prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Improve Early Stopping API
  See discussion in #2090
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `flax/training/early_stopping.py`
  Content:
  ```
  1 # Copyright 2023 The Flax Authors.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #     http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 
  15 """Early stopping."""
  16 
  17 import math
  18 from flax import struct
  19 
  20 
  21 class EarlyStopping(struct.PyTreeNode):
  22   """Early stopping to avoid overfitting during training.
  23 
  24   The following example stops training early if the difference between losses
  25   recorded in the current epoch and previous epoch is less than 1e-3
  26   consecutively for 2 times::
  27 
  28     early_stop = EarlyStopping(min_delta=1e-3, patience=2)
  29     for epoch in range(1, num_epochs+1):
  30       rng, input_rng = jax.random.split(rng)
  31       optimizer, train_metrics = train_epoch(
  32           optimizer, train_ds, config.batch_size, epoch, input_rng)
  33       _, early_stop = early_stop.update(train_metrics['loss'])
  34       if early_stop.should_stop:
  35         print('Met early stopping criteria, breaking...')
  36         break
  37 
  38   Attributes:
  39     min_delta: Minimum delta between updates to be considered an
  40         improvement.
  41     patience: Number of steps of no improvement before stopping.
  42     best_metric: Current best metric value.
  43     patience_count: Number of steps since last improving update.
  44     should_stop: Whether the training loop should stop to avoid
  45         overfitting.
  46   """
  47 
  48   min_delta: float = 0
  49   patience: int = 0
  50   best_metric: float = float('inf')
  51   patience_count: int = 0
  52   should_stop: bool = False
  53 
  54   def reset(self):
  55     return self.replace(
  56         best_metric=float('inf'), patience_count=0, should_stop=False
  57     )
  58 
  59   def update(self, metric):
  60     """Update the state based on metric.
  61 
  62     Returns:
  63       A pair (has_improved, early_stop), where `has_improved` is True when there
  64       was an improvement greater than `min_delta` from the previous
  65       `best_metric` and `early_stop` is the updated `EarlyStop` object.
  66     """
  67 
  68     if (
  69         math.isinf(self.best_metric)
  70         or self.best_metric - metric > self.min_delta
  71     ):
  72       return True, self.replace(best_metric=metric, patience_count=0)
  73     else:
  74       should_stop = self.patience_count >= self.patience or self.should_stop
  75       return False, self.replace(
  76           patience_count=self.patience_count + 1, should_stop=should_stop
  77       )
  78
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/flax/training/early_stopping.py b/flax/training/early_stopping.py
  --- a/flax/training/early_stopping.py
  +++ b/flax/training/early_stopping.py
  @@ -30,7 +30,7 @@
         rng, input_rng = jax.random.split(rng)
         optimizer, train_metrics = train_epoch(
             optimizer, train_ds, config.batch_size, epoch, input_rng)
  -      _, early_stop = early_stop.update(train_metrics['loss'])
  +      early_stop = early_stop.update(train_metrics['loss'])
         if early_stop.should_stop:
           print('Met early stopping criteria, breaking...')
           break
  @@ -43,6 +43,8 @@
       patience_count: Number of steps since last improving update.
       should_stop: Whether the training loop should stop to avoid
           overfitting.
  +    has_improved: Whether the metric has improved greater or
  +      equal to the min_delta in the last `.update` call.
     """
   
     min_delta: float = 0
  @@ -50,28 +52,29 @@
     best_metric: float = float('inf')
     patience_count: int = 0
     should_stop: bool = False
  +  has_improved: bool = False
   
     def reset(self):
       return self.replace(
  -        best_metric=float('inf'), patience_count=0, should_stop=False
  +        best_metric=float('inf'), patience_count=0, should_stop=False, has_improved=False
       )
   
     def update(self, metric):
       """Update the state based on metric.
   
       Returns:
  -      A pair (has_improved, early_stop), where `has_improved` is True when there
  -      was an improvement greater than `min_delta` from the previous
  -      `best_metric` and `early_stop` is the updated `EarlyStop` object.
