text: # Copyright 2020 Huawei Technologies Co., Ltd
  #
  # 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.
  # ============================================================================
  """
  The context of mindspore, used to configure the current execution environment,
  including execution mode, execution backend and other feature switches.
  """
  import os
  import threading
  from collections import namedtuple
  from types import FunctionType
  from mindspore import log as logger
  from mindspore._c_expression import MSContext
  from mindspore._checkparam import args_type_check
  from mindspore.parallel._auto_parallel_context import _set_auto_parallel_context, _get_auto_parallel_context, \
      _reset_auto_parallel_context
  
  __all__ = ['GRAPH_MODE', 'PYNATIVE_MODE', 'set_context', 'get_context', 'set_auto_parallel_context',
             'get_auto_parallel_context', 'reset_auto_parallel_context']
  
  GRAPH_MODE = 0
  PYNATIVE_MODE = 1
  
  
  def _make_directory(path):
      """Make directory."""
      real_path = None
      if path is None or not isinstance(path, str) or path.strip() == "":
          raise ValueError(f"Input path `{path}` is invalid type")
  
      # convert the relative paths
      path = os.path.realpath(path)
      logger.debug("The absolute path is %r", path)
  
      # check whether the path is already existed and has written permissions
      if os.path.exists(path):
          real_path = path
      else:
          # All exceptions need to be caught because create directory maybe have some limit(permissions)
          logger.debug("The directory(%s) doesn't exist, will create it", path)
          try:
              os.makedirs(path)
              real_path = path
          except PermissionError as e:
              logger.error(f"No write permission on the directory `{path}, error = {e}")
              raise ValueError(f"No write permission on the directory `{path}`.")
      return real_path
  
  
  class _ThreadLocalInfo(threading.local):
      """
      Thread local Info used for store thread local attributes.
      """
  
      def __init__(self):
          super(_ThreadLocalInfo, self).__init__()
          self._reserve_class_name_in_scope = True
  
      @property
      def reserve_class_name_in_scope(self):
          """Gets whether to save the network class name in the scope."""
          return self._reserve_class_name_in_scope
  
      @reserve_class_name_in_scope.setter
      def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
          """Sets whether to save the network class name in the scope."""
          if not isinstance(reserve_class_name_in_scope, bool):
              raise ValueError("Set reserve_class_name_in_scope value must be bool!")
          self._reserve_class_name_in_scope = reserve_class_name_in_scope
  
  
  _ContextRecord = namedtuple("_ContextRecord", ["is_pynative_mode", "switch_context_fn"])
  
  
  class _ContextSwitchInfo(threading.local):
      """
      Record of context switch information.
  
      Args:
          is_pynative (bool): Whether to adopt the PyNative mode.
      """
  
      def __init__(self, is_pynative):
          super(_ContextSwitchInfo, self).__init__()
          self.context_stack = []
          if is_pynative:
              self.push(True, None)
  
      def push(self, is_pynative, switch_context_fn):
          """
          Push a context switch record onto the stack.
  
          Args:
              is_pynative (bool): Whether context switch to PyNative mode.
              switch_context_fn (Function): A callable that executes the context switch.
          """
          if isinstance(switch_context_fn, FunctionType):
              switch_context_fn()
          self.context_stack.append(_ContextRecord(is_pynative, switch_context_fn))
  
      def pop(self):
          self.context_stack.pop()
  
  
  class _Context:
      """
      _Context is the environment in which operations are executed
  
      Note:
          Create a context through instantiating Context object is not recommended.
          should use context() to get the context since Context is singleton.
      """
      _instance = None
      _instance_lock = threading.Lock()
  
      def __init__(self):
          self._thread_local_info = _ThreadLocalInfo()
          self._context_switches = _ContextSwitchInfo(True)
          self._context_handle = MSContext.get_instance()
  
      def __new__(cls, *args, **kwargs):
          if cls._instance is None:
              cls._instance_lock.acquire()
              cls._instance = object.__new__(cls)
              cls._instance_lock.release()
          return cls._instance
  
      def __getattribute__(self, attr):
          value = object.__getattribute__(self, attr)
          if attr == "_context_handle" and value is None:
              raise ValueError("Context handle is none in context!!!")
          return value
  
      @property
      def mode(self):
          return self._context_handle.get_execution_mode()
  
      @mode.setter
      def mode(self, mode):
          """
          Switch between Graph mode and PyNative mode.
  
