two version of R2R are hereHEADmaster

1 files changed, 82 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_data_utils.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_data_utils.py
new file mode 100644
index 00000000..bde91d34
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+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_utils/_data_utils.py
@@ -0,0 +1,82 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import io
+import json
+import logging
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Dict, Union
+from urllib.parse import urljoin, urlparse
+
+import yaml
+from jsonschema import Draft7Validator, ValidationError
+from jsonschema.exceptions import best_match
+
+from azure.ai.ml._artifacts._artifact_utilities import get_datastore_info, get_storage_client
+from azure.ai.ml._artifacts._constants import INVALID_MLTABLE_METADATA_SCHEMA_ERROR, INVALID_MLTABLE_METADATA_SCHEMA_MSG
+from azure.ai.ml.constants._common import DefaultOpenEncoding
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+from azure.ai.ml.operations._datastore_operations import DatastoreOperations
+
+from ._http_utils import HttpPipeline
+from ._storage_utils import AzureMLDatastorePathUri
+from .utils import load_yaml
+
+module_logger = logging.getLogger(__name__)
+
+
+def download_mltable_metadata_schema(mltable_schema_url: str, requests_pipeline: HttpPipeline):
+    response = requests_pipeline.get(mltable_schema_url)
+    return response.json()
+
+
+def read_local_mltable_metadata_contents(*, path: str) -> Dict:
+    metadata_path = str(Path(path, "MLTable"))
+    return load_yaml(metadata_path)
+
+
+def read_remote_mltable_metadata_contents(
+    *,
+    base_uri: str,
+    datastore_operations: DatastoreOperations,
+    requests_pipeline: HttpPipeline,
+) -> Union[Dict, None]:
+    scheme = urlparse(base_uri).scheme
+    if scheme == "https":
+        response = requests_pipeline.get(urljoin(base_uri, "MLTable"))
+        yaml_file = io.BytesIO(response.content)
+        return yaml.safe_load(yaml_file)
+    if scheme == "azureml":
+        datastore_path_uri = AzureMLDatastorePathUri(base_uri)
+        datastore_info = get_datastore_info(datastore_operations, datastore_path_uri.datastore)
+        storage_client = get_storage_client(**datastore_info)
+        with TemporaryDirectory() as tmp_dir:
+            starts_with = datastore_path_uri.path.rstrip("/")
+            storage_client.download(f"{starts_with}/MLTable", tmp_dir)
+            downloaded_mltable_path = Path(tmp_dir, "MLTable")
+            with open(downloaded_mltable_path, "r", encoding=DefaultOpenEncoding.READ) as f:
+                return yaml.safe_load(f)
+    return None
+
+
+def validate_mltable_metadata(*, mltable_metadata_dict: Dict, mltable_schema: Dict):
+    # use json-schema to validate dict
+    error: Union[ValidationError, None] = best_match(Draft7Validator(mltable_schema).iter_errors(mltable_metadata_dict))
+    if error:
+        err_path = ".".join(error.path)
+        err_path = f"{err_path}: " if err_path != "" else ""
+        msg = INVALID_MLTABLE_METADATA_SCHEMA_ERROR.format(
+            jsonSchemaErrorPath=err_path,
+            jsonSchemaMessage=error.message,
+            invalidMLTableMsg=INVALID_MLTABLE_METADATA_SCHEMA_MSG,
+            invalidSchemaSnippet=json.dumps(error.schema, indent=2),
+        )
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=INVALID_MLTABLE_METADATA_SCHEMA_MSG,
+            error_type=ValidationErrorType.INVALID_VALUE,
+            target=ErrorTarget.DATA,
+            error_category=ErrorCategory.USER_ERROR,
+        )