We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
ImportError for get_rank function
While running the **preprocess.py** I am getting an ImportError of get_rank function from Pytorch. What I have found on the internet that it is now deprecated and is inside the deprecated module. I can send a pull request for a fix of that?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `onmt/utils/statistics.py`
Content:
```
1 """ Statistics calculation utility """
2 from __future__ import division
3 import time
4 import math
5 import sys
6 
7 from torch.distributed import get_rank
8 from onmt.utils.distributed import all_gather_list
9 from onmt.utils.logging import logger
10 
11 
12 class Statistics(object):
13     """
14     Accumulator for loss statistics.
15     Currently calculates:
16 
17     * accuracy
18     * perplexity
19     * elapsed time
20     """
21 
22     def __init__(self, loss=0, n_words=0, n_correct=0):
23         self.loss = loss
24         self.n_words = n_words
25         self.n_correct = n_correct
26         self.n_src_words = 0
27         self.start_time = time.time()
28 
29     @staticmethod
30     def all_gather_stats(stat, max_size=4096):
31         """
32         Gather a `Statistics` object accross multiple process/nodes
33 
34         Args:
35             stat(:obj:Statistics): the statistics object to gather
36                 accross all processes/nodes
37             max_size(int): max buffer size to use
38 
39         Returns:
40             `Statistics`, the update stats object
41         """
42         stats = Statistics.all_gather_stats_list([stat], max_size=max_size)
43         return stats[0]
44 
45     @staticmethod
46     def all_gather_stats_list(stat_list, max_size=4096):
47         """
48         Gather a `Statistics` list accross all processes/nodes
49 
50         Args:
51             stat_list(list([`Statistics`])): list of statistics objects to
52                 gather accross all processes/nodes
53             max_size(int): max buffer size to use
54 
55         Returns:
56             our_stats(list([`Statistics`])): list of updated stats
57         """
58         # Get a list of world_size lists with len(stat_list) Statistics objects
59         all_stats = all_gather_list(stat_list, max_size=max_size)
60 
61         our_rank = get_rank()
62         our_stats = all_stats[our_rank]
63         for other_rank, stats in enumerate(all_stats):
64             if other_rank == our_rank:
65                 continue
66             for i, stat in enumerate(stats):
67                 our_stats[i].update(stat, update_n_src_words=True)
68         return our_stats
69 
70     def update(self, stat, update_n_src_words=False):
71         """
72         Update statistics by suming values with another `Statistics` object
73 
74         Args:
75             stat: another statistic object
76             update_n_src_words(bool): whether to update (sum) `n_src_words`
77                 or not
78 
79         """
80         self.loss += stat.loss
81         self.n_words += stat.n_words
82         self.n_correct += stat.n_correct
83 
84         if update_n_src_words:
85             self.n_src_words += stat.n_src_words
86 
87     def accuracy(self):
88         """ compute accuracy """
89         return 100 * (self.n_correct / self.n_words)
90 
91     def xent(self):
92         """ compute cross entropy """
93         return self.loss / self.n_words
94 
95     def ppl(self):
96         """ compute perplexity """
97         return math.exp(min(self.loss / self.n_words, 100))
98 
99     def elapsed_time(self):
100         """ compute elapsed time """
101         return time.time() - self.start_time
102 
103     def output(self, step, num_steps, learning_rate, start):
104         """Write out statistics to stdout.
105 
106         Args:
107            step (int): current step
108            n_batch (int): total batches
109            start (int): start time of step.
