text: #
  # 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.
  #
  
  from pyspark.util import _parse_memory
  
  
  class ExecutorResourceRequest(object):
      """
      .. note:: Evolving
  
      An Executor resource request. This is used in conjunction with the ResourceProfile to
      programmatically specify the resources needed for an RDD that will be applied at the
      stage level.
  
      This is used to specify what the resource requirements are for an Executor and how
      Spark can find out specific details about those resources. Not all the parameters are
      required for every resource type. Resources like GPUs are supported and have same limitations
      as using the global spark configs spark.executor.resource.gpu.*. The amount, discoveryScript,
      and vendor parameters for resources are all the same parameters a user would specify through the
      configs: spark.executor.resource.{resourceName}.{amount, discoveryScript, vendor}.
  
      For instance, a user wants to allocate an Executor with GPU resources on YARN. The user has
      to specify the resource name (gpu), the amount or number of GPUs per Executor,
      the discovery script would be specified so that when the Executor starts up it can
      discovery what GPU addresses are available for it to use because YARN doesn't tell
      Spark that, then vendor would not be used because its specific for Kubernetes.
  
      See the configuration and cluster specific docs for more details.
  
      Use `pyspark.ExecutorResourceRequests` class as a convenience API.
  
      :param resourceName: Name of the resource
      :param amount: Amount requesting
      :param discoveryScript: Optional script used to discover the resources. This is required on some
          cluster managers that don't tell Spark the addresses of the resources
          allocated. The script runs on Executors startup to discover the addresses
          of the resources available.
      :param vendor: Vendor, required for some cluster managers
  
      .. versionadded:: 3.1.0
      """
      def __init__(self, resourceName, amount, discoveryScript="", vendor=""):
          self._name = resourceName
          self._amount = amount
          self._discovery_script = discoveryScript
          self._vendor = vendor
  
      @property
      def resourceName(self):
          return self._name
  
      @property
      def amount(self):
          return self._amount
  
      @property
      def discoveryScript(self):
          return self._discovery_script
  
      @property
      def vendor(self):
          return self._vendor
  
  
  class ExecutorResourceRequests(object):
  
      """
      .. note:: Evolving
  
      A set of Executor resource requests. This is used in conjunction with the
      :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the
      resources needed for an RDD that will be applied at the stage level.
  
      .. versionadded:: 3.1.0
      """
      _CORES = "cores"
      _MEMORY = "memory"
      _OVERHEAD_MEM = "memoryOverhead"
      _PYSPARK_MEM = "pyspark.memory"
  
      def __init__(self, _jvm=None, _requests=None):
          from pyspark import SparkContext
          _jvm = _jvm or SparkContext._jvm
          if _jvm is not None:
              self._java_executor_resource_requests = \
                  _jvm.org.apache.spark.resource.ExecutorResourceRequests()
              if _requests is not None:
                  for k, v in _requests.items():
                      if k == self._MEMORY:
                          self._java_executor_resource_requests.memory(str(v.amount))
                      elif k == self._OVERHEAD_MEM:
                          self._java_executor_resource_requests.memoryOverhead(str(v.amount))
                      elif k == self._PYSPARK_MEM:
                          self._java_executor_resource_requests.pysparkMemory(str(v.amount))
                      elif k == self._CORES:
                          self._java_executor_resource_requests.cores(v.amount)
                      else:
                          self._java_executor_resource_requests.resource(v.resourceName, v.amount,
                                                                         v.discoveryScript, v.vendor)
          else:
              self._java_executor_resource_requests = None
              self._executor_resources = {}
  
      def memory(self, amount):
          if self._java_executor_resource_requests is not None:
              self._java_executor_resource_requests.memory(amount)
          else:
              self._executor_resources[self._MEMORY] = ExecutorResourceRequest(self._MEMORY,
                                                                               _parse_memory(amount))
          return self
  
      def memoryOverhead(self, amount):
          if self._java_executor_resource_requests is not None:
              self._java_executor_resource_requests.memoryOverhead(amount)
          else:
              self._executor_resources[self._OVERHEAD_MEM] = \
                  ExecutorResourceRequest(self._OVERHEAD_MEM, _parse_memory(amount))
          return self
  
