code: # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you 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.
  """Meta Schedule CostModel."""
  import ctypes
  from typing import List
  
  import numpy as np  # type: ignore
  from tvm._ffi import register_object
  from tvm.runtime import Object
  
  from .. import _ffi_api
  from ..runner import RunnerResult
  from ..search_strategy import MeasureCandidate
  from ..tune_context import TuneContext
  from ..utils import _get_hex_address, check_override
  
  
  @register_object("meta_schedule.CostModel")
  class CostModel(Object):
      """Cost model."""
  
      def load(self, path: str) -> None:
          """Load the cost model from given file location.
  
          Parameters
          ----------
          path : str
              The file path.
          """
          _ffi_api.CostModelLoad(self, path)  # type: ignore # pylint: disable=no-member
  
      def save(self, path: str) -> None:
          """Save the cost model to given file location.
  
          Parameters
          ----------
          path : str
              The file path.
          """
          _ffi_api.CostModelSave(self, path)  # type: ignore # pylint: disable=no-member
  
      def update(
          self,
          context: TuneContext,
          candidates: List[MeasureCandidate],
          results: List[RunnerResult],
      ) -> None:
          """Update the cost model given running results.
  
          Parameters
          ----------
          context : TuneContext,
              The tuning context.
          candidates : List[MeasureCandidate]
              The measure candidates.
          results : List[RunnerResult]
              The running results of the measure candidates.
          """
          _ffi_api.CostModelUpdate(self, context, candidates, results)  # type: ignore # pylint: disable=no-member
  
      def predict(self, context: TuneContext, candidates: List[MeasureCandidate]) -> np.ndarray:
          """Update the cost model given running results.
  
          Parameters
          ----------
          context : TuneContext,
              The tuning context.
          candidates : List[MeasureCandidate]
              The measure candidates.
  
          Return
          ------
          result : np.ndarray
              The predicted normalized score.
          """
          n = len(candidates)
          results = np.zeros(shape=(n,), dtype="float64")
          _ffi_api.CostModelPredict(  # type: ignore # pylint: disable=no-member
              self,
              context,
              candidates,
              results.ctypes.data_as(ctypes.c_void_p),
          )
          return results
  
  
  @register_object("meta_schedule.PyCostModel")
  class PyCostModel(CostModel):
      """An abstract CostModel with customized methods on the python-side."""
  
      def __init__(self):
          """Constructor."""
  
          @check_override(self.__class__, CostModel)
          def f_load(path: str) -> None:
              self.load(path)
  
          @check_override(self.__class__, CostModel)
          def f_save(path: str) -> None:
              self.save(path)
  
          @check_override(self.__class__, CostModel)
          def f_update(
              context: TuneContext,
              candidates: List[MeasureCandidate],
              results: List[RunnerResult],
          ) -> None:
              self.update(context, candidates, results)
  
          @check_override(self.__class__, CostModel)
          def f_predict(context: TuneContext, candidates: List[MeasureCandidate], return_ptr) -> None:
              n = len(candidates)
              return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_double))
              array_wrapper = np.ctypeslib.as_array(return_ptr, shape=(n,))
              array_wrapper[:] = self.predict(context, candidates)
              assert (
                  array_wrapper.dtype == "float64"
              ), "ValueError: Invalid data type returned from CostModel Predict!"
  
          def f_as_string() -> str:
              return str(self)
  
          self.__init_handle_by_constructor__(
              _ffi_api.CostModelPyCostModel,  # type: ignore # pylint: disable=no-member
              f_load,
              f_save,
              f_update,
              f_predict,
              f_as_string,
          )
  
      def __str__(self) -> str:
          return f"{self.__class__.__name__}({_get_hex_address(self.handle)})"
  
apis: numpy.ctypeslib.as_array numpy.zeros