110         """
111         t = self.elapsed_time()
112         logger.info(
113             ("Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; " +
114              "lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec")
115             % (step, num_steps,
116                self.accuracy(),
117                self.ppl(),
118                self.xent(),
119                learning_rate,
120                self.n_src_words / (t + 1e-5),
121                self.n_words / (t + 1e-5),
122                time.time() - start))
123         sys.stdout.flush()
124 
125     def log_tensorboard(self, prefix, writer, learning_rate, step):
126         """ display statistics to tensorboard """
127         t = self.elapsed_time()
128         writer.add_scalar(prefix + "/xent", self.xent(), step)
129         writer.add_scalar(prefix + "/ppl", self.ppl(), step)
130         writer.add_scalar(prefix + "/accuracy", self.accuracy(), step)
131         writer.add_scalar(prefix + "/tgtper", self.n_words / t, step)
132         writer.add_scalar(prefix + "/lr", learning_rate, step)
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/onmt/utils/statistics.py b/onmt/utils/statistics.py
--- a/onmt/utils/statistics.py
+++ b/onmt/utils/statistics.py
@@ -4,8 +4,6 @@
 import math
 import sys
 
-from torch.distributed import get_rank
-from onmt.utils.distributed import all_gather_list
 from onmt.utils.logging import logger
 
 
@@ -55,6 +53,9 @@
         Returns:
             our_stats(list([`Statistics`])): list of updated stats
         """
+        from torch.distributed import get_rank
+        from onmt.utils.distributed import all_gather_list
+
         # Get a list of world_size lists with len(stat_list) Statistics objects
         all_stats = all_gather_list(stat_list, max_size=max_size)

{"golden_diff": "diff --git a/onmt/utils/statistics.py b/onmt/utils/statistics.py\n--- a/onmt/utils/statistics.py\n+++ b/onmt/utils/statistics.py\n@@ -4,8 +4,6 @@\n import math\n import sys\n \n-from torch.distributed import get_rank\n-from onmt.utils.distributed import all_gather_list\n from onmt.utils.logging import logger\n \n \n@@ -55,6 +53,9 @@\n         Returns:\n             our_stats(list([`Statistics`])): list of updated stats\n         \"\"\"\n+        from torch.distributed import get_rank\n+        from onmt.utils.distributed import all_gather_list\n+\n         # Get a list of world_size lists with len(stat_list) Statistics objects\n         all_stats = all_gather_list(stat_list, max_size=max_size)\n", "issue": "ImportError for get_rank function\nWhile running the **preprocess.py** I am getting an ImportError of get_rank function from Pytorch. What I have found on the internet that it is now deprecated and is inside the deprecated module. I can send a pull request for a fix of that?\n", "before_files": [{"content": "\"\"\" Statistics calculation utility \"\"\"\nfrom __future__ import division\nimport time\nimport math\nimport sys\n\nfrom torch.distributed import get_rank\nfrom onmt.utils.distributed import all_gather_list\nfrom onmt.utils.logging import logger\n\n\nclass Statistics(object):\n    \"\"\"\n    Accumulator for loss statistics.\n    Currently calculates:\n\n    * accuracy\n    * perplexity\n    * elapsed time\n    \"\"\"\n\n    def __init__(self, loss=0, n_words=0, n_correct=0):\n        self.loss = loss\n        self.n_words = n_words\n        self.n_correct = n_correct\n        self.n_src_words = 0\n        self.start_time = time.time()\n\n    @staticmethod\n    def all_gather_stats(stat, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` object accross multiple process/nodes\n\n        Args:\n            stat(:obj:Statistics): the statistics object to gather\n                accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            `Statistics`, the update stats object\n        \"\"\"\n        stats = Statistics.all_gather_stats_list([stat], max_size=max_size)\n        return stats[0]\n\n    @staticmethod\n    def all_gather_stats_list(stat_list, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` list accross all processes/nodes\n\n        Args:\n            stat_list(list([`Statistics`])): list of statistics objects to\n                gather accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            our_stats(list([`Statistics`])): list of updated stats\n        \"\"\"\n        # Get a list of world_size lists with len(stat_list) Statistics objects\n        all_stats = all_gather_list(stat_list, max_size=max_size)\n\n        our_rank = get_rank()\n        our_stats = all_stats[our_rank]\n        for other_rank, stats in enumerate(all_stats):\n            if other_rank == our_rank:\n                continue\n            for i, stat in enumerate(stats):\n                our_stats[i].update(stat, update_n_src_words=True)\n        return our_stats\n\n    def update(self, stat, update_n_src_words=False):\n        \"\"\"\n        Update statistics by suming values with another `Statistics` object\n\n        Args:\n            stat: another statistic object\n            update_n_src_words(bool): whether to update (sum) `n_src_words`\n                or not\n\n        \"\"\"\n        self.loss += stat.loss\n        self.n_words += stat.n_words\n        self.n_correct += stat.n_correct\n\n        if update_n_src_words:\n            self.n_src_words += stat.n_src_words\n\n    def accuracy(self):\n        \"\"\" compute accuracy \"\"\"\n        return 100 * (self.n_correct / self.n_words)\n\n    def xent(self):\n        \"\"\" compute cross entropy \"\"\"\n        return self.loss / self.n_words\n\n    def ppl(self):\n        \"\"\" compute perplexity \"\"\"\n        return math.exp(min(self.loss / self.n_words, 100))\n\n    def elapsed_time(self):\n        \"\"\" compute elapsed time \"\"\"\n        return time.time() - self.start_time\n\n    def output(self, step, num_steps, learning_rate, start):\n        \"\"\"Write out statistics to stdout.