"""
---
title: "Project"
slug: "sdk-ref-project"
hidden: false
metadata:
  title: "Project"
  description: "Encord SDK Project class"
category: "64e481b57b6027003f20aaa0"
---
"""

import datetime
from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
from uuid import UUID

from encord.client import EncordClientProject
from encord.common.deprecated import deprecated
from encord.constants.model import AutomationModels, Device
from encord.http.bundle import Bundle
from encord.http.v2.api_client import ApiClient
from encord.objects import LabelRowV2, OntologyStructure
from encord.ontology import Ontology
from encord.orm.analytics import (
    CollaboratorTimer,
    CollaboratorTimerParams,
    CollaboratorTimersGroupBy,
)
from encord.orm.cloud_integration import CloudIntegration
from encord.orm.dataset import Image, Video
from encord.orm.group import ProjectGroup
from encord.orm.label_log import LabelLog
from encord.orm.label_row import (
    AnnotationTaskStatus,
    LabelRow,
    LabelRowMetadata,
    LabelStatus,
    ShadowDataState,
)
from encord.orm.model import ModelConfiguration, ModelTrainingWeights, TrainingMetadata
from encord.orm.project import CopyDatasetOptions, CopyLabelsOptions, ProjectDataset, ProjectDTO, ProjectType
from encord.orm.project import Project as OrmProject
from encord.project_ontology.classification_type import ClassificationType
from encord.project_ontology.object_type import ObjectShape
from encord.project_ontology.ontology import Ontology as LegacyOntology
from encord.utilities.coco.datastructure import CategoryID, FrameIndex, ImageID
from encord.utilities.hash_utilities import convert_to_uuid
from encord.utilities.project_user import ProjectUser, ProjectUserRole
from encord.workflow import Workflow


class Project:
    """
    Access project related data and manipulate the project.
    """

    def __init__(
        self,
        client: EncordClientProject,
        project_instance: ProjectDTO,
        ontology: Ontology,
        api_client: ApiClient,
    ):
        self._client = client
        self._project_instance = project_instance
        self._ontology = ontology

        if project_instance.workflow:
            self._workflow = Workflow(api_client, project_instance.project_hash, project_instance.workflow)

    @property
    def project_hash(self) -> str:
        """
        Get the project hash (i.e. the Project ID).
        """
        # Keeping the interface backward compatible, so converting UUID to str for now
        return str(self._project_instance.project_hash)

    @property
    def title(self) -> str:
        """
        Get the title of the project.
        """
        return self._project_instance.title

    @property
    def description(self) -> str:
        """
        Get the description of the project.
        """
        return self._project_instance.description

    @property
    def created_at(self) -> datetime.datetime:
        """
        Get the time the project was created at.
        """
        return self._project_instance.created_at

    @property
    def last_edited_at(self) -> datetime.datetime:
        """
        Get the time the project was last edited at.
        """
        return self._project_instance.last_edited_at

    @property
    @deprecated(version="0.1.95", alternative=".ontology_structure")
    def ontology(self) -> Dict[str, Any]:
        """
        Get the ontology of the project.

        DEPRECATED: Prefer using the :meth:`encord.Project.ontology_structure` method.
        This method returns the same structure as :meth:`encord.Project.ontology_structure`, just in
        raw python dictionary format.
        """
        return self._ontology.structure.to_dict()

    @property
    def ontology_hash(self) -> str:
        """
        Get the ontology hash of the project's ontology.
        """
        return self._ontology.ontology_hash

    @property
    def ontology_structure(self) -> OntologyStructure:
        """
        Get the ontology structure of the project's ontology.
        """
        return self._ontology.structure

    @property
    @deprecated(version="0.1.117", alternative=".list_datasets")
    def datasets(self) -> List[Dict[str, Any]]:
        """
        DEPRECATED: Prefer using the :meth:`encord.project.list_datasets` class to work with the data.

        Get the info about datasets associated with this project.
        """
        return [project_dataset.to_dict(by_alias=False) for project_dataset in self.list_datasets()]

    @property
    def project_type(self) -> ProjectType:
        """
        Get the project type.
        """
        return self._project_instance.project_type

    @property
    @deprecated(version="0.1.104", alternative=".list_label_rows_v2")
    def label_rows(self) -> dict:
        """
        Get the label rows.
        DEPRECATED: Prefer using :meth:`list_label_rows_v2()` method and :meth:`LabelRowV2` class to work with the data.

