text: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  from __future__ import annotations
  
  from collections import OrderedDict, defaultdict
  from datetime import datetime
  from typing import (
      TYPE_CHECKING,
      DefaultDict,
      Dict,
      List,
      MutableMapping,
      NamedTuple,
      Optional,
      Union,
  )
  
  import numpy as np
  from ax.core.arm import Arm
  from ax.core.base_trial import BaseTrial
  from ax.core.generator_run import GeneratorRun, GeneratorRunType
  from ax.core.trial import immutable_once_run
  from ax.core.types import TCandidateMetadata
  from ax.utils.common.docutils import copy_doc
  from ax.utils.common.equality import datetime_equals, equality_typechecker
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
  
  
  logger = get_logger(__name__)
  
  
  if TYPE_CHECKING:
      # import as module to make sphinx-autodoc-typehints happy
      from ax import core  # noqa F401  # pragma: no cover
  
  
  class AbandonedArm(NamedTuple):
      """Tuple storing metadata of arm that has been abandoned within
      a BatchTrial.
      """
  
      name: str
      time: datetime
      reason: Optional[str] = None
  
      @equality_typechecker
      def __eq__(self, other: AbandonedArm) -> bool:
          return (
              self.name == other.name
              and self.reason == other.reason
              and datetime_equals(self.time, other.time)
          )
  
  
  class GeneratorRunStruct(NamedTuple):
      """Stores GeneratorRun object as well as the weight with which it was added."""
  
      generator_run: GeneratorRun
      weight: float
  
  
  class BatchTrial(BaseTrial):
      """Batched trial that has multiple attached arms, meant to be
      *deployed and evaluated together*, and possibly arm weights, which are
      a measure of how much of the total resources allocated to evaluating
      a batch should go towards evaluating the specific arm. For instance,
      for field experiments the weights could describe the fraction of the
      total experiment population assigned to the different treatment arms.
      Interpretation of the weights is defined in Runner.
  
      NOTE: A `BatchTrial` is not just a trial with many arms; it is a trial,
      for which it is important that the arms are evaluated simultaneously, e.g.
      in an A/B test where the evaluation results are subject to nonstationarity.
      For cases where multiple arms are evaluated separately and independently of
      each other, use multiple `Trial` objects with a single arm each.
  
      Args:
          experiment: Experiment, to which this trial is attached
          generator_run: GeneratorRun, associated with this trial. This can a
              also be set later through `add_arm` or `add_generator_run`, but a
              trial's associated generator run is immutable once set.
          trial_type: Type of this trial, if used in MultiTypeExperiment.
          optimize_for_power: Whether to optimize the weights of arms in this
              trial such that the experiment's power to detect effects of
              certain size is as high as possible. Refer to documentation of
              `BatchTrial.set_status_quo_and_optimize_power` for more detail.
          ttl_seconds: If specified, trials will be considered failed after
              this many seconds since the time the trial was ran, unless the
              trial is completed before then. Meant to be used to detect
              'dead' trials, for which the evaluation process might have
              crashed etc., and which should be considered failed after
              their 'time to live' has passed.
          index: If specified, the trial's index will be set accordingly.
              This should generally not be specified, as in the index will be
              automatically determined based on the number of existing trials.
              This is only used for the purpose of loading from storage.
      """
  
      def __init__(
          self,
          experiment: core.experiment.Experiment,
          generator_run: Optional[GeneratorRun] = None,
          trial_type: Optional[str] = None,
          optimize_for_power: Optional[bool] = False,
          ttl_seconds: Optional[int] = None,
          index: Optional[int] = None,
      ) -> None:
          super().__init__(
              experiment=experiment,
              trial_type=trial_type,
              ttl_seconds=ttl_seconds,
              index=index,
          )
          self._generator_run_structs: List[GeneratorRunStruct] = []
          self._abandoned_arms_metadata: Dict[str, AbandonedArm] = {}
          self._status_quo: Optional[Arm] = None
          self._status_quo_weight_override: Optional[float] = None
          if generator_run is not None:
              self.add_generator_run(generator_run=generator_run)
  
          self.optimize_for_power = optimize_for_power
          status_quo = experiment.status_quo
          if optimize_for_power:
              if status_quo is None:
                  raise ValueError(
                      "Can only optimize for power if experiment has a status quo."
                  )
              self.set_status_quo_and_optimize_power(status_quo=status_quo)
          else:
              # Set the status quo for tracking purposes
              # It will not be included in arm_weights
              self._status_quo = status_quo
  
      @property
      def experiment(self) -> core.experiment.Experiment:
          """The experiment this batch belongs to."""
          return self._experiment
  
      @property
      def index(self) -> int:
          """The index of this batch within the experiment's batch list."""
          return self._index
  
      @property
      def generator_run_structs(self) -> List[GeneratorRunStruct]:
          """List of generator run structs attached to this trial.
  
