"""This file and its contents are licensed under the Apache License 2.0. Please see the included NOTICE for copyright information and LICENSE for a copy of the license.
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"""
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import logging
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from collections import OrderedDict
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from typing import Any, Iterable, Tuple
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from urllib.parse import unquote
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import ujson as json
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from core.feature_flags import flag_set
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from core.utils.common import int_from_request
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from data_manager.models import View
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from data_manager.prepare_params import PrepareParams
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from django.conf import settings
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from rest_framework.generics import get_object_or_404
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from tasks.models import Task
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TASKS = 'tasks:'
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logger = logging.getLogger(__name__)
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class DataManagerException(Exception):
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pass
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def get_all_columns(project, *_):
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"""Make columns info for the frontend data manager"""
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result = {'columns': []}
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# frontend uses MST data model, so we need two directional referencing parent <-> child
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task_data_children = []
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i = 0
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data_types = OrderedDict()
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# add data types from config again
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project_data_types = {}
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for key, value in project.data_types.items():
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# skip keys from Repeater tag, because we already have its base data,
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# e.g.: skip 'image[{{idx}}]' because we have 'image' list already
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if '[' not in key:
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project_data_types[key] = value
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data_types.update(project_data_types.items())
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# all data types from import data
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all_data_columns = project.summary.all_data_columns
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logger.info(f'get_all_columns: project_id={project.id} {all_data_columns=} {data_types=}')
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if all_data_columns:
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data_types.update({key: 'Unknown' for key in all_data_columns if key not in data_types})
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logger.info(f'get_all_columns: project_id={project.id} {data_types=}')
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# remove $undefined$ if there is one type at least in labeling config, because it will be resolved automatically
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if len(project_data_types) > 0:
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data_types.pop(settings.DATA_UNDEFINED_NAME, None)
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logger.info(f'get_all_columns: project_id={project.id} {data_types=} {project_data_types=}')
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for key, data_type in list(data_types.items()): # make data types from labeling config first
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column = {
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'id': key,
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'title': key if key != settings.DATA_UNDEFINED_NAME else 'data',
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'type': data_type if data_type in ['Image', 'Audio', 'AudioPlus', 'Video', 'Unknown'] else 'String',
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'target': 'tasks',
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'parent': 'data',
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'visibility_defaults': {
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'explore': True,
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'labeling': key in project_data_types or key == settings.DATA_UNDEFINED_NAME,
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},
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'project_defined': True,
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}
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result['columns'].append(column)
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task_data_children.append(column['id'])
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i += 1
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remove_members_schema = flag_set('fflag_feat_fit_449_datamanager_filter_members_short', user='auto')
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# --- Data root ---
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data_root = {
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'id': 'data',
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'title': 'data',
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'type': 'List',
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'target': 'tasks',
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'children': task_data_children,
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'project_defined': False,
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}
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result['columns'] += [
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# --- Tasks ---
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{
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'id': 'id',
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'title': 'ID',
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'type': 'Number',
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'help': 'Task ID',
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'target': 'tasks',
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'visibility_defaults': {'explore': True, 'labeling': False},
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'project_defined': False,
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}
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]
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result['columns'] += [
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{
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'id': 'inner_id',
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'title': 'Inner ID',
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'type': 'Number',
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'help': 'Internal task ID starting from 1 for the current project',
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'target': 'tasks',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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}
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]
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if remove_members_schema:
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project_members = []
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else:
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project_members = project.all_members.values_list('id', flat=True)
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result['columns'] += [
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{
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'id': 'completed_at',
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'title': 'Completed',
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'type': 'Datetime',
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'target': 'tasks',
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'help': 'Last annotation date',
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'visibility_defaults': {'explore': True, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'total_annotations',
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'title': 'Annotations',
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'type': 'Number',
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'target': 'tasks',
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'help': 'Total annotations per task',
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'visibility_defaults': {'explore': True, 'labeling': True},
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'project_defined': False,
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},
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{
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'id': 'cancelled_annotations',
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'title': 'Cancelled',
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'type': 'Number',
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'target': 'tasks',
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'help': 'Total cancelled (skipped) annotations',
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'visibility_defaults': {'explore': True, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'total_predictions',
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'title': 'Predictions',
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'type': 'Number',
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'target': 'tasks',
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'help': 'Total predictions per task',
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'visibility_defaults': {'explore': True, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'annotators',
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'title': 'Annotated by',
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'type': 'List',
