We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
matplotlib.use('Agg') fail
It result in plot fail 
```
_tkinter.TclError: no display name and no $DISPLAY environment variable
```
I fixed this by applying a patch on `espnet/nets/pytorch_backend/transformer/plot.py`
```
@@ -1,5 +1,6 @@
 import logging
-
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt

 from espnet.asr import asr_utils
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `espnet/nets/pytorch_backend/transformer/plot.py`
Content:
```
1 import logging
2 
3 import matplotlib.pyplot as plt
4 
5 from espnet.asr import asr_utils
6 
7 
8 def _plot_and_save_attention(att_w, filename):
9     # dynamically import matplotlib due to not found error
10     from matplotlib.ticker import MaxNLocator
11     import os
12     d = os.path.dirname(filename)
13     if not os.path.exists(d):
14         os.makedirs(d)
15     w, h = plt.figaspect(1.0 / len(att_w))
16     fig = plt.Figure(figsize=(w * 2, h * 2))
17     axes = fig.subplots(1, len(att_w))
18     if len(att_w) == 1:
19         axes = [axes]
20     for ax, aw in zip(axes, att_w):
21         # plt.subplot(1, len(att_w), h)
22         ax.imshow(aw, aspect="auto")
23         ax.set_xlabel("Input")
24         ax.set_ylabel("Output")
25         ax.xaxis.set_major_locator(MaxNLocator(integer=True))
26         ax.yaxis.set_major_locator(MaxNLocator(integer=True))
27     fig.tight_layout()
28     return fig
29 
30 
31 def savefig(plot, filename):
32     plot.savefig(filename)
33     plt.clf()
34 
35 
36 def plot_multi_head_attention(data, attn_dict, outdir, suffix="png", savefn=savefig):
37     """Plot multi head attentions
38 
39     :param dict data: utts info from json file
40     :param dict[str, torch.Tensor] attn_dict: multi head attention dict.
41         values should be torch.Tensor (head, input_length, output_length)
42     :param str outdir: dir to save fig
43     :param str suffix: filename suffix including image type (e.g., png)
44     :param savefn: function to save
45     """
46     for name, att_ws in attn_dict.items():
47         for idx, att_w in enumerate(att_ws):
48             filename = "%s/%s.%s.%s" % (
49                 outdir, data[idx][0], name, suffix)
50             dec_len = int(data[idx][1]['output'][0]['shape'][0])
51             enc_len = int(data[idx][1]['input'][0]['shape'][0])
52             if "encoder" in name:
53                 att_w = att_w[:, :enc_len, :enc_len]
54             elif "decoder" in name:
55                 if "self" in name:
56                     att_w = att_w[:, :dec_len, :dec_len]
57                 else:
58                     att_w = att_w[:, :dec_len, :enc_len]
59             else:
60                 logging.warning("unknown name for shaping attention")
61             fig = _plot_and_save_attention(att_w, filename)
62             savefn(fig, filename)
63 
64 
65 class PlotAttentionReport(asr_utils.PlotAttentionReport):
66     def plotfn(self, *args, **kwargs):
67         plot_multi_head_attention(*args, **kwargs)
68 
69     def __call__(self, trainer):
70         attn_dict = self.get_attention_weights()
71         suffix = "ep.{.updater.epoch}.png".format(trainer)
72         self.plotfn(self.data, attn_dict, self.outdir, suffix, savefig)
73 
74     def get_attention_weights(self):
75         batch = self.converter([self.transform(self.data)], self.device)
76         if isinstance(batch, tuple):
77             att_ws = self.att_vis_fn(*batch)
78         elif isinstance(batch, dict):
79             att_ws = self.att_vis_fn(**batch)
80         return att_ws
81 
82     def log_attentions(self, logger, step):
83         def log_fig(plot, filename):
84             from os.path import basename
85             logger.add_figure(basename(filename), plot, step)
86             plt.clf()
87 
88         attn_dict = self.get_attention_weights()
89         self.plotfn(self.data, attn_dict, self.outdir, "", log_fig)
90
```
