B лde@sdZddlZddlZddlZddlZddlZddlmZddlm Z e a ej ej eddfZe dddZe d d d Ze d d dZddZddZddZdS)zBUtilities related to TensorFlow exception stack trace prettifying.N) tf_decorator) tf_exportz..z(debugging.is_traceback_filtering_enabledcCsttdd}|S)aCheck whether traceback filtering is currently enabled. See also `tf.debugging.enable_traceback_filtering()` and `tf.debugging.disable_traceback_filtering()`. Note that filtering out internal frames from the tracebacks of exceptions raised by TensorFlow code is the default behavior. Returns: True if traceback filtering is enabled (e.g. if `tf.debugging.enable_traceback_filtering()` was called), and False otherwise (e.g. if `tf.debugging.disable_traceback_filtering()` was called). valueT)getattr_ENABLE_TRACEBACK_FILTERING)rrX/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/util/traceback_utils.pyis_traceback_filtering_enabled s r z$debugging.enable_traceback_filteringcCs2tjjdkstjjdkr(tdtjdt_dS)aEnable filtering out TensorFlow-internal frames in exception stack traces. Raw TensorFlow stack traces involve many internal frames, which can be challenging to read through, while not being actionable for end users. By default, TensorFlow filters internal frames in most exceptions that it raises, to keep stack traces short, readable, and focused on what's actionable for end users (their own code). If you have previously disabled traceback filtering via `tf.debugging.disable_traceback_filtering()`, you can re-enable it via `tf.debugging.enable_traceback_filtering()`. Raises: RuntimeError: If Python version is not at least 3.7. zVTraceback filtering is only available with Python 3.7 or higher. This Python version: TN)sys version_infomajorminor RuntimeErrorversionrrrrrrenable_traceback_filtering3srz%debugging.disable_traceback_filteringcCs dt_dS)aQDisable filtering out TensorFlow-internal frames in exception stack traces. Raw TensorFlow stack traces involve many internal frames, which can be challenging to read through, while not being actionable for end users. By default, TensorFlow filters internal frames in most exceptions that it raises, to keep stack traces short, readable, and focused on what's actionable for end users (their own code). Calling `tf.debugging.disable_traceback_filtering` disables this filtering mechanism, meaning that TensorFlow exceptions stack traces will include all frames, in particular TensorFlow-internal ones. **If you are debugging a TensorFlow-internal issue, you need to call `tf.debugging.disable_traceback_filtering`**. To re-enable traceback filtering afterwards, you can call `tf.debugging.enable_traceback_filtering()`. FN)rrrrrrdisable_traceback_filteringLsrcCsxtD]}||krdSqWdS)NFT)_EXCLUDED_PATHS)fnameZ exclusionrrr include_framecs rcCstd}tt|}x2t|D]&\}}t|jjrt|||j |}qW|dkrp|rp|d\}}t|||j |}|S)N) list tracebackwalk_tbreversedrf_code co_filenametypes TracebackTypef_lasti)tbZnew_tbZtb_listfline_norrr_process_traceback_framesjs   r$cs4tjjdkstjjdkrSfdd}t|S)a{Decorator to filter out TF-internal stack trace frames in exceptions. Raw TensorFlow stack traces involve many internal frames, which can be challenging to read through, while not being actionable for end users. By default, TensorFlow filters internal frames in most exceptions that it raises, to keep stack traces short, readable, and focused on what's actionable for end users (their own code). Arguments: fn: The function or method to decorate. Any exception raised within the function will be reraised with its internal stack trace frames filtered out. Returns: Decorated function or method. r r c syts||SWntk r.||SXd}zHy ||Stk rx}zt|j}||dWdd}~XYnXWd~XdS)N)r NameError Exceptionr$ __traceback__with_traceback)argskwargsZ filtered_tbe)fnrr error_handlers   "z'filter_traceback..error_handler)r r rrrmake_decorator)r,r-r)r,rfilter_tracebackvs r/)__doc__osr threadingrrtensorflow.python.utilr tensorflow.python.util.tf_exportrlocalrpathabspathjoin__file__rr rrrr$r/rrrrs