text: import abc
  import typing
  import pickle
  from dataclasses import dataclass
  from functools import reduce
  
  from typing import Iterable, Tuple, Union
  import pipeline_dp
  from pipeline_dp import dp_computations
  from pipeline_dp import aggregate_params
  import numpy as np
  
  @dataclass
  class AccumulatorParams:
      accumulator_type: type
      constructor_params: typing.Any
  
  
  def merge(accumulators: typing.Iterable['Accumulator']) -> 'Accumulator':
      """Merges the accumulators."""
      return reduce(lambda acc1, acc2: acc1.add_accumulator(acc2), accumulators)
  
  
  def create_accumulator_params(
      aggregation_params: pipeline_dp.AggregateParams,
      budget_accountant: pipeline_dp.BudgetAccountant
  ) -> typing.List[AccumulatorParams]:
      accumulator_params = []
      if pipeline_dp.Metrics.COUNT in aggregation_params.metrics:
          # TODO: populate CountParams from budget_accountant when it is ready
          accumulator_params.append(AccumulatorParams(
              accumulator_type=CountAccumulator,
              constructor_params=CountParams()))
      else:
          raise NotImplemented()  # implementation will be done later
      return accumulator_params
  
  
  class Accumulator(abc.ABC):
      """Base class for all accumulators.
  
      Accumulators are objects that encapsulate aggregations and computations of
      differential private metrics.
    """
  
      @abc.abstractmethod
      def add_value(self, value):
          """Adds the value to each of the accumulator.
      Args:
        value: value to be added.
  
      Returns: self.
      """
          pass
  
      def _check_mergeable(self, accumulator: 'Accumulator'):
          if not isinstance(accumulator, type(self)):
              raise TypeError(
                  f"The accumulator to be added is not of the same type: "
                  f"{accumulator.__class__.__name__} != "
                  f"{self.__class__.__name__}")
  
      @abc.abstractmethod
      def add_accumulator(self, accumulator: 'Accumulator') -> 'Accumulator':
          """Merges the accumulator to self and returns self.
  
         Sub-class implementation is responsible for checking that types of
         self and accumulator are the same.
        Args:
          accumulator:
  
        Returns: self
      """
          pass
  
      @abc.abstractmethod
      def compute_metrics(self):
          """Computes and returns the result of aggregation."""
          pass
  
      def serialize(self):
          return pickle.dumps(self)
  
      @classmethod
      def deserialize(cls, serialized_obj: str):
          deserialized_obj = pickle.loads(serialized_obj)
          if not isinstance(deserialized_obj, cls):
              raise TypeError("The deserialized object is not of the right type.")
          return deserialized_obj
  
  
  class CompoundAccumulator(Accumulator):
      """Accumulator for computing multiple metrics.
  
      CompoundAccumulator contains one or more accumulators of other types for
      computing multiple metrics.
      For example it can contain [CountAccumulator,  SumAccumulator].
      CompoundAccumulator delegates all operations to the internal accumulators.
    """
  
      def __init__(self, accumulators: typing.Iterable['Accumulator']):
          self.accumulators = accumulators
  
      def add_value(self, value):
          for accumulator in self.accumulators:
              accumulator.add_value(value)
          return self
  
      def add_accumulator(self, accumulator: 'CompoundAccumulator') -> \
        'CompoundAccumulator':
          """Merges the accumulators of the CompoundAccumulators.
  
