docs: configurator (plus removing dead code) (#226)

---------

Signed-off-by: Nina Xu <19981858+nina-xu@users.noreply.github.com>

Author: nina-xu

src/nemo_safe_synthesizer/config/autoconfig.py

@@ -11,15 +11,11 @@
 
 from __future__ import annotations
 
-import inspect
 import math
 import re
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Callable
+from typing import TYPE_CHECKING, Any
 
 import pandas as pd
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import core_schema
 
 from ..defaults import DEFAULT_MAX_SEQ_LENGTH, MAX_ROPE_SCALING_FACTOR
 from ..llm.metadata import ModelMetadata
@@ -353,48 +349,3 @@ def resolve(self) -> SafeSynthesizerParameters:
         privacy_params.update(self._determine_delta())
 
         return self._build_updated_params(training_params, data_params, privacy_params)
-
-
-@dataclass(frozen=True)
-class AutoParamsValidator:
-    """Pydantic validator that passes ``"auto"`` strings through unchanged.
-
-    For non-``"auto"`` values, delegates to ``value_func`` for validation.
-    Used as a field-level validator on parameters that accept either a concrete
-    value or the ``"auto"`` sentinel.
-    """
-
-    value_func: Callable[[Any], bool]
-    """Predicate that returns ``True`` if the value is acceptable."""
-
-    def validate(self, value, _info):
-        """Pass ``"auto"`` through unchanged; otherwise delegate to ``value_func``.
-
-        Args:
-            value: The raw field value to validate.
-            _info: Pydantic validation context (unused).
-
-        Returns:
-            The validated value, or ``"auto"`` for deferred resolution by
-            :class:`AutoConfigResolver`.
-
-        Raises:
-            ValueError: If ``value`` is not ``"auto"`` and ``value_func``
-                rejects it.
-        """
-        if isinstance(value, str) and value == "auto":
-            return value
-        elif self.value_func(value):
-            return value
-        else:
-            raise ValueError(f"AutoParam validation failed: {inspect.getsource(self.value_func)}, got {value}")
-
-    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
-        """Register :meth:`validate` as a Pydantic after-validator.
-
-        Wraps the base schema from ``handler`` so that :meth:`validate`
-        runs after normal type coercion.
-        """
-        return core_schema.with_info_after_validator_function(
-            self.validate, handler(source_type), field_name=handler.field_name
-        )

src/nemo_safe_synthesizer/configurator/__init__.py

@@ -1,63 +1,2 @@
 # SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
-
-"""
-Configurator: A mini-library for Pydantic-based configuration systems with strong typing support.
-
-This package provides a comprehensive set of tools for building type-safe, validated
-configuration systems using Pydantic v2. It's designed for developers who need
-sophisticated parameter management with excellent static analysis support.
-
-Key Components:
-
-Parameter Wrappers (parameter.py):
-    - Parameter: Generic wrapper for configuration values with Pydantic integration
-    - AutoParam: Parameter with automatic type-safe update capabilities
-    - UnsetParam: For handling unset/default parameter states
-    - ValidNoneParam: For parameters where None is a valid value
-
-Validation & Collections (parameters.py):
-    - Parameters: Abstract base class for organizing parameter collections
-    - DependsOnValidator: Conditional field validation based on other fields
-    - ValueValidator: Custom validation functions for parameter values
-    - YAML serialization support for configuration persistence
-
-Usage Examples:
-
-Basic Parameter Usage:
-    >>> from nemo_safe_synthesizer.configurator.parameter import Parameter, AutoParam
-    >>>
-    >>> # Simple parameter
-    >>> max_size = Parameter(name="max_size", value=1000)
-    >>>
-    >>> # Auto-updating parameter
-    >>> batch_size = AutoParam(name="batch_size", value=32)
-    >>> batch_size.autoupdate(64)  # Safe type-checked update
-
-Parameter Collections:
-    >>> from nemo_safe_synthesizer.configurator.parameters import Parameters
-    >>> from nemo_safe_synthesizer.configurator.parameter import AutoParam
-    >>> from typing import Annotated
-    >>> from pydantic import Field
-    >>> class DatabaseConfig(Parameters):
-    ...     host: Annotated[AutoParam[str], Field(default=AutoParam(name="host", value="localhost")]
-    ...     port: Annotated[AutoParam[int], Field(default=AutoParam(name="port", value=5432)]
-    >>>
-    >>> config = DatabaseConfig()
-    >>> config.host
-    AutoParam(name='host', value='localhost')
-
-Conditional Validation:
-    >>> from nemo_safe_synthesizer.configurator.parameters import Parameters
-    >>> from nemo_safe_synthesizer.configurator.validators import DependsOnValidator
-    >>> from nemo_safe_synthesizer.configurator.parameter import AutoParam
-    >>> from typing import Annotated
-    >>>
-    >>> class AdvancedConfig(Parameters):
-    ...     debug_mode: bool = False
-    ...     verbose_logging: Annotated[
-    ...         Parameter[bool],
-    ...         DependsOnValidator(depends_on="debug_mode", depends_on_func=lambda x: x is True),
-    ...         Field(default=Parameter(name="verbose_logging", value=False)),
-    ...     ]
-"""