
    BVh                     b    d Z ddlmZ ddlmZ ddlmZ  G d de      Z edg      d	        Z	y
)zHProvides a method for reading events from an event file via an iterator.    )	event_pb2)	tf_record)	tf_exportc                   &    e Zd ZdZd Zd Zd ZeZy)_SummaryIteratorz2Yields `Event` protocol buffers from a given path.c                 8    t        j                  |      | _        y N)r   tf_record_iterator_tf_record_iterator)selfpaths     Z/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/summary/summary_iterator.py__init__z_SummaryIterator.__init__   s    (;;DAD    c                     | S r	    )r   s    r   __iter__z_SummaryIterator.__iter__   s    Kr   c                 j    t        | j                        }t        j                  j	                  |      S r	   )nextr   r   Event
FromString)r   rs     r   __next__z_SummaryIterator.__next__    s'    T%%&A??%%a((r   N)__name__
__module____qualname____doc__r   r   r   r   r   r   r   r   r      s    :B) 
$r   r   ztrain.summary_iterator)v1c                     t        |       S )a  Returns a iterator for reading `Event` protocol buffers from an event file.

  You can use this function to read events written to an event file. It returns
  a Python iterator that yields `Event` protocol buffers.

  Example: Print the contents of an events file.

  ```python
  for e in tf.compat.v1.train.summary_iterator(path to events file):
      print(e)
  ```

  Example: Print selected summary values.

  ```python
  # This example supposes that the events file contains summaries with a
  # summary value tag 'loss'.  These could have been added by calling
  # `add_summary()`, passing the output of a scalar summary op created with
  # with: `tf.compat.v1.summary.scalar('loss', loss_tensor)`.
  for e in tf.compat.v1.train.summary_iterator(path to events file):
      for v in e.summary.value:
          if v.tag == 'loss':
              print(tf.make_ndarray(v.tensor))
  ```
  Example: Continuously check for new summary values.

  ```python
  summaries = tf.compat.v1.train.summary_iterator(path to events file)
  while True:
    for e in summaries:
        for v in e.summary.value:
            if v.tag == 'loss':
                print(tf.make_ndarray(v.tensor))
    # Wait for a bit before checking the file for any new events
    time.sleep(wait time)
  ```

  See the protocol buffer definitions of
  [Event](https://www.tensorflow.org/code/tensorflow/core/util/event.proto)
  and
  [Summary](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  for more information about their attributes.

  Args:
    path: The path to an event file created by a `SummaryWriter`.

  Returns:
    A iterator that yields `Event` protocol buffers
  )r   )r   s    r   summary_iteratorr    '   s    h 
$	r   N)
r   tensorflow.core.utilr   tensorflow.python.lib.ior    tensorflow.python.util.tf_exportr   objectr   r    r   r   r   <module>r%      s=     O * . 6v   '()3  *3 r   