\"weight_decay\\\": weight_decay},\\n {\\\"params\\\": nowd_group, \\\"weight_decay\\\": 0.0},\\n ]\\n optimizer = self.optimizer_class(params, **defaults)\\n else:\\n optimizer = self.optimizer_class(self.parameters(), **self.optimizer_params)\\n lr_scheduler = self.lr_scheduler_class(optimizer, **self.lr_scheduler_params)\\n return [optimizer], [lr_scheduler]\",\n \"def configure_optimizer(learning_rate):\\n\\tif train_config['optimizer'] == 'adadelta':\\n\\t\\toptimizer = tf.train.AdadeltaOptimizer(learning_rate,\\n\\t\\t rho=train_config['adadelta_rho'],\\n\\t\\t epsilon=train_config['opt_epsilon'])\\n\\telif train_config['optimizer'] == 'dadgrad':\\n\\t\\toptimizer = tf.train.AdagradDAOptimizer(\\n\\t\\t\\tlearning_rate,\\n\\t\\t\\tinitial_gradient_squared_accumulator_value=train_config['adagrad_initial_accumulator_value'])\\n\\telif train_config['optimizer'] == 'adam':\\n\\t\\toptimizer = tf.train.AdamOptimizer(\\n\\t\\t\\tlearning_rate,\\n\\t\\t\\tbeta1=train_config['adam_beta1'],\\n\\t\\t\\tbeta2=train_config['adam_beta2'],\\n\\t\\t\\tepsilon=train_config['opt_epsilon'])\\n\\telif train_config['optimizer'] == 'ftrl':\\n\\t\\toptimizer = tf.train.FtrlOptimizer(\\n\\t\\t\\tlearning_rate,\\n\\t\\t\\tlearning_rate_power=train_config['ftrl_learning_rate_power'],\\n\\t\\t\\tinitial_accumulator_value=train_config['ftrl_initial_accumulator_value'],\\n\\t\\t\\tl1_regularization_strength=train_config['ftrl_l1'],\\n\\t\\t\\tl2_regularization_strength=train_config['ftrl_l2'])\\n\\telif train_config['optimizer'] == 'momentum':\\n\\t\\toptimizer = tf.train.MomentumOptimizer(\\n\\t\\t\\tlearning_rate,\\n\\t\\t\\tmomentum=train_config['momentum'],\\n\\t\\t\\tname='Momentum')\\n\\telif train_config['optimizer'] == 'rmsprop':\\n\\t\\toptimizer = tf.train.RMSPropOptimizer(\\n\\t\\t\\tlearning_rate,\\n\\t\\t\\tdecay=train_config['rmsprop_decay'],\\n\\t\\t\\tmomentum=train_config['rmsprop_momentum'],\\n\\t\\t\\tepsilon=train_config['opt_epsilon'])\\n\\telif train_config['optimizer'] == 'sgd':\\n\\t\\toptimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n\\telse:\\n\\t\\traise ValueError('Optimizer [%s] was not recognized' % train_config['optimizer'])\\n\\treturn optimizer\",\n \"def optimizer_factory(config, model):\\n params = model.parameters()\\n\\n optimizer = config[\\\"loss\\\"].get(\\\"optimizer\\\", \\\"Adam\\\")\\n lr = config[\\\"loss\\\"].get(\\\"lr\\\", 1e-3)\\n momentum = config[\\\"loss\\\"].get(\\\"momentum\\\", 0.9)\\n\\n if optimizer == \\\"SGD\\\":\\n return optim.SGD(params, lr=lr, momentum=momentum)\\n elif optimizer == \\\"Adam\\\":\\n return optim.Adam(params, lr=lr)\\n else:\\n raise NotImplementedError()\",\n \"def get_optimizer(params, custom_scheduler=None):\\n other_args = {}\\n if \\\"clipnorm\\\" in params:\\n other_args[\\\"clipnorm\\\"] = params.clipnorm\\n if \\\"clipvalue\\\" in params:\\n other_args[\\\"clipvalue\\\"] = params.clipvalue\\n\\n if params.lr_decay_policy == \\\"exp\\\":\\n lr = tf.keras.optimizers.schedules.ExponentialDecay(params.lr,\\n params.decay_steps,\\n params.decay_rate,\\n staircase=params.staircase)\\n else:\\n lr = params.init_lr\\n if params.optimizer == \\\"adam\\\":\\n beta_1 = params.get(\\\"beta_1\\\", 0.9)\\n beta_2 = params.get(\\\"beta_2\\\", 0.999)\\n optimizer = tf.keras.optimizers.Adam(lr, beta_1=beta_1, beta_2=beta_2, **other_args)\\n elif params.optimizer == \\\"sgd\\\":\\n momentum = params.get(\\\"momentum\\\", 0.9)\\n optimizer = tf.keras.optimizers.SGD(lr, momentum=momentum, **other_args)\\n else:\\n raise ValueError(\\\"unsupported optimizer: {}\\\".format(params.optimizer))\\n return optimizer\",\n \"def exp_lr_scheduler(optimizer, epoch, init_lr, lr_decay_epoch):\\n\\n lr = init_lr * (0.1**((epoch-1) // lr_decay_epoch))\\n\\n if (epoch-1) % lr_decay_epoch == 0:\\n print('LR is set to {}'.format(lr))\\n # log_value('lr',lr,epoch)\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def add_optimizers_to_graph(self):\\n with tf.device(self.params.device):\\n with self.graph.as_default():\\n with tf.compat.v1.variable_scope(\\\"optimizers\\\") as scope:\\n self.grads_and_vars = list() # [sch_idx][weight_idx]\\n self.apply_grads = list() # [sch_idx][weight_idx]\\n self.learning_rates = list() # [sch_idx][weight_idx]\\n if self.params.optimizer == \\\"lbfgsb\\\":\\n self.minimizer = None\\n #self.minimizer = tfp.optimizer.lbfgs_minimize(\\n # value_and_gradients_function=self.loss_value_and_grad,#self.total_loss,\\n # initial_position=self.w_init,#self.trainable_variables,\\n # max_iterations=self.params.maxiter)\\n #self.minimizer = tf.contrib.opt.ScipyOptimizerInterface(self.total_loss,\\n # options={\\\"maxiter\\\":self.params.maxiter}) # Default method is L-BFGSB\\n for schedule_idx, sch in enumerate(self.params.schedule):\\n sch_grads_and_vars = list() # [weight_idx]\\n sch_apply_grads = list() # [weight_idx]\\n sch_lrs = list() # [weight_idx]\\n #Construct weight ops\\n weight_ops = [self.trainable_variables[weight] for weight in sch[\\\"weights\\\"]]\\n for w_idx, weight in enumerate(sch[\\\"weights\\\"]):\\n weight_name = weight.split(\\\"/\\\")[-1].split(\\\":\\\")[0]\\n learning_rates = tf.compat.v1.train.exponential_decay(\\n learning_rate=sch[\\\"weight_lr\\\"][w_idx],\\n global_step=self.global_step,\\n decay_steps=sch[\\\"decay_steps\\\"][w_idx],\\n decay_rate=sch[\\\"decay_rate\\\"][w_idx],\\n staircase=sch[\\\"staircase\\\"][w_idx],\\n name=\\\"annealing_schedule_\\\"+weight_name)\\n sch_lrs.append(learning_rates)\\n if self.params.optimizer == \\\"sgd\\\":\\n optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rates,\\n name=\\\"grad_optimizer_\\\"+weight_name)\\n elif self.params.optimizer == \\\"adam\\\":\\n optimizer = tf.compat.v1.train.AdamOptimizer(learning_rates, beta1=0.9, beta2=0.99,\\n epsilon=1e-07, name=\\\"adam_optimizer_\\\"+weight_name)\\n elif self.params.optimizer == \\\"adadelta\\\":\\n optimizer = tf.compat.v1.train.AdadeltaOptimizer(learning_rates, epsilon=1e-07,\\n name=\\\"adadelta_optimizer_\\\"+weight_name)\\n elif self.params.optimizer == \\\"lbfgsb\\\":\\n optimizer = None\\n else:\\n assert False, (\\\"Optimizer \\\"+self.params.optimizer+\\\" is not supported.\\\")\\n weight_op = self.trainable_variables[weight]\\n sch_grads_and_vars.append(self.compute_weight_gradients(optimizer, weight_op))\\n gstep = self.global_step if w_idx == 0 else None # Only increment once\\n if self.params.optimizer == \\\"lbfgsb\\\": # BFGS doesn't actually need the update op\\n if w_idx == 0:\\n sch_apply_grads.append(tf.compat.v1.assign_add(self.global_step, 1))\\n else:\\n sch_apply_grads.append(None)\\n else:\\n sch_apply_grads.append(optimizer.apply_gradients(sch_grads_and_vars[w_idx],\\n global_step=gstep))\\n self.learning_rates.append(sch_lrs)\\n self.grads_and_vars.append(sch_grads_and_vars)\\n self.apply_grads.append(sch_apply_grads)\\n self.optimizers_added = True\",\n \"def __configure_optimizer(self, learning_rate):\\n if self.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=self.adadelta_rho,\\n epsilon=self.opt_epsilon)\\n elif self.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=self.adagrad_initial_accumulator_value)\\n elif self.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=self.adam_beta1,\\n beta2=self.adam_beta2,\\n epsilon=self.opt_epsilon)\\n elif self.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=self.ftrl_learning_rate_power,\\n initial_accumulator_value=self.ftrl_initial_accumulator_value,\\n l1_regularization_strength=self.ftrl_l1,\\n l2_regularization_strength=self.ftrl_l2)\\n elif self.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=self.momentum,\\n name='Momentum')\\n elif self.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=self.rmsprop_decay,\\n momentum=self.rmsprop_momentum,\\n epsilon=self.opt_epsilon)\\n elif self.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n else:\\n raise ValueError('Optimizer [%s] was not recognized', self.optimizer)\\n return optimizer\",\n \"def get_lr_scheduler(self):\\n try:\\n scheduler_name = self.configs.OPTIM.LR_SCHEDULER.SCHEDULER_NAME\\n except AttributeError:\\n scheduler_name = None\\n\\n if scheduler_name is None:\\n lr_scheduler = None\\n\\n elif scheduler_name == 'plateau':\\n factor = self.configs.OPTIM.LR_SCHEDULER.FACTOR\\n patience = self.configs.OPTIM.LR_SCHEDULER.PATIENCE\\n\\n lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\\n optimizer=self.optimizer,\\n mode='min',\\n factor=factor,\\n patience=patience,\\n verbose=False,\\n threshold=1e-4,\\n threshold_mode='rel',\\n cooldown=0,\\n min_lr=1e-6, # default min_lr is 0\\n eps=1e-8,\\n )\\n\\n elif scheduler_name == 'cyclic':\\n base_lr = self.configs.OPTIM.LR_SCHEDULER.BASE_LR\\n max_lr = self.configs.OPTIM.LR_SCHEDULER.MAX_LR\\n\\n lr_scheduler = torch.optim.lr_scheduler.CyclicLR(\\n optimizer=self.optimizer,\\n base_lr=base_lr,\\n max_lr=max_lr,\\n step_size_up=2000,\\n step_size_down=None,\\n mode='triangular',\\n gamma=1.0,\\n scale_fn=None,\\n scale_mode='cycle',\\n cycle_momentum=True,\\n base_momentum=0.8,\\n max_momentum=0.9,\\n last_epoch=-1,\\n )\\n\\n else:\\n LOGGER.error('Invalid learning rate scheduler: %s', scheduler_name)\\n raise NotImplementedError\\n\\n return lr_scheduler\",\n \"def configure_optimizers(self):\\n model = self.model\\n no_decay = [\\\"bias\\\", \\\"LayerNorm.weight\\\"]\\n optimizer_grouped_parameters = [\\n {\\n \\\"params\\\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": self.hparams.weight_decay,\\n },\\n {\\n \\\"params\\\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],\\n \\\"weight_decay\\\": 0.0,\\n },\\n ]\\n if self.hparams.lamb:\\n optimizer_reduced_precision_type = self.config.dtype if self.hparams.allreduce_post_accumulation_half_precision else None\\n optimizer = FusedMixedPrecisionLamb(\\n optimizer_grouped_parameters,\\n lr=self.hparams.learning_rate,\\n eps=self.hparams.adam_epsilon,\\n max_grad_norm=self.hparams.gradient_clip_val,\\n reduced_precision_dtype=optimizer_reduced_precision_type)\\n elif self.hparams.allreduce_post_accumulation_half_precision:\\n raise ValueError(\\\"--allreduce_post_accumulation_half_precision is only supported on LAMB optimizer\\\")\\n elif self.hparams.adafactor:\\n optimizer = Adafactor(\\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, scale_parameter=False, relative_step=False\\n )\\n else:\\n optimizer = FusedAdam(\\n optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)\\n self.opt = optimizer\\n\\n scheduler = self.get_lr_scheduler()\\n\\n return [optimizer], [scheduler]\",\n \"def build_optimizer(\\n self, lr: Union[tf.keras.optimizers.schedules.LearningRateSchedule,\\n float]):\\n\\n optimizer_dict = self._optimizer_config.as_dict()\\n optimizer_dict['learning_rate'] = lr\\n\\n optimizer = OPTIMIZERS_CLS[self._optimizer_type](**optimizer_dict)\\n return optimizer\",\n \"def fetch_optimizer(args, model):\\n optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wdecay, eps=args.epsilon)\\n\\n scheduler = optim.lr_scheduler.OneCycleLR(optimizer, args.lr, args.num_steps + 100,\\n pct_start=0.05, cycle_momentum=False, anneal_strategy='linear')\\n\\n return optimizer, scheduler\",\n \"def build_optimizer(opt_config, learning_rate):\\n if opt_config.opt_method == 'SGD':\\n print('Using SGD as the optimizer', file=sys.stderr)\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n elif opt_config.opt_method == 'Adam':\\n print('Using Adam as the optimizer', file=sys.stderr)\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate, beta1=opt_config.adam_beta1,\\n beta2=opt_config.adam_beta2, epsilon=opt_config.adam_epsilon\\n )\\n else:\\n raise ValueError(\\n 'Unknown optimization method {0}!'.format(opt_config.opt_method))\\n return optimizer\",\n \"def _build_optimizers(self):\\r\\n self._optimize_ops = []\\r\\n all_trainable_variables = tf.trainable_variables()\\r\\n all_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\\r\\n all_reg_losses = tf.losses.get_regularization_losses()\\r\\n for spec in self._learning_schedule:\\r\\n optimize_ops = []\\r\\n update_ops = []\\r\\n loss_terms = spec['loss_terms_to_optimize']\\r\\n reg_losses = []\\r\\n assert isinstance(loss_terms, dict)\\r\\n for loss_term_key, prefixes in loss_terms.items():\\r\\n assert loss_term_key in self.loss_terms['train'].keys()\\r\\n variables_to_train = []\\r\\n for prefix in prefixes:\\r\\n variables_to_train += [\\r\\n v for v in all_trainable_variables\\r\\n if v.name.startswith(prefix)\\r\\n ]\\r\\n update_ops += [\\r\\n o for o in all_update_ops\\r\\n if o.name.startswith(prefix)\\r\\n ]\\r\\n reg_losses += [\\r\\n l for l in all_reg_losses\\r\\n if l.name.startswith(prefix)\\r\\n ]\\r\\n\\r\\n optimizer_class = tf.train.AdamOptimizer\\r\\n optimizer = optimizer_class(\\r\\n learning_rate=self.learning_rate_multiplier * spec['learning_rate'],\\r\\n # beta1=0.9,\\r\\n # beta2=0.999,\\r\\n )\\r\\n final_loss = self.loss_terms['train'][loss_term_key]\\r\\n if len(reg_losses) > 0:\\r\\n final_loss += tf.reduce_sum(reg_losses)\\r\\n with tf.control_dependencies(update_ops):\\r\\n gradients, variables = zip(*optimizer.compute_gradients(\\r\\n loss=final_loss,\\r\\n var_list=variables_to_train,\\r\\n aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N,\\r\\n ))\\r\\n # gradients, _ = tf.clip_by_global_norm(gradients, 5.0) # TODO: generalize\\r\\n optimize_op = optimizer.apply_gradients(zip(gradients, variables))\\r\\n optimize_ops.append(optimize_op)\\r\\n self._optimize_ops.append(optimize_ops)\\r\\n logger.info('Built optimizer for: %s' % ', '.join(loss_terms.keys()))\",\n \"def adjust_learning_rate_by_schedule(config, optimizer, epoch, decrease_rate=0.1):\\n if not isinstance(optimizer, torch.optim.SGD):\\n return\\n #lr = config.lr * (0.1 ** (epoch // 10))\\n if epoch and epoch % 10 == 0:\\n for i, param_group in enumerate(optimizer.param_groups):\\n param_group['lr'] *= decrease_rate\\n logger.info('Setting learning layer=i, rate=%.6f', i, param_group['lr'])\",\n \"def optimizer_creator(model, config):\\n return torch.optim.SGD(model.parameters(), lr=config.get(\\\"lr\\\", 1e-4))\",\n \"def lr_scheduler(self, lr_init, global_step):\\n pass\",\n \"def create_learning_rate_scheduler(\\n factors: str = 'constant * linear_warmup * rsqrt_decay',\\n base_learning_rate: float = 0.5,\\n warmup_steps: int = 1000,\\n decay_factor: float = 0.5,\\n steps_per_decay: int = 20000,\\n steps_per_cycle: int = 100000,\\n step_offset: int = 0,\\n min_learning_rate: float = 1e-8) -> LearningRateCallable:\\n factors = [n.strip() for n in factors.split('*')]\\n\\n def step_fn(step: jnp.ndarray) -> jnp.ndarray:\\n \\\"\\\"\\\"Step to learning rate function.\\\"\\\"\\\"\\n step = jnp.maximum(0, step - step_offset)\\n ret = 1.0\\n for name in factors:\\n if name == 'constant':\\n ret *= base_learning_rate\\n elif name == 'linear_warmup':\\n ret *= jnp.minimum(1.0, step / warmup_steps)\\n elif name == 'linear_decay':\\n ret *= base_learning_rate * jnp.minimum(\\n step / warmup_steps, 1.0 + decay_factor * (warmup_steps - step))\\n elif name == 'rsqrt_decay':\\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\\n elif name == 'rsqrt_normalized_decay':\\n ret *= jnp.sqrt(warmup_steps)\\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\\n elif name == 'decay_every':\\n ret *= (decay_factor**(step // steps_per_decay))\\n elif name == 'cosine_decay':\\n progress = jnp.maximum(0.0,\\n (step - warmup_steps) / float(steps_per_cycle))\\n ret *= jnp.maximum(0.0,\\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\\n else:\\n raise ValueError('Unknown factor %s.' % name)\\n ret = jnp.maximum(ret, min_learning_rate)\\n return jnp.asarray(ret, dtype=jnp.float32)\\n\\n return step_fn\",\n \"def exp_lr_scheduler(optimizer, epoch, lr_decay_epoch=100,lr_decay_factor=0.5):\\n init_lr = optimizer.param_groups[0]['lr']\\n if epoch > 0 and (epoch % lr_decay_epoch == 0):\\n lr = init_lr*lr_decay_factor\\n print('\\\\n LR is set to {}'.format(lr))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n\\n return optimizer\",\n \"def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):\\n global_step = tf.compat.v1.train.get_or_create_global_step()\\n\\n learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)\\n\\n # Implements linear decay of the learning rate.\\n learning_rate = tf.compat.v1.train.polynomial_decay(\\n learning_rate,\\n global_step,\\n num_train_steps,\\n end_learning_rate=0.0,\\n power=1.0,\\n cycle=False)\\n\\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\\n if num_warmup_steps:\\n global_steps_int = tf.cast(global_step, tf.int32)\\n warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)\\n\\n global_steps_float = tf.cast(global_steps_int, tf.float32)\\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\\n\\n warmup_percent_done = global_steps_float / warmup_steps_float\\n warmup_learning_rate = init_lr * warmup_percent_done\\n\\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\\n learning_rate = (\\n (1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)\\n\\n # It is recommended that you use this optimizer for fine tuning, since this\\n # is how the model was trained (note that the Adam m/v variables are NOT\\n # loaded from init_checkpoint.)\\n optimizer = AdamWeightDecayOptimizer(\\n learning_rate=learning_rate,\\n weight_decay_rate=0.01,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=1e-6,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"])\\n\\n if use_tpu:\\n optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)\\n\\n tvars = tf.compat.v1.trainable_variables()\\n grads = tf.gradients(loss, tvars)\\n\\n # This is how the model was pre-trained.\\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)\\n\\n train_op = optimizer.apply_gradients(\\n zip(grads, tvars), global_step=global_step)\\n\\n # Normally the global step update is done inside of `apply_gradients`.\\n # However, `AdamWeightDecayOptimizer` doesn't do this. But if you use\\n # a different optimizer, you should probably take this line out.\\n new_global_step = global_step + 1\\n train_op = tf.group(train_op, [global_step.assign(new_global_step)])\\n return train_op\",\n \"def create_custom_optimizer(tvars, loss, bert_init_lr, task_init_lr, num_train_steps, num_warmup_steps, use_tpu, global_step=None, freeze=-1, task_opt='adam', eps=1e-6):\\n if global_step is None:\\n global_step = tf.train.get_or_create_global_step()\\n\\n bert_learning_rate = tf.constant(value=bert_init_lr, shape=[], dtype=tf.float32)\\n task_learning_rate = tf.constant(value=task_init_lr, shape=[], dtype=tf.float32)\\n\\n # Implements linear decay of the learning rate.\\n bert_learning_rate = tf.train.polynomial_decay(\\n bert_learning_rate,\\n global_step,\\n num_train_steps,\\n end_learning_rate=0.0,\\n power=1.0,\\n cycle=False)\\n task_learning_rate = tf.train.polynomial_decay(\\n task_learning_rate,\\n global_step,\\n num_train_steps,\\n end_learning_rate=0.0,\\n power=1.0,\\n cycle=False)\\n\\n ## TODO DEBUG\\n print(f'type task learning rate: {type(task_learning_rate)} ##################')\\n\\n\\n # Implements linear warmup. I.e., if global_step < num_warmup_steps, the\\n # learning rate will be `global_step/num_warmup_steps * init_lr`.\\n if num_warmup_steps:\\n global_steps_int = tf.cast(global_step, tf.int32)\\n warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)\\n\\n global_steps_float = tf.cast(global_steps_int, tf.float32)\\n warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)\\n\\n warmup_percent_done = global_steps_float / warmup_steps_float\\n bert_warmup_learning_rate = bert_init_lr * warmup_percent_done\\n task_warmup_learning_rate = task_init_lr * warmup_percent_done\\n\\n is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)\\n bert_learning_rate = (\\n (1.0 - is_warmup) * bert_learning_rate + is_warmup * bert_warmup_learning_rate)\\n\\n # It is recommended that you use this optimizer for fine tuning, since this\\n # is how the model was trained (note that the Adam m/v variables are NOT\\n # loaded from init_checkpoint.)\\n bert_optimizer = AdamWeightDecayOptimizer(\\n learning_rate=bert_learning_rate,\\n weight_decay_rate=0.01,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=eps,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"])\\n if task_opt == 'adam_weight_decay':\\n task_optimizer = AdamWeightDecayOptimizer(\\n learning_rate=task_learning_rate,\\n weight_decay_rate=0.01,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=eps\\n )\\n elif task_opt == 'adam':\\n task_optimizer = tf.train.AdamOptimizer(\\n learning_rate=task_learning_rate)\\n else:\\n raise NotImplementedError('Check optimizer. {} is invalid.'.format(task_opt))\\n\\n # tvars = tf.trainable_variables()\\n bert_vars, task_vars = [], []\\n for var in tvars:\\n if var.name.startswith('bert'):\\n can_optimize = False\\n if var.name.startswith('bert/encoder/layer_') and int(var.name.split('/')[2][len('layer_'):]) >= freeze:\\n can_optimize = True\\n if freeze == -1 or can_optimize:\\n bert_vars.append(var)\\n else:\\n task_vars.append(var)\\n print('bert:task', len(bert_vars), len(task_vars))\\n grads = tf.gradients(loss, bert_vars + task_vars)\\n bert_grads = grads[:len(bert_vars)]\\n task_grads = grads[len(bert_vars):]\\n\\n # This is how the model was pre-trained.\\n (bert_grads, _) = tf.clip_by_global_norm(bert_grads, clip_norm=1.0)\\n (task_grads, _) = tf.clip_by_global_norm(task_grads, clip_norm=1.0)\\n\\n # global_step1 = tf.Print(global_step, [global_step], 'before')\\n bert_train_op = bert_optimizer.apply_gradients(\\n zip(bert_grads, bert_vars), global_step=global_step)\\n task_train_op = task_optimizer.apply_gradients(\\n zip(task_grads, task_vars), global_step=global_step)\\n if task_opt == 'adam_weight_decay':\\n new_global_step = global_step + 1\\n train_op = tf.group(bert_train_op, task_train_op, [global_step.assign(new_global_step)])\\n else:\\n train_op = tf.group(bert_train_op, task_train_op)\\n return train_op\",\n \"def setup_optimiser(self):\\n self.optimiser = ScheduledOptim(\\n optim.Adam(\\n filter(lambda x: x.requires_grad, self.model.parameters()),\\n betas=(0.9, 0.98), eps=1e-09, lr=self.opt.learning_rate),\\n self.opt.d_model, self.opt.n_warmup_steps)\\n if self.opt.verbose:\\n print(\\\"[Info] optimiser configured.\\\")\",\n \"def adjust_learning_rate(args,optimizer, epoch):\\n lr = args.lr\\n schedule = args.lr_schedule\\n # schedule from TRADES repo (different from paper due to bug there)\\n if schedule == 'trades':\\n if epoch >= 0.75 * args.epochs:\\n lr = args.lr * 0.1\\n # schedule as in TRADES paper\\n elif schedule == 'trades_fixed':\\n if epoch >= 0.75 * args.epochs:\\n lr = args.lr * 0.1\\n if epoch >= 0.9 * args.epochs:\\n lr = args.lr * 0.01\\n if epoch >= args.epochs:\\n lr = args.lr * 0.001\\n # cosine schedule\\n elif schedule == 'cosine':\\n lr = args.lr * 0.5 * (1 + np.cos((epoch - 1) / args.epochs * np.pi))\\n # schedule as in WRN paper\\n elif schedule == 'wrn':\\n if epoch >= 0.3 * args.epochs:\\n lr = args.lr * 0.2\\n if epoch >= 0.6 * args.epochs:\\n lr = args.lr * 0.2 * 0.2\\n if epoch >= 0.8 * args.epochs:\\n lr = args.lr * 0.2 * 0.2 * 0.2\\n else:\\n raise ValueError('Unkown LR schedule %s' % schedule)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def _create_optimizer(self):\\n\\n with tf.name_scope(\\\"optimizer\\\"):\\n self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)\",\n \"def get_scheduler(optimizer, recipe):\\n\\n # <optimizer>, \\\"stages__*__lr_scheduler\\\"\\n return get_instance(optimizer, **recipe)\",\n \"def exp_lr_scheduler(optimizer, epoch, lr_decay=0.1, lr_decay_epoch=7):\\n if epoch % lr_decay_epoch or epoch>35:\\n return optimizer\\n \\n for param_group in optimizer.param_groups:\\n param_group['lr'] *= lr_decay\\n return optimizer\",\n \"def configure_optimizers(self):\\n\\n optimizer = self.optimizer(self.parameters(), lr=self.lr)\\n\\n return optimizer\",\n \"def adam_optim(config = None, global_step = None):\\n learning_rate = config[\\\"learning_rate\\\"]\\n\\n beta1 = config.get('beta1', 0.9)\\n beta2 = config.get('beta2', 0.999)\\n epsilon = config.get('epsilon', 1e-8)\\n \\n train_step = tf.train.AdamOptimizer(learning_rate, beta1, beta2, epsilon)\\n\\n return train_step\",\n \"def configure_optimizers(self):\\n optimizer = torch.optim.Adam(\\n self.parameters(), lr=self.hparams[\\\"learning_rate\\\"]\\n )\\n return optimizer\",\n \"def rnn_learning_rate_schedule(dataset):\\n model, prefix = make_rnn(non_negative=True)\\n # model.set_params(**{prefix + \\\"val\\\": 0.1})\\n\\n # base = no schedule\\n print(\\\"Baseline RNN\\\")\\n # model.set_params(**{prefix + \\\"history_file\\\": \\\"nn_default.csv\\\"})\\n cross_validate(dataset, model)\\n # model.fit(dataset.inputs, dataset.outputs)\\n\\n # higher init, decay set to reach the same at 40 epochs\\n print(\\\"RNN with decay\\\")\\n model.set_params(**{prefix + \\\"lr_decay\\\": \\\"DecreasingLearningRateScheduler\\\"})\\n # model.fit(dataset.inputs, dataset.outputs)\\n cross_validate(dataset, model)\",\n \"def create_learning_rate_scheduler(\\n base_learning_rate=0.5,\\n factors='constant * linear_warmup * rsqrt_normalized_decay',\\n warmup_steps=16000,\\n decay_factor=0.5,\\n steps_per_decay=50000,\\n steps_per_cycle=100000):\\n factors = [n.strip() for n in factors.split('*')]\\n\\n def step_fn(step):\\n \\\"\\\"\\\"Step to learning rate function.\\\"\\\"\\\"\\n ret = 1.0\\n for name in factors:\\n if name == 'constant':\\n ret *= base_learning_rate\\n elif name == 'linear_warmup':\\n ret *= jnp.minimum(1.0, step / warmup_steps)\\n elif name == 'rsqrt_decay':\\n ret /= jnp.sqrt(jnp.maximum(1.0, step - warmup_steps))\\n elif name == 'rsqrt_normalized_decay':\\n ret *= jnp.sqrt(warmup_steps)\\n ret /= jnp.sqrt(jnp.maximum(step, warmup_steps))\\n elif name == 'decay_every':\\n ret *= (decay_factor**(step // steps_per_decay))\\n elif name == 'cosine_decay':\\n progress = jnp.maximum(0.0,\\n (step - warmup_steps) / float(steps_per_cycle))\\n ret *= jnp.maximum(0.0,\\n 0.5 * (1.0 + jnp.cos(jnp.pi * (progress % 1.0))))\\n else:\\n raise ValueError('Unknown factor %s.' % name)\\n return jnp.asarray(ret, dtype=jnp.float32)\\n\\n return step_fn\",\n \"def update_learning_rate(self) -> None:\\n optimizer = list(self.optimizers.values())[0]\\n old_lr = optimizer.param_groups[0]['lr']\\n for name, scheduler in self.schedulers.items():\\n if name == 'generator' and self.opt.generator_scheduler_name == 'plateau':\\n scheduler.step(self.metric)\\n elif name == 'discriminator' and self.opt.discriminator_scheduler_name == 'plateau':\\n scheduler.step(self.metric)\\n else:\\n scheduler.step()\\n\\n lr = optimizer.param_groups[0]['lr']\\n print('learning rate %.7f -> %.7f' % (old_lr, lr))\\n return\",\n \"def _adjust_learning_rate_resnet(optimizer, epoch):\\n\\n if epoch == 90:\\n return lr_scheduler.MultiStepLR(optimizer, [30, 60, 80])\\n elif epoch == 270: # autoaugment\\n return lr_scheduler.MultiStepLR(optimizer, [90, 180, 240])\\n else:\\n raise ValueError('invalid epoch=%d for resnet scheduler' % epoch)\",\n \"def configure_optimizer(learning_rate):\\n if FLAGS.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=FLAGS.adadelta_rho,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\\n elif FLAGS.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=FLAGS.adam_beta1,\\n beta2=FLAGS.adam_beta2,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\\n l1_regularization_strength=FLAGS.ftrl_l1,\\n l2_regularization_strength=FLAGS.ftrl_l2)\\n elif FLAGS.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=FLAGS.momentum,\\n name='Momentum')\\n elif FLAGS.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=FLAGS.rmsprop_decay,\\n momentum=FLAGS.rmsprop_momentum,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n elif FLAGS.optimizer == \\\"adamweightdecay\\\":\\n optimizer = AdamWeightDecayOptimizer(\\n learning_rate=learning_rate,\\n weight_decay_rate=0.01,\\n beta_1=FLAGS.adam_beta1,\\n beta_2=FLAGS.adam_beta2,\\n epsilon=FLAGS.opt_epsilon,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"])\\n else:\\n raise ValueError('Optimizer [%s] was not recognized' % FLAGS.optimizer)\\n return optimizer\",\n \"def configure_optimizers(self):\\n optimizer = torch.optim.Adam(\\n self.parameters(),\\n lr=self.hparams.learning_rate,\\n weight_decay=self.hparams.weight_decay,\\n )\\n return optimizer\",\n \"def configure_optimizer(learning_rate):\\r\\n if FLAGS.optimizer == 'adadelta':\\r\\n optimizer = tf.train.AdadeltaOptimizer(learning_rate, \\r\\n rho=FLAGS.adadelta_rho,epsilon=FLAGS.opt_epsilon)\\r\\n elif FLAGS.optimizer == 'adagrad':\\r\\n optimizer = tf.train.AdagradOptimizer(learning_rate,\\r\\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\\r\\n elif FLAGS.optimizer == 'adam':\\r\\n optimizer = tf.train.AdamOptimizer(learning_rate,\\r\\n beta1=FLAGS.adam_beta1,beta2=FLAGS.adam_beta2,epsilon=FLAGS.opt_epsilon)\\r\\n elif FLAGS.optimizer == 'ftrl':\\r\\n optimizer = tf.train.FtrlOptimizer(learning_rate,learning_rate_power=FLAGS.ftrl_learning_rate_power,\\r\\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\\r\\n l1_regularization_strength=FLAGS.ftrl_l1,l2_regularization_strength=FLAGS.ftrl_l2)\\r\\n elif FLAGS.optimizer == 'momentum':\\r\\n optimizer = tf.train.MomentumOptimizer(learning_rate,\\r\\n momentum=FLAGS.momentum,name='Momentum')\\r\\n elif FLAGS.optimizer == 'rmsprop':\\r\\n optimizer = tf.train.RMSPropOptimizer(learning_rate,decay=FLAGS.rmsprop_decay,\\r\\n momentum=FLAGS.rmsprop_momentum,epsilon=FLAGS.opt_epsilon)\\r\\n elif FLAGS.optimizer == 'sgd':\\r\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)\\r\\n else:\\r\\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\\r\\n return optimizer\",\n \"def update_learning_rate(self):\\n for scheduler in self.schedulers:\\n if self.opt.lr_policy == 'plateau':\\n scheduler.step(self.metric)\\n else:\\n scheduler.step()\\n\\n lr = self.optimizers[0].param_groups[0]['lr']\\n print('learning rate = %.7f' % lr)\",\n \"def configure_optimizers(self):\\n optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\\n return optimizer\",\n \"def configure_optimizer(learning_rate):\\n if hp.