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_archive/openrpc/tools/tools_py.py

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+from inspect import isclass
+from typing import Any, Dict, List
+
+from pydantic import BaseModel
+
+
+def get_pydantic_type(schema: Dict[str, Any]) -> str:
+    """
+    Convert OpenRPC schema types to Pydantic types.
+
+    Args:
+        schema: OpenRPC schema dictionary
+
+    Returns:
+        String representation of the Pydantic type
+    """
+    if "type" in schema:
+        if schema["type"] == "string":
+            if "format" in schema and schema["format"] == "email":
+                return "Email"
+            return "str"
+        elif schema["type"] == "integer":
+            return "int"
+        elif schema["type"] == "array":
+            items_type = get_pydantic_type(schema["items"])
+            return f"List[{items_type}]"
+        elif schema["type"] == "object":
+            return "dict"
+        elif schema["type"] == "boolean":
+            return "bool"
+        elif schema["type"] == "null":
+            return "None"
+    elif "$ref" in schema:
+        ref_name = schema["$ref"].split("/")[-1]
+        return ref_name
+    elif "anyOf" in schema:
+        types = [get_pydantic_type(sub_schema) for sub_schema in schema["anyOf"]]
+        if "None" in types:
+            # Remove 'None' from the types list
+            types = [t for t in types if t != "None"]
+            if len(types) == 1:
+                return f"Optional[{types[0]}]"
+            else:
+                return f"Optional[Union[{', '.join(types)}]]"
+        else:
+            return f"Union[{', '.join(types)}]"
+
+    return "Any"
+
+
+def get_return_type(method_result: Dict[str, Any]) -> str:
+    """
+    Get the return type from a method result schema.
+
+    Args:
+        method_result: Method result dictionary containing schema or $ref
+
+    Returns:
+        String representation of the return type
+    """
+    if "schema" in method_result:
+        schema = method_result["schema"]
+        if "type" in schema:
+            return get_pydantic_type(schema)
+        elif "$ref" in schema:
+            ref_name = schema["$ref"].split("/")[-1]
+            return ref_name
+        elif "anyOf" in schema:
+            schema_list = schema["anyOf"]
+            if isinstance(schema_list, list):
+                return " | ".join(get_pydantic_type(sub_schema) for sub_schema in schema_list)
+            return "Any"
+    elif "$ref" in method_result:  # Handle $ref at the top level
+        ref_path = method_result["$ref"]
+        if isinstance(ref_path, str):
+            return ref_path.split("/")[-1]
+    return ""
+
+
+def topological_sort(schema_dict: Dict[str, Any]) -> List[str]:
+    visited = set()
+    stack = []
+    sorted_classes = []
+
+    def dfs(class_name: str) -> None:
+        visited.add(class_name)
+        if class_name in schema_dict:
+            for prop in schema_dict[class_name].get("properties", {}).values():
+                if "$ref" in prop:
+                    ref_name = prop["$ref"].split("/")[-1]
+                    if ref_name not in visited:
+                        dfs(ref_name)
+        stack.append(class_name)
+
+    for class_name in schema_dict:
+        if class_name not in visited:
+            dfs(class_name)
+
+    while stack:
+        sorted_classes.append(stack.pop())
+
+    return sorted_classes
+
+
+def create_example_object(cls: type[BaseModel]) -> BaseModel:
+    """
+    Create an example object from a Pydantic model class using field examples.
+
+    Args:
+        cls: A Pydantic BaseModel class
+
+    Returns:
+        An instance of the provided model class with example data
+
+    Raises:
+        ValueError: If cls is not a valid Pydantic BaseModel class
+    """
+    if not isclass(cls) or not issubclass(cls, BaseModel):
+        raise ValueError(f"{cls} is not a valid pydantic BaseModel class.")
+
+    example_data = {}
+
+    for field_name, field_info in cls.model_fields.items():
+        examples = field_info.examples
+        if examples:
+            example_data[field_name] = examples[0]
+
+    return cls(**example_data)