prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Remove FireReset wrapper for atari environments
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `gym/wrappers/atari_preprocessing.py`
  Content:
  ```
  1 import numpy as np
  2 
  3 import gym
  4 from gym.spaces import Box
  5 from gym.wrappers import TimeLimit
  6 
  7 
  8 class AtariPreprocessing(gym.Wrapper):
  9     r"""Atari 2600 preprocessings. 
  10 
  11     This class follows the guidelines in 
  12     Machado et al. (2018), "Revisiting the Arcade Learning Environment: 
  13     Evaluation Protocols and Open Problems for General Agents".
  14 
  15     Specifically:
  16 
  17     * NoopReset: obtain initial state by taking random number of no-ops on reset. 
  18     * FireReset: take action on reset for environments that are fixed until firing. 
  19     * Frame skipping: 4 by default
  20     * Max-pooling: most recent two observations
  21     * Termination signal when a life is lost: turned off by default. Not recommended by Machado et al. (2018).
  22     * Resize to a square image: 84x84 by default
  23     * Grayscale observation: optional
  24     * Scale observation: optional
  25 
  26     Args:
  27         env (Env): environment
  28         noop_max (int): max number of no-ops
  29         frame_skip (int): the frequency at which the agent experiences the game. 
  30         screen_size (int): resize Atari frame
  31         terminal_on_life_loss (bool): if True, then step() returns done=True whenever a
  32             life is lost. 
  33         grayscale_obs (bool): if True, then gray scale observation is returned, otherwise, RGB observation
  34             is returned.
  35         scale_obs (bool): if True, then observation normalized in range [0,1] is returned. It also limits memory
  36             optimization benefits of FrameStack Wrapper.
  37     """
  38 
  39     def __init__(self, env, noop_max=30, frame_skip=4, screen_size=84, terminal_on_life_loss=False, grayscale_obs=True,
  40                  scale_obs=False):
  41         super().__init__(env)
  42         assert frame_skip > 0
  43         assert screen_size > 0
  44 
  45         self.noop_max = noop_max
  46         assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
  47 
  48         self.frame_skip = frame_skip
  49         self.screen_size = screen_size
  50         self.terminal_on_life_loss = terminal_on_life_loss
  51         self.grayscale_obs = grayscale_obs
  52         self.scale_obs = scale_obs
  53 
  54         # buffer of most recent two observations for max pooling
  55         if grayscale_obs:
  56             self.obs_buffer = [np.empty(env.observation_space.shape[:2], dtype=np.uint8),
  57                                np.empty(env.observation_space.shape[:2], dtype=np.uint8)]
  58         else:
  59             self.obs_buffer = [np.empty(env.observation_space.shape, dtype=np.uint8),
  60                                np.empty(env.observation_space.shape, dtype=np.uint8)]
  61 
  62         self.ale = env.unwrapped.ale
  63         self.lives = 0
  64         self.game_over = False
  65 
  66         _low, _high, _obs_dtype = (0, 255, np.uint8) if not scale_obs else (0, 1, np.float32)
  67         if grayscale_obs:
  68             self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size), dtype=_obs_dtype)
  69         else:
  70             self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size, 3), dtype=_obs_dtype)
  71 
  72     def step(self, action):
  73         R = 0.0
  74 
  75         for t in range(self.frame_skip):
  76             _, reward, done, info = self.env.step(action)
  77             R += reward
  78             self.game_over = done
  79 
  80             if self.terminal_on_life_loss:
  81                 new_lives = self.ale.lives()
  82                 done = done or new_lives < self.lives
  83                 self.lives = new_lives
  84 
  85             if done:
  86                 break
  87             if t == self.frame_skip - 2:
  88                 if self.grayscale_obs:
  89                     self.ale.getScreenGrayscale(self.obs_buffer[0])
  90                 else:
  91                     self.ale.getScreenRGB2(self.obs_buffer[0])
  92             elif t == self.frame_skip - 1:
  93                 if self.grayscale_obs:
  94                     self.ale.getScreenGrayscale(self.obs_buffer[1])
  95                 else:
  96                     self.ale.getScreenRGB2(self.obs_buffer[1])
  97         return self._get_obs(), R, done, info
  98 
  99     def reset(self, **kwargs):
  100         # NoopReset
  101         self.env.reset(**kwargs)
