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()

```

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])

{"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"}]}