          Args:
              mode (int): GRAPH_MODE or PYNATIVE_MODE.
          """
          self._context_handle.set_execution_mode(mode)
          if mode == PYNATIVE_MODE:
              if self.enable_debug_runtime:
                  self.set_backend_policy("vm")
              self._context_switches.push(True, None)
          else:
              if self.enable_debug_runtime:
                  self.set_backend_policy("ge")
              self._context_switches.push(False, None)
  
      def set_backend_policy(self, policy):
          success = self._context_handle.set_backend_policy(policy)
          if not success:
              raise RuntimeError("Backend policy must be one of ge, vm, ms.")
  
      @property
      def precompile_only(self):
          return self._context_handle.get_precompile_only()
  
      @precompile_only.setter
      def precompile_only(self, precompile_only):
          self._context_handle.set_precompile_only(precompile_only)
  
      @property
      def save_graphs(self):
          return self._context_handle.get_save_graphs_flag()
  
      @save_graphs.setter
      def save_graphs(self, save_graphs_flag):
          self._context_handle.set_save_graphs_flag(save_graphs_flag)
  
      @property
      def save_graphs_path(self):
          return self._context_handle.get_save_graphs_path()
  
      @save_graphs_path.setter
      def save_graphs_path(self, save_graphs_path):
          self._context_handle.set_save_graphs_path(_make_directory(save_graphs_path))
  
      @property
      def device_target(self):
          return self._context_handle.get_device_target()
  
      @device_target.setter
      def device_target(self, target):
          success = self._context_handle.set_device_target(target)
          if not success:
              raise ValueError("Target device name is invalid!!!")
  
      @property
      def device_id(self):
          return self._context_handle.get_device_id()
  
      @device_id.setter
      def device_id(self, device_id):
          if device_id < 0 or device_id > 4095:
              raise ValueError("Device id must be in [0, 4095], but got {}".format(device_id))
          success = self._context_handle.set_device_id(device_id)
          if not success:
              raise RuntimeError("Device id set failed!!!")
  
      @property
      def enable_loop_sink(self):
          return self._context_handle.get_loop_sink_flag()
  
      @enable_loop_sink.setter
      def enable_loop_sink(self, enable_loop_sink):
          self._context_handle.set_loop_sink_flag(enable_loop_sink)
  
      @property
      def enable_mem_reuse(self):
          return self._context_handle.get_enable_mem_reuse()
  
      @enable_mem_reuse.setter
      def enable_mem_reuse(self, enable_mem_reuse):
          self._context_handle.set_enable_mem_reuse(enable_mem_reuse)
  
      @property
      def save_ms_model(self):
          return self._context_handle.get_save_ms_model_flag()
  
      @save_ms_model.setter
      def save_ms_model(self, save_ms_model_flag):
          self._context_handle.set_save_ms_model_flag(save_ms_model_flag)
  
      @property
      def save_ms_model_path(self):
          return self._context_handle.get_save_ms_model_path()
  
      @save_ms_model_path.setter
      def save_ms_model_path(self, save_ms_model_path):
          self._context_handle.set_save_ms_model_path(save_ms_model_path)
  
      @property
      def enable_auto_mixed_precision(self):
          return self._context_handle.get_auto_mixed_precision_flag()
  
      @enable_auto_mixed_precision.setter
      def enable_auto_mixed_precision(self, enable_auto_mixed_precision):
          self._context_handle.set_auto_mixed_precision_flag(enable_auto_mixed_precision)
  
      @property
      def enable_reduce_precision(self):
          return self._context_handle.get_enable_reduce_precision_flag()
  
      @enable_reduce_precision.setter
      def enable_reduce_precision(self, enable_reduce_precision):
          self._context_handle.set_enable_reduce_precision_flag(enable_reduce_precision)
  
      @property
      def enable_dump(self):
          return self._context_handle.get_enable_dump()
  
      @enable_dump.setter
      def enable_dump(self, enable_dump):
          self._context_handle.set_enable_dump(enable_dump)
  
      @property
      def save_dump_path(self):
          return self._context_handle.get_save_dump_path()
  
