test/mocking_classes.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional

import torch
import torch.nn as nn
from tensordict import TensorDict, TensorDictBase
from tensordict.nn import TensorDictModuleBase
from tensordict.utils import expand_right, NestedKey

from torchrl.data.tensor_specs import (
    Binary,
    Bounded,
    Categorical,
    Composite,
    MultiOneHot,
    NonTensor,
    OneHot,
    TensorSpec,
    Unbounded,
)
from torchrl.data.utils import consolidate_spec
from torchrl.envs.common import EnvBase
from torchrl.envs.model_based.common import ModelBasedEnvBase
from torchrl.envs.utils import _terminated_or_truncated

spec_dict = {
    "bounded": Bounded,
    "one_hot": OneHot,
    "categorical": Categorical,
    "unbounded": Unbounded,
    "binary": Binary,
    "mult_one_hot": MultiOneHot,
    "composite": Composite,
}

default_spec_kwargs = {
    OneHot: {"n": 7},
    Categorical: {"n": 7},
    Bounded: {"minimum": -torch.ones(4), "maximum": torch.ones(4)},
    Unbounded: {
        "shape": [
            7,
        ]
    },
    Binary: {"n": 7},
    MultiOneHot: {"nvec": [7, 3, 5]},
    Composite: {},
}


def make_spec(spec_str):
    target_class = spec_dict[spec_str]
    return target_class(**default_spec_kwargs[target_class])


class _MockEnv(EnvBase):
    @classmethod
    def __new__(
        cls,
        *args,
        **kwargs,
    ):
        for key, item in list(cls._output_spec["full_observation_spec"].items()):
            cls._output_spec["full_observation_spec"][key] = item.to(
                torch.get_default_dtype()
            )
        reward_spec = cls._output_spec["full_reward_spec"]
        if isinstance(reward_spec, Composite):
            reward_spec = Composite(
                {
                    key: item.to(torch.get_default_dtype())
                    for key, item in reward_spec.items(True, True)
                },
                shape=reward_spec.shape,
                device=reward_spec.device,
            )
        else:
            reward_spec = reward_spec.to(torch.get_default_dtype())
        cls._output_spec["full_reward_spec"] = reward_spec
        if not isinstance(cls._output_spec["full_reward_spec"], Composite):
            cls._output_spec["full_reward_spec"] = Composite(
                reward=cls._output_spec["full_reward_spec"],
                shape=cls._output_spec["full_reward_spec"].shape[:-1],
            )
        if not isinstance(cls._output_spec["full_done_spec"], Composite):
            cls._output_spec["full_done_spec"] = Composite(
                done=cls._output_spec["full_done_spec"].clone(),
                terminated=cls._output_spec["full_done_spec"].clone(),
                shape=cls._output_spec["full_done_spec"].shape[:-1],
            )
        if not isinstance(cls._input_spec["full_action_spec"], Composite):
            cls._input_spec["full_action_spec"] = Composite(
                action=cls._input_spec["full_action_spec"],
                shape=cls._input_spec["full_action_spec"].shape[:-1],
            )
        dtype = kwargs.pop("dtype", torch.get_default_dtype())
        for spec in (cls._output_spec, cls._input_spec):
            if dtype != torch.get_default_dtype():
                for key, val in list(spec.items(True, True)):
                    if val.dtype == torch.get_default_dtype():
                        val = val.to(dtype)
                        spec[key] = val
        return super().__new__(cls, *args, **kwargs)

    def __init__(
        self,
        *args,
        seed: int = 100,
        **kwargs,
    ):
        super().__init__(
            device=kwargs.pop("device", "cpu"),
            allow_done_after_reset=kwargs.pop("allow_done_after_reset", False),
        )
        self.set_seed(seed)
        self.is_closed = False

    @property
    def maxstep(self):
        return 100

    def _set_seed(self, seed: Optional[int]):
        self.seed = seed
        self.counter = seed % 17  # make counter a small number

    def custom_fun(self):
        return 0

    custom_attr = 1

    @property
    def custom_prop(self):
        return 2

    @property
    def custom_td(self):
        return TensorDict({"a": torch.zeros(3)}, [])


class MockSerialEnv(EnvBase):
    """A simple counting env that is reset after a predifined max number of steps."""

    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if action_spec is None:
            action_spec = Unbounded(
                (
                    *batch_size,
                    1,
                )
            )
        if observation_spec is None:
            observation_spec = Composite(
                observation=Unbounded(
                    (
                        *batch_size,
                        1,
                    )
                ),
                shape=batch_size,
            )
        if reward_spec is None:
            reward_spec = Unbounded(
                (
                    *batch_size,
                    1,
                )
            )
        if done_spec is None:
            done_spec = Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))
        if state_spec is None:
            state_spec = Composite(shape=batch_size)
        input_spec = Composite(
            full_action_spec=action_spec, full_state_spec=state_spec, shape=batch_size
        )
        cls._output_spec = Composite(shape=batch_size)
        cls._output_spec["full_reward_spec"] = reward_spec
        cls._output_spec["full_done_spec"] = done_spec
        cls._output_spec["full_observation_spec"] = observation_spec
        cls._input_spec = input_spec

        if not isinstance(cls._output_spec["full_reward_spec"], Composite):
            cls._output_spec["full_reward_spec"] = Composite(
                reward=cls._output_spec["full_reward_spec"], shape=batch_size
            )
        if not isinstance(cls._output_spec["full_done_spec"], Composite):
            cls._output_spec["full_done_spec"] = Composite(
                done=cls._output_spec["full_done_spec"], shape=batch_size
            )
        if not isinstance(cls._input_spec["full_action_spec"], Composite):
            cls._input_spec["full_action_spec"] = Composite(
                action=cls._input_spec["full_action_spec"], shape=batch_size
            )
        return super().__new__(*args, **kwargs)

    def __init__(self, device="cpu"):
        super(MockSerialEnv, self).__init__(device=device)
        self.is_closed = False

    def _set_seed(self, seed: Optional[int]):
        assert seed >= 1
        self.seed = seed
        self.counter = seed % 17  # make counter a small number
        self.max_val = max(self.counter + 100, self.counter * 2)

    def _step(self, tensordict):
        self.counter += 1
        n = torch.tensor(
            [self.counter], device=self.device, dtype=torch.get_default_dtype()
        )
        done = self.counter >= self.max_val
        done = torch.tensor([done], dtype=torch.bool, device=self.device)
        return TensorDict(
            {
                "reward": n,
                "done": done,
                "terminated": done.clone(),
                "observation": n.clone(),
            },
            batch_size=[],
            device=self.device,
        )

    def _reset(self, tensordict: TensorDictBase = None, **kwargs) -> TensorDictBase:
        self.max_val = max(self.counter + 100, self.counter * 2)

        n = torch.tensor(
            [self.counter], device=self.device, dtype=torch.get_default_dtype()
        )
        done = self.counter >= self.max_val
        done = torch.tensor([done], dtype=torch.bool, device=self.device)
        return TensorDict(
            {"done": done, "terminated": done.clone(), "observation": n},
            [],
            device=self.device,
        )

    def rand_step(self, tensordict: Optional[TensorDictBase] = None) -> TensorDictBase:
        return self.step(tensordict)


class MockBatchedLockedEnv(EnvBase):
    """Mocks an env whose batch_size defines the size of the output tensordict"""

