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carla_env / src /core /env_server /interfaces.py

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# SPDX-License-Identifier: BSD-3-Clause

import inspect
from abc import ABC, abstractmethod
from typing import (
    Any,
    Dict,
    Generic,
    List,
    Optional,
    Protocol,
    Tuple,
    TYPE_CHECKING,
    TypeVar,
)

from typing_extensions import TypedDict

from .types import Action, EnvironmentMetadata, Observation, State

if TYPE_CHECKING:
    from openenv.core.rubrics import Rubric

ActT = TypeVar("ActT", bound=Action)
ObsT = TypeVar("ObsT", bound=Observation)
StateT = TypeVar("StateT", bound=State)


class Message(TypedDict):
    """A message in a conversation.
    Compatible with Huggingface chat template format.
    """

    role: str
    content: str


class ModelTokenizer(Protocol):
    """Protocol for tokenizers that support chat templates.
    This protocol defines the interface that tokenizers must implement
    to work with chat-based environments. It's compatible with
    Huggingface transformers tokenizers.
    """

    def apply_chat_template(
        self,
        conversation: list[Message],
        tokenize: bool = True,
        return_tensors: str | None = None,
        **kwargs: Any,
    ) -> Any:
        """Apply a chat template to format and optionally tokenize a conversation.
        Args:
            conversation (`list[Message]`):
                List of message dictionaries with 'role' and 'content'.
            tokenize (`bool`, *optional*, defaults to `True`):
                Whether to tokenize the output.
            return_tensors (`str`, *optional*):
                Format for returned tensors ('pt' for PyTorch).
            **kwargs:
                Additional arguments.
        Returns:
            Formatted and optionally tokenized conversation.
        """
        ...

    def decode(
        self, token_ids: Any, skip_special_tokens: bool = False, **kwargs: Any
    ) -> str:
        """Decode token IDs back to text.
        Args:
            token_ids (`Any`):
                Token IDs to decode.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether to skip special tokens in output.
            **kwargs:
                Additional arguments.
        Returns:
            `str`: Decoded text string.
        """
        ...


class TaskProvider(Protocol):
    """
    Optional task discovery API for dataset-backed environments.
    An environment implements this protocol structurally — declare the methods on
    an [`~openenv.core.env_server.interfaces.Environment`] subclass, without
    inheriting from `TaskProvider`. When the methods are present,
    [`~openenv.core.env_server.http_server.HTTPEnvServer`] exposes them as HTTP
    routes under `/{env_name}/…`; when they are absent, those routes return
    `501 Not Implemented`. Each method may be sync or async.
    Task provider methods are for metadata/discovery only and should be
    side-effect-free. They must be callable on a freshly constructed
    environment instance because HTTP compatibility routes may create a
    short-lived instance solely for task discovery.
    Selecting a task is not part of this protocol — pass the chosen split and
    index to `reset()` instead. See the
    [Task API guide](https://huggingface.co/docs/openenv/guides/task-api).
    Examples:
    ```python
    env.list_splits()          # ["train", "test"]
    env.num_tasks("test")      # 7595
    env.get_task("test", 12)   # {"id": "test-12", "index": 12}
    env.reset(split="test", index=12)
    ```
    """

    def list_splits(self) -> list[Any]:
        """
        Return task split descriptors supported by this environment.
        Returns:
            `list[Any]`: Split descriptors. Plain strings, dicts, and Pydantic
                models are all accepted; the server normalizes each entry to
                `{"name": ..., "type": ...}`.
        """
        ...

    def list_tasks(self, split: str) -> list[Any]:
        """
        Return all task specs for a split.
        Args:
            split (`str`):
                Task split name.
        Returns:
            `list[Any]`: Task specs for the split. Environments backed by very
                large or streamed splits may return a bounded preview, but
                `num_tasks` should still report the true total.
        """
        ...

    def num_tasks(self, split: str) -> int:
        """
        Return the number of task specs in a split.
        Args:
            split (`str`):
                Task split name.
        Returns:
            `int`: Number of task specs available in the split.
        """
        ...

