I am done

rl/Lib/site-packages/tensorboard/plugins/text/metadata.py

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+# 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.
+# ==============================================================================
+"""Internal information about the text plugin."""
+
+
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.text import plugin_data_pb2
+
+PLUGIN_NAME = "text"
+
+# The most recent value for the `version` field of the
+# `TextPluginData` proto.
+PROTO_VERSION = 0
+
+
+def create_summary_metadata(display_name, description):
+    """Create a `summary_pb2.SummaryMetadata` proto for text plugin data.
+
+    Returns:
+      A `summary_pb2.SummaryMetadata` protobuf object.
+    """
+    content = plugin_data_pb2.TextPluginData(version=PROTO_VERSION)
+    metadata = summary_pb2.SummaryMetadata(
+        display_name=display_name,
+        summary_description=description,
+        plugin_data=summary_pb2.SummaryMetadata.PluginData(
+            plugin_name=PLUGIN_NAME, content=content.SerializeToString()
+        ),
+    )
+    return metadata
+
+
+def parse_plugin_metadata(content):
+    """Parse summary metadata to a Python object.
+
+    Arguments:
+      content: The `content` field of a `SummaryMetadata` proto corresponding to
+        the text plugin.
+    Returns:
+      A `TextPluginData` protobuf object.
+    """
+    if not isinstance(content, bytes):
+        raise TypeError("Content type must be bytes")
+    if content == b"{}":
+        # Old-style JSON format. Equivalent to an all-default proto.
+        return plugin_data_pb2.TextPluginData()
+    result = plugin_data_pb2.TextPluginData.FromString(content)
+    if result.version == 0:
+        return result
+    # No other versions known at this time, so no migrations to do.
+    return result

rl/Lib/site-packages/tensorboard/plugins/text/plugin_data_pb2.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorboard/plugins/text/plugin_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import message as _message
+from google.protobuf import reflection as _reflection
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorboard/plugins/text/plugin_data.proto\x12\x0btensorboard\"!\n\x0eTextPluginData\x12\x0f\n\x07version\x18\x01 \x01(\x05\x62\x06proto3')
+
+
+
+_TEXTPLUGINDATA = DESCRIPTOR.message_types_by_name['TextPluginData']
+TextPluginData = _reflection.GeneratedProtocolMessageType('TextPluginData', (_message.Message,), {
+  'DESCRIPTOR' : _TEXTPLUGINDATA,
+  '__module__' : 'tensorboard.plugins.text.plugin_data_pb2'
+  # @@protoc_insertion_point(class_scope:tensorboard.TextPluginData)
+  })
+_sym_db.RegisterMessage(TextPluginData)
+
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _TEXTPLUGINDATA._serialized_start=59
+  _TEXTPLUGINDATA._serialized_end=92
+# @@protoc_insertion_point(module_scope)

rl/Lib/site-packages/tensorboard/plugins/text/summary.py

@@ -0,0 +1,116 @@
+# 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.
+# ==============================================================================
+"""Text summaries and TensorFlow operations to create them."""
+
+
+from tensorboard.plugins.text import metadata
+from tensorboard.plugins.text import summary_v2
+
+
+# Export V2 versions.
+text = summary_v2.text
+text_pb = summary_v2.text_pb
+
+
+def op(name, data, display_name=None, description=None, collections=None):
+    """Create a legacy text summary op.
+
+    Text data summarized via this plugin will be visible in the Text Dashboard
+    in TensorBoard. The standard TensorBoard Text Dashboard will render markdown
+    in the strings, and will automatically organize 1D and 2D tensors into tables.
+    If a tensor with more than 2 dimensions is provided, a 2D subarray will be
+    displayed along with a warning message. (Note that this behavior is not
+    intrinsic to the text summary API, but rather to the default TensorBoard text
+    plugin.)
+
+    Args:
+      name: A name for the generated node. Will also serve as a series name in
+        TensorBoard.
+      data: A string-type Tensor to summarize. The text must be encoded in UTF-8.
+      display_name: Optional name for this summary in TensorBoard, as a
+        constant `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+      collections: Optional list of ops.GraphKeys. The collections to which to add
+        the summary. Defaults to [Graph Keys.SUMMARIES].
+
+    Returns:
+      A TensorSummary op that is configured so that TensorBoard will recognize
+      that it contains textual data. The TensorSummary is a scalar `Tensor` of
+      type `string` which contains `Summary` protobufs.
+
+    Raises:
+      ValueError: If tensor has the wrong type.
+    """
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    if display_name is None:
+        display_name = name
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=display_name, description=description
+    )
+    with tf.name_scope(name):
+        with tf.control_dependencies([tf.assert_type(data, tf.string)]):
+            return tf.summary.tensor_summary(
+                name="text_summary",
+                tensor=data,
+                collections=collections,
+                summary_metadata=summary_metadata,
+            )
+
+
+def pb(name, data, display_name=None, description=None):
+    """Create a legacy text summary protobuf.
+
+    Arguments:
+      name: A name for the generated node. Will also serve as a series name in
+        TensorBoard.
+      data: A Python bytestring (of type bytes), or Unicode string. Or a numpy
+        data array of those types.
+      display_name: Optional name for this summary in TensorBoard, as a
+        `str`. Defaults to `name`.
+      description: Optional long-form description for this summary, as a
+        `str`. Markdown is supported. Defaults to empty.
+
+    Raises:
+      ValueError: If the type of the data is unsupported.
+
+    Returns:
+      A `tf.Summary` protobuf object.
+    """
+    # TODO(nickfelt): remove on-demand imports once dep situation is fixed.
+    import tensorflow.compat.v1 as tf
+
+    try:
+        tensor = tf.make_tensor_proto(data, dtype=tf.string)
+    except TypeError as e:
+        raise ValueError(e)
+
+    if display_name is None:
+        display_name = name
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=display_name, description=description
+    )
+    tf_summary_metadata = tf.SummaryMetadata.FromString(
+        summary_metadata.SerializeToString()
+    )
+    summary = tf.Summary()
+    summary.value.add(
+        tag="%s/text_summary" % name,
+        metadata=tf_summary_metadata,
+        tensor=tensor,
+    )
+    return summary