        .. code::

            from encord.orm.label_row import LabelRowMetadata

            project = user_client.get_project("[project_hash]")

            label_rows = LabelRowMetadata.from_list(project.label_rows)

        """
        return self._client.get_project(include_labels_metadata=True).label_rows

    def refetch_data(self) -> None:
        """
        The Project class will only fetch its properties once. Use this function if you suspect the state of those
        properties to be dirty.
        """
        self._project_instance = self._client.get_project_v2()

    def refetch_ontology(self) -> None:
        """
        Update the ontology for the project to reflect changes on the backend.
        """
        self._ontology.refetch_data()

    def get_project(self) -> OrmProject:
        """
        This function is exposed for convenience. You are encouraged to use the property accessors instead.
        """
        return self._client.get_project()

    @property
    def workflow(self) -> Workflow:
        """
        Get the workflow of the project.

        Available only for workflow projects.
        """
        assert (
            self.project_type == ProjectType.WORKFLOW
        ), "project.workflow property only available for workflow projects"
        return self._workflow

    def list_label_rows_v2(
        self,
        data_hashes: Optional[Union[List[str], List[UUID]]] = None,
        label_hashes: Optional[Union[List[str], List[UUID]]] = None,
        edited_before: Optional[Union[str, datetime.datetime]] = None,
        edited_after: Optional[Union[str, datetime.datetime]] = None,
        label_statuses: Optional[List[AnnotationTaskStatus]] = None,
        shadow_data_state: Optional[ShadowDataState] = None,
        data_title_eq: Optional[str] = None,
        data_title_like: Optional[str] = None,
        workflow_graph_node_title_eq: Optional[str] = None,
        workflow_graph_node_title_like: Optional[str] = None,
        include_workflow_graph_node: bool = True,
        include_client_metadata: bool = False,
        include_images_data: bool = False,
        include_all_label_branches: bool = False,
    ) -> List[LabelRowV2]:
        """
        List label rows with various filtering options.

        Args:
            data_hashes: List of data hashes to filter by.
            label_hashes: List of label hashes to filter by.
            edited_before: Optionally filter to only rows last edited before the specified time.
            edited_after: Optionally filter to only rows last edited after the specified time.
            label_statuses: Optionally filter to only those label rows that have one of the specified :class:`~encord.orm.label_row.AnnotationTaskStatus`es.
            shadow_data_state: Optionally filter by data type in Benchmark QA projects. See :class:`~encord.orm.label_row.ShadowDataState`.
            data_title_eq: Optionally filter by exact title match.
            data_title_like: Optionally filter by fuzzy title match; SQL syntax.
            workflow_graph_node_title_eq: Optionally filter by exact match with workflow node title.
            workflow_graph_node_title_like: Optionally filter by fuzzy match with workflow node title; SQL syntax.
            include_workflow_graph_node: Include workflow graph node metadata in all the results. True by default.
            include_client_metadata: Optionally include client metadata into the result of this query.
            include_images_data: Optionally include image group metadata into the result of this query.
            include_all_label_branches: Optionally include all label branches. They will be included as separate label row objects.

        Returns:
            A list of :class:`~encord.objects.LabelRowV2` instances for all the matching label rows.
        """
        label_row_metadatas = self._client.list_label_rows(
            edited_before,
            edited_after,
            label_statuses,
            shadow_data_state,
            data_hashes=data_hashes,
            label_hashes=label_hashes,
            include_uninitialised_labels=True,
            data_title_eq=data_title_eq,
            data_title_like=data_title_like,
            workflow_graph_node_title_eq=workflow_graph_node_title_eq,
            workflow_graph_node_title_like=workflow_graph_node_title_like,
            include_workflow_graph_node=include_workflow_graph_node,
            include_client_metadata=include_client_metadata,
            include_images_data=include_images_data,
            include_all_label_branches=include_all_label_branches,
        )

        label_rows = [
            LabelRowV2(label_row_metadata, self._client, self._ontology) for label_row_metadata in label_row_metadatas
        ]
        return label_rows

    def add_users(self, user_emails: List[str], user_role: ProjectUserRole) -> List[ProjectUser]:
        """
        Add users to the project.