    def get_task(self, split: str, index: int) -> Any:
        """
        Return one task spec by split and index.
        Args:
            split (`str`):
                Task split name.
            index (`int`):
                Task index within the split.
        Returns:
            `Any`: The task spec at that position.
        Raises:
            `IndexError`: If `index` is out of range for the split. The HTTP
                route converts this to a `400 Bad Request`.
        """
        ...

    def get_task_range(
        self,
        split: str,
        start: Optional[int] = None,
        stop: Optional[int] = None,
    ) -> list[Any]:
        """
        Return task specs for Python slice-style range bounds.
        Args:
            split (`str`):
                Task split name.
            start (`int`, *optional*):
                Inclusive start index. Defaults to the beginning of the split.
            stop (`int`, *optional*):
                Exclusive stop index. Defaults to the end of the split.
        Returns:
            `list[Any]`: Task specs in `[start, stop)`.
        """
        ...


class Transform(ABC, Generic[ObsT]):
    """Transform observations to add rewards, metrics, or other modifications.
    Transforms follow the TorchRL pattern where they take an observation
    and return a (potentially modified) observation. This allows for
    flexible reward computation and observation augmentation.
    """

    @abstractmethod
    def __call__(self, observation: ObsT) -> ObsT:
        """Transform an observation.
        Args:
            observation (`ObsT`):
                The input observation.
        Returns:
            `ObsT`: The transformed observation.
        """
        pass


class Environment(ABC, Generic[ActT, ObsT, StateT]):
    """Base class for all environment servers following Gym/Gymnasium API.
    See [rfcs/004-rubrics.md](https://github.com/huggingface/OpenEnv/blob/main/rfcs/004-rubrics.md) for rubric design details.
    Args:
        transform (`Transform`, *optional*):
            Optional transform to apply to observations.
        rubric (`Rubric`, *optional*):
            Optional rubric for reward computation. When provided, the
            rubric's output can be used to set the observation's reward in step().
    Attributes:
        SUPPORTS_CONCURRENT_SESSIONS (`bool`):
            Whether this environment supports concurrent sessions. When ``True``,
            multiple WebSocket connections can each have their own environment
            instance (up to ``max_concurrent_envs``). When ``False`` (default),
            the environment should only be used with a single session at a time.
            Set this to ``True`` in your subclass if the environment uses proper
            session isolation (unique working dirs, no shared mutable state, and
            external resources that can handle concurrent access).
        rubric (`Rubric`, *optional*):
            Optional rubric for computing rewards. Set in ``__init__`` and use in
            ``step()`` to compute observation rewards. Training infrastructure can
            access it for introspection:
            ```python
            for name, r in env.rubric.named_rubrics():
                print(f"{name}: {r.last_score}")
            ```
    """

    # Class-level flag indicating whether this environment supports concurrent sessions
    SUPPORTS_CONCURRENT_SESSIONS: bool = False

    REQUIRES_SINGLE_THREAD_EXECUTOR: bool = False

    # Optional rubric for reward computation
    rubric: Optional["Rubric"]

    def __init__(
        self,
        transform: Optional[Transform[ObsT]] = None,
        rubric: Optional["Rubric"] = None,
    ):
        self.transform = transform
        self.rubric = rubric

    @abstractmethod
    def reset(
        self,
        seed: Optional[int] = None,
        episode_id: Optional[str] = None,
        **kwargs: Any,
    ) -> ObsT:
        """Reset the environment and return initial observation."""
        pass

    async def reset_async(
        self,
        seed: Optional[int] = None,
        episode_id: Optional[str] = None,
        **kwargs: Any,
    ) -> ObsT:
        """Async version of reset. Default implementation calls sync reset.
        Override to provide true async implementation.
        """
        return self.reset(seed=seed, episode_id=episode_id, **kwargs)

    @abstractmethod
    def step(
        self,
        action: ActT,
        timeout_s: Optional[float] = None,
        **kwargs: Any,
    ) -> ObsT:
        """Take a step in the environment."""
        pass

    async def step_async(
        self,
        action: ActT,
        timeout_s: Optional[float] = None,
        **kwargs: Any,
    ) -> ObsT:
        """Async version of step. Default implementation calls sync step.
        Override to provide true async implementation.
        """
        return self.step(action, timeout_s=timeout_s, **kwargs)