rl/Lib/site-packages/tensorboard/plugins/text/summary_v2.py

@@ -0,0 +1,125 @@
+# Copyright 2018 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.
+# ==============================================================================
+"""Text summaries and TensorFlow operations to create them, V2 versions."""
+
+
+import numpy as np
+
+from tensorboard.compat import tf2 as tf
+from tensorboard.compat.proto import summary_pb2
+from tensorboard.plugins.text import metadata
+from tensorboard.util import tensor_util
+
+
+def text(name, data, step=None, description=None):
+    r"""Write a text summary.
+
+    See also `tf.summary.scalar`, `tf.summary.SummaryWriter`, `tf.summary.image`.
+
+    Writes text Tensor values for later visualization and analysis in TensorBoard.
+    Writes go to the current default summary writer.  Like `tf.summary.scalar`
+    points, text points are each associated with a `step` and a `name`.
+    All the points with the same `name` constitute a time series of text values.
+
+    For Example:
+    ```python
+    test_summary_writer = tf.summary.create_file_writer('test/logdir')
+    with test_summary_writer.as_default():
+        tf.summary.text('first_text', 'hello world!', step=0)
+        tf.summary.text('first_text', 'nice to meet you!', step=1)
+    ```
+
+    The text summary can also contain Markdown, and TensorBoard will render the text
+    as such.
+
+    ```python
+    with test_summary_writer.as_default():
+        text_data = '''
+              | *hello* | *there* |
+              |---------|---------|
+              | this    | is      |
+              | a       | table   |
+        '''
+        text_data = '\n'.join(l.strip() for l in text_data.splitlines())
+        tf.summary.text('markdown_text', text_data, step=0)
+    ```
+
+    Since text is Tensor valued, each text point may be a Tensor of string values.
+    rank-1 and rank-2 Tensors are rendered as tables in TensorBoard.  For higher ranked
+    Tensors, you'll see just a 2D slice of the data.  To avoid this, reshape the Tensor
+    to at most rank-2 prior to passing it to this function.
+
+    Demo notebook at
+    ["Displaying text data in TensorBoard"](https://www.tensorflow.org/tensorboard/text_summaries).
+
+    Arguments:
+      name: A name for this summary. The summary tag used for TensorBoard will
+        be this name prefixed by any active name scopes.
+      data: A UTF-8 string Tensor value.
+      step: Explicit `int64`-castable monotonic step value for this summary. If
+        omitted, this defaults to `tf.summary.experimental.get_step()`, which must
+        not be None.
+      description: Optional long-form description for this summary, as a
+        constant `str`. Markdown is supported. Defaults to empty.
+
+    Returns:
+      True on success, or false if no summary was emitted because no default
+      summary writer was available.
+
+    Raises:
+      ValueError: if a default writer exists, but no step was provided and
+        `tf.summary.experimental.get_step()` is None.
+    """
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=None, description=description
+    )
+    # TODO(https://github.com/tensorflow/tensorboard/issues/2109): remove fallback
+    summary_scope = (
+        getattr(tf.summary.experimental, "summary_scope", None)
+        or tf.summary.summary_scope
+    )
+    with summary_scope(name, "text_summary", values=[data, step]) as (tag, _):
+        tf.debugging.assert_type(data, tf.string)
+        return tf.summary.write(
+            tag=tag, tensor=data, step=step, metadata=summary_metadata
+        )
+
+
+def text_pb(tag, data, description=None):
+    """Create a text tf.Summary protobuf.
+
+    Arguments:
+      tag: String tag for the summary.
+      data: A Python bytestring (of type bytes), a Unicode string, or a numpy data
+        array of those types.
+      description: Optional long-form description for this summary, as a `str`.
+        Markdown is supported. Defaults to empty.
+
+    Raises:
+      TypeError: If the type of the data is unsupported.
+
+    Returns:
+      A `tf.Summary` protobuf object.
+    """
+    try:
+        tensor = tensor_util.make_tensor_proto(data, dtype=np.object_)
+    except TypeError as e:
+        raise TypeError("tensor must be of type string", e)
+    summary_metadata = metadata.create_summary_metadata(
+        display_name=None, description=description
+    )
+    summary = summary_pb2.Summary()
+    summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor)
+    return summary

