195 lines
9.0 KiB
Python
195 lines
9.0 KiB
Python
"""低端图片流水线工作流。
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这个文件里的 `workflow` 只负责“编排顺序”和“等待结果”，
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不直接访问数据库、不做真实 I/O。
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真正的落库和 mock 执行都放在 activity 里完成。
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"""
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from datetime import timedelta
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from temporalio import workflow
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from temporalio.common import RetryPolicy
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# Temporal workflow 代码需要尽量保持可重放（deterministic）。
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# 这里导入的模块会在 workflow 外部执行，所以用 imports_passed_through
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# 明确告诉 SDK：这些导入不是 workflow 重放逻辑的一部分。
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with workflow.unsafe.imports_passed_through():
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from app.domain.enums import OrderStatus, WorkflowStepName
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from app.infra.temporal.task_queues import (
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IMAGE_PIPELINE_CONTROL_TASK_QUEUE,
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IMAGE_PIPELINE_EXPORT_TASK_QUEUE,
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IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE,
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IMAGE_PIPELINE_QC_TASK_QUEUE,
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)
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from app.workers.activities.export_activities import run_export_activity
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from app.workers.activities.qc_activities import run_qc_activity
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from app.workers.activities.review_activities import mark_workflow_failed_activity
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from app.workers.activities.scene_activities import run_scene_activity
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from app.workers.activities.tryon_activities import prepare_model_activity, run_tryon_activity
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from app.workers.workflows.timeout_policy import DEFAULT_ACTIVITY_TIMEOUT, activity_timeout_for_task_queue
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from app.workers.workflows.types import (
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PipelineWorkflowInput,
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StepActivityInput,
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WorkflowFailureActivityInput,
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)
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ACTIVITY_RETRY_POLICY = RetryPolicy(
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initial_interval=timedelta(seconds=1),
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backoff_coefficient=2.0,
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maximum_attempts=3,
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)
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@workflow.defn
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class LowEndPipelineWorkflow:
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"""低端全自动工作流。
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它对应的是一条从头跑到尾、不需要人工介入的流水线：
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prepare_model -> tryon -> scene -> qc -> export
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"""
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@workflow.run
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async def run(self, payload: PipelineWorkflowInput) -> dict[str, int | str | None]:
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"""执行低端工作流主流程。
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可以把这里理解成“时序控制器”：
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1. 按顺序调 activity
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2. 把上一步产物传给下一步
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3. 出错时统一标记 workflow 失败
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"""
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# current_step 用来在异常时把失败位置持久化到数据库。
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current_step = WorkflowStepName.PREPARE_MODEL
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try:
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# 每个步骤都通过 execute_activity 触发真正执行。
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# workflow 自己不做计算，只负责调度。
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# prepare_model 的职责是把“模特资源 + 订单上下文”整理成后续可消费的人物底图。
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# 这一步产出的 prepared.asset_id 会作为 tryon 的 source_asset_id。
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prepared = await workflow.execute_activity(
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prepare_model_activity,
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StepActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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step_name=WorkflowStepName.PREPARE_MODEL,
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model_id=payload.model_id,
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pose_id=payload.pose_id,
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garment_asset_id=payload.garment_asset_id,
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scene_ref_asset_id=payload.scene_ref_asset_id,
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),
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task_queue=IMAGE_PIPELINE_CONTROL_TASK_QUEUE,
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start_to_close_timeout=activity_timeout_for_task_queue(IMAGE_PIPELINE_CONTROL_TASK_QUEUE),
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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current_step = WorkflowStepName.TRYON
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# 下游步骤通过 source_asset_id 引用上一步生成的资产。
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# tryon 是换装主步骤：把 prepared 模特图和 garment_asset_id 组合成试衣结果。
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# 它产出的 tryon_result.asset_id 是后续 scene / qc 的基础输入。
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tryon_result = await workflow.execute_activity(
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run_tryon_activity,
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StepActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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step_name=WorkflowStepName.TRYON,
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source_asset_id=prepared.asset_id,
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garment_asset_id=payload.garment_asset_id,
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),
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task_queue=IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE,