  +      The updated EarlyStopping class. The `.has_improved` attribute is True
  +      when there was an improvement greater than `min_delta` from the previous
  +      `best_metric`.
       """
   
       if (
           math.isinf(self.best_metric)
           or self.best_metric - metric > self.min_delta
       ):
  -      return True, self.replace(best_metric=metric, patience_count=0)
  +      return self.replace(best_metric=metric, patience_count=0, has_improved=True)
       else:
         should_stop = self.patience_count >= self.patience or self.should_stop
  -      return False, self.replace(
  -          patience_count=self.patience_count + 1, should_stop=should_stop
  +      return self.replace(
  +          patience_count=self.patience_count + 1, should_stop=should_stop, has_improved=False
         )
  
verification_info: {"golden_diff": "diff --git a/flax/training/early_stopping.py b/flax/training/early_stopping.py\n--- a/flax/training/early_stopping.py\n+++ b/flax/training/early_stopping.py\n@@ -30,7 +30,7 @@\n       rng, input_rng = jax.random.split(rng)\n       optimizer, train_metrics = train_epoch(\n           optimizer, train_ds, config.batch_size, epoch, input_rng)\n-      _, early_stop = early_stop.update(train_metrics['loss'])\n+      early_stop = early_stop.update(train_metrics['loss'])\n       if early_stop.should_stop:\n         print('Met early stopping criteria, breaking...')\n         break\n@@ -43,6 +43,8 @@\n     patience_count: Number of steps since last improving update.\n     should_stop: Whether the training loop should stop to avoid\n         overfitting.\n+    has_improved: Whether the metric has improved greater or\n+      equal to the min_delta in the last `.update` call.\n   \"\"\"\n \n   min_delta: float = 0\n@@ -50,28 +52,29 @@\n   best_metric: float = float('inf')\n   patience_count: int = 0\n   should_stop: bool = False\n+  has_improved: bool = False\n \n   def reset(self):\n     return self.replace(\n-        best_metric=float('inf'), patience_count=0, should_stop=False\n+        best_metric=float('inf'), patience_count=0, should_stop=False, has_improved=False\n     )\n \n   def update(self, metric):\n     \"\"\"Update the state based on metric.\n \n     Returns:\n-      A pair (has_improved, early_stop), where `has_improved` is True when there\n-      was an improvement greater than `min_delta` from the previous\n-      `best_metric` and `early_stop` is the updated `EarlyStop` object.\n+      The updated EarlyStopping class. The `.has_improved` attribute is True\n+      when there was an improvement greater than `min_delta` from the previous\n+      `best_metric`.\n     \"\"\"\n \n     if (\n         math.isinf(self.best_metric)\n         or self.best_metric - metric > self.min_delta\n     ):\n-      return True, self.replace(best_metric=metric, patience_count=0)\n+      return self.replace(best_metric=metric, patience_count=0, has_improved=True)\n     else:\n       should_stop = self.patience_count >= self.patience or self.should_stop\n-      return False, self.replace(\n-          patience_count=self.patience_count + 1, should_stop=should_stop\n+      return self.replace(\n+          patience_count=self.patience_count + 1, should_stop=should_stop, has_improved=False\n       )\n", "issue": "Improve Early Stopping API\nSee discussion in #2090\n", "before_files": [{"content": "# Copyright 2023 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Early stopping.\"\"\"\n\nimport math\nfrom flax import struct\n\n\nclass EarlyStopping(struct.PyTreeNode):\n  \"\"\"Early stopping to avoid overfitting during training.\n\n  The following example stops training early if the difference between losses\n  recorded in the current epoch and previous epoch is less than 1e-3\n  consecutively for 2 times::\n\n    early_stop = EarlyStopping(min_delta=1e-3, patience=2)\n    for epoch in range(1, num_epochs+1):\n      rng, input_rng = jax.random.split(rng)\n      optimizer, train_metrics = train_epoch(\n          optimizer, train_ds, config.batch_size, epoch, input_rng)\n      _, early_stop = early_stop.update(train_metrics['loss'])\n      if early_stop.should_stop:\n        print('Met early stopping criteria, breaking...')