Project

class encord.project.Project(client, project_instance, ontology, client_v2)

from encord.objects.project import ProjectDataset

project = user_client.get_project("<project_hash>")

project_datasets = ProjectDataset.from_list(project.datasets)

from encord.orm.label_row import LabelRowMetadata

project = user_client.get_project("<project_hash>")

label_rows = LabelRowMetadata.from_list(project.label_rows)

refetch_data()

    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()

refetch_ontology()

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

get_project()

    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()

list_label_rows_v2(data_hashes=None, label_hashes=None, edited_before=None, edited_after=None, label_statuses=None, shadow_data_state=None, data_title_eq=None, data_title_like=None, workflow_graph_node_title_eq=None, workflow_graph_node_title_like=None, include_all_branches=False)

    def list_label_rows_v2(
        self,
        data_hashes: Optional[List[str]] = None,
        label_hashes: Optional[List[str]] = 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,
    ) -> List[LabelRowV2]:
        """
        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: On 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

        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,
        )

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

add_users(user_emails, user_role)

copy_project(copy_datasets=False, copy_collaborators=False, copy_models=False, *, copy_labels=None, new_title=None, new_description=None)

submit_label_row_for_review(uid)

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

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

        Args:
            uid: A label_hash (uid) string.

        Returns:
            Bool.

        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)

add_datasets(dataset_hashes)

    def add_datasets(self, dataset_hashes: List[str]) -> bool:
        """
        Add a dataset to a project

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

        Returns:
            Bool.

        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

remove_datasets(dataset_hashes)

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

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

        Returns:
            Bool.

        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

list_models()

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,
)

    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.

        .. 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,
            )
        """
        return self._client.list_models()

get_training_metadata(model_iteration_uids, get_created_at=False, get_training_final_loss=False, get_model_training_labels=False)

    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 some 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.
        """
        return self._client.get_training_metadata(
            model_iteration_uids, get_created_at, get_training_final_loss, get_model_training_labels
        )

create_model_row(title, description, features, model)

    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 is id's of ontology objects
                      or classifications to be included in the model.
            model: the model type to be used.
                   For backwards compatibility purposes, we continuously allow strings
                   corresponding to the values of the :class:`.AutomationModels` Enum.

        Returns:
            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 than 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)

model_delete(uid)

model_inference(uid, file_paths=None, base64_strings=None, conf_thresh=0.6, iou_thresh=0.3, device=Device.CUDA, detection_frame_range=None, allocation_enabled=False, data_hashes=None, rdp_thresh=0.005)

    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 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 base 64 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 hash 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 less points in the segmented image

        Returns:
            Inference results: A dict 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,
        )

model_train(uid, label_rows=None, epochs=None, batch_size=24, weights=None, device=Device.CUDA)

    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 uid's (hashes) for training.
            epochs: Number of passes through training dataset.
            batch_size: Number of training examples utilized in one iteration.
            weights: Model weights.
            device: Device (CPU or CUDA, default is CUDA).

        Returns:
            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,
        )

object_interpolation(key_frames, objects_to_interpolate)

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

    def object_interpolation(
        self,
        key_frames,
        objects_to_interpolate,
    ):
        return self._client.object_interpolation(key_frames, objects_to_interpolate)

fitted_bounding_boxes(frames, video)

    def fitted_bounding_boxes(
        self,
        frames: dict,
        video: dict,
    ):
        """

        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:
            Fitting results: 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)

get_data(data_hash, get_signed_url=False)

    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 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)

get_websocket_url()

    def get_websocket_url(self) -> str:
        return self._client.get_websocket_url()

get_label_logs(user_hash=None, data_hash=None, from_unix_seconds=None, to_unix_seconds=None, after=None, before=None, user_email=None)

    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.
        """
        return self._client.get_label_logs(
            user_hash, data_hash, from_unix_seconds, to_unix_seconds, after, before, user_email
        )

get_cloud_integrations()

    def get_cloud_integrations(self) -> List[CloudIntegration]:
        return self._client.get_cloud_integrations()

create_bundle()

    def create_bundle(self) -> Bundle:
        """
        Initialises a bundle to reduce amount of network calls performed by the Encord SDK

        See the :class:`encord.http.bundle.Bundle` documentation for more details
        """
        return Bundle()

list_collaborator_timers(after, before=None, group_by_data_unit=True)

    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 for each collaborator that has worked on the project within a specified
        range of dates.