      @save_dump_path.setter
      def save_dump_path(self, save_dump_path):
          self._context_handle.set_save_dump_path(save_dump_path)
  
      @property
      def enable_profiling(self):
          return self._context_handle.get_enable_profiling()
  
      @enable_profiling.setter
      def enable_profiling(self, flag):
          self._context_handle.set_enable_profiling(flag)
  
      @property
      def profiling_options(self):
          return self._context_handle.get_profiling_options()
  
      @profiling_options.setter
      def profiling_options(self, option):
          options = ["training_trace", "task_trace", "task_trace:training_trace", "training_trace:task_trace", "op_trace"]
          if option not in options:
              raise ValueError("Profiling options must be in 'training_trace' 'task_trace' "
                               "'task_trace:training_trace' 'training_trace:task_trace' or 'op_trace'.")
          self._context_handle.set_profiling_options(option)
  
      @property
      def reserve_class_name_in_scope(self):
          """Gets whether to save the network class name in the scope."""
          return self._thread_local_info.reserve_class_name_in_scope
  
      @reserve_class_name_in_scope.setter
      def reserve_class_name_in_scope(self, reserve_class_name_in_scope):
          """Sets whether to save the network class name in the scope."""
          self._thread_local_info.reserve_class_name_in_scope = reserve_class_name_in_scope
  
      @property
      def graph_memory_max_size(self):
          return None
  
      @graph_memory_max_size.setter
      def graph_memory_max_size(self, graph_memory_max_size):
          if check_input_format(graph_memory_max_size):
              graph_memory_max_size_ = graph_memory_max_size[:-2] + " * 1024 * 1024 * 1024"
              self._context_handle.set_graph_memory_max_size(graph_memory_max_size_)
          else:
              raise ValueError("Context param graph_memory_max_size should be in correct format! Such as \"26GB\"")
  
      @property
      def variable_memory_max_size(self):
          return None
  
      @variable_memory_max_size.setter
      def variable_memory_max_size(self, variable_memory_max_size):
          if check_input_format(variable_memory_max_size):
              variable_memory_max_size_ = variable_memory_max_size[:-2] + " * 1024 * 1024 * 1024"
              self._context_handle.set_variable_memory_max_size(variable_memory_max_size_)
          else:
              raise ValueError("Context param variable_memory_max_size should be in correct format! Such as \"5GB\"")
  
      @property
      def enable_ge(self):
          return self._context_handle.get_backend_policy() == 'ge'
  
      @property
      def enable_debug_runtime(self):
          return self._thread_local_info.debug_runtime
  
      @enable_debug_runtime.setter
      def enable_debug_runtime(self, enable):
          thread_info = self._thread_local_info
          thread_info.debug_runtime = enable
  
  
  def check_input_format(x):
      import re
      pattern = r'[1-9][0-9]*(\.)?[0-9]*GB|0\.[0-9]*GB'
      result = re.match(pattern, x)
      return result is not None
  
  
  _k_context = None
  
  
  def _context():
      """
      Get the global _context, if context is not created, create a new one.
  
      Returns:
          _Context, the global context in PyNative mode.
      """
      global _k_context
      if _k_context is None:
          default_backend = 'debug'
          try:
              from mindspore import default_config
              default_backend = default_config.__backend__
          except ImportError:
              logger.error("import default config fail")
          _k_context = _Context()
          _k_context.enable_debug_runtime = False
          if default_backend == 'debug':
              _k_context.enable_debug_runtime = True
              default_backend = 'vm'
          _k_context.set_backend_policy(default_backend)
      return _k_context
  
  
  @args_type_check(device_num=int, global_rank=int, mirror_mean=bool, cast_before_mirror=bool, parallel_mode=str,
                   parameter_broadcast=bool, strategy_ckpt_load_file=str, strategy_ckpt_save_file=str)
  def set_auto_parallel_context(**kwargs):
      """
      Set auto parallel context.
  
      Note:
          Attribute name is required for setting attributes.
          If a program has tasks with different parallel modes, then before setting new parallel mode for
          next task, interface mindspore.context.reset_auto_parallel_context() needs to be called to reset
          the configuration.
  
