update openapi specs for different targets of balancing strategy (#1533)

* update openapi specs for different targets of balancing strategy

* regenerated with new endpoint

* regenerated with new endpoint

* set isDefault

---------

Co-authored-by: Jeremy A. Prescott <[email protected]>

lightly/openapi_generated/swagger_client/__init__.py

@@ -258,6 +258,7 @@
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry import SelectionConfigV4Entry
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_input import SelectionConfigV4EntryInput
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_strategy import SelectionConfigV4EntryStrategy
+from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_strategy_all_of import SelectionConfigV4EntryStrategyAllOf
 from lightly.openapi_generated.swagger_client.models.selection_input_predictions_name import SelectionInputPredictionsName
 from lightly.openapi_generated.swagger_client.models.selection_input_type import SelectionInputType
 from lightly.openapi_generated.swagger_client.models.selection_strategy_threshold_operation import SelectionStrategyThresholdOperation

lightly/openapi_generated/swagger_client/api/predictions_api.py

@@ -20,7 +20,7 @@
 from pydantic import validate_arguments, ValidationError
 from typing_extensions import Annotated
 
-from pydantic import Field, conint, conlist, constr, validator
+from pydantic import Field, StrictBool, conint, conlist, constr, validator
 
 from typing import List, Optional
 
@@ -711,20 +711,26 @@ def get_prediction_task_schemas_by_dataset_id_with_http_info(self, dataset_id :
             _request_auth=_params.get('_request_auth'))
 
     @validate_arguments
-    def get_predictions_by_dataset_id(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], prediction_uuid_timestamp : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. ")] = None, task_name : Annotated[Optional[constr(strict=True, min_length=1)], Field(description="If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name")] = None, **kwargs) -> List[List]:  # noqa: E501
+    def get_predictions_by_dataset_id(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], prediction_uuid_timestamp : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. ")] = None, page_size : Annotated[Optional[conint(strict=True, ge=1)], Field(description="pagination size/limit of the number of samples to return")] = None, page_offset : Annotated[Optional[conint(strict=True, ge=0)], Field(description="pagination offset")] = None, lean : Annotated[Optional[StrictBool], Field(description="if lean is set to true, all prediction singletons are returned without their \"heavy\" part. This is useful for large datasets where the full prediction singletons are not needed. e.g SEGMENTATION does not need to return the RLE ")] = None, task_name : Annotated[Optional[constr(strict=True, min_length=1)], Field(description="If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name")] = None, **kwargs) -> List[List]:  # noqa: E501
         """get_predictions_by_dataset_id  # noqa: E501
 
         Get all prediction singletons of all samples of a dataset ordered by the sample mapping  # noqa: E501
         This method makes a synchronous HTTP request by default. To make an
         asynchronous HTTP request, please pass async_req=True
 
-        >>> thread = api.get_predictions_by_dataset_id(dataset_id, prediction_uuid_timestamp, task_name, async_req=True)
+        >>> thread = api.get_predictions_by_dataset_id(dataset_id, prediction_uuid_timestamp, page_size, page_offset, lean, task_name, async_req=True)
         >>> result = thread.get()
 
         :param dataset_id: ObjectId of the dataset (required)
         :type dataset_id: str
         :param prediction_uuid_timestamp: Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. 
         :type prediction_uuid_timestamp: int
+        :param page_size: pagination size/limit of the number of samples to return
+        :type page_size: int
+        :param page_offset: pagination offset
+        :type page_offset: int
+        :param lean: if lean is set to true, all prediction singletons are returned without their \"heavy\" part. This is useful for large datasets where the full prediction singletons are not needed. e.g SEGMENTATION does not need to return the RLE 
+        :type lean: bool
         :param task_name: If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name
         :type task_name: str
         :param async_req: Whether to execute the request asynchronously.
@@ -741,23 +747,29 @@ def get_predictions_by_dataset_id(self, dataset_id : Annotated[constr(strict=Tru
         kwargs['_return_http_data_only'] = True
         if '_preload_content' in kwargs:
             raise ValueError("Error! Please call the get_predictions_by_dataset_id_with_http_info method with `_preload_content` instead and obtain raw data from ApiResponse.raw_data")
-        return self.get_predictions_by_dataset_id_with_http_info(dataset_id, prediction_uuid_timestamp, task_name, **kwargs)  # noqa: E501
+        return self.get_predictions_by_dataset_id_with_http_info(dataset_id, prediction_uuid_timestamp, page_size, page_offset, lean, task_name, **kwargs)  # noqa: E501
 
