text: # Copyright 2017 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.
  # ==============================================================================
  """The TensorBoard Text plugin."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import json
  import textwrap
  
  # pylint: disable=g-bad-import-order
  # Necessary for an internal test with special behavior for numpy.
  import numpy as np
  
  # pylint: enable=g-bad-import-order
  
  import six
  from werkzeug import wrappers
  
  from tensorboard import plugin_util
  from tensorboard.backend import http_util
  from tensorboard.data import provider
  from tensorboard.plugins import base_plugin
  from tensorboard.plugins.text import metadata
  from tensorboard.util import tensor_util
  
  # HTTP routes
  TAGS_ROUTE = "/tags"
  TEXT_ROUTE = "/text"
  
  
  WARNING_TEMPLATE = textwrap.dedent(
      """\
    **Warning:** This text summary contained data of dimensionality %d, but only \
    2d tables are supported. Showing a 2d slice of the data instead."""
  )
  
  _DEFAULT_DOWNSAMPLING = 100  # text tensors per time series
  
  
  def make_table_row(contents, tag="td"):
      """Given an iterable of string contents, make a table row.
  
      Args:
        contents: An iterable yielding strings.
        tag: The tag to place contents in. Defaults to 'td', you might want 'th'.
  
      Returns:
        A string containing the content strings, organized into a table row.
  
      Example: make_table_row(['one', 'two', 'three']) == '''
      <tr>
      <td>one</td>
      <td>two</td>
      <td>three</td>
      </tr>'''
      """
      columns = ("<%s>%s</%s>\n" % (tag, s, tag) for s in contents)
      return "<tr>\n" + "".join(columns) + "</tr>\n"
  
  
  def make_table(contents, headers=None):
      """Given a numpy ndarray of strings, concatenate them into a html table.
  
      Args:
        contents: A np.ndarray of strings. May be 1d or 2d. In the 1d case, the
          table is laid out vertically (i.e. row-major).
        headers: A np.ndarray or list of string header names for the table.
  
      Returns:
        A string containing all of the content strings, organized into a table.
  
      Raises:
        ValueError: If contents is not a np.ndarray.
        ValueError: If contents is not 1d or 2d.
        ValueError: If contents is empty.
        ValueError: If headers is present and not a list, tuple, or ndarray.
        ValueError: If headers is not 1d.
        ValueError: If number of elements in headers does not correspond to number
          of columns in contents.
      """
      if not isinstance(contents, np.ndarray):
          raise ValueError("make_table contents must be a numpy ndarray")
  
      if contents.ndim not in [1, 2]:
          raise ValueError(
              "make_table requires a 1d or 2d numpy array, was %dd"
              % contents.ndim
          )
  
      if headers:
          if isinstance(headers, (list, tuple)):
              headers = np.array(headers)
          if not isinstance(headers, np.ndarray):
              raise ValueError(
                  "Could not convert headers %s into np.ndarray" % headers
              )
          if headers.ndim != 1:
              raise ValueError("Headers must be 1d, is %dd" % headers.ndim)
          expected_n_columns = contents.shape[1] if contents.ndim == 2 else 1
          if headers.shape[0] != expected_n_columns:
              raise ValueError(
                  "Number of headers %d must match number of columns %d"
                  % (headers.shape[0], expected_n_columns)
              )
          header = "<thead>\n%s</thead>\n" % make_table_row(headers, tag="th")
      else:
          header = ""
  
      n_rows = contents.shape[0]
      if contents.ndim == 1:
          # If it's a vector, we need to wrap each element in a new list, otherwise
          # we would turn the string itself into a row (see test code)
          rows = (make_table_row([contents[i]]) for i in range(n_rows))
      else:
          rows = (make_table_row(contents[i, :]) for i in range(n_rows))
  
      return "<table>\n%s<tbody>\n%s</tbody>\n</table>" % (header, "".join(rows))
  
  
  def reduce_to_2d(arr):
      """Given a np.npdarray with nDims > 2, reduce it to 2d.
  
      It does this by selecting the zeroth coordinate for every dimension greater
      than two.
  
      Args:
        arr: a numpy ndarray of dimension at least 2.
  
      Returns:
        A two-dimensional subarray from the input array.
  