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=hp.adadelta_rho,\\n epsilon=hp.opt_epsilon)\\n elif hp.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=hp.adagrad_initial_accumulator_value)\\n elif hp.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=hp.adam_beta1,\\n beta2=hp.adam_beta2,\\n epsilon=hp.opt_epsilon)\\n elif hp.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=hp.ftrl_learning_rate_power,\\n initial_accumulator_value=hp.ftrl_initial_accumulator_value,\\n l1_regularization_strength=hp.ftrl_l1,\\n l2_regularization_strength=hp.ftrl_l2)\\n elif hp.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=hp.momentum,\\n name='Momentum')\\n elif hp.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=hp.rmsprop_decay,\\n momentum=hp.rmsprop_momentum,\\n epsilon=hp.opt_epsilon)\\n elif hp.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n else:\\n raise ValueError('Optimizer [%s] was not recognized', hp.optimizer)\\n return optimizer\",\n \"def _update_initial_learning_rate(configs, learning_rate):\\n\\n optimizer_type = get_optimizer_type(configs[\\\"train_config\\\"])\\n if optimizer_type == \\\"rms_prop_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.rms_prop_optimizer\\n elif optimizer_type == \\\"momentum_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.momentum_optimizer\\n elif optimizer_type == \\\"adam_optimizer\\\":\\n optimizer_config = configs[\\\"train_config\\\"].optimizer.adam_optimizer\\n else:\\n raise TypeError(\\\"Optimizer %s is not supported.\\\" % optimizer_type)\\n\\n learning_rate_type = get_learning_rate_type(optimizer_config)\\n if learning_rate_type == \\\"constant_learning_rate\\\":\\n constant_lr = optimizer_config.learning_rate.constant_learning_rate\\n constant_lr.learning_rate = learning_rate\\n elif learning_rate_type == \\\"exponential_decay_learning_rate\\\":\\n exponential_lr = (\\n optimizer_config.learning_rate.exponential_decay_learning_rate)\\n exponential_lr.initial_learning_rate = learning_rate\\n elif learning_rate_type == \\\"manual_step_learning_rate\\\":\\n manual_lr = optimizer_config.learning_rate.manual_step_learning_rate\\n original_learning_rate = manual_lr.initial_learning_rate\\n learning_rate_scaling = float(learning_rate) / original_learning_rate\\n manual_lr.initial_learning_rate = learning_rate\\n for schedule in manual_lr.schedule:\\n schedule.learning_rate *= learning_rate_scaling\\n elif learning_rate_type == \\\"cosine_decay_learning_rate\\\":\\n cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate\\n learning_rate_base = cosine_lr.learning_rate_base\\n warmup_learning_rate = cosine_lr.warmup_learning_rate\\n warmup_scale_factor = warmup_learning_rate / learning_rate_base\\n cosine_lr.learning_rate_base = learning_rate\\n cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate\\n else:\\n raise TypeError(\\\"Learning rate %s is not supported.\\\" % learning_rate_type)\",\n \"def _configure_optimizer(learning_rate):\\n if FLAGS.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=FLAGS.adadelta_rho,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\\n elif FLAGS.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=FLAGS.adam_beta1,\\n beta2=FLAGS.adam_beta2,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\\n l1_regularization_strength=FLAGS.ftrl_l1,\\n l2_regularization_strength=FLAGS.ftrl_l2)\\n elif FLAGS.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=FLAGS.momentum,\\n name='Momentum')\\n elif FLAGS.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=FLAGS.rmsprop_decay,\\n momentum=FLAGS.rmsprop_momentum,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n else:\\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\\n return optimizer\",\n \"def adjust_learning_rate(opt, optimizer, epoch):\\n epoch = copy.deepcopy(epoch)\\n lr = opt.maxlr\\n wd = opt.weightDecay\\n if opt.learningratescheduler == 'imagenetscheduler':\\n if epoch >= 1 and epoch <= 18:\\n lr = 1e-3\\n wd = 5e-5\\n elif epoch >= 19 and epoch <= 29:\\n lr = 5e-4\\n wd = 5e-5\\n elif epoch >= 30 and epoch <= 43:\\n lr = 1e-4\\n wd = 0\\n elif epoch >= 44 and epoch <= 52:\\n lr = 5e-5\\n wd = 0\\n elif epoch >= 53:\\n lr = 2e-5\\n wd = 0\\n if opt.optimType=='sgd':\\n lr *= 10\\n opt.lr = lr\\n opt.weightDecay = wd\\n if opt.learningratescheduler == 'decayscheduler':\\n while epoch >= opt.decayinterval:\\n lr = lr/opt.decaylevel\\n epoch = epoch - opt.decayinterval\\n lr = max(lr,opt.minlr)\\n opt.lr = lr\\n\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n param_group['weight_decay'] = wd\",\n \"def lr_decay(config, optimizer: optim.Optimizer, epoch: int) -> optim.Optimizer:\\n lr = config.learning_rate / (1 + config.lr_decay * (epoch - 1))\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n print('learning rate is set to: ', lr)\\n return optimizer\",\n \"def adjust_learning_rate(cfg, optimizer):\\n for idx, group in enumerate(optimizer.param_groups):\\n init_lr = cfg.TRAINING.LR\\n if 'step' not in group:\\n group['step'] = 0.\\n else:\\n group['step'] += 1.\\n\\n group['lr'] = init_lr * (\\n 1.0 - float(group['step']) * float(cfg.TRAINING.BATCH_SIZE) /\\n (cfg.TRAINING.N_TRIPLETS * float(cfg.TRAINING.EPOCHS)))\\n return\",\n \"def train_scheduler(self, tau, Tau):\\n # SAC-U doesn't have a trained scheduler\\n pass\",\n \"def get_optimizer(start_epoch, factor, opt_config, model):\\n optimizer = __optimizers[opt_config['optimizer']]\\n opt_params = {'parameter_list': model.parameters()}\\n for key in opt_config.keys():\\n if key == 'learning_rate':\\n lr = opt_config[key]\\n bo = [val*factor for val in lr['bound']]\\n decay_lr = fluid.dygraph.PiecewiseDecay(bo, lr['value'], start_epoch*factor)\\n opt_params['learning_rate'] = decay_lr\\n elif key == 'weight_decay':\\n regularization = fluid.regularizer.L2Decay(regularization_coeff=opt_config[key])\\n opt_params['regularization'] = regularization\\n elif key == 'momentum':\\n opt_params['momentum'] = opt_config[key]\\n\\n return optimizer(**opt_params)\",\n \"def get_lr_scheduler(scheduler_type: str,\\n updates_per_checkpoint: int,\\n learning_rate_half_life: int,\\n learning_rate_reduce_factor: float,\\n learning_rate_reduce_num_not_improved: int) -> Optional[LearningRateScheduler]:\\n if scheduler_type is None:\\n return None\\n if scheduler_type == \\\"fixed-rate-inv-sqrt-t\\\":\\n return LearningRateSchedulerInvSqrtT(updates_per_checkpoint, learning_rate_half_life)\\n elif scheduler_type == \\\"fixed-rate-inv-t\\\":\\n return LearningRateSchedulerInvT(updates_per_checkpoint, learning_rate_half_life)\\n elif scheduler_type == \\\"plateau-reduce\\\":\\n check_condition(learning_rate_reduce_factor is not None,\\n \\\"learning_rate_reduce_factor needed for plateau-reduce scheduler\\\")\\n check_condition(learning_rate_reduce_num_not_improved is not None,\\n \\\"learning_rate_reduce_num_not_improved needed for plateau-reduce scheduler\\\")\\n if learning_rate_reduce_factor >= 1.0:\\n logger.warning(\\\"Not using plateau-reduce learning rate scheduling: learning_rate_reduce_factor == 1.0\\\")\\n return None\\n return LearningRateSchedulerPlateauReduce(learning_rate_reduce_factor, learning_rate_reduce_num_not_improved)\\n else:\\n raise ValueError(\\\"Unknown learning rate scheduler type %s.\\\" % scheduler_type)\",\n \"def _configure_optimizer(learning_rate):\\n if FLAGS.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=FLAGS.adadelta_rho,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\\n elif FLAGS.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=FLAGS.adam_beta1,\\n beta2=FLAGS.adam_beta2,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\\n l1_regularization_strength=FLAGS.ftrl_l1,\\n l2_regularization_strength=FLAGS.ftrl_l2)\\n elif FLAGS.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=FLAGS.momentum,\\n name='Momentum')\\n elif FLAGS.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=FLAGS.rmsprop_decay,\\n momentum=FLAGS.rmsprop_momentum,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n else:\\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\\n return optimizer\",\n \"def _configure_optimizer(learning_rate):\\n if FLAGS.optimizer == 'adadelta':\\n optimizer = tf.train.AdadeltaOptimizer(\\n learning_rate,\\n rho=FLAGS.adadelta_rho,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'adagrad':\\n optimizer = tf.train.AdagradOptimizer(\\n learning_rate,\\n initial_accumulator_value=FLAGS.adagrad_initial_accumulator_value)\\n elif FLAGS.optimizer == 'adam':\\n optimizer = tf.train.AdamOptimizer(\\n learning_rate,\\n beta1=FLAGS.adam_beta1,\\n beta2=FLAGS.adam_beta2,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'ftrl':\\n optimizer = tf.train.FtrlOptimizer(\\n learning_rate,\\n learning_rate_power=FLAGS.ftrl_learning_rate_power,\\n initial_accumulator_value=FLAGS.ftrl_initial_accumulator_value,\\n l1_regularization_strength=FLAGS.ftrl_l1,\\n l2_regularization_strength=FLAGS.ftrl_l2)\\n elif FLAGS.optimizer == 'momentum':\\n optimizer = tf.train.MomentumOptimizer(\\n learning_rate,\\n momentum=FLAGS.momentum,\\n use_nesterov=True,\\n name='Momentum')\\n elif FLAGS.optimizer == 'rmsprop':\\n optimizer = tf.train.RMSPropOptimizer(\\n learning_rate,\\n decay=FLAGS.rmsprop_decay,\\n momentum=FLAGS.momentum,\\n epsilon=FLAGS.opt_epsilon)\\n elif FLAGS.optimizer == 'sgd':\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate)\\n else:\\n raise ValueError('Optimizer [%s] was not recognized', FLAGS.optimizer)\\n return optimizer\",\n \"def init_optimizer(network, config):\\n # define optimizer and loss\\n if config.optimizer == 'adadelta':\\n opt = torch.optim.Adadelta(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\\n elif config.optimizer == 'adam':\\n opt = torch.optim.Adam(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\\n elif config.optimizer == 'rmsprop':\\n opt = torch.optim.RMSprop(network.parameters(), lr=config.lr, weight_decay=config.weight_decay)\\n return opt\",\n \"def sgd_optim(config = None, global_step = None):\\n learning_rate = config[\\\"learning_rate\\\"]\\n \\n train_step = tf.train.GradientDescentOptimizer(learning_rate)\\n #train_step = tf.train.GradientDescentOptimizer(learning_rate)\\n return train_step\",\n \"def adjust_learning_rate(optimizer, epoch):\\n lr = config.optimizer.lr\\n schedule = config.lr_schedule if hasattr(config, 'lr_schedule') else 'fixed'\\n if schedule == 'fixed':\\n if epoch >= 0.75 * config.epochs:\\n lr = config.optimizer.lr * 0.1\\n if epoch >= 0.9 * config.epochs:\\n lr = config.optimizer.lr * 0.01\\n if epoch >= config.epochs:\\n lr = config.optimizer.lr * 0.001\\n # cosine schedule\\n elif schedule == 'cosine':\\n lr = config.optimizer.lr * 0.5 * (1 + np.cos((epoch - 1) / config.epochs * np.pi))\\n elif schedule == 'search':\\n if epoch >= 75:\\n lr = 0.01\\n if epoch >= 90:\\n lr = 0.001\\n else:\\n raise ValueError('Unkown LR schedule %s' % schedule)\\n for param_group in optimizer.param_groups:\\n param_group['lr'] = lr\\n return lr\",\n \"def create_optimizer(\\n loss, learning_rate, num_train_steps, weight_decay_rate=0.0, use_tpu=False,\\n warmup_steps=0, warmup_proportion=0, lr_decay_power=1.0,\\n layerwise_lr_decay_power=-1, n_transformer_layers=None, name=\\\"adamw\\\", var_map=None):\\n global_step = tf.train.get_or_create_global_step()\\n learning_rate = tf.train.polynomial_decay(\\n learning_rate,\\n global_step,\\n num_train_steps,\\n end_learning_rate=0.0,\\n power=lr_decay_power,\\n cycle=False)\\n warmup_steps = max(num_train_steps * warmup_proportion, warmup_steps)\\n learning_rate *= tf.minimum(\\n 1.0, tf.cast(global_step, tf.float32) / tf.cast(warmup_steps, tf.float32))\\n\\n if layerwise_lr_decay_power > 0:\\n learning_rate = _get_layer_lrs(learning_rate, layerwise_lr_decay_power,\\n n_transformer_layers)\\n if name == \\\"recadam\\\":\\n optimizer = RecAdamOptimizer(\\n learning_rate=learning_rate,\\n weight_decay_rate=weight_decay_rate,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=1e-6,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"],\\n anneal_k=0.5,\\n anneal_t0=500,\\n anneal_w=1.0,\\n pretrain_cof=5000.0,\\n pretrain_params=var_map\\n )\\n else:\\n optimizer = AdamWeightDecayOptimizer(\\n learning_rate=learning_rate,\\n weight_decay_rate=weight_decay_rate,\\n beta_1=0.9,\\n beta_2=0.999,\\n epsilon=1e-6,\\n exclude_from_weight_decay=[\\\"LayerNorm\\\", \\\"layer_norm\\\", \\\"bias\\\"])\\n if use_tpu:\\n optimizer = tf.tpu.CrossShardOptimizer(optimizer)\\n\\n tvars = tf.trainable_variables()\\n grads = tf.gradients(loss, tvars)\\n (grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)\\n train_op = optimizer.apply_gradients(\\n zip(grads, tvars), global_step=global_step)\\n new_global_step = global_step + 1\\n train_op = tf.group(train_op, [global_step.assign(new_global_step)])\\n return train_op\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8004487","0.7996875","0.79782903","0.7639045","0.73738545","0.7332055","0.7301991","0.7300177","0.7256197","0.7231383","0.71939045","0.7167134","0.7102816","0.7095163","0.7091584","0.7080664","0.70553064","0.69863945","0.69457966","0.69298434","0.69246566","0.69087213","0.6899894","0.6898814","0.6898814","0.68980885","0.6892201","0.6876291","0.68741214","0.68681276","0.6828521","0.6798622","0.6794513","0.6784053","0.67781496","0.6755208","0.6752043","0.6743361","0.67215276","0.67163163","0.66881466","0.6687533","0.6683921","0.66736054","0.6668565","0.6656884","0.66488534","0.6645855","0.6643918","0.6643656","0.6627215","0.6610758","0.6578478","0.6578131","0.65780944","0.6558038","0.654031","0.65327424","0.6525127","0.65246624","0.65193355","0.64852375","0.64780366","0.6465433","0.6450413","0.64331615","0.6426566","0.6425983","0.6420555","0.64115554","0.6406337","0.64059085","0.63987356","0.6392284","0.634756","0.63434196","0.63428944","0.6339918","0.6334646","0.6321556","0.63012695","0.6299378","0.6280681","0.62757593","0.62730044","0.62704897","0.6264176","0.6263958","0.6255439","0.6240878","0.6240148","0.6235289","0.6231087","0.6227252","0.6217971","0.621726","0.62156266","0.6208399","0.6204031","0.6199527"],"string":"[\n \"0.8004487\",\n \"0.7996875\",\n \"0.79782903\",\n \"0.7639045\",\n \"0.73738545\",\n \"0.7332055\",\n \"0.7301991\",\n \"0.7300177\",\n \"0.7256197\",\n \"0.7231383\",\n \"0.71939045\",\n \"0.7167134\",\n \"0.7102816\",\n \"0.7095163\",\n \"0.7091584\",\n \"0.7080664\",\n \"0.70553064\",\n \"0.69863945\",\n \"0.69457966\",\n \"0.69298434\",\n \"0.69246566\",\n \"0.69087213\",\n \"0.6899894\",\n \"0.6898814\",\n \"0.6898814\",\n \"0.68980885\",\n \"0.6892201\",\n \"0.6876291\",\n \"0.68741214\",\n \"0.68681276\",\n \"0.6828521\",\n \"0.6798622\",\n \"0.6794513\",\n \"0.6784053\",\n \"0.67781496\",\n \"0.6755208\",\n \"0.6752043\",\n \"0.6743361\",\n \"0.67215276\",\n \"0.67163163\",\n \"0.66881466\",\n \"0.6687533\",\n \"0.6683921\",\n \"0.66736054\",\n \"0.6668565\",\n \"0.6656884\",\n \"0.66488534\",\n \"0.6645855\",\n \"0.6643918\",\n \"0.6643656\",\n \"0.6627215\",\n \"0.6610758\",\n \"0.6578478\",\n \"0.6578131\",\n \"0.65780944\",\n \"0.6558038\",\n \"0.654031\",\n \"0.65327424\",\n \"0.6525127\",\n \"0.65246624\",\n \"0.65193355\",\n \"0.64852375\",\n \"0.64780366\",\n \"0.6465433\",\n \"0.6450413\",\n \"0.64331615\",\n \"0.6426566\",\n \"0.6425983\",\n \"0.6420555\",\n \"0.64115554\",\n \"0.6406337\",\n \"0.64059085\",\n \"0.63987356\",\n \"0.6392284\",\n \"0.634756\",\n \"0.63434196\",\n \"0.63428944\",\n \"0.6339918\",\n \"0.6334646\",\n \"0.6321556\",\n \"0.63012695\",\n \"0.6299378\",\n \"0.6280681\",\n \"0.62757593\",\n \"0.62730044\",\n \"0.62704897\",\n \"0.6264176\",\n \"0.6263958\",\n \"0.6255439\",\n \"0.6240878\",\n \"0.6240148\",\n \"0.6235289\",\n \"0.6231087\",\n \"0.6227252\",\n \"0.6217971\",\n \"0.621726\",\n \"0.62156266\",\n \"0.6208399\",\n \"0.6204031\",\n \"0.6199527\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75770074"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":17397,"cells":{"query":{"kind":"string","value":"Update the display_name or display_description for this osd."},"document":{"kind":"string","value":"def update(self, **kwargs):\n self.manager.update(self, **kwargs)"},"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 updateDisplay(self, msg):\n t = msg.data\n self.displayLbl.SetLabel(\"%s\" % t)\n self.SetTitle(\"%s\" % t)","def device_display_name(self, device_display_name):\n\n self._device_display_name = device_display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name):\n\n self._display_name = display_name","def display_name(self, display_name: str):\n\n self._display_name = display_name","def set_display_name(self, display_name):\n self._call('set_display_name', display_name=display_name)","async def change_display_name(self, display_name: str):\n re = await self.request.request(url=f'https://users.roblox.com/v1/users/authenticated')\n user_id = re['id']\n data = {\"newDisplayName\": f\"{display_name}\"}\n _ok = await self.request.request(url=f\"https://users.roblox.com/v1/users/{user_id}/display-names\", data=data,\n method=\"patch\")\n return _ok","def update_description(self, option, desc):\n _, command = self.__options[option]\n self.__options[option] = (desc, command)","def updateDisplayLabel(self, value=None):\n if value is None:\n value = self.param.value()\n self.displayLabel.setText(value.toString(self.param.opts['format']))","def setDisplayName( self, name ):\n self._displayName = name\n self._titleFont = None","def update_client(self, display_name, player=PLAYER_IDENTIFIER):\n self.state.update_client(display_name, player)","def set_description(self, desc: str) -> None:\n self.metadata.data[\"description\"] = desc","def set_description(self, sNewDescription):\n\t\tcall_sdk_function('PrlVmDev_SetDescription', self.handle, sNewDescription)","def updateNameAndDescription(self, name, desc):\n self.magneticfield.name = name\n self.magneticfield.description = desc\n\n self.magneticfield.writeFile()","def _update_display(self, loop=True):\n\n sensors_data = self.get_sensors_data()\n\n if self.current_item.entity_type is WeatherEntityType.TEMPERATURE:\n pixels = self.current_item.show_pixels(sensors_data[0])\n elif self.current_item.entity_type is WeatherEntityType.HUMIDITY:\n pixels = self.current_item.show_pixels(sensors_data[2])\n else:\n pixels = self.current_item.show_pixels(sensors_data[3])\n\n self._sense_hat.set_rotation(self.current_style.rotation)\n self._sense_hat.set_pixels(pixels)\n\n if loop:\n self._update_timer = self._start_timer(Config.UPDATE_INTERVAL, self._update_display)","def set_description(self, desc):\n super().set_description(desc, refresh=True)\n if self._pbar:\n self._pbar._set_description(self.desc)","def opt_display(self, display):\n key = get_enum_key(display, DISPLAYS)\n if key is not None:\n self.conf[\"display\"] = key\n self.display = DISPLAYS[key]\n print(\"Set display %r\" % key)\n else:\n print(\"Unknown display %r\" % display)","def update_displays(self):\n for key, value in self.lnp.settings:\n if key in list(self.controls.keys()):\n if isinstance(self.controls[key], Entry):\n self.controls[key].delete(0, END)\n self.controls[key].insert(0, value)\n else:\n self.controls[key][\"text\"] = (\n self.controls[key][\"text\"].split(':')[0] + ': ' +\n value)","def update_description(self, host, baseUrl, description):\n self._host = host\n self._urlBase = baseUrl\n self._description = description\n return","def update_display(self) -> None:\n if self.display is None:\n raise RuntimeError(\n \"Tried to update the display, but a display hasn't been \"\n \"created yet! To create a display for the renderer, you must \"\n \"call the `make_display()` method.\"\n )\n\n self.display.blit(self.surface, [0, 0])\n pygame.display.update()\n\n # Sounds:\n if self.audio_on and self.game.sound_cache is not None:\n sound_name = self.game.sound_cache\n self.sounds[sound_name].play()","def adjust_display(self, display: typing.List[typing.List[str]]):","def setLabel(self, desc):\n self.label = desc","def set_display_value(self, display_value):\n\n\t\tif display_value is not None and not isinstance(display_value, str):\n\t\t\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: display_value EXPECTED TYPE: str', None, None)\n\t\t\n\t\tself.__display_value = display_value\n\t\tself.__key_modified['display_value'] = 1","def set_description(self, sDescription):\n\t\tcall_sdk_function('PrlVirtNet_SetDescription', self.handle, sDescription)","def set_description(self, data):\n self._description = self._uni(data)","def Network_displayName(self, data):\n self.name = data['name']\n self._server.Send_publicInfo()","def setServiceDescription(self, description):\n with self.zeroconf.lock:\n self.zeroconf.outbox.put(description)","def updateDisplay(self):\n if self._displayPjt:\n self._displayPjt.updateim()\n if self._displayUsr:\n self._displayUsr.updateim()\n if self._displayVtk:\n self._displayVtk.updateim()","def set_description(self, sNewShareDescription):\n\t\tcall_sdk_function('PrlShare_SetDescription', self.handle, sNewShareDescription)","def set_description(self, description):\r\n self.__description = description","def _update_display(self, game, action):\n self._update_label(action.outer_pos, action.inner_pos, action.move)","def SetDescription(self, description):\n self.description = str(description)","def short_description(self, short_description):\n self._short_description = short_description","def request_description_update():\n global should_update_description\n should_update_description = True","def display(self, obj, parent, display_id=False, update=False):\n data, metadata = self.display_formatter.format(obj)\n\n if metadata is None:\n metadata = {}\n\n transient = {}\n if display_id:\n transient = {\"display_id\": display_id}\n\n content = {\"data\": data, \"metadata\": metadata, \"transient\": transient}\n\n self.session.send(\n self.iopub_socket,\n \"update_display_data\" if update else \"display_data\",\n content,\n parent=parent,\n ident=self._topic(\"display_data\"),\n )","def writeDescription(self,description):\n description = description[:min(255,len(description))]\n self.tes3.hedr.description = description\n self.tes3.hedr.setChanged()\n self.writeHedr()","def set_desc(self, desc: str):\n self._desc = desc","def set_description(self, description):\n self.__description = description","def short_description(self, short_description):\n\n self._short_description = short_description","def description(self, value):\n self._update_values('description', value)","def add_description(self, desc):\n self.description = desc","def description(self, new_description):\r\n self.set({\"description\": new_description})","def metadata_update(self, _):\n self.details.original_widget = YesNoWidget('Update metadata files?', self.__metadata_update)","def update(self):\n try:\n response = self.client.describe_stream(StreamName=self.stream)\n except Exception as exc:\n logger.exception(f'Failure trying to get stream: \"{self.stream}\".', exc)\n else:\n if response['ResponseMetadata']['HTTPStatusCode'] != 200:\n logger.error(f'Failure to describe stream \"{self.stream}\": {response}')\n else:\n self.description = response['StreamDescription']","def set_description(self, room_description):\n self.description = room_description","def driver_update_display(self):\n raise Exception(\"missing implementation \")","def set_description(desc):\n global last_description\n last_description = desc","def stat_description(self, stat_description):\n\n self._stat_description = stat_description","def data_source_display_name(self, data_source_display_name):\n\n self._data_source_display_name = data_source_display_name","def update_description_debounced(self, value):\n self.update_description(value)","def set_description(self, description):\n self.description = description","def setText(self, title: str, description: str):\n self.title.setText(title)\n self.description.setText(description)","def update_display(self):\r\n\r\n # The display.update() Updates the screen, making the new frame replace the old one. \r\n pg.display.update()\r\n \r\n # clock.tick sets a framerate for the game.\r\n # This is to make the game run at a stable fps \r\n self.clock.tick(cng.FRAMERATE)","def set_sdesc(self, sdesc: str):\n self._sdesc = sdesc","def set_desc(self, item_desc):\r\n self.description = item_desc","def set_description(self, descr):\n self._current_test_descr = descr","def change_display(self, obj):\n # Don't save until current_name is defined\n # If it's defined, save under the current paragraph name\n if self.current_name:\n self.save_paragraph()\n # Then change to new paragraph\n objs = self.plist.get_selected_objects()\n store, node = self.plist.get_selected()\n self.current_name = store.get_value(node, 0)\n self.current_p = objs[0]\n self.draw()","def setDescription(self, description):\n url = G.api + self.testId + '/snapshots/' + self.hash\n self.info = requests.put(url, auth=(G.username, G.authkey), data={'description':description})","def set_object_description(self, agent, Description):\n\n self.send_ObjectDescription(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Description]})","def __addDisplayNames(self, tree):\n \n display = self.__getStore(self.__data, \"display\")\n \n for key in [\"languages\", \"scripts\", \"territories\", \"variants\", \"keys\", \"types\", \"measurementSystemNames\"]:\n # make it a little bit shorter, there is not really any conflict potential\n if key == \"measurementSystemNames\":\n store = self.__getStore(display, \"Measure\")\n elif key == \"territories\":\n store = self.__getStore(display, \"Territory\")\n else:\n # remove last character \"s\" to force singular\n store = self.__getStore(display, key[:-1])\n \n for element in tree.findall(\"./localeDisplayNames/%s/*\" % key):\n if not element.get(\"draft\"):\n field = element.get(\"type\")\n if not field in store:\n store[camelCaseToUpper(field)] = element.text","def set_description(self, description):\n self._description = description","def configure(self, manager):\n success, display = manager.c.eval(\"self.core.display_name\")\n assert success\n self.env[\"WAYLAND_DISPLAY\"] = display","def provider_display_name(self, provider_display_name: str):\n\n self._provider_display_name = provider_display_name","def update(self) -> None:\n self._api.update()\n if self.available:\n self._attr_native_value = self._api.data[self.entity_description.key]\n else:\n self._attr_native_value = None","def description(self, description):\n self._description = description","def description(self, description):\n self._description = description","def description(self, description):\n self._description = description","def description(self, description):\n self._description = description","def set_longdescription(self, longdesc):\n self.longdescription(longdesc)","def _refresh(self):\n self._need_display_update = True\n self._update()","def description(self, value):\n self.definition.description = value","def update_info(self):\n self.execution_status_widget.update()\n self.execution_info_widget.update()\n self.cluster_widget.update() # update the cluster info even if it is not being displayed\n self.details.original_widget.update()","def UpdateDisplay(self):\n ##Jconf\n self.chJconf.Clear()\n for name in self.state.GetSurface(\"JconfDict\").GetNames():\n self.chJconf.Append(name)\n self.chJconf.SetStringSelection(self.state.GetSurface(\"JconfSelection\"))\n self.chJconf.Enable(self.state.IsEnabled(\"JconfDict\") == True and\n self.state.IsEnabled(\"JconfSelection\") == True and\n self.state.GetSurface(\"Xplorer\") == True)\n self.bEditJconf.Enable(self.state.IsEnabled(\"JconfDict\") and\n self.state.GetSurface(\"Xplorer\") == True)\n ##Name Server\n self.cbNameServer.SetValue(self.state.GetSurface(\"NameServer\"))\n self.cbNameServer.Enable(self.state.IsEnabled(\"NameServer\"))\n ##Conductor\n self.cbConductor.SetValue(self.state.GetSurface(\"Conductor\"))\n self.cbConductor.Enable(self.state.IsEnabled(\"Conductor\"))\n ##Xplorer\n self.cbXplorer.SetValue(self.state.GetSurface(\"Xplorer\"))\n self.cbXplorer.Enable(self.state.IsEnabled(\"Xplorer\"))\n ##Desktop Mode\n self.cbDesktop.SetValue(self.state.GetSurface(\"DesktopMode\"))\n self.cbDesktop.Enable(self.state.IsEnabled(\"DesktopMode\"))\n ##Xplorer Type\n if self.state.GetSurface(\"DesktopMode\"):\n self.rbXplorer.SetSelection(0)\n else:\n if (self.state.GetSurface(\"XplorerType\") == \"OSG-VEP\"):\n self.rbXplorer.SetSelection(0)\n else:\n self.rbXplorer.SetSelection(1)\n self.rbXplorer.Enable(self.state.IsEnabled(\"XplorerType\") == True and\n self.state.GetSurface(\"DesktopMode\") == False and\n self.state.GetSurface(\"Xplorer\") == True)\n ##Cluster Node button\n self.bCluster.Enable(CLUSTER_ENABLED and\n self.state.GetSurface(\"Xplorer\") == True and\n self.state.GetSurface(\"DesktopMode\") == False and\n self.state.GetSurface(\"XplorerType\") == \"OSG-VEPC\")\n return","def _update_from_pb(self, instance_pb):\n if not instance_pb.display_name: # Simple field (string)\n raise ValueError('Instance protobuf does not contain display_name')\n self.display_name = instance_pb.display_name","def updateScreenText(self) :\n\t\t# Update the text showing avatar's position\n\t\th = self.avatarNP.getH()\n\t\tif h < -180 : h += 360\n\t\telif h > 180 : h -= 360\n\t\tself.avPos.setText(\"Your location (x, y, dir): (%d, %d, %d)\"%\\\n\t\t\t(self.avatarNP.getX(), self.avatarNP.getY(), h))\n\n\t\t# update text showing visible avatars\n \t\tself.showNumVisible.setText(\"Visible avatars: \" + \\\n\t\t\t\t\t self.updateVisList())","def set_description(self, sNewVmDescription):\n\t\tcall_sdk_function('PrlVmCfg_SetDescription', self.handle, sNewVmDescription)","def update_help(self):\n if self.region_id is not None:\n controller.set_help_text(\"\"\"Drag to desired region width.