          Struct holds generator_run object and the weight with which it was added.
          """
          return self._generator_run_structs
  
      @property
      def arm_weights(self) -> MutableMapping[Arm, float]:
          """The set of arms and associated weights for the trial.
  
          These are constructed by merging the arms and weights from
          each generator run that is attached to the trial.
          """
          arm_weights = OrderedDict()
          if len(self._generator_run_structs) == 0 and self.status_quo is None:
              return arm_weights
          for struct in self._generator_run_structs:
              multiplier = struct.weight
              for arm, weight in struct.generator_run.arm_weights.items():
                  scaled_weight = weight * multiplier
                  if arm in arm_weights:
                      arm_weights[arm] += scaled_weight
                  else:
                      arm_weights[arm] = scaled_weight
          if self.status_quo is not None and self._status_quo_weight_override is not None:
              # If override is specified, this is the weight the status quo gets,
              # regardless of whether it appeared in any generator runs.
              # If no override is specified, status quo does not appear in arm_weights.
              arm_weights[self.status_quo] = self._status_quo_weight_override
          return arm_weights
  
      @arm_weights.setter
      def arm_weights(self, arm_weights: MutableMapping[Arm, float]) -> None:
          raise NotImplementedError("Use `trial.add_arms_and_weights`")
  
      @immutable_once_run
      def add_arm(self, arm: Arm, weight: float = 1.0) -> BatchTrial:
          """Add a arm to the trial.
  
          Args:
              arm: The arm to be added.
              weight: The weight with which this arm should be added.
  
          Returns:
              The trial instance.
          """
          return self.add_arms_and_weights(arms=[arm], weights=[weight])
  
      @immutable_once_run
      def add_arms_and_weights(
          self,
          arms: List[Arm],
          weights: Optional[List[float]] = None,
          multiplier: float = 1.0,
      ) -> BatchTrial:
          """Add arms and weights to the trial.
  
          Args:
              arms: The arms to be added.
              weights: The weights associated with the arms.
              multiplier: The multiplier applied to input weights before merging with
                  the current set of arms and weights.
  
          Returns:
              The trial instance.
          """
  
          return self.add_generator_run(
              generator_run=GeneratorRun(
                  arms=arms, weights=weights, type=GeneratorRunType.MANUAL.name
              ),
              multiplier=multiplier,
          )
  
      @immutable_once_run
      def add_generator_run(
          self, generator_run: GeneratorRun, multiplier: float = 1.0
      ) -> BatchTrial:
          """Add a generator run to the trial.
  
          The arms and weights from the generator run will be merged with
          the existing arms and weights on the trial, and the generator run
          object will be linked to the trial for tracking.
  
          Args:
              generator_run: The generator run to be added.
              multiplier: The multiplier applied to input weights before merging with
                  the current set of arms and weights.
  
          Returns:
              The trial instance.
          """
          # Copy the generator run, to preserve initial and skip mutations to arms.
          generator_run = generator_run.clone()
  
          # First validate generator run arms
          for arm in generator_run.arms:
              self.experiment.search_space.check_types(arm.parameters, raise_error=True)
  
          # Add names to arms
          # For those not yet added to this experiment, create a new name
          # Else, use the name of the existing arm
          for arm in generator_run.arms:
              self._check_existing_and_name_arm(arm)
  
          self._generator_run_structs.append(
              GeneratorRunStruct(generator_run=generator_run, weight=multiplier)
          )
          generator_run.index = len(self._generator_run_structs) - 1
  
          if self.status_quo is not None and self.optimize_for_power:
              self.set_status_quo_and_optimize_power(status_quo=not_none(self.status_quo))
  
          self._set_generation_step_index(
              generation_step_index=generator_run._generation_step_index
          )
          return self
  
      @property
      def status_quo(self) -> Optional[Arm]:
          """The control arm for this batch."""
          return self._status_quo
  
      @status_quo.setter
      def status_quo(self, status_quo: Optional[Arm]) -> None:
          raise NotImplementedError(
              "Use `set_status_quo_with_weight` or "
              "`set_status_quo_and_optimize_power` "
              "to set the status quo arm."
          )
  
      def unset_status_quo(self) -> None:
          """Set the status quo to None."""
          self._status_quo = None
  