\n\n        Args:\n           step (int): current step\n           n_batch (int): total batches\n           start (int): start time of step.\n        \"\"\"\n        t = self.elapsed_time()\n        logger.info(\n            (\"Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; \" +\n             \"lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec\")\n            % (step, num_steps,\n               self.accuracy(),\n               self.ppl(),\n               self.xent(),\n               learning_rate,\n               self.n_src_words / (t + 1e-5),\n               self.n_words / (t + 1e-5),\n               time.time() - start))\n        sys.stdout.flush()\n\n    def log_tensorboard(self, prefix, writer, learning_rate, step):\n        \"\"\" display statistics to tensorboard \"\"\"\n        t = self.elapsed_time()\n        writer.add_scalar(prefix + \"/xent\", self.xent(), step)\n        writer.add_scalar(prefix + \"/ppl\", self.ppl(), step)\n        writer.add_scalar(prefix + \"/accuracy\", self.accuracy(), step)\n        writer.add_scalar(prefix + \"/tgtper\", self.n_words / t, step)\n        writer.add_scalar(prefix + \"/lr\", learning_rate, step)\n", "path": "onmt/utils/statistics.py"}], "after_files": [{"content": "\"\"\" Statistics calculation utility \"\"\"\nfrom __future__ import division\nimport time\nimport math\nimport sys\n\nfrom onmt.utils.logging import logger\n\n\nclass Statistics(object):\n    \"\"\"\n    Accumulator for loss statistics.\n    Currently calculates:\n\n    * accuracy\n    * perplexity\n    * elapsed time\n    \"\"\"\n\n    def __init__(self, loss=0, n_words=0, n_correct=0):\n        self.loss = loss\n        self.n_words = n_words\n        self.n_correct = n_correct\n        self.n_src_words = 0\n        self.start_time = time.time()\n\n    @staticmethod\n    def all_gather_stats(stat, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` object accross multiple process/nodes\n\n        Args:\n            stat(:obj:Statistics): the statistics object to gather\n                accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            `Statistics`, the update stats object\n        \"\"\"\n        stats = Statistics.all_gather_stats_list([stat], max_size=max_size)\n        return stats[0]\n\n    @staticmethod\n    def all_gather_stats_list(stat_list, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` list accross all processes/nodes\n\n        Args:\n            stat_list(list([`Statistics`])): list of statistics objects to\n                gather accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            our_stats(list([`Statistics`])): list of updated stats\n        \"\"\"\n        from torch.distributed import get_rank\n        from onmt.utils.distributed import all_gather_list\n\n        # Get a list of world_size lists with len(stat_list) Statistics objects\n        all_stats = all_gather_list(stat_list, max_size=max_size)\n\n        our_rank = get_rank()\n        our_stats = all_stats[our_rank]\n        for other_rank, stats in enumerate(all_stats):\n            if other_rank == our_rank:\n                continue\n            for i, stat in enumerate(stats):\n                our_stats[i].update(stat, update_n_src_words=True)\n        return our_stats\n\n    def update(self, stat, update_n_src_words=False):\n        \"\"\"\n        Update statistics by suming values with another `Statistics` object\n\n        Args:\n            stat: another statistic object\n            update_n_src_words(bool): whether to update (sum) `n_src_words`\n                or not\n\n        \"\"\"\n        self.loss += stat.loss\n        self.n_words += stat.n_words\n        self.n_correct += stat.n_correct\n\n        if update_n_src_words:\n            self.n_src_words += stat.n_src_words\n\n    def accuracy(self):\n        \"\"\" compute accuracy \"\"\"\n        return 100 * (self.n_correct / self.n_words)\n\n    def xent(self):\n        \"\"\" compute cross entropy \"\"\"\n        return self.loss / self.n_words\n\n    def ppl(self):\n        \"\"\" compute perplexity \"\"\"\n        return math.exp(min(self.loss / self.n_words, 100))\n\n    def elapsed_time(self):\n        \"\"\" compute elapsed time \"\"\"\n        return time.time() - self.start_time\n\n    def output(self, step, num_steps, learning_rate, start):\n        \"\"\"Write out statistics to stdout.\n\n        Args:\n           step (int): current step\n           n_batch (int): total batches\n           start (int): start time of step.\n        \"\"\"\n        t = self.elapsed_time()\n        logger.info(\n            (\"Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; \" +\n             \"lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec\")\n            % (step, num_steps,\n               self.accuracy(),\n               self.ppl(),\n               self.xent(),\n               learning_rate,\n               self.n_src_words / (t + 1e-5),\n               self.n_words / (t + 1e-5),\n               time.time() - start))\n        sys.stdout.flush()\n\n    def log_tensorboard(self, prefix, writer, learning_rate, step):\n        \"\"\" display statistics to tensorboard \"\"\"\n        t = self.elapsed_time()\n        writer.add_scalar(prefix + \"/xent\", self.xent(), step)\n        writer.add_scalar(prefix + \"/ppl\", self.ppl(), step)\n        writer.add_scalar(prefix + \"/accuracy\", self.accuracy(), step)\n        writer.add_scalar(prefix + \"/tgtper\", self.n_words / t, step)\n        writer.add_scalar(prefix + \"/lr\", learning_rate, step)\n", "path": "onmt/utils/statistics.py"}]}