import json
from typing import Dict, Optional

from typing_extensions import Literal

from aws_lambda_powertools import Logger, Tracer
from aws_lambda_powertools.utilities.batch import (
    BatchProcessor,
    EventType,
    process_partial_response,
)
from aws_lambda_powertools.utilities.parser import BaseModel, field_validator
from aws_lambda_powertools.utilities.parser.models import (
    DynamoDBStreamChangedRecordModel,
    DynamoDBStreamRecordModel,
)
from aws_lambda_powertools.utilities.typing import LambdaContext


class Order(BaseModel):
    item: dict


class OrderDynamoDB(BaseModel):
    Message: Order

    # auto transform json string
    # so Pydantic can auto-initialize nested Order model
    @field_validator("Message", mode="before")
    def transform_message_to_dict(cls, value: Dict[Literal["S"], str]):
        return json.loads(value["S"])


class OrderDynamoDBChangeRecord(DynamoDBStreamChangedRecordModel):  # type: ignore[override]
    NewImage: Optional[OrderDynamoDB]
    OldImage: Optional[OrderDynamoDB]


class OrderDynamoDBRecord(DynamoDBStreamRecordModel):  # type: ignore[override]
    dynamodb: OrderDynamoDBChangeRecord


processor = BatchProcessor(event_type=EventType.DynamoDBStreams, model=OrderDynamoDBRecord)
tracer = Tracer()
logger = Logger()


@tracer.capture_method
def record_handler(record: OrderDynamoDBRecord):
    if record.dynamodb.NewImage and record.dynamodb.NewImage.Message:
        logger.info(record.dynamodb.NewImage.Message.item)
        return record.dynamodb.NewImage.Message.item


@logger.inject_lambda_context
@tracer.capture_lambda_handler
def lambda_handler(event, context: LambdaContext):
    return process_partial_response(event=event, record_handler=record_handler, processor=processor, context=context)