     @validate_arguments
-    def get_predictions_by_dataset_id_with_http_info(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], prediction_uuid_timestamp : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. ")] = None, task_name : Annotated[Optional[constr(strict=True, min_length=1)], Field(description="If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name")] = None, **kwargs) -> ApiResponse:  # noqa: E501
+    def get_predictions_by_dataset_id_with_http_info(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], prediction_uuid_timestamp : Annotated[Optional[conint(strict=True, ge=0)], Field(description="Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. ")] = None, page_size : Annotated[Optional[conint(strict=True, ge=1)], Field(description="pagination size/limit of the number of samples to return")] = None, page_offset : Annotated[Optional[conint(strict=True, ge=0)], Field(description="pagination offset")] = None, lean : Annotated[Optional[StrictBool], Field(description="if lean is set to true, all prediction singletons are returned without their \"heavy\" part. This is useful for large datasets where the full prediction singletons are not needed. e.g SEGMENTATION does not need to return the RLE ")] = None, task_name : Annotated[Optional[constr(strict=True, min_length=1)], Field(description="If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name")] = None, **kwargs) -> ApiResponse:  # noqa: E501
         """get_predictions_by_dataset_id  # noqa: E501
 
         Get all prediction singletons of all samples of a dataset ordered by the sample mapping  # noqa: E501
         This method makes a synchronous HTTP request by default. To make an
         asynchronous HTTP request, please pass async_req=True
 
-        >>> thread = api.get_predictions_by_dataset_id_with_http_info(dataset_id, prediction_uuid_timestamp, task_name, async_req=True)
+        >>> thread = api.get_predictions_by_dataset_id_with_http_info(dataset_id, prediction_uuid_timestamp, page_size, page_offset, lean, task_name, async_req=True)
         >>> result = thread.get()
 
         :param dataset_id: ObjectId of the dataset (required)
         :type dataset_id: str
         :param prediction_uuid_timestamp: Deprecated, currently ignored. The timestamp of when the actual predictions were created. This is used as a peg to version predictions. E.g one could upload predictions on day 1 and then create new predictions with an improved model on day 30. One can then upload the new predictions to the same dataset. 
         :type prediction_uuid_timestamp: int
+        :param page_size: pagination size/limit of the number of samples to return
+        :type page_size: int
+        :param page_offset: pagination offset
+        :type page_offset: int
+        :param lean: if lean is set to true, all prediction singletons are returned without their \"heavy\" part. This is useful for large datasets where the full prediction singletons are not needed. e.g SEGMENTATION does not need to return the RLE 
+        :type lean: bool
         :param task_name: If provided, only gets all prediction singletons of all samples of a dataset that were yielded by a specific prediction task name
         :type task_name: str
         :param async_req: Whether to execute the request asynchronously.
@@ -790,6 +802,9 @@ def get_predictions_by_dataset_id_with_http_info(self, dataset_id : Annotated[co
         _all_params = [
             'dataset_id',
             'prediction_uuid_timestamp',
+            'page_size',
+            'page_offset',
+            'lean',
             'task_name'
         ]
         _all_params.extend(
@@ -830,6 +845,24 @@ def get_predictions_by_dataset_id_with_http_info(self, dataset_id : Annotated[co
                 _params['prediction_uuid_timestamp'].value if hasattr(_params['prediction_uuid_timestamp'], 'value') else _params['prediction_uuid_timestamp']
             ))
 
