Module adcp.types.domains.registries

Types the AdCP registries schemas declare.

Importing from the domain says which variant you mean, where the flat adcp.types namespace can only bind one class per name:

from adcp.types.domains.registries import <Type>

A type this domain declares in more than one schema is not here: import it from its own schema's module, adcp.types.domains.registries.<schema>. Nothing here is renamed.

Auto-generated from the generated domain tree. DO NOT EDIT MANUALLY. Generation date: 2026-10-04 18:45:11 UTC

Sub-modules

adcp.types.domains.registries.v1_canonical_mapping

Classes

class Dimensions (**data: Any)

Expand source code

class Dimensions(AdCPBaseModel):
    width: SchemaInt | None = None
    height: SchemaInt | None = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var height : int | None

var model_config

var width : int | None

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class Mapping (**data: Any)

Expand source code

class Mapping(AdCPBaseModel):
    v1_pattern: Annotated[
        V1Pattern | V1Pattern1,
        Field(description='Match pattern. Carries either format_id_glob OR structural, not both.'),
    ]
    v2: V2
    deprecated: Annotated[
        StrictBool | None,
        Field(
            description='When true, this mapping is retained for backward-compatibility but should not be used for new mappings. SDKs SHOULD emit lint warnings when matching a deprecated entry.'
        ),
    ] = False
    notes: Annotated[
        str | None,
        Field(description='Optional human-readable explanation, examples, or rationale.'),
    ] = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var deprecated : bool | None

var model_config

var notes : str | None

var v1_pattern : V1Pattern | V1Pattern1

var v2 : V2

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class ParameterMapping (**data: Any)

Expand source code

class ParameterMapping(AdCPBaseModel):
    model_config = ConfigDict(
        extra='forbid',
    )
    source_field: Annotated[
        str, Field(description='Field read from the matched v1 format_id object.', min_length=1)
    ]
    target_parameter: Annotated[
        str, Field(description='Field written into the projected v2 params object.', min_length=1)
    ]
    transform: Annotated[
        Transform | None,
        Field(
            description='Value transform applied during forwarding. identity copies the value; singleton_array wraps it in a one-element array.'
        ),
    ] = Transform.identity

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var model_config

var source_field : str

var target_parameter : str

var transform : Transform | None

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class Structural (**data: Any)

Expand source code

class Structural(AdCPBaseModel):
    model_config = ConfigDict(
        extra='allow',
    )
    asset_types: Annotated[
        list[str] | None,
        Field(
            description="Set of asset_type values that must appear in the format's slots (in any order, any count)."
        ),
    ] = None
    vast_versions: Annotated[
        list[str] | None,
        Field(description="VAST version constraints. Strings like '>=4.0', '4.x', '4.2'."),
    ] = None
    daast_versions: list[str] | None = None
    dimensions: Dimensions | None = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var asset_types : list[str] | None

var daast_versions : list[str] | None

var dimensions : Dimensions | None

var model_config

var vast_versions : list[str] | None

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class Transform (*args, **kwds)

Expand source code

class Transform(StrEnum):
    identity = 'identity'
    singleton_array = 'singleton_array'

Enum where members are also (and must be) strings

Ancestors

• enum.StrEnum
• builtins.str
• enum.ReprEnum
• enum.Enum

Class variables

var identity

var singleton_array

class V1Pattern (**data: Any)

Expand source code

class V1Pattern(AdCPBaseModel):
    format_id_glob: Annotated[
        str,
        Field(
            description="Glob pattern matched against v1 format_id.id. Examples: 'iab_mrec_300x250', 'iab_leaderboard_*', 'meta_*_reels'."
        ),
    ]

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var format_id_glob : str

var model_config

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class V1Pattern1 (**data: Any)

Expand source code

class V1Pattern1(AdCPBaseModel):
    structural: Annotated[
        Structural,
        Field(
            description="Structural match against the format's slot shape, asset types, and version constraints."
        ),
    ]

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var model_config

var structural : Structural

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class V1V2CanonicalFormatMappingRegistry (**data: Any)

Expand source code

class V1V2CanonicalFormatMappingRegistry(AdCPBaseModel):
    version: Annotated[
        str, Field(description='Semver of this registry. Bumped on every published change.')
    ]
    last_updated: Annotated[
        date | None, Field(description='ISO date of the last published change.')
    ] = None
    mappings: Annotated[
        list[Mapping],
        Field(
            description='Ordered list of v1 → v2 mappings. SDKs apply mappings in order and use the first match.'
        ),
    ]

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var last_updated : datetime.date | None

var mappings : list[Mapping]

var model_config

var version : str

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary

class V2 (**data: Any)

Expand source code

class V2(AdCPBaseModel):
    canonical: Annotated[str, Field(description='v2 canonical format the v1 pattern projects to.')]
    parameters: Annotated[
        dict[str, Any] | None,
        Field(
            description='Optional parameters that narrow the canonical (e.g., width/height, vast_version). When present, become the params on the projected v2 ProductFormatDeclaration. The shape MUST be valid params for the named canonical.'
        ),
    ] = None
    parameter_mappings: Annotated[
        list[ParameterMapping] | None,
        Field(
            description='Machine-readable forwarding rules for parameters carried by a structured v1 format_id. SDKs copy each present source_field to the target_parameter after applying transform; absent source fields leave the target parameter omitted. Static v2.parameters are applied before these forwarded values.',
            min_length=1,
        ),
    ] = None

Base model for AdCP types with spec-compliant serialization.

Defaults to extra='ignore' so unknown fields from newer spec versions are silently dropped rather than causing validation errors. Generated types whose schemas set additionalProperties: true override this with extra='allow' in their own model_config.

Set ADCP_STRICT_VALIDATION=1 in the environment ("1", "true", "yes", "on" are accepted) to flip the default to extra='forbid'. Use this during spec upgrades to catch silently-dropped renamed fields in tests. See :func:_resolve_extra_policy.

Important

The env var is resolved once at module import time. Set it in your shell or CI environment before import adcp runs — mutating os.environ["ADCP_STRICT_VALIDATION"] after the first adcp import has no effect on already-imported model classes (they captured the policy at class-body evaluation).

Consumers who want per-model strict validation can override model_config on their subclass.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Ancestors

• AdCPBaseModel
• pydantic.main.BaseModel

Class variables

var canonical : str

var model_config

var parameter_mappings : list[ParameterMapping] | None

var parameters : dict[str, typing.Any] | None

Inherited members

• AdCPBaseModel:
  • model_dump
  • model_dump_json
  • model_summary