    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if action_spec is None:
            action_spec = Unbounded(
                (
                    *batch_size,
                    1,
                )
            )
        if state_spec is None:
            state_spec = Composite(
                observation=Unbounded(
                    (
                        *batch_size,
                        1,
                    )
                ),
                shape=batch_size,
            )
        if observation_spec is None:
            observation_spec = Composite(
                observation=Unbounded(
                    (
                        *batch_size,
                        1,
                    )
                ),
                shape=batch_size,
            )
        if reward_spec is None:
            reward_spec = Unbounded(
                (
                    *batch_size,
                    1,
                )
            )
        if done_spec is None:
            done_spec = Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))
        cls._output_spec = Composite(shape=batch_size)
        cls._output_spec["full_reward_spec"] = reward_spec
        cls._output_spec["full_done_spec"] = done_spec
        cls._output_spec["full_observation_spec"] = observation_spec
        cls._input_spec = Composite(
            full_action_spec=action_spec,
            full_state_spec=state_spec,
            shape=batch_size,
        )
        if not isinstance(cls._output_spec["full_reward_spec"], Composite):
            cls._output_spec["full_reward_spec"] = Composite(
                reward=cls._output_spec["full_reward_spec"], shape=batch_size
            )
        if not isinstance(cls._output_spec["full_done_spec"], Composite):
            cls._output_spec["full_done_spec"] = Composite(
                done=cls._output_spec["full_done_spec"], shape=batch_size
            )
        if not isinstance(cls._input_spec["full_action_spec"], Composite):
            cls._input_spec["full_action_spec"] = Composite(
                action=cls._input_spec["full_action_spec"], shape=batch_size
            )
        return super().__new__(cls, *args, **kwargs)

    def __init__(self, device="cpu", batch_size=None):
        super(MockBatchedLockedEnv, self).__init__(device=device, batch_size=batch_size)
        self.counter = 0

    rand_step = MockSerialEnv.rand_step

    def _set_seed(self, seed: Optional[int]):
        assert seed >= 1
        self.seed = seed
        self.counter = seed % 17  # make counter a small number
        self.max_val = max(self.counter + 100, self.counter * 2)

    def _step(self, tensordict):
        if len(self.batch_size):
            leading_batch_size = (
                tensordict.shape[: -len(self.batch_size)]
                if tensordict is not None
                else []
            )
        else:
            leading_batch_size = tensordict.shape if tensordict is not None else []
        self.counter += 1
        # We use tensordict.batch_size instead of self.batch_size since this method will also be used by MockBatchedUnLockedEnv
        n = (
            torch.full(
                [*leading_batch_size, *self.observation_spec["observation"].shape],
                self.counter,
            )
            .to(self.device)
            .to(torch.get_default_dtype())
        )
        done = self.counter >= self.max_val
        done = torch.full(
            (*leading_batch_size, *self.batch_size, 1),
            done,
            dtype=torch.bool,
            device=self.device,
        )
        return TensorDict(
            {"reward": n, "done": done, "terminated": done.clone(), "observation": n},
            batch_size=tensordict.batch_size,
            device=self.device,
        )

    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
        self.max_val = max(self.counter + 100, self.counter * 2)
        batch_size = self.batch_size
        if len(batch_size):
            leading_batch_size = (
                tensordict.shape[: -len(self.batch_size)]
                if tensordict is not None
                else []
            )
        else:
            leading_batch_size = tensordict.shape if tensordict is not None else []

        n = (
            torch.full(
                [*leading_batch_size, *self.observation_spec["observation"].shape],
                self.counter,
            )
            .to(self.device)
            .to(torch.get_default_dtype())
        )
        done = self.counter >= self.max_val
        done = torch.full(
            (*leading_batch_size, *batch_size, 1),
            done,
            dtype=torch.bool,
            device=self.device,
        )
        return TensorDict(
            {"done": done, "terminated": done.clone(), "observation": n},
            [
                *leading_batch_size,
                *batch_size,
            ],
            device=self.device,
        )


class MockBatchedUnLockedEnv(MockBatchedLockedEnv):
    """Mocks an env whose batch_size does not define the size of the output tensordict.

    The size of the output tensordict is defined by the input tensordict itself.

    """

    def __init__(self, device="cpu", batch_size=None):
        super(MockBatchedUnLockedEnv, self).__init__(
            batch_size=batch_size, device=device
        )

    @classmethod
    def __new__(cls, *args, **kwargs):
        return super().__new__(cls, *args, _batch_locked=False, **kwargs)


class DiscreteActionVecMockEnv(_MockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=False,
        categorical_action_encoding=False,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        size = cls.size = 7
        if observation_spec is None:
            cls.out_key = "observation"
            observation_spec = Composite(
                observation=Unbounded(shape=torch.Size([*batch_size, size])),
                observation_orig=Unbounded(shape=torch.Size([*batch_size, size])),
                shape=batch_size,
            )
        if action_spec is None:
            if categorical_action_encoding:
                action_spec_cls = Categorical
                action_spec = action_spec_cls(n=7, shape=batch_size)
            else:
                action_spec_cls = OneHot
                action_spec = action_spec_cls(n=7, shape=(*batch_size, 7))
        if reward_spec is None:
            reward_spec = Composite(reward=Unbounded(shape=(1,)))
        if done_spec is None:
            done_spec = Composite(
                terminated=Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))
            )

        if state_spec is None:
            cls._out_key = "observation_orig"
            state_spec = Composite(
                {
                    cls._out_key: observation_spec["observation"],
                },
                shape=batch_size,
            )
        cls._output_spec = Composite(shape=batch_size)
        cls._output_spec["full_reward_spec"] = reward_spec
        cls._output_spec["full_done_spec"] = done_spec
        cls._output_spec["full_observation_spec"] = observation_spec
        cls._input_spec = Composite(
            full_action_spec=action_spec,
            full_state_spec=state_spec,
            shape=batch_size,
        )
        cls.from_pixels = from_pixels
        cls.categorical_action_encoding = categorical_action_encoding
        return super().__new__(*args, **kwargs)

    def _get_in_obs(self, obs):
        return obs

    def _get_out_obs(self, obs):
        return obs

    def _reset(self, tensordict: TensorDictBase = None) -> TensorDictBase:
        self.counter += 1
        state = torch.zeros(self.size) + self.counter
        if tensordict is None:
            tensordict = TensorDict({}, self.batch_size, device=self.device)
        tensordict = tensordict.empty().set(self.out_key, self._get_out_obs(state))
        tensordict = tensordict.set(self._out_key, self._get_out_obs(state))
        tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool))
        tensordict.set(
            "terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)
        )
        return tensordict