+        if _params.get('page_size') is not None:  # noqa: E501
+            _query_params.append((
+                'pageSize',
+                _params['page_size'].value if hasattr(_params['page_size'], 'value') else _params['page_size']
+            ))
+
+        if _params.get('page_offset') is not None:  # noqa: E501
+            _query_params.append((
+                'pageOffset',
+                _params['page_offset'].value if hasattr(_params['page_offset'], 'value') else _params['page_offset']
+            ))
+
+        if _params.get('lean') is not None:  # noqa: E501
+            _query_params.append((
+                'lean',
+                _params['lean'].value if hasattr(_params['lean'], 'value') else _params['lean']
+            ))
+
         if _params.get('task_name') is not None:  # noqa: E501
             _query_params.append((
                 'taskName',

lightly/openapi_generated/swagger_client/api/samples_api.py

@@ -1527,7 +1527,7 @@ def get_samples_partial_by_dataset_id_with_http_info(self, dataset_id : Annotate
             _request_auth=_params.get('_request_auth'))
 
     @validate_arguments
-    def update_sample_by_id(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], sample_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the sample")], sample_update_request : Annotated[SampleUpdateRequest, Field(..., description="The updated sample to set")], enable_dataset_update : Optional[StrictBool] = None, **kwargs) -> None:  # noqa: E501
+    def update_sample_by_id(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], sample_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the sample")], sample_update_request : Annotated[SampleUpdateRequest, Field(..., description="The updated sample to set")], enable_dataset_update : Annotated[Optional[StrictBool], Field(description="Deprecated, is ignored! If we should also update the dataset with the new sample information (like total size)")] = None, **kwargs) -> None:  # noqa: E501
         """update_sample_by_id  # noqa: E501
 
         update a specific sample of a dataset  # noqa: E501
@@ -1543,7 +1543,7 @@ def update_sample_by_id(self, dataset_id : Annotated[constr(strict=True), Field(
         :type sample_id: str
         :param sample_update_request: The updated sample to set (required)
         :type sample_update_request: SampleUpdateRequest
-        :param enable_dataset_update:
+        :param enable_dataset_update: Deprecated, is ignored! If we should also update the dataset with the new sample information (like total size)
         :type enable_dataset_update: bool
         :param async_req: Whether to execute the request asynchronously.
         :type async_req: bool, optional
@@ -1562,7 +1562,7 @@ def update_sample_by_id(self, dataset_id : Annotated[constr(strict=True), Field(
         return self.update_sample_by_id_with_http_info(dataset_id, sample_id, sample_update_request, enable_dataset_update, **kwargs)  # noqa: E501
 
     @validate_arguments
-    def update_sample_by_id_with_http_info(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], sample_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the sample")], sample_update_request : Annotated[SampleUpdateRequest, Field(..., description="The updated sample to set")], enable_dataset_update : Optional[StrictBool] = None, **kwargs) -> ApiResponse:  # noqa: E501
+    def update_sample_by_id_with_http_info(self, dataset_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the dataset")], sample_id : Annotated[constr(strict=True), Field(..., description="ObjectId of the sample")], sample_update_request : Annotated[SampleUpdateRequest, Field(..., description="The updated sample to set")], enable_dataset_update : Annotated[Optional[StrictBool], Field(description="Deprecated, is ignored! If we should also update the dataset with the new sample information (like total size)")] = None, **kwargs) -> ApiResponse:  # noqa: E501
         """update_sample_by_id  # noqa: E501
 
         update a specific sample of a dataset  # noqa: E501
@@ -1578,7 +1578,7 @@ def update_sample_by_id_with_http_info(self, dataset_id : Annotated[constr(stric
         :type sample_id: str
         :param sample_update_request: The updated sample to set (required)
         :type sample_update_request: SampleUpdateRequest
-        :param enable_dataset_update:
+        :param enable_dataset_update: Deprecated, is ignored! If we should also update the dataset with the new sample information (like total size)
         :type enable_dataset_update: bool
         :param async_req: Whether to execute the request asynchronously.
         :type async_req: bool, optional

lightly/openapi_generated/swagger_client/models/__init__.py

@@ -225,6 +225,7 @@
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry import SelectionConfigV4Entry
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_input import SelectionConfigV4EntryInput
 from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_strategy import SelectionConfigV4EntryStrategy
+from lightly.openapi_generated.swagger_client.models.selection_config_v4_entry_strategy_all_of import SelectionConfigV4EntryStrategyAllOf
 from lightly.openapi_generated.swagger_client.models.selection_input_predictions_name import SelectionInputPredictionsName
 from lightly.openapi_generated.swagger_client.models.selection_input_type import SelectionInputType
 from lightly.openapi_generated.swagger_client.models.selection_strategy_threshold_operation import SelectionStrategyThresholdOperation

lightly/openapi_generated/swagger_client/models/datasource_config_s3_delegated_access.py

