prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Learning rate printing
  Now it will only print 0.00. Maybe we should print it with scientific notation.
  
  </issue>
  <code>
  [start of speechbrain/utils/train_logger.py]
  1 import logging
  2 from speechbrain.utils.edit_distance import wer_summary
  3 
  4 logger = logging.getLogger(__name__)
  5 
  6 
  7 class TrainLogger:
  8     """Abstract class defining an interface for training loggers."""
  9 
  10     def log_stats(
  11         self,
  12         stats_meta,
  13         train_stats=None,
  14         valid_stats=None,
  15         test_stats=None,
  16         verbose=False,
  17     ):
  18         """Log the stats for one epoch.
  19 
  20         Arguments
  21         ---------
  22         stats_meta : dict of str:scalar pairs
  23             Meta information about the stats (e.g. epoch, learning-rate, etc.)
  24         train_stats : dict of str:list pairs
  25             Each loss type is represented with a str : list pair including
  26             all the values for the training pass.
  27         valid_stats : dict of str:list pairs
  28             Each loss type is represented with a str : list pair including
  29             all the values for the validation pass.
  30         test_stats : dict of str:list pairs
  31             Each loss type is represented with a str : list pair including
  32             all the values for the test pass.
  33         verbose : bool
  34             Whether to also put logging information to the standard logger.
  35         """
  36         raise NotImplementedError
  37 
  38 
  39 class FileTrainLogger(TrainLogger):
  40     """Text logger of training information
  41 
  42     Arguments
  43     ---------
  44     save_file : str
  45         The file to use for logging train information.
  46     summary_fns : dict of str:function pairs
  47         Each summary function should take a list produced as output
  48         from a training/validation pass and summarize it to a single scalar.
  49     """
  50 
  51     def __init__(self, save_file, summary_fns):
  52         self.save_file = save_file
  53         self.summary_fns = summary_fns
  54 
  55     def _item_to_string(self, key, value, dataset=None):
  56         """Convert one item to string, handling floats"""
  57         if isinstance(value, float):
  58             value = f"{value:.2f}"
  59         if dataset is not None:
  60             key = f"{dataset} {key}"
  61         return f"{key}: {value}"
  62 
  63     def _stats_to_string(self, stats, dataset=None):
  64         """Convert all stats to a single string summary"""
  65         return ", ".join(
  66             [self._item_to_string(k, v, dataset) for k, v in stats.items()]
  67         )
  68 
  69     def log_stats(
  70         self,
  71         stats_meta,
  72         train_stats=None,
  73         valid_stats=None,
  74         test_stats=None,
  75         verbose=True,
  76     ):
  77         """See TrainLogger.log_stats()"""
  78         string_summary = self._stats_to_string(stats_meta)
  79         for dataset, stats in [
  80             ("train", train_stats),
  81             ("valid", valid_stats),
  82             ("test", test_stats),
  83         ]:
  84             if stats is None:
  85                 continue
  86             summary = {}
  87             for stat, value_list in stats.items():
  88                 summary[stat] = self.summary_fns[stat](value_list)
  89             string_summary += " - " + self._stats_to_string(summary, dataset)
  90 
  91         with open(self.save_file, "a") as fout:
  92             print(string_summary, file=fout)
  93         if verbose:
  94             logger.info(string_summary)
  95 
  96 
  97 class TensorboardLogger(TrainLogger):
  98     """Logs training information in the format required by Tensorboard.