    @property
    @abstractmethod
    def state(self) -> StateT:
        """Get the current environment state."""
        pass

    def get_metadata(self) -> EnvironmentMetadata:
        """
        Get metadata about this environment.
        Override this method to provide custom metadata for the environment.
        Default implementation returns basic metadata derived from class name.
        Returns:
            [`EnvironmentMetadata`] with environment information.
        """
        return EnvironmentMetadata(
            name=self.__class__.__name__,
            description=f"{self.__class__.__name__} environment",
            version="1.0.0",
        )

    def _apply_transform(self, observation: ObsT) -> ObsT:
        """Apply transform if one is provided."""
        if self.transform is not None:
            return self.transform(observation)
        return observation

    def _apply_rubric(self, action: ActT, observation: ObsT) -> float:
        """Apply rubric if one is provided.
        Args:
            action (`ActT`):
                The action taken by the agent.
            observation (`ObsT`):
                The resulting observation.
        Returns:
            `float`: Reward value from the rubric, or 0.0 if no rubric is set.
        Call this in `step()` to compute and assign the reward:
        ```python
        def step(self, action: MyAction, ...) -> MyObservation:
            # ... execute action and create observation ...
            observation.reward = self._apply_rubric(action, observation)
            return observation
        ```
        """
        if self.rubric is not None:
            return self.rubric(action, observation)
        return 0.0

    async def _apply_rubric_async(self, action: ActT, observation: ObsT) -> float:
        """Apply rubric asynchronously if one is provided.
        Args:
            action (`ActT`):
                The action taken by the agent.
            observation (`ObsT`):
                The resulting observation.
        Returns:
            `float`: Reward value from the rubric, or 0.0 if no rubric is set.
        Call this in `step_async()` to compute and assign the reward:
        ```python
        async def step_async(self, action: MyAction, ...) -> MyObservation:
            # ... execute action and create observation ...
            observation.reward = await self._apply_rubric_async(action, observation)
            return observation
        ```
        """
        if self.rubric is not None:
            result = self.rubric(action, observation)
            # If rubric returns a coroutine, await it
            if inspect.iscoroutine(result):
                return await result
            return result
        return 0.0

    def _reset_rubric(self) -> None:
        """Reset the rubric state if one is provided.
        Call this in `reset()` to clear any trajectory state in the rubric:
        ```python
        def reset(self, ...) -> MyObservation:
            self._reset_rubric()
            # ... create initial observation ...
            return observation
        ```
        """
        if self.rubric is not None:
            self.rubric.reset()

    async def _reset_rubric_async(self) -> None:
        """Reset the rubric state asynchronously if one is provided.
        Call this in `reset_async()` to clear any trajectory state in the rubric:
        ```python
        async def reset_async(self, ...) -> MyObservation:
            await self._reset_rubric_async()
            # ... create initial observation ...
            return observation
        ```
        """
        if self.rubric is not None:
            # Check if rubric has async reset method
            if hasattr(self.rubric, "reset_async"):
                result = self.rubric.reset_async()
                if inspect.iscoroutine(result):
                    await result
            else:
                self.rubric.reset()

    def render_web(self, observation: Dict[str, Any]) -> Optional[str]:
        """
        HTML that draws an observation in the web interface at `/web`.
        Override it to show the environment's state (a board, a page, a plot).
        The default draws nothing and the interface lists the observation's fields.
        Args:
            observation (`dict`):
                The serialized observation, as returned by `reset()` or `step()`,
                plus top-level `reward` and `done`.
        """
        return None

    def web_actions(
        self, observation: Dict[str, Any]
    ) -> List[Tuple[str, Dict[str, Any]]]:
        """
        One-click actions for the web interface at `/web`, as `(label, action)` pairs.
        Override it when the valid actions are known from the observation (the legal
        moves of a game). Each action is the dict `step()` receives. The default
        offers none and the interface shows the action's form only.
        Args:
            observation (`dict`):
                The serialized observation, as returned by `reset()` or `step()`,
                plus top-level `reward` and `done`.
        """
        return []

    def close(self) -> None:
        """Clean up resources used by the environment.
        Override this method to implement custom cleanup logic.
        Called when the environment is being destroyed or reset.
        """
        pass