--- 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/espnet/nets/pytorch_backend/transformer/plot.py b/espnet/nets/pytorch_backend/transformer/plot.py
--- a/espnet/nets/pytorch_backend/transformer/plot.py
+++ b/espnet/nets/pytorch_backend/transformer/plot.py
@@ -1,8 +1,7 @@
 import logging
 
-import matplotlib.pyplot as plt
-
 from espnet.asr import asr_utils
+import matplotlib.pyplot as plt
 
 
 def _plot_and_save_attention(att_w, filename):

{"golden_diff": "diff --git a/espnet/nets/pytorch_backend/transformer/plot.py b/espnet/nets/pytorch_backend/transformer/plot.py\n--- a/espnet/nets/pytorch_backend/transformer/plot.py\n+++ b/espnet/nets/pytorch_backend/transformer/plot.py\n@@ -1,8 +1,7 @@\n import logging\n \n-import matplotlib.pyplot as plt\n-\n from espnet.asr import asr_utils\n+import matplotlib.pyplot as plt\n \n \n def _plot_and_save_attention(att_w, filename):\n", "issue": "matplotlib.use('Agg') fail\nIt result in plot fail \r\n```\r\n_tkinter.TclError: no display name and no $DISPLAY environment variable\r\n```\r\nI fixed this by applying a patch on `espnet/nets/pytorch_backend/transformer/plot.py`\r\n```\r\n@@ -1,5 +1,6 @@\r\n import logging\r\n-\r\n+import matplotlib\r\n+matplotlib.use('Agg')\r\n import matplotlib.pyplot as plt\r\n\r\n from espnet.asr import asr_utils\r\n```\n", "before_files": [{"content": "import logging\n\nimport matplotlib.pyplot as plt\n\nfrom espnet.asr import asr_utils\n\n\ndef _plot_and_save_attention(att_w, filename):\n    # dynamically import matplotlib due to not found error\n    from matplotlib.ticker import MaxNLocator\n    import os\n    d = os.path.dirname(filename)\n    if not os.path.exists(d):\n        os.makedirs(d)\n    w, h = plt.figaspect(1.0 / len(att_w))\n    fig = plt.Figure(figsize=(w * 2, h * 2))\n    axes = fig.subplots(1, len(att_w))\n    if len(att_w) == 1:\n        axes = [axes]\n    for ax, aw in zip(axes, att_w):\n        # plt.subplot(1, len(att_w), h)\n        ax.imshow(aw, aspect=\"auto\")\n        ax.set_xlabel(\"Input\")\n        ax.set_ylabel(\"Output\")\n        ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n        ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n    fig.tight_layout()\n    return fig\n\n\ndef savefig(plot, filename):\n    plot.savefig(filename)\n    plt.clf()\n\n\ndef plot_multi_head_attention(data, attn_dict, outdir, suffix=\"png\", savefn=savefig):\n    \"\"\"Plot multi head attentions\n\n    :param dict data: utts info from json file\n    :param dict[str, torch.Tensor] attn_dict: multi head attention dict.\n        values should be torch.Tensor (head, input_length, output_length)\n    :param str outdir: dir to save fig\n    :param str suffix: filename suffix including image type (e.g., png)\n    :param savefn: function to save\n    \"\"\"\n    for name, att_ws in attn_dict.items():\n        for idx, att_w in enumerate(att_ws):\n            filename = \"%s/%s.%s.%s\" % (\n                outdir, data[idx][0], name, suffix)\n            dec_len = int(data[idx][1]['output'][0]['shape'][0])\n            enc_len = int(data[idx][1]['input'][0]['shape'][0])\n            if \"encoder\" in name:\n                att_w = att_w[:, :enc_len, :enc_len]\n            elif \"decoder\" in name:\n                if \"self\" in name:\n                    att_w = att_w[:, :dec_len, :dec_len]\n                else:\n                    att_w = att_w[:, :dec_len, :enc_len]\n            else:\n                logging.warning(\"unknown name for shaping attention\")\n            fig = _plot_and_save_attention(att_w, filename)\n            savefn(fig, filename)\n\n\nclass PlotAttentionReport(asr_utils.PlotAttentionReport):\n    def plotfn(self, *args, **kwargs):\n        plot_multi_head_attention(*args, **kwargs)\n\n    def __call__(self, trainer):\n        attn_dict = self.get_attention_weights()\n        suffix = \"ep.