<br/>\\n\n <b>R</b> to set the region<br/>\\n\n <b>D</b> to delete/cancel the current region<br/>\n <b>*</b> to extend to maximum on this side\n \"\"\")\n else:\n controller.set_help_text(\"\"\"<b> Edit Regions: </b><br/>\\n\n <b>R</b> to start a new region<br/>\\n\n <b>E</b> to edit nearest region<br/>\\n\n <b>D</b> to delete the nearest region\n \"\"\")","def set_description(self):\n if 'description' not in self.data:\n if self.verbose:\n click.echo('Adding empty descriptions to root')\n self.data['description'] = ''","def changeDisplay1(self):\n\n print (\"--Changing to display 1--\")\n self.display1Button.setDown(True)\n self.display2Button.setDown(False)\n self.statustext.setText(\"Changed to Display 1\")\n self.photo.setPixmap(QtGui.QPixmap(self.firstScreen))\n self.ActivePhoto = self.firstScreen","def AddLowerDisplayInfo(self, display_info):\n if not self._format:\n self._format = display_info.format\n if display_info.transforms:\n transforms = dict(display_info.transforms)\n transforms.update(self.transforms)\n self._transforms = transforms\n if display_info.aliases:\n aliases = dict(display_info.aliases)\n aliases.update(self._aliases)\n self._aliases = aliases","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description","def description(self, description):\n\n self._description = description"],"string":"[\n \"def updateDisplay(self, msg):\\n t = msg.data\\n self.displayLbl.SetLabel(\\\"%s\\\" % t)\\n self.SetTitle(\\\"%s\\\" % t)\",\n \"def device_display_name(self, device_display_name):\\n\\n self._device_display_name = device_display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name):\\n\\n self._display_name = display_name\",\n \"def display_name(self, display_name: str):\\n\\n self._display_name = display_name\",\n \"def set_display_name(self, display_name):\\n self._call('set_display_name', display_name=display_name)\",\n \"async def change_display_name(self, display_name: str):\\n re = await self.request.request(url=f'https://users.roblox.com/v1/users/authenticated')\\n user_id = re['id']\\n data = {\\\"newDisplayName\\\": f\\\"{display_name}\\\"}\\n _ok = await self.request.request(url=f\\\"https://users.roblox.com/v1/users/{user_id}/display-names\\\", data=data,\\n method=\\\"patch\\\")\\n return _ok\",\n \"def update_description(self, option, desc):\\n _, command = self.__options[option]\\n self.__options[option] = (desc, command)\",\n \"def updateDisplayLabel(self, value=None):\\n if value is None:\\n value = self.param.value()\\n self.displayLabel.setText(value.toString(self.param.opts['format']))\",\n \"def setDisplayName( self, name ):\\n self._displayName = name\\n self._titleFont = None\",\n \"def update_client(self, display_name, player=PLAYER_IDENTIFIER):\\n self.state.update_client(display_name, player)\",\n \"def set_description(self, desc: str) -> None:\\n self.metadata.data[\\\"description\\\"] = desc\",\n \"def set_description(self, sNewDescription):\\n\\t\\tcall_sdk_function('PrlVmDev_SetDescription', self.handle, sNewDescription)\",\n \"def updateNameAndDescription(self, name, desc):\\n self.magneticfield.name = name\\n self.magneticfield.description = desc\\n\\n self.magneticfield.writeFile()\",\n \"def _update_display(self, loop=True):\\n\\n sensors_data = self.get_sensors_data()\\n\\n if self.current_item.entity_type is WeatherEntityType.TEMPERATURE:\\n pixels = self.current_item.show_pixels(sensors_data[0])\\n elif self.current_item.entity_type is WeatherEntityType.HUMIDITY:\\n pixels = self.current_item.show_pixels(sensors_data[2])\\n else:\\n pixels = self.current_item.show_pixels(sensors_data[3])\\n\\n self._sense_hat.set_rotation(self.current_style.rotation)\\n self._sense_hat.set_pixels(pixels)\\n\\n if loop:\\n self._update_timer = self._start_timer(Config.UPDATE_INTERVAL, self._update_display)\",\n \"def set_description(self, desc):\\n super().set_description(desc, refresh=True)\\n if self._pbar:\\n self._pbar._set_description(self.desc)\",\n \"def opt_display(self, display):\\n key = get_enum_key(display, DISPLAYS)\\n if key is not None:\\n self.conf[\\\"display\\\"] = key\\n self.display = DISPLAYS[key]\\n print(\\\"Set display %r\\\" % key)\\n else:\\n print(\\\"Unknown display %r\\\" % display)\",\n \"def update_displays(self):\\n for key, value in self.lnp.settings:\\n if key in list(self.controls.keys()):\\n if isinstance(self.controls[key], Entry):\\n self.controls[key].delete(0, END)\\n self.controls[key].insert(0, value)\\n else:\\n self.controls[key][\\\"text\\\"] = (\\n self.controls[key][\\\"text\\\"].split(':')[0] + ': ' +\\n value)\",\n \"def update_description(self, host, baseUrl, description):\\n self._host = host\\n self._urlBase = baseUrl\\n self._description = description\\n return\",\n \"def update_display(self) -> None:\\n if self.display is None:\\n raise RuntimeError(\\n \\\"Tried to update the display, but a display hasn't been \\\"\\n \\\"created yet! To create a display for the renderer, you must \\\"\\n \\\"call the `make_display()` method.\\\"\\n )\\n\\n self.display.blit(self.surface, [0, 0])\\n pygame.display.update()\\n\\n # Sounds:\\n if self.audio_on and self.game.sound_cache is not None:\\n sound_name = self.game.sound_cache\\n self.sounds[sound_name].play()\",\n \"def adjust_display(self, display: typing.List[typing.List[str]]):\",\n \"def setLabel(self, desc):\\n self.label = desc\",\n \"def set_display_value(self, display_value):\\n\\n\\t\\tif display_value is not None and not isinstance(display_value, str):\\n\\t\\t\\traise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: display_value EXPECTED TYPE: str', None, None)\\n\\t\\t\\n\\t\\tself.__display_value = display_value\\n\\t\\tself.__key_modified['display_value'] = 1\",\n \"def set_description(self, sDescription):\\n\\t\\tcall_sdk_function('PrlVirtNet_SetDescription', self.handle, sDescription)\",\n \"def set_description(self, data):\\n self._description = self._uni(data)\",\n \"def Network_displayName(self, data):\\n self.name = data['name']\\n self._server.Send_publicInfo()\",\n \"def setServiceDescription(self, description):\\n with self.zeroconf.lock:\\n self.zeroconf.outbox.put(description)\",\n \"def updateDisplay(self):\\n if self._displayPjt:\\n self._displayPjt.updateim()\\n if self._displayUsr:\\n self._displayUsr.updateim()\\n if self._displayVtk:\\n self._displayVtk.updateim()\",\n \"def set_description(self, sNewShareDescription):\\n\\t\\tcall_sdk_function('PrlShare_SetDescription', self.handle, sNewShareDescription)\",\n \"def set_description(self, description):\\r\\n self.__description = description\",\n \"def _update_display(self, game, action):\\n self._update_label(action.outer_pos, action.inner_pos, action.move)\",\n \"def SetDescription(self, description):\\n self.description = str(description)\",\n \"def short_description(self, short_description):\\n self._short_description = short_description\",\n \"def request_description_update():\\n global should_update_description\\n should_update_description = True\",\n \"def display(self, obj, parent, display_id=False, update=False):\\n data, metadata = self.display_formatter.format(obj)\\n\\n if metadata is None:\\n metadata = {}\\n\\n transient = {}\\n if display_id:\\n transient = {\\\"display_id\\\": display_id}\\n\\n content = {\\\"data\\\": data, \\\"metadata\\\": metadata, \\\"transient\\\": transient}\\n\\n self.session.send(\\n self.iopub_socket,\\n \\\"update_display_data\\\" if update else \\\"display_data\\\",\\n content,\\n parent=parent,\\n ident=self._topic(\\\"display_data\\\"),\\n )\",\n \"def writeDescription(self,description):\\n description = description[:min(255,len(description))]\\n self.tes3.hedr.description = description\\n self.tes3.hedr.setChanged()\\n self.writeHedr()\",\n \"def set_desc(self, desc: str):\\n self._desc = desc\",\n \"def set_description(self, description):\\n self.__description = description\",\n \"def short_description(self, short_description):\\n\\n self._short_description = short_description\",\n \"def description(self, value):\\n self._update_values('description', value)\",\n \"def add_description(self, desc):\\n self.description = desc\",\n \"def description(self, new_description):\\r\\n self.set({\\\"description\\\": new_description})\",\n \"def metadata_update(self, _):\\n self.details.original_widget = YesNoWidget('Update metadata files?', self.__metadata_update)\",\n \"def update(self):\\n try:\\n response = self.client.describe_stream(StreamName=self.stream)\\n except Exception as exc:\\n logger.exception(f'Failure trying to get stream: \\\"{self.stream}\\\".', exc)\\n else:\\n if response['ResponseMetadata']['HTTPStatusCode'] != 200:\\n logger.error(f'Failure to describe stream \\\"{self.stream}\\\": {response}')\\n else:\\n self.description = response['StreamDescription']\",\n \"def set_description(self, room_description):\\n self.description = room_description\",\n \"def driver_update_display(self):\\n raise Exception(\\\"missing implementation \\\")\",\n \"def set_description(desc):\\n global last_description\\n last_description = desc\",\n \"def stat_description(self, stat_description):\\n\\n self._stat_description = stat_description\",\n \"def data_source_display_name(self, data_source_display_name):\\n\\n self._data_source_display_name = data_source_display_name\",\n \"def update_description_debounced(self, value):\\n self.update_description(value)\",\n \"def set_description(self, description):\\n self.description = description\",\n \"def setText(self, title: str, description: str):\\n self.title.setText(title)\\n self.description.setText(description)\",\n \"def update_display(self):\\r\\n\\r\\n # The display.update() Updates the screen, making the new frame replace the old one. \\r\\n pg.display.update()\\r\\n \\r\\n # clock.tick sets a framerate for the game.\\r\\n # This is to make the game run at a stable fps \\r\\n self.clock.tick(cng.FRAMERATE)\",\n \"def set_sdesc(self, sdesc: str):\\n self._sdesc = sdesc\",\n \"def set_desc(self, item_desc):\\r\\n self.description = item_desc\",\n \"def set_description(self, descr):\\n self._current_test_descr = descr\",\n \"def change_display(self, obj):\\n # Don't save until current_name is defined\\n # If it's defined, save under the current paragraph name\\n if self.current_name:\\n self.save_paragraph()\\n # Then change to new paragraph\\n objs = self.plist.get_selected_objects()\\n store, node = self.plist.get_selected()\\n self.current_name = store.get_value(node, 0)\\n self.current_p = objs[0]\\n self.draw()\",\n \"def setDescription(self, description):\\n url = G.api + self.testId + '/snapshots/' + self.hash\\n self.info = requests.put(url, auth=(G.username, G.authkey), data={'description':description})\",\n \"def set_object_description(self, agent, Description):\\n\\n self.send_ObjectDescription(agent, agent.agent_id, agent.session_id, {1:[self.LocalID, Description]})\",\n \"def __addDisplayNames(self, tree):\\n \\n display = self.__getStore(self.__data, \\\"display\\\")\\n \\n for key in [\\\"languages\\\", \\\"scripts\\\", \\\"territories\\\", \\\"variants\\\", \\\"keys\\\", \\\"types\\\", \\\"measurementSystemNames\\\"]:\\n # make it a little bit shorter, there is not really any conflict potential\\n if key == \\\"measurementSystemNames\\\":\\n store = self.__getStore(display, \\\"Measure\\\")\\n elif key == \\\"territories\\\":\\n store = self.__getStore(display, \\\"Territory\\\")\\n else:\\n # remove last character \\\"s\\\" to force singular\\n store = self.__getStore(display, key[:-1])\\n \\n for element in tree.findall(\\\"./localeDisplayNames/%s/*\\\" % key):\\n if not element.get(\\\"draft\\\"):\\n field = element.get(\\\"type\\\")\\n if not field in store:\\n store[camelCaseToUpper(field)] = element.text\",\n \"def set_description(self, description):\\n self._description = description\",\n \"def configure(self, manager):\\n success, display = manager.c.eval(\\\"self.core.display_name\\\")\\n assert success\\n self.env[\\\"WAYLAND_DISPLAY\\\"] = display\",\n \"def provider_display_name(self, provider_display_name: str):\\n\\n self._provider_display_name = provider_display_name\",\n \"def update(self) -> None:\\n self._api.update()\\n if self.available:\\n self._attr_native_value = self._api.data[self.entity_description.key]\\n else:\\n self._attr_native_value = None\",\n \"def description(self, description):\\n self._description = description\",\n \"def description(self, description):\\n self._description = description\",\n \"def description(self, description):\\n self._description = description\",\n \"def description(self, description):\\n self._description = description\",\n \"def set_longdescription(self, longdesc):\\n self.longdescription(longdesc)\",\n \"def _refresh(self):\\n self._need_display_update = True\\n self._update()\",\n \"def description(self, value):\\n self.definition.description = value\",\n \"def update_info(self):\\n self.execution_status_widget.update()\\n self.execution_info_widget.update()\\n self.cluster_widget.update() # update the cluster info even if it is not being displayed\\n self.details.original_widget.update()\",\n \"def UpdateDisplay(self):\\n ##Jconf\\n self.chJconf.Clear()\\n for name in self.state.GetSurface(\\\"JconfDict\\\").GetNames():\\n self.chJconf.Append(name)\\n self.chJconf.SetStringSelection(self.state.GetSurface(\\\"JconfSelection\\\"))\\n self.chJconf.Enable(self.state.IsEnabled(\\\"JconfDict\\\") == True and\\n self.state.IsEnabled(\\\"JconfSelection\\\") == True and\\n self.state.GetSurface(\\\"Xplorer\\\") == True)\\n self.bEditJconf.Enable(self.state.IsEnabled(\\\"JconfDict\\\") and\\n self.state.GetSurface(\\\"Xplorer\\\") == True)\\n ##Name Server\\n self.cbNameServer.SetValue(self.state.GetSurface(\\\"NameServer\\\"))\\n self.cbNameServer.Enable(self.state.IsEnabled(\\\"NameServer\\\"))\\n ##Conductor\\n self.cbConductor.SetValue(self.state.GetSurface(\\\"Conductor\\\"))\\n self.cbConductor.Enable(self.state.IsEnabled(\\\"Conductor\\\"))\\n ##Xplorer\\n self.cbXplorer.SetValue(self.state.GetSurface(\\\"Xplorer\\\"))\\n self.cbXplorer.Enable(self.state.IsEnabled(\\\"Xplorer\\\"))\\n ##Desktop Mode\\n self.cbDesktop.SetValue(self.state.GetSurface(\\\"DesktopMode\\\"))\\n self.cbDesktop.Enable(self.state.IsEnabled(\\\"DesktopMode\\\"))\\n ##Xplorer Type\\n if self.state.GetSurface(\\\"DesktopMode\\\"):\\n self.rbXplorer.SetSelection(0)\\n else:\\n if (self.state.GetSurface(\\\"XplorerType\\\") == \\\"OSG-VEP\\\"):\\n self.rbXplorer.SetSelection(0)\\n else:\\n self.rbXplorer.SetSelection(1)\\n self.rbXplorer.Enable(self.state.IsEnabled(\\\"XplorerType\\\") == True and\\n self.state.GetSurface(\\\"DesktopMode\\\") == False and\\n self.state.GetSurface(\\\"Xplorer\\\") == True)\\n ##Cluster Node button\\n self.bCluster.Enable(CLUSTER_ENABLED and\\n self.state.GetSurface(\\\"Xplorer\\\") == True and\\n self.state.GetSurface(\\\"DesktopMode\\\") == False and\\n self.state.GetSurface(\\\"XplorerType\\\") == \\\"OSG-VEPC\\\")\\n return\",\n \"def _update_from_pb(self, instance_pb):\\n if not instance_pb.display_name: # Simple field (string)\\n raise ValueError('Instance protobuf does not contain display_name')\\n self.display_name = instance_pb.display_name\",\n \"def updateScreenText(self) :\\n\\t\\t# Update the text showing avatar's position\\n\\t\\th = self.avatarNP.getH()\\n\\t\\tif h < -180 : h += 360\\n\\t\\telif h > 180 : h -= 360\\n\\t\\tself.avPos.setText(\\\"Your location (x, y, dir): (%d, %d, %d)\\\"%\\\\\\n\\t\\t\\t(self.avatarNP.getX(), self.avatarNP.getY(), h))\\n\\n\\t\\t# update text showing visible avatars\\n \\t\\tself.showNumVisible.setText(\\\"Visible avatars: \\\" + \\\\\\n\\t\\t\\t\\t\\t self.updateVisList())\",\n \"def set_description(self, sNewVmDescription):\\n\\t\\tcall_sdk_function('PrlVmCfg_SetDescription', self.handle, sNewVmDescription)\",\n \"def update_help(self):\\n if self.region_id is not None:\\n controller.set_help_text(\\\"\\\"\\\"Drag to desired region width.<br/>\\\\n\\n <b>R</b> to set the region<br/>\\\\n\\n <b>D</b> to delete/cancel the current region<br/>\\n <b>*</b> to extend to maximum on this side\\n \\\"\\\"\\\")\\n else:\\n controller.set_help_text(\\\"\\\"\\\"<b> Edit Regions: </b><br/>\\\\n\\n <b>R</b> to start a new region<br/>\\\\n\\n <b>E</b> to edit nearest region<br/>\\\\n\\n <b>D</b> to delete the nearest region\\n \\\"\\\"\\\")\",\n \"def set_description(self):\\n if 'description' not in self.data:\\n if self.verbose:\\n click.echo('Adding empty descriptions to root')\\n self.data['description'] = ''\",\n \"def changeDisplay1(self):\\n\\n print (\\\"--Changing to display 1--\\\")\\n self.display1Button.setDown(True)\\n self.display2Button.setDown(False)\\n self.statustext.setText(\\\"Changed to Display 1\\\")\\n self.photo.setPixmap(QtGui.QPixmap(self.firstScreen))\\n self.ActivePhoto = self.firstScreen\",\n \"def AddLowerDisplayInfo(self, display_info):\\n if not self._format:\\n self._format = display_info.format\\n if display_info.transforms:\\n transforms = dict(display_info.transforms)\\n transforms.update(self.transforms)\\n self._transforms = transforms\\n if display_info.aliases:\\n aliases = dict(display_info.aliases)\\n aliases.update(self._aliases)\\n self._aliases = aliases\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\",\n \"def description(self, description):\\n\\n self._description = description\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63896036","0.6336821","0.6326636","0.6326636","0.6326636","0.6326636","0.6326636","0.6326636","0.6314018","0.6314018","0.6314018","0.6314018","0.6314018","0.6314018","0.62420577","0.6225104","0.61178285","0.6005843","0.5882068","0.58664495","0.58609277","0.5845469","0.5830161","0.5763741","0.576062","0.57551545","0.5656342","0.5640462","0.56342036","0.56014985","0.55912197","0.5589739","0.55804586","0.5566686","0.5551142","0.55484927","0.553487","0.5531851","0.55068266","0.55030864","0.5494947","0.5480133","0.5477972","0.54551005","0.5454149","0.5445203","0.54309845","0.5411202","0.54020673","0.54010457","0.53986067","0.5385068","0.53831","0.53787684","0.53772086","0.5374033","0.5365687","0.5350823","0.5350485","0.5347794","0.5335469","0.5328674","0.5324169","0.5320499","0.527705","0.52726835","0.52688605","0.5261883","0.52611685","0.5255533","0.5252302","0.5249426","0.5247638","0.5246382","0.5241417","0.5241417","0.5241417","0.5241417","0.5237043","0.52344376","0.52299505","0.5219414","0.5218051","0.5216774","0.5216221","0.5191609","0.5180432","0.5178692","0.5177369","0.5172642","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972","0.5165972"],"string":"[\n \"0.63896036\",\n \"0.6336821\",\n \"0.6326636\",\n \"0.6326636\",\n \"0.6326636\",\n \"0.6326636\",\n \"0.6326636\",\n \"0.6326636\",\n \"0.6314018\",\n \"0.6314018\",\n \"0.6314018\",\n \"0.6314018\",\n \"0.6314018\",\n \"0.6314018\",\n \"0.62420577\",\n \"0.6225104\",\n \"0.61178285\",\n \"0.6005843\",\n \"0.5882068\",\n \"0.58664495\",\n \"0.58609277\",\n \"0.5845469\",\n \"0.5830161\",\n \"0.5763741\",\n \"0.576062\",\n \"0.57551545\",\n \"0.5656342\",\n \"0.5640462\",\n \"0.56342036\",\n \"0.56014985\",\n \"0.55912197\",\n \"0.5589739\",\n \"0.55804586\",\n \"0.5566686\",\n \"0.5551142\",\n \"0.55484927\",\n \"0.553487\",\n \"0.5531851\",\n \"0.55068266\",\n \"0.55030864\",\n \"0.5494947\",\n \"0.5480133\",\n \"0.5477972\",\n \"0.54551005\",\n \"0.5454149\",\n \"0.5445203\",\n \"0.54309845\",\n \"0.5411202\",\n \"0.54020673\",\n \"0.54010457\",\n \"0.53986067\",\n \"0.5385068\",\n \"0.53831\",\n \"0.53787684\",\n \"0.53772086\",\n \"0.5374033\",\n \"0.5365687\",\n \"0.5350823\",\n \"0.5350485\",\n \"0.5347794\",\n \"0.5335469\",\n \"0.5328674\",\n \"0.5324169\",\n \"0.5320499\",\n \"0.527705\",\n \"0.52726835\",\n \"0.52688605\",\n \"0.5261883\",\n \"0.52611685\",\n \"0.5255533\",\n \"0.5252302\",\n \"0.5249426\",\n \"0.5247638\",\n \"0.5246382\",\n \"0.5241417\",\n \"0.5241417\",\n \"0.5241417\",\n \"0.5241417\",\n \"0.5237043\",\n \"0.52344376\",\n \"0.52299505\",\n \"0.5219414\",\n \"0.5218051\",\n \"0.5216774\",\n \"0.5216221\",\n \"0.5191609\",\n \"0.5180432\",\n \"0.5178692\",\n \"0.5177369\",\n \"0.5172642\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\",\n \"0.5165972\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17398,"cells":{"query":{"kind":"string","value":"Delete the specified osd ignoring its current state."},"document":{"kind":"string","value":"def force_delete(self):\n self.manager.force_delete(self)"},"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 remove(self, purge, timeout, force):\n # Set the CRUSH weight to 0.\n hookenv.log('Reweighting OSD', hookenv.DEBUG)\n reweight_osd(self.osd_id)\n\n # Ensure that the OSD is safe to stop and destroy.\n end = (datetime.datetime.now() +\n datetime.timedelta(seconds=timeout * 60))\n safe_stop, safe_destroy = False, False\n\n while True:\n if not safe_stop and safe_to_stop(self.osd_id):\n safe_stop = True\n if not safe_destroy and safe_to_destroy(self.osd_id):\n safe_destroy = True\n\n if safe_stop and safe_destroy:\n break\n\n curr = datetime.datetime.now()\n if curr >= end:\n if force:\n hookenv.log(\n 'OSD not safe to destroy, but \"force\" was specified',\n hookenv.DEBUG)\n break\n\n raise RemoveException(\n 'timed out waiting for an OSD to be safe to destroy')\n time.sleep(min(1, (end - curr).total_seconds()))\n\n # Stop the OSD service.\n hookenv.log('Stopping the OSD service', hookenv.DEBUG)\n charms_ceph.utils.stop_osd(self.osd_id[4:])\n charms_ceph.utils.disable_osd(self.osd_id[4:])\n unit_filename = \\\n '/run/systemd/system/ceph-osd.target.wants/ceph-osd@{}.service' \\\n .format(self.osd_id[4:])\n if os.path.exists(unit_filename):\n os.remove(unit_filename)\n\n subprocess.check_call(['systemctl', 'daemon-reload'])\n\n # Remove the OSD from the cluster.\n hookenv.log('Destroying the OSD', hookenv.DEBUG)\n destroy(self.osd_id, purge)\n report = self.report.setdefault(self.osd_device,\n {'osd-ids': self.osd_id})\n\n if self.bcache_backing:\n # Remove anything related to bcache.\n size = int(ceil(device_size(self.bcache_caching)))\n caching = get_parent_device(self.bcache_caching)\n report.update({'cache-devices': caching, 'partition-size': size})\n bcache_remove(self.device, self.bcache_backing,\n self.bcache_caching)\n else:\n remove_lvm(self.device)\n wipefs_safely(self.device)","def safe_to_destroy(osd_id, timeout=300):\n ret = subprocess.call([\n 'ceph', '--id', 'osd-removal',\n 'osd', 'safe-to-destroy', osd_id\n ], timeout=timeout)\n return ret == 0","def destroy(osd_id, purge=False, timeout=600):\n for _ in range(10):\n # We might get here before the OSD is marked as down. As such,\n # retry if the error code is EBUSY.\n try:\n subprocess.check_call([\n 'ceph', '--id', 'osd-removal', 'osd',\n 'purge' if purge else 'destroy',\n osd_id, '--yes-i-really-mean-it'\n ], timeout=timeout)\n return\n except subprocess.CalledProcessError as e:\n if e.returncode != errno.EBUSY:\n raise\n time.sleep(0.1)","def delete(**_):\n\n volume_id = utils.get_external_resource_id_or_raise(\n 'delete EBS volume', ctx.instance)\n\n if _delete_external_volume():\n return\n\n ctx.logger.debug('Deleting EBS volume: {0}'.format(volume_id))\n\n if not _delete_volume(volume_id):\n return ctx.operation.retry(\n message='Failed to delete volume {0}.'\n .format(volume_id))\n\n utils.unassign_runtime_property_from_resource(\n constants.ZONE, ctx.instance)\n\n utils.unassign_runtime_property_from_resource(\n constants.EXTERNAL_RESOURCE_ID, ctx.instance)\n\n ctx.logger.info(\n 'Deleted EBS volume: {0}.'\n .format(volume_id))","def delete(self):\n\n lod_history = self.repo._get_lod_history(self.lod)\n assert lod_history.exists()\n lod_history.update(self.repo._youngest, None)\n self._mark_deleted()","def delete(self):\r\n return self.connection.delete_volume(self.id)","def delete(args, config):\n print('Deletes a selected HPC fleet with name \"{}\"'.format(args.fleet_name))","def bdev_ocf_delete(client, name):\n params = {'name': name}\n\n return client.call('bdev_ocf_delete', params)","def bdev_daos_delete(client, name):\n params = {'name': name}\n return client.call('bdev_daos_delete', params)","def remove(self):\n self._switch.odlclient._request(self._path, method=\"delete\")","def delete(openstack_resource):\n openstack_resource.delete()","async def delete(self, ctx: \"IceTeaContext\", *, otag: TagConverter):\n tag: models.Tag = otag\n if tag.alias:\n if ctx.author.guild_permissions.administrator or tag.author == ctx.author.id:\n try:\n await tag.delete()\n await ctx.send(\"aliases deleted\")\n except:\n await ctx.send(\"Alias unsuccessfully deleted\")\n elif not tag.alias:\n if ctx.author.guild_permissions.administrator or tag.author == ctx.author.id:\n try:\n await tag.delete()\n await ctx.send(\"Tag and all aliases deleted\")\n except:\n await ctx.send(\"Tag unsuccessfully deleted\")\n else:\n await ctx.send(\"No Tag with that name found\")","def fusion_api_delete_storage_volume(self, name=None, uri=None, param='', api=None, headers=None):\n return self.volume.delete(name=name, uri=uri, param=param, api=api, headers=headers)","def delete(ctx: click.Context, repository_path):\n root_commands.cmd_delete(ctx.obj, repository_path)","def delete(self, obj=None):\n pass","def delete(self, *args):\n if self.cur == Win.left:\n self.commands.delpl([])\n else:\n cur_song = self.rightwin.highlighted()\n\n self.rightwin.delete(cur_song)\n\n if not self.rightwin.data:\n self.switch_view_left()","def delete(self, src):\n\n if self.noop:\n logger.info(\"No-Op Delete: %s.tar\" % self.bucket + src)\n else:\n logger.info(\"Trying to delete %s.tar\" % self.bucket + src)\n self.client.delete(self.bucket + src + \".tar\")","def do_delete_configured_volume(self, arg):\n args = self.parse_arguments(arg)\n if len(args) == 0:\n self.perror(\"No storage specified.\")\n return\n self.do_coroutine(self._localStorageRoutines.delete_configured_volume_routine(args[0]))","def delete_one(self, session: Optional[ClientSession] = None) -> DeleteOne:\n return self.delete(session=session)","def del_ost(self, dev_ind: int) -> None:\n self._ost_mut.acquire()\n self.ost = list(filter(lambda o: o.indo != dev_ind, self.ost))\n if not self.ost:\n self.kill()\n self._ost_mut.release()","def delete(self):\n del self.shx.atoms[self.index]","def delete(self, oid):\n path = '/servers/%s' % oid\n res = self.client.call(path, 'DELETE', data='', \n token=self.manager.identity.token)\n self.logger.debug('Delete openstack server: %s' % truncate(res))\n return res[0]","def delete(self):\n self.vera.delete_scene(self)","def delete():","def delete(self):\n self.current_revision.delete()","def fusion_api_delete_os_deploymentserver(self, name=None, uri=None, param='', api=None, headers=None):\n return self.osds.delete(name=name, uri=uri, param=param, api=api, headers=headers)","def vm_diskdelete(args):\n name = args.name\n diskname = args.diskname\n pool = args.pool\n config = Kconfig(client=args.client, debug=args.debug, region=args.region, zone=args.zone, namespace=args.namespace)\n k = config.k\n if diskname is None:\n common.pprint(\"Missing diskname. Leaving...\", color='red')\n os._exit(1)\n common.pprint(\"Deleting disk %s\" % diskname)\n k.delete_disk(name=name, diskname=diskname, pool=pool)\n return","def do_command(self, args):\n ostypeops = dbops.OsTypes()\n ostypeops.delete(args)","def svn_fs_delete(*args):\r\n return _fs.svn_fs_delete(*args)","def delete(self, controller, virtual_drive='all'):\n return self.run('/c{} /v{} del force'.format(controller, virtual_drive))","def delete(self, **kwargs):\n\n rst = self.del_sngl_pair(kwargs)\n return rst","def __delete__(self, instance):\n self._lib_vscf_ecc.vscf_ecc_delete(self.ctx)","def delete(self, show_id, ep_id, rel_id, session):\n try:\n db.show_by_id(show_id, session=session)\n except NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\n try:\n db.episode_by_id(ep_id, session)\n except NoResultFound:\n raise NotFoundError('episode with ID %s not found' % ep_id)\n try:\n release = db.episode_release_by_id(rel_id, session)\n except NoResultFound:\n raise NotFoundError('release with ID %s not found' % rel_id)\n if not db.episode_in_show(show_id, ep_id):\n raise BadRequest(f'episode with id {ep_id} does not belong to show {show_id}')\n if not db.release_in_episode(ep_id, rel_id):\n raise BadRequest(f'release id {rel_id} does not belong to episode {ep_id}')\n args = delete_parser.parse_args()\n if args.get('forget'):\n fire_event('forget', release.title)\n\n db.delete_episode_release_by_id(rel_id)\n return success_response(\n 'successfully deleted release %d from episode %d' % (rel_id, ep_id)\n )","def delete(self, *args, **kw):\n kw['method'] = 'DELETE'\n return self.open(*args, **kw)","def delete_index(self):\n es = self.get_es()\n if es.head(self.es_index):\n es.delete(self.es_index)","def delete(self, handle):\n self.LogCommand()\n tclcode = \"stc::delete \" + handle\n\n result = self.Exec(tclcode)\n logging.debug(\" - Python result - \" + str(result))\n return result","def volume_delete_by_storage(context, storage_id):\n _volume_get_query(context).filter_by(storage_id=storage_id).delete()","def deletePod(peaPodInstance,**kwargs):\n\treturnToHome=True\n\treturnToHome=kwargs.get(\"returnToHome\",returnToHome)\n\n\tchoice=messagebox.askokcancel(\"Sure\",\"Are you sure you want to delete this pod?