We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
ProgressBar iters/sec is too low in CPU mode
The 'iters/sec' speed displayed in the `ProgressBar` extension seems too low when running a model in CPU mode. I suspect this is due to the use of `time.clock()` instead of `time.time()`. `time.clock()` apparently measures the total time spent in all CPU cores, which can cause the measured time to (sometimes greatly) exceed the wall clock time.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `chainer/training/extensions/progress_bar.py`
Content:
```
1 from __future__ import division
2 import datetime
3 import sys
4 import time
5 
6 from chainer.training import extension
7 from chainer.training import trigger
8 
9 
10 class ProgressBar(extension.Extension):
11 
12     """Trainer extension to print a progress bar and recent training status.
13 
14     This extension prints a progress bar at every call. It watches the current
15     iteration and epoch to print the bar.
16 
17     Args:
18         training_length (tuple): Length of whole training. It consists of an
19             integer and either ``'epoch'`` or ``'iteration'``. If this value is
20             omitted and the stop trigger of the trainer is
21             :class:`IntervalTrigger`, this extension uses its attributes to
22             determine the length of the training.
23         update_interval (int): Number of iterations to skip printing the
24             progress bar.
25         bar_length (int): Length of the progress bar in characters.
26         out: Stream to print the bar. Standard output is used by default.
27 
28     """
29     def __init__(self, training_length=None, update_interval=100,
30                  bar_length=50, out=sys.stdout):
31         self._training_length = training_length
32         self._status_template = None
33         self._update_interval = update_interval
34         self._bar_length = bar_length
35         self._out = out
36         self._recent_timing = []
37 
38     def __call__(self, trainer):
39         training_length = self._training_length
40 
41         # initialize some attributes at the first call
42         if training_length is None:
43             t = trainer.stop_trigger
44             if not isinstance(t, trigger.IntervalTrigger):
45                 raise TypeError(
46                     'cannot retrieve the training length from %s' % type(t))
47             training_length = self._training_length = t.period, t.unit
48 
49         stat_template = self._status_template
50         if stat_template is None:
51             stat_template = self._status_template = (
52                 '{0.iteration:10} iter, {0.epoch} epoch / %s %ss\n' %
53                 training_length)
54 
55         length, unit = training_length
56         out = self._out
57 
58         iteration = trainer.updater.iteration
59 
60         # print the progress bar
61         if iteration % self._update_interval == 0:
62             epoch = trainer.updater.epoch_detail
63             recent_timing = self._recent_timing
64             now = time.clock()
65 
66             if len(recent_timing) >= 1:
67                 out.write('\033[J')
68 
69                 if unit == 'iteration':
70                     rate = iteration / length
71                 else:
72                     rate = epoch / length
73 
74                 bar_length = self._bar_length
75                 marks = '#' * int(rate * bar_length)
76                 out.write('     total [{}{}] {:6.2%}\n'.format(
77                     marks, '.' * (bar_length - len(marks)), rate))
78 
79                 epoch_rate = epoch - int(epoch)
80                 marks = '#' * int(epoch_rate * bar_length)
81                 out.write('this epoch [{}{}] {:6.2%}\n'.format(
82                     marks, '.' * (bar_length - len(marks)), epoch_rate))
83 
84                 status = stat_template.format(trainer.updater)
85                 out.write(status)
86 
87                 old_t, old_e, old_sec = recent_timing[0]
88                 speed_t = (iteration - old_t) / (now - old_sec)
89                 speed_e = (epoch - old_e) / (now - old_sec)
90                 if unit == 'iteration':
91                     estimated_time = (length - iteration) / speed_t
92                 else:
93                     estimated_time = (length - epoch) / speed_e
94                 out.write('{:10.5g} iters/sec. Estimated time to finish: {}.\n'
95                           .format(speed_t,
96                                   datetime.timedelta(seconds=estimated_time)))
97 
98                 # move the cursor to the head of the progress bar
99                 out.write('\033[4A')
100                 out.flush()
101 
102                 if len(recent_timing) > 100:
103                     del recent_timing[0]
104 
105             recent_timing.append((iteration, epoch, now))
106 
107     def finalize(self):
108         # delete the progress bar
109         out = self._out
110         out.write('\033[J')
111         out.flush()
112
```
