<commit_before># Copyright 2016 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.
# ==============================================================================

"""Operations for writing summary data, for use in analysis and visualization.

See the [Summaries and
TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard) guide.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from google.protobuf import json_format as _json_format

# exports Summary, SummaryDescription, Event, TaggedRunMetadata, SessionLog
# pylint: disable=unused-import
from tensorflow.core.framework.summary_pb2 import Summary
from tensorflow.core.framework.summary_pb2 import SummaryDescription
from tensorflow.core.framework.summary_pb2 import SummaryMetadata as _SummaryMetadata  # pylint: enable=unused-import
from tensorflow.core.util.event_pb2 import Event
from tensorflow.core.util.event_pb2 import SessionLog
from tensorflow.core.util.event_pb2 import TaggedRunMetadata
# pylint: enable=unused-import

from tensorflow.python.distribute import summary_op_util as _distribute_summary_op_util
from tensorflow.python.eager import context as _context
from tensorflow.python.framework import constant_op as _constant_op
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import ops as _ops
from tensorflow.python.ops import gen_logging_ops as _gen_logging_ops
from tensorflow.python.ops import gen_summary_ops as _gen_summary_ops  # pylint: disable=unused-import
from tensorflow.python.ops import summary_op_util as _summary_op_util

# exports FileWriter, FileWriterCache
# pylint: disable=unused-import
from tensorflow.python.summary.writer.writer import FileWriter
from tensorflow.python.summary.writer.writer_cache import FileWriterCache
# pylint: enable=unused-import

from tensorflow.python.util import compat as _compat
from tensorflow.python.util.tf_export import tf_export


@tf_export(v1=['summary.scalar'])
def scalar(name, tensor, collections=None, family=None):
  """Outputs a `Summary` protocol buffer containing a single scalar value.

  The generated Summary has a Tensor.proto containing the input Tensor.

  Args:
    name: A name for the generated node. Will also serve as the series name in
      TensorBoard.
    tensor: A real numeric Tensor containing a single value.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.

  Raises:
    ValueError: If tensor has the wrong shape or type.
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')
  with _summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    val = _gen_logging_ops.scalar_summary(tags=tag, values=tensor, name=scope)
    _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val


@tf_export(v1=['summary.image'])
def image(name, tensor, max_outputs=3, collections=None, family=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_outputs` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` in the outputted Summary.Value protobufs is generated based on the
  name, with a suffix depending on the max_outputs setting:

  *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
  *  If `max_outputs` is greater than 1, the summary value tags are
     generated sequentially as '*name*/image/0', '*name*/image/1', etc.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_outputs: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [_ops.GraphKeys.SUMMARIES]
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')
  with _summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    val = _gen_logging_ops.image_summary(
        tag=tag, tensor=tensor, max_images=max_outputs, name=scope)
    _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val


@tf_export(v1=['summary.histogram'])
def histogram(name, values, collections=None, family=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with a histogram.

  Adding a histogram summary makes it possible to visualize your data's
  distribution in TensorBoard. You can see a detailed explanation of the
  TensorBoard histogram dashboard
  [here](https://www.tensorflow.org/get_started/tensorboard_histograms).

  The generated
  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  has one summary value containing a histogram for `values`.

  This op reports an `InvalidArgument` error if any value is not finite.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    values: A real numeric `Tensor`. Any shape. Values to use to
      build the histogram.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')<commit_msg>Add TF2 migration block for tf.compat.v1.summary.scalar, tf.compat.v1.summary.image, tf.compat.v1.summary.histogram.<commit_after># Copyright 2016 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.
# ==============================================================================

"""Operations for writing summary data, for use in analysis and visualization.

