#6694 [BUG] when using `from __future__ import annotations` the pydantic type transformer doesn't properly recognize types Issue created by gverkes ### Flyte & Flytekit version flytekit 1.16.1 ### Describe the bug When using

from __future__ import annotations

in Python, Pydantic BaseModel fields fail to be properly typed in Flyte tasks. The

PydanticTransformer.get_literal_type()

method directly accesses

__annotations__

which contains string literals instead of actual type objects when future annotations are enabled. This causes Flyte to treat all fields as unsupported types and fall back to PickleFile serialization. Root cause: In

flytekit/extras/pydantic_transformer/transformer.py:28

, the code uses: fields = t.annotations.items() With from

__future__ import annotations

,

__annotations__

contains strings like

'str'

and

'int'

instead of the actual

str

and

int

type objects. This breaks

TypeEngine.to_literal_type()

on line 32, causing it to fail to recognize standard Python types. Expected behavior: Flyte should properly handle Pydantic models regardless of whether future annotations are used, since this is a standard Python feature (PEP 563) and will become the default behavior in Python 3.14. Actual behavior: Field types are misidentified as unsupported, triggering warnings like:

WARNING - Unsupported Type str found, Flyte will default to use PickleFile as the transport.
  WARNING - Unsupported Type int found, Flyte will default to use PickleFile as the transport.

Solution: Use Pydantic's built-in field APIs (

model_fields

for v2,

__fields__

for v1) which properly resolve string annotations to actual type objects. ### Expected behavior Flyte should properly recognize and handle all field types in Pydantic BaseModel classes, regardless of whether

from __future__ import annotations

is used. Standard Python types like

str

,

int

,

float

,

list

, etc. should be correctly identified and serialized using their appropriate Flyte literal types, not fall back to PickleFile. When a task with a Pydantic BaseModel parameter is defined, the type system should: 1. Correctly extract the actual Python types from the model's fields 2. Convert them to appropriate Flyte literal types 3. Generate proper type metadata for the workflow This should work consistently whether future annotations are enabled or not, since PEP 563 is a standard Python feature and will become the default in Python 3.14. ### Additional context to reproduce 1. Create a Python file with `from future import annotations`: from future import annotations from pydantic import BaseModel from flytekit import task, workflow class MyModel(BaseModel): name: str age: int @task def process_model(model: MyModel) -> str: return f"{model.name} is {model.age} years old" @workflow def my_workflow(model: MyModel) -> str: return process_model(model=model) if name == "__main__": test_model = MyModel(name="Alice", age=30) result = my_workflow(model=test_model) print(result) 1. Run the script:

python test_script.py

1. Observe the warnings in the output:

WARNING - Unsupported Type str found, Flyte will default to use PickleFile as the transport.
WARNING - Unsupported Type int found, Flyte will default to use PickleFile as the transport.

1. Without from

__future__ import annotations

, the same code works correctly without warnings. Environment: • Python: 3.8+ (future annotations available from 3.7+) • Pydantic: Both v1 and v2 affected • Flytekit: Current version (issue exists in the transformer code) ### Screenshots No response ### Are you sure this issue hasn't been raised already? • Yes ### Have you read the Code of Conduct? • Yes flyteorg/flyte