\")\n\tif choice:\n\t\tcurrentMaster=masterPod.currentMasterPod\n\t\tfor pea in currentMaster.peas:\n\t\t\tcurrentPeaPod=currentMaster.peas[pea]\n\t\t\tif currentPeaPod == peaPodInstance:\n\t\t\t\tdel currentMaster.peas[pea]\n\t\t\t\tbreak\n\t\t#Save\n\t\tcurrentMaster.save()\n\n\t\tif returnToHome:\n\t\t\tgoHome()","def delete(self, client):\n log(\"Deleting %s\" % self, self.opt)\n client.delete(self.path)","def delete_snapshot(self, snapshot):\n aname = \"cinder_v%s.delete_snapshot\" % self.version\n with atomic.ActionTimer(self, aname):\n self._get_client().volume_snapshots.delete(snapshot)\n bench_utils.wait_for_status(\n snapshot,\n ready_statuses=[\"deleted\"],\n check_deletion=True,\n update_resource=self._update_resource,\n timeout=CONF.openstack.cinder_volume_delete_timeout,\n check_interval=(CONF.openstack\n .cinder_volume_delete_poll_interval)\n )","def delete_command():\n global selected_tuple\n backend.delete(selected_tuple[0])","def delete(self, *args, **kwargs):\n pass","def delete(self, *args, **kwargs):\n pass","def deleteDisk(positive, alias=None, async=True, disk_id=None):\n disk_obj = DISKS_API.find(disk_id, attribute='id') if disk_id else (\n DISKS_API.find(alias)\n )\n # TODO: add async parameter to delete method once it's supported\n status = DISKS_API.delete(disk_obj, positive)\n if not status and positive:\n logger.error(\"Failed to delete disk %s\", alias)\n return status","def delete(self):\n Texture2D.delete_glo(self._ctx, self._glo)\n self._glo.value = 0","def delete(self, api_path, *args, **kwargs):\n\n return self._do_operation(u'delete', api_path, *args, **kwargs)","def bdev_aio_delete(client, name):\n params = {'name': name}\n return client.call('bdev_aio_delete', params)","def delete(self, req, id):\n context = req.environ['nova.context']\n self._image_service.delete(context, id)\n return webob.exc.HTTPNoContent()","def snap_delete_by_volumename(mnode, volname):\n\n cmd = \"gluster snapshot delete volume %s --mode=script\" % volname\n return g.run(mnode, cmd)","def DELETE(self, uri):\n def body(conn, cur):\n self.enforce_right('owner', uri)\n if web.ctx.ermrest_history_snaptime is not None:\n raise exception.Forbidden('deletion of catalog at previous revision')\n if web.ctx.ermrest_history_snaprange is not None:\n # should not be possible bug check anyway...\n raise NotImplementedError('deletion of catalog with snapshot range')\n self.set_http_etag( web.ctx.ermrest_catalog_model.etag() )\n self.http_check_preconditions(method='DELETE')\n self.emit_headers()\n return True\n\n def post_commit(destroy):\n web.ctx.ermrest_registry.unregister(self.catalog_id)\n web.ctx.status = '204 No Content'\n return ''\n\n return self.perform(body, post_commit)","def delete(self, obj):\n raise NotImplementedError","async def delete(self, job):\n # nothing to delete if it doesn't exist\n info = await self.middleware.call('gluster.volume.exists_and_started', CTDB_VOL_NAME)\n if not info['exists']:\n return\n\n # stop the gluster volume\n if info['started']:\n options = {'args': (CTDB_VOL_NAME,), 'kwargs': {'force': True}}\n job.set_progress(33, f'Stopping gluster volume {CTDB_VOL_NAME!r}')\n await self.middleware.call('gluster.method.run', volume.stop, options)\n\n # finally, we delete it\n job.set_progress(66, f'Deleting gluster volume {CTDB_VOL_NAME!r}')\n await self.middleware.call('gluster.method.run', volume.delete, {'args': (CTDB_VOL_NAME,)})\n job.set_progress(100, f'Successfully deleted {CTDB_VOL_NAME!r}')","def delete_representation(self, index=None):\n del self.representations[index]","def delete(self):\n for lv in self.logical_volumes:\n self.delete_lv(lv_name=lv)\n\n super().delete()","def delete(self, index):\n del self.data[index]","def delete(self, obj=None):\n if obj:\n self.__session.delete(obj)","def delete(self, obj=None):\n if obj:\n self.__session.delete(obj)","def delete(self, obj=None):\n if obj:\n self.__session.delete(obj)","def delete(self, obj=None):\n if obj:\n self.__session.delete(obj)","def delete_volume(self, volume):\n nfs_share = volume.get('provider_location')\n if nfs_share:\n nms = self.share2nms[nfs_share]\n vol, parent_folder = self._get_share_datasets(nfs_share)\n folder = '%s/%s/%s' % (vol, parent_folder, volume['name'])\n mount_path = self.remote_path(volume).strip(\n '/%s' % self.VOLUME_FILE_NAME)\n if mount_path in self._remotefsclient._read_mounts():\n cinder.privsep.fs.umount(mount_path)\n try:\n props = nms.folder.get_child_props(folder, 'origin') or {}\n nms.folder.destroy(folder, '-r')\n except utils.NexentaException as exc:\n if 'does not exist' in exc.args[0]:\n LOG.info('Folder %s does not exist, it was '\n 'already deleted.', folder)\n return\n raise\n self._get_capacity_info(nfs_share)\n origin = props.get('origin')\n if origin and self._is_clone_snapshot_name(origin):\n try:\n nms.snapshot.destroy(origin, '')\n except utils.NexentaException as exc:\n if 'does not exist' in exc.args[0]:\n LOG.info('Snapshot %s does not exist, it was '\n 'already deleted.', origin)\n return\n raise","def delete(self, volume_id):\n self.client().volumes.delete(volume_id)","def delete(self, api_path, *args, **kwargs):\n\n\t\treturn self._do_operation(u'delete', api_path, *args, **kwargs)","def delete(self, sid):\n return self.delete_instance(sid)","def delete(self, sid):\n return self.delete_instance(sid)","def database_volume_delete(volume_uuid):\n db = database_get()\n session = db.session()\n query = session.query(model.Volume)\n query.filter(model.Volume.uuid == volume_uuid).delete()\n session.commit()","def delete(self, obj=None):\n\n if obj:\n self.__session.delete(obj)","def delete(vmname, deldisk=True):\n\n dom = _conn.lookupByName(vmname)\n if dom.isActive():\n dom.destroy()\n infokeeper.update_status_vm(vmname, Instance.STATUS_POWER_OFF)\n dom.undefine()\n infokeeper.delete_vm(vmname)\n if deldisk:\n os.remove(os.path.join(base_disk_path, dom.name() + '.img'))\n return 'VM %s deleted' % vmname","def delete_document(obj):\n index = obj.get_index_name()\n doc_type = obj.get_document_type()\n try:\n ES.delete(index=index, doc_type=doc_type, id=obj.pk)\n except NotFoundError:\n raise DocumentNotFound(obj.get_index_name(), obj.pk)","def test_aws_service_api_volume_delete(self):\n pass","def delete(obj, path, ignore_missing=False):\n return glom(obj, Delete(path, ignore_missing=ignore_missing))","def delete(self, op_id: str) -> Response:\n\n authorized: bool = Users.objects.get(id=get_jwt_identity()).roles.organization or \\\n Users.objects.get(id=get_jwt_identity()).roles.admin\n\n if authorized:\n try:\n output = Opportunity.objects.get(id=op_id).delete()\n except ValidationError as e:\n return bad_request(e.message)\n return jsonify(output)\n else:\n return forbidden()","def delete(self, obj):\n self.session.delete(obj)","def delete_volume(self, volume_path, data_isolated=False):\n\n path = self._get_path(volume_path)\n log.info(\"delete_volume: {0}\".format(path))\n\n # Create the trash folder if it doesn't already exist\n trash = os.path.join(self.volume_prefix, \"_deleting\")\n self._mkdir_p(trash)\n\n # We'll move it to here\n trashed_volume = os.path.join(trash, volume_path.volume_id)\n\n # Move the volume's data to the trash folder\n try:\n self.fs.stat(path)\n except cephfs.ObjectNotFound:\n log.warning(\"Trying to delete volume '{0}' but it's already gone\".format(\n path))\n else:\n self.fs.rename(path, trashed_volume)\n\n # Delete the volume meta file, if it's not already deleted\n vol_meta_path = self._volume_metadata_path(volume_path)\n try:\n self.fs.unlink(vol_meta_path)\n except cephfs.ObjectNotFound:\n pass","def delete(self, obj=None):\n if obj is not None:\n self.__session.delete(obj)","def delete(self, obj=None):\n if obj is not None:\n self.__session.delete(obj)","def delete(self, obj=None):\n if obj is not None:\n self.__session.delete(obj)","def processDeleteCommand(self, objId):\n editor = self._parent\n obj = editor.findWithUUID(objId)\n if obj:\n print(\"DELETE FOR\",objId)\n # delete from object cache\n if objId in editor._total['objects']:\n del editor._total['objects'][objId]\n # clear uuid\n obj.opensim.uuid = \"\"\n scene = editor.get_current_scene()\n # unlink\n scene.objects.unlink(obj)\n editor.queueRedraw()","def delete(isvgAppliance, uuid, check_mode=False, force=False):\n if force is True or _exists(isvgAppliance, uuid) is True:\n if check_mode is True:\n return isvgAppliance.create_return_object(changed=True)\n else:\n return isvgAppliance.invoke_delete(\n \"Delete a snmp object\",\n \"/rsp_snmp_objs/{0}\".format(uuid))\n\n return isvgAppliance.create_return_object()","def delete(self):\n pdbox._args.get(\"dryrun\") or os.remove(self.path)\n pdbox.info(\"Deleted %s\" % self.path)","def delete_key_vault_command(client: KeyVaultClient, args: dict[str, Any], params: dict[str, Any]) -> CommandResults:\n\n vault_name = args['vault_name']\n # subscription_id and resource_group_name arguments can be passed as command arguments or as configuration parameters,\n # if both are passed as arguments, the command arguments will be used.\n subscription_id = get_from_args_or_params(params=params, args=args, key='subscription_id')\n resource_group_name = get_from_args_or_params(params=params,\n args=args, key='resource_group_name')\n\n response = client.delete_key_vault_request(subscription_id=subscription_id,\n resource_group_name=resource_group_name,\n vault_name=vault_name)\n message = \"\"\n if response.get('status_code') == 200:\n message = f'Deleted Key Vault {vault_name} successfully.'\n elif response.get('status_code') == 204:\n message = f'Key Vault {vault_name} does not exists.'\n\n return CommandResults(\n readable_output=message\n )","def vault_delete(self, vault_delete):\n self._vault_delete = vault_delete","def delete(self,force=False):\n if self.state == ObjState.created or self.state == ObjState.absent:\n return (0,'')\n else:\n return (1,'cannot delete a live disk (only absent or user created ones)')","def delete(self, obj=None):\n\n if obj is not None:\n self.__session.delete(obj)","def delete(self, name):\n result = self.cm.find_name(name)\n path = result[0]['path']\n delete_path = Path(f'{path}/{name}')\n try:\n os.system(f\"rmdir {delete_path}\")\n result[0]['State'] = 'deleted'\n result = self.update_dict(result)\n except:\n Console.error(\"volume is either not empty or not exist\")\n return result","def delete(ctx):\n click.echo('deleting')\n ctx.delete()\n click.echo('done')","def delete_volume(self, volObj, removeMode='ONLY_ME', **kwargs):\n if kwargs:\n for key, value in kwargs.iteritems():\n if key =='autoUnmap' and value ==True:\n # Find all mapped SDS to this volObj\n # Call unmap for all of them\n if self.get_volume_all_sdcs_mapped(volObj):\n try:\n self.conn.cluster.unmap_volume_from_sdc(volObj, enableMapAllSdcs=False)\n except:\n raise RuntimeError(\"delete_volume() - enableMapAllSdcs error\")\n else: # All SDS not enabled so loop through all mapped SDCs of volume and remove one by one \n for sdc in self.get_sdc_for_volume(volObj):\n try:\n self.unmap_volume_from_sdc(volObj, self.get_sdc_by_id(sdc['sdcId']))\n except:\n raise RuntimeError(\"delete_volume() - unmap_volume_from_sdc() error\")\n # TODO:\n # Check if object parameters are the correct ones, otherwise throw error\n self.conn.connection._check_login()\n deleteVolumeDict = {'removeMode': removeMode}\n try:\n response = self.conn.connection._do_post(\"{}/{}{}/{}\".format(self.conn.connection._api_url, \"instances/Volume::\", volObj.id, 'action/removeVolume'), json=deleteVolumeDict)\n except:\n raise RuntimeError(\"delete_volume() - Communication error with ScaleIO Gateway\")\n return response","def delete(self, obj=None):\n if obj is not None:\n key = \"{}.{}\".format(type(obj).__name__, obj.id)\n try:\n del self.__objects[key]\n except KeyError:\n pass","def delete(self, *args, **kwargs):\n return self.handle_delete_request()","def delete(self):\n self._client.delete(self)","def delete(self):\n # gid must be specified for deletion\n gid = self.get_query_argument('gid')\n self.write(self._rpc.aria2.remove(self._token, gid))","def delete(self):\n self.log.info('Deleting')\n self._state = PonPort.State.DELETING\n self._cancel_deferred()","def delete_pos(self, pos):\n yield from self.command('delete {}'.format(pos))\n return True","def document_delete(index_name, doc_type, doc_id):\n resp = es.delete(index=index_name, doc_type=doc_type, id=doc_id)\n print(resp)","async def delete_one(self, where):\n\n pass","def delete_at_index(self, idx):\n self.timeseries = np.delete(self.timeseries, idx, axis=1)\n del self.ch_name[idx]\n del self.units[idx]","def delete_scene(command):\n namespace = app.main(command)\n assert namespace.command == 'ds' or namespace.command == \"deletescene\"\n if \"test\" in command:\n assert namespace.name == \"test\"","def do_command(self, args):\n vendorops = dbops.Vendors()\n vendorops.delete(args)","def logical_drive_delete(controller_id, logical_drive_id):\n try:\n ld = controller.LogicalDrive(controller_id, logical_drive_id)\n info = ld.get_info()\n x = ld.delete()\n return dict(x)\n except:\n return {\"controller\": controller_id,\n \"logical_drive\": logical_drive_id,\n \"status\": \"Failed to delete\"}","def fusion_api_delete_logical_enclosure(self, name=None, uri=None, param='', api=None, headers=None):\n return self.logical_enclosure.delete(name=name, uri=uri, param=param, api=api, headers=headers)","def delete(self, image_path=None):\n current_app.mnt_mutex.acquire()\n unmount_image(image_path)\n current_app.mnt_mutex.release()","def delete_snapshot(self, dest, source=None):\n raise NotImplementedYet()"],"string":"[\n \"def remove(self, purge, timeout, force):\\n # Set the CRUSH weight to 0.\\n hookenv.log('Reweighting OSD', hookenv.DEBUG)\\n reweight_osd(self.osd_id)\\n\\n # Ensure that the OSD is safe to stop and destroy.\\n end = (datetime.datetime.now() +\\n datetime.timedelta(seconds=timeout * 60))\\n safe_stop, safe_destroy = False, False\\n\\n while True:\\n if not safe_stop and safe_to_stop(self.osd_id):\\n safe_stop = True\\n if not safe_destroy and safe_to_destroy(self.osd_id):\\n safe_destroy = True\\n\\n if safe_stop and safe_destroy:\\n break\\n\\n curr = datetime.datetime.now()\\n if curr >= end:\\n if force:\\n hookenv.log(\\n 'OSD not safe to destroy, but \\\"force\\\" was specified',\\n hookenv.DEBUG)\\n break\\n\\n raise RemoveException(\\n 'timed out waiting for an OSD to be safe to destroy')\\n time.sleep(min(1, (end - curr).total_seconds()))\\n\\n # Stop the OSD service.\\n hookenv.log('Stopping the OSD service', hookenv.DEBUG)\\n charms_ceph.utils.stop_osd(self.osd_id[4:])\\n charms_ceph.utils.disable_osd(self.osd_id[4:])\\n unit_filename = \\\\\\n '/run/systemd/system/ceph-osd.target.wants/ceph-osd@{}.service' \\\\\\n .format(self.osd_id[4:])\\n if os.path.exists(unit_filename):\\n os.remove(unit_filename)\\n\\n subprocess.check_call(['systemctl', 'daemon-reload'])\\n\\n # Remove the OSD from the cluster.\\n hookenv.log('Destroying the OSD', hookenv.DEBUG)\\n destroy(self.osd_id, purge)\\n report = self.report.setdefault(self.osd_device,\\n {'osd-ids': self.osd_id})\\n\\n if self.bcache_backing:\\n # Remove anything related to bcache.\\n size = int(ceil(device_size(self.bcache_caching)))\\n caching = get_parent_device(self.bcache_caching)\\n report.update({'cache-devices': caching, 'partition-size': size})\\n bcache_remove(self.device, self.bcache_backing,\\n self.bcache_caching)\\n else:\\n remove_lvm(self.device)\\n wipefs_safely(self.device)\",\n \"def safe_to_destroy(osd_id, timeout=300):\\n ret = subprocess.call([\\n 'ceph', '--id', 'osd-removal',\\n 'osd', 'safe-to-destroy', osd_id\\n ], timeout=timeout)\\n return ret == 0\",\n \"def destroy(osd_id, purge=False, timeout=600):\\n for _ in range(10):\\n # We might get here before the OSD is marked as down. As such,\\n # retry if the error code is EBUSY.\\n try:\\n subprocess.check_call([\\n 'ceph', '--id', 'osd-removal', 'osd',\\n 'purge' if purge else 'destroy',\\n osd_id, '--yes-i-really-mean-it'\\n ], timeout=timeout)\\n return\\n except subprocess.CalledProcessError as e:\\n if e.returncode != errno.EBUSY:\\n raise\\n time.sleep(0.1)\",\n \"def delete(**_):\\n\\n volume_id = utils.get_external_resource_id_or_raise(\\n 'delete EBS volume', ctx.instance)\\n\\n if _delete_external_volume():\\n return\\n\\n ctx.logger.debug('Deleting EBS volume: {0}'.format(volume_id))\\n\\n if not _delete_volume(volume_id):\\n return ctx.operation.retry(\\n message='Failed to delete volume {0}.'\\n .format(volume_id))\\n\\n utils.unassign_runtime_property_from_resource(\\n constants.ZONE, ctx.instance)\\n\\n utils.unassign_runtime_property_from_resource(\\n constants.EXTERNAL_RESOURCE_ID, ctx.instance)\\n\\n ctx.logger.info(\\n 'Deleted EBS volume: {0}.'\\n .format(volume_id))\",\n \"def delete(self):\\n\\n lod_history = self.repo._get_lod_history(self.lod)\\n assert lod_history.exists()\\n lod_history.update(self.repo._youngest, None)\\n self._mark_deleted()\",\n \"def delete(self):\\r\\n return self.connection.delete_volume(self.id)\",\n \"def delete(args, config):\\n print('Deletes a selected HPC fleet with name \\\"{}\\\"'.format(args.fleet_name))\",\n \"def bdev_ocf_delete(client, name):\\n params = {'name': name}\\n\\n return client.call('bdev_ocf_delete', params)\",\n \"def bdev_daos_delete(client, name):\\n params = {'name': name}\\n return client.call('bdev_daos_delete', params)\",\n \"def remove(self):\\n self._switch.odlclient._request(self._path, method=\\\"delete\\\")\",\n \"def delete(openstack_resource):\\n openstack_resource.delete()\",\n \"async def delete(self, ctx: \\\"IceTeaContext\\\", *, otag: TagConverter):\\n tag: models.Tag = otag\\n if tag.alias:\\n if ctx.author.guild_permissions.administrator or tag.author == ctx.author.id:\\n try:\\n await tag.delete()\\n await ctx.send(\\\"aliases deleted\\\")\\n except:\\n await ctx.send(\\\"Alias unsuccessfully deleted\\\")\\n elif not tag.alias:\\n if ctx.author.guild_permissions.administrator or tag.author == ctx.author.id:\\n try:\\n await tag.delete()\\n await ctx.send(\\\"Tag and all aliases deleted\\\")\\n except:\\n await ctx.send(\\\"Tag unsuccessfully deleted\\\")\\n else:\\n await ctx.send(\\\"No Tag with that name found\\\")\",\n \"def fusion_api_delete_storage_volume(self, name=None, uri=None, param='', api=None, headers=None):\\n return self.volume.delete(name=name, uri=uri, param=param, api=api, headers=headers)\",\n \"def delete(ctx: click.Context, repository_path):\\n root_commands.cmd_delete(ctx.obj, repository_path)\",\n \"def delete(self, obj=None):\\n pass\",\n \"def delete(self, *args):\\n if self.cur == Win.left:\\n self.commands.delpl([])\\n else:\\n cur_song = self.rightwin.highlighted()\\n\\n self.rightwin.delete(cur_song)\\n\\n if not self.rightwin.data:\\n self.switch_view_left()\",\n \"def delete(self, src):\\n\\n if self.noop:\\n logger.info(\\\"No-Op Delete: %s.tar\\\" % self.bucket + src)\\n else:\\n logger.info(\\\"Trying to delete %s.tar\\\" % self.bucket + src)\\n self.client.delete(self.bucket + src + \\\".tar\\\")\",\n \"def do_delete_configured_volume(self, arg):\\n args = self.parse_arguments(arg)\\n if len(args) == 0:\\n self.perror(\\\"No storage specified.\\\")\\n return\\n self.do_coroutine(self._localStorageRoutines.delete_configured_volume_routine(args[0]))\",\n \"def delete_one(self, session: Optional[ClientSession] = None) -> DeleteOne:\\n return self.delete(session=session)\",\n \"def del_ost(self, dev_ind: int) -> None:\\n self._ost_mut.acquire()\\n self.ost = list(filter(lambda o: o.indo != dev_ind, self.ost))\\n if not self.ost:\\n self.kill()\\n self._ost_mut.release()\",\n \"def delete(self):\\n del self.shx.atoms[self.index]\",\n \"def delete(self, oid):\\n path = '/servers/%s' % oid\\n res = self.client.call(path, 'DELETE', data='', \\n token=self.manager.identity.token)\\n self.logger.debug('Delete openstack server: %s' % truncate(res))\\n return res[0]\",\n \"def delete(self):\\n self.vera.delete_scene(self)\",\n \"def delete():\",\n \"def delete(self):\\n self.current_revision.delete()\",\n \"def fusion_api_delete_os_deploymentserver(self, name=None, uri=None, param='', api=None, headers=None):\\n return self.osds.delete(name=name, uri=uri, param=param, api=api, headers=headers)\",\n \"def vm_diskdelete(args):\\n name = args.name\\n diskname = args.diskname\\n pool = args.pool\\n config = Kconfig(client=args.client, debug=args.debug, region=args.region, zone=args.zone, namespace=args.namespace)\\n k = config.k\\n if diskname is None:\\n common.pprint(\\\"Missing diskname. Leaving...\\\", color='red')\\n os._exit(1)\\n common.pprint(\\\"Deleting disk %s\\\" % diskname)\\n k.delete_disk(name=name, diskname=diskname, pool=pool)\\n return\",\n \"def do_command(self, args):\\n ostypeops = dbops.OsTypes()\\n ostypeops.delete(args)\",\n \"def svn_fs_delete(*args):\\r\\n return _fs.svn_fs_delete(*args)\",\n \"def delete(self, controller, virtual_drive='all'):\\n return self.run('/c{} /v{} del force'.format(controller, virtual_drive))\",\n \"def delete(self, **kwargs):\\n\\n rst = self.del_sngl_pair(kwargs)\\n return rst\",\n \"def __delete__(self, instance):\\n self._lib_vscf_ecc.vscf_ecc_delete(self.ctx)\",\n \"def delete(self, show_id, ep_id, rel_id, session):\\n try:\\n db.show_by_id(show_id, session=session)\\n except NoResultFound:\\n raise NotFoundError('show with ID %s not found' % show_id)\\n try:\\n db.episode_by_id(ep_id, session)\\n except NoResultFound:\\n raise NotFoundError('episode with ID %s not found' % ep_id)\\n try:\\n release = db.episode_release_by_id(rel_id, session)\\n except NoResultFound:\\n raise NotFoundError('release with ID %s not found' % rel_id)\\n if not db.episode_in_show(show_id, ep_id):\\n raise BadRequest(f'episode with id {ep_id} does not belong to show {show_id}')\\n if not db.release_in_episode(ep_id, rel_id):\\n raise BadRequest(f'release id {rel_id} does not belong to episode {ep_id}')\\n args = delete_parser.parse_args()\\n if args.get('forget'):\\n fire_event('forget', release.title)\\n\\n db.delete_episode_release_by_id(rel_id)\\n return success_response(\\n 'successfully deleted release %d from episode %d' % (rel_id, ep_id)\\n )\",\n \"def delete(self, *args, **kw):\\n kw['method'] = 'DELETE'\\n return self.open(*args, **kw)\",\n \"def delete_index(self):\\n es = self.get_es()\\n if es.head(self.es_index):\\n es.delete(self.es_index)\",\n \"def delete(self, handle):\\n self.LogCommand()\\n tclcode = \\\"stc::delete \\\" + handle\\n\\n result = self.Exec(tclcode)\\n logging.debug(\\\" - Python result - \\\" + str(result))\\n return result\",\n \"def volume_delete_by_storage(context, storage_id):\\n _volume_get_query(context).filter_by(storage_id=storage_id).delete()\",\n \"def deletePod(peaPodInstance,**kwargs):\\n\\treturnToHome=True\\n\\treturnToHome=kwargs.get(\\\"returnToHome\\\",returnToHome)\\n\\n\\tchoice=messagebox.askokcancel(\\\"Sure\\\",\\\"Are you sure you want to delete this pod?\\\")\\n\\tif choice:\\n\\t\\tcurrentMaster=masterPod.currentMasterPod\\n\\t\\tfor pea in currentMaster.peas:\\n\\t\\t\\tcurrentPeaPod=currentMaster.peas[pea]\\n\\t\\t\\tif currentPeaPod == peaPodInstance:\\n\\t\\t\\t\\tdel currentMaster.peas[pea]\\n\\t\\t\\t\\tbreak\\n\\t\\t#Save\\n\\t\\tcurrentMaster.save()\\n\\n\\t\\tif returnToHome:\\n\\t\\t\\tgoHome()\",\n \"def delete(self, client):\\n log(\\\"Deleting %s\\\" % self, self.opt)\\n client.delete(self.path)\",\n \"def delete_snapshot(self, snapshot):\\n aname = \\\"cinder_v%s.delete_snapshot\\\" % self.version\\n with atomic.ActionTimer(self, aname):\\n self._get_client().volume_snapshots.delete(snapshot)\\n bench_utils.wait_for_status(\\n snapshot,\\n ready_statuses=[\\\"deleted\\\"],\\n check_deletion=True,\\n update_resource=self._update_resource,\\n timeout=CONF.openstack.cinder_volume_delete_timeout,\\n check_interval=(CONF.openstack\\n .cinder_volume_delete_poll_interval)\\n )\",\n \"def delete_command():\\n global selected_tuple\\n backend.delete(selected_tuple[0])\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def delete(self, *args, **kwargs):\\n pass\",\n \"def deleteDisk(positive, alias=None, async=True, disk_id=None):\\n disk_obj = DISKS_API.find(disk_id, attribute='id') if disk_id else (\\n DISKS_API.find(alias)\\n )\\n # TODO: add async parameter to delete method once it's supported\\n status = DISKS_API.delete(disk_obj, positive)\\n if not status and positive:\\n logger.error(\\\"Failed to delete disk %s\\\", alias)\\n return status\",\n \"def delete(self):\\n Texture2D.delete_glo(self._ctx, self._glo)\\n self._glo.value = 0\",\n \"def delete(self, api_path, *args, **kwargs):\\n\\n return self._do_operation(u'delete', api_path, *args, **kwargs)\",\n \"def bdev_aio_delete(client, name):\\n params = {'name': name}\\n return client.call('bdev_aio_delete', params)\",\n \"def delete(self, req, id):\\n context = req.environ['nova.context']\\n self._image_service.delete(context, id)\\n return webob.exc.HTTPNoContent()\",\n \"def snap_delete_by_volumename(mnode, volname):\\n\\n cmd = \\\"gluster snapshot delete volume %s --mode=script\\\" % volname\\n return g.run(mnode, cmd)\",\n \"def DELETE(self, uri):\\n def body(conn, cur):\\n self.enforce_right('owner', uri)\\n if web.ctx.ermrest_history_snaptime is not None:\\n raise exception.Forbidden('deletion of catalog at previous revision')\\n if web.ctx.ermrest_history_snaprange is not None:\\n # should not be possible bug check anyway...\\n raise NotImplementedError('deletion of catalog with snapshot range')\\n self.set_http_etag( web.ctx.ermrest_catalog_model.etag() )\\n self.http_check_preconditions(method='DELETE')\\n self.emit_headers()\\n return True\\n\\n def post_commit(destroy):\\n web.ctx.ermrest_registry.unregister(self.catalog_id)\\n web.ctx.status = '204 No Content'\\n return ''\\n\\n return self.perform(body, post_commit)\",\n \"def delete(self, obj):\\n raise NotImplementedError\",\n \"async def delete(self, job):\\n # nothing to delete if it doesn't exist\\n info = await self.middleware.call('gluster.volume.exists_and_started', CTDB_VOL_NAME)\\n if not info['exists']:\\n return\\n\\n # stop the gluster volume\\n if info['started']:\\n options = {'args': (CTDB_VOL_NAME,), 'kwargs': {'force': True}}\\n job.set_progress(33, f'Stopping gluster volume {CTDB_VOL_NAME!r}')\\n await self.middleware.call('gluster.method.run', volume.stop, options)\\n\\n # finally, we delete it\\n job.set_progress(66, f'Deleting gluster volume {CTDB_VOL_NAME!r}')\\n await self.middleware.call('gluster.method.run', volume.delete, {'args': (CTDB_VOL_NAME,)})\\n job.set_progress(100, f'Successfully deleted {CTDB_VOL_NAME!r}')\",\n \"def delete_representation(self, index=None):\\n del self.representations[index]\",\n \"def delete(self):\\n for lv in self.logical_volumes:\\n self.delete_lv(lv_name=lv)\\n\\n super().delete()\",\n \"def delete(self, index):\\n del self.data[index]\",\n \"def delete(self, obj=None):\\n if obj:\\n self.__session.delete(obj)\",\n \"def delete(self, obj=None):\\n if obj:\\n self.__session.delete(obj)\",\n \"def delete(self, obj=None):\\n if obj:\\n self.__session.delete(obj)\",\n \"def delete(self, obj=None):\\n if obj:\\n self.__session.delete(obj)\",\n \"def delete_volume(self, volume):\\n nfs_share = volume.get('provider_location')\\n if nfs_share:\\n nms = self.share2nms[nfs_share]\\n vol, parent_folder = self._get_share_datasets(nfs_share)\\n folder = '%s/%s/%s' % (vol, parent_folder, volume['name'])\\n mount_path = self.remote_path(volume).strip(\\n '/%s' % self.VOLUME_FILE_NAME)\\n if mount_path in self._remotefsclient._read_mounts():\\n cinder.privsep.fs.umount(mount_path)\\n try:\\n props = nms.folder.get_child_props(folder, 'origin') or {}\\n nms.folder.destroy(folder, '-r')\\n except utils.NexentaException as exc:\\n if 'does not exist' in exc.args[0]:\\n LOG.info('Folder %s does not exist, it was '\\n 'already deleted.', folder)\\n return\\n raise\\n self._get_capacity_info(nfs_share)\\n origin = props.get('origin')\\n if origin and self._is_clone_snapshot_name(origin):\\n try:\\n nms.snapshot.destroy(origin, '')\\n except utils.NexentaException as exc:\\n if 'does not exist' in exc.args[0]:\\n LOG.info('Snapshot %s does not exist, it was '\\n 'already deleted.', origin)\\n return\\n raise\",\n \"def delete(self, volume_id):\\n self.client().volumes.delete(volume_id)\",\n \"def delete(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'delete', api_path, *args, **kwargs)\",\n \"def delete(self, sid):\\n return self.delete_instance(sid)\",\n \"def delete(self, sid):\\n return self.delete_instance(sid)\",\n \"def database_volume_delete(volume_uuid):\\n db = database_get()\\n session = db.session()\\n query = session.query(model.Volume)\\n query.filter(model.Volume.uuid == volume_uuid).delete()\\n session.commit()\",\n \"def delete(self, obj=None):\\n\\n if obj:\\n self.