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'target': 'tasks',
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'help': 'All users who completed the task',
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**({'schema': {'items': project_members}} if not remove_members_schema else {}),
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'visibility_defaults': {'explore': True, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'annotations_results',
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'title': 'Annotation results',
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'type': 'String',
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'target': 'tasks',
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'help': 'Annotation results stacked over all annotations',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'annotations_ids',
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'title': 'Annotation IDs',
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'type': 'String',
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'target': 'tasks',
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'help': 'Annotation IDs stacked over all annotations',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'predictions_score',
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'title': 'Prediction score',
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'type': 'Number',
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'target': 'tasks',
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'help': 'Average prediction score over all task predictions',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'predictions_model_versions',
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'title': 'Prediction model versions',
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'type': 'List',
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'target': 'tasks',
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'help': 'Model versions aggregated over all predictions',
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'schema': {'items': project.get_model_versions(), 'multiple': True},
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'predictions_results',
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'title': 'Prediction results',
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'type': 'String',
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'target': 'tasks',
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'help': 'Prediction results stacked over all predictions',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'file_upload',
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'title': 'Upload filename',
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'type': 'String',
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'target': 'tasks',
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'help': 'Filename of uploaded file',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'storage_filename',
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'title': 'Storage filename',
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'type': 'String',
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'target': 'tasks',
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'help': 'Filename from import storage',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'created_at',
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'title': 'Created at',
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'type': 'Datetime',
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'target': 'tasks',
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'help': 'Task creation time',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'updated_at',
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'title': 'Updated at',
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'type': 'Datetime',
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'target': 'tasks',
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'help': 'Task update time',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'updated_by',
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'title': 'Updated by',
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'type': 'List',
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'target': 'tasks',
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'help': 'User who did the last task update',
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**({'schema': {'items': project_members}} if not remove_members_schema else {}),
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'avg_lead_time',
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'title': 'Lead Time',
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'type': 'Number',
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'help': 'Average lead time over all annotations (seconds)',
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'target': 'tasks',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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{
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'id': 'draft_exists',
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'title': 'Drafts',
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'type': 'Boolean',
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'help': 'True if at least one draft exists for the task',
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'target': 'tasks',
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'visibility_defaults': {'explore': False, 'labeling': False},
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'project_defined': False,
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},
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]
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result['columns'].append(data_root)
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return result
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def get_prepare_params(request, project):
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"""This function extract prepare_params from
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* view_id if it's inside of request data
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* selectedItems, filters, ordering if they are in request and there is no view id
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"""
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# use filters and selected items from view
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view_id = int_from_request(request.GET, 'view', 0) or int_from_request(request.data, 'view', 0)
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if view_id > 0:
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view = get_object_or_404(View, pk=view_id)
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if view.project.pk != project.pk:
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raise DataManagerException('Project and View mismatch')
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prepare_params = view.get_prepare_tasks_params(add_selected_items=True)
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prepare_params.request = request
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# use filters and selected items from request if it's specified
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else:
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# query arguments from url
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if 'query' in request.GET:
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data = json.loads(unquote(request.GET['query']))
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# data payload from body
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else:
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data = request.data
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selected = data.get('selectedItems', {'all': True, 'excluded': []})
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if not isinstance(selected, dict):
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if isinstance(selected, str):
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# try to parse JSON string
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try:
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selected = json.loads(selected)
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except Exception as e:
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logger.error(f'Error parsing selectedItems: {e}')
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raise DataManagerException(
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'selectedItems must be JSON encoded string for dict: {"all": [true|false], '
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'"excluded | included": [...task_ids...]}. '
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f'Found: {selected}'
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)
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else:
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raise DataManagerException(
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'selectedItems must be dict: {"all": [true|false], '
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'"excluded | included": [...task_ids...]}. '
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f'Found type: {type(selected)} with value: {selected}'
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)
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filters = data.get('filters', None)
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ordering = data.get('ordering', [])
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prepare_params = PrepareParams(
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project=project.id, selectedItems=selected, data=data, filters=filters, ordering=ordering, request=request
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)
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return prepare_params
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def get_prepared_queryset(request, project):
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prepare_params = get_prepare_params(request, project)
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queryset = Task.prepared.only_filtered(prepare_params=prepare_params)
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return queryset
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def evaluate_predictions(tasks):
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"""
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Call the given ML backend to retrieve predictions with the task queryset as an input.