rl/Lib/site-packages/tensorboard/plugins/text/text_plugin.py

@@ -0,0 +1,289 @@
+# 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."""
+
+
+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
+
+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
+
+# 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[tuple(slices)]
+
+
+def text_array_to_html(text_arr, enable_markdown):
+    """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.
+      enable_markdown: boolean, whether to enable Markdown
+
+    Returns:
+      The array converted to html.
+    """
+    if not text_arr.shape:
+        # It is a scalar. No need to put it in a table.
+        if enable_markdown:
+            return plugin_util.markdown_to_safe_html(text_arr.item())
+        else:
+            return plugin_util.safe_html(text_arr.item())
+    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)
+    if enable_markdown:
+        table = plugin_util.markdowns_to_safe_html(
+            text_arr.reshape(-1),
+            lambda xs: make_table(np.array(xs).reshape(text_arr.shape)),
+        )
+    else:
+        # Convert utf-8 bytes to str. The built-in np.char.decode doesn't work on
+        # object arrays, and converting to an numpy chararray is lossy.
+        decode = lambda bs: bs.decode("utf-8") if isinstance(bs, bytes) else bs
+        text_arr_str = np.array(
+            [decode(bs) for bs in text_arr.reshape(-1)]
+        ).reshape(text_arr.shape)
+        table = plugin_util.safe_html(make_table(text_arr_str))
+    return warning + table
+
+
+def process_event(wall_time, step, string_ndarray, enable_markdown):
+    """Convert a text event into a JSON-compatible response."""
+    html = text_array_to_html(string_ndarray, enable_markdown)
+    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._downsample_to = (context.sampling_hints or {}).get(
+            self.plugin_name, _DEFAULT_DOWNSAMPLING
+        )
+        self._data_provider = context.data_provider
+        self._version_checker = plugin_util._MetadataVersionChecker(
+            data_kind="text",
+            latest_known_version=0,
+        )
+
+    def is_active(self):
+        return False  # `list_plugins` as called by TB core suffices
+
+    def frontend_metadata(self):
+        return base_plugin.FrontendMetadata(element_name="tf-text-dashboard")
+
+    def index_impl(self, ctx, experiment):
+        mapping = self._data_provider.list_tensors(
+            ctx,
+            experiment_id=experiment,
+            plugin_name=metadata.PLUGIN_NAME,
+        )
+        result = {run: [] for run in mapping}
+        for run, tag_to_content in mapping.items():
+            for tag, metadatum in tag_to_content.items():
+                md = metadata.parse_plugin_metadata(metadatum.plugin_content)
+                if not self._version_checker.ok(md.version, run, tag):
+                    continue
+                result[run].append(tag)
+        return result
+
+    @wrappers.Request.application
+    def tags_route(self, request):
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        index = self.index_impl(ctx, experiment)
+        return http_util.Respond(request, index, "application/json")
+
+    def text_impl(self, ctx, run, tag, experiment, enable_markdown):
+        all_text = self._data_provider.read_tensors(
+            ctx,
+            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, enable_markdown)
+            for d in text
+        ]
+
+    @wrappers.Request.application
+    def text_route(self, request):
+        ctx = plugin_util.context(request.environ)
+        experiment = plugin_util.experiment_id(request.environ)
+        run = request.args.get("run")
+        tag = request.args.get("tag")
+        markdown_arg = request.args.get("markdown")
+        enable_markdown = markdown_arg != "false"  # Default to enabled.
+        response = self.text_impl(ctx, run, tag, experiment, enable_markdown)
+        return http_util.Respond(request, response, "application/json")
+
+    def get_plugin_apps(self):
+        return {
+            TAGS_ROUTE: self.tags_route,
+            TEXT_ROUTE: self.text_route,
+        }