See the [Summaries and
TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard) guide.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from google.protobuf import json_format as _json_format

# exports Summary, SummaryDescription, Event, TaggedRunMetadata, SessionLog
# pylint: disable=unused-import
from tensorflow.core.framework.summary_pb2 import Summary
from tensorflow.core.framework.summary_pb2 import SummaryDescription
from tensorflow.core.framework.summary_pb2 import SummaryMetadata as _SummaryMetadata  # pylint: enable=unused-import
from tensorflow.core.util.event_pb2 import Event
from tensorflow.core.util.event_pb2 import SessionLog
from tensorflow.core.util.event_pb2 import TaggedRunMetadata
# pylint: enable=unused-import

from tensorflow.python.distribute import summary_op_util as _distribute_summary_op_util
from tensorflow.python.eager import context as _context
from tensorflow.python.framework import constant_op as _constant_op
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import ops as _ops
from tensorflow.python.ops import gen_logging_ops as _gen_logging_ops
from tensorflow.python.ops import gen_summary_ops as _gen_summary_ops  # pylint: disable=unused-import
from tensorflow.python.ops import summary_op_util as _summary_op_util

# exports FileWriter, FileWriterCache
# pylint: disable=unused-import
from tensorflow.python.summary.writer.writer import FileWriter
from tensorflow.python.summary.writer.writer_cache import FileWriterCache
# pylint: enable=unused-import

from tensorflow.python.util import compat as _compat
from tensorflow.python.util.tf_export import tf_export


@tf_export(v1=['summary.scalar'])
def scalar(name, tensor, collections=None, family=None):
  """Outputs a `Summary` protocol buffer containing a single scalar value.

  The generated Summary has a Tensor.proto containing the input Tensor.

  Args:
    name: A name for the generated node. Will also serve as the series name in
      TensorBoard.
    tensor: A real numeric Tensor containing a single value.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.

  Raises:
    ValueError: If tensor has the wrong shape or type.

  @compatibility(TF2)
  This API is not compatible with eager execution or `tf.function`. To migrate
  to TF2, please use `tf.summary.scalar` instead. Please check
  [Migrating tf.summary usage to
  TF 2.0](https://www.tensorflow.org/tensorboard/migrate#in_tf_1x) for concrete
  steps for migration. `tf.summary.scalar` can also log training metrics in
  Keras, you can check [Logging training metrics in
  Keras](https://www.tensorflow.org/tensorboard/scalars_and_keras) for detials.

  #### How to Map Arguments

  | TF1 Arg Name  | TF2 Arg Name    | Note                                   |
  | :------------ | :-------------- | :------------------------------------- |
  | `name`        | `name`          | -                                      |
  | `tensor`      | `data`          | -                                      |
  | -             | `step`          | Explicit int64-castable monotonic step |
  :               :                 : value. If omitted, this defaults to    :
  :               :                 : `tf.summary.experimental.get_step()`.  :
  | `collections` | Not Supported   | -                                      |
  | `family`      | Removed         | Please use `tf.name_scope` instead to  |
  :               :                 : manage summary name prefix.            :
  | -             | `description`   | Optional long-form `str` description   |
  :               :                 : for the summary. Markdown is supported.:
  :               :                 : Defaults to empty.                     :

  @end_compatibility
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')
  with _summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    val = _gen_logging_ops.scalar_summary(tags=tag, values=tensor, name=scope)
    _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val


@tf_export(v1=['summary.image'])
def image(name, tensor, max_outputs=3, collections=None, family=None):
  """Outputs a `Summary` protocol buffer with images.

  The summary has up to `max_outputs` summary values containing images. The
  images are built from `tensor` which must be 4-D with shape `[batch_size,
  height, width, channels]` and where `channels` can be:

  *  1: `tensor` is interpreted as Grayscale.
  *  3: `tensor` is interpreted as RGB.
  *  4: `tensor` is interpreted as RGBA.

  The images have the same number of channels as the input tensor. For float
  input, the values are normalized one image at a time to fit in the range
  `[0, 255]`.  `uint8` values are unchanged.  The op uses two different
  normalization algorithms:

  *  If the input values are all positive, they are rescaled so the largest one
     is 255.

  *  If any input value is negative, the values are shifted so input value 0.0
     is at 127.  They are then rescaled so that either the smallest value is 0,
     or the largest one is 255.

  The `tag` in the outputted Summary.Value protobufs is generated based on the
  name, with a suffix depending on the max_outputs setting:

  *  If `max_outputs` is 1, the summary value tag is '*name*/image'.
  *  If `max_outputs` is greater than 1, the summary value tags are
     generated sequentially as '*name*/image/0', '*name*/image/1', etc.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
      width, channels]` where `channels` is 1, 3, or 4.
    max_outputs: Max number of batch elements to generate images for.
    collections: Optional list of ops.GraphKeys.  The collections to add the
      summary to.  Defaults to [_ops.GraphKeys.SUMMARIES]
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.