__session.delete(obj)\",\n \"def delete(vmname, deldisk=True):\\n\\n dom = _conn.lookupByName(vmname)\\n if dom.isActive():\\n dom.destroy()\\n infokeeper.update_status_vm(vmname, Instance.STATUS_POWER_OFF)\\n dom.undefine()\\n infokeeper.delete_vm(vmname)\\n if deldisk:\\n os.remove(os.path.join(base_disk_path, dom.name() + '.img'))\\n return 'VM %s deleted' % vmname\",\n \"def delete_document(obj):\\n index = obj.get_index_name()\\n doc_type = obj.get_document_type()\\n try:\\n ES.delete(index=index, doc_type=doc_type, id=obj.pk)\\n except NotFoundError:\\n raise DocumentNotFound(obj.get_index_name(), obj.pk)\",\n \"def test_aws_service_api_volume_delete(self):\\n pass\",\n \"def delete(obj, path, ignore_missing=False):\\n return glom(obj, Delete(path, ignore_missing=ignore_missing))\",\n \"def delete(self, op_id: str) -> Response:\\n\\n authorized: bool = Users.objects.get(id=get_jwt_identity()).roles.organization or \\\\\\n Users.objects.get(id=get_jwt_identity()).roles.admin\\n\\n if authorized:\\n try:\\n output = Opportunity.objects.get(id=op_id).delete()\\n except ValidationError as e:\\n return bad_request(e.message)\\n return jsonify(output)\\n else:\\n return forbidden()\",\n \"def delete(self, obj):\\n self.session.delete(obj)\",\n \"def delete_volume(self, volume_path, data_isolated=False):\\n\\n path = self._get_path(volume_path)\\n log.info(\\\"delete_volume: {0}\\\".format(path))\\n\\n # Create the trash folder if it doesn't already exist\\n trash = os.path.join(self.volume_prefix, \\\"_deleting\\\")\\n self._mkdir_p(trash)\\n\\n # We'll move it to here\\n trashed_volume = os.path.join(trash, volume_path.volume_id)\\n\\n # Move the volume's data to the trash folder\\n try:\\n self.fs.stat(path)\\n except cephfs.ObjectNotFound:\\n log.warning(\\\"Trying to delete volume '{0}' but it's already gone\\\".format(\\n path))\\n else:\\n self.fs.rename(path, trashed_volume)\\n\\n # Delete the volume meta file, if it's not already deleted\\n vol_meta_path = self._volume_metadata_path(volume_path)\\n try:\\n self.fs.unlink(vol_meta_path)\\n except cephfs.ObjectNotFound:\\n pass\",\n \"def delete(self, obj=None):\\n if obj is not None:\\n self.__session.delete(obj)\",\n \"def delete(self, obj=None):\\n if obj is not None:\\n self.__session.delete(obj)\",\n \"def delete(self, obj=None):\\n if obj is not None:\\n self.__session.delete(obj)\",\n \"def processDeleteCommand(self, objId):\\n editor = self._parent\\n obj = editor.findWithUUID(objId)\\n if obj:\\n print(\\\"DELETE FOR\\\",objId)\\n # delete from object cache\\n if objId in editor._total['objects']:\\n del editor._total['objects'][objId]\\n # clear uuid\\n obj.opensim.uuid = \\\"\\\"\\n scene = editor.get_current_scene()\\n # unlink\\n scene.objects.unlink(obj)\\n editor.queueRedraw()\",\n \"def delete(isvgAppliance, uuid, check_mode=False, force=False):\\n if force is True or _exists(isvgAppliance, uuid) is True:\\n if check_mode is True:\\n return isvgAppliance.create_return_object(changed=True)\\n else:\\n return isvgAppliance.invoke_delete(\\n \\\"Delete a snmp object\\\",\\n \\\"/rsp_snmp_objs/{0}\\\".format(uuid))\\n\\n return isvgAppliance.create_return_object()\",\n \"def delete(self):\\n pdbox._args.get(\\\"dryrun\\\") or os.remove(self.path)\\n pdbox.info(\\\"Deleted %s\\\" % self.path)\",\n \"def delete_key_vault_command(client: KeyVaultClient, args: dict[str, Any], params: dict[str, Any]) -> CommandResults:\\n\\n vault_name = args['vault_name']\\n # subscription_id and resource_group_name arguments can be passed as command arguments or as configuration parameters,\\n # if both are passed as arguments, the command arguments will be used.\\n subscription_id = get_from_args_or_params(params=params, args=args, key='subscription_id')\\n resource_group_name = get_from_args_or_params(params=params,\\n args=args, key='resource_group_name')\\n\\n response = client.delete_key_vault_request(subscription_id=subscription_id,\\n resource_group_name=resource_group_name,\\n vault_name=vault_name)\\n message = \\\"\\\"\\n if response.get('status_code') == 200:\\n message = f'Deleted Key Vault {vault_name} successfully.'\\n elif response.get('status_code') == 204:\\n message = f'Key Vault {vault_name} does not exists.'\\n\\n return CommandResults(\\n readable_output=message\\n )\",\n \"def vault_delete(self, vault_delete):\\n self._vault_delete = vault_delete\",\n \"def delete(self,force=False):\\n if self.state == ObjState.created or self.state == ObjState.absent:\\n return (0,'')\\n else:\\n return (1,'cannot delete a live disk (only absent or user created ones)')\",\n \"def delete(self, obj=None):\\n\\n if obj is not None:\\n self.__session.delete(obj)\",\n \"def delete(self, name):\\n result = self.cm.find_name(name)\\n path = result[0]['path']\\n delete_path = Path(f'{path}/{name}')\\n try:\\n os.system(f\\\"rmdir {delete_path}\\\")\\n result[0]['State'] = 'deleted'\\n result = self.update_dict(result)\\n except:\\n Console.error(\\\"volume is either not empty or not exist\\\")\\n return result\",\n \"def delete(ctx):\\n click.echo('deleting')\\n ctx.delete()\\n click.echo('done')\",\n \"def delete_volume(self, volObj, removeMode='ONLY_ME', **kwargs):\\n if kwargs:\\n for key, value in kwargs.iteritems():\\n if key =='autoUnmap' and value ==True:\\n # Find all mapped SDS to this volObj\\n # Call unmap for all of them\\n if self.get_volume_all_sdcs_mapped(volObj):\\n try:\\n self.conn.cluster.unmap_volume_from_sdc(volObj, enableMapAllSdcs=False)\\n except:\\n raise RuntimeError(\\\"delete_volume() - enableMapAllSdcs error\\\")\\n else: # All SDS not enabled so loop through all mapped SDCs of volume and remove one by one \\n for sdc in self.get_sdc_for_volume(volObj):\\n try:\\n self.unmap_volume_from_sdc(volObj, self.get_sdc_by_id(sdc['sdcId']))\\n except:\\n raise RuntimeError(\\\"delete_volume() - unmap_volume_from_sdc() error\\\")\\n # TODO:\\n # Check if object parameters are the correct ones, otherwise throw error\\n self.conn.connection._check_login()\\n deleteVolumeDict = {'removeMode': removeMode}\\n try:\\n response = self.conn.connection._do_post(\\\"{}/{}{}/{}\\\".format(self.conn.connection._api_url, \\\"instances/Volume::\\\", volObj.id, 'action/removeVolume'), json=deleteVolumeDict)\\n except:\\n raise RuntimeError(\\\"delete_volume() - Communication error with ScaleIO Gateway\\\")\\n return response\",\n \"def delete(self, obj=None):\\n if obj is not None:\\n key = \\\"{}.{}\\\".format(type(obj).__name__, obj.id)\\n try:\\n del self.__objects[key]\\n except KeyError:\\n pass\",\n \"def delete(self, *args, **kwargs):\\n return self.handle_delete_request()\",\n \"def delete(self):\\n self._client.delete(self)\",\n \"def delete(self):\\n # gid must be specified for deletion\\n gid = self.get_query_argument('gid')\\n self.write(self._rpc.aria2.remove(self._token, gid))\",\n \"def delete(self):\\n self.log.info('Deleting')\\n self._state = PonPort.State.DELETING\\n self._cancel_deferred()\",\n \"def delete_pos(self, pos):\\n yield from self.command('delete {}'.format(pos))\\n return True\",\n \"def document_delete(index_name, doc_type, doc_id):\\n resp = es.delete(index=index_name, doc_type=doc_type, id=doc_id)\\n print(resp)\",\n \"async def delete_one(self, where):\\n\\n pass\",\n \"def delete_at_index(self, idx):\\n self.timeseries = np.delete(self.timeseries, idx, axis=1)\\n del self.ch_name[idx]\\n del self.units[idx]\",\n \"def delete_scene(command):\\n namespace = app.main(command)\\n assert namespace.command == 'ds' or namespace.command == \\\"deletescene\\\"\\n if \\\"test\\\" in command:\\n assert namespace.name == \\\"test\\\"\",\n \"def do_command(self, args):\\n vendorops = dbops.Vendors()\\n vendorops.delete(args)\",\n \"def logical_drive_delete(controller_id, logical_drive_id):\\n try:\\n ld = controller.LogicalDrive(controller_id, logical_drive_id)\\n info = ld.get_info()\\n x = ld.delete()\\n return dict(x)\\n except:\\n return {\\\"controller\\\": controller_id,\\n \\\"logical_drive\\\": logical_drive_id,\\n \\\"status\\\": \\\"Failed to delete\\\"}\",\n \"def fusion_api_delete_logical_enclosure(self, name=None, uri=None, param='', api=None, headers=None):\\n return self.logical_enclosure.delete(name=name, uri=uri, param=param, api=api, headers=headers)\",\n \"def delete(self, image_path=None):\\n current_app.mnt_mutex.acquire()\\n unmount_image(image_path)\\n current_app.mnt_mutex.release()\",\n \"def delete_snapshot(self, dest, source=None):\\n raise NotImplementedYet()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58212245","0.5652357","0.5631923","0.56308943","0.561431","0.55853283","0.5472813","0.5385417","0.5382336","0.5376866","0.5364507","0.5355651","0.535345","0.53411007","0.5332679","0.5301693","0.5286166","0.5260226","0.52445686","0.52367926","0.5229653","0.52057874","0.5203734","0.52017","0.5196558","0.5194816","0.5185825","0.51701945","0.51521254","0.5136393","0.5130294","0.5124084","0.51076245","0.5103426","0.5087527","0.50864404","0.50795764","0.50767595","0.507371","0.5073147","0.50613433","0.5053841","0.5053841","0.50488853","0.50478196","0.5039514","0.5039444","0.50375587","0.5030435","0.5024565","0.5020474","0.50180024","0.50130534","0.5007741","0.5002058","0.49965698","0.49965698","0.49965698","0.49965698","0.49881262","0.49873516","0.49862844","0.49855506","0.49855506","0.4980889","0.4979033","0.49638844","0.49578324","0.49545306","0.49509627","0.4949233","0.49439102","0.49406642","0.49402273","0.49402273","0.49402273","0.4938901","0.49380836","0.49376643","0.4936752","0.4935231","0.49329042","0.4930207","0.49293828","0.49290627","0.49278632","0.4925926","0.4923826","0.4923472","0.49220413","0.49182278","0.49146974","0.4912122","0.49103594","0.490392","0.49011165","0.48999143","0.48977175","0.48966235","0.48857298","0.4881579"],"string":"[\n \"0.58212245\",\n \"0.5652357\",\n \"0.5631923\",\n \"0.56308943\",\n \"0.561431\",\n \"0.55853283\",\n \"0.5472813\",\n \"0.5385417\",\n \"0.5382336\",\n \"0.5376866\",\n \"0.5364507\",\n \"0.5355651\",\n \"0.535345\",\n \"0.53411007\",\n \"0.5332679\",\n \"0.5301693\",\n \"0.5286166\",\n \"0.5260226\",\n \"0.52445686\",\n \"0.52367926\",\n \"0.5229653\",\n \"0.52057874\",\n \"0.5203734\",\n \"0.52017\",\n \"0.5196558\",\n \"0.5194816\",\n \"0.5185825\",\n \"0.51701945\",\n \"0.51521254\",\n \"0.5136393\",\n \"0.5130294\",\n \"0.5124084\",\n \"0.51076245\",\n \"0.5103426\",\n \"0.5087527\",\n \"0.50864404\",\n \"0.50795764\",\n \"0.50767595\",\n \"0.507371\",\n \"0.5073147\",\n \"0.50613433\",\n \"0.5053841\",\n \"0.5053841\",\n \"0.50488853\",\n \"0.50478196\",\n \"0.5039514\",\n \"0.5039444\",\n \"0.50375587\",\n \"0.5030435\",\n \"0.5024565\",\n \"0.5020474\",\n \"0.50180024\",\n \"0.50130534\",\n \"0.5007741\",\n \"0.5002058\",\n \"0.49965698\",\n \"0.49965698\",\n \"0.49965698\",\n \"0.49965698\",\n \"0.49881262\",\n \"0.49873516\",\n \"0.49862844\",\n \"0.49855506\",\n \"0.49855506\",\n \"0.4980889\",\n \"0.4979033\",\n \"0.49638844\",\n \"0.49578324\",\n \"0.49545306\",\n \"0.49509627\",\n \"0.4949233\",\n \"0.49439102\",\n \"0.49406642\",\n \"0.49402273\",\n \"0.49402273\",\n \"0.49402273\",\n \"0.4938901\",\n \"0.49380836\",\n \"0.49376643\",\n \"0.4936752\",\n \"0.4935231\",\n \"0.49329042\",\n \"0.4930207\",\n \"0.49293828\",\n \"0.49290627\",\n \"0.49278632\",\n \"0.4925926\",\n \"0.4923826\",\n \"0.4923472\",\n \"0.49220413\",\n \"0.49182278\",\n \"0.49146974\",\n \"0.4912122\",\n \"0.49103594\",\n \"0.490392\",\n \"0.49011165\",\n \"0.48999143\",\n \"0.48977175\",\n \"0.48966235\",\n \"0.48857298\",\n \"0.4881579\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17399,"cells":{"query":{"kind":"string","value":"Get a list of all osds."},"document":{"kind":"string","value":"def list(self, detailed=False, search_opts=None, paginate_opts=None):\n if search_opts is None:\n search_opts = {}\n\n if paginate_opts is None:\n paginate_opts = {}\n\n qparams = {}\n\n for opt, val in search_opts.iteritems():\n if val:\n qparams[opt] = val\n\n for opt, val in paginate_opts.iteritems():\n if val:\n qparams[opt] = val\n\n query_string = \"?%s\" % urllib.urlencode(qparams) if qparams else \"\"\n\n detail = \"\"\n if detailed == 'detail_filter_and_sort':\n detail = \"/detail_filter_and_sort\"\n elif detailed:\n detail = \"/detail\"\n\n ret = self._list(\"/osds%s%s\" % (detail, query_string),\n \"osds\")\n return ret"},"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_list(self):\n return self.__repository.get_all()","def list(self):\n return self._list(self._path())","def list(self):\n return self.connection.get(self.service)","def list(self):\n return self._get_list()","def getAll(self):\n return self.__lst","def getList(self):","def getList(self):","def get_all(cls):\n if Model.data_connector:\n with Model.data_connector.u_lock:\n return Model.data_connector.get_all_objects(cls)\n \n return []","def all(self):\n return self.client.request_with_method(Methods.LIST % self.name)['items']","def get_soma_objects(self):\n\n msg_store = MessageStoreProxy(database=\"soma2data\", collection=\"soma2\")\n objs = msg_store.query(SOMA2Object._type, message_query={\"map_name\":self.soma_map,\"config\":self.soma_conf})\n print \"queried soma2 objects >> \", objs\n self.soma_objects = ce.get_soma_objects()\n print \"hard coded objects >> \", [self.soma_objects[r].keys() for r in self.soma_objects.keys()]","def get_all_offices():\n return [vars(office) for office in OFFICES]","def list(self):\n url = self._resource_name\n return self._get(url)","def osd_list(self):\n def unique_list_of_dicts(l):\n return reduce(lambda x, y: x if y in x else x + [y], l, [])\n\n tree = self.osd_tree()\n nodes = tree['nodes']\n if 'stray' in tree:\n nodes += tree['stray']\n for node in nodes:\n if u'depth' in node:\n del node[u'depth']\n nodes = unique_list_of_dicts(nodes)\n osdlists = list(unique_list_of_dicts([node for node in nodes if node['type'] == 'osd']))\n hostlists = list(unique_list_of_dicts([node for node in nodes if node['type'] == 'host']))\n # add host info in osdlist\n for osdlist in osdlists:\n for hostlist in hostlists:\n if osdlist[\"id\"] in hostlist[\"children\"]:\n osdlist[\"host\"] = hostlist[\"name\"]\n break\n return osdlists","def getList(self):\n pass","def get_all(self):\n url = self._dbname + '/_all'\n return self._connection.get(url).json()","def get_list_of_ontologies(self):\n try:\n con = self.getOntologyDatabaseConnection()\n column_values = con.cursor()\n con.cursor().callproc('get_list_of_ontologies', [column_values])\n query_results=[]\n for row in column_values:\n if row[0] is None:\n continue\n query_results.append(row)\n return query_results\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)\n return False","def get_all_orders(): \n data = order_obj.get_all_orders()\n return data","def fetchall(self):\n return list(self._results)","def fetch_all(self):\n return list(iter(self))","def list(parsed=True):\n return Client.get_client().list(parsed=parsed)","def list(self) -> List[Organisation]:\n ...","def all(self) -> list[dict[str, Any]]:\n return self.client.get(self._url())","def get_all_data():\n return jsonify(service.get_all_data())","def get_all_objects(self, table_name: str) -> list:\n print(f\"INFO Get all objects from table: {table_name}\")\n\n if not self.mydb:\n print(f\"INFO Conn ong is empty. Opening new connection...\")\n self.open_connection()\n\n mycursor = self.mydb.cursor()\n\n mycursor.execute(f\"SELECT * FROM {table_name}\")\n\n myresult = mycursor.fetchall()\n\n res = []\n for x in myresult:\n res.append(x)\n print(f\"INFO Got items: {len(res)}\")\n return res","def get(self):\r\n return get_all()","def fetchall(self) -> list:\n return self.cursor.fetchall()","def listObjects(instance):\n # Get a cursor from the DB connection.\n cursor = Conection.connect(DB_USER, DB_PASSWD, instance, DB_HOST)\n \n # Compose the SQL query to find all the orbits/SSM objects. We do this with \n # a simle query to the derivedobjects table since we realy only need the\n # ssm_id values.\n maxMJD = completedPrecoveryMaxDate(instance)\n if(maxMJD == None):\n return([], None)\n \n sql = 'select distinct(ssm_id) from derivedobjects where ssm_id is not null'\n sql += ' and status = \"I\"'\n # sql += ' and updated >= \"%s\"' %(minModifiedDate)\n # <-- end if\n \n nRes = cursor.execute(sql)\n return([x[0] for x in cursor.fetchall()], float(maxMJD))","def listaServicios(self):\n portal_catalog = getToolByName(self, 'portal_catalog')\n mt = portal_catalog.searchResults(portal_type = 'ServeiTIC',\n path={\"query\": '/'.join(self.getParentNode().getParentNode().getPhysicalPath()),\"depth\":2},\n sort_on='Date')\n new_list=[]\n for f in mt:\n new_list.append(f.Title)\n new_list.sort()\n return new_list","def get_all(self, name):\n\t\tpass","def read_all():\n # Create the list of example_objects from our data\n example_objects = ExampleObject.query.order_by(ExampleObject.field2).all()\n\n # Serialize the data for the response\n example_object_schema = ExampleObjectSchema(many=True)\n data = example_object_schema.dump(example_objects)\n return data","def show_list():\n\n response = []\n docs = SUPERHEROES.stream()\n for doc in docs:\n response.append(doc.to_dict())\n return jsonify(response), 201","def get_all_shopping_list():\n all_shopping_lists = ShoppingList.query.all()\n return create_shopping_list_output(all_shopping_lists)","def get_all(self):\n self.data = list(self.data)\n return self.data","def all(self):\n return (self.__objects)","def list(self):\n return self.objects.all()","def GetObjects(self): \r\n return self.model.GetObjects()","def get_all(self):\n return self.__fetcher.get_fetched()","def getList(self):\n\treturn self.list","def listaServicios(self):\n portal_catalog = getToolByName(self, 'portal_catalog')\n mt = portal_catalog.searchResults(portal_type = 'Servei',sort_on='Date')\n new_list=[]\n for f in mt:\n new_list.append(f.Title)\n new_list.sort()\n return new_list","def all(self):\n return self.__objects","def all(self):\n return self.__objects","def all(self):\n return self.__objects","def all(self):\n return self.__objects","def all(self):\n return self.__objects","def all(self):\n return self.__objects","def get_all(self):\n return [_ for _ in self]","def list(self):","def get_all_orders():","def ls():\n return dynamodb.ls(OrganizationModel)","def list(self):\n return self.request(\"GET\")","def getList(self):\n return self.position.exportToList()","def list(self, request):\n return self._get_filtered_results(request, columns=[])","def List(cls):\n return WordList.query().fetch_async()","def get_all_objects():\n gcl = gc.get_objects()\n olist = []\n seen = {}\n # Just in case:\n seen[id(gcl)] = None\n seen[id(olist)] = None\n seen[id(seen)] = None\n # _getr does the real work.\n _getr(gcl, olist, seen)\n return olist","def get_all(self):\n return self.__items","def list(self):\n if not self.model:\n raise NameError('database model has not been set.')\n\n with self.session() as session:\n query = self.get_query(session)\n data = query.all()\n return data","def list(self):\n return self.results_list","def get_all_by_status(status):\n return OaiData.objects.filter(status=status).all()","def obj_list(self):\n return self._obj_list","def query_all(cls)->List:\n database.cursor.execute(\"SELECT * FROM {}\".format(cls.table_name))\n items = database.cursor.fetchall()\n return [cls.to_object(item) for item in items]","def get_all():\n if not SERVICE_DIR:\n raise CommandExecutionError(\"Could not find service directory.\")\n # - List all daemontools services in\n return sorted(os.listdir(SERVICE_DIR))","def list():","def list():","def list(self, *args):\n return []","def objects_rst(self):\n return [_.as_rst for _ in self.objects]","def get_service_list():\n service_dict = requests.get('http://consul:8500/v1/catalog/services').json()\n service_list = []\n for s in service_dict:\n service_list.append(s)\n return service_list","def all(self):\n return list(self)","def get_scnlist_all(self):\n logger.debug(\"Creating Database Engine and Session.\")\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\n ses = session_sqlalc()\n logger.debug(\"Perform query to find scenes which need downloading.\")\n query_result = ses.query(EDDSentinel1ASF).order_by(EDDSentinel1ASF.Acquisition_Date.asc()).all()\n scns = list()\n if query_result is not None:\n for record in query_result:\n scns.append(record.PID)\n ses.close()\n logger.debug(\"Closed the database session.\")\n return scns","def fetch_list(self):\n\t\treturn self.fetch(self.list_url % ART_SERVER_HOST)","def list_all():\n\n return (_conn.listDefinedDomains() +\n [_conn.lookupByID(id).name() for id in _conn.listDomainsID()])","def getList(self):\n return self.list","def get_all(self, object):\n self.lock.acquire()\n result = self.__Session.query(object).all()\n self.lock.release()\n return result","def get_objects(self):\n return self._objects","def fetch_all(self):\n with self.__connection.cursor() as cursor:\n query = \"SELECT * FROM %s\" % self.__schema\n cursor.execute(query)\n return cursor.fetchall()","def _list(self):\n raise NotImplementedError","def study_list_all(self):\n envelope_copy = et.fromstring(self.envelope)\n body = [i for i in envelope_copy.iterfind(\n './/se:Body', {'se': self.ns_se})][0]\n\n method_name = 'listAllRequest'\n list_all = et.Element(et.QName(self.ns_study, method_name))\n body.append(list_all)\n\n envelope = et.tostring(envelope_copy)\n response = self.request(\n self.ocws_url, envelope, method_name, self.ns_study)\n return response","def getstationaryobslist(self):\n\n stationaryobslist = [self.__tablecm]\n return stationaryobslist","def generate_osd_list(ceph_cluster: Ceph):\n client = ceph_cluster.get_ceph_object(\"installer\")\n ceph_osds = ceph_cluster.get_ceph_objects(\"osd\")\n osd_nodes = set()\n disk_list = set()\n for osd in ceph_osds:\n osd_nodes.add(osd.node.vmshortname)\n osd_node_list = list(osd_nodes)\n log.info(osd_node_list)\n for osn in osd_node_list:\n for osd in ceph_osds:\n if osd.node.vmshortname == osn:\n for i in osd.node.vm_node.volumes:\n disk_list.add(i)\n osd_disk_list = list(disk_list)\n log.info(osd_disk_list)\n log.info(len(osd_disk_list))\n dump_osd_data(client, osn, osd_disk_list)\n disk_list.clear()\n osd_disk_list.clear()","def stations():\n\n return station_list","def get_all_objects():\n gcl = gc.get_objects()\n olist = []\n seen = {}\n # Just in case:\n seen[id(gcl)] = None\n seen[id(olist)] = None\n seen[id(seen)] = None\n # _getr does the real work.\n _getr(gcl, olist, seen)\n return olist","def find_all():\n return ItopapiPrototype.find_all(ItopapiOSLicence)","def fetch_all(): \n client, index_name = connection_es()\n res = client.search(index = index_name+\"*\")\n return res","def list(self) -> List[ListClientDto]:\n raise NotImplementedError","def get_all():\n return SavedQuery.get_all()","def do_list(self, _):\n devices = []\n for source in self._target.devices:\n devices.append({\n 'name': source.device['name'],\n 'path': source.device['path'],\n })\n return devices","def list():\n trucks = Foodtruck.query.all()\n return jsonify(foodtrucks=[truck.to_dict() for truck in trucks])","def list_all(self):\n\n url = 'equipamento/list/'\n\n code, xml = self.submit(None, 'GET', url)\n\n return self.response(code, xml)","def get_services(self):\r\n return get_service_list()","def get_all(self):\n res = self.es.search(index=self.index, doc_type=self.doc_type, body={'query': {'match_all': {}}})\n if not res['hits']['total']:\n return []\n return [Dashboard.from_kibana(hit) for hit in res['hits']['hits']]","def list(refresh):\n # This works too, but is much slower:\n # ogrinfo WFS:http://openmaps.gov.bc.ca/geo/ows?VERSION=1.1.0\n for table in bcdata.list_tables(refresh):\n click.echo(table)","def listAll(self):\n red = self.dbConnect()\n return red.keys()","def list_all():\n if os.path.exists(DATA_DIR):\n return os.listdir(DATA_DIR)\n return []","def getItems(self):\n for object in self.database:\n print(object)","def getObjects(self, idList=None):\n\n if idList is None:\n idList = ''\n if idList in ['', 'published', 'unpublished']:\n return self.iterateAllPaginated('objects/%s' % idList, func=vsdModels.APIObject._create)\n\n items = []\n for curId in idList:\n items.append(self.getObject(curId))\n return items","def get_list ( self, object_class_id ) :\n name_list = []\n stmt = \"select name from \" + self.get_table_name ( object_class_id )\n self.oracle_cursor.arraysize = 100000\n self.oracle_cursor.execute(stmt)\n resultset = self.oracle_cursor.fetchmany()\n if resultset :\n for row in resultset :\n name_list.append(str(row[0]))\n return name_list","def data_list(self):\n # return [data_object, data_object, ...]\n return self._data_list","def all(cls):\n api = BuslineAPI()\n try:\n objects = api.all()\n except ApiException:\n objects = cls.objects.all()\n return objects","def get_all_locations():\n rs = run_query('''select * from zlrz_office_location''')\n return [] if rs is None else list(map(lambda t: Location(t[1], t[2], t[3], t[4], t[5], t[0]), rs))","def get(self):\n devs = Device.query.all()\n lista = []\n for d in devs:\n lista.append(d.json())\n return lista","def dataObjects(self):\n\t\treturn self._objects"],"string":"[\n \"def get_list(self):\\n return self.__repository.get_all()\",\n \"def list(self):\\n return self._list(self._path())\",\n \"def list(self):\\n return self.connection.get(self.service)\",\n \"def list(self):\\n return self._get_list()\",\n \"def getAll(self):\\n return self.__lst\",\n \"def getList(self):\",\n \"def getList(self):\",\n \"def get_all(cls):\\n if Model.data_connector:\\n with Model.data_connector.u_lock:\\n return Model.data_connector.get_all_objects(cls)\\n \\n return []\",\n \"def all(self):\\n return self.client.request_with_method(Methods.LIST % self.name)['items']\",\n \"def get_soma_objects(self):\\n\\n msg_store = MessageStoreProxy(database=\\\"soma2data\\\", collection=\\\"soma2\\\")\\n objs = msg_store.query(SOMA2Object._type, message_query={\\\"map_name\\\":self.soma_map,\\\"config\\\":self.soma_conf})\\n print \\\"queried soma2 objects >> \\\", objs\\n self.soma_objects = ce.get_soma_objects()\\n print \\\"hard coded objects >> \\\", [self.soma_objects[r].keys() for r in self.soma_objects.keys()]\",\n \"def get_all_offices():\\n return [vars(office) for office in OFFICES]\",\n \"def list(self):\\n url = self._resource_name\\n return self._get(url)\",\n \"def osd_list(self):\\n def unique_list_of_dicts(l):\\n return reduce(lambda x, y: x if y in x else x + [y], l, [])\\n\\n tree = self.osd_tree()\\n nodes = tree['nodes']\\n if 'stray' in tree:\\n nodes += tree['stray']\\n for node in nodes:\\n if u'depth' in node:\\n del node[u'depth']\\n nodes = unique_list_of_dicts(nodes)\\n osdlists = list(unique_list_of_dicts([node for node in nodes if node['type'] == 'osd']))\\n hostlists = list(unique_list_of_dicts([node for node in nodes if node['type'] == 'host']))\\n # add host info in osdlist\\n for osdlist in osdlists:\\n for hostlist in hostlists:\\n if osdlist[\\\"id\\\"] in hostlist[\\\"children\\\"]:\\n osdlist[\\\"host\\\"] = hostlist[\\\"name\\\"]\\n break\\n return osdlists\",\n \"def getList(self):\\n pass\",\n \"def get_all(self):\\n url = self._dbname + '/_all'\\n return self._connection.get(url).json()\",\n \"def get_list_of_ontologies(self):\\n try:\\n con = self.getOntologyDatabaseConnection()\\n column_values = con.cursor()\\n con.cursor().callproc('get_list_of_ontologies', [column_values])\\n query_results=[]\\n for row in column_values:\\n if row[0] is None:\\n continue\\n query_results.append(row)\\n return query_results\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\\n return False\",\n \"def get_all_orders(): \\n data = order_obj.get_all_orders()\\n return data\",\n \"def fetchall(self):\\n return list(self._results)\",\n \"def fetch_all(self):\\n return list(iter(self))\",\n \"def list(parsed=True):\\n return Client.get_client().list(parsed=parsed)\",\n \"def list(self) -> List[Organisation]:\\n ...\",\n \"def all(self) -> list[dict[str, Any]]:\\n return self.client.get(self._url())\",\n \"def get_all_data():\\n return jsonify(service.get_all_data())\",\n \"def get_all_objects(self, table_name: str) -> list:\\n print(f\\\"INFO Get all objects from table: {table_name}\\\")\\n\\n if not self.mydb:\\n print(f\\\"INFO Conn ong is empty. Opening new connection...\\\")\\n self.open_connection()\\n\\n mycursor = self.mydb.cursor()\\n\\n mycursor.execute(f\\\"SELECT * FROM {table_name}\\\")\\n\\n myresult = mycursor.fetchall()\\n\\n res = []\\n for x in myresult:\\n res.append(x)\\n print(f\\\"INFO Got items: {len(res)}\\\")\\n return res\",\n \"def get(self):\\r\\n return get_all()\",\n \"def fetchall(self) -> list:\\n return self.cursor.fetchall()\",\n \"def listObjects(instance):\\n # Get a cursor from the DB connection.\\n cursor = Conection.connect(DB_USER, DB_PASSWD, instance, DB_HOST)\\n \\n # Compose the SQL query to find all the orbits/SSM objects. We do this with \\n # a simle query to the derivedobjects table since we realy only need the\\n # ssm_id values.\\n maxMJD = completedPrecoveryMaxDate(instance)\\n if(maxMJD == None):\\n return([], None)\\n \\n sql = 'select distinct(ssm_id) from derivedobjects where ssm_id is not null'\\n sql += ' and status = \\\"I\\\"'\\n # sql += ' and updated >= \\\"%s\\\"' %(minModifiedDate)\\n # <-- end if\\n \\n nRes = cursor.execute(sql)\\n return([x[0] for x in cursor.fetchall()], float(maxMJD))\",\n \"def listaServicios(self):\\n portal_catalog = getToolByName(self, 'portal_catalog')\\n mt = portal_catalog.searchResults(portal_type = 'ServeiTIC',\\n path={\\\"query\\\": '/'.join(self.getParentNode().getParentNode().getPhysicalPath()),\\\"depth\\\":2},\\n sort_on='Date')\\n new_list=[]\\n for f in mt:\\n new_list.append(f.Title)\\n new_list.sort()\\n return new_list\",\n \"def get_all(self, name):\\n\\t\\tpass\",\n \"def read_all():\\n # Create the list of example_objects from our data\\n example_objects = ExampleObject.query.order_by(ExampleObject.field2).all()\\n\\n # Serialize the data for the response\\n example_object_schema = ExampleObjectSchema(many=True)\\n data = example_object_schema.dump(example_objects)\\n return data\",\n \"def show_list():\\n\\n response = []\\n docs = SUPERHEROES.stream()\\n for doc in docs:\\n response.append(doc.to_dict())\\n return jsonify(response), 201\",\n \"def get_all_shopping_list():\\n all_shopping_lists = ShoppingList.query.all()\\n return create_shopping_list_output(all_shopping_lists)\",\n \"def get_all(self):\\n self.data = list(self.data)\\n return self.data\",\n \"def all(self):\\n return (self.