--- 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/chainer/training/extensions/progress_bar.py b/chainer/training/extensions/progress_bar.py
--- a/chainer/training/extensions/progress_bar.py
+++ b/chainer/training/extensions/progress_bar.py
@@ -61,7 +61,7 @@
         if iteration % self._update_interval == 0:
             epoch = trainer.updater.epoch_detail
             recent_timing = self._recent_timing
-            now = time.clock()
+            now = time.time()
 
             if len(recent_timing) >= 1:
                 out.write('\033[J')

{"golden_diff": "diff --git a/chainer/training/extensions/progress_bar.py b/chainer/training/extensions/progress_bar.py\n--- a/chainer/training/extensions/progress_bar.py\n+++ b/chainer/training/extensions/progress_bar.py\n@@ -61,7 +61,7 @@\n         if iteration % self._update_interval == 0:\n             epoch = trainer.updater.epoch_detail\n             recent_timing = self._recent_timing\n-            now = time.clock()\n+            now = time.time()\n \n             if len(recent_timing) >= 1:\n                 out.write('\\033[J')\n", "issue": "ProgressBar iters/sec is too low in CPU mode\nThe 'iters/sec' speed displayed in the `ProgressBar` extension seems too low when running a model in CPU mode. I suspect this is due to the use of `time.clock()` instead of `time.time()`. `time.clock()` apparently measures the total time spent in all CPU cores, which can cause the measured time to (sometimes greatly) exceed the wall clock time.\n\n", "before_files": [{"content": "from __future__ import division\nimport datetime\nimport sys\nimport time\n\nfrom chainer.training import extension\nfrom chainer.training import trigger\n\n\nclass ProgressBar(extension.Extension):\n\n    \"\"\"Trainer extension to print a progress bar and recent training status.\n\n    This extension prints a progress bar at every call. It watches the current\n    iteration and epoch to print the bar.\n\n    Args:\n        training_length (tuple): Length of whole training. It consists of an\n            integer and either ``'epoch'`` or ``'iteration'``. If this value is\n            omitted and the stop trigger of the trainer is\n            :class:`IntervalTrigger`, this extension uses its attributes to\n            determine the length of the training.\n        update_interval (int): Number of iterations to skip printing the\n            progress bar.\n        bar_length (int): Length of the progress bar in characters.\n        out: Stream to print the bar. Standard output is used by default.\n\n    \"\"\"\n    def __init__(self, training_length=None, update_interval=100,\n                 bar_length=50, out=sys.stdout):\n        self._training_length = training_length\n        self._status_template = None\n        self._update_interval = update_interval\n        self._bar_length = bar_length\n        self._out = out\n        self._recent_timing = []\n\n    def __call__(self, trainer):\n        training_length = self._training_length\n\n        # initialize some attributes at the first call\n        if training_length is None:\n            t = trainer.stop_trigger\n            if not isinstance(t, trigger.IntervalTrigger):\n                raise TypeError(\n                    'cannot retrieve the training length from %s' % type(t))\n            training_length = self._training_length = t.period, t.unit\n\n        stat_template = self._status_template\n        if stat_template is None:\n            stat_template = self._status_template = (\n                '{0.iteration:10} iter, {0.epoch} epoch / %s %ss\\n' %\n                training_length)\n\n        length, unit = training_length\n        out = self._out\n\n        iteration = trainer.updater.iteration\n\n        # print the progress bar\n        if iteration % self._update_interval == 0:\n            epoch = trainer.updater.epoch_detail\n            recent_timing = self._recent_timing\n            now = time.clock()\n\n            if len(recent_timing) >= 1:\n                out.write('\\033[J')\n\n                if unit == 'iteration':\n                    rate = iteration / length\n                else:\n                    rate = epoch / length\n\n                bar_length = self._bar_length\n                marks = '#' * int(rate * bar_length)\n                out.write('     total [{}{}] {:6.2%}\\n'.format(\n                    marks, '.' * (bar_length - len(marks)), rate))\n\n                epoch_rate = epoch - int(epoch)\n                marks = '#' * int(epoch_rate * bar_length)\n                out.write('this epoch [{}{}] {:6.2%}\\n'.format(\n                    marks, '.' * (bar_length - len(marks)), epoch_rate))\n\n                status = stat_template.format(trainer.updater)\n                out.write(status)\n\n                old_t, old_e, old_sec = recent_timing[0]\n                speed_t = (iteration - old_t) / (now - old_sec)\n                speed_e = (epoch - old_e) / (now - old_sec)\n                if unit == 'iteration':\n                    estimated_time = (length - iteration) / speed_t\n                else:\n                    estimated_time = (length - epoch) / speed_e\n                out.write('{:10.5g} iters/sec. Estimated time to finish: {}.