	# SPDX-License-Identifier: BSD-3-Clause

	import inspect
	from abc import ABC, abstractmethod
	from typing import (
	Any,
	Dict,
	Generic,
	List,
	Optional,
	Protocol,
	Tuple,
	TYPE_CHECKING,
	TypeVar,
	)

	from typing_extensions import TypedDict

	from .types import Action, EnvironmentMetadata, Observation, State

	if TYPE_CHECKING:
	from openenv.core.rubrics import Rubric

	ActT = TypeVar("ActT", bound=Action)
	ObsT = TypeVar("ObsT", bound=Observation)
	StateT = TypeVar("StateT", bound=State)


	class Message(TypedDict):
	"""A message in a conversation.

	Compatible with Huggingface chat template format.
	"""

	role: str
	content: str


	class ModelTokenizer(Protocol):
	"""Protocol for tokenizers that support chat templates.

	This protocol defines the interface that tokenizers must implement
	to work with chat-based environments. It's compatible with
	Huggingface transformers tokenizers.
	"""

	def apply_chat_template(
	self,
	conversation: list[Message],
	tokenize: bool = True,
	return_tensors: str \| None = None,
	**kwargs: Any,
	) -> Any:
	"""Apply a chat template to format and optionally tokenize a conversation.

	Args:
	conversation (`list[Message]`):
	List of message dictionaries with 'role' and 'content'.
	tokenize (`bool`, optional, defaults to `True`):
	Whether to tokenize the output.
	return_tensors (`str`, optional):
	Format for returned tensors ('pt' for PyTorch).
	**kwargs:
	Additional arguments.

	Returns:
	Formatted and optionally tokenized conversation.
	"""
	...

	def decode(
	self, token_ids: Any, skip_special_tokens: bool = False, **kwargs: Any
	) -> str:
	"""Decode token IDs back to text.

	Args:
	token_ids (`Any`):
	Token IDs to decode.
	skip_special_tokens (`bool`, optional, defaults to `False`):
	Whether to skip special tokens in output.
	**kwargs:
	Additional arguments.

	Returns:
	`str`: Decoded text string.
	"""
	...


	class TaskProvider(Protocol):
	"""
	Optional task discovery API for dataset-backed environments.

	An environment implements this protocol structurally — declare the methods on
	an [`~openenv.core.env_server.interfaces.Environment`] subclass, without
	inheriting from `TaskProvider`. When the methods are present,
	[`~openenv.core.env_server.http_server.HTTPEnvServer`] exposes them as HTTP
	routes under `/{env_name}/…`; when they are absent, those routes return
	`501 Not Implemented`. Each method may be sync or async.

	Task provider methods are for metadata/discovery only and should be
	side-effect-free. They must be callable on a freshly constructed
	environment instance because HTTP compatibility routes may create a
	short-lived instance solely for task discovery.

	Selecting a task is not part of this protocol — pass the chosen split and
	index to `reset()` instead. See the
	[Task API guide](https://huggingface.co/docs/openenv/guides/task-api).

	Examples:

	```python
	env.list_splits() # ["train", "test"]
	env.num_tasks("test") # 7595
	env.get_task("test", 12) # {"id": "test-12", "index": 12}
	env.reset(split="test", index=12)
	```
	"""

	def list_splits(self) -> list[Any]:
	"""
	Return task split descriptors supported by this environment.

	Returns:
	`list[Any]`: Split descriptors. Plain strings, dicts, and Pydantic
	models are all accepted; the server normalizes each entry to
	`{"name": ..., "type": ...}`.
	"""
	...

	def list_tasks(self, split: str) -> list[Any]:
	"""
	Return all task specs for a split.

	Args:
	split (`str`):
	Task split name.

	Returns:
	`list[Any]`: Task specs for the split. Environments backed by very
	large or streamed splits may return a bounded preview, but
	`num_tasks` should still report the true total.
	"""
	...

	def num_tasks(self, split: str) -> int:
	"""
	Return the number of task specs in a split.

	Args:
	split (`str`):
	Task split name.

	Returns:
	`int`: Number of task specs available in the split.
	"""
	...

	def get_task(self, split: str, index: int) -> Any:
	"""
	Return one task spec by split and index.