  102         noops = self.env.unwrapped.np_random.randint(1, self.noop_max + 1) if self.noop_max > 0 else 0
  103         for _ in range(noops):
  104             _, _, done, _ = self.env.step(0)
  105             if done:
  106                 self.env.reset(**kwargs)
  107 
  108         # FireReset
  109         action_meanings = self.env.unwrapped.get_action_meanings()
  110         if action_meanings[1] == 'FIRE' and len(action_meanings) >= 3:
  111             self.env.step(1)
  112             self.env.step(2)
  113 
  114         self.lives = self.ale.lives()
  115         if self.grayscale_obs:
  116             self.ale.getScreenGrayscale(self.obs_buffer[0])
  117         else:
  118             self.ale.getScreenRGB2(self.obs_buffer[0])
  119         self.obs_buffer[1].fill(0)
  120         return self._get_obs()
  121 
  122     def _get_obs(self):
  123         import cv2
  124         if self.frame_skip > 1:  # more efficient in-place pooling
  125             np.maximum(self.obs_buffer[0], self.obs_buffer[1], out=self.obs_buffer[0])
  126         obs = cv2.resize(self.obs_buffer[0], (self.screen_size, self.screen_size), interpolation=cv2.INTER_AREA)
  127 
  128         if self.scale_obs:
  129             obs = np.asarray(obs, dtype=np.float32) / 255.0
  130         else:
  131             obs = np.asarray(obs, dtype=np.uint8)
  132         return obs
  133
  ```
  --- 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/gym/wrappers/atari_preprocessing.py b/gym/wrappers/atari_preprocessing.py
  --- a/gym/wrappers/atari_preprocessing.py
  +++ b/gym/wrappers/atari_preprocessing.py
  @@ -15,7 +15,6 @@
       Specifically:
   
       * NoopReset: obtain initial state by taking random number of no-ops on reset. 
  -    * FireReset: take action on reset for environments that are fixed until firing. 
       * Frame skipping: 4 by default
       * Max-pooling: most recent two observations
       * Termination signal when a life is lost: turned off by default. Not recommended by Machado et al. (2018).
  @@ -105,12 +104,6 @@
               if done:
                   self.env.reset(**kwargs)
   
  -        # FireReset
  -        action_meanings = self.env.unwrapped.get_action_meanings()
  -        if action_meanings[1] == 'FIRE' and len(action_meanings) >= 3:
  -            self.env.step(1)
  -            self.env.step(2)
  -
           self.lives = self.ale.lives()
           if self.grayscale_obs:
               self.ale.getScreenGrayscale(self.obs_buffer[0])
  
verification_info: {"golden_diff": "diff --git a/gym/wrappers/atari_preprocessing.py b/gym/wrappers/atari_preprocessing.py\n--- a/gym/wrappers/atari_preprocessing.py\n+++ b/gym/wrappers/atari_preprocessing.py\n@@ -15,7 +15,6 @@\n     Specifically:\n \n     * NoopReset: obtain initial state by taking random number of no-ops on reset. \n-    * FireReset: take action on reset for environments that are fixed until firing. \n     * Frame skipping: 4 by default\n     * Max-pooling: most recent two observations\n     * Termination signal when a life is lost: turned off by default. Not recommended by Machado et al. (2018).\n@@ -105,12 +104,6 @@\n             if done:\n                 self.env.reset(**kwargs)\n \n-        # FireReset\n-        action_meanings = self.env.unwrapped.get_action_meanings()\n-        if action_meanings[1] == 'FIRE' and len(action_meanings) >= 3:\n-            self.env.step(1)\n-            self.env.step(2)\n-\n         self.lives = self.ale.lives()\n         if self.grayscale_obs:\n             self.ale.getScreenGrayscale(self.obs_buffer[0])\n", "issue": "Remove FireReset wrapper for atari environments\n\n", "before_files": [{"content": "import numpy as np\n\nimport gym\nfrom gym.spaces import Box\nfrom gym.wrappers import TimeLimit\n\n\nclass AtariPreprocessing(gym.Wrapper):\n    r\"\"\"Atari 2600 preprocessings. \n\n    This class follows the guidelines in \n    Machado et al. (2018), \"Revisiting the Arcade Learning Environment: \n    Evaluation Protocols and Open Problems for General Agents\".\n\n    Specifically:\n\n    * NoopReset: obtain initial state by taking random number of no-ops on reset. \n    * FireReset: take action on reset for environments that are fixed until firing. \n    * Frame skipping: 4 by default\n    * Max-pooling: most recent two observations\n    * Termination signal when a life is lost: turned off by default. Not recommended by Machado et al. (2018).