cursus.steps.configs.config_currency_conversion_step

Currency Conversion Configuration with Self-Contained Derivation Logic

This module implements the configuration class for SageMaker Processing steps for currency conversion, using a self-contained design where each field is properly categorized according to the three-tier design: 1. Essential User Inputs (Tier 1) - Required fields that must be provided by users 2. System Fields (Tier 2) - Fields with reasonable defaults that can be overridden 3. Derived Fields (Tier 3) - Fields calculated from other fields, private with read-only properties

class CurrencyConversionMappingConfig(*, marketplace_id, currency_code, conversion_rate)

Bases: BaseModel

Single currency conversion mapping entry.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private attributes with properties)

marketplace_id: str

currency_code: str

conversion_rate: float

classmethod validate_rate(v)

Ensure conversion rate is positive.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class CurrencyConversionDictConfig(*, mappings)

Bases: BaseModel

Currency conversion dictionary with mappings.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private attributes with properties)

mappings: List[CurrencyConversionMappingConfig]

classmethod validate_mappings(v)

Validate mappings list.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class CurrencyConversionConfig(*, author, bucket, role, region, service_name, pipeline_version, model_class='xgboost', current_date=<factory>, framework_version='2.1.0', py_version='py310', source_dir=None, enable_caching=False, use_secure_pypi=False, max_runtime_seconds=172800, project_root_folder, processing_instance_count=1, processing_volume_size=500, processing_instance_type_large='ml.m5.4xlarge', processing_instance_type_small='ml.m5.2xlarge', use_large_processing_instance=False, skip_volume_kms=None, processing_source_dir=None, processing_entry_point='currency_conversion.py', processing_script_arguments=None, processing_framework_version='1.2-1', currency_conversion_vars, currency_conversion_dict, job_type='training', currency_code_field=None, marketplace_id_field=None, default_currency='USD', n_workers=50, **extra_data)

Bases: ProcessingStepConfigBase

Configuration for the Currency Conversion step with three-tier field categorization. Inherits from ProcessingStepConfigBase.

Fields are categorized into: - Tier 1: Essential User Inputs - Required from users - Tier 2: System Fields - Default values that can be overridden - Tier 3: Derived Fields - Private with read-only property access

This configuration follows the specification-driven approach where inputs and outputs are defined by step specifications and script contracts, not by hardcoded dictionaries.

currency_conversion_vars: List[str]

currency_conversion_dict: CurrencyConversionDictConfig

job_type: str

processing_entry_point: str

currency_code_field: str | None

marketplace_id_field: str | None

default_currency: str

n_workers: int

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property environment_variables: Dict[str, str]

Generate environment variables for the currency conversion script.

Returns:

Dictionary of environment variables

get_environment_variables(declared_env_vars=None)

Generate the currency-conversion env vars (the single env source; FZ 31e1d3g).

declared_env_vars is accepted for the builder’s names-driven contract but ignored — these values are all computed (JSON-encoded conversion vars/dict), so this config returns its full computed env dict regardless of the declared name set.

classmethod validate_vars(v)

Validate currency conversion variables list.

classmethod validate_job_type(v)

Validate job_type is lowercase alphanumeric (with underscores) — matches the peers’ validator (RiskTableMapping/Tabular) and the script’s choices.

classmethod validate_entry_point_relative(v)

Ensure processing_entry_point is a non‐empty relative path.

validate_currency_fields()

Ensure at least one of currency_code_field or marketplace_id_field is not None.

initialize_derived_fields()

Initialize all derived fields once after validation.

get_public_init_fields()

Override get_public_init_fields to include currency conversion specific fields.

Returns:

Dictionary of field names to values for child initialization

Return type:

Dict[str, Any]

model_dump(**kwargs)

Override model_dump to include derived properties.

get_job_arguments()

CLI args — config is the single source (FZ 31e1d3h).

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• context (Any) – The context.

processing_instance_count: int

processing_volume_size: int

processing_instance_type_large: str

processing_instance_type_small: str

use_large_processing_instance: bool

skip_volume_kms: bool | None

processing_source_dir: str | None

processing_script_arguments: List[str] | None

processing_framework_version: str

author: str

bucket: str

role: str

region: str

service_name: str

pipeline_version: str

model_class: str

current_date: str

framework_version: str

py_version: str

source_dir: str | None

enable_caching: bool

use_secure_pypi: bool

max_runtime_seconds: int

project_root_folder: str