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If backend is not specified, we'll assume the tasks' project only has one associated
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ML backend, and use that backend.
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"""
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if not tasks:
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return
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project = tasks[0].project
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backend = project.ml_backend
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if backend:
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return backend.predict_tasks(tasks=tasks)
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def filters_ordering_selected_items_exist(data):
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return data.get('filters') or data.get('ordering') or data.get('selectedItems')
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def custom_filter_expressions(*args, **kwargs):
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pass
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def preprocess_filter(_filter, *_):
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return _filter
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def preprocess_field_name(raw_field_name, project) -> Tuple[str, bool]:
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"""Transform a field name (as specified in the datamanager views endpoint) to
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a django ORM field name. Also handle dotted accesses to task.data.
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Edit with care; it's critical that this function not be changed in ways that
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introduce vulnerabilities in the vein of the ORM Leak (see #5012). In particular
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it is not advisable to use `replace` or other calls that replace all instances
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of a string within this function.
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Returns: Django ORM field name: str, Sort is ascending: bool
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"""
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field_name = raw_field_name
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ascending = True
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# Descending marker `-` may come at the beginning of the string
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if field_name.startswith('-'):
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ascending = False
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field_name = field_name[1:]
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# For security reasons, these must only be removed when they fall at the beginning of the string (or after `-`).
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optional_prefixes = ['filter:', 'tasks:']
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for prefix in optional_prefixes:
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if field_name.startswith(prefix):
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field_name = field_name[len(prefix) :]
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# Descending marker may also come after other prefixes. Double negative is not allowed.
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if ascending and field_name.startswith('-'):
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ascending = False
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field_name = field_name[1:]
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if field_name.startswith('data.'):
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# process as $undefined$ only if real_name is from labeling config, not from task.data
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real_name = field_name.replace('data.', '')
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common_data_columns = project.summary.common_data_columns
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real_name_suitable = (
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# there is only one object tag in labeling config
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# and requested filter name == value from object tag
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len(project.data_types.keys()) == 1
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and real_name in project.data_types.keys()
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# file was uploaded before labeling config is set, `data.data` is system predefined name
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or len(project.data_types.keys()) == 0
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and real_name == 'data'
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)
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if (
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real_name_suitable
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# common data columns are not None
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and common_data_columns
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# $undefined$ is in common data columns, in all tasks
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and settings.DATA_UNDEFINED_NAME in common_data_columns
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):
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field_name = f'data__{settings.DATA_UNDEFINED_NAME}'
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else:
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field_name = field_name.replace('data.', 'data__')
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return field_name, ascending
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def intersperse(items: Iterable, separator: Any) -> list:
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"""
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Create a list with a separator between each item in the passed iterable `items`
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for example, intersperse(['one', 'two', 'three'], 0) == ['one', 0, 'two', 0, 'three']
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"""
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output = []
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for item in items:
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output.append(item)
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output.append(separator)
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# if there are no items, there will be no last separator to remove
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if output:
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output.pop()
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return output
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