        Args:
            user_emails: List of user emails to be added.
            user_role: The user role to assign to all users.

        Returns:
            List[ProjectUser]: A list of ProjectUser objects representing the added users.

        Raises:
            AuthorisationError: If the project API key is invalid.
            ResourceNotFoundError: If no project exists by the specified project EntityId.
            UnknownError: If an error occurs while adding the users to the project.
        """
        return self._client.add_users(user_emails, user_role)

    def list_groups(self) -> Iterable[ProjectGroup]:
        """
        List all groups that have access to a particular project.

        Returns:
            Iterable[ProjectGroup]: An iterable of ProjectGroup objects.
        """
        project_hash = convert_to_uuid(self.project_hash)
        page = self._client.list_groups(project_hash)
        yield from page.results

    def add_group(self, group_hash: Union[List[UUID], UUID], user_role: ProjectUserRole):
        """
        Add a group to the project.

        Args:
            group_hash: List of group hashes or a single group hash to be added.
            user_role: User role that the group will be given.

        Returns:
            None
        """
        project_hash = convert_to_uuid(self.project_hash)
        if isinstance(group_hash, UUID):
            group_hash = [group_hash]
        self._client.add_groups(project_hash, group_hash, user_role)

    def remove_group(self, group_hash: Union[List[UUID], UUID]):
        """
        Remove a group from the project.

        Args:
            group_hash: List of group hashes or a single group hash to be removed.

        Returns:
            None
        """
        if isinstance(group_hash, UUID):
            group_hash = [group_hash]
        self._client.remove_groups(group_hash)

    def copy_project(
        self,
        copy_datasets: Union[bool, CopyDatasetOptions] = False,
        copy_collaborators=False,
        copy_models=False,
        *,
        copy_labels: Optional[CopyLabelsOptions] = None,
        new_title: Optional[str] = None,
        new_description: Optional[str] = None,
    ) -> str:
        """
        Copy the current project into a new one with copied contents including settings, datasets, and users.
        Labels and models are optional.

        Args:
            copy_datasets: If True, the datasets of the existing project are copied over, and new tasks are created from those datasets.
            copy_collaborators: If True, all users of the existing project are copied over with their current roles.
                                If label and/or annotator reviewer mapping is set, this will also be copied over.
            copy_models: If True, all models with their training information will be copied into the new project.
            copy_labels: Options for copying labels, defined in `CopyLabelsOptions`.
            new_title: When provided, will be used as the title for the new project.
            new_description: When provided, will be used as the description for the new project.

        Returns:
            str: The EntityId of the newly created project.

        Raises:
            AuthorisationError: If the project API key is invalid.
            ResourceNotFoundError: If no project exists by the specified project EntityId.
            UnknownError: If an error occurs while copying the project.
        """
        return self._client.copy_project(
            new_title=new_title,
            new_description=new_description,
            copy_datasets=copy_datasets,
            copy_collaborators=copy_collaborators,
            copy_models=copy_models,
            copy_labels=copy_labels,
        )

    def submit_label_row_for_review(self, uid: str):
        """
        Submit a label row for review.

        **Note:** This method is not supported for workflow-based projects. See the documentation about the workflows.

        Args:
            uid: A label_hash (uid) string.

        Returns:
            bool: True if the submission was successful, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while submitting for review.
            OperationNotAllowed: If the write operation is not allowed by the API key.
        """
        return self._client.submit_label_row_for_review(uid)

    def add_datasets(self, dataset_hashes: List[str]) -> bool:
        """
        Add datasets to the project.

        Args:
            dataset_hashes: List of dataset hashes of the datasets to be added.

        Returns:
            bool: True if the datasets were successfully added, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If one or more datasets don't exist by the specified dataset_hashes.
            UnknownError: If an error occurs while adding the datasets to the project.
            OperationNotAllowed: If the write operation is not allowed by the API key.
        """
        res = self._client.add_datasets(dataset_hashes)
        self.refetch_data()
        return res

    def remove_datasets(self, dataset_hashes: List[str]) -> bool:
        """
        Remove datasets from the project.

        Args:
            dataset_hashes: List of dataset hashes of the datasets to be removed.

