# Copyright 2019 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.
# ==============================================================================
"""Experimental public APIs for the HParams plugin.

These are porcelain on top of `api_pb2` (`api.proto`) and `summary.py`.
"""

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

import abc
import hashlib
import json
import random
import time

import numpy as np
import six

from tensorboard.compat import tf2 as tf
from tensorboard.compat.proto import summary_pb2
from tensorboard.plugins.hparams import api_pb2
from tensorboard.plugins.hparams import metadata
from tensorboard.plugins.hparams import plugin_data_pb2


def hparams(hparams, trial_id=None, start_time_secs=None):
    # NOTE: Keep docs in sync with `hparams_pb` below.
    """Write hyperparameter values for a single trial.

    Args:
      hparams: A `dict` mapping hyperparameters to the values used in this
        trial. Keys should be the names of `HParam` objects used in an
        experiment, or the `HParam` objects themselves. Values should be
        Python `bool`, `int`, `float`, or `string` values, depending on
        the type of the hyperparameter. The corresponding numpy types,
        like `np.float32`, are also permitted.
      trial_id: An optional `str` ID for the set of hyperparameter values
        used in this trial. Defaults to a hash of the hyperparameters.
      start_time_secs: The time that this trial started training, as
        seconds since epoch. Defaults to the current time.

    Returns:
      A tensor whose value is `True` on success, or `False` if no summary
      was written because no default summary writer was available.
    """
    pb = hparams_pb(
        hparams=hparams, trial_id=trial_id, start_time_secs=start_time_secs,
    )
    return _write_summary("hparams", pb)


def hparams_pb(hparams, trial_id=None, start_time_secs=None):
    # NOTE: Keep docs in sync with `hparams` above.
    """Create a summary encoding hyperparameter values for a single trial.

    Args:
      hparams: A `dict` mapping hyperparameters to the values used in this
        trial. Keys should be the names of `HParam` objects used in an
        experiment, or the `HParam` objects themselves. Values should be
        Python `bool`, `int`, `float`, or `string` values, depending on
        the type of the hyperparameter.
      trial_id: An optional `str` ID for the set of hyperparameter values
        used in this trial. Defaults to a hash of the hyperparameters.
      start_time_secs: The time that this trial started training, as
        seconds since epoch. Defaults to the current time.

    Returns:
      A TensorBoard `summary_pb2.Summary` message.
    """
    if start_time_secs is None:
        start_time_secs = time.time()
    hparams = _normalize_hparams(hparams)
    group_name = _derive_session_group_name(trial_id, hparams)

    session_start_info = plugin_data_pb2.SessionStartInfo(
        group_name=group_name, start_time_secs=start_time_secs,
    )
    for hp_name in sorted(hparams):
        hp_value = hparams[hp_name]
        if isinstance(hp_value, bool):
            session_start_info.hparams[hp_name].bool_value = hp_value
        elif isinstance(hp_value, (float, int)):
            session_start_info.hparams[hp_name].number_value = hp_value
        elif isinstance(hp_value, six.string_types):
            session_start_info.hparams[hp_name].string_value = hp_value
        else:
            raise TypeError(
                "hparams[%r] = %r, of unsupported type %r"
                % (hp_name, hp_value, type(hp_value))
            )

    return _summary_pb(
        metadata.SESSION_START_INFO_TAG,
        plugin_data_pb2.HParamsPluginData(
            session_start_info=session_start_info
        ),
    )


def hparams_config(hparams, metrics, time_created_secs=None):
    # NOTE: Keep docs in sync with `hparams_config_pb` below.
    """Write a top-level experiment configuration.

    This configuration describes the hyperparameters and metrics that will
    be tracked in the experiment, but does not record any actual values of
    those hyperparameters and metrics. It can be created before any models
    are actually trained.

    Args:
      hparams: A list of `HParam` values.
      metrics: A list of `Metric` values.
      time_created_secs: The time that this experiment was created, as
        seconds since epoch. Defaults to the current time.

    Returns:
      A tensor whose value is `True` on success, or `False` if no summary
      was written because no default summary writer was available.
    """
    pb = hparams_config_pb(
        hparams=hparams, metrics=metrics, time_created_secs=time_created_secs,
    )
    return _write_summary("hparams_config", pb)


def hparams_config_pb(hparams, metrics, time_created_secs=None):
    # NOTE: Keep docs in sync with `hparams_config` above.
    """Create a top-level experiment configuration.