\n        break\n\n  Attributes:\n    min_delta: Minimum delta between updates to be considered an\n        improvement.\n    patience: Number of steps of no improvement before stopping.\n    best_metric: Current best metric value.\n    patience_count: Number of steps since last improving update.\n    should_stop: Whether the training loop should stop to avoid\n        overfitting.\n  \"\"\"\n\n  min_delta: float = 0\n  patience: int = 0\n  best_metric: float = float('inf')\n  patience_count: int = 0\n  should_stop: bool = False\n\n  def reset(self):\n    return self.replace(\n        best_metric=float('inf'), patience_count=0, should_stop=False\n    )\n\n  def update(self, metric):\n    \"\"\"Update the state based on metric.\n\n    Returns:\n      A pair (has_improved, early_stop), where `has_improved` is True when there\n      was an improvement greater than `min_delta` from the previous\n      `best_metric` and `early_stop` is the updated `EarlyStop` object.\n    \"\"\"\n\n    if (\n        math.isinf(self.best_metric)\n        or self.best_metric - metric > self.min_delta\n    ):\n      return True, self.replace(best_metric=metric, patience_count=0)\n    else:\n      should_stop = self.patience_count >= self.patience or self.should_stop\n      return False, self.replace(\n          patience_count=self.patience_count + 1, should_stop=should_stop\n      )\n", "path": "flax/training/early_stopping.py"}], "after_files": [{"content": "# Copyright 2023 The Flax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Early stopping.\"\"\"\n\nimport math\nfrom flax import struct\n\n\nclass EarlyStopping(struct.PyTreeNode):\n  \"\"\"Early stopping to avoid overfitting during training.\n\n  The following example stops training early if the difference between losses\n  recorded in the current epoch and previous epoch is less than 1e-3\n  consecutively for 2 times::\n\n    early_stop = EarlyStopping(min_delta=1e-3, patience=2)\n    for epoch in range(1, num_epochs+1):\n      rng, input_rng = jax.random.split(rng)\n      optimizer, train_metrics = train_epoch(\n          optimizer, train_ds, config.batch_size, epoch, input_rng)\n      early_stop = early_stop.update(train_metrics['loss'])\n      if early_stop.should_stop:\n        print('Met early stopping criteria, breaking...')\n        break\n\n  Attributes:\n    min_delta: Minimum delta between updates to be considered an\n        improvement.\n    patience: Number of steps of no improvement before stopping.\n    best_metric: Current best metric value.\n    patience_count: Number of steps since last improving update.\n    should_stop: Whether the training loop should stop to avoid\n        overfitting.\n    has_improved: Whether the metric has improved greater or\n      equal to the min_delta in the last `.update` call.\n  \"\"\"\n\n  min_delta: float = 0\n  patience: int = 0\n  best_metric: float = float('inf')\n  patience_count: int = 0\n  should_stop: bool = False\n  has_improved: bool = False\n\n  def reset(self):\n    return self.replace(\n        best_metric=float('inf'), patience_count=0, should_stop=False, has_improved=False\n    )\n\n  def update(self, metric):\n    \"\"\"Update the state based on metric.\n\n    Returns:\n      The updated EarlyStopping class. The `.has_improved` attribute is True\n      when there was an improvement greater than `min_delta` from the previous\n      `best_metric`.\n    \"\"\"\n\n    if (\n        math.isinf(self.best_metric)\n        or self.best_metric - metric > self.min_delta\n    ):\n      return self.replace(best_metric=metric, patience_count=0, has_improved=True)\n    else:\n      should_stop = self.patience_count >= self.patience or self.should_stop\n      return self.replace(\n          patience_count=self.patience_count + 1, should_stop=should_stop, has_improved=False\n      )\n", "path": "flax/training/early_stopping.py"}]}