        Returns:
            bool: True if the datasets were successfully removed, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no dataset exists by the specified dataset_hash (uid).
            UnknownError: If an error occurs while removing the datasets from the project.
            OperationNotAllowed: If the operation is not allowed by the API key.
        """
        res = self._client.remove_datasets(dataset_hashes)
        self.refetch_data()
        return res

    @deprecated(version="0.1.95", alternative=".ontology_structure")
    def get_project_ontology(self) -> LegacyOntology:
        """
        DEPRECATED: Prefer using the `ontology_structure` property accessor instead.

        Returns:
            LegacyOntology: The project's ontology.
        """
        return self._client.get_project_ontology()

    @deprecated("0.1.102", alternative="encord.ontology.Ontology class")
    def add_object(self, name: str, shape: ObjectShape) -> bool:
        """
        DEPRECATED: Prefer using :class:`Ontology [encord.ontology.Ontology]` to manipulate ontology.

        Add an object to an ontology.

        ATTENTION: This legacy method will affect all the projects sharing the same ontology.

        Args:
            name: The name of the object.
            shape: The shape of the object. (BOUNDING_BOX, POLYGON, POLYLINE, or KEY_POINT)

        Returns:
            bool: True if the object was added successfully, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while adding the object to the project ontology.
            OperationNotAllowed: If the operation is not allowed by the API key.
            ValueError: If invalid arguments are supplied in the function call.
        """
        res = self._client.add_object(name, shape)
        self.refetch_ontology()
        return res

    @deprecated("0.1.102", alternative="encord.ontology.Ontology class")
    def add_classification(
        self,
        name: str,
        classification_type: ClassificationType,
        required: bool,
        options: Optional[Iterable[str]] = None,
    ):
        """
        DEPRECATED: Prefer using :class:`Ontology encord.ontology.Ontology` to manipulate ontology.

        Add a classification to an ontology.

        ATTENTION: This legacy method will affect all the projects sharing the same ontology.

        Args:
            name: The name of the classification.
            classification_type: The classification type (RADIO, TEXT, or CHECKLIST).
            required: Whether this classification is required by the annotator.
            options: The list of options for the classification (to be set to None for texts).

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while adding the classification to the project ontology.
            OperationNotAllowed: If the operation is not allowed by the API key.
            ValueError: If invalid arguments are supplied in the function call.
        """
        res = self._client.add_classification(name, classification_type, required, options)
        self.refetch_ontology()
        return res

    def list_models(self) -> List[ModelConfiguration]:
        """
        List all models that are associated with the project. Use the
        :meth:`encord.project.Project.get_training_metadata` to get more metadata about each training instance.

        Example:

        .. code::

            from encord.utilities.project_utilities import get_all_model_iteration_uids

            project = client_instance.get_project([project_hash])

            model_configurations = project.list_models()
            all_model_iteration_uids = get_all_model_iteration_uids(model_configurations)
            training_metadata = project.get_training_metadata(
                all_model_iteration_uids,
                get_model_training_labels=True,
            )

        Returns:
            List[ModelConfiguration]: A list of ModelConfiguration objects representing the models associated with the project.
        """
        return self._client.list_models()

    def get_training_metadata(
        self,
        model_iteration_uids: Iterable[str],
        get_created_at: bool = False,
        get_training_final_loss: bool = False,
        get_model_training_labels: bool = False,
    ) -> List[TrainingMetadata]:
        """
        Given a list of model_iteration_uids, get metadata around each model_iteration.

        Args:
            model_iteration_uids: The model iteration uids.
            get_created_at: Whether the `created_at` field should be retrieved.
            get_training_final_loss: Whether the `training_final_loss` field should be retrieved.
            get_model_training_labels: Whether the `model_training_labels` field should be retrieved.

        Returns:
            List[TrainingMetadata]: A list of TrainingMetadata objects containing the requested metadata.
        """
        return self._client.get_training_metadata(
            model_iteration_uids,
            get_created_at,
            get_training_final_loss,
            get_model_training_labels,
        )

    def create_model_row(
        self,
        title: str,
        description: str,
        features: List[str],
        model: Union[AutomationModels, str],
    ) -> str:
        """
        Create a model row.

        Args:
            title: Model title.
            description: Model description.
            features: List of feature_node_hashes which are IDs of ontology objects or classifications to be included in the model.
            model: The model type to be used. For backwards compatibility purposes, strings corresponding to the values of
                the :class:`.AutomationModels` Enum are also allowed.