      def pysparkMemory(self, amount):
          if self._java_executor_resource_requests is not None:
              self._java_executor_resource_requests.pysparkMemory(amount)
          else:
              self._executor_resources[self._PYSPARK_MEM] = \
                  ExecutorResourceRequest(self._PYSPARK_MEM, _parse_memory(amount))
          return self
  
      def cores(self, amount):
          if self._java_executor_resource_requests is not None:
              self._java_executor_resource_requests.cores(amount)
          else:
              self._executor_resources[self._CORES] = ExecutorResourceRequest(self._CORES, amount)
          return self
  
      def resource(self, resourceName, amount, discoveryScript="", vendor=""):
          if self._java_executor_resource_requests is not None:
              self._java_executor_resource_requests.resource(resourceName, amount, discoveryScript,
                                                             vendor)
          else:
              self._executor_resources[resourceName] = \
                  ExecutorResourceRequest(resourceName, amount, discoveryScript, vendor)
          return self
  
      @property
      def requests(self):
          if self._java_executor_resource_requests is not None:
              result = {}
              execRes = self._java_executor_resource_requests.requestsJMap()
              for k, v in execRes.items():
                  result[k] = ExecutorResourceRequest(v.resourceName(), v.amount(),
                                                      v.discoveryScript(), v.vendor())
              return result
          else:
              return self._executor_resources
  
  
  class TaskResourceRequest(object):
      """
      .. note:: Evolving
  
      A task resource request. This is used in conjuntion with the
      :class:`pyspark.resource.ResourceProfile` to programmatically specify the resources
      needed for an RDD that will be applied at the stage level. The amount is specified
      as a Double to allow for saying you want more than 1 task per resource. Valid values
      are less than or equal to 0.5 or whole numbers.
      Use :class:`pyspark.resource.TaskResourceRequests` class as a convenience API.
  
      :param resourceName: Name of the resource
      :param amount: Amount requesting as a Double to support fractional resource requests.
          Valid values are less than or equal to 0.5 or whole numbers.
  
      .. versionadded:: 3.1.0
      """
      def __init__(self, resourceName, amount):
          self._name = resourceName
          self._amount = float(amount)
  
      @property
      def resourceName(self):
          return self._name
  
      @property
      def amount(self):
          return self._amount
  
  
  class TaskResourceRequests(object):
  
      """
      .. note:: Evolving
  
      A set of task resource requests. This is used in conjuntion with the
      :class:`pyspark.resource.ResourceProfileBuilder` to programmatically specify the resources
      needed for an RDD that will be applied at the stage level.
  
      .. versionadded:: 3.1.0
      """
  
      _CPUS = "cpus"
  
      def __init__(self, _jvm=None, _requests=None):
          from pyspark import SparkContext
          _jvm = _jvm or SparkContext._jvm
          if _jvm is not None:
              self._java_task_resource_requests = \
                  SparkContext._jvm.org.apache.spark.resource.TaskResourceRequests()
              if _requests is not None:
                  for k, v in _requests.items():
                      if k == self._CPUS:
                          self._java_task_resource_requests.cpus(int(v.amount))
                      else:
                          self._java_task_resource_requests.resource(v.resourceName, v.amount)
          else:
              self._java_task_resource_requests = None
              self._task_resources = {}
  
      def cpus(self, amount):
          if self._java_task_resource_requests is not None:
              self._java_task_resource_requests.cpus(amount)
          else:
              self._task_resources[self._CPUS] = TaskResourceRequest(self._CPUS, amount)
          return self
  
      def resource(self, resourceName, amount):
          if self._java_task_resource_requests is not None:
              self._java_task_resource_requests.resource(resourceName, float(amount))
          else:
              self._task_resources[resourceName] = TaskResourceRequest(resourceName, amount)
          return self
  
      @property
      def requests(self):
          if self._java_task_resource_requests is not None:
              result = {}
              taskRes = self._java_task_resource_requests.requestsJMap()
              for k, v in taskRes.items():
                  result[k] = TaskResourceRequest(v.resourceName(), v.amount())
              return result
          else:
              return self._task_resources
  