__objects)\",\n \"def list(self):\\n return self.objects.all()\",\n \"def GetObjects(self): \\r\\n return self.model.GetObjects()\",\n \"def get_all(self):\\n return self.__fetcher.get_fetched()\",\n \"def getList(self):\\n\\treturn self.list\",\n \"def listaServicios(self):\\n portal_catalog = getToolByName(self, 'portal_catalog')\\n mt = portal_catalog.searchResults(portal_type = 'Servei',sort_on='Date')\\n new_list=[]\\n for f in mt:\\n new_list.append(f.Title)\\n new_list.sort()\\n return new_list\",\n \"def all(self):\\n return self.__objects\",\n \"def all(self):\\n return self.__objects\",\n \"def all(self):\\n return self.__objects\",\n \"def all(self):\\n return self.__objects\",\n \"def all(self):\\n return self.__objects\",\n \"def all(self):\\n return self.__objects\",\n \"def get_all(self):\\n return [_ for _ in self]\",\n \"def list(self):\",\n \"def get_all_orders():\",\n \"def ls():\\n return dynamodb.ls(OrganizationModel)\",\n \"def list(self):\\n return self.request(\\\"GET\\\")\",\n \"def getList(self):\\n return self.position.exportToList()\",\n \"def list(self, request):\\n return self._get_filtered_results(request, columns=[])\",\n \"def List(cls):\\n return WordList.query().fetch_async()\",\n \"def get_all_objects():\\n gcl = gc.get_objects()\\n olist = []\\n seen = {}\\n # Just in case:\\n seen[id(gcl)] = None\\n seen[id(olist)] = None\\n seen[id(seen)] = None\\n # _getr does the real work.\\n _getr(gcl, olist, seen)\\n return olist\",\n \"def get_all(self):\\n return self.__items\",\n \"def list(self):\\n if not self.model:\\n raise NameError('database model has not been set.')\\n\\n with self.session() as session:\\n query = self.get_query(session)\\n data = query.all()\\n return data\",\n \"def list(self):\\n return self.results_list\",\n \"def get_all_by_status(status):\\n return OaiData.objects.filter(status=status).all()\",\n \"def obj_list(self):\\n return self._obj_list\",\n \"def query_all(cls)->List:\\n database.cursor.execute(\\\"SELECT * FROM {}\\\".format(cls.table_name))\\n items = database.cursor.fetchall()\\n return [cls.to_object(item) for item in items]\",\n \"def get_all():\\n if not SERVICE_DIR:\\n raise CommandExecutionError(\\\"Could not find service directory.\\\")\\n # - List all daemontools services in\\n return sorted(os.listdir(SERVICE_DIR))\",\n \"def list():\",\n \"def list():\",\n \"def list(self, *args):\\n return []\",\n \"def objects_rst(self):\\n return [_.as_rst for _ in self.objects]\",\n \"def get_service_list():\\n service_dict = requests.get('http://consul:8500/v1/catalog/services').json()\\n service_list = []\\n for s in service_dict:\\n service_list.append(s)\\n return service_list\",\n \"def all(self):\\n return list(self)\",\n \"def get_scnlist_all(self):\\n logger.debug(\\\"Creating Database Engine and Session.\\\")\\n db_engine = sqlalchemy.create_engine(self.db_info_obj.dbConn)\\n session_sqlalc = sqlalchemy.orm.sessionmaker(bind=db_engine)\\n ses = session_sqlalc()\\n logger.debug(\\\"Perform query to find scenes which need downloading.\\\")\\n query_result = ses.query(EDDSentinel1ASF).order_by(EDDSentinel1ASF.Acquisition_Date.asc()).all()\\n scns = list()\\n if query_result is not None:\\n for record in query_result:\\n scns.append(record.PID)\\n ses.close()\\n logger.debug(\\\"Closed the database session.\\\")\\n return scns\",\n \"def fetch_list(self):\\n\\t\\treturn self.fetch(self.list_url % ART_SERVER_HOST)\",\n \"def list_all():\\n\\n return (_conn.listDefinedDomains() +\\n [_conn.lookupByID(id).name() for id in _conn.listDomainsID()])\",\n \"def getList(self):\\n return self.list\",\n \"def get_all(self, object):\\n self.lock.acquire()\\n result = self.__Session.query(object).all()\\n self.lock.release()\\n return result\",\n \"def get_objects(self):\\n return self._objects\",\n \"def fetch_all(self):\\n with self.__connection.cursor() as cursor:\\n query = \\\"SELECT * FROM %s\\\" % self.__schema\\n cursor.execute(query)\\n return cursor.fetchall()\",\n \"def _list(self):\\n raise NotImplementedError\",\n \"def study_list_all(self):\\n envelope_copy = et.fromstring(self.envelope)\\n body = [i for i in envelope_copy.iterfind(\\n './/se:Body', {'se': self.ns_se})][0]\\n\\n method_name = 'listAllRequest'\\n list_all = et.Element(et.QName(self.ns_study, method_name))\\n body.append(list_all)\\n\\n envelope = et.tostring(envelope_copy)\\n response = self.request(\\n self.ocws_url, envelope, method_name, self.ns_study)\\n return response\",\n \"def getstationaryobslist(self):\\n\\n stationaryobslist = [self.__tablecm]\\n return stationaryobslist\",\n \"def generate_osd_list(ceph_cluster: Ceph):\\n client = ceph_cluster.get_ceph_object(\\\"installer\\\")\\n ceph_osds = ceph_cluster.get_ceph_objects(\\\"osd\\\")\\n osd_nodes = set()\\n disk_list = set()\\n for osd in ceph_osds:\\n osd_nodes.add(osd.node.vmshortname)\\n osd_node_list = list(osd_nodes)\\n log.info(osd_node_list)\\n for osn in osd_node_list:\\n for osd in ceph_osds:\\n if osd.node.vmshortname == osn:\\n for i in osd.node.vm_node.volumes:\\n disk_list.add(i)\\n osd_disk_list = list(disk_list)\\n log.info(osd_disk_list)\\n log.info(len(osd_disk_list))\\n dump_osd_data(client, osn, osd_disk_list)\\n disk_list.clear()\\n osd_disk_list.clear()\",\n \"def stations():\\n\\n return station_list\",\n \"def get_all_objects():\\n gcl = gc.get_objects()\\n olist = []\\n seen = {}\\n # Just in case:\\n seen[id(gcl)] = None\\n seen[id(olist)] = None\\n seen[id(seen)] = None\\n # _getr does the real work.\\n _getr(gcl, olist, seen)\\n return olist\",\n \"def find_all():\\n return ItopapiPrototype.find_all(ItopapiOSLicence)\",\n \"def fetch_all(): \\n client, index_name = connection_es()\\n res = client.search(index = index_name+\\\"*\\\")\\n return res\",\n \"def list(self) -> List[ListClientDto]:\\n raise NotImplementedError\",\n \"def get_all():\\n return SavedQuery.get_all()\",\n \"def do_list(self, _):\\n devices = []\\n for source in self._target.devices:\\n devices.append({\\n 'name': source.device['name'],\\n 'path': source.device['path'],\\n })\\n return devices\",\n \"def list():\\n trucks = Foodtruck.query.all()\\n return jsonify(foodtrucks=[truck.to_dict() for truck in trucks])\",\n \"def list_all(self):\\n\\n url = 'equipamento/list/'\\n\\n code, xml = self.submit(None, 'GET', url)\\n\\n return self.response(code, xml)\",\n \"def get_services(self):\\r\\n return get_service_list()\",\n \"def get_all(self):\\n res = self.es.search(index=self.index, doc_type=self.doc_type, body={'query': {'match_all': {}}})\\n if not res['hits']['total']:\\n return []\\n return [Dashboard.from_kibana(hit) for hit in res['hits']['hits']]\",\n \"def list(refresh):\\n # This works too, but is much slower:\\n # ogrinfo WFS:http://openmaps.gov.bc.ca/geo/ows?VERSION=1.1.0\\n for table in bcdata.list_tables(refresh):\\n click.echo(table)\",\n \"def listAll(self):\\n red = self.dbConnect()\\n return red.keys()\",\n \"def list_all():\\n if os.path.exists(DATA_DIR):\\n return os.listdir(DATA_DIR)\\n return []\",\n \"def getItems(self):\\n for object in self.database:\\n print(object)\",\n \"def getObjects(self, idList=None):\\n\\n if idList is None:\\n idList = ''\\n if idList in ['', 'published', 'unpublished']:\\n return self.iterateAllPaginated('objects/%s' % idList, func=vsdModels.APIObject._create)\\n\\n items = []\\n for curId in idList:\\n items.append(self.getObject(curId))\\n return items\",\n \"def get_list ( self, object_class_id ) :\\n name_list = []\\n stmt = \\\"select name from \\\" + self.get_table_name ( object_class_id )\\n self.oracle_cursor.arraysize = 100000\\n self.oracle_cursor.execute(stmt)\\n resultset = self.oracle_cursor.fetchmany()\\n if resultset :\\n for row in resultset :\\n name_list.append(str(row[0]))\\n return name_list\",\n \"def data_list(self):\\n # return [data_object, data_object, ...]\\n return self._data_list\",\n \"def all(cls):\\n api = BuslineAPI()\\n try:\\n objects = api.all()\\n except ApiException:\\n objects = cls.objects.all()\\n return objects\",\n \"def get_all_locations():\\n rs = run_query('''select * from zlrz_office_location''')\\n return [] if rs is None else list(map(lambda t: Location(t[1], t[2], t[3], t[4], t[5], t[0]), rs))\",\n \"def get(self):\\n devs = Device.query.all()\\n lista = []\\n for d in devs:\\n lista.append(d.json())\\n return lista\",\n \"def dataObjects(self):\\n\\t\\treturn self._objects\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6780782","0.6703935","0.66433823","0.6626435","0.65891266","0.6533394","0.6533394","0.65089464","0.6494575","0.64734656","0.64625776","0.6453494","0.644844","0.6447506","0.64162856","0.6404984","0.6335044","0.63190335","0.6300186","0.62992346","0.6296229","0.62930113","0.6282265","0.62771726","0.62419623","0.623803","0.6233411","0.621422","0.6207585","0.61982566","0.61886203","0.61609256","0.6150969","0.61506456","0.61408925","0.6139678","0.6130089","0.61275274","0.6125276","0.61250806","0.61250806","0.61250806","0.61250806","0.61250806","0.61250806","0.6120436","0.6097818","0.609513","0.60933936","0.60886943","0.60883355","0.60842675","0.6072681","0.6072673","0.6070747","0.6058961","0.6056825","0.6037518","0.603485","0.6029944","0.602697","0.6024824","0.6024824","0.602318","0.6021342","0.60183495","0.6010849","0.6010399","0.60084516","0.60027415","0.6000879","0.59887755","0.5982408","0.5982219","0.5979498","0.59782183","0.59755385","0.59732276","0.595351","0.5949507","0.5945797","0.5940188","0.5939142","0.593342","0.5924281","0.5922746","0.5921078","0.5907503","0.5899843","0.5898741","0.58960944","0.5894895","0.5894467","0.5889768","0.58837944","0.58776027","0.58760417","0.58760226","0.58700085","0.5868951"],"string":"[\n \"0.6780782\",\n \"0.6703935\",\n \"0.66433823\",\n \"0.6626435\",\n \"0.65891266\",\n \"0.6533394\",\n \"0.6533394\",\n \"0.65089464\",\n \"0.6494575\",\n \"0.64734656\",\n \"0.64625776\",\n \"0.6453494\",\n \"0.644844\",\n \"0.6447506\",\n \"0.64162856\",\n \"0.6404984\",\n \"0.6335044\",\n \"0.63190335\",\n \"0.6300186\",\n \"0.62992346\",\n \"0.6296229\",\n \"0.62930113\",\n \"0.6282265\",\n \"0.62771726\",\n \"0.62419623\",\n \"0.623803\",\n \"0.6233411\",\n \"0.621422\",\n \"0.6207585\",\n \"0.61982566\",\n \"0.61886203\",\n \"0.61609256\",\n \"0.6150969\",\n \"0.61506456\",\n \"0.61408925\",\n \"0.6139678\",\n \"0.6130089\",\n \"0.61275274\",\n \"0.6125276\",\n \"0.61250806\",\n \"0.61250806\",\n \"0.61250806\",\n \"0.61250806\",\n \"0.61250806\",\n \"0.61250806\",\n \"0.6120436\",\n \"0.6097818\",\n \"0.609513\",\n \"0.60933936\",\n \"0.60886943\",\n \"0.60883355\",\n \"0.60842675\",\n \"0.6072681\",\n \"0.6072673\",\n \"0.6070747\",\n \"0.6058961\",\n \"0.6056825\",\n \"0.6037518\",\n \"0.603485\",\n \"0.6029944\",\n \"0.602697\",\n \"0.6024824\",\n \"0.6024824\",\n \"0.602318\",\n \"0.6021342\",\n \"0.60183495\",\n \"0.6010849\",\n \"0.6010399\",\n \"0.60084516\",\n \"0.60027415\",\n \"0.6000879\",\n \"0.59887755\",\n \"0.5982408\",\n \"0.5982219\",\n \"0.5979498\",\n \"0.59782183\",\n \"0.59755385\",\n \"0.59732276\",\n \"0.595351\",\n \"0.5949507\",\n \"0.5945797\",\n \"0.5940188\",\n \"0.5939142\",\n \"0.593342\",\n \"0.5924281\",\n \"0.5922746\",\n \"0.5921078\",\n \"0.5907503\",\n \"0.5899843\",\n \"0.5898741\",\n \"0.58960944\",\n \"0.5894895\",\n \"0.5894467\",\n \"0.5889768\",\n \"0.58837944\",\n \"0.58776027\",\n \"0.58760417\",\n \"0.58760226\",\n \"0.58700085\",\n \"0.5868951\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62678444"},"document_rank":{"kind":"string","value":"24"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":173,"numItemsPerPage":100,"numTotalItems":95772,"offset":17300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjIxMzI4MSwianRpIjoiZmY2MWNlYzMtOTQzZC00NmMzLWFhZTgtYTgzZmQxMmY3OTZlIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyMTY4ODEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.90HzgI1wwexLsNsroTxvCYYr8OD5iII295Lhb26X_AKzSgRLELr9slDGY_wxCcYLn1idw9JCqqZsYpZOEptPDw","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 |
|---|---|---|---|---|---|---|
Simulates a mouse wheel movement | def wheel(ticks):
m = PyMouse()
m.scroll(ticks) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ev_mousewheel(self, event: MouseWheel) -> None:",
"def on_mouse_wheel(self, e): # pragma: no cover\n super(TraceView, self).on_mouse_wheel(e)\n if e.modifiers == ('Alt',):\n start, end = self._interval\n delay = e.delta * (end - start) * .1\n self.shift(-delay)... | [
"0.70724034",
"0.6867929",
"0.6845834",
"0.6725395",
"0.6706772",
"0.66839576",
"0.66106194",
"0.64879376",
"0.6433302",
"0.6428224",
"0.6424929",
"0.63497424",
"0.6329097",
"0.62871855",
"0.62500674",
"0.62429804",
"0.62024975",
"0.61738163",
"0.6163825",
"0.6155793",
"0.613... | 0.79435927 | 0 |
Compresses a byte array with the xz binary | def compress(value):
process = Popen(["xz", "--compress", "--force"], stdin=PIPE, stdout=PIPE)
return process.communicate(value)[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def swap_byte(byte_array, index):\n\n if byte_array[index] == 0:\n changed_byte_array = byte_array[0:index] + b\"\\xff\" + byte_array[index + 1 :]\n changed_byte_array = byte_array[0:index] + b\"\\x00\" + byte_array[index + 1 :]\n return changed_byte_array",
"def test_compress_2(self):\n t... | [
"0.5709028",
"0.56780964",
"0.5647832",
"0.5616013",
"0.56071866",
"0.55910605",
"0.55739576",
"0.55164623",
"0.5504677",
"0.5499852",
"0.5489234",
"0.5489234",
"0.5486275",
"0.5466205",
"0.54346704",
"0.54300076",
"0.540067",
"0.53756106",
"0.53609854",
"0.53576124",
"0.5354... | 0.5999412 | 1 |
Decompresses a byte array with the xz binary | def decompress(value):
process = Popen(["xz", "--decompress", "--stdout", "--force"],
stdin=PIPE, stdout=PIPE)
return process.communicate(value)[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_decompress_2(self):\n b_array = bytearray([3]) + bytearray(b'abcdef')\\\n + bytearray([0, 32]) + bytearray([0, 113])\n actual = LZ77.decompress(b_array)\n expected = 'abcdefdeabc'\n self.assertEqual(actual, expected)",
"def test_decompress_1(self):\n ... | [
"0.6248445",
"0.6211954",
"0.62091017",
"0.6167395",
"0.60436386",
"0.5956435",
"0.59318906",
"0.5930538",
"0.5903139",
"0.5897572",
"0.5881671",
"0.5857352",
"0.5821568",
"0.57875955",
"0.578303",
"0.5764637",
"0.5753524",
"0.57527006",
"0.5750076",
"0.5742337",
"0.5739909",... | 0.58198994 | 14 |
Compress the file at 'path' with the xz binary | def compress_file(path):
process = Popen(["xz", "--compress", "--force", "--stdout", path], stdout=PIPE)
return process.communicate()[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def zip_file(file_path: str) -> str:\n zip_file_path: str = file_path + \".gz\"\n\n print(f\"Compressing {file_path} into {zip_file_path}\")\n timestamp=path.getmtime(file_path)\n with open(file_path, \"rb\") as read_stream:\n with gzip.open(zip_file_path, \"wb\") as write_stream:\n s... | [
"0.7248893",
"0.6594647",
"0.6594647",
"0.64348674",
"0.64283776",
"0.6283818",
"0.62432826",
"0.6221492",
"0.6214257",
"0.6159141",
"0.6146967",
"0.6120605",
"0.6105703",
"0.60970914",
"0.6093049",
"0.6085339",
"0.6084827",
"0.6059032",
"0.6022121",
"0.60189515",
"0.59890485... | 0.7983229 | 1 |
Shows a specific plane within 3D data. | def show_plane(axis, plane, cmap="gray", title=None):
axis.imshow(plane, cmap=cmap)
axis.set_xticks([])
axis.set_yticks([])
if title:
axis.set_title(title)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_plane(unit_normal, x_array, y_array, fore):\n # print'unit normal = ', unit_normal\n z = (((unit_normal[0] * (fore[0] - x_array)) + (unit_normal[1] * (fore[1] - y_array))) / unit_normal[2]) + fore[2]\n # print 'plane numbers\\n', z\n return z",
"def plane(self):\r\n from lsst.analysis... | [
"0.6780227",
"0.666365",
"0.6577502",
"0.6563549",
"0.63409144",
"0.624834",
"0.6219757",
"0.6199828",
"0.6173089",
"0.6126187",
"0.6115501",
"0.6100461",
"0.6099733",
"0.6040154",
"0.6024716",
"0.6006487",
"0.5987519",
"0.59277785",
"0.5921929",
"0.5920941",
"0.5902615",
"... | 0.67553663 | 1 |
Draws a cube in a 3D plot. | def slice_in_3d(axis, shape, plane):
Z = np.array([[0, 0, 0],
[1, 0, 0],
[1, 1, 0],
[0, 1, 0],
[0, 0, 1],
[1, 0, 1],
[1, 1, 1],
[0, 1, 1]])
Z = Z * shape
r = [-1, 1]
X, Y = np... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_cube(self, window):\n size = pygame.display.get_surface().get_size()\n width = (size[0]/4)\n\n window.fill((000,000,000))\n\n self.draw_face(\"U\", window, (0 + (width*1), 0 + (width*0)), width)\n self.draw_face(\"L\", window, (0 + (width*0), 0 + (width*1)), width)\n ... | [
"0.7407902",
"0.71406615",
"0.71255124",
"0.7051842",
"0.6929713",
"0.6927209",
"0.69070977",
"0.69030863",
"0.68954325",
"0.6841298",
"0.67818475",
"0.6753072",
"0.675133",
"0.67188823",
"0.6653603",
"0.6642409",
"0.6613349",
"0.6610248",
"0.65382105",
"0.65379065",
"0.65249... | 0.0 | -1 |
Allows to explore 2D slices in 3D data. | def slice_explorer(data, cmap='gray'):
data_len = len(data)
@interact(plane=(0, data_len-1), continuous_update=False)
def display_slice(plane=data_len/2):
fig, axis = plt.subplots(figsize=(20, 7))
axis_3d = fig.add_subplot(133, projection='3d')
show_plane(axis, data[plane], title='P... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take_slice(img_3D, view):\n input_type = isinstance(img_3D, np.ndarray)\n if input_type:\n img_3D = [img_3D]\n img_shape = img_3D[0].shape\n if view == \"sag\":\n slice_pos = np.random.randint(int(0.2 * img_shape[0]), int(0.8 * img_shape[0]))\n imgs_2D = [imgg_3D[slice_pos, :, ... | [
"0.7107621",
"0.6738053",
"0.6709929",
"0.6706025",
"0.6513878",
"0.64924455",
"0.6423735",
"0.63151467",
"0.63149804",
"0.6307343",
"0.62615204",
"0.6126598",
"0.6090667",
"0.607543",
"0.6070433",
"0.60660744",
"0.60097694",
"0.6005962",
"0.5995597",
"0.597353",
"0.59621847"... | 0.7134606 | 0 |
Helper function for plotting histograms. | def plot_hist(axis, data, title=None):
axis.hist(data.ravel(), bins=256)
axis.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
if title:
axis.set_title(title)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_histogram(self,ax=None,**kwargs):\n if not ax:\n fig = plt.figure()\n ax = fig.add_subplot(111)\n probs,bins,patches = ax.hist(self.scores_list,normed=True,label=\"Sample\",**kwargs)\n ax.vlines(self.xhat,*ax.get_ylim(),label='Mean',color='r')\n ax.legend(... | [
"0.76307493",
"0.7626514",
"0.74735564",
"0.7414809",
"0.7405838",
"0.7366627",
"0.7359895",
"0.7336036",
"0.73091656",
"0.73068434",
"0.71873444",
"0.71678686",
"0.7161124",
"0.7120898",
"0.70793045",
"0.70793045",
"0.70793045",
"0.7075434",
"0.707193",
"0.7068348",
"0.70412... | 0.6881642 | 30 |
Generates a 3D surface plot for the specified region. | def plot_3d_surface(data, labels, region=3, spacing=(1.0, 1.0, 1.0)):
properties = measure.regionprops(labels, intensity_image=data)
# skimage.measure.marching_cubes expects ordering (row, col, plane).
# We need to transpose the data:
volume = (labels == properties[region].label).transpose(1, 2, 0)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_surface(self):\n X, Y = np.meshgrid(self.x, self.y)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.plot_surface(X=X, Y=Y, Z=self.z)\n plt.show()",
"def plot3d(data):\n assert span1 == span2\n span = span1\n # ---------------------- crea... | [
"0.6976554",
"0.68625677",
"0.6843619",
"0.6560246",
"0.6512885",
"0.6510929",
"0.6456214",
"0.63602376",
"0.63443196",
"0.63097036",
"0.6258106",
"0.62344956",
"0.61818576",
"0.61670333",
"0.61499923",
"0.6146061",
"0.6144096",
"0.61362255",
"0.60683507",
"0.60314703",
"0.60... | 0.7583633 | 0 |
If no mapping_method, then analysis run is set up. | def run_parallel(pid, call_method_id, run_id='gwas', kinship_method='ibd'):
job_id = '%s_%s_%d_%d' % (run_id, kinship_method, call_method_id, pid)
file_prefix = env.env['results_dir'] + job_id
#Cluster specific parameters
shstr = '#!/bin/bash\n'
shstr += '#$ -S /bin/bash... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _do_mapping(self):\n pass",
"def analysis_setup(self):\n pass",
"def applyMapping(self):\n pass",
"def requires_mapping(self):",
"def process_field_mapping(self, analysis, observable: Observable, result, result_field, result_time=None) -> None:\n pass",
"def setup_mapping(... | [
"0.6723257",
"0.672305",
"0.65543103",
"0.60905826",
"0.5807275",
"0.572249",
"0.57076865",
"0.5566315",
"0.55566597",
"0.5527906",
"0.5521116",
"0.55162174",
"0.5465503",
"0.5437137",
"0.5386988",
"0.5373432",
"0.53393596",
"0.5198328",
"0.5180411",
"0.51791704",
"0.5174413"... | 0.0 | -1 |
Connect current container to the environment containers network. | def connect_to_containers_network():
logging.info("Connecting to the environment network")
container_id = get_current_container_id()
subprocess.check_output(
'docker network connect subsystem_tests-network {container_id}'.format(container_id=container_id),
shell=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect(self, container_name: str, aliases: list[str] = None,\n ipv4: str | None = None) -> None:\n self.log.debug(\n f\"Connecting {container_name} to network '{self.network_name}'\")\n self.network.connect(\n container_name, aliases=aliases, ipv4_address=ipv... | [
"0.68715274",
"0.6033247",
"0.5958551",
"0.5952797",
"0.5917014",
"0.5764563",
"0.57549083",
"0.57498366",
"0.57448465",
"0.573062",
"0.57062066",
"0.5695735",
"0.5688441",
"0.5684623",
"0.56554174",
"0.56166863",
"0.56042194",
"0.5562665",
"0.5559789",
"0.5551756",
"0.552874... | 0.83500123 | 0 |
Return the current container ID. | def get_current_container_id():
with open('/proc/self/cgroup', 'rt') as cgroup_file:
for line in cgroup_file.readlines():
return re.sub(r'^docker-', '', re.sub(r'\.scope$', '', re.sub(r'^.*\/', '', line.strip()))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def container_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"container_id\")",
"def containerID(self):\n return self._container",
"def container_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"container_id\")",
"def cont_to_id(self):\n r... | [
"0.86177015",
"0.85974866",
"0.822979",
"0.81850547",
"0.7676669",
"0.7595466",
"0.7591976",
"0.7085673",
"0.69835997",
"0.69442016",
"0.69008535",
"0.6894379",
"0.6839231",
"0.68319935",
"0.6813734",
"0.6774681",
"0.67729616",
"0.66315556",
"0.6575164",
"0.6575164",
"0.65332... | 0.80170023 | 4 |
Overrides Die.roll() so that in addition to rolling the dice, it sets the die's value based on the currentValue. | def roll(self):
self.currentValue = choice(self.possibleValues)
self.value = AngryDie.ANGRY_VALUES[self.currentValue]
return self.currentValue | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def roll_dice(self):\n self.roll = (random.randint(1,6), random.randint(1,6))\n return self.roll",
"def roll(self):\n #dieValue = [] \n self._value = random.randrange(Die.SIDES) + 1\n self._update()\n #dieValue.append(self._value)\n #print(dieValue)\n #p... | [
"0.72756755",
"0.72380686",
"0.71869373",
"0.71206564",
"0.70240617",
"0.69059175",
"0.68994266",
"0.685288",
"0.682175",
"0.67554736",
"0.66809267",
"0.66572595",
"0.66572595",
"0.6561393",
"0.64926106",
"0.6468559",
"0.6467723",
"0.6460948",
"0.64546245",
"0.64188683",
"0.6... | 0.7436479 | 0 |
A helper method that, given a valid faceValue, will update the die's currentValue and value to match the passed faceValue. | def setDieFaceValue(self, faceValue):
if faceValue in AngryDie.ANGRY_VALUES:
self.currentValue = faceValue
self.value = AngryDie.ANGRY_VALUES[faceValue] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setFace(self, value):\n self.face = value",
"def setFace(self, value):\n self.face = value",
"def test_currentValue_is_updated_to_roll_value(self):\n rolled_value = self.new_die.roll()\n if rolled_value == self.new_die.currentValue:\n self.assertTrue(True, \"currentVa... | [
"0.568083",
"0.568083",
"0.5458344",
"0.54526615",
"0.52289414",
"0.5217452",
"0.5184717",
"0.5160611",
"0.51151603",
"0.511195",
"0.50905704",
"0.506557",
"0.50020486",
"0.49880826",
"0.49478003",
"0.49093857",
"0.4904442",
"0.48498568",
"0.48484898",
"0.4839955",
"0.4830702... | 0.81279725 | 0 |
Drive the Angry Dice game for the user. Welcomes them to the game and prints the instructions, then prompts them with the die values and what they want to roll until they advance through all the stages and win. | def main(self):
text = "Welcome to Angry Dice! Roll the two dice until you get thru the 3 Stages!\n" \
"Stage 1 you need to roll 1 & 2\n" \
"Stage 2 you need to roll ANGRY & 4\n" \
"Stage 3 you need to roll 5 & 6\n" \
"You can lock a die needed for your current... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def play(self):\n\n input(\"\"\"\nWelcome to Angry Dice! Roll the two dice until you get thru the 3 Stages!\nStage 1 you need to roll 1 & 2\nStage 2 you need to roll ANGRY & 4\nStage 3 you need to roll 5 & 6\nYou can lock a die needed for your current stage\nand just roll the other one, but beware!\nIf you ... | [
"0.7677212",
"0.75570935",
"0.68514156",
"0.6303217",
"0.6295013",
"0.6294594",
"0.620361",
"0.61641127",
"0.609266",
"0.6076957",
"0.60398626",
"0.6020962",
"0.6014037",
"0.60069793",
"0.60060954",
"0.5999888",
"0.5986961",
"0.5982929",
"0.59672225",
"0.5925162",
"0.59180194... | 0.7522587 | 2 |
Roll the dice passed in the list. | def roll_the_dice(self, dice):
if type(dice) == list:
for die in dice:
die.roll() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def roll(dice):\n rolled_dice = []\n for die in dice[1]:\n rolled_dice.append(randint(1, CUBE_DICE_MAX_VALUE()))\n dice[1] = rolled_dice\n return dice",
"def roll_the_dice(self, index):\n # first roll\n first_roll_result = self._rolls_list[index].roll_dice()\n print(f'FIRS... | [
"0.78297305",
"0.76451164",
"0.76148355",
"0.7406287",
"0.73938185",
"0.73751354",
"0.7322575",
"0.7311594",
"0.7302911",
"0.72434473",
"0.7187141",
"0.71589607",
"0.7152758",
"0.71263856",
"0.7106611",
"0.7098338",
"0.7085768",
"0.70856994",
"0.7061517",
"0.70429206",
"0.704... | 0.8384107 | 0 |
Print both die values, as well as the current stage. | def print_dice(self):
stage_to_print = 3 if self.current_stage == 4 else self.current_stage
print("You rolled:\n a = [ {} ]\n b = [ {} ]\n\nYou are in Stage {}"
.format(self.die_a, self.die_b, stage_to_print)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_current_dice(self):\n print(\"You rolled:\\n a = [ {} ]\\n b = [ {} ]\\n\".\n format(self.die_a, self.die_b))",
"def print_hand(self):\n if self.cheating:\n print(\"You're cheating!\")\n print(\"until you reroll it!\")\n print(\"\"\"\nYo... | [
"0.6175284",
"0.5993719",
"0.5895301",
"0.5708749",
"0.56729364",
"0.5624648",
"0.5600944",
"0.55977625",
"0.55730206",
"0.5530365",
"0.5493459",
"0.5430469",
"0.538948",
"0.53874636",
"0.5384385",
"0.53282684",
"0.52871954",
"0.5283577",
"0.527334",
"0.52729154",
"0.5268641"... | 0.7377811 | 0 |
Prompt the user for input, and return the dice they want to roll. | def determine_roll(self):
dice_to_roll = []
to_roll = input("Roll dice: ")
if 'a' in to_roll:
dice_to_roll.append(self.die_a)
if 'b' in to_roll:
dice_to_roll.append(self.die_b)
return dice_to_roll | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def roll_dice(player: int) -> int:\n sides = 6\n roll_again = input(\"Player {}: Press ENTER to roll your dice...\".format(player))\n num_rolled = roll(sides)\n print(\"You rolled {}.\".format(num_rolled))\n return num_rolled",
"def dice_roller():\n\n print('Use the xDy+z format to roll the dic... | [
"0.75145817",
"0.73645425",
"0.73168164",
"0.71421844",
"0.71233743",
"0.7100476",
"0.70834786",
"0.7075699",
"0.7033684",
"0.70306945",
"0.70136887",
"0.7012352",
"0.692733",
"0.6923469",
"0.68955934",
"0.6895005",
"0.6886274",
"0.68699884",
"0.6821573",
"0.6815672",
"0.6813... | 0.75796336 | 0 |
Check the state of the game and if conditions are met to advance the player to the next stage. | def check_stage(self):
#Initalize target and goal_stage to stage1 values
target = 3
goal_stage = 2
# Set target and goal_stage if current stage is not 1
if self.current_stage == 2:
target = 7
goal_stage = 3
elif self.current_stage == 3:
target = 11
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def advance_check(self):\n values = [self.die_a.value, self.die_b.value]\n if self.stage == 3:\n if not self.cheating and \"5\" in values and \"6\" in values:\n return True\n if self.stage == 2 and \"ANGRY\" in values and \"4\" in values:\n self.stage = 3\n... | [
"0.6904773",
"0.680145",
"0.6782129",
"0.6746176",
"0.672846",
"0.67035085",
"0.6647363",
"0.66338056",
"0.660885",
"0.65491366",
"0.6462665",
"0.6453733",
"0.6429156",
"0.6387714",
"0.63038003",
"0.62731713",
"0.62604964",
"0.6260225",
"0.621723",
"0.62048525",
"0.6200238",
... | 0.7219316 | 0 |
Checks to see if both dice are Angry, if so, sets current_stage to 1 | def check_angry(self):
if self.die_a.value == 3 and self.die_b.value == 3:
print("WOW, you're ANGRY!\nTime to go back to Stage 1!")