      Raises:
        ValueError: If the argument is not a numpy ndarray, or the dimensionality
          is too low.
      """
      if not isinstance(arr, np.ndarray):
          raise ValueError("reduce_to_2d requires a numpy.ndarray")
  
      ndims = len(arr.shape)
      if ndims < 2:
          raise ValueError("reduce_to_2d requires an array of dimensionality >=2")
      # slice(None) is equivalent to `:`, so we take arr[0,0,...0,:,:]
      slices = ([0] * (ndims - 2)) + [slice(None), slice(None)]
      return arr[slices]
  
  
  def text_array_to_html(text_arr):
      """Take a numpy.ndarray containing strings, and convert it into html.
  
      If the ndarray contains a single scalar string, that string is converted to
      html via our sanitized markdown parser. If it contains an array of strings,
      the strings are individually converted to html and then composed into a table
      using make_table. If the array contains dimensionality greater than 2,
      all but two of the dimensions are removed, and a warning message is prefixed
      to the table.
  
      Args:
        text_arr: A numpy.ndarray containing strings.
  
      Returns:
        The array converted to html.
      """
      if not text_arr.shape:
          # It is a scalar. No need to put it in a table, just apply markdown
          return plugin_util.markdown_to_safe_html(np.asscalar(text_arr))
      warning = ""
      if len(text_arr.shape) > 2:
          warning = plugin_util.markdown_to_safe_html(
              WARNING_TEMPLATE % len(text_arr.shape)
          )
          text_arr = reduce_to_2d(text_arr)
      table = plugin_util.markdowns_to_safe_html(
          text_arr.reshape(-1),
          lambda xs: make_table(np.array(xs).reshape(text_arr.shape)),
      )
      return warning + table
  
  
  def process_event(wall_time, step, string_ndarray):
      """Convert a text event into a JSON-compatible response."""
      html = text_array_to_html(string_ndarray)
      return {
          "wall_time": wall_time,
          "step": step,
          "text": html,
      }
  
  
  class TextPlugin(base_plugin.TBPlugin):
      """Text Plugin for TensorBoard."""
  
      plugin_name = metadata.PLUGIN_NAME
  
      def __init__(self, context):
          """Instantiates TextPlugin via TensorBoard core.
  
          Args:
            context: A base_plugin.TBContext instance.
          """
          self._multiplexer = context.multiplexer
          self._downsample_to = (context.sampling_hints or {}).get(
              self.plugin_name, _DEFAULT_DOWNSAMPLING
          )
          if context.flags and context.flags.generic_data != "false":
              self._data_provider = context.data_provider
          else:
              self._data_provider = None
  
      def is_active(self):
          """Determines whether this plugin is active.
  
          This plugin is only active if TensorBoard sampled any text summaries.
  
          Returns:
            Whether this plugin is active.
          """
          if self._data_provider:
              return False  # `list_plugins` as called by TB core suffices
          if not self._multiplexer:
              return False
          return bool(
              self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)
          )
  
      def frontend_metadata(self):
          return base_plugin.FrontendMetadata(element_name="tf-text-dashboard")
  
      def index_impl(self, experiment):
          if self._data_provider:
              mapping = self._data_provider.list_tensors(
                  experiment_id=experiment, plugin_name=metadata.PLUGIN_NAME,
              )
          else:
              mapping = self._multiplexer.PluginRunToTagToContent(
                  metadata.PLUGIN_NAME
              )
          return {
              run: list(tag_to_content)
              for (run, tag_to_content) in six.iteritems(mapping)
          }
  
      @wrappers.Request.application
      def tags_route(self, request):
          experiment = plugin_util.experiment_id(request.environ)
          index = self.index_impl(experiment)
          return http_util.Respond(request, index, "application/json")
  
      def text_impl(self, run, tag, experiment):
          if self._data_provider:
              all_text = self._data_provider.read_tensors(
                  experiment_id=experiment,
                  plugin_name=metadata.PLUGIN_NAME,
                  downsample=self._downsample_to,
                  run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
              )
              text = all_text.get(run, {}).get(tag, None)
              if text is None:
                  return []
              return [process_event(d.wall_time, d.step, d.numpy) for d in text]
  
          try:
              text_events = self._multiplexer.Tensors(run, tag)
          except KeyError:
              text_events = []
          return [
              process_event(
                  e.wall_time, e.step, tensor_util.make_ndarray(e.tensor_proto)
              )
              for e in text_events
          ]
  
      @wrappers.Request.application
      def text_route(self, request):
          experiment = plugin_util.experiment_id(request.environ)
          run = request.args.get("run")
          tag = request.args.get("tag")
          response = self.text_impl(run, tag, experiment)
          return http_util.Respond(request, response, "application/json")
  
      def get_plugin_apps(self):
          return {
              TAGS_ROUTE: self.tags_route,
              TEXT_ROUTE: self.text_route,
          }
  