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        tensordict = tensordict.to(self.device)
        a = tensordict.get("action")

        if not self.categorical_action_encoding:
            assert (a.sum(-1) == 1).all()

        obs = self._get_in_obs(tensordict.get(self._out_key)) + a / self.maxstep
        tensordict = tensordict.empty()

        tensordict.set(self.out_key, self._get_out_obs(obs))
        tensordict.set(self._out_key, self._get_out_obs(obs))

        done = torch.isclose(obs, torch.ones_like(obs) * (self.counter + 1))
        reward = done.any(-1).unsqueeze(-1)

        # set done to False
        done = torch.zeros_like(done).all(-1).unsqueeze(-1)
        tensordict.set("reward", reward.to(torch.get_default_dtype()))
        tensordict.set("done", done)
        tensordict.set("terminated", done.clone())
        return tensordict


class ContinuousActionVecMockEnv(_MockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=False,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        size = cls.size = 7
        if observation_spec is None:
            cls.out_key = "observation"
            observation_spec = Composite(
                observation=Unbounded(shape=torch.Size([*batch_size, size])),
                observation_orig=Unbounded(shape=torch.Size([*batch_size, size])),
                shape=batch_size,
            )
        if action_spec is None:
            action_spec = Bounded(
                -1,
                1,
                (
                    *batch_size,
                    7,
                ),
            )
        if reward_spec is None:
            reward_spec = Unbounded(shape=(*batch_size, 1))
        if done_spec is None:
            done_spec = Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))

        if state_spec is None:
            cls._out_key = "observation_orig"
            state_spec = Composite(
                {
                    cls._out_key: observation_spec["observation"],
                },
                shape=batch_size,
            )
        cls._output_spec = Composite(shape=batch_size)
        cls._output_spec["full_reward_spec"] = reward_spec
        cls._output_spec["full_done_spec"] = done_spec
        cls._output_spec["full_observation_spec"] = observation_spec
        cls._input_spec = Composite(
            full_action_spec=action_spec,
            full_state_spec=state_spec,
            shape=batch_size,
        )
        cls.from_pixels = from_pixels
        return super().__new__(cls, *args, **kwargs)

    def _get_in_obs(self, obs):
        return obs

    def _get_out_obs(self, obs):
        return obs

    def _reset(self, tensordict: TensorDictBase) -> TensorDictBase:
        self.counter += 1
        self.step_count = 0
        # state = torch.zeros(self.size) + self.counter
        if tensordict is None:
            tensordict = TensorDict({}, self.batch_size, device=self.device)

        tensordict = tensordict.empty()
        tensordict.update(self.observation_spec.rand())
        # tensordict.set("next_" + self.out_key, self._get_out_obs(state))
        # tensordict.set("next_" + self._out_key, self._get_out_obs(state))
        tensordict.set("done", torch.zeros(*tensordict.shape, 1, dtype=torch.bool))
        tensordict.set(
            "terminated", torch.zeros(*tensordict.shape, 1, dtype=torch.bool)
        )
        return tensordict

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        self.step_count += 1
        tensordict = tensordict.to(self.device)
        a = tensordict.get("action")

        obs = self._obs_step(self._get_in_obs(tensordict.get(self._out_key)), a)

        tensordict = tensordict.empty()  # empty tensordict

        tensordict.set(self.out_key, self._get_out_obs(obs))
        tensordict.set(self._out_key, self._get_out_obs(obs))

        done = torch.isclose(obs, torch.ones_like(obs) * (self.counter + 1))
        while done.shape != tensordict.shape:
            done = done.any(-1)
        done = reward = done.unsqueeze(-1)
        tensordict.set("reward", reward.to(torch.get_default_dtype()))
        tensordict.set("done", done)
        tensordict.set("terminated", done)
        return tensordict

    def _obs_step(self, obs, a):
        return obs + a / self.maxstep


class DiscreteActionVecPolicy(TensorDictModuleBase):
    in_keys = ["observation"]
    out_keys = ["action"]

    def _get_in_obs(self, tensordict):
        obs = tensordict.get(*self.in_keys)
        return obs

    def __call__(self, tensordict):
        obs = self._get_in_obs(tensordict)
        max_obs = (obs == obs.max(dim=-1, keepdim=True)[0]).cumsum(-1).argmax(-1)
        k = tensordict.get(*self.in_keys).shape[-1]
        max_obs = (max_obs + 1) % k
        action = torch.nn.functional.one_hot(max_obs, k)
        tensordict.set(*self.out_keys, action)
        return tensordict


class DiscreteActionConvMockEnv(DiscreteActionVecMockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=True,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if observation_spec is None:
            cls.out_key = "pixels"
            observation_spec = Composite(
                pixels=Unbounded(shape=torch.Size([*batch_size, 1, 7, 7])),
                pixels_orig=Unbounded(shape=torch.Size([*batch_size, 1, 7, 7])),
                shape=batch_size,
            )
        if action_spec is None:
            action_spec = OneHot(7, shape=(*batch_size, 7))
        if reward_spec is None:
            reward_spec = Unbounded(shape=(*batch_size, 1))
        if done_spec is None:
            done_spec = Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))

        if state_spec is None:
            cls._out_key = "pixels_orig"
            state_spec = Composite(
                {
                    cls._out_key: observation_spec["pixels_orig"].clone(),
                },
                shape=batch_size,
            )
        return super().__new__(
            *args,
            observation_spec=observation_spec,
            action_spec=action_spec,
            reward_spec=reward_spec,
            state_spec=state_spec,
            from_pixels=from_pixels,
            done_spec=done_spec,
            **kwargs,
        )

    def _get_out_obs(self, obs):
        obs = torch.diag_embed(obs, 0, -2, -1).unsqueeze(0)
        return obs

    def _get_in_obs(self, obs):
        return obs.diagonal(0, -1, -2).squeeze()


class DiscreteActionConvMockEnvNumpy(DiscreteActionConvMockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=True,
        categorical_action_encoding=False,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if observation_spec is None:
            cls.out_key = "pixels"
            observation_spec = Composite(
                pixels=Unbounded(shape=torch.Size([*batch_size, 7, 7, 3])),
                pixels_orig=Unbounded(shape=torch.Size([*batch_size, 7, 7, 3])),
                shape=batch_size,
            )
        if action_spec is None:
            action_spec_cls = Categorical if categorical_action_encoding else OneHot
            action_spec = action_spec_cls(7, shape=(*batch_size, 7))
        if state_spec is None:
            cls._out_key = "pixels_orig"
            state_spec = Composite(
                {
                    cls._out_key: observation_spec["pixels_orig"],
                },
                shape=batch_size,
            )

        return super().__new__(
            *args,
            observation_spec=observation_spec,
            action_spec=action_spec,
            reward_spec=reward_spec,
            state_spec=state_spec,
            from_pixels=from_pixels,
            categorical_action_encoding=categorical_action_encoding,
            **kwargs,
        )

    def _get_out_obs(self, obs):
        obs = torch.diag_embed(obs, 0, -2, -1).unsqueeze(-1)
        obs = obs.expand(*obs.shape[:-1], 3)
        return obs

    def _get_in_obs(self, obs):
        return obs.diagonal(0, -2, -3)[..., 0, :]

    def _obs_step(self, obs, a):
        return obs + a.unsqueeze(-1) / self.maxstep


class ContinuousActionConvMockEnv(ContinuousActionVecMockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=True,
        pixel_shape=None,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if pixel_shape is None:
            pixel_shape = [1, 7, 7]
        if observation_spec is None:
            cls.out_key = "pixels"
            observation_spec = Composite(
                pixels=Unbounded(shape=torch.Size([*batch_size, *pixel_shape])),
                pixels_orig=Unbounded(shape=torch.Size([*batch_size, *pixel_shape])),
                shape=batch_size,
            )

        if action_spec is None:
            action_spec = Bounded(-1, 1, [*batch_size, pixel_shape[-1]])
        if reward_spec is None:
            reward_spec = Unbounded(shape=(*batch_size, 1))
        if done_spec is None:
            done_spec = Categorical(2, dtype=torch.bool, shape=(*batch_size, 1))
        if state_spec is None:
            cls._out_key = "pixels_orig"
            state_spec = Composite(
                {cls._out_key: observation_spec["pixels"]}, shape=batch_size
            )
        return super().__new__(
            *args,
            observation_spec=observation_spec,
            action_spec=action_spec,
            reward_spec=reward_spec,
            from_pixels=from_pixels,
            state_spec=state_spec,
            done_spec=done_spec,
            **kwargs,
        )

    def _get_out_obs(self, obs):
        obs = torch.diag_embed(obs, 0, -2, -1)
        return obs

    def _get_in_obs(self, obs):
        obs = obs.diagonal(0, -1, -2)
        return obs


class ContinuousActionConvMockEnvNumpy(ContinuousActionConvMockEnv):
    @classmethod
    def __new__(
        cls,
        *args,
        observation_spec=None,
        action_spec=None,
        state_spec=None,
        reward_spec=None,
        done_spec=None,
        from_pixels=True,
        **kwargs,
    ):
        batch_size = kwargs.setdefault("batch_size", torch.Size([]))
        if observation_spec is None:
            cls.out_key = "pixels"
            observation_spec = Composite(
                pixels=Unbounded(shape=torch.Size([*batch_size, 7, 7, 3])),
                pixels_orig=Unbounded(shape=torch.Size([*batch_size, 7, 7, 3])),
            )
        return super().__new__(
            *args,
            observation_spec=observation_spec,
            action_spec=action_spec,
            reward_spec=reward_spec,
            state_spec=state_spec,
            from_pixels=from_pixels,
            **kwargs,
        )

    def _get_out_obs(self, obs):
        obs = torch.diag_embed(obs, 0, -2, -1).unsqueeze(-1)
        obs = obs.expand(*obs.shape[:-1], 3)
        return obs

    def _get_in_obs(self, obs):
        return obs.diagonal(0, -2, -3)[..., 0, :]

    def _obs_step(self, obs, a):
        return obs + a / self.maxstep


class DiscreteActionConvPolicy(DiscreteActionVecPolicy):
    in_keys = ["pixels"]
    out_keys = ["action"]

    def _get_in_obs(self, tensordict):
        obs = tensordict.get(*self.in_keys).diagonal(0, -1, -2).squeeze()
        return obs


class DummyModelBasedEnvBase(ModelBasedEnvBase):
    """Dummy environnement for Model Based RL sota-implementations.

    This class is meant to be used to test the model based environnement.

    Args:
        world_model (WorldModel): the world model to use for the environnement.
        device (str or torch.device, optional): the device to use for the environnement.
        dtype (torch.dtype, optional): the dtype to use for the environnement.
        batch_size (sequence of int, optional): the batch size to use for the environnement.
    """

    def __init__(
        self,
        world_model,
        device="cpu",
        dtype=None,
        batch_size=None,
    ):
        super().__init__(
            world_model,
            device=device,
            batch_size=batch_size,
        )
        self.observation_spec = Composite(
            hidden_observation=Unbounded(
                (
                    *self.batch_size,
                    4,
                )
            ),
            shape=self.batch_size,
        )
        self.state_spec = Composite(
            hidden_observation=Unbounded(
                (
                    *self.batch_size,
                    4,
                )
            ),
            shape=self.batch_size,
        )
        self.action_spec = Unbounded(
            (
                *self.batch_size,
                1,
            )
        )
        self.reward_spec = Unbounded(
            (
                *self.batch_size,
                1,
            )
        )

    def _reset(self, tensordict: TensorDict, **kwargs) -> TensorDict:
        td = TensorDict(
            {
                "hidden_observation": self.state_spec["hidden_observation"].rand(),
            },
            batch_size=self.batch_size,
            device=self.device,
        )
        return td


class ActionObsMergeLinear(nn.Module):
    def __init__(self, in_size, out_size):
        super().__init__()
        self.linear = nn.Linear(in_size, out_size)

    def forward(self, observation, action):
        return self.linear(torch.cat([observation, action], dim=-1))


class CountingEnvCountPolicy(TensorDictModuleBase):
    def __init__(self, action_spec: TensorSpec, action_key: NestedKey = "action"):
        super().__init__()
        self.action_spec = action_spec
        self.action_key = action_key
        self.in_keys = []
        self.out_keys = [action_key]

    def __call__(self, td: TensorDictBase) -> TensorDictBase:
        return td.set(self.action_key, self.action_spec.zero() + 1)


class CountingEnvCountModule(nn.Module):
    def __init__(self, action_spec: TensorSpec):
        super().__init__()
        self.action_spec = action_spec

    def forward(self, t):
        return self.action_spec.zero() + 1


class CountingEnv(EnvBase):
    """An env that is done after a given number of steps.

    The action is the count increment.

    """

    def __init__(self, max_steps: int = 5, start_val: int = 0, **kwargs):
        super().__init__(**kwargs)
        self.max_steps = max_steps
        self.start_val = start_val

        self.observation_spec = Composite(
            observation=Unbounded(
                (
                    *self.batch_size,
                    1,
                ),
                dtype=torch.int32,
                device=self.device,
            ),
            shape=self.batch_size,
            device=self.device,
        )
        self.reward_spec = Unbounded(
            (
                *self.batch_size,
                1,
            ),
            device=self.device,
        )
        self.done_spec = Categorical(
            2,
            dtype=torch.bool,
            shape=(
                *self.batch_size,
                1,
            ),
            device=self.device,
        )
        self.action_spec = Binary(n=1, shape=[*self.batch_size, 1], device=self.device)
        self.register_buffer(
            "count",
            torch.zeros((*self.batch_size, 1), device=self.device, dtype=torch.int),
        )

    def _set_seed(self, seed: Optional[int]):
        torch.manual_seed(seed)

    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
        if tensordict is not None and "_reset" in tensordict.keys():
            _reset = tensordict.get("_reset")
            self.count[_reset] = self.start_val
        else:
            self.count[:] = self.start_val
        return TensorDict(
            source={
                "observation": self.count.clone(),
                "done": self.count > self.max_steps,
                "terminated": self.count > self.max_steps,
            },
            batch_size=self.batch_size,
            device=self.device,
        )

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        action = tensordict.get(self.action_key)
        self.count += action.to(
            dtype=torch.int,
            device=self.action_spec.device if self.device is None else self.device,
        )
        tensordict = TensorDict(
            source={
                "observation": self.count.clone(),
                "done": self.count > self.max_steps,
                "terminated": self.count > self.max_steps,
                "reward": torch.zeros_like(self.count, dtype=torch.float),
            },
            batch_size=self.batch_size,
            device=self.device,
        )
        return tensordict


class IncrementingEnv(CountingEnv):
    # Same as CountingEnv but always increments the count by 1 regardless of the action.
    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        self.count += 1  # The only difference with CountingEnv.
        tensordict = TensorDict(
            source={
                "observation": self.count.clone(),
                "done": self.count > self.max_steps,
                "terminated": self.count > self.max_steps,
                "reward": torch.zeros_like(self.count, dtype=torch.float),
            },
            batch_size=self.batch_size,
            device=self.device,
        )
        return tensordict


class NestedCountingEnv(CountingEnv):
    # an env with nested reward and done states
    def __init__(
        self,
        max_steps: int = 5,
        start_val: int = 0,
        nest_obs_action: bool = True,
        nest_done: bool = True,
        nest_reward: bool = True,
        nested_dim: int = 3,
        has_root_done: bool = False,
        **kwargs,
    ):
        super().__init__(max_steps=max_steps, start_val=start_val, **kwargs)

        self.nested_dim = nested_dim
        self.has_root_done = has_root_done

        self.nested_obs_action = nest_obs_action
        self.nested_done = nest_done
        self.nested_reward = nest_reward

        if self.nested_obs_action:
            self.observation_spec = Composite(
                {
                    "data": Composite(
                        {
                            "states": self.observation_spec["observation"]
                            .unsqueeze(-1)
                            .expand(*self.batch_size, self.nested_dim, 1)
                        },
                        shape=(
                            *self.batch_size,
                            self.nested_dim,
                        ),
                    )
                },
                shape=self.batch_size,
            )
            self.action_spec = Composite(
                {
                    "data": Composite(
                        {
                            "action": self.action_spec.unsqueeze(-1).expand(
                                *self.batch_size, self.nested_dim, 1
                            )
                        },
                        shape=(
                            *self.batch_size,
                            self.nested_dim,
                        ),
                    )
                },
                shape=self.batch_size,
            )

        if self.nested_reward:
            self.reward_spec = Composite(
                {
                    "data": Composite(
                        {
                            "reward": self.reward_spec.unsqueeze(-1).expand(
                                *self.batch_size, self.nested_dim, 1
                            )
                        },
                        shape=(
                            *self.batch_size,
                            self.nested_dim,
                        ),
                    )
                },
                shape=self.batch_size,
            )

        if self.nested_done:
            done_spec = self.full_done_spec.unsqueeze(-1).expand(
                *self.batch_size, self.nested_dim
            )
            done_spec = Composite(
                {"data": done_spec},
                shape=self.batch_size,
            )
            if self.has_root_done:
                done_spec["done"] = Categorical(
                    2,
                    shape=(
                        *self.batch_size,
                        1,
                    ),
                    dtype=torch.bool,
                )
            self.done_spec = done_spec

    def _reset(self, tensordict):

        # check that reset works as expected
        if tensordict is not None:
            if self.nested_done:
                if not self.has_root_done:
                    assert "_reset" not in tensordict.keys()
            else:
                assert ("data", "_reset") not in tensordict.keys(True)

        tensordict_reset = super()._reset(tensordict)

        if self.nested_done:
            for done_key in self.done_keys:
                if isinstance(done_key, str):
                    continue
                else:
                    done = tensordict_reset.pop(done_key[-1], None)
                if done is None:
                    continue
                tensordict_reset.set(
                    done_key,
                    (done.unsqueeze(-2).expand(*self.batch_size, self.nested_dim, 1)),
                )
        if self.nested_obs_action:
            obs = tensordict_reset.pop("observation")
            tensordict_reset.set(
                ("data", "states"),
                (obs.unsqueeze(-1).expand(*self.batch_size, self.nested_dim, 1)),
            )
        if "data" in tensordict_reset.keys():
            tensordict_reset.get("data").batch_size = (
                *self.batch_size,
                self.nested_dim,
            )
        return tensordict_reset

    def _step(self, tensordict):
        if self.nested_obs_action:
            tensordict = tensordict.clone()
            tensordict["data"].batch_size = self.batch_size
            tensordict[self.action_key] = tensordict[self.action_key].max(-2)[0]
        next_tensordict = super()._step(tensordict)
        if self.nested_obs_action:
            tensordict[self.action_key] = (
                tensordict[self.action_key]
                .unsqueeze(-1)
                .expand(*self.batch_size, self.nested_dim, 1)
            )
        if "data" in tensordict.keys():
            tensordict["data"].batch_size = (*self.batch_size, self.nested_dim)
        if self.nested_done:
            for done_key in self.done_keys:
                if isinstance(done_key, str):
                    continue
                else:
                    done = next_tensordict.pop(done_key[-1], None)
                if done is None:
                    continue
                next_tensordict.set(
                    done_key,
                    (done.unsqueeze(-1).expand(*self.batch_size, self.nested_dim, 1)),
                )
        if self.nested_obs_action:
            next_tensordict.set(
                ("data", "states"),
                (
                    next_tensordict.pop("observation")
                    .unsqueeze(-1)
                    .expand(*self.batch_size, self.nested_dim, 1)
                ),
            )
        if self.nested_reward:
            next_tensordict.set(
                self.reward_key,
                (
                    next_tensordict.pop("reward")
                    .unsqueeze(-1)
                    .expand(*self.batch_size, self.nested_dim, 1)
                ),
            )
        if "data" in next_tensordict.keys():
            next_tensordict.get("data").batch_size = (*self.batch_size, self.nested_dim)
        return next_tensordict


class CountingBatchedEnv(EnvBase):
    """An env that is done after a given number of steps.

    The action is the count increment.

    Unlike ``CountingEnv``, different envs of the batch can have different max_steps
    """

    def __init__(
        self,
        max_steps: torch.Tensor = None,
        start_val: torch.Tensor = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        if max_steps is None:
            max_steps = torch.tensor(5)
        if start_val is None:
            start_val = torch.zeros((), dtype=torch.int32)
        if not max_steps.shape == self.batch_size:
            raise RuntimeError("batch_size and max_steps shape must match.")

        self.max_steps = max_steps

        self.observation_spec = Composite(
            observation=Unbounded(
                (
                    *self.batch_size,
                    1,
                ),
                dtype=torch.int32,
            ),
            shape=self.batch_size,
        )
        self.reward_spec = Unbounded(
            (
                *self.batch_size,
                1,
            )
        )
        self.done_spec = Categorical(
            2,
            dtype=torch.bool,
            shape=(
                *self.batch_size,
                1,
            ),
        )
        self.action_spec = Binary(n=1, shape=[*self.batch_size, 1])

        self.count = torch.zeros(
            (*self.batch_size, 1), device=self.device, dtype=torch.int
        )
        if start_val.numel() == self.batch_size.numel():
            self.start_val = start_val.view(*self.batch_size, 1)
        elif start_val.numel() <= 1:
            self.start_val = start_val.expand_as(self.count)

    def _set_seed(self, seed: Optional[int]):
        torch.manual_seed(seed)

    def _reset(self, tensordict: TensorDictBase, **kwargs) -> TensorDictBase:
        if tensordict is not None and "_reset" in tensordict.keys():
            _reset = tensordict.get("_reset")
            self.count[_reset] = self.start_val[_reset].view_as(self.count[_reset])
        else:
            self.count[:] = self.start_val.view_as(self.count)
        return TensorDict(
            source={
                "observation": self.count.clone(),
                "done": self.count > self.max_steps.view_as(self.count),
                "terminated": self.count > self.max_steps.view_as(self.count),
            },
            batch_size=self.batch_size,
            device=self.device,
        )

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        action = tensordict.get("action")
        self.count += action.to(torch.int).view_as(self.count)
        tensordict = TensorDict(
            source={
                "observation": self.count.clone(),
                "done": self.count > self.max_steps.unsqueeze(-1),
                "terminated": self.count > self.max_steps.unsqueeze(-1),
                "reward": torch.zeros_like(self.count, dtype=torch.float),
            },
            batch_size=self.batch_size,
            device=self.device,
        )
        return tensordict


class HeterogeneousCountingEnvPolicy(TensorDictModuleBase):
    def __init__(self, full_action_spec: TensorSpec, count: bool = True):
        super().__init__()
        self.full_action_spec = full_action_spec
        self.count = count

    def __call__(self, td: TensorDictBase) -> TensorDictBase:
        action_td = self.full_action_spec.zero()
        if self.count:
            action_td.apply_(lambda x: x + 1)
        return td.update(action_td)


class HeterogeneousCountingEnv(EnvBase):
    """A heterogeneous, counting Env."""

    def __init__(self, max_steps: int = 5, start_val: int = 0, **kwargs):
        super().__init__(**kwargs)
        self.n_nested_dim = 3
        self.max_steps = max_steps
        self.start_val = start_val

        count = torch.zeros((*self.batch_size, 1), device=self.device, dtype=torch.int)
        count[:] = self.start_val

        self.register_buffer("count", count)
        self._make_specs()

    def _make_specs(self):
        obs_specs = []
        action_specs = []
        for index in range(self.n_nested_dim):
            obs_specs.append(self.get_agent_obs_spec(index))
            action_specs.append(self.get_agent_action_spec(index))
        obs_specs = torch.stack(obs_specs, dim=0)
        obs_spec_unlazy = consolidate_spec(obs_specs)
        action_specs = torch.stack(action_specs, dim=0)

        self.unbatched_observation_spec = Composite(
            lazy=obs_spec_unlazy,
            state=Unbounded(shape=(64, 64, 3)),
            device=self.device,
        )

        self.unbatched_action_spec = Composite(
            lazy=action_specs,
            device=self.device,
        )
        self.unbatched_reward_spec = Composite(
            {
                "lazy": Composite(
                    {"reward": Unbounded(shape=(self.n_nested_dim, 1))},
                    shape=(self.n_nested_dim,),
                )
            },
            device=self.device,
        )
        self.unbatched_done_spec = Composite(
            {
                "lazy": Composite(
                    {
                        "done": Categorical(
                            n=2,
                            shape=(self.n_nested_dim, 1),
                            dtype=torch.bool,
                        ),
                    },
                    shape=(self.n_nested_dim,),
                )
            },
            device=self.device,
        )

        self.action_spec = self.unbatched_action_spec.expand(
            *self.batch_size, *self.unbatched_action_spec.shape
        )
        self.observation_spec = self.unbatched_observation_spec.expand(
            *self.batch_size, *self.unbatched_observation_spec.shape
        )
        self.reward_spec = self.unbatched_reward_spec.expand(
            *self.batch_size, *self.unbatched_reward_spec.shape
        )
        self.done_spec = self.unbatched_done_spec.expand(
            *self.batch_size, *self.unbatched_done_spec.shape
        )

    def get_agent_obs_spec(self, i):
        camera = Bounded(low=0, high=200, shape=(7, 7, 3))
        vector_3d = Unbounded(shape=(3,))
        vector_2d = Unbounded(shape=(2,))
        lidar = Bounded(low=0, high=5, shape=(8,))

        tensor_0 = Unbounded(shape=(1,))
        tensor_1 = Bounded(low=0, high=3, shape=(1, 2))
        tensor_2 = Unbounded(shape=(1, 2, 3))

        if i == 0:
            return Composite(
                {
                    "camera": camera,
                    "lidar": lidar,
                    "vector": vector_3d,
                    "tensor_0": tensor_0,
                },
                device=self.device,
            )
        elif i == 1:
            return Composite(
                {
                    "camera": camera,
                    "lidar": lidar,
                    "vector": vector_2d,
                    "tensor_1": tensor_1,
                },
                device=self.device,
            )
        elif i == 2:
            return Composite(
                {
                    "camera": camera,
                    "vector": vector_2d,
                    "tensor_2": tensor_2,
                },
                device=self.device,
            )
        else:
            raise ValueError(f"Index {i} undefined for index 3")

    def get_agent_action_spec(self, i):
        action_3d = Bounded(low=-1, high=1, shape=(3,))
        action_2d = Bounded(low=-1, high=1, shape=(2,))

        # Some have 2d action and some 3d
        # TODO Introduce composite heterogeneous actions
        if i == 0:
            ret = action_3d
        elif i == 1:
            ret = action_2d
        elif i == 2:
            ret = action_2d
        else:
            raise ValueError(f"Index {i} undefined for index 3")

        return Composite({"action": ret})

    def _reset(
        self,
        tensordict: TensorDictBase = None,
        **kwargs,
    ) -> TensorDictBase:
        if tensordict is not None and self.reset_keys[0] in tensordict.keys(True):
            _reset = tensordict.get(self.reset_keys[0]).squeeze(-1).any(-1)
            self.count[_reset] = self.start_val
        else:
            self.count[:] = self.start_val

        reset_td = self.observation_spec.zero()
        reset_td.apply_(lambda x: x + expand_right(self.count, x.shape))
        reset_td.update(self.output_spec["full_done_spec"].zero())

        assert reset_td.batch_size == self.batch_size
        for key in reset_td.keys(True):
            assert "_reset" not in key
        return reset_td

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        actions = torch.zeros_like(self.count.squeeze(-1), dtype=torch.bool)
        for i in range(self.n_nested_dim):
            action = tensordict["lazy"][..., i]["action"]
            action = action[..., 0].to(torch.bool)
            actions += action

        self.count += actions.unsqueeze(-1).to(torch.int)

        td = self.observation_spec.zero()
        td.apply_(lambda x: x + expand_right(self.count, x.shape))
        td.update(self.output_spec["full_done_spec"].zero())
        td.update(self.output_spec["full_reward_spec"].zero())

        assert td.batch_size == self.batch_size
        for done_key in self.done_keys:
            td[done_key] = expand_right(
                self.count > self.max_steps,
                self.full_done_spec[done_key].shape,
            )

        return td

    def _set_seed(self, seed: Optional[int]):
        torch.manual_seed(seed)


class MultiKeyCountingEnvPolicy(TensorDictModuleBase):
    def __init__(
        self,
        full_action_spec: TensorSpec,
        count: bool = True,
        deterministic: bool = False,
    ):
        super().__init__()
        if not deterministic and not count:
            raise ValueError("Not counting policy is always deterministic")

        self.full_action_spec = full_action_spec
        self.count = count
        self.deterministic = deterministic

    def __call__(self, td: TensorDictBase) -> TensorDictBase:
        action_td = self.full_action_spec.zero()
        if self.count:
            if self.deterministic:
                action_td["nested_1", "action"] += 1
                action_td["nested_2", "azione"] += 1
                action_td["action"][..., 1] = 1
            else:
                # We choose an action at random
                choice = torch.randint(0, 3, ()).item()
                if choice == 0:
                    action_td["nested_1", "action"] += 1
                elif choice == 1:
                    action_td["nested_2", "azione"] += 1
                else:
                    action_td["action"][..., 1] = 1
        return td.update(action_td)


class MultiKeyCountingEnv(EnvBase):
    def __init__(self, max_steps: int = 5, start_val: int = 0, **kwargs):
        super().__init__(**kwargs)

        self.max_steps = max_steps
        self.start_val = start_val
        self.nested_dim_1 = 3
        self.nested_dim_2 = 2

        count = torch.zeros((*self.batch_size, 1), device=self.device, dtype=torch.int)
        count_nested_1 = torch.zeros(
            (*self.batch_size, self.nested_dim_1, 1),
            device=self.device,
            dtype=torch.int,
        )
        count_nested_2 = torch.zeros(
            (*self.batch_size, self.nested_dim_2, 1),
            device=self.device,
            dtype=torch.int,
        )

        count[:] = self.start_val
        count_nested_1[:] = self.start_val
        count_nested_2[:] = self.start_val

        self.register_buffer("count", count)
        self.register_buffer("count_nested_1", count_nested_1)
        self.register_buffer("count_nested_2", count_nested_2)

        self.make_specs()

        self.action_spec = self.unbatched_action_spec.expand(
            *self.batch_size, *self.unbatched_action_spec.shape
        )
        self.observation_spec = self.unbatched_observation_spec.expand(
            *self.batch_size, *self.unbatched_observation_spec.shape
        )
        self.reward_spec = self.unbatched_reward_spec.expand(
            *self.batch_size, *self.unbatched_reward_spec.shape
        )
        self.done_spec = self.unbatched_done_spec.expand(
            *self.batch_size, *self.unbatched_done_spec.shape
        )

    def make_specs(self):
        self.unbatched_observation_spec = Composite(
            nested_1=Composite(
                observation=Bounded(low=0, high=200, shape=(self.nested_dim_1, 3)),
                shape=(self.nested_dim_1,),
            ),
            nested_2=Composite(
                observation=Unbounded(shape=(self.nested_dim_2, 2)),
                shape=(self.nested_dim_2,),
            ),
            observation=Unbounded(
                shape=(
                    10,
                    10,
                    3,
                )
            ),
        )

        self.unbatched_action_spec = Composite(
            nested_1=Composite(
                action=Categorical(n=2, shape=(self.nested_dim_1,)),
                shape=(self.nested_dim_1,),
            ),
            nested_2=Composite(
                azione=Bounded(low=0, high=100, shape=(self.nested_dim_2, 1)),
                shape=(self.nested_dim_2,),
            ),
            action=OneHot(n=2),
        )

        self.unbatched_reward_spec = Composite(
            nested_1=Composite(
                gift=Unbounded(shape=(self.nested_dim_1, 1)),
                shape=(self.nested_dim_1,),
            ),
            nested_2=Composite(
                reward=Unbounded(shape=(self.nested_dim_2, 1)),
                shape=(self.nested_dim_2,),
            ),
            reward=Unbounded(shape=(1,)),
        )

        self.unbatched_done_spec = Composite(
            nested_1=Composite(
                done=Categorical(
                    n=2,
                    shape=(self.nested_dim_1, 1),
                    dtype=torch.bool,
                ),
                terminated=Categorical(
                    n=2,
                    shape=(self.nested_dim_1, 1),
                    dtype=torch.bool,
                ),
                shape=(self.nested_dim_1,),
            ),
            nested_2=Composite(
                done=Categorical(
                    n=2,
                    shape=(self.nested_dim_2, 1),
                    dtype=torch.bool,
                ),
                terminated=Categorical(
                    n=2,
                    shape=(self.nested_dim_2, 1),
                    dtype=torch.bool,
                ),
                shape=(self.nested_dim_2,),
            ),
            # done at the root always prevail
            done=Categorical(
                n=2,
                shape=(1,),
                dtype=torch.bool,
            ),
            terminated=Categorical(
                n=2,
                shape=(1,),
                dtype=torch.bool,
            ),
        )

    def _reset(
        self,
        tensordict: TensorDictBase = None,
        **kwargs,
    ) -> TensorDictBase:
        reset_all = False
        if tensordict is not None:
            _reset = tensordict.get("_reset", None)
            if _reset is not None:
                self.count[_reset.squeeze(-1)] = self.start_val
                self.count_nested_1[_reset.squeeze(-1)] = self.start_val
                self.count_nested_2[_reset.squeeze(-1)] = self.start_val
            else:
                reset_all = True

        if tensordict is None or reset_all:
            self.count[:] = self.start_val
            self.count_nested_1[:] = self.start_val
            self.count_nested_2[:] = self.start_val

        reset_td = self.observation_spec.zero()
        reset_td["observation"] += expand_right(
            self.count, reset_td["observation"].shape
        )
        reset_td["nested_1", "observation"] += expand_right(
            self.count_nested_1, reset_td["nested_1", "observation"].shape
        )
        reset_td["nested_2", "observation"] += expand_right(
            self.count_nested_2, reset_td["nested_2", "observation"].shape
        )

        reset_td.update(self.output_spec["full_done_spec"].zero())

        assert reset_td.batch_size == self.batch_size

        return reset_td

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:

        # Each action has a corresponding reward, done, and observation
        reward = self.output_spec["full_reward_spec"].zero()
        done = self.output_spec["full_done_spec"].zero()
        td = self.observation_spec.zero()

        one_hot_action = tensordict["action"]
        one_hot_action = one_hot_action.long().argmax(-1).unsqueeze(-1)
        reward["reward"] += one_hot_action.to(torch.float)
        self.count += one_hot_action.to(torch.int)
        td["observation"] += expand_right(self.count, td["observation"].shape)
        done["done"] = self.count > self.max_steps
        done["terminated"] = self.count > self.max_steps

        discrete_action = tensordict["nested_1"]["action"].unsqueeze(-1)
        reward["nested_1"]["gift"] += discrete_action.to(torch.float)
        self.count_nested_1 += discrete_action.to(torch.int)
        td["nested_1", "observation"] += expand_right(
            self.count_nested_1, td["nested_1", "observation"].shape
        )
        done["nested_1", "done"] = self.count_nested_1 > self.max_steps
        done["nested_1", "terminated"] = self.count_nested_1 > self.max_steps

        continuous_action = tensordict["nested_2"]["azione"]
        reward["nested_2"]["reward"] += continuous_action.to(torch.float)
        self.count_nested_2 += continuous_action.to(torch.bool)
        td["nested_2", "observation"] += expand_right(
            self.count_nested_2, td["nested_2", "observation"].shape
        )
        done["nested_2", "done"] = self.count_nested_2 > self.max_steps
        done["nested_2", "terminated"] = self.count_nested_2 > self.max_steps

        td.update(done)
        td.update(reward)

        assert td.batch_size == self.batch_size
        return td

    def _set_seed(self, seed: Optional[int]):
        torch.manual_seed(seed)


class EnvWithMetadata(EnvBase):
    def __init__(self):
        super().__init__()
        self.observation_spec = Composite(
            tensor=Unbounded(3),
            non_tensor=NonTensor(shape=()),
        )
        self.state_spec = Composite(
            non_tensor=NonTensor(shape=()),
        )
        self.reward_spec = Unbounded(1)
        self.action_spec = Unbounded(1)

    def _reset(self, tensordict):
        data = self.observation_spec.zero()
        data.set_non_tensor("non_tensor", 0)
        data.update(self.full_done_spec.zero())
        return data

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        data = self.observation_spec.zero()
        data.set_non_tensor("non_tensor", tensordict["non_tensor"] + 1)
        data.update(self.full_done_spec.zero())
        data.update(self.full_reward_spec.zero())
        return data

    def _set_seed(self, seed: Optional[int]):
        return seed


class AutoResettingCountingEnv(CountingEnv):
    def _step(self, tensordict):
        tensordict = super()._step(tensordict)
        if tensordict["done"].any():
            td_reset = super().reset()
            tensordict.update(td_reset.exclude(*self.done_keys))
        return tensordict

    def _reset(self, tensordict=None):
        if tensordict is not None and "_reset" in tensordict:
            raise RuntimeError
        return super()._reset(tensordict)


class AutoResetHeteroCountingEnv(HeterogeneousCountingEnv):
    def __init__(self, max_steps: int = 5, start_val: int = 0, **kwargs):
        super().__init__(**kwargs)
        self.n_nested_dim = 3
        self.max_steps = max_steps
        self.start_val = start_val

        count = torch.zeros(
            (*self.batch_size, self.n_nested_dim, 1),
            device=self.device,
            dtype=torch.int,
        )
        count[:] = self.start_val

        self.register_buffer("count", count)
        self._make_specs()

    def _step(self, tensordict):
        for i in range(self.n_nested_dim):
            action = tensordict["lazy"][..., i]["action"]
            action = action[..., 0].to(torch.bool)
            self.count[..., i, 0] += action

        td = self.observation_spec.zero()
        for done_key in self.done_keys:
            td[done_key] = self.count > self.max_steps

        any_done = _terminated_or_truncated(
            td,
            full_done_spec=self.output_spec["full_done_spec"],
            key=None,
        )
        if any_done:
            self.count[td["lazy", "done"]] = 0

        for i in range(self.n_nested_dim):
            lazy = tensordict["lazy"][..., i]
            for obskey in self.observation_spec.keys(True, True):
                if isinstance(obskey, tuple) and obskey[0] == "lazy":
                    lazy[obskey[1:]] += expand_right(
                        self.count[..., i, 0], lazy[obskey[1:]].shape
                    ).clone()
        td.update(self.full_done_spec.zero())
        td.update(self.full_reward_spec.zero())

        assert td.batch_size == self.batch_size
        return td

    def _reset(self, tensordict=None):
        if tensordict is not None and self.reset_keys[0] in tensordict.keys(True):
            raise RuntimeError
        self.count[:] = self.start_val

        reset_td = self.observation_spec.zero()
        reset_td.update(self.full_done_spec.zero())
        assert reset_td.batch_size == self.batch_size
        return reset_td


class EnvWithDynamicSpec(EnvBase):
    def __init__(self, max_count=5):
        super().__init__(batch_size=())
        self.observation_spec = Composite(
            observation=Unbounded(shape=(3, -1, 2)),
        )
        self.action_spec = Bounded(low=-1, high=1, shape=(2,))
        self.full_done_spec = Composite(
            done=Binary(1, shape=(1,), dtype=torch.bool),
            terminated=Binary(1, shape=(1,), dtype=torch.bool),
            truncated=Binary(1, shape=(1,), dtype=torch.bool),
        )
        self.reward_spec = Unbounded((1,), dtype=torch.float)
        self.count = 0
        self.max_count = max_count

    def _reset(self, tensordict=None):
        self.count = 0
        data = TensorDict(
            {
                "observation": torch.full(
                    (3, self.count + 1, 2),
                    self.count,
                    dtype=self.observation_spec["observation"].dtype,
                )
            }
        )
        data.update(self.done_spec.zero())
        return data

    def _step(
        self,
        tensordict: TensorDictBase,
    ) -> TensorDictBase:
        self.count += 1
        done = self.count >= self.max_count
        observation = TensorDict(
            {
                "observation": torch.full(
                    (3, self.count + 1, 2),
                    self.count,
                    dtype=self.observation_spec["observation"].dtype,
                )
            }
        )
        done = self.full_done_spec.zero() | done
        reward = self.full_reward_spec.zero()
        return observation.update(done).update(reward)

    def _set_seed(self, seed: Optional[int]):
        self.manual_seed = seed
        return seed