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inference

pyvider.hcl.parser.inference

Automatic CTY type inference from Python data structures.

This module provides HCL-specific wrappers around pyvider-cty's canonical type inference implementation, which correctly handles lists, objects, and all CTY types with sophisticated element type analysis and caching.

Functions

auto_infer_cty_type 

auto_infer_cty_type(raw_data: Any) -> CtyValue[Any]

Automatically infer CTY type from raw Python data.

This function takes Python data structures (typically from HCL parsing) and automatically infers appropriate CTY types using pyvider-cty's canonical inference implementation.

Parameters:

Name Type Description Default
raw_data Any

Python data structure to infer types for

 required

Returns:

Type Description
CtyValue[Any]

CTY value with inferred types
Example

data = {"name": "test", "count": 5} result = auto_infer_cty_type(data) isinstance(result.type, CtyObject) True

Note

This delegates to pyvider.cty.conversion.infer_cty_type_from_raw() which provides sophisticated type inference including: - List element type analysis (e.g., [1,2,3] → list(number)) - Object attribute inference - Type unification for mixed collections - Caching and cycle detection

Source code in pyvider/hcl/parser/inference.py 
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def auto_infer_cty_type(raw_data: Any) -> CtyValue[Any]:
    """Automatically infer CTY type from raw Python data.

    This function takes Python data structures (typically from HCL parsing)
    and automatically infers appropriate CTY types using pyvider-cty's
    canonical inference implementation.

    Args:
        raw_data: Python data structure to infer types for

    Returns:
        CTY value with inferred types

    Example:
        >>> data = {"name": "test", "count": 5}
        >>> result = auto_infer_cty_type(data)
        >>> isinstance(result.type, CtyObject)
        True

    Note:
        This delegates to pyvider.cty.conversion.infer_cty_type_from_raw()
        which provides sophisticated type inference including:
        - List element type analysis (e.g., [1,2,3] → list(number))
        - Object attribute inference
        - Type unification for mixed collections
        - Caching and cycle detection
    """
    # Use pyvider-cty's canonical inference implementation
    inferred_type = infer_cty_type_from_raw(raw_data)
    return inferred_type.validate(raw_data)  # type: ignore[no-any-return]