      The expectation is that the internal accumulators are of the same type and
      are in the same order."""
          self._check_mergeable(accumulator)
          if len(accumulator.accumulators) != len(self.accumulators):
              raise ValueError(
                  "Accumulators in the input are not of the same size." +
                  f" Expected size = {len(self.accumulators)}" +
                  f" received size = {len(accumulator.accumulators)}.")
  
          for pos, (base_accumulator_type, to_add_accumulator_type) in enumerate(
                  zip(self.accumulators, accumulator.accumulators)):
              if type(base_accumulator_type) != type(to_add_accumulator_type):
                  raise TypeError(
                      "The type of the accumulators don't match at "
                      f"index {pos}. {type(base_accumulator_type).__name__} "
                      f"!= {type(to_add_accumulator_type).__name__}.")
  
          for (base_accumulator,
               to_add_accumulator) in zip(self.accumulators,
                                          accumulator.accumulators):
              base_accumulator.add_accumulator(to_add_accumulator)
          return self
  
      def compute_metrics(self):
          """Computes and returns a list of metrics computed by internal
      accumulators."""
          return [
              accumulator.compute_metrics() for accumulator in self.accumulators
          ]
  
  
  class AccumulatorFactory:
      """Factory for producing the appropriate Accumulator depending on the
      AggregateParams and BudgetAccountant."""
  
      def __init__(self, params: pipeline_dp.AggregateParams,
                   budget_accountant: pipeline_dp.BudgetAccountant):
          self._params = params
          self._budget_accountant = budget_accountant
  
      def initialize(self):
          self._accumulator_params = create_accumulator_params(
              self._params, self._budget_accountant)
  
      def create(self, values: typing.List) -> Accumulator:
          accumulators = []
          for accumulator_param in self._accumulator_params:
              accumulators.append(
                  accumulator_param.accumulator_type(
                      accumulator_param.constructor_params, values))
  
          # No need to create CompoundAccumulator if there is only 1 accumulator.
          if len(accumulators) == 1:
              return accumulators[0]
  
          return CompoundAccumulator(accumulators)
  
  
  @dataclass
  class CountParams:
      pass
  
  
  class CountAccumulator(Accumulator):
  
      def __init__(self, params: CountParams, values):
          self._count = len(values)
  
      def add_value(self, value):
          self._count += 1
  
      def add_accumulator(self,
                          accumulator: 'CountAccumulator') -> 'CountAccumulator':
          self._check_mergeable(accumulator)
          self._count += accumulator._count
          return self
  
      def compute_metrics(self) -> float:
          # TODO: add differential privacy
          return self._count
  
  
  @dataclass
  class VectorSummationParams:
      noise_kind: aggregate_params.NoiseKind
      max_norm: float
  
  _FloatVector = Union[Tuple[float], np.ndarray]
  
  class VectorSummationAccumulator(Accumulator):
      _vec_sum: np.ndarray
  
      def __init__(self, params: VectorSummationParams,
                   values: Iterable[_FloatVector]) -> None:
          self._params = params
          self._vec_sum = None
          for val in values:
              self.add_value(val)
  
      def add_value(self, value: _FloatVector):
          if not isinstance(value, np.ndarray):
              value = np.array(value)
  
          if self._vec_sum is None:
              self._vec_sum = value
          else:
              if self._vec_sum.shape != value.shape:
                  raise TypeError(
                      f"Shape mismatch: {self._vec_sum.shape} != {value.shape}")
              self._vec_sum += value
          return self
  
      def add_accumulator(
          self, accumulator: 'VectorSummationAccumulator'
      ) -> 'VectorSummationAccumulator':
          self._check_mergeable(accumulator)
          self.add_value(accumulator._vec_sum)
          return self
  
      def compute_metrics(self):
          # TODO - add DP anonymization
          if self._vec_sum is None:
              raise IndexError("No data provided for metrics computation.")
          return self._vec_sum
  
  
  class SumParams:
      noise: dp_computations.MeanVarParams
  
  
  class SumAccumulator(Accumulator):
  
      def __init__(self, params: SumParams, values):
          self._sum = sum(values)
          self._params = params
  
      def add_value(self, value):
          self._sum += value
  
      def add_accumulator(self,
                          accumulator: 'SumAccumulator') -> 'SumAccumulator':
          self._check_mergeable(accumulator)
          self._sum += accumulator._sum
  
      def compute_metrics(self) -> float:
          return pipeline_dp.dp_computations.compute_dp_sum(
              self._sum, self._params.noise)
  