    This configuration describes the hyperparameters and metrics that will
    be tracked in the experiment, but does not record any actual values of
    those hyperparameters and metrics. It can be created before any models
    are actually trained.

    Args:
      hparams: A list of `HParam` values.
      metrics: A list of `Metric` values.
      time_created_secs: The time that this experiment was created, as
        seconds since epoch. Defaults to the current time.

    Returns:
      A TensorBoard `summary_pb2.Summary` message.
    """
    hparam_infos = []
    for hparam in hparams:
        info = api_pb2.HParamInfo(
            name=hparam.name,
            description=hparam.description,
            display_name=hparam.display_name,
        )
        domain = hparam.domain
        if domain is not None:
            domain.update_hparam_info(info)
        hparam_infos.append(info)
    metric_infos = [metric.as_proto() for metric in metrics]
    experiment = api_pb2.Experiment(
        hparam_infos=hparam_infos,
        metric_infos=metric_infos,
        time_created_secs=time_created_secs,
    )
    return _summary_pb(
        metadata.EXPERIMENT_TAG,
        plugin_data_pb2.HParamsPluginData(experiment=experiment),
    )


def _normalize_hparams(hparams):
    """Normalize a dict keyed by `HParam`s and/or raw strings.

    Args:
      hparams: A `dict` whose keys are `HParam` objects and/or strings
        representing hyperparameter names, and whose values are
        hyperparameter values. No two keys may have the same name.

    Returns:
      A `dict` whose keys are hyperparameter names (as strings) and whose
      values are the corresponding hyperparameter values, after numpy
      normalization (see `_normalize_numpy_value`).

    Raises:
      ValueError: If two entries in `hparams` share the same
        hyperparameter name.
    """
    result = {}
    for (k, v) in six.iteritems(hparams):
        if isinstance(k, HParam):
            k = k.name
        if k in result:
            raise ValueError("multiple values specified for hparam %r" % (k,))
        result[k] = _normalize_numpy_value(v)
    return result


def _normalize_numpy_value(value):
    """Convert a Python or Numpy scalar to a Python scalar.

    For instance, `3.0`, `np.float32(3.0)`, and `np.float64(3.0)` all
    map to `3.0`.

    Args:
      value: A Python scalar (`int`, `float`, `str`, or `bool`) or
        rank-0 `numpy` equivalent (e.g., `np.int64`, `np.float32`).

    Returns:
      A Python scalar equivalent to `value`.
    """
    if isinstance(value, np.generic):
        return value.item()
    else:
        return value


def _derive_session_group_name(trial_id, hparams):
    if trial_id is not None:
        if not isinstance(trial_id, six.string_types):
            raise TypeError(
                "`trial_id` should be a `str`, but got: %r" % (trial_id,)
            )
        return trial_id
    # Use `json.dumps` rather than `str` to ensure invariance under string
    # type (incl. across Python versions) and dict iteration order.
    jparams = json.dumps(hparams, sort_keys=True, separators=(",", ":"))
    return hashlib.sha256(jparams.encode("utf-8")).hexdigest()


def _write_summary(name, pb):
    """Write a summary, returning the writing op.

    Args:
      name: As passed to `summary_scope`.
      pb: A `summary_pb2.Summary` message.

    Returns:
      A tensor whose value is `True` on success, or `False` if no summary
      was written because no default summary writer was available.
    """
    raw_pb = pb.SerializeToString()
    summary_scope = (
        getattr(tf.summary.experimental, "summary_scope", None)
        or tf.summary.summary_scope
    )
    with summary_scope(name):
        return tf.summary.experimental.write_raw_pb(raw_pb, step=0)


def _summary_pb(tag, hparams_plugin_data):
    """Create a summary holding the given `HParamsPluginData` message.

    Args:
      tag: The `str` tag to use.
      hparams_plugin_data: The `HParamsPluginData` message to use.

    Returns:
      A TensorBoard `summary_pb2.Summary` message.
    """
    summary = summary_pb2.Summary()
    summary_metadata = metadata.create_summary_metadata(hparams_plugin_data)
    value = summary.value.add(
        tag=tag, metadata=summary_metadata, tensor=metadata.NULL_TENSOR
    )
    return summary


class HParam(object):
    """A hyperparameter in an experiment.

    This class describes a hyperparameter in the abstract. It ranges
    over a domain of values, but is not bound to any particular value.
    """

    def __init__(self, name, domain=None, display_name=None, description=None):
        """Create a hyperparameter object.

