two version of R2R are here HEAD master

1 files changed, 192 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_internal/entities/spark.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_internal/entities/spark.py
new file mode 100644
index 00000000..345fa5f2
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+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_internal/entities/spark.py
@@ -0,0 +1,192 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Dict, List, Optional, Union
+
+from marshmallow import Schema
+
+from ..._schema import PathAwareSchema
+from ...constants._job.job import RestSparkConfKey
+from ...entities import Environment, SparkJobEntry
+from ...entities._job.parameterized_spark import DUMMY_IMAGE, ParameterizedSpark
+from ...entities._job.spark_job_entry_mixin import SparkJobEntryMixin
+from .._schema.component import InternalSparkComponentSchema
+from ..entities import InternalComponent
+from .environment import InternalEnvironment
+
+
+class InternalSparkComponent(
+    InternalComponent, ParameterizedSpark, SparkJobEntryMixin
+):  # pylint: disable=too-many-instance-attributes, too-many-ancestors
+    """Internal Spark Component
+    This class is used to handle internal spark component.
+    It can be loaded from internal spark component yaml or from rest object of an internal spark component.
+    But after loaded, its structure will be the same as spark component.
+    """
+
+    def __init__(
+        self,
+        entry: Union[Dict[str, str], SparkJobEntry, None] = None,
+        py_files: Optional[List[str]] = None,
+        jars: Optional[List[str]] = None,
+        files: Optional[List[str]] = None,
+        archives: Optional[List[str]] = None,
+        driver_cores: Optional[int] = None,
+        driver_memory: Optional[str] = None,
+        executor_cores: Optional[int] = None,
+        executor_memory: Optional[str] = None,
+        executor_instances: Optional[int] = None,
+        dynamic_allocation_enabled: Optional[bool] = None,
+        dynamic_allocation_min_executors: Optional[int] = None,
+        dynamic_allocation_max_executors: Optional[int] = None,
+        conf: Optional[Dict[str, str]] = None,
+        args: Optional[str] = None,
+        **kwargs,
+    ):
+        SparkJobEntryMixin.__init__(self, entry=entry, **kwargs)
+        # environment.setter has been overridden in ParameterizedSpark, so we need to pop it out here
+        environment = kwargs.pop("environment", None)
+        InternalComponent.__init__(self, **kwargs)
+        # Pop it to avoid passing multiple values for code in ParameterizedSpark.__init__
+        code = kwargs.pop("code", None)
+        ParameterizedSpark.__init__(
+            self,
+            code=self.base_path,
+            entry=entry,
+            py_files=py_files,
+            jars=jars,
+            files=files,
+            archives=archives,
+            conf=conf,
+            environment=environment,
+            args=args,
+            **kwargs,
+        )
+        self.code = code
+        # For pipeline spark job, we also allow user to set driver_cores, driver_memory and so on by setting conf.
+        # If root level fields are not set by user, we promote conf setting to root level to facilitate subsequent
+        # verification. This usually happens when we use to_component(SparkJob) or builder function spark() as a node
+        # in pipeline sdk
+        conf = conf or {}
+        self.driver_cores = driver_cores or conf.get(RestSparkConfKey.DRIVER_CORES, None)
+        self.driver_memory = driver_memory or conf.get(RestSparkConfKey.DRIVER_MEMORY, None)
+        self.executor_cores = executor_cores or conf.get(RestSparkConfKey.EXECUTOR_CORES, None)
+        self.executor_memory = executor_memory or conf.get(RestSparkConfKey.EXECUTOR_MEMORY, None)
+        self.executor_instances = executor_instances or conf.get(RestSparkConfKey.EXECUTOR_INSTANCES, None)
+        self.dynamic_allocation_enabled = dynamic_allocation_enabled or conf.get(
+            RestSparkConfKey.DYNAMIC_ALLOCATION_ENABLED, None
+        )
+        self.dynamic_allocation_min_executors = dynamic_allocation_min_executors or conf.get(
+            RestSparkConfKey.DYNAMIC_ALLOCATION_MIN_EXECUTORS, None
+        )
+        self.dynamic_allocation_max_executors = dynamic_allocation_max_executors or conf.get(
+            RestSparkConfKey.DYNAMIC_ALLOCATION_MAX_EXECUTORS, None
+        )
+
+        self.conf = conf
+        self.args = args
+
+    @classmethod
+    def _create_schema_for_validation(cls, context) -> Union[PathAwareSchema, Schema]:
+        return InternalSparkComponentSchema(context=context)
+
+    @property  # type: ignore[override]
+    def environment(self) -> Optional[Union[Environment, str]]:
+        """Get the environment of the component.
+
+        :return: The environment of the component.
+        :rtype: Optional[Union[Environment, str]]]
+        """
+        if isinstance(self._environment, Environment) and self._environment.image is None:
+            return Environment(conda_file=self._environment.conda_file, image=DUMMY_IMAGE)
+        return self._environment
+
+    @environment.setter
+    def environment(self, value):
+        """Set the environment of the component.
+
+        :param value: The environment of the component.
+        :type value: Union[str, Environment, dict]
+        :return: No return
+        :rtype: None
+        """
+        if value is None or isinstance(value, (str, Environment)):
+            self._environment = value
+        elif isinstance(value, dict):
+            internal_environment = InternalEnvironment(**value)
+            internal_environment.resolve(self.base_path)
+            self._environment = Environment(
+                name=internal_environment.name,
+                version=internal_environment.version,
+            )
+            if internal_environment.conda:
+                self._environment.conda_file = {
+                    "dependencies": internal_environment.conda[InternalEnvironment.CONDA_DEPENDENCIES]
+                }
+            if internal_environment.docker:
+                self._environment.image = internal_environment.docker["image"]
+            # we suppose that loaded internal spark component won't be used to create another internal spark component
+            # so the environment construction here can be simplified
+        else:
+            raise ValueError(f"Unsupported environment type: {type(value)}")
+
+    @property
+    def jars(self) -> Optional[List[str]]:
+        """Get the jars of the component.
+
+        :return: The jars of the component.
+        :rtype: Optional[List[str]]
+        """
+        return self._jars
+
+    @jars.setter
+    def jars(self, value: Union[str, List[str]]):
+        """Set the jars of the component.
+
+        :param value: The jars of the component.
+        :type value: Union[str, List[str]]
+        :return: No return
+        :rtype: None
+        """
+        if isinstance(value, str):
+            value = [value]
+        self._jars = value
+
+    @property
+    def py_files(self) -> Optional[List[str]]:
+        """Get the py_files of the component.
+
+        :return: The py_files of the component.
+        :rtype: Optional[List[str]]
+        """
+        return self._py_files
+
+    @py_files.setter
+    def py_files(self, value):
+        """Set the py_files of the component.
+
+        :param value: The py_files of the component.
+        :type value: Union[str, List[str]]
+        :return: No return
+        :rtype: None
+        """
+        if isinstance(value, str):
+            value = [value]
+        self._py_files = value
+
+    def _to_dict(self) -> Dict:
+        result = super()._to_dict()
+        return result
+
+    def _to_rest_object(self):
+        result = super()._to_rest_object()
+        if "pyFiles" in result.properties.component_spec:
+            result.properties.component_spec["py_files"] = result.properties.component_spec.pop("pyFiles")
+        return result
+
+    @classmethod
+    def _from_rest_object_to_init_params(cls, obj) -> Dict:
+        if "py_files" in obj.properties.component_spec:
+            obj.properties.component_spec["pyFiles"] = obj.properties.component_spec.pop("py_files")
+        result = super()._from_rest_object_to_init_params(obj)
+        return result