1 files changed, 229 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_endpoint_utils.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_endpoint_utils.py
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_endpoint_utils.py
@@ -0,0 +1,229 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import ast
+import concurrent.futures
+import logging
+import time
+from concurrent.futures import Future
+from pathlib import Path
+from typing import Any, Callable, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models import DataVersion, UriFileJobOutput
+from azure.ai.ml._utils._arm_id_utils import is_ARM_id_for_resource, is_registry_id_for_resource
+from azure.ai.ml._utils._logger_utils import initialize_logger_info
+from azure.ai.ml.constants._common import ARM_ID_PREFIX, AzureMLResourceType, DefaultOpenEncoding, LROConfigurations
+from azure.ai.ml.entities import BatchDeployment
+from azure.ai.ml.entities._assets._artifacts.code import Code
+from azure.ai.ml.entities._deployment.deployment import Deployment
+from azure.ai.ml.entities._deployment.model_batch_deployment import ModelBatchDeployment
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, MlException, ValidationErrorType, ValidationException
+from azure.ai.ml.operations._operation_orchestrator import OperationOrchestrator
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceNotFoundError,
+    ServiceRequestTimeoutError,
+    map_error,
+)
+from azure.core.polling import LROPoller
+from azure.core.rest import HttpResponse
+from azure.mgmt.core.exceptions import ARMErrorFormat
+
+module_logger = logging.getLogger(__name__)
+initialize_logger_info(module_logger, terminator="")
+
+
+def get_duration(start_time: float) -> None:
+    """Calculates the duration of the Long running operation took to finish.
+
+    :param start_time: Start time
+    :type start_time: float
+    """
+    end_time = time.time()
+    duration = divmod(int(round(end_time - start_time)), 60)
+    module_logger.warning("(%sm %ss)\n", duration[0], duration[1])
+
+
+def polling_wait(
+    poller: Union[LROPoller, Future],
+    message: Optional[str] = None,
+    start_time: Optional[float] = None,
+    is_local=False,
+    timeout=LROConfigurations.POLLING_TIMEOUT,
+) -> Any:
+    """Print out status while polling and time of operation once completed.
+
+    :param poller: An poller which will return status update via function done().
+    :type poller: Union[LROPoller, concurrent.futures.Future]
+    :param (str, optional) message: Message to print out before starting operation write-out.
+    :param (float, optional) start_time: Start time of operation.
+    :param (bool, optional) is_local: If poller is for a local endpoint, so the timeout is removed.
+    :param (int, optional) timeout: New value to overwrite the default timeout.
+    """
+    module_logger.warning("%s", message)
+    if is_local:
+        # We removed timeout on local endpoints in case it takes a long time
+        # to pull image or install conda env.
+
+        # We want user to be able to see that.
+
+        while not poller.done():
+            module_logger.warning(".")
+            time.sleep(LROConfigurations.SLEEP_TIME)
+    else:
+        poller.result(timeout=timeout)
+
+    if poller.done():
+        module_logger.warning("Done ")
+    else:
+        module_logger.warning("Timeout waiting for long running operation")
+
+    if start_time:
+        get_duration(start_time)
+
+
+def local_endpoint_polling_wrapper(func: Callable, message: str, **kwargs) -> Any:
+    """Wrapper for polling local endpoint operations.
+
+    :param func: Name of the endpoint.
+    :type func: Callable
+    :param message: Message to print out before starting operation write-out.
+    :type message: str
+    :return: The type returned by Func
+    """
+    pool = concurrent.futures.ThreadPoolExecutor()
+    start_time = time.time()
+    event = pool.submit(func, **kwargs)
+    polling_wait(poller=event, start_time=start_time, message=message, is_local=True)
+    return event.result()
+
+
+def validate_response(response: HttpResponse) -> None:
+    """Validates the response of POST requests, throws on error.
+
+    :param HttpResponse response: the response of a POST requests
+    :raises Exception: Raised when response is not json serializable
+    :raises HttpResponseError: Raised when the response signals that an error occurred
+    """
+    r_json = {}
+
+    if response.status_code not in [200, 201]:
+        # We need to check for an empty response body or catch the exception raised.
+        # It is possible the server responded with a 204 No Content response, and json parsing fails.