  99 
  100     Arguments
  101     ---------
  102     save_dir : str
  103         A directory for storing all the relevant logs
  104 
  105     Raises
  106     ------
  107     ImportError if Tensorboard is not installed.
  108     """
  109 
  110     def __init__(self, save_dir):
  111         self.save_dir = save_dir
  112 
  113         # Raises ImportError if TensorBoard is not installed
  114         from torch.utils.tensorboard import SummaryWriter
  115 
  116         self.writer = SummaryWriter(self.save_dir)
  117         self.global_step = {"train": {}, "valid": {}, "meta": 0}
  118 
  119     def log_stats(
  120         self,
  121         stats_meta,
  122         train_stats=None,
  123         valid_stats=None,
  124         test_stats=None,
  125         verbose=False,
  126     ):
  127         """See TrainLogger.log_stats()"""
  128         self.global_step["meta"] += 1
  129         for name, value in stats_meta.items():
  130             self.writer.add_scalar(name, value, self.global_step["meta"])
  131 
  132         for dataset, stats in [
  133             ("train", train_stats),
  134             ("valid", valid_stats),
  135             ("test", test_stats),
  136         ]:
  137             if stats is None:
  138                 continue
  139             for stat, value_list in stats.items():
  140                 if stat not in self.global_step[dataset]:
  141                     self.global_step[dataset][stat] = 0
  142                 tag = f"{stat}/{dataset}"
  143                 for value in value_list:
  144                     new_global_step = self.global_step[dataset][stat] + 1
  145                     self.writer.add_scalar(tag, value, new_global_step)
  146                     self.global_step[dataset][stat] = new_global_step
  147 
  148 
  149 def summarize_average(stat_list):
  150     return float(sum(stat_list) / len(stat_list))
  151 
  152 
  153 def summarize_error_rate(stat_list):
  154     summary = wer_summary(stat_list)
  155     return summary["WER"]
  156
  [end of speechbrain/utils/train_logger.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/speechbrain/utils/train_logger.py b/speechbrain/utils/train_logger.py
  --- a/speechbrain/utils/train_logger.py
  +++ b/speechbrain/utils/train_logger.py
  @@ -54,8 +54,10 @@
   
       def _item_to_string(self, key, value, dataset=None):
           """Convert one item to string, handling floats"""
  -        if isinstance(value, float):
  +        if isinstance(value, float) and 0.01 < value < 100.0:
               value = f"{value:.2f}"
  +        elif isinstance(value, float):
  +            value = f"{value:.2e}"
           if dataset is not None:
               key = f"{dataset} {key}"
           return f"{key}: {value}"
  
verification_info: {"golden_diff": "diff --git a/speechbrain/utils/train_logger.py b/speechbrain/utils/train_logger.py\n--- a/speechbrain/utils/train_logger.py\n+++ b/speechbrain/utils/train_logger.py\n@@ -54,8 +54,10 @@\n \n     def _item_to_string(self, key, value, dataset=None):\n         \"\"\"Convert one item to string, handling floats\"\"\"\n-        if isinstance(value, float):\n+        if isinstance(value, float) and 0.01 < value < 100.0:\n             value = f\"{value:.2f}\"\n+        elif isinstance(value, float):\n+            value = f\"{value:.2e}\"\n         if dataset is not None:\n             key = f\"{dataset} {key}\"\n         return f\"{key}: {value}\"\n", "issue": "Learning rate printing\nNow it will only print 0.00. Maybe we should print it with scientific notation.\n", "before_files": [{"content": "import logging\nfrom speechbrain.utils.edit_distance import wer_summary\n\nlogger = logging.getLogger(__name__)\n\n\nclass TrainLogger:\n    \"\"\"Abstract class defining an interface for training loggers.\"\"\"\n\n    def log_stats(\n        self,\n        stats_meta,\n        train_stats=None,\n        valid_stats=None,\n        test_stats=None,\n        verbose=False,\n    ):\n        \"\"\"Log the stats for one epoch.\n\n        Arguments\n        ---------\n        stats_meta : dict of str:scalar pairs\n            Meta information about the stats (e.g. epoch, learning-rate, etc.)\n        train_stats : dict of str:list pairs\n            Each loss type is represented with a str : list pair including\n            all the values for the training pass.