self.current_stage = 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_angry_dice(self):\n if self.die_a.current_value == \"ANGRY\" and self.die_b.current_value == \"ANGRY\":\n print(\"WOW, you're ANGRY!\\nTime to go back to Stage 1!\")\n self.game_stage = 1",
"def check_stage(self):\n\n #Initalize target and goal_stage to stage1 values\... | [
"0.7947746",
"0.68952584",
"0.67912954",
"0.66577834",
"0.657209",
"0.65228903",
"0.6070311",
"0.6039068",
"0.5986948",
"0.58281",
"0.57704735",
"0.5715772",
"0.5712143",
"0.5680398",
"0.56088614",
"0.5588602",
"0.55773765",
"0.5547491",
"0.55223674",
"0.5517609",
"0.5482465"... | 0.74593896 | 1 |
In Stage 3, they can only hold a 5 valued die. If they hold a 6, they'll be found cheating and thus, cannot win, or advance to the next stage. | def check_cheating(self, dice=[]):
#Assume they're not cheating until proven guilty
self.cheating = False
if self.current_stage == 3:
if self.die_a not in dice and (self.die_a.value == 6):
print("You're cheating! You cannot lock a 6! You cannot win "
"until you... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def advance_check(self):\n values = [self.die_a.value, self.die_b.value]\n if self.stage == 3:\n if not self.cheating and \"5\" in values and \"6\" in values:\n return True\n if self.stage == 2 and \"ANGRY\" in values and \"4\" in values:\n self.stage = 3\n... | [
"0.7061071",
"0.6833772",
"0.6340773",
"0.63359606",
"0.6290116",
"0.628443",
"0.6203253",
"0.6173044",
"0.6135499",
"0.61070085",
"0.61038446",
"0.60700893",
"0.6055959",
"0.60029256",
"0.59974277",
"0.5941838",
"0.5928955",
"0.59277576",
"0.590705",
"0.5904662",
"0.5890709"... | 0.7265603 | 0 |
This function locates all nearby cities within num_hops from the given city. It maintains a set of all the cities visited from the starting city at each hop. After completion, it removes the original city from the list of results | def find_nearby_cities(graph: TeleportGraph, city: str, num_hops: int = 1) -> set:
if num_hops == 0:
return set()
start_city_node = graph.find_city_node(city)
city_nodes = {start_city_node}
for i in range(num_hops):
related_cities = set()
# for every city in the current set,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def FindDHopCities(self, X, d):\n # G = nx.Graph()\n # G.add_nodes_from(self.nodes)\n # G.add_edges_from(self.edges)\n\n # airports_id_in_city = self.airports.loc[self.airports['city'] == X, 'airport_id'].to_list()\n\n # cities_h_hop = set()\n # for airport in airports_id_... | [
"0.6298028",
"0.60487",
"0.58636826",
"0.58009905",
"0.558688",
"0.5529587",
"0.5491075",
"0.54383576",
"0.54128426",
"0.5362866",
"0.5285198",
"0.5187931",
"0.5150512",
"0.5140834",
"0.5108585",
"0.5091598",
"0.506311",
"0.5040769",
"0.503374",
"0.4991658",
"0.49839562",
"... | 0.73122805 | 0 |
This function determines if two cities can be reached in the graph. This algorithm uses a breadthfirst search approach | def does_route_exist(graph: TeleportGraph, start_city: str, end_city: str) -> bool:
queue = Queue()
start_city_node = graph.find_city_node(start_city)
queue.put(start_city_node)
# keep track of the nodes we've visited - if we do not do this, we'll wind up in an infinite loop because since
# we're ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def are_connected(self, person1, person2):\n\n possible_nodes = Queue()\n seen = set()\n possible_nodes.enqueue(person1)\n seen.add(person1)\n\n while not possible_nodes.is_empty():\n person = possible_nodes.dequeue()\n print(\"checking\", person)\n ... | [
"0.67384744",
"0.6349388",
"0.63051564",
"0.6257954",
"0.6152257",
"0.6132436",
"0.6108083",
"0.60494477",
"0.60445905",
"0.59779936",
"0.59365493",
"0.5929931",
"0.59287775",
"0.5921574",
"0.58920145",
"0.58911324",
"0.58680403",
"0.5858796",
"0.58010995",
"0.57815397",
"0.5... | 0.67146933 | 1 |
This function uses a closure to wrap the is_loop function in the context it needs to execute. It builds a Path object by traversing the tree in a depthfirst searchlike manner. The Path object itself is a stack, with the latest node in the path on the top | def does_loop_exist(graph: TeleportGraph, city: str) -> bool:
start_node = graph.find_city_node(city)
visited_inner_nodes = set()
def is_loop(path: Path, node: CityNode) -> bool:
# check the current path + node combination to see if they form a loop
new_path = path + node
if node !... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def depthFirstSearch(problem):\n \"*** YOUR CODE HERE ***\"\n fringeList = util.Stack()\n print \"fringeList\",fringeList\n closedList = {str(problem.getStartState()): ([])} #Hash Map to maintain state to path\n print \"closed list:\", closedList\n isGoalStateArrived = False\n\n # Push start state into frin... | [
"0.6329693",
"0.61533785",
"0.6149285",
"0.61012757",
"0.60317045",
"0.6018394",
"0.5991409",
"0.5937781",
"0.5935834",
"0.5900034",
"0.5898311",
"0.5897564",
"0.58851016",
"0.58820933",
"0.587812",
"0.5874348",
"0.5860418",
"0.5849348",
"0.5839746",
"0.58242905",
"0.58104414... | 0.0 | -1 |
Starts this module and displays its menu. User can access default special features from here. User can go back to main menu from here. | def start_module():
menu_for_store = ["Show Table",
"Add",
"Remove",
"Update",
"Item by Durability Time",
"Average Durability Time by Manufacturer",
"Back to main menu"]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.menu()",
"def start(self) -> None:\n self.execute_startup_menu()\n self.execute_main_menu()",
"def menu(self):\n from mainmenu import Menu\n gm = Menu(self.screen)\n gm.run()",
"def run(self) -> None:\n\n MainMenuController().show_menu(... | [
"0.7938095",
"0.7741795",
"0.77349406",
"0.76457036",
"0.7281473",
"0.7106561",
"0.7106561",
"0.7041256",
"0.6893074",
"0.6865796",
"0.6844933",
"0.6782512",
"0.6730884",
"0.67242575",
"0.6687903",
"0.66862607",
"0.66724086",
"0.6643579",
"0.66138124",
"0.6591422",
"0.6482712... | 0.6132973 | 43 |
Asks user for input and adds it into the table. | def add(table):
new_list_to_add = []
new_list_to_add.append(common.generate_random(table))
new_list_to_add.extend(ui.get_inputs(["Please add the Name: "],""))
new_list_to_add.extend(ui.get_inputs(["Please add the Manufacturer: "],""))
new_list_to_add.extend(ui.get_inputs(["Please add the Year of P... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(table):\n\n generated = common.generate_random(table)\n\n list_labels = ['Title: ', 'Manufacturer: ', 'Price: ', 'Number in stock: ']\n\n inputs = list_labels[:]\n\n while not inputs[2].isdigit() or not inputs[3].isdigit():\n inputs = ui.get_inputs(list_labels, 'Provide data: ')\n\n i... | [
"0.72043127",
"0.70753807",
"0.692427",
"0.6833963",
"0.6790875",
"0.676084",
"0.6621706",
"0.66021085",
"0.65713584",
"0.6525232",
"0.6510655",
"0.64706945",
"0.64340395",
"0.6290243",
"0.62401277",
"0.6178783",
"0.61465514",
"0.61439264",
"0.59972376",
"0.5993154",
"0.59722... | 0.66343117 | 6 |
Remove a record with a given id from the table. | def remove(table, id_):
common.toremoveid("inventory/inventory.csv",data_manager.get_table_from_file("inventory/inventory.csv"),id_) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(table, id_):\n table, successful = common.remove_record(table, id_)\n\n if not successful:\n ui.print_error_message('Error!')\n\n return table",
"def remove(table, id_):\n\n record = common.find_id(table, id_[0])\n if record in table:\n table = common.remove_record(table, ... | [
"0.8176463",
"0.8054362",
"0.80142987",
"0.76159215",
"0.74309653",
"0.72869754",
"0.72543085",
"0.7076186",
"0.70404893",
"0.7039547",
"0.6975638",
"0.69384336",
"0.6901097",
"0.6890645",
"0.6853109",
"0.6842799",
"0.6817162",
"0.68161863",
"0.68117946",
"0.6789418",
"0.6776... | 0.6802873 | 19 |
Updates specified record in the table. Ask users for new data. | def update(table, id_):
for i in table:
if i[0] == id_:
i[1] = ui.get_inputs(["What should i update the titel to: "],"")
i[2] = ui.get_inputs(["What should I update the manufacturer to? "],"")
i[3] = ui.get_inputs(["What should I update the year of purchase to? "],"")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(table,record_id='',message='',next='',\n readonly_fields='',hidden_fields='',default_fields=''):\n\n PluginWikiWidgets._set_field_attributes(table, readonly_fields,hidden_fields,default_fields)\n if not record_id: record_id=request.args(-1)\n if not record_id.isdigit()... | [
"0.7245048",
"0.7108982",
"0.7071606",
"0.7058902",
"0.6944672",
"0.6926919",
"0.6925757",
"0.6912551",
"0.6864421",
"0.67838085",
"0.67801327",
"0.67352724",
"0.67352724",
"0.6695691",
"0.66325665",
"0.65575486",
"0.653736",
"0.65354466",
"0.6529351",
"0.6521863",
"0.6492877... | 0.0 | -1 |
Reads the specified project Gutenberg book. Header comments, punctuation, and whitespace are stripped away. The function returns a list of the words used in the book as a list. All words are converted to lower case. | def get_word_list(file_name):
storyEdit = []
#Reads the file starting after the beginning
f = open(file_name,'r')
lines = f.readlines()
curr_line = 0
while lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:
curr_line += 1
lines = lines[curr_line+1:]
#Loops through each row, making ever... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_book_words(infile):\t\n\timport string\n\t\n\tfin = open(infile) \n\tlines = fin.readlines()\n\twords = []\n\tfor line in lines[25:]: #skipping over the header information\n\t\tline = line.replace('-', ' ')\n\t\tt = line.split()\n\t\tfor word in t:\n\t\t\tword = word.strip(string.punctuation + string.whit... | [
"0.72380835",
"0.7035844",
"0.69978523",
"0.6959783",
"0.6928538",
"0.68596375",
"0.68219995",
"0.6537841",
"0.62804574",
"0.6134441",
"0.60707104",
"0.6049446",
"0.6033682",
"0.60183454",
"0.60149145",
"0.5994137",
"0.59453297",
"0.5919479",
"0.58863664",
"0.586905",
"0.5842... | 0.67661035 | 7 |
Takes a list of words as input and returns a list of the n most frequently occurring words ordered from most to least frequently occurring. | def get_top_n_words(word_list, n):
#Uses Counter function to create tuples of words and number of instances of word
wordCount = Counter(word_list)
topWords = []
orderedByFrequency = sorted(wordCount, key=wordCount.get, reverse=True)
#create list of inputted 'n' top words
for i in range (0 , n):
topWords.app... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_top_n_words(word_list, n):\n d = dict()\n for w in word_list:\n d[w] = d.get(w, 0) + 1\n ordered_by_frequency = sorted(d, key=d.get, reverse=True)\n return ordered_by_frequency[0:n]",
"def get_top_n_words(word_list, n):\n word_counts = dict()\n\n for word in word_list:\n f... | [
"0.83019364",
"0.8259304",
"0.82464784",
"0.8063228",
"0.8041614",
"0.7969055",
"0.79085237",
"0.78822184",
"0.77217156",
"0.7661636",
"0.7626515",
"0.75896144",
"0.755189",
"0.7525962",
"0.748399",
"0.7470999",
"0.7364696",
"0.71175003",
"0.70950264",
"0.70950264",
"0.706640... | 0.8294504 | 1 |
Generate N RGB colors for cmap. | def generate(N, cmap='Set1', method='matplotlib', keep_alpha=False, scheme='rgb', verbose='info'):
# Set the logger
set_logger(verbose=verbose)
listlen = 4 if keep_alpha else 3
if method=='seaborn':
sns = _check_seaborn()
color_list = sns.color_palette(cmap, N)
else:
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_n_colors(n, cmap_name='tab20'):\n pt_region_colormap = plt.get_cmap(cmap_name)\n max_i = len(pt_region_colormap.colors)\n return [pt_region_colormap(i % max_i) for i in range(n)]",
"def get_cmap(n):\n cmap_fn = plt.cm.get_cmap('hsv', n+1)\n colors = [cmap_fn(i + 1)[:3] for i in range(... | [
"0.8178953",
"0.77302057",
"0.76631385",
"0.76402104",
"0.73800164",
"0.7310844",
"0.72412497",
"0.72399986",
"0.70891625",
"0.70848805",
"0.705759",
"0.6974212",
"0.69218963",
"0.6902083",
"0.68446606",
"0.6824279",
"0.6822179",
"0.68052876",
"0.6770785",
"0.67608047",
"0.66... | 0.6841107 | 15 |
Convert RGB colorrange to hex. | def rgb2hex(colors, keep_alpha=False):
if isinstance(colors, list):
colors = np.array(colors)
if len(colors.shape)==1:
colors = np.array([colors])
if not keep_alpha:
colors = colors[:, 0:3]
hexcolors = list(map(lambda x: matplotlib.colors.to_hex(x, keep_alpha=keep_alpha), colors... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rgb_hexify(rgb: Iterable[int]) -> str:\n return ''.join(\n list(map(\n lambda x: hex(abs(x))[2:].zfill(2),\n rgb\n ))[::-1]\n )",
"def rgb_to_hex(cls, r, g, b):\n return '#%02x%02x%02x' % (int(r), int(g), int(b))",
"def rgb2hex(cls, rgb):\r\n if not t... | [
"0.7687512",
"0.7467669",
"0.74484754",
"0.7410516",
"0.7389656",
"0.7368666",
"0.7366835",
"0.73552287",
"0.73433214",
"0.73162735",
"0.7264452",
"0.7255768",
"0.7255768",
"0.7255768",
"0.7242387",
"0.7229792",
"0.72228086",
"0.7195485",
"0.71832603",
"0.7121302",
"0.7120350... | 0.6617863 | 51 |
Convert hex colorrange to RGBA. | def hex2rgba(colors):
if 'str' in str(type(colors)):
colors = np.array([colors])
rgbcolors = list(map(lambda x: matplotlib.colors.to_rgba(x), colors))
return np.array(rgbcolors) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hex_to_rgba(h, alpha):\n return tuple([int(h.lstrip('#')[i:i + 2], 16) for i in (0, 2, 4)] + [alpha])",
"def normalize_rgb_colors_to_hex(css):\n log.debug(\"Converting all rgba to hexadecimal color values.\")\n regex = re.compile(r\"rgb\\s*\\(\\s*([0-9,\\s]+)\\s*\\)\")\n match = regex.search(css)... | [
"0.74167764",
"0.70776886",
"0.6812988",
"0.6806883",
"0.66850585",
"0.66562873",
"0.66246355",
"0.66192836",
"0.6602443",
"0.6597313",
"0.6596636",
"0.65546227",
"0.65298957",
"0.6514845",
"0.6510283",
"0.65088314",
"0.64748806",
"0.6454721",
"0.6445255",
"0.64353675",
"0.64... | 0.71970624 | 1 |
Convert hex colorrange to RGB. | def hex2rgb(colors):
if 'str' in str(type(colors)):
colors = np.array([colors])
rgbcolors = list(map(lambda x: matplotlib.colors.to_rgb(x), colors))
return np.array(rgbcolors) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hex2rgb(cls, hex):\r\n valid_char = '#1234567890abcdef'\r\n conditions = (hex[0] == '#',\r\n len(hex) == 7,\r\n all(c in valid_char for c in hex))\r\n if not all(conditions):\r\n raise ValueError\r\n return int(hex[1:3], 16), int(... | [
"0.80397415",
"0.79226583",
"0.78379315",
"0.78092486",
"0.7805608",
"0.7804425",
"0.77812",
"0.77641577",
"0.7762018",
"0.77501935",
"0.774766",
"0.77383685",
"0.7595601",
"0.75911206",
"0.75911206",
"0.75911206",
"0.75911206",
"0.7569708",
"0.7568768",
"0.75653166",
"0.7564... | 0.74308246 | 26 |
Convert hex to rgb. | def _hex2rgb(c_hex):
# Pass 16 to the integer function for change of base
return [int(c_hex[i:i + 2], 16) for i in range(1, 6, 2)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hex2rgb(cls, hex):\r\n valid_char = '#1234567890abcdef'\r\n conditions = (hex[0] == '#',\r\n len(hex) == 7,\r\n all(c in valid_char for c in hex))\r\n if not all(conditions):\r\n raise ValueError\r\n return int(hex[1:3], 16), int(... | [
"0.83853227",
"0.8192587",
"0.8075392",
"0.80479324",
"0.8018519",
"0.79461384",
"0.7929786",
"0.78758806",
"0.78482735",
"0.780748",
"0.7790779",
"0.77845526",
"0.77800864",
"0.77697533",
"0.77679956",
"0.77160627",
"0.77045107",
"0.76989084",
"0.76989084",
"0.76989084",
"0.... | 0.76010317 | 27 |
Generate colors from input list. This function creates unique colors based on the input list y and the cmap. When the gradient hex color is defined, such as '000000', a gradient coloring space is created between two colors. The start color of the particular y, using the cmap and The end color is the defined gradient, s... | def fromlist(y, X=None, cmap='Set1', gradient=None, method='matplotlib', scheme='rgb', opaque_type='per_class', verbose='info'):
# Set the logger
set_logger(verbose=verbose)
# make unique
y = np.array(y)
uiy = np.unique(y)
# Get colors
colors_unique = generate(len(uiy), cmap=cmap, method=me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_color_gradient():\n colors = []\n step = 10\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': green, 'blue': 0})\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': 0, 'green'... | [
"0.69815975",
"0.6241875",
"0.608826",
"0.6028528",
"0.5951059",
"0.58788097",
"0.58780885",
"0.58316827",
"0.5818387",
"0.5781078",
"0.56648606",
"0.5645055",
"0.5637384",
"0.56352425",
"0.56185704",
"0.55861217",
"0.5581655",
"0.5577476",
"0.5556196",
"0.5546131",
"0.554457... | 0.7146094 | 0 |
Return a gradient list of (n) colors between two hex colors. start_hex and finish_hex should be the full sixdigit color string, inlcuding the number sign ("FFFFFF") | def linear_gradient(start_hex, finish_hex="#FFFFFF", n=10):
if finish_hex=='opaque': finish_hex=start_hex
# Starting and ending colors in RGB form
s = _hex2rgb(start_hex)
f = _hex2rgb(finish_hex)
# Initilize a list of the output colors with the starting color
RGB_list = [s]
# Calcuate a colo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_color_gradient():\n colors = []\n step = 10\n for red, green in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': red, 'green': green, 'blue': 0})\n for green, blue in zip(range(255,-step, -step), range(0, 255, step)):\n colors.append({'red': 0, 'green'... | [
"0.7464628",
"0.730974",
"0.6747355",
"0.64438266",
"0.6420821",
"0.6330026",
"0.6233631",
"0.61494654",
"0.614863",
"0.60044503",
"0.5992193",
"0.5977377",
"0.59677243",
"0.5953647",
"0.5910034",
"0.590775",
"0.59075046",
"0.58731186",
"0.58708364",
"0.586321",
"0.5853994",
... | 0.7984741 | 0 |
Create a linear gradient between two values. | def _incremental_steps(start, end, steps, stepsize=None):
if stepsize is None: step_size = (end - start) / np.maximum((steps - 1), 1)
gradient = []
for i in range(steps):
value = start + step_size * i
gradient.append(value)
return gradient[0:steps] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linear_gradient(value, start, end, colour_list=None):\n\n # Translate the end colour to RGB arrays if necessary.\n if isinstance(start, str):\n # Default (search the molmol list then the X11 list).\n if colour_list == None:\n try:\n start = molmol_colours(start)\n ... | [
"0.7255634",
"0.69585824",
"0.6585821",
"0.6522588",
"0.65146476",
"0.6445859",
"0.6416497",
"0.6357481",
"0.63406503",
"0.6292611",
"0.62760484",
"0.6210663",
"0.61864525",
"0.60238576",
"0.6018492",
"0.6007702",
"0.59936553",
"0.5974485",
"0.59592235",
"0.5944983",
"0.59406... | 0.0 | -1 |
Color to dictionary. Takes in a list of RGB sublists and returns dictionary of colors in RGB and hex form for use in a graphing function defined later on. | def _color_dict(gradient):
hex_colors = [_rgb2hex(RGB) for RGB in gradient]
rgb_colors = np.c_[[RGB[0] for RGB in gradient], [RGB[1] for RGB in gradient], [RGB[2] for RGB in gradient]]
return {'hex': hex_colors, 'rgb': rgb_colors} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assigning_colors():\n rgb_colors = {}\n for name, hex in matplotlib.colors.cnames.items():\n color = []\n # So the values are from 0-255 and not 0-1\n for i in matplotlib.colors.to_rgb(hex):\n color.append(int(i * 255))\n\n color = tuple(color)\n rgb_colors[n... | [
"0.72993577",
"0.7166082",
"0.71234846",
"0.7105684",
"0.67915034",
"0.66504467",
"0.6633686",
"0.6622999",
"0.656271",
"0.65496135",
"0.6542531",
"0.64886117",
"0.64846194",
"0.63931656",
"0.63931257",
"0.6381189",
"0.6377055",
"0.6372067",
"0.62352693",
"0.62340975",
"0.623... | 0.7698112 | 0 |
Check whether the input is a valid hex color code. | def is_hex_color(color, verbose='info'):
# Set the logger
set_logger(verbose=verbose)
if not isinstance(color, str):
logger.info('Hex [%s] should be of type string' %(str(color)))
return False
if color.startswith('#'):
color = color[1:]
else:
logger.info('Hex [%s] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_valid_hex(self):\n self.assertTrue(is_valid_hex('#aabb11'))\n self.assertTrue(is_valid_hex('#000'))\n self.assertTrue(is_valid_hex('#aaa'))\n self.assertFalse(is_valid_hex('black'))\n self.assertFalse(is_valid_hex('bl(ack'))",
"def is_valid_hex(hex_code: str) -> boo... | [
"0.78903884",
"0.78054225",
"0.7551353",
"0.7472038",
"0.73835933",
"0.7371952",
"0.73323774",
"0.7326407",
"0.71489763",
"0.70746005",
"0.70680034",
"0.6905422",
"0.687528",
"0.679516",
"0.67354673",
"0.67342633",
"0.67073137",
"0.67032605",
"0.6660225",
"0.6614718",
"0.6614... | 0.760875 | 2 |
Set gradient on density color. This function determines the density of the data and adds a transparency column. If samples are in dense areas, transparency values are towards 1 (visible), whereas isn nonedense areas, the transparency values are towards 0 (not visible). | def gradient_on_density_color(X, c_rgb, labels, opaque_type='per_class', showfig=False, verbose='info'):
# Set the logger
set_logger(verbose=verbose)
if labels is None: labels = np.repeat(0, X.shape[0])
from scipy.stats import gaussian_kde
uilabels = np.unique(labels)
# Add the transparency col... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isothermal_depth_wyrtki1964_gradient(da_PT):\n\n # make land mask based on surface layer\n da_mask = da_PT.isel(z=0)*0.+1.\n\n # calculate drho/dz\n da_PT_dz = da_PT.differentiate('z') # kg/m^4\n\n # interpolate to finer vertical resolution (2.5m)\n da_interp = da_PT_dz.interp(z=np.arange(0,d... | [
"0.57073355",
"0.57073355",
"0.56205153",
"0.5285244",
"0.52843755",
"0.5108466",
"0.5072132",
"0.50284564",
"0.49939755",
"0.49933887",
"0.49889076",
"0.4987362",
"0.4982678",
"0.4981954",
"0.49721134",
"0.49690628",
"0.49479908",
"0.49206397",
"0.4919995",
"0.49167046",
"0.... | 0.65256983 | 0 |
Convert old verbosity to the new one. | def convert_verbose_to_new(verbose):
# In case the new verbosity is used, convert to the old one.
if verbose is None: verbose=0
if not isinstance(verbose, str) and verbose<10:
status_map = {
'None': 'silent',
0: 'silent',
6: 'silent',
1: 'critical',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verbosity(v):\n assert v in [0,1,2] # debug, warn, info\n GLOBAL['VERBOSITY'] = v",
"def test_increase_verbosity(self):\n # Start from a known state.\n set_level(logging.INFO)\n assert get_level() == logging.INFO\n # INFO -> VERBOSE.\n increase_verbosity()\n ... | [
"0.6408217",
"0.6038145",
"0.60295814",
"0.5947771",
"0.5929938",
"0.5909877",
"0.5623561",
"0.5614202",
"0.55986595",
"0.55324614",
"0.55215126",
"0.55148864",
"0.54819477",
"0.5481862",
"0.5461689",
"0.5380808",
"0.53354967",
"0.531541",
"0.52798617",
"0.52721906",
"0.52597... | 0.74339193 | 0 |
Set the logger for verbosity messages. | def set_logger(verbose: [str, int] = 'info'):
# Set 0 and None as no messages.
if (verbose==0) or (verbose is None):
verbose=60
verbose = convert_verbose_to_new(verbose)
# Convert str to levels
if isinstance(verbose, str):
levels = {'silent': 60,
'off': 60,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_verbosity(self, verbosity):\n if verbosity == 0:\n self.__logger.setLevel(logging.CRITICAL)\n if verbosity == 1:\n self.__logger.setLevel(logging.ERROR)\n if verbosity == 2:\n self.__logger.setLevel(logging.WARNING)\n if verbosity == 3:\n ... | [
"0.8115755",
"0.78855675",
"0.73544574",
"0.72481287",
"0.7224838",
"0.71247065",
"0.711234",
"0.7095071",
"0.7076668",
"0.7050104",
"0.70436734",
"0.68502855",
"0.6829931",
"0.6782882",
"0.6740046",
"0.67367077",
"0.67337465",
"0.6728373",
"0.6710663",
"0.6692917",
"0.669114... | 0.7289997 | 3 |
Set the logger for verbosity messages. | def disable_tqdm():
return (True if (logger.getEffectiveLevel()>=30) else False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_verbosity(self, verbosity):\n if verbosity == 0:\n self.__logger.setLevel(logging.CRITICAL)\n if verbosity == 1:\n self.__logger.setLevel(logging.ERROR)\n if verbosity == 2:\n self.__logger.setLevel(logging.WARNING)\n if verbosity == 3:\n ... | [
"0.81160295",
"0.78849846",
"0.73537046",
"0.7289292",
"0.7246877",
"0.72242355",
"0.71231896",
"0.7111531",
"0.70946926",
"0.70777905",
"0.7048882",
"0.70435476",
"0.68507385",
"0.68290937",
"0.67830193",
"0.67398274",
"0.673591",
"0.67333853",
"0.6726991",
"0.67109823",
"0.... | 0.0 | -1 |
Deploy de PyArweb en python.org.ar. | def deploy():
git_pull()
if confirm("Install/upgrade requirements with pip?"):
install_requeriments()
django_command('collectstatic')
django_command('migrate')
restart() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deploy():",
"def deploy():\n build()\n copy()\n install()",
"def deploy():\n require(\"hosts\", provided_by=[production, staging])\n env.release = time.strftime(\"%Y-%m-%d_%H:%M:%S\")\n upload_tar_from_git()\n install_requirements()\n setup_webserver()\n symlink_current_release()... | [
"0.69113123",
"0.65194166",
"0.64792144",
"0.6451945",
"0.6413485",
"0.61965394",
"0.60336876",
"0.5960515",
"0.5960515",
"0.5960515",
"0.5945097",
"0.58856344",
"0.5848106",
"0.58176446",
"0.58134186",
"0.5800298",
"0.5773971",
"0.5745045",
"0.57371277",
"0.5698382",
"0.5692... | 0.5520349 | 42 |
Restart gunicorn sending HUP signal to his pid. | def restart():
run('kill -HUP $(cat /tmp/pyar_web.pid)') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reload_gunicorn():\n puts(yellow(\"Reload gunicorn graceful\"))\n sudo('kill -HUP `cat %s`' % (env.gunicorn_pidpath), user=env.app_user)",
"def handle(self, *args, **options):\n try:\n with open(\"/gunicorn.pid\") as f:\n pid = int(f.read().strip())\n os.... | [
"0.7595875",
"0.7515631",
"0.74177647",
"0.7061338",
"0.702291",
"0.6820605",
"0.6762132",
"0.67176133",
"0.67176133",
"0.65728974",
"0.656298",
"0.6393916",
"0.63589066",
"0.6325044",
"0.63238925",
"0.6298795",
"0.6288653",
"0.627927",
"0.62728465",
"0.6186012",
"0.61211467"... | 0.6968212 | 5 |
Return size of folder at path. | def folder_size(path):
return sum(getsize(f) for f in os.listdir('.') if isfile(f)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def folder_size(path: str) -> str:\r\n return (\r\n subprocess.check_output([\"du\", \"-sh\", \"-B1\", path]).split()[0].decode(\"utf-8\")\r\n )",
"def get_size_from_path(path):\n if not os.path.isdir(path):\n return os.path.getsize(path)\n\n size = 0\n\n for dirname, dirs, files in ... | [
"0.8283314",
"0.8165242",
"0.80046517",
"0.78010786",
"0.77946424",
"0.7767517",
"0.77278864",
"0.77143127",
"0.77143127",
"0.769937",
"0.7691966",
"0.7543926",
"0.7538644",
"0.7486155",
"0.7480786",
"0.7466756",
"0.74297",
"0.74095184",
"0.73598933",
"0.7322922",
"0.7322922"... | 0.8572386 | 0 |
Yield all filenames in a path. | def list_directory_files(path, folders=False):
for f in os.listdir(path):
if f[0] == '.':
continue
current_path = os.path.join(path, f)
if folders is False:
if os.path.isfile(current_path):
if os.path.getsize(current_path) != 0:
yie... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_files(path):\n if os.path.isfile(path):\n yield path\n elif os.path.isdir(path):\n for dirpath, _, filenames in os.walk(path):\n for f in filenames:\n yield os.path.join(dirpath, f)\n else:\n raise RuntimeError('Path %s is invalid' % path)",
"def a... | [
"0.8316498",
"0.8162758",
"0.7978636",
"0.77177733",
"0.7645544",
"0.7496099",
"0.7371728",
"0.73671514",
"0.7325709",
"0.7240269",
"0.72048444",
"0.71422756",
"0.7079702",
"0.707557",
"0.70674306",
"0.70605344",
"0.6950866",
"0.69340396",
"0.6924985",
"0.6914971",
"0.6914575... | 0.0 | -1 |
Print out debugging information string string to be printed (in) | def debug(string):
if verbose:
print string
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def debug(string):\n if conf.DEBUG:\n outputs.print_debug(string)",
"def output_debug_info(self):",
"def debugPrint(text: str):\r\n if DEBUG:\r\n print(text)",
"def debug_string(self):\n\n raise NotImplementedError",
"def debug():",
"def debug_print(text):\r\n if settings.de... | [
"0.76916933",
"0.7513169",
"0.7507592",
"0.73979014",
"0.73887455",
"0.7374867",
"0.7366604",
"0.7354347",
"0.7354347",
"0.7234283",
"0.7234283",
"0.71581537",
"0.70975",
"0.7073775",
"0.7070248",
"0.7070248",
"0.7016923",
"0.6961993",
"0.6890409",
"0.68704146",
"0.6854564",
... | 0.7761138 | 0 |
Log the node, time, and the string string to be printed (in) | def nilog(string):
node=platform.node()
now=time.time()
nano= "%.10f" %now
utct = time.strftime("%Y-%m-%dT%H:%M:%S")
logger.info('NILOG: ' + node + ',' + nano + ',' + utct + ',' + string)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Log(self, times):\n\n print '--'\n print times.PrettyPrintLog()\n\n return",
"def log(text):\n print \"%s: %s\" % (str(datetime.datetime.now()), text)",
"def log_time(label: str) -> None:\n print(label, datetime.now())",
"def print_log(*content):\n now = datetime.datetime.now().strftime(\... | [
"0.7125287",
"0.6977599",
"0.6731128",
"0.6702986",
"0.66663325",
"0.66369194",
"0.65915567",
"0.6529379",
"0.6510782",
"0.6506993",
"0.6426057",
"0.6324537",
"0.6316714",
"0.629932",
"0.6274224",
"0.6270883",
"0.626235",
"0.62466824",
"0.6237593",
"0.62242323",
"0.6213401",
... | 0.67103255 | 3 |
Do a NetInf PUBLISH for one file file_name is the file to do now | def pubone(file_name,alg,host):
hash_alg=alg
scheme="ni"
rform="json"
ext="{ \"meta\": { \"pubdirs\" : \"yep\" } }"
# record start time of this
stime=time.time()
# Create NIdigester for use with form encoder and StreamingHTTP
ni_digester = NIdigester()
# Install the template URL b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish(self, filename):\n # 1) Encrypt file\n # 2) Publish to remote cloud server\n # 3) Wait for the result\n # 4) Store results in files located inside RAM folder",
"def detect(self, filename):\n self.publish(filename)",
"def publishUploads(self, manualVerify = True):\... | [
"0.64287066",
"0.60361385",
"0.5941095",
"0.59345895",
"0.5917054",
"0.57990074",
"0.57726157",
"0.57401097",
"0.57236075",
"0.57073116",
"0.5628994",
"0.55332154",
"0.5481817",
"0.54700124",
"0.54092836",
"0.54011065",
"0.5339769",
"0.53389674",
"0.5331978",
"0.5296641",
"0.... | 0.6248407 | 1 |
Command line program to perform a NetInf 'publish' operation using http convergence layer. Uses NIproc global instance of NI operations class | def py_nipubdir():
# Options parsing and verification stuff
usage = "%%prog -d <pathname of content directory> -n <FQDN of netinf node> [-a <hash alg>] [-m NN] [-c count]"
parser = OptionParser(usage)
parser.add_option("-d", "--dir", dest="dir_name",
type="string",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish():\n pass",
"def hydronn():\n from hydronn.bin import extract_data\n from hydronn.bin import extract_retrieval_data\n from hydronn.bin import train\n from hydronn.bin import retrieve\n from hydronn.bin import evaluate\n\n description = (\"HYDRONN: A NRT precipitation retrieval fo... | [
"0.5767328",
"0.5661973",
"0.5631436",
"0.55480254",
"0.54211825",
"0.54131734",
"0.5406113",
"0.53765",
"0.53701484",
"0.53538346",
"0.5352672",
"0.5315707",
"0.5301884",
"0.5267521",
"0.5263778",
"0.5224786",
"0.52056885",
"0.5168929",
"0.51480085",
"0.5146815",
"0.5140904"... | 0.5670017 | 1 |
This will initialize a worker process | def init_audio_builder(_encode_queue, _app_config, _lock, _only_wav, _dump_sequencer_log):
global sequence_builder
WordNetCache._lock = _lock
sequence_builder = SequenceBuilder(app_config=_app_config,
encode_queue=_encode_queue,
o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(self,init):\n logger.info('*** initialize: worker id=%d',self._agent.wid)\n self.commands = {'initialize':None, 'before_do_work':None, 'after_do_work':None, 'finalize':None}\n self.commands.update(init.get(self._agent.wid,{}))\n exec_command(self.commands['initialize'])",... | [
"0.73232734",
"0.73105544",
"0.72356534",
"0.714579",
"0.70134914",
"0.69839424",
"0.6903663",
"0.6903414",
"0.6900731",
"0.6741941",
"0.6730824",
"0.67168313",
"0.67149097",
"0.6712364",
"0.6679937",
"0.6669801",
"0.6663036",
"0.6583762",
"0.6555774",
"0.6551152",
"0.6539077... | 0.0 | -1 |
This will be executed as payload from a worker process | def make_audio_track(language_pair, items, part_number):
global sequence_builder
try:
sequence_builder.make_audio_track(language_pair, items, part_number)
except Exception as e:
print(str(e))
print_exc() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _worker(self, args):\n pass",
"def exec_worker(self, endpoint, args, request):\n raise NotImplementedError",
"def run(self):\r\n self.env.process(self.rw_pifo_sm())",
"def process(self):\n pass",
"def executor(self):",
"def do_work(self):",
"def docker_worker():",
"def... | [
"0.6918521",
"0.6761025",
"0.6694109",
"0.6624723",
"0.6477929",
"0.64630187",
"0.6447768",
"0.644027",
"0.6413022",
"0.63529164",
"0.63503426",
"0.63503426",
"0.63503426",
"0.634449",
"0.6313756",
"0.6266754",
"0.6241326",
"0.6200068",
"0.61842644",
"0.61830354",
"0.6179496"... | 0.0 | -1 |
Generate cached and grouped activity items for personal and universal news. Args | def build_activity(self, user_id):
activity_ids = self.raw_activity_links_collection.get_activity_ids_for_user(user_id)
friend_ids = self.friends_collection.getFriends(user_id, limit=None)
personal_items = self.raw_activity_items_collection.get_activity_items(activity_ids)
universal_ve... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activities_to_jsonfeed(activities, actor=None, title=None, feed_url=None,\n home_page_url=None):\n try:\n iter(activities)\n except TypeError:\n raise TypeError('activities must be iterable')\n\n if isinstance(activities, (dict, str)):\n raise TypeError('activities may not... | [
"0.58782476",
"0.58245516",
"0.5714196",
"0.54559016",
"0.5373944",
"0.5279887",
"0.52462864",
"0.52327186",
"0.52325124",
"0.5232471",
"0.5232227",
"0.5223817",
"0.5216105",
"0.51968014",
"0.51924586",
"0.519236",
"0.51744807",
"0.51639336",
"0.51562124",
"0.5153761",
"0.509... | 0.6624148 | 0 |
This initializes the C fitting library. | def initializeC(self, image):
super(CPupilFit, self).initializeC(image)
self.mfit = self.clib.pfitInitialize(self.pupil_fn.getCPointer(),
self.rqe,
self.scmos_cal,
self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__ (self) :\n self.loadCSPAD2x2CalibParsDefault()",
"def initialize(self):\n self.write_model(path=PATH.GRAD, suffix='new')\n\n if PAR.RANDOM_OVER_IT or optimize.iter == 1:\n self.get_random_frequencies()\n\n print('Generating synthetics')\n system.run('sol... | [
"0.6538869",
"0.640215",
"0.62104684",
"0.6187296",
"0.61846364",
"0.6099485",
"0.602922",
"0.60163647",
"0.5997682",
"0.5991749",
"0.597411",
"0.5933944",
"0.5894047",
"0.58402103",
"0.57940716",
"0.57654905",
"0.57517904",
"0.5741623",
"0.57360727",
"0.57360727",
"0.5736072... | 0.64429295 | 1 |
Pass new peaks to the C library. | def newPeaks(self, peaks, peaks_type):
c_peaks = self.formatPeaks(peaks, peaks_type)
self.clib.pfitNewPeaks(self.mfit,
c_peaks,
ctypes.c_char_p(peaks_type.encode()),
c_peaks.shape[0]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def changePeaks(self):\n # Change the number of peaks\n if self.minpeaks is not None and self.maxpeaks is not None:\n npeaks = len(self.peaks_function)\n u = self.random.random()\n r = self.maxpeaks - self.minpeaks\n if u < 0.5:\n # Remove n ... | [
"0.64907897",
"0.6027173",
"0.57900417",
"0.57882077",
"0.5668051",
"0.56633043",
"0.5647525",
"0.5629544",
"0.562596",
"0.5559616",
"0.5544985",
"0.5536392",
"0.5512018",
"0.5455464",
"0.5427178",
"0.54028904",
"0.5350657",
"0.5269623",
"0.52637213",
"0.525326",
"0.52431154"... | 0.70837945 | 0 |
Test that addEventListener gets flagged appropriately. | def test_addEventListener():
err = _do_test_raw("""
x.addEventListener("click", function() {}, true);
x.addEventListener("click", function() {}, true, false);
""")
assert not err.failed()
assert not err.notices
err = _do_test_raw("""
x.addEventListener("click", function() {}, true, tru... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_subscribe_one_listener(self):\n def listener():\n pass\n EVENT_MANAGER.subscribe('test_listener', listener)\n self.assertIn(listener, EVENT_MANAGER._listeners['test_listener'])",