\n    * Resize to a square image: 84x84 by default\n    * Grayscale observation: optional\n    * Scale observation: optional\n\n    Args:\n        env (Env): environment\n        noop_max (int): max number of no-ops\n        frame_skip (int): the frequency at which the agent experiences the game. \n        screen_size (int): resize Atari frame\n        terminal_on_life_loss (bool): if True, then step() returns done=True whenever a\n            life is lost. \n        grayscale_obs (bool): if True, then gray scale observation is returned, otherwise, RGB observation\n            is returned.\n        scale_obs (bool): if True, then observation normalized in range [0,1] is returned. It also limits memory\n            optimization benefits of FrameStack Wrapper.\n    \"\"\"\n\n    def __init__(self, env, noop_max=30, frame_skip=4, screen_size=84, terminal_on_life_loss=False, grayscale_obs=True,\n                 scale_obs=False):\n        super().__init__(env)\n        assert frame_skip > 0\n        assert screen_size > 0\n\n        self.noop_max = noop_max\n        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'\n\n        self.frame_skip = frame_skip\n        self.screen_size = screen_size\n        self.terminal_on_life_loss = terminal_on_life_loss\n        self.grayscale_obs = grayscale_obs\n        self.scale_obs = scale_obs\n\n        # buffer of most recent two observations for max pooling\n        if grayscale_obs:\n            self.obs_buffer = [np.empty(env.observation_space.shape[:2], dtype=np.uint8),\n                               np.empty(env.observation_space.shape[:2], dtype=np.uint8)]\n        else:\n            self.obs_buffer = [np.empty(env.observation_space.shape, dtype=np.uint8),\n                               np.empty(env.observation_space.shape, dtype=np.uint8)]\n\n        self.ale = env.unwrapped.ale\n        self.lives = 0\n        self.game_over = False\n\n        _low, _high, _obs_dtype = (0, 255, np.uint8) if not scale_obs else (0, 1, np.float32)\n        if grayscale_obs:\n            self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size), dtype=_obs_dtype)\n        else:\n            self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size, 3), dtype=_obs_dtype)\n\n    def step(self, action):\n        R = 0.0\n\n        for t in range(self.frame_skip):\n            _, reward, done, info = self.env.step(action)\n            R += reward\n            self.game_over = done\n\n            if self.terminal_on_life_loss:\n                new_lives = self.ale.lives()\n                done = done or new_lives < self.lives\n                self.lives = new_lives\n\n            if done:\n                break\n            if t == self.frame_skip - 2:\n                if self.grayscale_obs:\n                    self.ale.getScreenGrayscale(self.obs_buffer[0])\n                else:\n                    self.ale.getScreenRGB2(self.obs_buffer[0])\n            elif t == self.frame_skip - 1:\n                if self.grayscale_obs:\n                    self.ale.getScreenGrayscale(self.obs_buffer[1])\n                else:\n                    self.ale.getScreenRGB2(self.obs_buffer[1])\n        return self._get_obs(), R, done, info\n\n    def reset(self, **kwargs):\n        # NoopReset\n        self.env.reset(**kwargs)\n        noops = self.env.unwrapped.np_random.randint(1, self.noop_max + 1) if self.noop_max > 0 else 0\n        for _ in range(noops):\n            _, _, done, _ = self.env.step(0)\n            if done:\n                self.env.reset(**kwargs)\n\n        # FireReset\n        action_meanings = self.env.unwrapped.get_action_meanings()\n        if action_meanings[1] == 'FIRE' and len(action_meanings) >= 3:\n            self.env.step(1)\n            self.env.step(2)\n\n        self.lives = self.ale.lives()\n        if self.grayscale_obs:\n            self.ale.getScreenGrayscale(self.obs_buffer[0])\n        else:\n            self.ale.getScreenRGB2(self.obs_buffer[0])\n        self.obs_buffer[1].fill(0)\n        return self._get_obs()\n\n    def _get_obs(self):\n        import cv2\n        if self.frame_skip > 1:  # more efficient in-place pooling\n            np.maximum(self.obs_buffer[0], self.obs_buffer[1], out=self.obs_buffer[0])\n        obs = cv2.resize(self.obs_buffer[0], (self.screen_size, self.screen_size), interpolation=cv2.INTER_AREA)\n\n        if self.scale_obs:\n            obs = np.asarray(obs, dtype=np.float32) / 255.0\n        else:\n            obs = np.asarray(obs, dtype=np.uint8)\n        return obs\n", "path": "gym/wrappers/atari_preprocessing.py"}], "after_files": [{"content": "import numpy as np\n\nimport gym\nfrom gym.spaces import Box\nfrom gym.wrappers import TimeLimit\n\n\nclass AtariPreprocessing(gym.Wrapper):\n    r\"\"\"Atari 2600 preprocessings. \n\n    This class follows the guidelines in \n    Machado et al. (2018), \"Revisiting the Arcade Learning Environment: \n    Evaluation Protocols and Open Problems for General Agents\".