        Returns:
            str: The uid of the added model row.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ModelFeaturesInconsistentError: If a feature type is different from what is supported by the model (e.g. if creating a classification model using a bounding box).
        """
        return self._client.create_model_row(title, description, features, model)

    def model_delete(self, uid: str) -> bool:
        """
        Delete a model created on the platform.

        Args:
            uid: A model_hash (uid) string.

        Returns:
            bool: True if the model was successfully deleted, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no model exists by the specified model_hash (uid).
            UnknownError: If an error occurs during deletion.
        """
        return self._client.model_delete(uid)

    def model_inference(
        self,
        uid: str,
        file_paths: Optional[List[str]] = None,
        base64_strings: Optional[List[bytes]] = None,
        conf_thresh: float = 0.6,
        iou_thresh: float = 0.3,
        device: Device = Device.CUDA,
        detection_frame_range: Optional[List[int]] = None,
        allocation_enabled: bool = False,
        data_hashes: Optional[List[str]] = None,
        rdp_thresh: float = 0.005,
    ):
        """
        Run inference with a model trained on the platform.

        The image(s)/video(s) can be provided either as local file paths, base64 strings, or as data hashes if the
        data is already uploaded on the Encord platform.

        Args:
            uid: A model_iteration_hash (uid) string.
            file_paths: List of local file paths to image(s) or video(s) - if running inference on files.
            base64_strings: List of base64 strings of image(s) or video(s) - if running inference on base64 strings.
            conf_thresh: Confidence threshold (default 0.6).
            iou_thresh: Intersection over union threshold (default 0.3).
            device: Device (CPU or CUDA, default is CUDA).
            detection_frame_range: Detection frame range (for videos).
            allocation_enabled: Object UID allocation (tracking) enabled (disabled by default).
            data_hashes: List of hashes of the videos/image_groups you'd like to run inference on.
            rdp_thresh: Parameter specifying the polygon coarseness to be used while running inference. The higher the
                        value, the fewer points in the segmented image.

        Returns:
            dict: A dictionary of inference results.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no model exists by the specified model_iteration_hash (uid).
            UnknownError: If an error occurs while running inference.
            FileTypeNotSupportedError: If the file type is not supported for inference (has to be an image or video).
            FileSizeNotSupportedError: If the file size is too big to be supported.
            DetectionRangeInvalidError: If a detection range is invalid for video inference.
        """
        return self._client.model_inference(
            uid,
            file_paths,
            base64_strings,
            conf_thresh,
            iou_thresh,
            device,
            detection_frame_range,
            allocation_enabled,
            data_hashes,
            rdp_thresh,
        )

    def model_train(
        self,
        uid: str,
        label_rows: Optional[List[str]] = None,
        epochs: Optional[int] = None,
        batch_size: int = 24,
        weights: Optional[ModelTrainingWeights] = None,
        device: Device = Device.CUDA,
    ):
        """
        Train a model created on the platform.

        Args:
            uid: A model_hash (uid) string.
            label_rows: List of label row uids (hashes) for training.
            epochs: Number of passes through the training dataset.
            batch_size: Number of training examples utilized in one iteration.
            weights: Model weights.
            device: Device (CPU or CUDA, default is CUDA).

        Returns:
            ModelIteration: A model iteration object.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ModelWeightsInconsistentError: If the passed model weights are incompatible with the selected model.
            ResourceNotFoundError: If no model exists by the specified model_hash (uid).
            UnknownError: If an error occurs during training.
        """
        return self._client.model_train(
            uid,
            label_rows,
            epochs,
            batch_size,
            weights,
            device,
        )

    def object_interpolation(
        self,
        key_frames,
        objects_to_interpolate,
    ):
        """
        Run object interpolation algorithm on project labels (requires an editor ontology and feature uids).

        Interpolation is supported for bounding box, polygon, and keypoint.

        Args:
            key_frames: Labels for frames to be interpolated. Key frames are consumed in the form::

                {
                    "[frame_number]": {
                        "objects": [
                            {
                                "objectHash": "[object_hash]",
                                "featureHash": "[feature_hash]",
                                "polygon": {
                                    "0": { "x": x1, "y": y1, },
                                    "1": { "x": x2, "y": y2, },
                                    # ...,
                                }
                            },
                            # ...
                        ]
                    },
                    # ...,
                }

            objects_to_interpolate: List of object uids (hashes) of objects to interpolate.