\n        valid_stats : dict of str:list pairs\n            Each loss type is represented with a str : list pair including\n            all the values for the validation pass.\n        test_stats : dict of str:list pairs\n            Each loss type is represented with a str : list pair including\n            all the values for the test pass.\n        verbose : bool\n            Whether to also put logging information to the standard logger.\n        \"\"\"\n        raise NotImplementedError\n\n\nclass FileTrainLogger(TrainLogger):\n    \"\"\"Text logger of training information\n\n    Arguments\n    ---------\n    save_file : str\n        The file to use for logging train information.\n    summary_fns : dict of str:function pairs\n        Each summary function should take a list produced as output\n        from a training/validation pass and summarize it to a single scalar.\n    \"\"\"\n\n    def __init__(self, save_file, summary_fns):\n        self.save_file = save_file\n        self.summary_fns = summary_fns\n\n    def _item_to_string(self, key, value, dataset=None):\n        \"\"\"Convert one item to string, handling floats\"\"\"\n        if isinstance(value, float):\n            value = f\"{value:.2f}\"\n        if dataset is not None:\n            key = f\"{dataset} {key}\"\n        return f\"{key}: {value}\"\n\n    def _stats_to_string(self, stats, dataset=None):\n        \"\"\"Convert all stats to a single string summary\"\"\"\n        return \", \".join(\n            [self._item_to_string(k, v, dataset) for k, v in stats.items()]\n        )\n\n    def log_stats(\n        self,\n        stats_meta,\n        train_stats=None,\n        valid_stats=None,\n        test_stats=None,\n        verbose=True,\n    ):\n        \"\"\"See TrainLogger.log_stats()\"\"\"\n        string_summary = self._stats_to_string(stats_meta)\n        for dataset, stats in [\n            (\"train\", train_stats),\n            (\"valid\", valid_stats),\n            (\"test\", test_stats),\n        ]:\n            if stats is None:\n                continue\n            summary = {}\n            for stat, value_list in stats.items():\n                summary[stat] = self.summary_fns[stat](value_list)\n            string_summary += \" - \" + self._stats_to_string(summary, dataset)\n\n        with open(self.save_file, \"a\") as fout:\n            print(string_summary, file=fout)\n        if verbose:\n            logger.info(string_summary)\n\n\nclass TensorboardLogger(TrainLogger):\n    \"\"\"Logs training information in the format required by Tensorboard.\n\n    Arguments\n    ---------\n    save_dir : str\n        A directory for storing all the relevant logs\n\n    Raises\n    ------\n    ImportError if Tensorboard is not installed.\n    \"\"\"\n\n    def __init__(self, save_dir):\n        self.save_dir = save_dir\n\n        # Raises ImportError if TensorBoard is not installed\n        from torch.utils.tensorboard import SummaryWriter\n\n        self.writer = SummaryWriter(self.save_dir)\n        self.global_step = {\"train\": {}, \"valid\": {}, \"meta\": 0}\n\n    def log_stats(\n        self,\n        stats_meta,\n        train_stats=None,\n        valid_stats=None,\n        test_stats=None,\n        verbose=False,\n    ):\n        \"\"\"See TrainLogger.log_stats()\"\"\"\n        self.global_step[\"meta\"] += 1\n        for name, value in stats_meta.items():\n            self.writer.add_scalar(name, value, self.global_step[\"meta\"])\n\n        for dataset, stats in [\n            (\"train\", train_stats),\n            (\"valid\", valid_stats),\n            (\"test\", test_stats),\n        ]:\n            if stats is None:\n                continue\n            for stat, value_list in stats.items():\n                if stat not in self.global_step[dataset]:\n                    self.global_step[dataset][stat] = 0\n                tag = f\"{stat}/{dataset}\"\n                for value in value_list:\n                    new_global_step = self.global_step[dataset][stat] + 1\n                    self.writer.add_scalar(tag, value, new_global_step)\n                    self.global_step[dataset][stat] = new_global_step\n\n\ndef summarize_average(stat_list):\n    return float(sum(stat_list) / len(stat_list))\n\n\ndef summarize_error_rate(stat_list):\n    summary = wer_summary(stat_list)\n    return summary[\"WER\"]\n", "path": "speechbrain/utils/train_logger.py"}]}