{.updater.epoch}.png\".format(trainer)\n        self.plotfn(self.data, attn_dict, self.outdir, suffix, savefig)\n\n    def get_attention_weights(self):\n        batch = self.converter([self.transform(self.data)], self.device)\n        if isinstance(batch, tuple):\n            att_ws = self.att_vis_fn(*batch)\n        elif isinstance(batch, dict):\n            att_ws = self.att_vis_fn(**batch)\n        return att_ws\n\n    def log_attentions(self, logger, step):\n        def log_fig(plot, filename):\n            from os.path import basename\n            logger.add_figure(basename(filename), plot, step)\n            plt.clf()\n\n        attn_dict = self.get_attention_weights()\n        self.plotfn(self.data, attn_dict, self.outdir, \"\", log_fig)\n", "path": "espnet/nets/pytorch_backend/transformer/plot.py"}], "after_files": [{"content": "import logging\n\nfrom espnet.asr import asr_utils\nimport matplotlib.pyplot as plt\n\n\ndef _plot_and_save_attention(att_w, filename):\n    # dynamically import matplotlib due to not found error\n    from matplotlib.ticker import MaxNLocator\n    import os\n    d = os.path.dirname(filename)\n    if not os.path.exists(d):\n        os.makedirs(d)\n    w, h = plt.figaspect(1.0 / len(att_w))\n    fig = plt.Figure(figsize=(w * 2, h * 2))\n    axes = fig.subplots(1, len(att_w))\n    if len(att_w) == 1:\n        axes = [axes]\n    for ax, aw in zip(axes, att_w):\n        # plt.subplot(1, len(att_w), h)\n        ax.imshow(aw, aspect=\"auto\")\n        ax.set_xlabel(\"Input\")\n        ax.set_ylabel(\"Output\")\n        ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n        ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n    fig.tight_layout()\n    return fig\n\n\ndef savefig(plot, filename):\n    plot.savefig(filename)\n    plt.clf()\n\n\ndef plot_multi_head_attention(data, attn_dict, outdir, suffix=\"png\", savefn=savefig):\n    \"\"\"Plot multi head attentions\n\n    :param dict data: utts info from json file\n    :param dict[str, torch.Tensor] attn_dict: multi head attention dict.\n        values should be torch.Tensor (head, input_length, output_length)\n    :param str outdir: dir to save fig\n    :param str suffix: filename suffix including image type (e.g., png)\n    :param savefn: function to save\n    \"\"\"\n    for name, att_ws in attn_dict.items():\n        for idx, att_w in enumerate(att_ws):\n            filename = \"%s/%s.%s.%s\" % (\n                outdir, data[idx][0], name, suffix)\n            dec_len = int(data[idx][1]['output'][0]['shape'][0])\n            enc_len = int(data[idx][1]['input'][0]['shape'][0])\n            if \"encoder\" in name:\n                att_w = att_w[:, :enc_len, :enc_len]\n            elif \"decoder\" in name:\n                if \"self\" in name:\n                    att_w = att_w[:, :dec_len, :dec_len]\n                else:\n                    att_w = att_w[:, :dec_len, :enc_len]\n            else:\n                logging.warning(\"unknown name for shaping attention\")\n            fig = _plot_and_save_attention(att_w, filename)\n            savefn(fig, filename)\n\n\nclass PlotAttentionReport(asr_utils.PlotAttentionReport):\n    def plotfn(self, *args, **kwargs):\n        plot_multi_head_attention(*args, **kwargs)\n\n    def __call__(self, trainer):\n        attn_dict = self.get_attention_weights()\n        suffix = \"ep.{.updater.epoch}.png\".format(trainer)\n        self.plotfn(self.data, attn_dict, self.outdir, suffix, savefig)\n\n    def get_attention_weights(self):\n        batch = self.converter([self.transform(self.data)], self.device)\n        if isinstance(batch, tuple):\n            att_ws = self.att_vis_fn(*batch)\n        elif isinstance(batch, dict):\n            att_ws = self.att_vis_fn(**batch)\n        return att_ws\n\n    def log_attentions(self, logger, step):\n        def log_fig(plot, filename):\n            from os.path import basename\n            logger.add_figure(basename(filename), plot, step)\n            plt.clf()\n\n        attn_dict = self.get_attention_weights()\n        self.plotfn(self.data, attn_dict, self.outdir, \"\", log_fig)\n", "path": "espnet/nets/pytorch_backend/transformer/plot.py"}]}