        Returns:
            dict: Full set of filled frames including interpolated objects.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while running interpolation.
        """
        return self._client.object_interpolation(key_frames, objects_to_interpolate)

    def fitted_bounding_boxes(
        self,
        frames: dict,
        video: dict,
    ):
        """
        Fit bounding boxes to the given frames of a video.

        Args:
            frames: Labels for frames to be fitted. Frames are consumed in the form::

                {
                    "[frame_number]": {
                        "objects": [
                            {
                                "objectHash": "[object_hash]",
                                "featureHash": "[feature_hash]",
                                "polygon": {
                                    "0": { "x": x1, "y": y1, },
                                    "1": { "x": x2, "y": y2, },
                                    # ...,
                                }
                            },
                            # ...
                        ]
                    },
                    # ...,
                }

            video: Metadata of the video for which bounding box fitting needs to be run::

                {
                    "width": w,
                    "height": h,
                }

        Returns:
            dict: Full set of filled frames including fitted objects.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while running interpolation.
        """
        return self._client.fitted_bounding_boxes(frames, video)

    def get_data(self, data_hash: str, get_signed_url: bool = False) -> Tuple[Optional[Video], Optional[List[Image]]]:
        """
        Retrieve information about a video or image group.

        Args:
            data_hash: The uid of the data object.
            get_signed_url: Optionally return signed URLs for timed public access to that resource (default False).

        Returns:
            A tuple consisting of the video (if it exists) and a list of individual images (if they exist).

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while retrieving the object.
        """
        return self._client.get_data(data_hash, get_signed_url)

    def get_label_logs(
        self,
        user_hash: Optional[str] = None,
        data_hash: Optional[str] = None,
        from_unix_seconds: Optional[int] = None,
        to_unix_seconds: Optional[int] = None,
        after: Optional[datetime.datetime] = None,
        before: Optional[datetime.datetime] = None,
        user_email: Optional[str] = None,
    ) -> List[LabelLog]:
        """
        Get label logs, which represent the actions taken in the UI to create labels.

        All arguments can be left as `None` if no filtering should be applied.

        Args:
            user_hash: Filter the label logs by the user.
            data_hash: Filter the label logs by the data_hash.
            from_unix_seconds: Filter the label logs to only include labels after this timestamp. **Deprecated**: use parameter **after** instead.
            to_unix_seconds: Filter the label logs to only include labels before this timestamp. **Deprecated**: use parameter **before** instead.
            after: Filter the label logs to only include labels after the specified time.
            before: Filter the label logs to only include labels before the specified time.
            user_email: Filter by the annotator email.

        Returns:
            List of label logs.
        """
        return self._client.get_label_logs(
            user_hash,
            data_hash,
            from_unix_seconds,
            to_unix_seconds,
            after,
            before,
            user_email,
        )

    def get_cloud_integrations(self) -> List[CloudIntegration]:
        """
        Get the list of cloud integrations.

        Returns:
            List of CloudIntegration objects.
        """
        return self._client.get_cloud_integrations()

    @deprecated(version="0.1.104", alternative=".list_label_rows_v2")
    def list_label_rows(
        self,
        edited_before: Optional[Union[str, datetime.datetime]] = None,
        edited_after: Optional[Union[str, datetime.datetime]] = None,
        label_statuses: Optional[List[AnnotationTaskStatus]] = None,
        shadow_data_state: Optional[ShadowDataState] = None,
        *,
        include_uninitialised_labels=False,
        label_hashes: Optional[List[str]] = None,
        data_hashes: Optional[List[str]] = None,
    ) -> List[LabelRowMetadata]:
        """
        DEPRECATED - use `list_label_rows_v2` to manage label rows instead.

        Args:
            edited_before: Optionally filter to only rows last edited before the specified time.
            edited_after: Optionally filter to only rows last edited after the specified time.
            label_statuses: Optionally filter to only those label rows that have one of the specified AnnotationTaskStatus.
            shadow_data_state: Optionally filter by data type in Benchmark QA projects. See ShadowDataState.
            include_uninitialised_labels: Whether to return only label rows that are "created" and have a label_hash (default).
                If set to `True`, this will return all label rows, including those that do not have a label_hash.
            data_hashes: List of data hashes to filter by.
            label_hashes: List of label hashes to filter by.

