# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities related to Keras exception stack trace prettifying."""

import inspect
import os
import sys
import traceback
import types
import tensorflow.compat.v2 as tf


_EXCLUDED_PATHS = (
    os.path.abspath(os.path.join(__file__, '..', '..')),
    os.path.join('tensorflow', 'python'),
)


def include_frame(fname):
  for exclusion in _EXCLUDED_PATHS:
    if exclusion in fname:
      return False
  return True


def _process_traceback_frames(tb):
  """Iterate through traceback frames and return a new, filtered traceback."""
  last_tb = None
  tb_list = list(traceback.walk_tb(tb))
  for f, line_no in reversed(tb_list):
    if include_frame(f.f_code.co_filename):
      last_tb = types.TracebackType(last_tb, f, f.f_lasti, line_no)
  if last_tb is None and tb_list:
    # If no frames were kept during filtering, create a new traceback
    # from the outermost function.
    f, line_no = tb_list[-1]
    last_tb = types.TracebackType(last_tb, f, f.f_lasti, line_no)
  return last_tb


def filter_traceback(fn):
  """Filter out Keras-internal stack trace frames in exceptions raised by fn."""
  if sys.version_info.major != 3 or sys.version_info.minor < 7:
    return fn

  def error_handler(*args, **kwargs):
    if not tf.debugging.is_traceback_filtering_enabled():
      return fn(*args, **kwargs)

    filtered_tb = None
    try:
      return fn(*args, **kwargs)
    except Exception as e:  # pylint: disable=broad-except
      filtered_tb = _process_traceback_frames(e.__traceback__)
      raise e.with_traceback(filtered_tb) from None
    finally:
      del filtered_tb

  return tf.__internal__.decorator.make_decorator(fn, error_handler)


def inject_argument_info_in_traceback(fn, object_name=None):
  """Add information about call argument values to an error message.

  Arguments:
    fn: Function to wrap. Exceptions raised by the this function will be
      re-raised with additional information added to the error message,
      displaying the values of the different arguments that the function
      was called with.
    object_name: String, display name of the class/function being called,
      e.g. `'layer "layer_name" (LayerClass)'`.

  Returns:
    A wrapped version of `fn`.
  """
  def error_handler(*args, **kwargs):
    signature = None
    bound_signature = None
    try:
      return fn(*args, **kwargs)
    except Exception as e:  # pylint: disable=broad-except
      if hasattr(e, '_keras_call_info_injected'):
        # Only inject info for the innermost failing call
        raise e
      signature = inspect.signature(fn)
      try:
        # The first argument is `self`, so filter it out
        bound_signature = signature.bind(*args, **kwargs)
      except TypeError:
        # Likely unbindable arguments
        raise e

      # Add argument context
      arguments_context = []
      for arg in list(signature.parameters.values()):
        if arg.name in bound_signature.arguments:
          value = format_argument_value(bound_signature.arguments[arg.name])
        else:
          value = arg.default
        arguments_context.append(f'  • {arg.name}={value}')

      if arguments_context:
        arguments_context = '\n'.join(arguments_context)
        # Get original error message and append information to it.
        if isinstance(e, tf.errors.OpError):
          message = e.message
        elif e.args:
          # Canonically, the 1st argument in an exception is the error message.
          # This works for all built-in Python exceptions.
          message = e.args[0]
        else:
          message = ''
        message = (
            'Exception encountered when calling '
            f'{object_name if object_name else fn.__name__}.\n\n'
            f'{message}\n\n'
            f'Call arguments received:\n{arguments_context}')

        # Reraise exception, with added context
        if isinstance(e, tf.errors.OpError):
          new_e = e.__class__(e.node_def, e.op, message, e.error_code)
        else:
          try:
            # For standard exceptions such as ValueError, TypeError, etc.
            new_e = e.__class__(message)
          except TypeError:
            # For any custom error that doesn't have a standard signature.
            new_e = RuntimeError(message)
        new_e._keras_call_info_injected = True  # pylint: disable=protected-access
      else:
        new_e = e
      raise new_e.with_traceback(e.__traceback__) from None
    finally:
      del signature
      del bound_signature
  return tf.__internal__.decorator.make_decorator(fn, error_handler)


def format_argument_value(value):
  if isinstance(value, tf.Tensor):
    # Simplified representation for eager / graph tensors
    # to keep messages readable
    return f'tf.Tensor(shape={value.shape}, dtype={value.dtype.name})'
  return repr(value)

@@ -3628,16 +3628,53 @@
  value =
+ tf.nest.map_structure(%0A             
  format_
@@ -3687,17 +3687,18 @@
 nt_value
-(
+, 
 bound_si

Improve formatting of nested layer input arguments in traceback filtering.