+        if response.status_code != 204:
+            try:
+                r_json = response.json()
+            except ValueError as e:
+                # exception is not in the json format
+                msg = response.content.decode("utf-8")
+                raise MlException(message=msg, no_personal_data_message=msg) from e
+        failure_msg = r_json.get("error", {}).get("message", response)
+        error_map = {
+            401: ClientAuthenticationError,
+            404: ResourceNotFoundError,
+            408: ServiceRequestTimeoutError,
+            409: ResourceExistsError,
+            424: HttpResponseError,
+        }
+        map_error(status_code=response.status_code, response=response, error_map=error_map)
+        raise HttpResponseError(response=response, message=failure_msg, error_format=ARMErrorFormat)
+
+
+def upload_dependencies(deployment: Deployment, orchestrators: OperationOrchestrator) -> None:
+    """Upload code, dependency, model dependencies. For BatchDeployment only register compute.
+
+    :param Deployment deployment: Endpoint deployment object.
+    :param OperationOrchestrator orchestrators: Operation Orchestrator.
+    """
+
+    module_logger.debug("Uploading the dependencies for deployment %s", deployment.name)
+
+    # Create a code asset if code is not already an ARM ID
+    if (
+        deployment.code_configuration
+        and not is_ARM_id_for_resource(deployment.code_configuration.code, AzureMLResourceType.CODE)
+        and not is_registry_id_for_resource(deployment.code_configuration.code)
+    ):
+        if deployment.code_configuration.code.startswith(ARM_ID_PREFIX):
+            deployment.code_configuration.code = orchestrators.get_asset_arm_id(
+                deployment.code_configuration.code[len(ARM_ID_PREFIX) :],
+                azureml_type=AzureMLResourceType.CODE,
+            )
+        else:
+            deployment.code_configuration.code = orchestrators.get_asset_arm_id(
+                Code(base_path=deployment._base_path, path=deployment.code_configuration.code),
+                azureml_type=AzureMLResourceType.CODE,
+            )
+
+    if not is_registry_id_for_resource(deployment.environment):
+        deployment.environment = (
+            orchestrators.get_asset_arm_id(deployment.environment, azureml_type=AzureMLResourceType.ENVIRONMENT)
+            if deployment.environment
+            else None
+        )
+    if not is_registry_id_for_resource(deployment.model):
+        deployment.model = (
+            orchestrators.get_asset_arm_id(deployment.model, azureml_type=AzureMLResourceType.MODEL)
+            if deployment.model
+            else None
+        )
+    if isinstance(deployment, (BatchDeployment, ModelBatchDeployment)) and deployment.compute:
+        deployment.compute = orchestrators.get_asset_arm_id(
+            deployment.compute, azureml_type=AzureMLResourceType.COMPUTE
+        )
+
+
+def validate_scoring_script(deployment):
+    score_script_path = Path(deployment.base_path).joinpath(
+        deployment.code_configuration.code, deployment.scoring_script
+    )
+    try:
+        with open(score_script_path, "r", encoding=DefaultOpenEncoding.READ) as script:
+            contents = script.read()
+            try:
+                ast.parse(contents, score_script_path)
+            except SyntaxError as err:
+                err.filename = err.filename.split("/")[-1]
+                msg = (
+                    f"Failed to submit deployment {deployment.name} due to syntax errors "
+                    f"in scoring script {err.filename}.\nError on line {err.lineno}: "
+                    f"{err.text}\nIf you wish to bypass this validation use --skip-script-validation paramater."
+                )
+
+                np_msg = (
+                    "Failed to submit deployment due to syntax errors in deployment script."
+                    "\n If you wish to bypass this validation use --skip-script-validation paramater."
+                )
+                raise ValidationException(
+                    message=msg,
+                    target=(
+                        ErrorTarget.BATCH_DEPLOYMENT
+                        if isinstance(deployment, BatchDeployment)
+                        else ErrorTarget.ONLINE_DEPLOYMENT
+                    ),
+                    no_personal_data_message=np_msg,
+                    error_category=ErrorCategory.USER_ERROR,
+                    error_type=ValidationErrorType.CANNOT_PARSE,
+                ) from err
+    except OSError as err:
+        raise MlException(
+            message=f"Failed to open scoring script {err.filename}.",
+            no_personal_data_message="Failed to open scoring script.",
+        ) from err
+
+
+def convert_v1_dataset_to_v2(output_data_set: DataVersion, file_name: str) -> Dict[str, Any]:
+    if file_name:
+        v2_dataset = UriFileJobOutput(
+            uri=f"azureml://datastores/{output_data_set.datastore_id}/paths/{output_data_set.path}/{file_name}"
+        ).serialize()
+    else:
+        v2_dataset = UriFileJobOutput(
+            uri=f"azureml://datastores/{output_data_set.datastore_id}/paths/{output_data_set.path}"
+        ).serialize()
+    return {"output_name": v2_dataset}