      Args:
          device_num (int): Available device number, the value must be in [1, 4096]. Default: 1.
          global_rank (int): Global rank id, the value must be in [0, 4095]. Default: 0.
          mirror_mean (bool): Whether to perform mean operator after all-reduce of mirror.
                       "stand_alone" do not support mirror_mean. Default: False.
          cast_before_mirror (bool): Insert Mirror Op after the cast if this flag is True.
                       "stand_alone", "data_parallel" and "hybrid_parallel" do not support
                       cast_before_mirror. Default: True.
          parallel_mode (str): There are five kinds of parallel modes, "stand_alone", "data_parallel",
                       "hybrid_parallel", "semi_auto_parallel" and "auto_parallel". Default: "stand_alone".
  
                       - stand_alone: Only one processor working.
  
                       - data_parallel: Distributing the data across different processors.
  
                       - hybrid_parallel: Achieving data parallelism and model parallelism manually.
  
                       - semi_auto_parallel: Achieving data parallelism and model parallelism by
                         setting parallel strategies.
  
                       - auto_parallel: Achieving parallelism automatically.
          parameter_broadcast (bool): Indicating whether to broadcast parameters before training.
                         "stand_alone", "semi_auto_parallel" and "auto_parallel" do not support parameter
                         broadcast. Default: False.
          strategy_ckpt_load_file (str): The path to load parallel strategy checkpoint. Default: ''
          strategy_ckpt_save_file (str): The path to save parallel strategy checkpoint. Default: ''
  
      Raises:
          ValueError: If input key is not attribute in auto parallel context.
  
      Examples:
          >>> context.set_auto_parallel_context(device_num=8)
          >>> context.set_auto_parallel_context(global_rank=0)
          >>> context.set_auto_parallel_context(mirror_mean=True)
          >>> context.set_auto_parallel_context(cast_before_mirror=False)
          >>> context.set_auto_parallel_context(parallel_mode="auto_parallel")
          >>> context.set_auto_parallel_context(parameter_broadcast=False)
          >>> context.set_auto_parallel_context(strategy_ckpt_load_file="./strategy_stage1.ckpt")
          >>> context.set_auto_parallel_context(strategy_ckpt_save_file="./strategy_stage1.ckpt")
      """
      _set_auto_parallel_context(**kwargs)
  
  
  def get_auto_parallel_context(attr_key):
      """
      Gets auto parallel context attribute value according to the key.
  
      Args:
          attr_key (str): The key of the attribute.
  
      Returns:
          Returns attribute value according to the key.
  
      Raises:
          ValueError: If input key is not attribute in auto parallel context.
      """
      return _get_auto_parallel_context(attr_key)
  
  
  def reset_auto_parallel_context():
      """
      Reset auto parallel context attributes to the default values:
  
      - device_num: 1.
      - global_rank: 0.
      - mirror_mean: False.
      - cast_before_mirror: True.
      - parallel_mode: "stand_alone".
      - parameter_broadcast: False.
      - strategy_ckpt_load_file: "".
      - strategy_ckpt_save_file: "".
      """
      _reset_auto_parallel_context()
  
  
  @args_type_check(mode=int, precompile_only=bool, device_target=str, device_id=int, save_graphs=bool,
                   save_graphs_path=str, enable_loop_sink=bool, enable_mem_reuse=bool, save_ms_model=bool,
                   save_ms_model_path=str, enable_auto_mixed_precision=bool, enable_dump=bool, save_dump_path=str,
                   enable_reduce_precision=bool, graph_memory_max_size=str,
                   variable_memory_max_size=str, enable_profiling=bool, profiling_options=str)
  def set_context(**kwargs):
      """
      Sets context for running environment.
  
      Context should be configured before running your program. If there is no configuration,
      the "Ascend" device target will be used by default. GRAPH_MODE or
      PYNATIVE_MODE can be set by `mode` attribute and both modes support all backends, default
      mode is PYNATIVE_MODE.
  
      When the `save_graphs` attribute is set to True, attribute of `save_graphs_path` is used to set the
      intermediate compilation graph storage path. By default, the graphs are saved in the current directory.
      As for other configurations and arguments, please refer to the corresponding module
      description, the configuration is optional and can be enabled when needed.
  
      Note:
          Attribute name is required for setting attributes.
          If need to config graph max memory size and variable max memory size, one must make sure:
              The sum of graph_memory_max_size and variable_memory_max_size should be less than total memory size of
              a device, while the total memory is supposed to be no more than 256GB.
  