"def test_mouseevents():\n\n err = _do_test_raw(\"window.addEventListener('mousemove', fun... | [
"0.63600886",
"0.63525814",
"0.629856",
"0.6232452",
"0.60886395",
"0.58877194",
"0.5872726",
"0.57898706",
"0.5751316",
"0.5695141",
"0.56895745",
"0.5664929",
"0.56027156",
"0.5536601",
"0.54882324",
"0.54466474",
"0.5415127",
"0.5400491",
"0.5400491",
"0.5400491",
"0.54004... | 0.85027266 | 0 |
Tests that createElement calls are filtered properly | def test_createElement():
assert not _do_test_raw("""
var x = "foo";
x.createElement();
x.createElement("foo");
""").failed()
assert _do_test_raw("""
var x = "foo";
x.createElement("script");
""").failed()
assert _do_test_raw("""
var x = "foo";
x.createElement(bar);
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_render_element2():\n elem = hr.Element()\n elem.append(\"this is some text\")\n elem.append(\"and this is some more text\")\n\n # This uses the render_results utility above\n file_contents = render_result(elem).strip()\n\n # making sure the content got in there.\n assert \"this is som... | [
"0.6099641",
"0.5910537",
"0.56316715",
"0.5482185",
"0.54426223",
"0.54182744",
"0.5415149",
"0.5406062",
"0.53996783",
"0.53621614",
"0.5355254",
"0.5328316",
"0.5324107",
"0.5315543",
"0.5293697",
"0.5273912",
"0.5255659",
"0.5250865",
"0.5217955",
"0.51548404",
"0.5153654... | 0.7561235 | 0 |
Tests that createElementNS calls are filtered properly | def test_createElementNS():
assert not _do_test_raw("""
var x = "foo";
x.createElementNS();
x.createElementNS("foo");
x.createElementNS("foo", "bar");
""").failed()
assert _do_test_raw("""
var x = "foo";
x.createElementNS("foo", "script");
""").failed()
assert _do_test_raw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_createElement():\n\n assert not _do_test_raw(\"\"\"\n var x = \"foo\";\n x.createElement();\n x.createElement(\"foo\");\n \"\"\").failed()\n\n assert _do_test_raw(\"\"\"\n var x = \"foo\";\n x.createElement(\"script\");\n \"\"\").failed()\n\n assert _do_test_raw(\"\"\"\n v... | [
"0.6553644",
"0.5767339",
"0.56110704",
"0.5496338",
"0.5460686",
"0.5308755",
"0.52212656",
"0.5208479",
"0.5199475",
"0.5189086",
"0.5153285",
"0.51497877",
"0.5131236",
"0.51135963",
"0.51049906",
"0.50972146",
"0.5070891",
"0.50700235",
"0.50475746",
"0.5033134",
"0.50266... | 0.7780457 | 0 |
Tests that warnings on SQL methods are emitted properly | def test_sql_methods():
err = _do_test_raw("""
x.executeSimpleSQL("foo " + y);
""")
assert err.warnings[0]['id'][-1] == 'executeSimpleSQL_dynamic'
err = _do_test_raw("""
x.createStatement("foo " + y);
""")
assert err.warnings[0]['id'][-1] == 'executeSimpleSQL_dynamic'
err ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def warning(self, *args, **kwargs):",
"def warn():\n pass",
"def test_query_wrapper_operational_error(self):\n\n _session = self.sessionmaker()\n\n _session.begin()\n self.addCleanup(_session.rollback)\n q = _session.query(self.Foo).filter(\n self.Foo.count... | [
"0.63846934",
"0.6327814",
"0.6292967",
"0.6227003",
"0.61164504",
"0.606927",
"0.60324985",
"0.6029675",
"0.60110146",
"0.6006412",
"0.6006412",
"0.6006412",
"0.6006412",
"0.6006412",
"0.6006412",
"0.6006412",
"0.6006412",
"0.5999971",
"0.5977941",
"0.5971273",
"0.59604573",... | 0.73811924 | 0 |
Tests that setAttribute calls are blocked successfully | def test_setAttribute():
assert not _do_test_raw("""
var x = "foo";
x.setAttribute();
x.setAttribute("foo");
x.setAttribute("foo", "bar");
""").failed()
assert _do_test_raw("""
var x = "foo";
x.setAttribute("onfoo", "bar");
""").failed() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_set_attribute():\n elem = hr.Element(\"this is some text\", id=\"spam\", style=\"eggs\")\n elem.set_attributes(holy=\"grail\", answer=42)\n\n assert (\n get_opening_line(elem)\n == '<html id=\"spam\" style=\"eggs\" holy=\"grail\" answer=\"42\">'\n )",
"def testSetAttributeActio... | [
"0.6553509",
"0.6533299",
"0.6466073",
"0.62271565",
"0.60845727",
"0.600334",
"0.5973352",
"0.59136784",
"0.5868815",
"0.5804294",
"0.58023375",
"0.57952565",
"0.57601655",
"0.5696862",
"0.56905687",
"0.5690205",
"0.5658569",
"0.5632546",
"0.563241",
"0.56156796",
"0.5583333... | 0.7886286 | 0 |
This makes sure that unknown function calls still have their arguments traversed. | def test_callexpression_argument_traversal():
DECLARATIONS = (
'function foo(x){}',
'var foo = function foo(x){}',
'var foo = (x) => {}',
'var foo = (x) => undefined',
)
for declaration in DECLARATIONS:
assert not _do_test_raw("""
%s;
foo({"bar":funct... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def derive_args(func):\n args = inspect.getfullargspec(func).args\n if args and is_selfish_name(args[0]):\n del args[0]\n return args",
"def ignore(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\n pass",
"def needs_arglist(self):\n True",
... | [
"0.61856985",
"0.5807541",
"0.5802105",
"0.57924193",
"0.5757364",
"0.5753708",
"0.5750814",
"0.5709718",
"0.5687455",
"0.5621083",
"0.56190723",
"0.55581003",
"0.5552606",
"0.55483025",
"0.55393267",
"0.5535397",
"0.5522945",
"0.55209094",
"0.5517479",
"0.55152714",
"0.55129... | 0.531404 | 47 |
Test that insertAdjacentHTML works the same as innerHTML. | def test_insertAdjacentHTML():
assert not _do_test_raw("""
var x = foo();
x.insertAdjacentHTML("foo bar", "<div></div>");
""").failed()
assert _do_test_raw("""
var x = foo();
x.insertAdjacentHTML("foo bar", "<div onclick=\\"foo\\"></div>");
""").failed()
# Test without declaration... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_createElement():\n\n assert not _do_test_raw(\"\"\"\n var x = \"foo\";\n x.createElement();\n x.createElement(\"foo\");\n \"\"\").failed()\n\n assert _do_test_raw(\"\"\"\n var x = \"foo\";\n x.createElement(\"script\");\n \"\"\").failed()\n\n assert _do_test_raw(\"\"\"\n v... | [
"0.6206021",
"0.54315954",
"0.5343544",
"0.5333806",
"0.5289093",
"0.52715856",
"0.52019536",
"0.51820296",
"0.5170138",
"0.51649594",
"0.51338845",
"0.5128217",
"0.51144373",
"0.50881356",
"0.506577",
"0.5063727",
"0.50439453",
"0.50368094",
"0.50320894",
"0.49574444",
"0.49... | 0.84246767 | 0 |
Test that `nsIFile.launch()` is flagged. | def test_nsIFile_launch():
assert _do_test_raw('foo.launch()').failed() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_fail_launch_file(self):\n args = self.args.copy()\n # Pass a string instead of a list\n args[\"traj_file\"] = \"nofile.xtc\"\n with pytest.raises(FileNotFoundError) as err:\n UI.launch(**args)\n assert \"nofile.xtc does not exist.\" in str(err.value)",
"def ... | [
"0.64032733",
"0.6122187",
"0.5883255",
"0.5880068",
"0.5665483",
"0.5528563",
"0.552782",
"0.55099374",
"0.5467473",
"0.5463001",
"0.544874",
"0.5420529",
"0.5306385",
"0.5299579",
"0.52987474",
"0.52926964",
"0.52705574",
"0.52683437",
"0.52574074",
"0.5248173",
"0.52363783... | 0.82881325 | 0 |
Test that `.openDialog("")` throws doesn't throw an error for chrome/local URIs. | def test_openDialog_pass(self):
self.run_script("""
foo.openDialog("foo")
foo.openDialog("chrome://foo/bar")
""")
self.assert_silent() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_openDialog(self):\n\n def test_uri(self, uri):\n self.setUp()\n self.setup_err()\n self.run_script('foo.openDialog(\"%s\")' % uri)\n self.assert_failed(with_warnings=True)\n\n uris = ['http://foo/bar/',\n 'https://foo/bar/',\n ... | [
"0.6870198",
"0.61324924",
"0.6001495",
"0.5948701",
"0.59436876",
"0.58810073",
"0.56357914",
"0.56282413",
"0.5534864",
"0.5530544",
"0.55118865",
"0.5507113",
"0.5468689",
"0.545093",
"0.53994143",
"0.5359619",
"0.5342453",
"0.5341253",
"0.5336293",
"0.5319729",
"0.5292852... | 0.7835892 | 0 |
Test that `.openDialog(bar)` throws doesn't throw an error where `bar` is a dirty object. | def test_openDialog_flag_var(self):
self.run_script("""
foo.openDialog(bar)
""")
self.assert_notices() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_openDialog_pass(self):\n self.run_script(\"\"\"\n foo.openDialog(\"foo\")\n foo.openDialog(\"chrome://foo/bar\")\n \"\"\")\n self.assert_silent()",
"def test_openWindowWithWrongSettingsFile(self):\n self.createWrongSettingsFile()\n return self.assertRaise... | [
"0.664462",
"0.6096759",
"0.5865762",
"0.56994104",
"0.5670863",
"0.5574597",
"0.5482802",
"0.54300237",
"0.5427492",
"0.53748256",
"0.5308122",
"0.52538544",
"0.5234245",
"0.52235",
"0.5222695",
"0.52182746",
"0.52126163",
"0.5170296",
"0.516994",
"0.51174134",
"0.5110923",
... | 0.63953465 | 1 |
Test that `.openDialog("")` throws an error where is a nonchrome, nonrelative URL. | def test_openDialog(self):
def test_uri(self, uri):
self.setUp()
self.setup_err()
self.run_script('foo.openDialog("%s")' % uri)
self.assert_failed(with_warnings=True)
uris = ['http://foo/bar/',
'https://foo/bar/',
'ftp://f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_openDialog_pass(self):\n self.run_script(\"\"\"\n foo.openDialog(\"foo\")\n foo.openDialog(\"chrome://foo/bar\")\n \"\"\")\n self.assert_silent()",
"def error_open_mess(url: str) -> None:\n meta = MainData()\n print(('{0}Can not open URL: {1} {2}{3}').format(meta... | [
"0.76250166",
"0.63414615",
"0.60822976",
"0.5849271",
"0.5837435",
"0.5826389",
"0.57790226",
"0.57463026",
"0.5706456",
"0.5685249",
"0.5682213",
"0.56506664",
"0.56181157",
"0.5609045",
"0.5588032",
"0.55650854",
"0.5546954",
"0.55328673",
"0.5530209",
"0.55300903",
"0.551... | 0.6708838 | 1 |
select from mydb.item_item where category='suncare' | def test_categoryQuery(self) -> None:
result = self.entries.filter(category__iexact='suncare')
self.assertGreater(len(result), 0)
result = self.entries.filter(category__iexact='xxxxxx')
self.assertEqual(len(result), 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def oreDbQuery():\n # TODO: Change TABLE name to the Crop Slection table that has not yet been Created :-(\n # TODO: Currently using the old DB for Crop Lookup table\n\n c.execute('SELECT DISTINCT Crop, GrpNo, GrpName, SubGrpNo, SubGrpName, Category FROM CCA')\n\n return c.fetchall()",
"def get_items... | [
"0.61683613",
"0.59296685",
"0.5827819",
"0.57827365",
"0.5734394",
"0.5708885",
"0.5661912",
"0.5578158",
"0.55745226",
"0.5478331",
"0.53940827",
"0.538089",
"0.53388613",
"0.5329586",
"0.53203094",
"0.5273206",
"0.52608246",
"0.52469826",
"0.52403355",
"0.52397007",
"0.522... | 0.5704614 | 6 |
select name from mydb.item_item where ingredients not like '%multimedia%' and ingredients not like '%provision%' | def test_excludeIngredientQuery(self) -> None:
ingredient0 = 'multimedia'
ingredient1 = 'provision'
result = self.entries.exclude(Q(ingredients__icontains=ingredient0) | Q(ingredients__icontains=ingredient1))
self.assertEqual(988, len(result))
queries = (Q(ingredients__icontains... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self):\n return super(BeefRecipes, self).execute().where(lower(col('ingredients')).like(\"%beef%\"))",
"def test_search_by_bad_ingredients(self):\n recipe_id = self.request_mgr.search_by_ingredients(['asdfadsfa'])\n self.assertEqual(recipe_id, None)",
"def test_includeIngredien... | [
"0.620481",
"0.6070535",
"0.5824319",
"0.56567574",
"0.5471123",
"0.540554",
"0.54015297",
"0.53638273",
"0.5333347",
"0.53282493",
"0.5299963",
"0.5287664",
"0.52692205",
"0.5224078",
"0.52074933",
"0.5206623",
"0.52025026",
"0.5187782",
"0.5176583",
"0.5147688",
"0.51246583... | 0.6608633 | 0 |
select name from mydb.item_item where ingredients like '%multimedia%' and ingredients like '%provision%' | def test_includeIngredientQuery(self) -> None:
ingredient0 = 'multimedia'
ingredient1 = 'provision'
result = self.entries.filter(Q(ingredients__icontains=ingredient0) & Q(ingredients__icontains=ingredient1))
self.assertEqual(1, len(result)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self):\n return super(BeefRecipes, self).execute().where(lower(col('ingredients')).like(\"%beef%\"))",
"def search_recipe(ingredients):\n\n params = '+'.join(ingredients.split())\n url_search = SEARCH_URL.format(params)\n response = req.get(url_search)\n\n return response.content",... | [
"0.6647757",
"0.6191605",
"0.59739184",
"0.59232444",
"0.5843114",
"0.5770647",
"0.57508814",
"0.57090604",
"0.56950915",
"0.56343377",
"0.55926657",
"0.5586077",
"0.555635",
"0.55286276",
"0.5510413",
"0.55007166",
"0.5458444",
"0.5436161",
"0.5435352",
"0.5406655",
"0.54020... | 0.63518983 | 1 |
This cuts out all values of "arg" from the string! | def cutting(value,arg):
return value.replace(arg,'working') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cut_string(value, arg):\n\n return value.replace(arg, '')",
"def cut(value, arg):\n return value.replace(arg, '') # we can replace arg with ''. We also need to register it",
"def cut_str(value, arg):\n\n return value.replace(arg,'')",
"def parse_args(string):\n return re.findall('[-=][^ ]*', ... | [
"0.71844476",
"0.6835684",
"0.6774667",
"0.67090833",
"0.66573966",
"0.66573966",
"0.6640773",
"0.64334327",
"0.62785816",
"0.624245",
"0.62198776",
"0.61978364",
"0.61978364",
"0.61978364",
"0.61978364",
"0.61978364",
"0.6171914",
"0.6171914",
"0.6155647",
"0.6096736",
"0.60... | 0.55772555 | 51 |
Initializes a new state object. | def __init__(self,
qpos: Optional[np.ndarray] = None,
qvel: Optional[np.ndarray] = None,
qacc: Optional[np.ndarray] = None):
self.qpos = qpos
self.qvel = qvel
self.qacc = qacc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n raise NotImplementedError('cannot create independent state')",
"def __init__(self, state):\n self.state = state",
"def __init__(self, state=State.NORMAL):\n self.state = state",
"def __init__(self, init_state):\n self._curr_state = init_state",
"def initial... | [
"0.8221085",
"0.8184259",
"0.81162816",
"0.79742545",
"0.7853358",
"0.77889585",
"0.77679455",
"0.7708608",
"0.7664172",
"0.75280225",
"0.75117093",
"0.7510387",
"0.74730545",
"0.74558854",
"0.74364495",
"0.74086964",
"0.73885727",
"0.73800635",
"0.7345474",
"0.7326453",
"0.7... | 0.0 | -1 |
Returns the time (total sum of timesteps) since the last reset. | def time(self) -> float:
return self.sim_scene.data.time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTime(self):\n return self.step / (self.max_step + int(self.include))",
"def getTime(self) -> float:\n return self.t",
"def get(self):\n if self.running:\n return self.accumulated_time + pg.time.get_ticks() - self.start_time\n else:\n return self.accumula... | [
"0.7168588",
"0.71211964",
"0.70225096",
"0.6981281",
"0.69380325",
"0.69172233",
"0.6909712",
"0.68915546",
"0.6887088",
"0.68044907",
"0.6802243",
"0.6790637",
"0.67853886",
"0.67688805",
"0.6760488",
"0.67461056",
"0.67229843",
"0.6677072",
"0.6672788",
"0.6668022",
"0.666... | 0.6420324 | 42 |
Processes the configuration for a group. | def _process_group(self, **config_kwargs) -> RobotGroupConfig:
return RobotGroupConfig(self.sim_scene, **config_kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_group_from_config(self):\n\n group_file_name = \"cicada/config/group.yaml\"\n if os.path.isfile(group_file_name):\n self.group_data = dict()\n with open(group_file_name, 'r') as stream:\n self.group_data = yaml.safe_load(stream)\n self.all_grou... | [
"0.63921624",
"0.6276154",
"0.5917572",
"0.58930755",
"0.5878203",
"0.5875567",
"0.5822448",
"0.57812375",
"0.57412446",
"0.57304585",
"0.5726534",
"0.57034045",
"0.5700766",
"0.56856185",
"0.5675106",
"0.56413394",
"0.56358737",
"0.5601757",
"0.55887634",
"0.55571437",
"0.55... | 0.7236225 | 0 |
Runs one timestep of the robot for the given control. | def step(self,
control_groups: Dict[str, np.ndarray],
denormalize: bool = True):
group_controls = []
for group_name, control_values in control_groups.items():
config = self.get_config(group_name)
# Ignore if this is a hardware-only group.
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_step(self):\n self.control_instance.run_step()",
"def run_single(self):\n self.run_sim_time(1)",
"def run(self, T=10, x0=None, control=None):\n\n self.init(control=control, x0=x0)\n \n for i in range(round(T / self.dt)):\n self.step()\n\n # check... | [
"0.6852948",
"0.65564185",
"0.6438402",
"0.6255005",
"0.6224126",
"0.6203932",
"0.6151068",
"0.60931796",
"0.60704327",
"0.60700077",
"0.60636765",
"0.60082597",
"0.5996422",
"0.5982675",
"0.5972462",
"0.5972462",
"0.5952304",
"0.5924543",
"0.5899932",
"0.5890532",
"0.5871303... | 0.0 | -1 |
Moves the robot to the given initial state. | def set_state(self, state_groups: Dict[str, RobotState], **kwargs):
group_states = []
for group_name, state in state_groups.items():
config = self.get_config(group_name)
# Clip the position and velocity to the configured bounds.
clipped_state = RobotState(qpos=state.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self, state):\n raise NotImplementedError(\"Need to implement this method\")",
"def set_state(self, state):\n if self.state == CHANNEL_MOVE_STATE_NONE:\n self.state = state",
"def initial_step(self, state, action):\n next_state = self.state_transition(state, action)\n ... | [
"0.67893064",
"0.6242464",
"0.6183469",
"0.61543345",
"0.6112133",
"0.6078262",
"0.60704833",
"0.60522616",
"0.59689504",
"0.596739",
"0.594087",
"0.5933569",
"0.59301543",
"0.5927122",
"0.5907014",
"0.5901719",
"0.58989894",
"0.5897002",
"0.58676326",
"0.58672667",
"0.586587... | 0.0 | -1 |
Returns the initial states for the given groups. | def get_initial_state(
self,
groups: Union[str, Sequence[str]],
) -> Union[RobotState, Sequence[RobotState]]:
if isinstance(groups, str):
configs = [self.get_config(groups)]
else:
configs = [self.get_config(name) for name in groups]
states = []... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_initial_states(self):\n raise NotImplementedError()",
"def initial_states(self):\n return self._initial_states",
"def initial_states(self):\n return list(self.iter_initial_states())",
"def states_initial(self):\n return self.states(\"Initial = YES\")",
"def _get_group_st... | [
"0.6517598",
"0.63916826",
"0.61768365",
"0.61699396",
"0.6043793",
"0.59128195",
"0.5887704",
"0.58768594",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
"0.58085155",
... | 0.7757164 | 0 |
Returns the states for the given group configurations. | def _get_group_states(
self, configs: Sequence[RobotGroupConfig]) -> Sequence[RobotState]:
states = []
for config in configs:
state = RobotState()
# Return a blank state if this is a hardware-only group.
if config.qpos_indices is None:
stat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_states(self):\n raise NotImplementedError()",
"def get_states():\n try:\n ''' Returns a list of states in list named result '''\n data = State.select()\n return ListStyle.list(data, request), 200\n except Exception as e:\n abort(500)",
"def get_all_states(self):... | [
"0.5906376",
"0.587641",
"0.58613956",
"0.5801155",
"0.57883763",
"0.57819426",
"0.5745295",
"0.5735361",
"0.5593753",
"0.5529911",
"0.55296177",
"0.54799014",
"0.5479514",
"0.54587895",
"0.5449307",
"0.5433292",
"0.5427357",
"0.5345912",
"0.5342106",
"0.5340869",
"0.5320486"... | 0.7487228 | 0 |
Sets the robot joints to the given states. | def _set_group_states(
self, group_states: Sequence[Tuple[RobotGroupConfig, RobotState]]):
for config, state in group_states:
if config.qpos_indices is None:
continue
if state.qpos is not None:
self.sim_scene.data.qpos[config.qpos_indices] = st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_joint_states(self, joints: List[str], position: List[float] = None,\n velocity: List[float] = None) -> NoReturn:\n assert all([j in self.joints.names for j in joints]), 'all values of joints must also be in self.joints'\n \n if position is not None:\n ... | [
"0.7340826",
"0.6836892",
"0.6627433",
"0.6219958",
"0.6123465",
"0.59864044",
"0.5968934",
"0.59389764",
"0.5932464",
"0.590818",
"0.58539706",
"0.5794332",
"0.57893485",
"0.57693374",
"0.57496977",
"0.5736451",
"0.57093275",
"0.56946826",
"0.56944317",
"0.5574347",
"0.55688... | 0.51125777 | 52 |
Applies the given control values to the robot. | def _perform_timestep(
self,
group_controls: Sequence[Tuple[RobotGroupConfig, np.ndarray]]):
for config, control in group_controls:
indices = config.actuator_indices
assert len(indices) == len(control)
self.sim_scene.data.ctrl[indices] = control
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_controls(self, control_operations: dict):\n control_index = 1\n for id, operations in control_operations.items():\n link = self.pumps[id] if id in self.pumps else self.valves[id] if id in self.valves else self.pipes[id]\n for op in operations:\n epamodule... | [
"0.60483235",
"0.5974363",
"0.5956806",
"0.59566313",
"0.5827852",
"0.5825056",
"0.5769332",
"0.5761806",
"0.5739416",
"0.5700603",
"0.5652478",
"0.5604202",
"0.5581627",
"0.5543662",
"0.5541567",
"0.5541567",
"0.5541567",
"0.5541567",
"0.5541567",
"0.5541567",
"0.5541567",
... | 0.53014547 | 33 |
Applies observation noise to the given state. | def _apply_observation_noise(self, state: RobotState,
config: RobotGroupConfig):
if config.sim_observation_noise is None or self.random_state is None:
return
# Define the noise calculation.
def noise(value_range: np.ndarray):
amplitude = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_noise(self):\n self.noise = torch.normal(0.5, .2, self.state.shape).double()\n self.noise *= torch.sqrt(2 *\n self.vars['T']*torch.tensor(self.vars['dt']))",
"def apply_noise(self, input):\n mask = np.random.binomial(1, 1-self.noise_prob, len(input)) ... | [
"0.7100496",
"0.6829454",
"0.6615999",
"0.65508354",
"0.6535193",
"0.65046763",
"0.6397168",
"0.6329152",
"0.6270984",
"0.62694496",
"0.6267069",
"0.6266234",
"0.62525165",
"0.6244945",
"0.6227903",
"0.620435",
"0.6183446",
"0.6166714",
"0.61635095",
"0.616012",
"0.6097126",
... | 0.79560375 | 0 |
Denormalizes the given action. | def _denormalize_action(self, action: np.ndarray,
config: RobotGroupConfig) -> np.ndarray:
if config.denormalize_center.shape != action.shape:
raise ValueError(
'Action shape ({}) does not match actuator shape: ({})'.format(
action.shap... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reverse_action(self, action):\n low = self.action_space.low\n high = self.action_space.high\n\n scale_factor = (high - low) / 2\n reloc_factor = high - scale_factor\n\n action = (action - reloc_factor) / scale_factor\n action = np.clip(action, -1.0, 1.0)\n\n ret... | [
"0.59685934",
"0.5911025",
"0.5838376",
"0.57880056",
"0.5760996",
"0.5746722",
"0.5595164",
"0.5518302",
"0.5501854",
"0.5424955",
"0.53894466",
"0.5361105",
"0.5326777",
"0.5308367",
"0.52543795",
"0.50769705",
"0.503363",
"0.5022165",
"0.5016263",
"0.50093585",
"0.50074387... | 0.77185875 | 0 |
Clips the action using the given configuration. | def _apply_action_bounds(self, action: np.ndarray,
config: RobotGroupConfig) -> np.ndarray:
if config.control_mode == ControlMode.JOINT_POSITION:
# Apply position bounds.
if config.qpos_range is not None:
action = np.clip(action, config.qpos_r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flip_action_bits(action: LoggingActions, bits: int) -> int:\n\n if LoggingActions.has_action(action, bits):\n return LoggingActions.remove_actions_from_bits([action], bits)\n else:\n return LoggingActions.add_actions_to_bits([action], bits)",
"def reverse_action(self, action):\n lo... | [
"0.5486495",
"0.5229196",
"0.49610138",
"0.48103568",
"0.48022357",
"0.4784404",
"0.47646475",
"0.4745638",
"0.46950883",
"0.46798447",
"0.46327832",
"0.46148506",
"0.46142355",
"0.46142194",
"0.45783988",
"0.45724455",
"0.4569896",
"0.45685962",
"0.45600906",
"0.45600906",
"... | 0.0 | -1 |
Create a fragment used to display the XBlock to a student. `context` is a dictionary used to configure the display (unused) Returns a `Fragment` object specifying the HTML, CSS, and JavaScript to display. | def student_view(self, context=None): # pylint: disable=W0613
# Load the HTML fragment from within the package and fill in the template
html_str = pkg_resources.resource_string(__name__,
"static/html/thumbs.html").decode('utf-8')
frag = Fragment... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def student_view(self, context=None):\n html = self.resource_string(self.html_path)\n fragment = Fragment(html.format(self=self))\n fragment.add_css(self.resource_string(self.css_path))\n fragment.add_javascript(self.resource_string(self.js_path))\n fragment.initialize_js('WhoWhe... | [
"0.803043",
"0.7568872",
"0.74260396",
"0.71374416",
"0.7077496",
"0.6981609",
"0.6979395",
"0.69643986",
"0.695315",
"0.695193",
"0.6940515",
"0.68856263",
"0.6771916",
"0.6755776",
"0.66533804",
"0.6626741",
"0.6586341",
"0.65374935",
"0.6487344",
"0.64490306",
"0.64487976"... | 0.5214808 | 52 |
Update the vote count in response to a user action. | def vote(self, data, suffix=''): # pylint: disable=unused-argument
# Here is where we would prevent a student from voting twice, but then
# we couldn't click more than once in the demo!
#
# if self.voted:
# log.error("cheater!")
# return
vote... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def up_vote(cls, user, message):\r\n pass",
"def up_vote(cls, user, message):\n pass",
"def relation_upvote(request, pk):\n try:\n relation = Relation.objects.get(pk=pk)\n except Relation.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.metho... | [
"0.7239073",
"0.7119304",
"0.668565",
"0.6511972",
"0.6503739",
"0.6354325",
"0.63113993",
"0.6275616",
"0.6274778",
"0.62731385",
"0.62333953",
"0.62169063",
"0.61895573",
"0.61686796",
"0.61535746",
"0.60905564",
"0.6069016",
"0.5999043",
"0.5989211",
"0.5989211",
"0.593912... | 0.53030753 | 98 |
A canned scenario for display in the workbench. | def workbench_scenarios():
return [
("filethumbs",
"""\
<vertical_demo>
<filethumbs/>
<filethumbs/>
<filethumbs/>
</vertical_demo>
""")
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def workbench_scenarios():\n return [\n (\"Oppia Embedding\",\n \"\"\"<vertical_demo>\n <oppia oppiaid=\"0\" src=\"https://www.oppia.org\" width=\"700\" />\n </vertical_demo>\n \"\"\"),\n ]",
"def workbench_scenarios():\n retur... | [
"0.69502115",
"0.6878325",
"0.687794",
"0.68425786",
"0.6841508",
"0.6824964",
"0.6769337",
"0.6758825",
"0.6684726",
"0.6679064",
"0.66781527",
"0.6648049",
"0.6647093",
"0.6620656",
"0.6456912",
"0.64337105",
"0.6387073",
"0.6322783",
"0.63063073",
"0.627672",
"0.6198262",
... | 0.6202967 | 20 |
Performs HTTP POST call. | def _request(self, path):
url = urllib.parse.urljoin(CONF.gerrit.url, path)
request = urllib.request.Request(url)
try:
sock = urllib.request.urlopen(request)
response = sock.read()
except Exception:
with excutils.save_and_reraise_exception():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_POST(self,):\n self.http_method = 'POST'\n self.response()",
"def post(self, *args, **kwargs):\n return self._requests_call(util.requests_post, *args, **kwargs)",
"def do_POST(self):\r\n self._send_handler_response('POST')",
"def _post(self, *args, **kwargs):\n return se... | [
"0.84838665",
"0.82153636",
"0.8085671",
"0.79459065",
"0.78568274",
"0.78214186",
"0.7749993",
"0.77131903",
"0.7710755",
"0.7684489",
"0.76350945",
"0.76104945",
"0.7589615",
"0.75877327",
"0.75125647",
"0.74714005",
"0.7465422",
"0.7449667",
"0.7407969",
"0.7389761",
"0.73... | 0.0 | -1 |
This validates the post request as a whole and not just a field. During read, the validator practically skips. During post, each orderline unit is validated. If an orderline has more units than available product units, the order is not accepted. | def validate(self, attrs):
exception_body = []
for orderline in attrs.get('orderlines', []):
product = orderline['product']
# If orderline has less units than available, all good.
if orderline['units'] <= product.units:
continue
# else er... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(self):\n cleaned_data = super().clean()\n variant = cleaned_data.get('variant')\n quantity = cleaned_data.get('quantity')\n if variant and quantity is not None:\n try:\n variant.check_quantity(quantity)\n except InsufficientStock as e:\n ... | [
"0.5745162",
"0.552804",
"0.55063504",
"0.55063504",
"0.5504187",
"0.54778343",
"0.54469776",
"0.5445624",
"0.5444358",
"0.54384",
"0.54326487",
"0.5432112",
"0.5422848",
"0.54169387",
"0.5380987",
"0.53740376",
"0.5372109",
"0.5350199",
"0.5332469",
"0.5322742",
"0.5322059",... | 0.64973056 | 0 |
Runs after validation to create an order and associated orderlines. Prices are not locked as prices can always change despite being accepted. Until the order is confirmed by our agent, the prices are NOT locked in. If an 'accepted' order is shown on UI, the client should show prices from the associated product of an or... | def create(self, validated_data):
orderlines = validated_data.pop('orderlines', None)
if not (orderlines and len(orderlines)):
raise EmptyOrderException
# Create order and associated orderlines
order = models.Order.objects.create(**validated_data)
for orderline in or... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create(self, values):\n res = super(PurchaseOrderLine, self).create(values)\n states = ['purchase', 'done']\n if res.order_id.state in states:\n raise UserError(_('You can not create an additional purchase order line in a confirmed order '))\n return res",
"def test_05_... | [
"0.65118515",
"0.64460814",
"0.6433476",
"0.6251967",
"0.6173412",
"0.6157046",
"0.6104601",
"0.60722667",
"0.60429686",
"0.59729445",
"0.59326535",
"0.5884068",
"0.58812374",
"0.58809453",
"0.5860326",
"0.58583325",
"0.5857815",
"0.58318114",
"0.5813478",
"0.58035976",
"0.57... | 0.62860453 | 3 |
Actual updation of an order to confirmed/cancellation/delivery status happens here. There are only two valid transitions acceptable 1. From accepted to confirmed. 2. From confirmed to cancelled/delivered. | def update(self, instance, validated_data):
# If an order is cancelled or delivered, it cannot be modified.
if instance.status == CANCELLED or instance.status == DELIVERED:
raise exceptions.PermissionDenied('This order cannot be modified.')
# If an order is already confirmed but UI... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_order(self):\n response = self.api_test_client.put('{}/orders/1'.format(\n self.BASE_URL), json={'order_status': 'accepted'})\n\n self.assertEqual(response.status_code, 201)\n self.assertTrue(\n response_as_json(response)['order']['status_updated_on'])\n ... | [
"0.68926567",
"0.6892195",
"0.66556543",
"0.6604199",
"0.64407927",
"0.6413585",
"0.6380169",
"0.6379697",
"0.6355502",
"0.63130534",
"0.63076985",
"0.6285451",
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"0.613097",
"0.6118931",
"0.6096293",
"0.6087185",
"0.60793054",
"0.6068147",
"0.6027666",
"0.6010716"... | 0.76192147 | 0 |
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,
value
... | {
"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... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) | {
"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 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, SearchResourceShareInvitationReqBody):
return False
return self.__dict__ == other.__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):
return not self == other | {
"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 ... | [
"0.84568954",
"0.83923674",
"0.81447554",
"0.81407183",
"0.81326675",
"0.80941343",
"0.8092415",
"0.8092415",
"0.8092415",
"0.8085536",
"0.8085536",
"0.8076502",
"0.8076502",
"0.8066026"
] | 0.0 | -1 |
Create lr scheduler based on config. note that lr_scheduler must accept a optimizer that has been restored. | def build(optimizer_config, optimizer, total_step):
optimizer_type = optimizer_config.WhichOneof('optimizer')
if optimizer_type == 'rms_prop_optimizer':
config = optimizer_config.rms_prop_optimizer
lr_scheduler = _create_learning_rate_scheduler(
config.learning_rate, optimizer, total_step=total_step)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_lr_scheduler(\n cfg: CfgNode, optimizer: torch.optim.Optimizer\n) -> torch.optim.lr_scheduler._LRScheduler:\n name = cfg.SOLVER.LR_SCHEDULER_NAME\n if name == \"WarmupMultiStepLR\":\n return WarmupMultiStepLR(\n optimizer,\n cfg.SOLVER.STEPS,\n cfg.SOLVER.... | [
"0.8214888",
"0.81373125",
"0.8030262",
"0.7877491",
"0.7863203",
"0.777814",
"0.7759245",
"0.76554245",
"0.7646608",
"0.7585435",
"0.748087",
"0.74216324",
"0.7285573",
"0.7153245",
"0.7007013",
"0.69302773",
"0.6923065",
"0.6863463",
"0.68190587",
"0.6761453",
"0.6728476",
... | 0.81571466 | 1 |
Create optimizer learning rate scheduler based on config. | def _create_learning_rate_scheduler(learning_rate_config, optimizer, total_step):
lr_scheduler = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'multi_phase':
config = learning_rate_config.multi_phase
lr_phases = []
mom_phases = []
for phase_cfg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scheduler_creator(optimizer, config):\n return torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)",
"def build(optimizer_config, optimizer, total_step):\n optimizer_type = optimizer_config.WhichOneof('optimizer')\n\n if optimizer_type == 'rms_prop_optimizer':\n config = optimizer_conf... | [
"0.8004487",
"0.7996875",
"0.79782903",
"0.7639045",
"0.73738545",
"0.7332055",
"0.7301991",
"0.7300177",
"0.7256197",
"0.7231383",
"0.71939045",
"0.7167134",
"0.7102816",
"0.7095163",
"0.7091584",
"0.7080664",
"0.70553064",
"0.69863945",
"0.69457966",
"0.69298434",
"0.692465... | 0.75770074 | 4 |
Update the display_name or display_description for this osd. | def update(self, **kwargs):
self.manager.update(self, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateDisplay(self, msg):\n t = msg.data\n self.displayLbl.SetLabel(\"%s\" % t)\n self.SetTitle(\"%s\" % t)",
"def device_display_name(self, device_display_name):\n\n self._device_display_name = device_display_name",
"def display_name(self, display_name):\n self._display_... | [
"0.63896036",
"0.6336821",
"0.6326636",
"0.6326636",
"0.6326636",
"0.6326636",
"0.6326636",
"0.6326636",
"0.6314018",
"0.6314018",
"0.6314018",
"0.6314018",
"0.6314018",
"0.6314018",
"0.62420577",
"0.6225104",
"0.61178285",
"0.6005843",
"0.5882068",
"0.58664495",
"0.58609277"... | 0.0 | -1 |
Delete the specified osd ignoring its current state. | def force_delete(self):
self.manager.force_delete(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self, purge, timeout, force):\n # Set the CRUSH weight to 0.\n hookenv.log('Reweighting OSD', hookenv.DEBUG)\n reweight_osd(self.osd_id)\n\n # Ensure that the OSD is safe to stop and destroy.\n end = (datetime.datetime.now() +\n datetime.timedelta(seconds... | [
"0.58212245",
"0.5652357",
"0.5631923",
"0.56308943",
"0.561431",
"0.55853283",
"0.5472813",
"0.5385417",
"0.5382336",
"0.5376866",
"0.5364507",
"0.5355651",
"0.535345",
"0.53411007",
"0.5332679",
"0.5301693",
"0.5286166",
"0.5260226",
"0.52445686",
"0.52367926",
"0.5229653",... | 0.0 | -1 |
Get a list of all osds. | def list(self, detailed=False, search_opts=None, paginate_opts=None):
if search_opts is None:
search_opts = {}
if paginate_opts is None:
paginate_opts = {}
qparams = {}
for opt, val in search_opts.iteritems():
if val:
qparams[opt] = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_list(self):\n return self.__repository.get_all()",
"def list(self):\n return self._list(self._path())",
"def list(self):\n return self.connection.get(self.service)",
"def list(self):\n return self._get_list()",
"def getAll(self):\n return self.__lst",
"def getLi... | [
"0.6780782",
"0.6703935",
"0.66433823",
"0.6626435",
"0.65891266",
"0.6533394",
"0.6533394",
"0.65089464",
"0.6494575",
"0.64734656",
"0.64625776",
"0.6453494",
"0.644844",
"0.6447506",
"0.64162856",
"0.6404984",
"0.6335044",
"0.63190335",
"0.6300186",
"0.62992346",
"0.629622... | 0.62678444 | 24 |
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