o xi@s\ddlmZddlZddlZddlZddlZddlmZddlm Z d dd Z dd d Z dS)) annotationsN)Any)filesreturnboolcCstjtjp dtjvS)z1Returns whether we're in a SageMaker environment.SM_TRAINING_ENV)ospathexistssm_filesSM_PARAM_CONFIGenvironrrV/home/ubuntu/.local/lib/python3.10/site-packages/wandb/integration/sagemaker/config.pyis_using_sagemaker srdict[str, Any]cCsi}tjtjrOtd|d<ttj2}t| D]"\}}| d}t d|r1t |}n t d|r;t|}|||<qWdn1sJwYtjd}ruz |t|W|Stjyttjdd d Y|Sw|S) aParses SageMaker configuration. Returns: A dictionary of SageMaker config keys/values or an empty dict if not found. SM_TRAINING_ENV is a json string of the training environment variables set by SageMaker and is only available when running in SageMaker, but not in local mode. SM_TRAINING_ENV is set by the SageMaker container and contains arguments such as hyperparameters and arguments passed to the training job. TRAINING_JOB_NAMEsagemaker_training_job_name"z ^-?[\d]+$z ^-?[.\d]+$Nrz5Failed to parse SM_TRAINING_ENV not valid JSON string) stacklevel)r r r r r getenvopenjsonloaditemsstriprematchintfloatrgetupdateloadsJSONDecodeErrorwarningswarn)conffidkeyvalcastenvrrrparse_sm_configs2       r.)rr)rr) __future__rrr rr&typingrrr rr.rrrrs