      Args:
          mode (int): Running in GRAPH_MODE(0) or PYNATIVE_MODE(1). Default: PYNATIVE_MODE.
          device_target (str): The target device to run, support "Ascend", "GPU", "CPU". Default: "Ascend".
          device_id (int): Id of target device, the value must be in [0, device_num_per_host-1],
                      while device_num_per_host should no more than 4096. Default: 0.
          save_graphs (bool): Whether to save graphs. Default: False.
          enable_loop_sink (bool): Whether to enable loop sink. Default: True.
          enable_mem_reuse (bool): Whether to enable memory reuse. Default: True.
          save_ms_model (bool): Whether to save lite model converted by graph. Default: False.
          save_ms_model_path (str): Path to save converted lite model. Default: "."
          save_graphs_path (str): Path to save graphs. Default: "."
          enable_auto_mixed_precision (bool): Whether to enable auto mixed precision. Default: True.
          reserve_class_name_in_scope (bool) : Whether to save the network class name in the scope. Default: True.
          enable_reduce_precision (bool): Whether to enable precision reduction. Default: True.
          enable_dump (bool): Whether to enable dump. Default: False.
          save_dump_path (str): When the program is executed on Ascend, operators can dump data here.
              The root dump path is configured in /home/HwHiAiUser/ide_daemon/ide_daemon.cfg.
              So the real dump path is "{configured root dump path}/{`save_dump_path`}". Default: ".".
          graph_memory_max_size (str): Sets graph memory max size. Default: "26GB".
          variable_memory_max_size (str): Sets variable memory max size. Default: "5GB".
          enable_profiling (bool): Whether to open profiling. Default: False.
          profiling_options (str): Sets profiling collection options, operators can profiling data here.
              Profiling collection options, the values are as follows, supporting the collection of multiple data.
  
              - training_trace: collect iterative trajectory data, that is, the training task and software information of
                the AI software stack, to achieve performance analysis of the training task, focusing on data
                enhancement, forward and backward calculation, gradient aggregation update and other related data.
  
              - task_trace: collect task trajectory data, that is, the hardware information of the HWTS/AICore of
                the Ascend 910 processor, and analyze the information of start and end of the task.
  
              - op_trace: collect single operator performance data.
              The profiling can choose training_trace, task_trace, training_trace and task_trace combination and
              separated by colons; single operator can choose op_trace, op_trace cannot be combined with
              training_trace and task_trace. Default: "training_trace".
  
      Raises:
          ValueError: If input key is not an attribute in context.
  
      Examples:
          >>> context.set_context(mode=context.GRAPH_MODE)
          >>> context.set_context(mode=context.PYNATIVE_MODE)
          >>> context.set_context(device_target="Ascend")
          >>> context.set_context(device_id=0)
          >>> context.set_context(save_graphs=True, save_graphs_path="./model.ms")
          >>> context.set_context(enable_mem_reuse=True)
          >>> context.set_context(enable_reduce_precision=True)
          >>> context.set_context(save_ms_model=True, save_ms_model_path=".")
          >>> context.set_context(enable_dump=True, save_dump_path=".")
          >>> context.set_context(reserve_class_name_in_scope=True)
          >>> context.set_context(graph_memory_max_size="25GB")
          >>> context.set_context(variable_memory_max_size="6GB")
          >>> context.set_context(mode=context.GRAPH_MODE,
          >>>                     device_target="Ascend",device_id=0, save_graphs=True,
          >>>                     save_graphs_path="/mindspore")
          >>> context.set_context(enable_profiling=True, profiling_options="training_trace")
      """
      for key, value in kwargs.items():
          if not hasattr(_context(), key):
              raise ValueError("Set context keyword %s is not recognized!" % key)
          setattr(_context(), key, value)
  
  
  def get_context(attr_key):
      """
      Gets context attribute value according to the input key.
  
      Args:
          attr_key (str): The key of the attribute.
  
      Returns:
          Object, The value of given attribute key.
  
      Raises:
          ValueError: If input key is not an attribute in context.
      """
      if not hasattr(_context(), attr_key):
          raise ValueError("Get context keyword %s is not recognized!" % attr_key)
      return getattr(_context(), attr_key)
  