      @immutable_once_run
      def set_status_quo_with_weight(self, status_quo: Arm, weight: float) -> BatchTrial:
          """Sets status quo arm with given weight. This weight *overrides* any
          weight the status quo has from generator runs attached to this batch.
          Thus, this function is not the same as using add_arm, which will
          result in the weight being additive over all generator runs.
          """
          # Assign a name to this arm if none exists
          if weight is not None and weight <= 0.0:
              raise ValueError("Status quo weight must be positive.")
  
          if status_quo is not None:
              self.experiment.search_space.check_types(
                  status_quo.parameters, raise_error=True
              )
              self.experiment._name_and_store_arm_if_not_exists(
                  arm=status_quo, proposed_name="status_quo_" + str(self.index)
              )
          self._status_quo = status_quo
          self._status_quo_weight_override = weight
          return self
  
      @immutable_once_run
      def set_status_quo_and_optimize_power(self, status_quo: Arm) -> BatchTrial:
          """Adds a status quo arm to the batch and optimizes for power.
  
          NOTE: this optimization based on the arms that are currently attached
          to the batch. If you add more arms later, you should re-run this function.
          If you want the optimization to happen automatically,
          set batch.optimize_for_power = True.
  
          This function will maximize power across the multiple pair-wise
          comparisons of existing arms against the status_quo.
  
          Specifically, this function assigns sqrt(sum_weights) weight to the
          status quo, where sum_weights is the sum of the weights of the existing
          arms, excluding the status quo. This will be optimal in terms of
          statistical power in the case where:
              1) status quo is the only arm to compare against,
              2) all other arms are of equal interest.
          """
          if len(self.arms) == 0:
              # If status quo is the only arm, just set its weight to 1
              # Can't use logic below, because it will choose 0
              self.set_status_quo_with_weight(status_quo=status_quo, weight=1)
              return self
  
          # arm_weights should always have at least one arm now
          arm_weights = not_none(self.arm_weights)
          sum_weights = sum(w for arm, w in arm_weights.items() if arm != status_quo)
          optimal_status_quo_weight_override = np.sqrt(sum_weights)
          self.set_status_quo_with_weight(
              status_quo=status_quo, weight=optimal_status_quo_weight_override
          )
          return self
  
      @property
      def arms(self) -> List[Arm]:
          """All arms contained in the trial."""
          arm_weights = self.arm_weights
          return [] if arm_weights is None else list(arm_weights.keys())
  
      @property
      def weights(self) -> List[float]:
          """Weights corresponding to arms contained in the trial."""
          arm_weights = self.arm_weights
          return [] if arm_weights is None else list(arm_weights.values())
  
      @property
      def arms_by_name(self) -> Dict[str, Arm]:
          """Map from arm name to object for all arms in trial."""
          arms_by_name = {}
          for arm in self.arms:
              if not arm.has_name:
                  raise ValueError(  # pragma: no cover
                      "Arms attached to a trial must have a name."
                  )
              arms_by_name[arm.name] = arm
          return arms_by_name
  
      @property
      def abandoned_arms(self) -> List[Arm]:
          """List of arms that have been abandoned within this trial"""
          return [
              self.arms_by_name[arm.name]
              for arm in self._abandoned_arms_metadata.values()
          ]
  
      # pyre-ignore[6]: T77111662.
      @copy_doc(BaseTrial.generator_runs)
      @property
      def generator_runs(self) -> List[GeneratorRun]:
          return [grs.generator_run for grs in self.generator_run_structs]
  
      @property
      def abandoned_arms_metadata(self) -> List[AbandonedArm]:
          return list(self._abandoned_arms_metadata.values())
  
      @property
      def is_factorial(self) -> bool:
          """Return true if the trial's arms are a factorial design with
          no linked factors.
          """
          # To match the model behavior, this should probably actually be pulled
          # from exp.parameters. However, that seems rather ugly when this function
          # intuitively should just depend on the arms.
          sufficient_factors = all(len(arm.parameters or []) >= 2 for arm in self.arms)
          if not sufficient_factors:
              return False
          param_levels: DefaultDict[str, Dict[Union[str, float], int]] = defaultdict(dict)
          for arm in self.arms:
              for param_name, param_value in arm.parameters.items():
                  # Expected `Union[float, str]` for 2nd anonymous parameter to call
                  # `dict.__setitem__` but got `Optional[Union[bool, float, str]]`.
                  # pyre-fixme[6]: Expected `Union[float, str]` for 1st param but got `...
                  param_levels[param_name][param_value] = 1
          param_cardinality = 1
          for param_values in param_levels.values():
              param_cardinality *= len(param_values)
          return len(self.arms) == param_cardinality
  
      def run(self) -> BatchTrial:
          return checked_cast(BatchTrial, super().run())
  
      def normalized_arm_weights(
          self, total: float = 1, trunc_digits: Optional[int] = None
      ) -> MutableMapping[Arm, float]:
          """Returns arms with a new set of weights normalized
          to the given total.
  