	Args:
	split (`str`):
	Task split name.
	index (`int`):
	Task index within the split.

	Returns:
	`Any`: The task spec at that position.

	Raises:
	`IndexError`: If `index` is out of range for the split. The HTTP
	route converts this to a `400 Bad Request`.
	"""
	...

	def get_task_range(
	self,
	split: str,
	start: Optional[int] = None,
	stop: Optional[int] = None,
	) -> list[Any]:
	"""
	Return task specs for Python slice-style range bounds.

	Args:
	split (`str`):
	Task split name.
	start (`int`, optional):
	Inclusive start index. Defaults to the beginning of the split.
	stop (`int`, optional):
	Exclusive stop index. Defaults to the end of the split.

	Returns:
	`list[Any]`: Task specs in `[start, stop)`.
	"""
	...


	class Transform(ABC, Generic[ObsT]):
	"""Transform observations to add rewards, metrics, or other modifications.

	Transforms follow the TorchRL pattern where they take an observation
	and return a (potentially modified) observation. This allows for
	flexible reward computation and observation augmentation.
	"""

	@abstractmethod
	def __call__(self, observation: ObsT) -> ObsT:
	"""Transform an observation.

	Args:
	observation (`ObsT`):
	The input observation.

	Returns:
	`ObsT`: The transformed observation.
	"""
	pass


	class Environment(ABC, Generic[ActT, ObsT, StateT]):
	"""Base class for all environment servers following Gym/Gymnasium API.

	See [rfcs/004-rubrics.md](https://github.com/huggingface/OpenEnv/blob/main/rfcs/004-rubrics.md) for rubric design details.

	Args:
	transform (`Transform`, optional):
	Optional transform to apply to observations.
	rubric (`Rubric`, optional):
	Optional rubric for reward computation. When provided, the
	rubric's output can be used to set the observation's reward in step().

	Attributes:
	SUPPORTS_CONCURRENT_SESSIONS (`bool`):
	Whether this environment supports concurrent sessions. When ``True``,
	multiple WebSocket connections can each have their own environment
	instance (up to ``max_concurrent_envs``). When ``False`` (default),
	the environment should only be used with a single session at a time.

	Set this to ``True`` in your subclass if the environment uses proper
	session isolation (unique working dirs, no shared mutable state, and
	external resources that can handle concurrent access).
	rubric (`Rubric`, optional):
	Optional rubric for computing rewards. Set in ``__init__`` and use in
	``step()`` to compute observation rewards. Training infrastructure can
	access it for introspection:

	```python
	for name, r in env.rubric.named_rubrics():
	print(f"{name}: {r.last_score}")
	```
	"""

	# Class-level flag indicating whether this environment supports concurrent sessions
	SUPPORTS_CONCURRENT_SESSIONS: bool = False

	REQUIRES_SINGLE_THREAD_EXECUTOR: bool = False

	# Optional rubric for reward computation
	rubric: Optional["Rubric"]

	def __init__(
	self,
	transform: Optional[Transform[ObsT]] = None,
	rubric: Optional["Rubric"] = None,
	):
	self.transform = transform
	self.rubric = rubric

	@abstractmethod
	def reset(
	self,
	seed: Optional[int] = None,
	episode_id: Optional[str] = None,
	**kwargs: Any,
	) -> ObsT:
	"""Reset the environment and return initial observation."""
	pass

	async def reset_async(
	self,
	seed: Optional[int] = None,
	episode_id: Optional[str] = None,
	**kwargs: Any,
	) -> ObsT:
	"""Async version of reset. Default implementation calls sync reset.

	Override to provide true async implementation.
	"""
	return self.reset(seed=seed, episode_id=episode_id, **kwargs)

	@abstractmethod
	def step(
	self,
	action: ActT,
	timeout_s: Optional[float] = None,
	**kwargs: Any,
	) -> ObsT:
	"""Take a step in the environment."""
	pass

	async def step_async(
	self,
	action: ActT,
	timeout_s: Optional[float] = None,
	**kwargs: Any,
	) -> ObsT:
	"""Async version of step. Default implementation calls sync step.