        Args:
          name: A string ID for this hyperparameter, which should be unique
            within an experiment.
          domain: An optional `Domain` object describing the values that
            this hyperparameter can take on.
          display_name: An optional human-readable display name (`str`).
          description: An optional Markdown string describing this
            hyperparameter.

        Raises:
          ValueError: If `domain` is not a `Domain`.
        """
        self._name = name
        self._domain = domain
        self._display_name = display_name
        self._description = description
        if not isinstance(self._domain, (Domain, type(None))):
            raise ValueError("not a domain: %r" % (self._domain,))

    def __str__(self):
        return "<HParam %r: %s>" % (self._name, self._domain)

    def __repr__(self):
        fields = [
            ("name", self._name),
            ("domain", self._domain),
            ("display_name", self._display_name),
            ("description", self._description),
        ]
        fields_string = ", ".join("%s=%r" % (k, v) for (k, v) in fields)
        return "HParam(%s)" % fields_string

    @property
    def name(self):
        return self._name

    @property
    def domain(self):
        return self._domain

    @property
    def display_name(self):
        return self._display_name

    @property
    def description(self):
        return self._description


@six.add_metaclass(abc.ABCMeta)
class Domain(object):
    """The domain of a hyperparameter.

    Domains are restricted to values of the simple types `float`, `int`,
    `str`, and `bool`.
    """

    @abc.abstractproperty
    def dtype(self):
        """Data type of this domain: `float`, `int`, `str`, or `bool`."""
        pass

    @abc.abstractmethod
    def sample_uniform(self, rng=random):
        """Sample a value from this domain uniformly at random.

        Args:
          rng: A `random.Random` interface; defaults to the `random` module
            itself.

        Raises:
          IndexError: If the domain is empty.
        """
        pass

    @abc.abstractmethod
    def update_hparam_info(self, hparam_info):
        """Update an `HParamInfo` proto to include this domain.

        This should update the `type` field on the proto and exactly one of
        the `domain` variants on the proto.

        Args:
          hparam_info: An `api_pb2.HParamInfo` proto to modify.
        """
        pass


class IntInterval(Domain):
    """A domain that takes on all integer values in a closed interval."""

    def __init__(self, min_value=None, max_value=None):
        """Create an `IntInterval`.

        Args:
          min_value: The lower bound (inclusive) of the interval.
          max_value: The upper bound (inclusive) of the interval.

        Raises:
          TypeError: If `min_value` or `max_value` is not an `int`.
          ValueError: If `min_value > max_value`.
        """
        if not isinstance(min_value, int):
            raise TypeError("min_value must be an int: %r" % (min_value,))
        if not isinstance(max_value, int):
            raise TypeError("max_value must be an int: %r" % (max_value,))
        if min_value > max_value:
            raise ValueError("%r > %r" % (min_value, max_value))
        self._min_value = min_value
        self._max_value = max_value

    def __str__(self):
        return "[%s, %s]" % (self._min_value, self._max_value)

    def __repr__(self):
        return "IntInterval(%r, %r)" % (self._min_value, self._max_value)

    @property
    def dtype(self):
        return int

    @property
    def min_value(self):
        return self._min_value

    @property
    def max_value(self):
        return self._max_value

    def sample_uniform(self, rng=random):
        return rng.randint(self._min_value, self._max_value)

    def update_hparam_info(self, hparam_info):
        hparam_info.type = (
            api_pb2.DATA_TYPE_FLOAT64
        )  # TODO(#1998): Add int dtype.
        hparam_info.domain_interval.min_value = self._min_value
        hparam_info.domain_interval.max_value = self._max_value


class RealInterval(Domain):
    """A domain that takes on all real values in a closed interval."""

    def __init__(self, min_value=None, max_value=None):
        """Create a `RealInterval`.

        Args:
          min_value: The lower bound (inclusive) of the interval.
          max_value: The upper bound (inclusive) of the interval.