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

        Returns:
            Iterable[CollaboratorTimer]
        """

        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,
        )

        while True:
            page = self._client_v2.get(
                Path("analytics/collaborators/timers"), params=params, result_type=Page[CollaboratorTimer]
            )

            for result in page.results:
                yield result

            if page.next_page_token is not None:
                params.page_token = page.next_page_token
            else:
                break

list_groups()

project_hash = convert_to_uuid(self.project_hash)
page = self._client.list_groups(project_hash)
yield from page.results

add_group(group_hash, user_role)

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)

remove_group(group_hash)

if isinstance(group_hash, UUID):
    group_hash = [group_hash]
self._client.remove_groups(group_hash)

get_project_ontology()

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

add_object(name, shape)

    def add_object(self, name: str, shape: ObjectShape) -> bool:
        """
        Add 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:
            True if the object was added successfully and 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 add te 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_data()
        return res

add_classification(name, classification_type, required, options=None)

    def add_classification(
        self,
        name: str,
        classification_type: ClassificationType,
        required: bool,
        options: Optional[Iterable[str]] = None,
    ):
        """
        Add 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 add te 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_data()
        return res

list_label_rows(edited_before=None, edited_after=None, label_statuses=None, shadow_data_state=None, *, include_uninitialised_labels=False, label_hashes=None, data_hashes=None)

    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 :class:`~encord.orm.label_row.AnnotationTaskStatus`
            shadow_data_state: On Optionally filter by data type in Benchmark QA projects. See :class:`~encord.orm.label_row.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 :class:`~encord.orm.label_row.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,
        )

set_label_status(label_hash, label_status)

    def set_label_status(self, label_hash: str, label_status: LabelStatus) -> bool:
        """
        DEPRECATED - this function is currently not maintained.

        Set the label status for a label row to a desired value.

        Args:
            self: Encord client object.
            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:
            Bool.

        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)

get_label_row(uid, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)

    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
        :meth:`encord.project.Project.get_label_rows` instead.

        A code example using the `include_object_feature_hashes` and `include_classification_feature_hashes`
        filters can be found in :meth:`encord.project.Project.get_label_row`.


        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,
        )

get_label_rows(uids, get_signed_url=True, *, include_object_feature_hashes=None, include_classification_feature_hashes=None, include_reviews=False)

# Code example of using the object filters.
from encord.objects.common import Shape
from encord.objects.ontology_structure import OntologyStructure

project = ... # assuming you already have instantiated this Project object

# Get all feature hashes of the objects which are of type `Shape.BOUNDING_BOX`
ontology = OntologyStructure.from_dict(project.ontology)
only_bounding_box_feature_hashes = set()
for object_ in ontology.objects:
    if object_.shape == Shape.BOUNDING_BOX:
        only_bounding_box_feature_hashes.add(object_.feature_node_hash)

no_classification_feature_hashes = set()  # deliberately left empty

# Get all labels of tasks that have already been initiated.
# Include only labels of bounding boxes and exclude all
# classifications
label_hashes = []
for label_row in project.label_rows:
    # Trying to run `get_label_row` on a label_row without a `label_hash` would fail.
    if label_row["label_hash"] is not None:
        label_hashes.append(label_row["label_hash"])

all_labels = project.get_label_rows(
    label_hashes,
    include_object_feature_hashes=only_bounding_box_feature_hashes,
    include_classification_feature_hashes=no_classification_feature_hashes,
)

    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.

        This return is undefined behaviour if any of the uids are invalid (i.e. it may randomly fail or randomly
        succeed and should not be relied upon).

        .. code::

                # Code example of using the object filters.
                from encord.objects.common import Shape
                from encord.objects.ontology_structure import OntologyStructure

                project = ... # assuming you already have instantiated this Project object

                # Get all feature hashes of the objects which are of type `Shape.BOUNDING_BOX`
                ontology = OntologyStructure.from_dict(project.ontology)
                only_bounding_box_feature_hashes = set()
                for object_ in ontology.objects:
                    if object_.shape == Shape.BOUNDING_BOX:
                        only_bounding_box_feature_hashes.add(object_.feature_node_hash)

                no_classification_feature_hashes = set()  # deliberately left empty

                # Get all labels of tasks that have already been initiated.
                # Include only labels of bounding boxes and exclude all
                # classifications
                label_hashes = []
                for label_row in project.label_rows:
                    # Trying to run `get_label_row` on a label_row without a `label_hash` would fail.
                    if label_row["label_hash"] is not None:
                        label_hashes.append(label_row["label_hash"])

                all_labels = project.get_label_rows(
                    label_hashes,
                    include_object_feature_hashes=only_bounding_box_feature_hashes,
                    include_classification_feature_hashes=no_classification_feature_hashes,
                )

        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.