\\n'\n                          .format(speed_t,\n                                  datetime.timedelta(seconds=estimated_time)))\n\n                # move the cursor to the head of the progress bar\n                out.write('\\033[4A')\n                out.flush()\n\n                if len(recent_timing) > 100:\n                    del recent_timing[0]\n\n            recent_timing.append((iteration, epoch, now))\n\n    def finalize(self):\n        # delete the progress bar\n        out = self._out\n        out.write('\\033[J')\n        out.flush()\n", "path": "chainer/training/extensions/progress_bar.py"}], "after_files": [{"content": "from __future__ import division\nimport datetime\nimport sys\nimport time\n\nfrom chainer.training import extension\nfrom chainer.training import trigger\n\n\nclass ProgressBar(extension.Extension):\n\n    \"\"\"Trainer extension to print a progress bar and recent training status.\n\n    This extension prints a progress bar at every call. It watches the current\n    iteration and epoch to print the bar.\n\n    Args:\n        training_length (tuple): Length of whole training. It consists of an\n            integer and either ``'epoch'`` or ``'iteration'``. If this value is\n            omitted and the stop trigger of the trainer is\n            :class:`IntervalTrigger`, this extension uses its attributes to\n            determine the length of the training.\n        update_interval (int): Number of iterations to skip printing the\n            progress bar.\n        bar_length (int): Length of the progress bar in characters.\n        out: Stream to print the bar. Standard output is used by default.\n\n    \"\"\"\n    def __init__(self, training_length=None, update_interval=100,\n                 bar_length=50, out=sys.stdout):\n        self._training_length = training_length\n        self._status_template = None\n        self._update_interval = update_interval\n        self._bar_length = bar_length\n        self._out = out\n        self._recent_timing = []\n\n    def __call__(self, trainer):\n        training_length = self._training_length\n\n        # initialize some attributes at the first call\n        if training_length is None:\n            t = trainer.stop_trigger\n            if not isinstance(t, trigger.IntervalTrigger):\n                raise TypeError(\n                    'cannot retrieve the training length from %s' % type(t))\n            training_length = self._training_length = t.period, t.unit\n\n        stat_template = self._status_template\n        if stat_template is None:\n            stat_template = self._status_template = (\n                '{0.iteration:10} iter, {0.epoch} epoch / %s %ss\\n' %\n                training_length)\n\n        length, unit = training_length\n        out = self._out\n\n        iteration = trainer.updater.iteration\n\n        # print the progress bar\n        if iteration % self._update_interval == 0:\n            epoch = trainer.updater.epoch_detail\n            recent_timing = self._recent_timing\n            now = time.time()\n\n            if len(recent_timing) >= 1:\n                out.write('\\033[J')\n\n                if unit == 'iteration':\n                    rate = iteration / length\n                else:\n                    rate = epoch / length\n\n                bar_length = self._bar_length\n                marks = '#' * int(rate * bar_length)\n                out.write('     total [{}{}] {:6.2%}\\n'.format(\n                    marks, '.' * (bar_length - len(marks)), rate))\n\n                epoch_rate = epoch - int(epoch)\n                marks = '#' * int(epoch_rate * bar_length)\n                out.write('this epoch [{}{}] {:6.2%}\\n'.format(\n                    marks, '.' * (bar_length - len(marks)), epoch_rate))\n\n                status = stat_template.format(trainer.updater)\n                out.write(status)\n\n                old_t, old_e, old_sec = recent_timing[0]\n                speed_t = (iteration - old_t) / (now - old_sec)\n                speed_e = (epoch - old_e) / (now - old_sec)\n                if unit == 'iteration':\n                    estimated_time = (length - iteration) / speed_t\n                else:\n                    estimated_time = (length - epoch) / speed_e\n                out.write('{:10.5g} iters/sec. Estimated time to finish: {}.\\n'\n                          .format(speed_t,\n                                  datetime.timedelta(seconds=estimated_time)))\n\n                # move the cursor to the head of the progress bar\n                out.write('\\033[4A')\n                out.flush()\n\n                if len(recent_timing) > 100:\n                    del recent_timing[0]\n\n            recent_timing.append((iteration, epoch, now))\n\n    def finalize(self):\n        # delete the progress bar\n        out = self._out\n        out.write('\\033[J')\n        out.flush()\n", "path": "chainer/training/extensions/progress_bar.py"}]}