        Returns:
            A list of LabelRowMetadata instances for all the matching label rows.

        Raises:
            UnknownError: If an error occurs while retrieving the data.
        """
        return self._client.list_label_rows(
            edited_before,
            edited_after,
            label_statuses,
            shadow_data_state,
            include_uninitialised_labels=include_uninitialised_labels,
            label_hashes=label_hashes,
            data_hashes=data_hashes,
        )

    def set_label_status(self, label_hash: str, label_status: LabelStatus) -> bool:
        """
        Set the label status for a label row to a desired value.

        Args:
            label_hash: Unique identifier of the label row whose status is to be updated.
            label_status: The new status that needs to be set.

        Returns:
            True if the label status was successfully updated, False otherwise.

        Raises:
            AuthorisationError: If the label_hash provided is invalid or not a member of the project.
            UnknownError: If an error occurs while updating the status.
        """
        return self._client.set_label_status(label_hash, label_status)

    @deprecated(version="0.1.123", alternative=".list_label_rows_v2")
    def get_label_row(
        self,
        uid: str,
        get_signed_url: bool = True,
        *,
        include_object_feature_hashes: Optional[Set[str]] = None,
        include_classification_feature_hashes: Optional[Set[str]] = None,
        include_reviews: bool = False,
    ) -> LabelRow:
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Retrieve label row. If you need to retrieve multiple label rows, prefer using get_label_rows instead.

        Args:
            uid: A label_hash (uid) string.
            get_signed_url: Whether to generate signed urls to the data asset. Generating these should be disabled if the signed urls are not used to speed up the request.
            include_object_feature_hashes: If None all the objects will be included. Otherwise, only objects labels will be included of which the feature_hash has been added.
            include_classification_feature_hashes: If None all the classifications will be included. Otherwise, only classification labels will be included of which the feature_hash has been added.
            include_reviews: Whether to request read only information about the reviews of the label row.

        Returns:
            LabelRow: A label row instance.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no label exists by the specified label_hash (uid).
            UnknownError: If an error occurs while retrieving the label.
            OperationNotAllowed: If the read operation is not allowed by the API key.
        """
        return self._client.get_label_row(
            uid,
            get_signed_url,
            include_object_feature_hashes=include_object_feature_hashes,
            include_classification_feature_hashes=include_classification_feature_hashes,
            include_reviews=include_reviews,
        )

    @deprecated(version="0.1.123", alternative=".list_label_rows_v2")
    def get_label_rows(
        self,
        uids: List[str],
        get_signed_url: bool = True,
        *,
        include_object_feature_hashes: Optional[Set[str]] = None,
        include_classification_feature_hashes: Optional[Set[str]] = None,
        include_reviews: bool = False,
    ) -> List[LabelRow]:
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Retrieve a list of label rows. Duplicates will be dropped. The result will come back in a random order.

        Args:
            uids: A list of label_hash (uid).
            get_signed_url: Whether to generate signed urls to the data asset. Generating these should be disabled if the signed urls are not used to speed up the request.
            include_object_feature_hashes: If None all the objects will be included. Otherwise, only objects labels will be included of which the feature_hash has been added.
            include_classification_feature_hashes: If None all the classifications will be included. Otherwise, only classification labels will be included of which the feature_hash has been added.
            include_reviews: Whether to request read only information about the reviews of the label row.

        Returns:
            List of LabelRow instances.

        Raises:
            MultiLabelLimitError: If too many labels were requested. Check the error's maximum_labels_allowed field to read the most up to date error limit.
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no label exists by the specified label_hash (uid).
            UnknownError: If an error occurs while retrieving the label.
            OperationNotAllowed: If the read operation is not allowed by the API key.
        """
        return self._client.get_label_rows(
            uids,
            get_signed_url,
            include_object_feature_hashes=include_object_feature_hashes,
            include_classification_feature_hashes=include_classification_feature_hashes,
            include_reviews=include_reviews,
        )

    @deprecated(version="0.1.123", alternative=".list_label_rows_v2")
    def save_label_row(self, uid, label, validate_before_saving: bool = False):
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Save an existing label row.

        If you have a series of frame labels and have not updated answer
        dictionaries, call the construct_answer_dictionaries utility function
        to do so prior to saving labels.