@@ -30,7 +30,7 @@ class DatasourceConfigS3DelegatedAccess(DatasourceConfigBase):
     """
     full_path: StrictStr = Field(..., alias="fullPath", description="path includes the bucket name and the path within the bucket where you have stored your information")
     s3_region: S3Region = Field(..., alias="s3Region")
-    s3_external_id: constr(strict=True, min_length=10) = Field(..., alias="s3ExternalId", description="The external ID specified when creating the role.")
+    s3_external_id: constr(strict=True, min_length=10) = Field(..., alias="s3ExternalId", description="The external ID specified when creating the role. More information can be found here: - https://docs.aws.amazon.com/IAM/latest/UserGuide/confused-deputy.html - https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_iam-condition-keys.html#ck_externalid ")
     s3_arn: constr(strict=True, min_length=12) = Field(..., alias="s3ARN", description="The ARN of the role you created")
     s3_server_side_encryption_kms_key: Optional[constr(strict=True, min_length=1)] = Field(None, alias="s3ServerSideEncryptionKMSKey", description="If set, Lightly Worker will automatically set the headers to use server side encryption https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html with this value as the appropriate KMS key arn. This will encrypt the files created by Lightly (crops, frames, thumbnails) in the S3 bucket. ")
     __properties = ["id", "purpose", "type", "thumbSuffix", "fullPath", "s3Region", "s3ExternalId", "s3ARN", "s3ServerSideEncryptionKMSKey"]

lightly/openapi_generated/swagger_client/models/datasource_config_s3_delegated_access_all_of.py

@@ -28,7 +28,7 @@ class DatasourceConfigS3DelegatedAccessAllOf(BaseModel):
     DatasourceConfigS3DelegatedAccessAllOf
     """
     s3_region: S3Region = Field(..., alias="s3Region")
-    s3_external_id: constr(strict=True, min_length=10) = Field(..., alias="s3ExternalId", description="The external ID specified when creating the role.")
+    s3_external_id: constr(strict=True, min_length=10) = Field(..., alias="s3ExternalId", description="The external ID specified when creating the role. More information can be found here: - https://docs.aws.amazon.com/IAM/latest/UserGuide/confused-deputy.html - https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_iam-condition-keys.html#ck_externalid ")
     s3_arn: constr(strict=True, min_length=12) = Field(..., alias="s3ARN", description="The ARN of the role you created")
     s3_server_side_encryption_kms_key: Optional[constr(strict=True, min_length=1)] = Field(None, alias="s3ServerSideEncryptionKMSKey", description="If set, Lightly Worker will automatically set the headers to use server side encryption https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html with this value as the appropriate KMS key arn. This will encrypt the files created by Lightly (crops, frames, thumbnails) in the S3 bucket. ")
     __properties = ["s3Region", "s3ExternalId", "s3ARN", "s3ServerSideEncryptionKMSKey"]

lightly/openapi_generated/swagger_client/models/delegated_access_external_ids_inner.py

@@ -26,7 +26,7 @@ class DelegatedAccessExternalIdsInner(BaseModel):
     """
     DelegatedAccessExternalIdsInner
     """
-    external_id: constr(strict=True, min_length=10) = Field(..., alias="externalId", description="The external ID specified when creating the role.")
+    external_id: constr(strict=True, min_length=10) = Field(..., alias="externalId", description="The external ID specified when creating the role. More information can be found here: - https://docs.aws.amazon.com/IAM/latest/UserGuide/confused-deputy.html - https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_iam-condition-keys.html#ck_externalid ")
     user_id: Optional[StrictStr] = Field(None, alias="userId")
     team_id: Optional[StrictStr] = Field(None, alias="teamId")
     __properties = ["externalId", "userId", "teamId"]