  @compatibility(TF2)
  This API is not compatible with eager execution and `tf.function`. To migrate
  to TF2, please use `tf.summary.image` instead. Please check
  [Migrating tf.summary usage to
  TF 2.0](https://www.tensorflow.org/tensorboard/migrate#in_tf_1x) for concrete
  steps for migration.

  #### How to Map Arguments

  | TF1 Arg Name  | TF2 Arg Name    | Note                                   |
  | :------------ | :-------------- | :------------------------------------- |
  | `name`        | `name`          | -                                      |
  | `tensor`      | `data`          | -                                      |
  | -             | `step`          | Explicit int64-castable monotonic step |
  :               :                 : value. If omitted, this defaults to    :
  :               :                 : `tf.summary.experimental.get_step()`.  :
  | `max_outputs` | `max_outputs`   | -                                      |
  | `collections` | Not Supported   | -                                      |
  | `family`      | Removed         | Please use `tf.name_scope` instead     |
  :               :                 : to manage summary name prefix.         :
  | -             | `description`   | Optional long-form `str` description   |
  :               :                 : for the summary. Markdown is supported.:
  :               :                 : Defaults to empty.                     :

  @end_compatibility
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')
  with _summary_op_util.summary_scope(
      name, family, values=[tensor]) as (tag, scope):
    val = _gen_logging_ops.image_summary(
        tag=tag, tensor=tensor, max_images=max_outputs, name=scope)
    _summary_op_util.collect(val, collections, [_ops.GraphKeys.SUMMARIES])
  return val


@tf_export(v1=['summary.histogram'])
def histogram(name, values, collections=None, family=None):
  # pylint: disable=line-too-long
  """Outputs a `Summary` protocol buffer with a histogram.

  Adding a histogram summary makes it possible to visualize your data's
  distribution in TensorBoard. You can see a detailed explanation of the
  TensorBoard histogram dashboard
  [here](https://www.tensorflow.org/get_started/tensorboard_histograms).

  The generated
  [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
  has one summary value containing a histogram for `values`.

  This op reports an `InvalidArgument` error if any value is not finite.

  Args:
    name: A name for the generated node. Will also serve as a series name in
      TensorBoard.
    values: A real numeric `Tensor`. Any shape. Values to use to
      build the histogram.
    collections: Optional list of graph collections keys. The new summary op is
      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    family: Optional; if provided, used as the prefix of the summary tag name,
      which controls the tab name used for display on Tensorboard.

  Returns:
    A scalar `Tensor` of type `string`. The serialized `Summary` protocol
    buffer.

  @compatibility(TF2)
  This API is not compatible with eager execution and `tf.function`. To migrate
  to TF2, please use `tf.summary.histogram` instead. Please check
  [Migrating tf.summary usage to
  TF 2.0](https://www.tensorflow.org/tensorboard/migrate#in_tf_1x) for concrete
  steps for migration.

  #### How to Map Arguments

  | TF1 Arg Name  | TF2 Arg Name    | Note                                   |
  | :------------ | :-------------- | :------------------------------------- |
  | `name`        | `name`          | -                                      |
  | `values`      | `data`          | -                                      |
  | -             | `step`          | Explicit int64-castable monotonic step |
  :               :                 : value. If omitted, this defaults to    :
  :               :                 : `tf.summary.experimental.get_step()`   :
  | -             | `buckets`       | Optional positive `int` specifying     |
  :               :                 : the histogram bucket number.           :
  | `collections` | Not Supported   | -                                      |
  | `family`      | Removed         | Please use `tf.name_scope` instead     |
  :               :                 : to manage summary name prefix.         :
  | -             | `description`   | Optional long-form `str` description   |
  :               :                 : for the summary. Markdown is supported.:
  :               :                 : Defaults to empty.                     :

  @end_compatibility
  """
  if _distribute_summary_op_util.skip_summary():
    return _constant_op.constant('')
{'fraction_non_alphanumeric': 0.09573555387332829, 'fraction_numerical': 0.006611153166792834, 'mean_word_length': 2.859758472925594, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 10, 'lorem ipsum': 0, 'www.': 12, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 40, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '5984669', 'n_tokens_mistral': 5524, 'n_tokens_neox': 5286, 'n_words': 2001}