	Override to provide true async implementation.
	"""
	return self.step(action, timeout_s=timeout_s, **kwargs)

	@property
	@abstractmethod
	def state(self) -> StateT:
	"""Get the current environment state."""
	pass

	def get_metadata(self) -> EnvironmentMetadata:
	"""
	Get metadata about this environment.

	Override this method to provide custom metadata for the environment.
	Default implementation returns basic metadata derived from class name.

	Returns:
	[`EnvironmentMetadata`] with environment information.
	"""
	return EnvironmentMetadata(
	name=self.__class__.__name__,
	description=f"{self.__class__.__name__} environment",
	version="1.0.0",
	)

	def _apply_transform(self, observation: ObsT) -> ObsT:
	"""Apply transform if one is provided."""
	if self.transform is not None:
	return self.transform(observation)
	return observation

	def _apply_rubric(self, action: ActT, observation: ObsT) -> float:
	"""Apply rubric if one is provided.

	Args:
	action (`ActT`):
	The action taken by the agent.
	observation (`ObsT`):
	The resulting observation.

	Returns:
	`float`: Reward value from the rubric, or 0.0 if no rubric is set.

	Call this in `step()` to compute and assign the reward:

	```python
	def step(self, action: MyAction, ...) -> MyObservation:
	# ... execute action and create observation ...
	observation.reward = self._apply_rubric(action, observation)
	return observation
	```
	"""
	if self.rubric is not None:
	return self.rubric(action, observation)
	return 0.0

	async def _apply_rubric_async(self, action: ActT, observation: ObsT) -> float:
	"""Apply rubric asynchronously if one is provided.

	Args:
	action (`ActT`):
	The action taken by the agent.
	observation (`ObsT`):
	The resulting observation.

	Returns:
	`float`: Reward value from the rubric, or 0.0 if no rubric is set.

	Call this in `step_async()` to compute and assign the reward:

	```python
	async def step_async(self, action: MyAction, ...) -> MyObservation:
	# ... execute action and create observation ...
	observation.reward = await self._apply_rubric_async(action, observation)
	return observation
	```
	"""
	if self.rubric is not None:
	result = self.rubric(action, observation)
	# If rubric returns a coroutine, await it
	if inspect.iscoroutine(result):
	return await result
	return result
	return 0.0

	def _reset_rubric(self) -> None:
	"""Reset the rubric state if one is provided.

	Call this in `reset()` to clear any trajectory state in the rubric:

	```python
	def reset(self, ...) -> MyObservation:
	self._reset_rubric()
	# ... create initial observation ...
	return observation
	```
	"""
	if self.rubric is not None:
	self.rubric.reset()

	async def _reset_rubric_async(self) -> None:
	"""Reset the rubric state asynchronously if one is provided.

	Call this in `reset_async()` to clear any trajectory state in the rubric:

	```python
	async def reset_async(self, ...) -> MyObservation:
	await self._reset_rubric_async()
	# ... create initial observation ...
	return observation
	```
	"""
	if self.rubric is not None:
	# Check if rubric has async reset method
	if hasattr(self.rubric, "reset_async"):
	result = self.rubric.reset_async()
	if inspect.iscoroutine(result):
	await result
	else:
	self.rubric.reset()

	def render_web(self, observation: Dict[str, Any]) -> Optional[str]:
	"""
	HTML that draws an observation in the web interface at `/web`.

	Override it to show the environment's state (a board, a page, a plot).
	The default draws nothing and the interface lists the observation's fields.

	Args:
	observation (`dict`):
	The serialized observation, as returned by `reset()` or `step()`,
	plus top-level `reward` and `done`.
	"""
	return None

	def web_actions(
	self, observation: Dict[str, Any]
	) -> List[Tuple[str, Dict[str, Any]]]:
	"""
	One-click actions for the web interface at `/web`, as `(label, action)` pairs.

	Override it when the valid actions are known from the observation (the legal
	moves of a game). Each action is the dict `step()` receives. The default
	offers none and the interface shows the action's form only.

	Args:
	observation (`dict`):
	The serialized observation, as returned by `reset()` or `step()`,
	plus top-level `reward` and `done`.
	"""
	return []

	def close(self) -> None:
	"""Clean up resources used by the environment.

	Override this method to implement custom cleanup logic.
	Called when the environment is being destroyed or reset.
	"""
	pass