        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,
        )

save_label_row(uid, label, validate_before_saving: bool = False)

    def save_label_row(self, uid, label):
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Save existing label row.

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

        Args:
            uid: A label_hash (uid) string.
            label: A label row instance.

        Returns:
            Bool.

        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)

create_label_row(uid)

    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 been 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)

import datetime
from pathlib import Path
from typing import Iterable, List, Optional, Set, Tuple, Union

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.http.v2.payloads import Page
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.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
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.project_user import ProjectUser, ProjectUserRole


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

    def __init__(
        self, client: EncordClientProject, project_instance: OrmProject, ontology: Ontology, client_v2: ApiClient
    ):
        self._client = client
        self._client_v2 = client_v2
        self._project_instance = project_instance
        self._ontology = ontology

    @property
    def project_hash(self) -> str:
        """
        Get the project hash (i.e. the Project ID).
        """
        return 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:
        """
        Get the ontology of the project.

        DEPRECATED: Prefer using the :meth:`encord.Project.ontology_structure` method.
        """
        return self._project_instance.editor_ontology

    @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
    def datasets(self) -> list:
        """
        Get the associated datasets.

        Prefer using the :meth:`encord.objects.project.ProjectDataset` class to work with the data.

        .. code::

            from encord.objects.project import ProjectDataset

            project = user_client.get_project("<project_hash>")

            project_datasets = ProjectDataset.from_list(project.datasets)

        """
        return self._project_instance.datasets

    @property
    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)

        """
        if self._project_instance.label_rows is None:
            self._project_instance = self._client.get_project(include_labels_metadata=True)
        return self._project_instance.label_rows

[docs]    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()

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

[docs]    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()

[docs]    def list_label_rows_v2(
        self,
        data_hashes: Optional[List[str]] = None,
        label_hashes: Optional[List[str]] = 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,
    ) -> List[LabelRowV2]:
        """
        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: On 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

        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,
        )

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

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

        Args:
            user_emails: list of user emails to be added
            user_role: the user role to assign to all users

        Returns:
            ProjectUser

        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)

[docs]    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: currently 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 title for the new project.

        Returns:
            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,
        )

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

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

        Args:
            uid: A label_hash (uid) string.

        Returns:
            Bool.

        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)

[docs]    def add_datasets(self, dataset_hashes: List[str]) -> bool:
        """
        Add a dataset to a project

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

        Returns:
            Bool.

        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

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

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

        Returns:
            Bool.

        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

[docs]    @deprecated(version="0.1.95", alternative=".ontology_structure")
    def get_project_ontology(self) -> LegacyOntology:
        """
        DEPRECATED - prefer using the `ontology_structure` property accessor instead.
        """
        return self._client.get_project_ontology()

[docs]    def add_object(self, name: str, shape: ObjectShape) -> bool:
        """
        Add 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:
            True if the object was added successfully and 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 add te 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_data()
        return res

[docs]    def add_classification(
        self,
        name: str,
        classification_type: ClassificationType,
        required: bool,
        options: Optional[Iterable[str]] = None,
    ):
        """
        Add 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 add te 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_data()
        return res

[docs]    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.

        .. 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,
            )
        """
        return self._client.list_models()

[docs]    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 some 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.
        """
        return self._client.get_training_metadata(
            model_iteration_uids, get_created_at, get_training_final_loss, get_model_training_labels
        )

[docs]    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 is id's of ontology objects
                      or classifications to be included in the model.
            model: the model type to be used.
                   For backwards compatibility purposes, we continuously allow strings
                   corresponding to the values of the :class:`.AutomationModels` Enum.

        Returns:
            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 than 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)

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

        Args:
            uid: A model_hash (uid) string.