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start_to_close_timeout=activity_timeout_for_task_queue(IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE),
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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scene_source_asset_id = tryon_result.asset_id
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if payload.scene_ref_asset_id is not None:
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current_step = WorkflowStepName.SCENE
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# scene 是可选步骤。
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# 有场景图时，用 scene_ref_asset_id 把 tryon 结果合成到目标背景；
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# 没有场景图时，直接沿用 tryon 结果继续往下走。
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scene_result = await workflow.execute_activity(
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run_scene_activity,
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StepActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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step_name=WorkflowStepName.SCENE,
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source_asset_id=tryon_result.asset_id,
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scene_ref_asset_id=payload.scene_ref_asset_id,
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),
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task_queue=IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE,
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start_to_close_timeout=activity_timeout_for_task_queue(IMAGE_PIPELINE_IMAGE_GEN_TASK_QUEUE),
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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scene_source_asset_id = scene_result.asset_id
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current_step = WorkflowStepName.QC
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# QC 是流程里的“闸门”。
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# 如果这里不通过，低端流程直接失败，不会再 export。
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# source_asset_id 指向当前链路里“最接近最终成品”的那张图：
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# 有场景时是 scene 结果，没有场景时就是 tryon 结果。
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qc_result = await workflow.execute_activity(
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run_qc_activity,
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StepActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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step_name=WorkflowStepName.QC,
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source_asset_id=scene_source_asset_id,
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),
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task_queue=IMAGE_PIPELINE_QC_TASK_QUEUE,
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start_to_close_timeout=activity_timeout_for_task_queue(IMAGE_PIPELINE_QC_TASK_QUEUE),
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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if not qc_result.passed:
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await self._mark_failed(payload, current_step, qc_result.message)
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return {"order_id": payload.order_id, "status": OrderStatus.FAILED.value, "final_asset_id": None}
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current_step = WorkflowStepName.EXPORT
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# candidate_asset_ids 是 QC 推荐可导出的候选资产。
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# 当前 MVP 只会返回一个候选；如果没有，就退回 scene 结果导出。
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# export 是最后的收口步骤：生成最终交付资产，并把订单状态写成 succeeded。
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final_result = await workflow.execute_activity(
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run_export_activity,
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StepActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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step_name=WorkflowStepName.EXPORT,
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source_asset_id=(qc_result.candidate_asset_ids or [scene_source_asset_id])[0],
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),
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task_queue=IMAGE_PIPELINE_EXPORT_TASK_QUEUE,
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start_to_close_timeout=activity_timeout_for_task_queue(IMAGE_PIPELINE_EXPORT_TASK_QUEUE),
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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return {
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"order_id": payload.order_id,
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"status": OrderStatus.SUCCEEDED.value,
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"final_asset_id": final_result.asset_id,
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}
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except Exception as exc:
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# workflow 出异常时，额外调一个 activity 把数据库状态补齐。
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await self._mark_failed(payload, current_step, str(exc))
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raise
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async def _mark_failed(
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self,
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payload: PipelineWorkflowInput,
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current_step: WorkflowStepName,
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message: str,
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) -> None:
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"""持久化失败状态。
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注意这里仍然通过 activity 落库，而不是在 workflow 里直连数据库。
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这样能保持 workflow 的职责单一：只编排，不做外部副作用。
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"""
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await workflow.execute_activity(
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mark_workflow_failed_activity,
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WorkflowFailureActivityInput(
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order_id=payload.order_id,
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workflow_run_id=payload.workflow_run_id,
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current_step=current_step,
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message=message,
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),
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task_queue=IMAGE_PIPELINE_CONTROL_TASK_QUEUE,
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start_to_close_timeout=DEFAULT_ACTIVITY_TIMEOUT,
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retry_policy=ACTIVITY_RETRY_POLICY,
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)
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