\n\n    Specifically:\n\n    * NoopReset: obtain initial state by taking random number of no-ops on reset. \n    * Frame skipping: 4 by default\n    * Max-pooling: most recent two observations\n    * Termination signal when a life is lost: turned off by default. Not recommended by Machado et al. (2018).\n    * Resize to a square image: 84x84 by default\n    * Grayscale observation: optional\n    * Scale observation: optional\n\n    Args:\n        env (Env): environment\n        noop_max (int): max number of no-ops\n        frame_skip (int): the frequency at which the agent experiences the game. \n        screen_size (int): resize Atari frame\n        terminal_on_life_loss (bool): if True, then step() returns done=True whenever a\n            life is lost. \n        grayscale_obs (bool): if True, then gray scale observation is returned, otherwise, RGB observation\n            is returned.\n        scale_obs (bool): if True, then observation normalized in range [0,1] is returned. It also limits memory\n            optimization benefits of FrameStack Wrapper.\n    \"\"\"\n\n    def __init__(self, env, noop_max=30, frame_skip=4, screen_size=84, terminal_on_life_loss=False, grayscale_obs=True,\n                 scale_obs=False):\n        super().__init__(env)\n        assert frame_skip > 0\n        assert screen_size > 0\n\n        self.noop_max = noop_max\n        assert env.unwrapped.get_action_meanings()[0] == 'NOOP'\n\n        self.frame_skip = frame_skip\n        self.screen_size = screen_size\n        self.terminal_on_life_loss = terminal_on_life_loss\n        self.grayscale_obs = grayscale_obs\n        self.scale_obs = scale_obs\n\n        # buffer of most recent two observations for max pooling\n        if grayscale_obs:\n            self.obs_buffer = [np.empty(env.observation_space.shape[:2], dtype=np.uint8),\n                               np.empty(env.observation_space.shape[:2], dtype=np.uint8)]\n        else:\n            self.obs_buffer = [np.empty(env.observation_space.shape, dtype=np.uint8),\n                               np.empty(env.observation_space.shape, dtype=np.uint8)]\n\n        self.ale = env.unwrapped.ale\n        self.lives = 0\n        self.game_over = False\n\n        _low, _high, _obs_dtype = (0, 255, np.uint8) if not scale_obs else (0, 1, np.float32)\n        if grayscale_obs:\n            self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size), dtype=_obs_dtype)\n        else:\n            self.observation_space = Box(low=_low, high=_high, shape=(screen_size, screen_size, 3), dtype=_obs_dtype)\n\n    def step(self, action):\n        R = 0.0\n\n        for t in range(self.frame_skip):\n            _, reward, done, info = self.env.step(action)\n            R += reward\n            self.game_over = done\n\n            if self.terminal_on_life_loss:\n                new_lives = self.ale.lives()\n                done = done or new_lives < self.lives\n                self.lives = new_lives\n\n            if done:\n                break\n            if t == self.frame_skip - 2:\n                if self.grayscale_obs:\n                    self.ale.getScreenGrayscale(self.obs_buffer[0])\n                else:\n                    self.ale.getScreenRGB2(self.obs_buffer[0])\n            elif t == self.frame_skip - 1:\n                if self.grayscale_obs:\n                    self.ale.getScreenGrayscale(self.obs_buffer[1])\n                else:\n                    self.ale.getScreenRGB2(self.obs_buffer[1])\n        return self._get_obs(), R, done, info\n\n    def reset(self, **kwargs):\n        # NoopReset\n        self.env.reset(**kwargs)\n        noops = self.env.unwrapped.np_random.randint(1, self.noop_max + 1) if self.noop_max > 0 else 0\n        for _ in range(noops):\n            _, _, done, _ = self.env.step(0)\n            if done:\n                self.env.reset(**kwargs)\n\n        self.lives = self.ale.lives()\n        if self.grayscale_obs:\n            self.ale.getScreenGrayscale(self.obs_buffer[0])\n        else:\n            self.ale.getScreenRGB2(self.obs_buffer[0])\n        self.obs_buffer[1].fill(0)\n        return self._get_obs()\n\n    def _get_obs(self):\n        import cv2\n        if self.frame_skip > 1:  # more efficient in-place pooling\n            np.maximum(self.obs_buffer[0], self.obs_buffer[1], out=self.obs_buffer[0])\n        obs = cv2.resize(self.obs_buffer[0], (self.screen_size, self.screen_size), interpolation=cv2.INTER_AREA)\n\n        if self.scale_obs:\n            obs = np.asarray(obs, dtype=np.float32) / 255.0\n        else:\n            obs = np.asarray(obs, dtype=np.uint8)\n        return obs\n", "path": "gym/wrappers/atari_preprocessing.py"}]}