        Returns:
            bool

        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 training.
        """

        return self._client.model_delete(uid)

[docs]    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 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 base 64 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 hash 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 less points in the segmented image

        Returns:
            Inference results: A dict 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,
        )

[docs]    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 uid's (hashes) for training.
            epochs: Number of passes through training dataset.
            batch_size: Number of training examples utilized in one iteration.
            weights: Model weights.
            device: Device (CPU or CUDA, default is CUDA).

        Returns:
            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,
        )

[docs]    def object_interpolation(
        self,
        key_frames,
        objects_to_interpolate,
    ):
        """
        Run object interpolation algorithm on project labels (requires an editor ontology and feature uid's).

        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 uid's (hashes) of objects to interpolate.

        Returns:
            Interpolation results: 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)

[docs]    def fitted_bounding_boxes(
        self,
        frames: dict,
        video: dict,
    ):
        """

        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:
            Fitting results: 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)

[docs]    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 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)

[docs]    def get_websocket_url(self) -> str:
        return self._client.get_websocket_url()

[docs]    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.
        """
        return self._client.get_label_logs(
            user_hash, data_hash, from_unix_seconds, to_unix_seconds, after, before, user_email
        )

[docs]    def get_cloud_integrations(self) -> List[CloudIntegration]:
        return self._client.get_cloud_integrations()

[docs]    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 :class:`~encord.orm.label_row.AnnotationTaskStatus`
            shadow_data_state: On Optionally filter by data type in Benchmark QA projects. See :class:`~encord.orm.label_row.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 :class:`~encord.orm.label_row.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,
        )

[docs]    def set_label_status(self, label_hash: str, label_status: LabelStatus) -> bool:
        """
        DEPRECATED - this function is currently not maintained.

        Set the label status for a label row to a desired value.

        Args:
            self: Encord client object.
            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:
            Bool.

        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)

[docs]    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
        :meth:`encord.project.Project.get_label_rows` instead.

        A code example using the `include_object_feature_hashes` and `include_classification_feature_hashes`
        filters can be found in :meth:`encord.project.Project.get_label_row`.


        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,
        )

[docs]    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.

        This return is undefined behaviour if any of the uids are invalid (i.e. it may randomly fail or randomly
        succeed and should not be relied upon).

        .. code::

                # Code example of using the object filters.
                from encord.objects.common import Shape
                from encord.objects.ontology_structure import OntologyStructure

                project = ... # assuming you already have instantiated this Project object

                # Get all feature hashes of the objects which are of type `Shape.BOUNDING_BOX`
                ontology = OntologyStructure.from_dict(project.ontology)
                only_bounding_box_feature_hashes = set()
                for object_ in ontology.objects:
                    if object_.shape == Shape.BOUNDING_BOX:
                        only_bounding_box_feature_hashes.add(object_.feature_node_hash)

                no_classification_feature_hashes = set()  # deliberately left empty

                # Get all labels of tasks that have already been initiated.
                # Include only labels of bounding boxes and exclude all
                # classifications
                label_hashes = []
                for label_row in project.label_rows:
                    # Trying to run `get_label_row` on a label_row without a `label_hash` would fail.
                    if label_row["label_hash"] is not None:
                        label_hashes.append(label_row["label_hash"])

                all_labels = project.get_label_rows(
                    label_hashes,
                    include_object_feature_hashes=only_bounding_box_feature_hashes,
                    include_classification_feature_hashes=no_classification_feature_hashes,
                )

        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.

        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,
        )

[docs]    def save_label_row(self, uid, label):
        """
        DEPRECATED: Prefer using the list_label_rows_v2 function to interact with label rows.

        Save existing label row.

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

        Args:
            uid: A label_hash (uid) string.
            label: A label row instance.

        Returns:
            Bool.

        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)

[docs]    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 been 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)

[docs]    def create_bundle(self) -> Bundle:
        """
        Initialises a bundle to reduce amount of network calls performed by the Encord SDK

        See the :class:`encord.http.bundle.Bundle` documentation for more details
        """
        return Bundle()

[docs]    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 for each collaborator that has worked on the project within a specified
        range of dates.

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

        Returns:
            Iterable[CollaboratorTimer]
        """

        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,
        )

        while True:
            page = self._client_v2.get(
                Path("analytics/collaborators/timers"), params=params, result_type=Page[CollaboratorTimer]
            )

            for result in page.results:
                yield result

            if page.next_page_token is not None:
                params.page_token = page.next_page_token
            else:
                break