          This method is useful for many runners where we need to normalize weights
          to a certain total without mutating the weights attached to a trial.
  
          Args:
              total: The total weight to which to normalize.
                  Default is 1, in which case arm weights
                  can be interpreted as probabilities.
              trunc_digits: The number of digits to keep. If the
                  resulting total weight is not equal to `total`, re-allocate
                  weight in such a way to maintain relative weights as best as
                  possible.
  
          Returns:
              Mapping from arms to the new set of weights.
  
          """
          weights = np.array(self.weights)
          if trunc_digits is not None:
              atomic_weight = 10 ** -trunc_digits
              # pyre-fixme[16]: `float` has no attribute `astype`.
              int_weights = (
                  (total / atomic_weight) * (weights / np.sum(weights))
              ).astype(int)
              n_leftover = int(total / atomic_weight) - np.sum(int_weights)
              int_weights[:n_leftover] += 1
              weights = int_weights * atomic_weight
          else:
              weights = weights * (total / np.sum(weights))
          return OrderedDict(zip(self.arms, weights))
  
      def mark_arm_abandoned(
          self, arm_name: str, reason: Optional[str] = None
      ) -> BatchTrial:
          """Mark a arm abandoned.
  
          Usually done after deployment when one arm causes issues but
          user wants to continue running other arms in the batch.
  
          Args:
              arm_name: The name of the arm to abandon.
              reason: The reason for abandoning the arm.
  
          Returns:
              The batch instance.
          """
          if arm_name not in self.arms_by_name:
              raise ValueError("Arm must be contained in batch.")
  
          abandoned_arm = AbandonedArm(name=arm_name, time=datetime.now(), reason=reason)
          self._abandoned_arms_metadata[arm_name] = abandoned_arm
          return self
  
      def clone(self) -> BatchTrial:
          """Clone the trial.
  
          Returns:
              A new instance of the trial.
          """
          new_trial = self._experiment.new_batch_trial()
          for struct in self._generator_run_structs:
              new_trial.add_generator_run(struct.generator_run, struct.weight)
          new_trial.trial_type = self._trial_type
          new_trial.runner = self._runner
          return new_trial
  
      def __repr__(self) -> str:
          return (
              "BatchTrial("
              f"experiment_name='{self._experiment._name}', "
              f"index={self._index}, "
              f"status={self._status})"
          )
  
      def _get_candidate_metadata_from_all_generator_runs(
          self,
      ) -> Dict[str, TCandidateMetadata]:
          """Retrieves combined candidate metadata from all generator runs on this
          batch trial in the form of { arm name -> candidate metadata} mapping.
  
          NOTE: this does not handle the case of the same arm appearing in multiple
          generator runs in the same trial: metadata from only one of the generator
          runs containing the arm will be retrieved.
          """
          cand_metadata = {}
          for gr_struct in self._generator_run_structs:
              gr = gr_struct.generator_run
              if gr.candidate_metadata_by_arm_signature:
                  gr_cand_metadata = gr.candidate_metadata_by_arm_signature
                  warn = False
                  for arm in gr.arms:
                      if arm.name in cand_metadata:
                          warn = True
                      if gr_cand_metadata:
                          # Reformat the mapping to be by arm name, since arm signature
                          # is not stored in Ax data.
                          cand_metadata[arm.name] = gr_cand_metadata.get(arm.signature)
                  if warn:
                      logger.warning(
                          "The same arm appears in multiple generator runs in batch "
                          f"{self.index}. Candidate metadata will only contain metadata "
                          "for one of those generator runs, and the candidate metadata "
                          "for the arm from another generator run will not be propagated."
                      )
          return cand_metadata
  
      def _get_candidate_metadata(self, arm_name: str) -> TCandidateMetadata:
          """Retrieves candidate metadata for a specific arm."""
          try:
              arm = self.arms_by_name[arm_name]
          except KeyError:
              raise ValueError(
                  f"Arm by name {arm_name} is not part of trial #{self.index}."
              )
          for gr_struct in self._generator_run_structs:
              gr = gr_struct.generator_run
              if gr and gr.candidate_metadata_by_arm_signature and arm in gr.arms:
                  return not_none(gr.candidate_metadata_by_arm_signature).get(
                      arm.signature
                  )
          return None
  