from gymnasium.spaces import Box
import numpy as np
import random
import tree  # pip install dm_tree
from typing import (
    List,
    Optional,
    Union,
)

from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.utils.annotations import override
from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType


class RandomPolicy(Policy):
    """Hand-coded policy that returns random actions."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # Whether for compute_actions, the bounds given in action_space
        # should be ignored (default: False). This is to test action-clipping
        # and any Env's reaction to bounds breaches.
        if self.config.get("ignore_action_bounds", False) and isinstance(
            self.action_space, Box
        ):
            self.action_space_for_sampling = Box(
                -float("inf"),
                float("inf"),
                shape=self.action_space.shape,
                dtype=self.action_space.dtype,
            )
        else:
            self.action_space_for_sampling = self.action_space

    @override(Policy)
    def init_view_requirements(self):
        super().init_view_requirements()
        # Disable for_training and action attributes for SampleBatch.INFOS column
        # since it can not be properly batched.
        vr = self.view_requirements[SampleBatch.INFOS]
        vr.used_for_training = False
        vr.used_for_compute_actions = False

    @override(Policy)
    def compute_actions(
        self,
        obs_batch: Union[List[TensorStructType], TensorStructType],
        state_batches: Optional[List[TensorType]] = None,
        prev_action_batch: Union[List[TensorStructType], TensorStructType] = None,
        prev_reward_batch: Union[List[TensorStructType], TensorStructType] = None,
        **kwargs
    ):
        # Alternatively, a numpy array would work here as well.
        # e.g.: np.array([random.choice([0, 1])] * len(obs_batch))
        obs_batch_size = len(tree.flatten(obs_batch)[0])
        return (
            [self.action_space_for_sampling.sample() for _ in range(obs_batch_size)],
            [],
            {},
        )

    @override(Policy)
    def learn_on_batch(self, samples):
        """No learning."""
        return {}

    @override(Policy)
    def compute_log_likelihoods(
        self,
        actions,
        obs_batch,
        state_batches=None,
        prev_action_batch=None,
        prev_reward_batch=None,
    ):
        return np.array([random.random()] * len(obs_batch))

    @override(Policy)
    def get_weights(self) -> ModelWeights:
        """No weights to save."""
        return {}

    @override(Policy)
    def set_weights(self, weights: ModelWeights) -> None:
        """No weights to set."""
        pass

    @override(Policy)
    def _get_dummy_batch_from_view_requirements(self, batch_size: int = 1):
        return SampleBatch(
            {
                SampleBatch.OBS: tree.map_structure(
                    lambda s: s[None], self.observation_space.sample()
                ),
            }
        )
