AVh$dZddlmZddlmZddlmZddlmZ ddlm Z ddlm Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZedgdGdde Z edgGdde Z!e Z"y)zImport router for file_io.)copy) create_dir) delete_file)delete_recursively) file_exists)FileIO)get_matching_files) is_directory)list_directory)recursive_create_dir)rename)stat)walk) deprecated) tf_exportio.gfile.GFile)z gfile.GFilez gfile.Openr)v1c$eZdZdZdfd ZxZS)GFilear File I/O wrappers without thread locking. The main roles of the `tf.io.gfile` module are: 1. To provide an API that is close to Python's file I/O objects, and 2. To provide an implementation based on TensorFlow's C++ FileSystem API. The C++ FileSystem API supports multiple file system implementations, including local files, Google Cloud Storage (using a `gs://` prefix, and HDFS (using an `hdfs://` prefix). TensorFlow exports these as `tf.io.gfile`, so that you can use these implementations for saving and loading checkpoints, writing to TensorBoard logs, and accessing training data (among other uses). However, if all your files are local, you can use the regular Python file API without any problem. *Note*: though similar to Python's I/O implementation, there are semantic differences to make `tf.io.gfile` more efficient for backing filesystems. For example, a write mode file will not be opened until the first write call to minimize RPC invocations in network filesystems. Once you obtain a `GFile` object, you can use it in most ways as you would any Python's file object: >>> with open("/tmp/x", "w") as f: ... f.write("asdf") 4 >>> with tf.io.gfile.GFile("/tmp/x") as f: ... f.read() 'asdf' The difference is that you can specify URI schemes to use other filesystems (e.g., `gs://` for GCS, `s3://` for S3, etc.), if they are supported. Using `file://` as an example, we have: >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f: ... f.write("qwert") ... f.write("asdf") >>> tf.io.gfile.GFile("file:///tmp/x").read() 'qwertasdf' You can also read all lines of a file directly: >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f: ... f.write("asdf\n") ... f.write("qwer\n") >>> tf.io.gfile.GFile("/tmp/x").readlines() ['asdf\n', 'qwer\n'] You can iterate over the lines: >>> with tf.io.gfile.GFile("file:///tmp/x", "w") as f: ... f.write("asdf\n") ... f.write("qwer\n") >>> for line in tf.io.gfile.GFile("/tmp/x"): ... print(line[:-1]) # removes the end of line character asdf qwer Random access read is possible if the underlying filesystem supports it: >>> with open("/tmp/x", "w") as f: ... f.write("asdfqwer") >>> f = tf.io.gfile.GFile("/tmp/x") >>> f.read(3) 'asd' >>> f.seek(4) >>> f.tell() 4 >>> f.read(3) 'qwe' >>> f.tell() 7 >>> f.close() c0tt| ||yN)namemode)superr__init__selfrr __class__s P/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/platform/gfile.pyrzGFile.__init__qs %T5r)__name__ __module__ __qualname____doc__r __classcell__rs@rrr$sIV66r rzgfile.FastGFilec<eZdZdZedddfd ZxZS) FastGFilea\File I/O wrappers without thread locking. Note, that this is somewhat like builtin Python file I/O, but there are semantic differences to make it more efficient for some backing filesystems. For example, a write mode file will not be opened until the first write call (to minimize RPC invocations in network filesystems). NzUse tf.gfile.GFile.c0tt| ||yr)rr*rrs rrzFastGFile.__init__s )T#D#9r r!)r#r$r%r&rrr'r(s@rr*r*us$d)*:+:r r*N)#r& tensorflow.python.lib.io.file_iorCopyrMkDirrRemoverDeleteRecursivelyrExistsr_FileIOr Globr IsDirectoryr ListDirectoryr MakeDirsr RenamerStatrWalk"tensorflow.python.util.deprecationr tensorflow.python.util.tf_exportrrr*Openr rr>s !9@BTB>GHLM=9996  OPM6GM6QM6`  !" : :# :" r