fix(core): remove four dead duplicate definitions shadowed by a later copy

sagemaker-core/src/sagemaker/core/remote_function/job.py

@@ -2155,13 +2155,6 @@ def _rule_statuses_changed(current_statuses, last_statuses):
     return False
 
 
-def _get_initial_job_state(description, status_key, wait):
-    """Placeholder docstring"""
-    status = description[status_key]
-    job_already_completed = status in ("Completed", "Failed", "Stopped")
-    return LogState.TAILING if wait and not job_already_completed else LogState.COMPLETE
-
-
 def _logs_init(boto_session, description, job):
     """Placeholder docstring"""
     if job == "Training":

sagemaker-core/src/sagemaker/core/training/configs.py

@@ -125,30 +125,6 @@ class SourceCode(BaseConfig):
         ".ipynb_checkpoints",
     ]
 
-class OutputDataConfig(shapes.OutputDataConfig):
-    """OutputDataConfig.
-
-    Provides the configuration for the output data location of the training job 
-    (will not be carried over to any model repository or deployment).
-
-    Parameters:
-        s3_output_path (Optional[StrPipeVar]):
-            The S3 URI where the output data will be stored. This is the location where the
-            training job will save its output data, such as model artifacts and logs.
-        kms_key_id (Optional[StrPipeVar]):
-            The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that
-            SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side
-            encryption.
-        compression_type (Optional[StrPipeVar]):
-            The model output compression type. Select None to output an uncompressed model,
-            recommended for large model outputs. Defaults to gzip.
-    """
-
-    s3_output_path: Optional[StrPipeVar] = None
-    kms_key_id: Optional[StrPipeVar] = None
-    compression_type: Optional[StrPipeVar] = None
-
-
 class Compute(shapes.ResourceConfig):
     """Compute.
 

sagemaker-core/src/sagemaker/core/utils/utils.py

@@ -64,18 +64,6 @@ def convert_to_snake_case(entity_name):
     return re.sub("([a-z0-9])([A-Z])", r"\1_\2", snake_case).lower()
 
 
-def snake_to_pascal(snake_str):
-    """
-    Convert a snake_case string to PascalCase.
-    Args:
-        snake_str (str): The snake_case string to be converted.
-    Returns:
-        str: The PascalCase string.
-    """
-    components = snake_str.split("_")
-    return "".join(x.title() for x in components[0:])
-
-
 def reformat_file_with_black(filename):
     try:
         # Run black with specific options using subprocess

sagemaker-serve/src/sagemaker/serve/model_builder_utils.py

@@ -3407,34 +3407,6 @@ def _detect_inference_image_from_training(self) -> None:
                     f"Could not detect inference image for training image: {training_image}"
                 )
 
-    def _extract_speculative_draft_model_provider(
-        self,
-        speculative_decoding_config: Optional[Dict] = None,
-    ) -> Optional[str]:
-        """Extracts speculative draft model provider from speculative decoding config.
-
-        Args:
-            speculative_decoding_config (Optional[Dict]): A speculative decoding config.
-
-        Returns:
-            Optional[str]: The speculative draft model provider.
-        """
-        if speculative_decoding_config is None:
-            return None
-
-        model_provider = speculative_decoding_config.get("ModelProvider", "").lower()
-
-        if model_provider == "jumpstart":
-            return "jumpstart"
-
-        if model_provider == "custom" or speculative_decoding_config.get("ModelSource"):
-            return "custom"
-
-        if model_provider == "sagemaker":
-            return "sagemaker"
-
-        return "auto"
-
     def get_huggingface_model_metadata(
         self, model_id: str, hf_hub_token: Optional[str] = None
     ) -> dict: