prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG]: Flasbax bug on LBF (type issue)
  ### Describe the bug
  In the AgentID wrapper, the new_agents_view type is not enforced to be consistent, so for LBF with flashbax a dtype error emerges.
  
  ### To Reproduce
  Steps to reproduce the behavior:
  1. Run LBF with flashbax.
  
  ### Expected behavior
  Expected the observation to be added easily to the buffer.
  
  ### Context (Environment)
   - Updated jumanji
  
  ### Additional context
  This is somewhat an exercise in opening an issue!
  
  ### Possible Solution
  Will make a PR soon! Basically a cast to the agents_view type.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `mava/wrappers/observation.py`
  Content:
  ```
  1 # Copyright 2022 InstaDeep Ltd. All rights reserved.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #     http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 
  15 from typing import Tuple, Union
  16 
  17 import chex
  18 import jax.numpy as jnp
  19 from jumanji import specs
  20 from jumanji.env import Environment
  21 from jumanji.types import TimeStep
  22 from jumanji.wrappers import Wrapper
  23 
  24 from mava.types import Observation, ObservationGlobalState, State
  25 
  26 
  27 class AgentIDWrapper(Wrapper):
  28     """A wrapper to add a one-hot vector as agent IDs to the original observation.
  29     It can be useful in multi-agent environments where agents require unique identification.
  30     """
  31 
  32     def __init__(self, env: Environment):
  33         super().__init__(env)
  34 
  35     def _add_agent_ids(
  36         self, timestep: TimeStep, num_agents: int
  37     ) -> Union[Observation, ObservationGlobalState]:
  38         """Adds agent IDs to the observation."""
  39         obs = timestep.observation
  40         agent_ids = jnp.eye(num_agents)
  41         agents_view = jnp.concatenate([agent_ids, obs.agents_view], axis=-1)
  42 
  43         return obs._replace(agents_view=agents_view)  # type: ignore
  44 
  45     def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:
  46         """Reset the environment."""
  47         state, timestep = self._env.reset(key)
  48         timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)
  49 
  50         return state, timestep
  51 
  52     def step(
  53         self,
  54         state: State,
  55         action: chex.Array,
  56     ) -> Tuple[State, TimeStep]:
  57         """Step the environment."""
  58         state, timestep = self._env.step(state, action)
  59         timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)
  60 
  61         return state, timestep
  62 
  63     def observation_spec(
  64         self,
  65     ) -> Union[specs.Spec[Observation], specs.Spec[ObservationGlobalState]]:
  66         """Specification of the observation of the selected environment."""
  67         obs_spec = self._env.observation_spec()
  68         num_obs_features = obs_spec.agents_view.shape[-1] + self._env.num_agents
  69         dtype = obs_spec.agents_view.dtype
  70         agents_view = specs.Array((self._env.num_agents, num_obs_features), dtype, "agents_view")
  71 
  72         return obs_spec.replace(agents_view=agents_view)
  73 
  74 
  75 class GlobalStateWrapper(Wrapper):
  76     """Wrapper for adding global state to an environment that follows the mava API.
  77 
  78     The wrapper includes a global environment state to be used by the centralised critic.
  79     Note here that since most environments do not have a global state, we create one
  80     by concatenating the observations of all agents.
  81     """
  82 
  83     def modify_timestep(self, timestep: TimeStep) -> TimeStep[ObservationGlobalState]:
  84         global_state = jnp.concatenate(timestep.observation.agents_view, axis=0)
  85         global_state = jnp.tile(global_state, (self._env.num_agents, 1))
  86 
  87         observation = ObservationGlobalState(
  88             global_state=global_state,
  89             agents_view=timestep.observation.agents_view,
  90             action_mask=timestep.observation.action_mask,
  91             step_count=timestep.observation.step_count,
  92         )
  93 
  94         return timestep.replace(observation=observation)
  95 
  96     def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:
  97         """Reset the environment. Updates the step count."""
  98         state, timestep = self._env.reset(key)
  99         return state, self.modify_timestep(timestep)
  100 
  101     def step(self, state: State, action: chex.Array) -> Tuple[State, TimeStep]:
  102         """Step the environment. Updates the step count."""
  103         state, timestep = self._env.step(state, action)
  104         return state, self.modify_timestep(timestep)
  105 
  106     def observation_spec(self) -> specs.Spec[ObservationGlobalState]:
  107         """Specification of the observation of the selected environment."""
  108 
  109         obs_spec = self._env.observation_spec()
  110         num_obs_features = obs_spec.agents_view.shape[-1]
  111         global_state = specs.Array(
  112             (self._env.num_agents, self._env.num_agents * num_obs_features),
  113             obs_spec.agents_view.dtype,
  114             "global_state",
  115         )
  116 
  117         return specs.Spec(
  118             ObservationGlobalState,
  119             "ObservationSpec",
  120             agents_view=obs_spec.agents_view,
  121             action_mask=obs_spec.action_mask,
  122             global_state=global_state,
  123             step_count=obs_spec.step_count,
  124         )
  125
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/mava/wrappers/observation.py b/mava/wrappers/observation.py
  --- a/mava/wrappers/observation.py
  +++ b/mava/wrappers/observation.py
  @@ -38,7 +38,11 @@
           """Adds agent IDs to the observation."""
           obs = timestep.observation
           agent_ids = jnp.eye(num_agents)
  -        agents_view = jnp.concatenate([agent_ids, obs.agents_view], axis=-1)
  +        agents_view = jnp.concatenate(
  +            [agent_ids, obs.agents_view],
  +            axis=-1,
  +            dtype=obs.agents_view.dtype,
  +        )
   
           return obs._replace(agents_view=agents_view)  # type: ignore
  
verification_info: {"golden_diff": "diff --git a/mava/wrappers/observation.py b/mava/wrappers/observation.py\n--- a/mava/wrappers/observation.py\n+++ b/mava/wrappers/observation.py\n@@ -38,7 +38,11 @@\n         \"\"\"Adds agent IDs to the observation.\"\"\"\n         obs = timestep.observation\n         agent_ids = jnp.eye(num_agents)\n-        agents_view = jnp.concatenate([agent_ids, obs.agents_view], axis=-1)\n+        agents_view = jnp.concatenate(\n+            [agent_ids, obs.agents_view],\n+            axis=-1,\n+            dtype=obs.agents_view.dtype,\n+        )\n \n         return obs._replace(agents_view=agents_view)  # type: ignore\n", "issue": "[BUG]: Flasbax bug on LBF (type issue)\n### Describe the bug\r\nIn the AgentID wrapper, the new_agents_view type is not enforced to be consistent, so for LBF with flashbax a dtype error emerges.\r\n\r\n### To Reproduce\r\nSteps to reproduce the behavior:\r\n1. Run LBF with flashbax.\r\n\r\n### Expected behavior\r\nExpected the observation to be added easily to the buffer.\r\n\r\n### Context (Environment)\r\n - Updated jumanji\r\n\r\n### Additional context\r\nThis is somewhat an exercise in opening an issue!\r\n\r\n### Possible Solution\r\nWill make a PR soon! Basically a cast to the agents_view type.\r\n\n", "before_files": [{"content": "# Copyright 2022 InstaDeep Ltd. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Tuple, Union\n\nimport chex\nimport jax.numpy as jnp\nfrom jumanji import specs\nfrom jumanji.env import Environment\nfrom jumanji.types import TimeStep\nfrom jumanji.wrappers import Wrapper\n\nfrom mava.types import Observation, ObservationGlobalState, State\n\n\nclass AgentIDWrapper(Wrapper):\n    \"\"\"A wrapper to add a one-hot vector as agent IDs to the original observation.\n    It can be useful in multi-agent environments where agents require unique identification.\n    \"\"\"\n\n    def __init__(self, env: Environment):\n        super().__init__(env)\n\n    def _add_agent_ids(\n        self, timestep: TimeStep, num_agents: int\n    ) -> Union[Observation, ObservationGlobalState]:\n        \"\"\"Adds agent IDs to the observation.\"\"\"\n        obs = timestep.observation\n        agent_ids = jnp.eye(num_agents)\n        agents_view = jnp.concatenate([agent_ids, obs.agents_view], axis=-1)\n\n        return obs._replace(agents_view=agents_view)  # type: ignore\n\n    def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:\n        \"\"\"Reset the environment.\"\"\"\n        state, timestep = self._env.reset(key)\n        timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)\n\n        return state, timestep\n\n    def step(\n        self,\n        state: State,\n        action: chex.Array,\n    ) -> Tuple[State, TimeStep]:\n        \"\"\"Step the environment.\"\"\"\n        state, timestep = self._env.step(state, action)\n        timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)\n\n        return state, timestep\n\n    def observation_spec(\n        self,\n    ) -> Union[specs.Spec[Observation], specs.Spec[ObservationGlobalState]]:\n        \"\"\"Specification of the observation of the selected environment.\"\"\"\n        obs_spec = self._env.observation_spec()\n        num_obs_features = obs_spec.agents_view.shape[-1] + self._env.num_agents\n        dtype = obs_spec.agents_view.dtype\n        agents_view = specs.Array((self._env.num_agents, num_obs_features), dtype, \"agents_view\")\n\n        return obs_spec.replace(agents_view=agents_view)\n\n\nclass GlobalStateWrapper(Wrapper):\n    \"\"\"Wrapper for adding global state to an environment that follows the mava API.\n\n    The wrapper includes a global environment state to be used by the centralised critic.\n    Note here that since most environments do not have a global state, we create one\n    by concatenating the observations of all agents.\n    \"\"\"\n\n    def modify_timestep(self, timestep: TimeStep) -> TimeStep[ObservationGlobalState]:\n        global_state = jnp.concatenate(timestep.observation.agents_view, axis=0)\n        global_state = jnp.tile(global_state, (self._env.num_agents, 1))\n\n        observation = ObservationGlobalState(\n            global_state=global_state,\n            agents_view=timestep.observation.agents_view,\n            action_mask=timestep.observation.action_mask,\n            step_count=timestep.observation.step_count,\n        )\n\n        return timestep.replace(observation=observation)\n\n    def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:\n        \"\"\"Reset the environment. Updates the step count.\"\"\"\n        state, timestep = self._env.reset(key)\n        return state, self.modify_timestep(timestep)\n\n    def step(self, state: State, action: chex.Array) -> Tuple[State, TimeStep]:\n        \"\"\"Step the environment. Updates the step count.\"\"\"\n        state, timestep = self._env.step(state, action)\n        return state, self.modify_timestep(timestep)\n\n    def observation_spec(self) -> specs.Spec[ObservationGlobalState]:\n        \"\"\"Specification of the observation of the selected environment.\"\"\"\n\n        obs_spec = self._env.observation_spec()\n        num_obs_features = obs_spec.agents_view.shape[-1]\n        global_state = specs.Array(\n            (self._env.num_agents, self._env.num_agents * num_obs_features),\n            obs_spec.agents_view.dtype,\n            \"global_state\",\n        )\n\n        return specs.Spec(\n            ObservationGlobalState,\n            \"ObservationSpec\",\n            agents_view=obs_spec.agents_view,\n            action_mask=obs_spec.action_mask,\n            global_state=global_state,\n            step_count=obs_spec.step_count,\n        )\n", "path": "mava/wrappers/observation.py"}], "after_files": [{"content": "# Copyright 2022 InstaDeep Ltd. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import Tuple, Union\n\nimport chex\nimport jax.numpy as jnp\nfrom jumanji import specs\nfrom jumanji.env import Environment\nfrom jumanji.types import TimeStep\nfrom jumanji.wrappers import Wrapper\n\nfrom mava.types import Observation, ObservationGlobalState, State\n\n\nclass AgentIDWrapper(Wrapper):\n    \"\"\"A wrapper to add a one-hot vector as agent IDs to the original observation.\n    It can be useful in multi-agent environments where agents require unique identification.\n    \"\"\"\n\n    def __init__(self, env: Environment):\n        super().__init__(env)\n\n    def _add_agent_ids(\n        self, timestep: TimeStep, num_agents: int\n    ) -> Union[Observation, ObservationGlobalState]:\n        \"\"\"Adds agent IDs to the observation.\"\"\"\n        obs = timestep.observation\n        agent_ids = jnp.eye(num_agents)\n        agents_view = jnp.concatenate(\n            [agent_ids, obs.agents_view],\n            axis=-1,\n            dtype=obs.agents_view.dtype,\n        )\n\n        return obs._replace(agents_view=agents_view)  # type: ignore\n\n    def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:\n        \"\"\"Reset the environment.\"\"\"\n        state, timestep = self._env.reset(key)\n        timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)\n\n        return state, timestep\n\n    def step(\n        self,\n        state: State,\n        action: chex.Array,\n    ) -> Tuple[State, TimeStep]:\n        \"\"\"Step the environment.\"\"\"\n        state, timestep = self._env.step(state, action)\n        timestep.observation = self._add_agent_ids(timestep, self._env.num_agents)\n\n        return state, timestep\n\n    def observation_spec(\n        self,\n    ) -> Union[specs.Spec[Observation], specs.Spec[ObservationGlobalState]]:\n        \"\"\"Specification of the observation of the selected environment.\"\"\"\n        obs_spec = self._env.observation_spec()\n        num_obs_features = obs_spec.agents_view.shape[-1] + self._env.num_agents\n        dtype = obs_spec.agents_view.dtype\n        agents_view = specs.Array((self._env.num_agents, num_obs_features), dtype, \"agents_view\")\n\n        return obs_spec.replace(agents_view=agents_view)\n\n\nclass GlobalStateWrapper(Wrapper):\n    \"\"\"Wrapper for adding global state to an environment that follows the mava API.\n\n    The wrapper includes a global environment state to be used by the centralised critic.\n    Note here that since most environments do not have a global state, we create one\n    by concatenating the observations of all agents.\n    \"\"\"\n\n    def modify_timestep(self, timestep: TimeStep) -> TimeStep[ObservationGlobalState]:\n        global_state = jnp.concatenate(timestep.observation.agents_view, axis=0)\n        global_state = jnp.tile(global_state, (self._env.num_agents, 1))\n\n        observation = ObservationGlobalState(\n            global_state=global_state,\n            agents_view=timestep.observation.agents_view,\n            action_mask=timestep.observation.action_mask,\n            step_count=timestep.observation.step_count,\n        )\n\n        return timestep.replace(observation=observation)\n\n    def reset(self, key: chex.PRNGKey) -> Tuple[State, TimeStep]:\n        \"\"\"Reset the environment. Updates the step count.\"\"\"\n        state, timestep = self._env.reset(key)\n        return state, self.modify_timestep(timestep)\n\n    def step(self, state: State, action: chex.Array) -> Tuple[State, TimeStep]:\n        \"\"\"Step the environment. Updates the step count.\"\"\"\n        state, timestep = self._env.step(state, action)\n        return state, self.modify_timestep(timestep)\n\n    def observation_spec(self) -> specs.Spec[ObservationGlobalState]:\n        \"\"\"Specification of the observation of the selected environment.\"\"\"\n\n        obs_spec = self._env.observation_spec()\n        num_obs_features = obs_spec.agents_view.shape[-1]\n        global_state = specs.Array(\n            (self._env.num_agents, self._env.num_agents * num_obs_features),\n            obs_spec.agents_view.dtype,\n            \"global_state\",\n        )\n\n        return specs.Spec(\n            ObservationGlobalState,\n            \"ObservationSpec\",\n            agents_view=obs_spec.agents_view,\n            action_mask=obs_spec.action_mask,\n            global_state=global_state,\n            step_count=obs_spec.step_count,\n        )\n", "path": "mava/wrappers/observation.py"}]}