        Raises:
          TypeError: If `min_value` or `max_value` is not an `float`.
          ValueError: If `min_value > max_value`.
        """
        if not isinstance(min_value, float):
            raise TypeError("min_value must be a float: %r" % (min_value,))
        if not isinstance(max_value, float):
            raise TypeError("max_value must be a float: %r" % (max_value,))
        if min_value > max_value:
            raise ValueError("%r > %r" % (min_value, max_value))
        self._min_value = min_value
        self._max_value = max_value

    def __str__(self):
        return "[%s, %s]" % (self._min_value, self._max_value)

    def __repr__(self):
        return "RealInterval(%r, %r)" % (self._min_value, self._max_value)

    @property
    def dtype(self):
        return float

    @property
    def min_value(self):
        return self._min_value

    @property
    def max_value(self):
        return self._max_value

    def sample_uniform(self, rng=random):
        return rng.uniform(self._min_value, self._max_value)

    def update_hparam_info(self, hparam_info):
        hparam_info.type = api_pb2.DATA_TYPE_FLOAT64
        hparam_info.domain_interval.min_value = self._min_value
        hparam_info.domain_interval.max_value = self._max_value


class Discrete(Domain):
    """A domain that takes on a fixed set of values.

    These values may be of any (single) domain type.
    """

    def __init__(self, values, dtype=None):
        """Construct a discrete domain.

        Args:
          values: A iterable of the values in this domain.
          dtype: The Python data type of values in this domain: one of
            `int`, `float`, `bool`, or `str`. If `values` is non-empty,
            `dtype` may be `None`, in which case it will be inferred as the
            type of the first element of `values`.

        Raises:
          ValueError: If `values` is empty but no `dtype` is specified.
          ValueError: If `dtype` or its inferred value is not `int`,
            `float`, `bool`, or `str`.
          TypeError: If an element of `values` is not an instance of
            `dtype`.
        """
        self._values = list(values)
        if dtype is None:
            if self._values:
                dtype = type(self._values[0])
            else:
                raise ValueError("Empty domain with no dtype specified")
        if dtype not in (int, float, bool, str):
            raise ValueError("Unknown dtype: %r" % (dtype,))
        self._dtype = dtype
        for value in self._values:
            if not isinstance(value, self._dtype):
                raise TypeError(
                    "dtype mismatch: not isinstance(%r, %s)"
                    % (value, self._dtype.__name__)
                )
        self._values.sort()

    def __str__(self):
        return "{%s}" % (", ".join(repr(x) for x in self._values))

    def __repr__(self):
        return "Discrete(%r)" % (self._values,)

    @property
    def dtype(self):
        return self._dtype

    @property
    def values(self):
        return list(self._values)

    def sample_uniform(self, rng=random):
        return rng.choice(self._values)

    def update_hparam_info(self, hparam_info):
        hparam_info.type = {
            int: api_pb2.DATA_TYPE_FLOAT64,  # TODO(#1998): Add int dtype.
            float: api_pb2.DATA_TYPE_FLOAT64,
            bool: api_pb2.DATA_TYPE_BOOL,
            str: api_pb2.DATA_TYPE_STRING,
        }[self._dtype]
        hparam_info.ClearField("domain_discrete")
        hparam_info.domain_discrete.extend(self._values)


class Metric(object):
    """A metric in an experiment.

    A metric is a real-valued function of a model. Each metric is
    associated with a TensorBoard scalar summary, which logs the
    metric's value as the model trains.
    """

    TRAINING = api_pb2.DATASET_TRAINING
    VALIDATION = api_pb2.DATASET_VALIDATION

    def __init__(
        self,
        tag,
        group=None,
        display_name=None,
        description=None,
        dataset_type=None,
    ):
        """

        Args:
          tag: The tag name of the scalar summary that corresponds to this
            metric (as a `str`).
          group: An optional string listing the subdirectory under the
            session's log directory containing summaries for this metric.
            For instance, if summaries for training runs are written to
            events files in `ROOT_LOGDIR/SESSION_ID/train`, then `group`
            should be `"train"`. Defaults to the empty string: i.e.,
            summaries are expected to be written to the session logdir.
          display_name: An optional human-readable display name.
          description: An optional Markdown string with a human-readable
            description of this metric, to appear in TensorBoard.
          dataset_type: Either `Metric.TRAINING` or `Metric.VALIDATION`, or
            `None`.
        """
        self._tag = tag
        self._group = group
        self._display_name = display_name
        self._description = description
        self._dataset_type = dataset_type
        if self._dataset_type not in (None, Metric.TRAINING, Metric.VALIDATION):
            raise ValueError("invalid dataset type: %r" % (self._dataset_type,))

    def as_proto(self):
        return api_pb2.MetricInfo(
            name=api_pb2.MetricName(group=self._group, tag=self._tag,),
            display_name=self._display_name,
            description=self._description,
            dataset_type=self._dataset_type,
        )