        Args:
            uid: A label_hash (uid) string.
            label: A label row instance.
            validate_before_saving: Enable stricter server-side integrity checks. Boolean, `False` by default.

        Returns:
            bool: True if the label row is successfully saved, False otherwise.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            ResourceNotFoundError: If no label exists by the specified label_hash (uid).
            UnknownError: If an error occurs while saving the label.
            OperationNotAllowed: If the write operation is not allowed by the API key.
            AnswerDictionaryError: If an object or classification instance is missing in answer dictionaries.
            CorruptedLabelError: If a blurb is corrupted (e.g., if the frame labels have more frames than the video).
        """
        return self._client.save_label_row(uid, label, validate_before_saving)

    @deprecated(version="0.1.123", alternative=".list_label_rows_v2")
    def create_label_row(self, uid: str):
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Create a label row (for data in a project not previously labeled).

        Args:
            uid: The data_hash (uid) of the data unit being labeled.
                Available in client.get_project().get('label_rows')
                where label_status is NOT_LABELLED.

        Returns:
            LabelRow: A label row instance.

        Raises:
            AuthenticationError: If the project API key is invalid.
            AuthorisationError: If access to the specified resource is restricted.
            UnknownError: If an error occurs while saving the label.
            OperationNotAllowed: If the write operation is not allowed by the API key.
            AnswerDictionaryError: If an object or classification instance is missing in answer dictionaries.
            CorruptedLabelError: If a blurb is corrupted (e.g., if the frame labels have more frames than the video).
            ResourceExistsError: If a label row already exists for this project data. Avoids overriding existing work.
        """
        return self._client.create_label_row(uid)

    def create_bundle(self, bundle_size: Optional[int] = None) -> Bundle:
        """
        Initializes a bundle to reduce the number of network calls performed by the Encord SDK.

        See the :class:`encord.http.bundle.Bundle` documentation for more details.

        Args:
            bundle_size: maximum number of items bundled. If more actions provided to the bundle, they will be
                automatically split into separate api calls.

        Returns:
            Bundle: An instance of the Bundle class.
        """
        return Bundle(bundle_size=bundle_size)

    def list_collaborator_timers(
        self,
        after: datetime.datetime,
        before: Optional[datetime.datetime] = None,
        group_by_data_unit: bool = True,
    ) -> Iterable[CollaboratorTimer]:
        """
        Provides information about time spent by each collaborator who has worked on the project within a specified
        range of dates.

        Args:
            after: The beginning of the period of interest.
            before: The end of the period of interest.
            group_by_data_unit: If True, time spent by a collaborator for each data unit is provided separately.
                                If False, all time spent in the scope of the project is aggregated together.

        Yields:
            CollaboratorTimer: Information about the time spent by each collaborator.
        """

        params = CollaboratorTimerParams(
            project_hash=self.project_hash,
            after=after,
            before=before,
            group_by=(CollaboratorTimersGroupBy.DATA_UNIT if group_by_data_unit else CollaboratorTimersGroupBy.PROJECT),
            page_size=100,
        )

        yield from self._client.get_collaborator_timers(params)

    def list_datasets(self) -> Iterable[ProjectDataset]:
        """
        List all datasets associated with the project.

        Returns:
            Iterable[ProjectDataset]: An iterable of ProjectDataset instances.
        """
        return self._client.list_project_datasets(self._project_instance.project_hash)

    def import_coco_labels(
        self,
        labels_dict: Dict[str, Any],
        category_id_to_feature_hash: Dict[CategoryID, str],
        image_id_to_frame_index: Dict[ImageID, FrameIndex],
    ) -> None:
        """Import labels from a COCO format into your Encord project

        Args:
            labels_dict (Dict[str, Any]): Raw label dictionary conforming to Encord format
            category_id_to_feature_hash (Dict[CategoryID, str]): Dictionary mapping category_id as used in the COCO data to the feature hash for the corresponding element in this Ontology
            image_id_to_frame_index (Dict[ImageID, FrameIndex]): Dictionary mapping int to FrameIndex(data_hash, frame_offset) which is used to identify the corresponding frame in the Encord setting
        """
        from encord.utilities.coco.datastructure import CocoRootModel
        from encord.utilities.coco.importer import import_coco_labels

        coco = CocoRootModel.from_dict(labels_dict)
        import_coco_labels(self, coco, category_id_to_feature_hash, image_id_to_frame_index)
