140 lines
4.3 KiB
Python
140 lines
4.3 KiB
Python
"""
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Type Dispatch Tables
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Type mappings for each output format.
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Used by generators to convert Python types to target framework types.
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"""
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from typing import Any, Callable, get_args
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# =============================================================================
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# Django Type Mappings
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# =============================================================================
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DJANGO_TYPES: dict[Any, str] = {
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str: "models.CharField(max_length={max_length}{opts})",
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int: "models.IntegerField({opts})",
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float: "models.FloatField({opts})",
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bool: "models.BooleanField(default={default})",
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"UUID": "models.UUIDField({opts})",
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"datetime": "models.DateTimeField({opts})",
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"dict": "models.JSONField(default=dict, blank=True)",
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"list": "models.JSONField(default=list, blank=True)",
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"text": "models.TextField(blank=True, default='')",
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"bigint": "models.BigIntegerField({opts})",
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"enum": "models.CharField(max_length=20, choices=Status.choices{opts})",
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}
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DJANGO_SPECIAL: dict[str, str] = {
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"id": "models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)",
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"created_at": "models.DateTimeField(auto_now_add=True)",
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"updated_at": "models.DateTimeField(auto_now=True)",
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}
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# =============================================================================
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# Pydantic Type Resolvers
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# =============================================================================
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def _get_list_inner(type_hint: Any) -> str:
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"""Get inner type of List[T] for Pydantic."""
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args = get_args(type_hint)
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if args and args[0] in (str, int, float, bool):
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return {str: "str", int: "int", float: "float", bool: "bool"}[args[0]]
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return "str"
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PYDANTIC_RESOLVERS: dict[Any, Callable[[Any], str]] = {
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str: lambda _: "str",
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int: lambda _: "int",
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float: lambda _: "float",
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bool: lambda _: "bool",
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"UUID": lambda _: "UUID",
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"datetime": lambda _: "datetime",
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"dict": lambda _: "Dict[str, Any]",
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"list": lambda base: f"List[{_get_list_inner(base)}]",
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"enum": lambda base: base.__name__,
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}
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# =============================================================================
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# TypeScript Type Resolvers
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# =============================================================================
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def _resolve_ts_list(base: Any) -> str:
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"""Resolve TypeScript list type."""
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args = get_args(base)
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if args:
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inner = args[0]
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if inner is str:
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return "string[]"
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elif inner is int or inner is float:
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return "number[]"
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elif inner is bool:
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return "boolean[]"
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return "string[]"
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TS_RESOLVERS: dict[Any, Callable[[Any], str]] = {
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str: lambda _: "string",
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int: lambda _: "number",
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float: lambda _: "number",
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bool: lambda _: "boolean",
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"UUID": lambda _: "string",
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"datetime": lambda _: "string",
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"dict": lambda _: "Record<string, unknown>",
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"list": _resolve_ts_list,
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"enum": lambda base: base.__name__,
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}
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# =============================================================================
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# Protobuf Type Resolvers
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# =============================================================================
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def _resolve_proto_list(base: Any) -> str:
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"""Resolve Protobuf repeated type."""
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args = get_args(base)
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if args:
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inner = args[0]
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if inner is str:
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return "repeated string"
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elif inner is int:
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return "repeated int32"
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elif inner is float:
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return "repeated float"
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elif inner is bool:
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return "repeated bool"
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return "repeated string"
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PROTO_RESOLVERS: dict[Any, Callable[[Any], str]] = {
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str: lambda _: "string",
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int: lambda _: "int32",
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float: lambda _: "float",
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bool: lambda _: "bool",
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"list": _resolve_proto_list,
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}
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# =============================================================================
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# Prisma Type Mappings
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# =============================================================================
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PRISMA_TYPES: dict[Any, str] = {
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str: "String",
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int: "Int",
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float: "Float",
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bool: "Boolean",
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"UUID": "String @default(uuid())",
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"datetime": "DateTime",
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"dict": "Json",
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"list": "Json",
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"bigint": "BigInt",
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}
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PRISMA_SPECIAL: dict[str, str] = {
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"id": "String @id @default(uuid())",
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"created_at": "DateTime @default(now())",
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"updated_at": "DateTime @updatedAt",
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}
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