text: """Serialization
  
  This module contains functionality for serializing TorchScript modules, notably:
      * torch.jit.save
      * torch.jit.load
  
  This is not intended to be imported directly; please use the exposed
  functionalities in `torch.jit`.
  """
  import os
  import pathlib
  
  import torch
  from torch._six import string_classes
  from torch.jit._recursive import wrap_cpp_module
  from torch.serialization import validate_cuda_device
  
  
  def save(m, f, _extra_files=None):
      r"""
      Save an offline version of this module for use in a separate process. The
      saved module serializes all of the methods, submodules, parameters, and
      attributes of this module. It can be loaded into the C++ API using
      ``torch::jit::load(filename)`` or into the Python API with
      :func:`torch.jit.load <torch.jit.load>`.
  
      To be able to save a module, it must not make any calls to native Python
      functions.  This means that all submodules must be subclasses of
      :class:`ScriptModule` as well.
  
      .. DANGER::
          All modules, no matter their device, are always loaded onto the CPU
          during loading.  This is different from :func:`torch.load`'s semantics
          and may change in the future.
  
      Args:
          m: A :class:`ScriptModule` to save.
          f: A file-like object (has to implement write and flush) or a string
             containing a file name.
          _extra_files: Map from filename to contents which will be stored as part of `f`.
  
      .. note::
          torch.jit.save attempts to preserve the behavior of some operators
          across versions. For example, dividing two integer tensors in
          PyTorch 1.5 performed floor division, and if the module
          containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6
          its division behavior will be preserved. The same module saved in
          PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the
          behavior of division changed in 1.6, and 1.5 does not know how to
          replicate the 1.6 behavior.
  
      Example:
  
      .. testcode::
  
          import torch
          import io
  
          class MyModule(torch.nn.Module):
              def forward(self, x):
                  return x + 10
  
          m = torch.jit.script(MyModule())
  
          # Save to file
          torch.jit.save(m, 'scriptmodule.pt')
          # This line is equivalent to the previous
          m.save("scriptmodule.pt")
  
          # Save to io.BytesIO buffer
          buffer = io.BytesIO()
          torch.jit.save(m, buffer)
  
          # Save with extra files
          extra_files = {'foo.txt': b'bar'}
          torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
      """
      if _extra_files is None:
          _extra_files = {}
      if isinstance(f, str) or isinstance(f, pathlib.Path):
          m.save(f, _extra_files=_extra_files)
      else:
          ret = m.save_to_buffer(_extra_files=_extra_files)
          f.write(ret)
  
  
  def load(f, map_location=None, _extra_files=None):
      r"""
      Load a :class:`ScriptModule` or :class:`ScriptFunction` previously
      saved with :func:`torch.jit.save <torch.jit.save>`
  
      All previously saved modules, no matter their device, are first loaded onto CPU,
      and then are moved to the devices they were saved from. If this fails (e.g.
      because the run time system doesn't have certain devices), an exception is
      raised.
  
      Args:
          f: a file-like object (has to implement read, readline, tell, and seek),
              or a string containing a file name
          map_location (string or torch.device): A simplified version of
              ``map_location`` in `torch.jit.save` used to dynamically remap
              storages to an alternative set of devices.
          _extra_files (dictionary of filename to content): The extra
              filenames given in the map would be loaded and their content
              would be stored in the provided map.
  
      Returns:
          A :class:`ScriptModule` object.
  
      Example:
  
      .. testcode::
  
          import torch
          import io
  
          torch.jit.load('scriptmodule.pt')
  
          # Load ScriptModule from io.BytesIO object
          with open('scriptmodule.pt', 'rb') as f:
              buffer = io.BytesIO(f.read())
  
          # Load all tensors to the original device
          torch.jit.load(buffer)
  
          # Load all tensors onto CPU, using a device
          buffer.seek(0)
          torch.jit.load(buffer, map_location=torch.device('cpu'))
  
          # Load all tensors onto CPU, using a string
          buffer.seek(0)
          torch.jit.load(buffer, map_location='cpu')
  
          # Load with extra files.
          extra_files = {'foo.txt': ''}  # values will be replaced with data
          torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
          print(extra_files['foo.txt'])
  
      .. testoutput::
          :hide:
  
          ...
  
      .. testcleanup::
  
          import os
          os.remove("scriptmodule.pt")
      """
  
      # TODO (tugsuu) Putting this on top level creates
      # circular import issue. We need to fix this later
      from torch.jit.operator_upgraders import populate_upgraders_map
      populate_upgraders_map()
  
      if isinstance(f, string_classes):
          if not os.path.exists(f):  # type: ignore[type-var]
              raise ValueError("The provided filename {} does not exist".format(f))  # type: ignore[str-bytes-safe]
          if os.path.isdir(f):
              raise ValueError("The provided filename {} is a directory".format(f))  # type: ignore[str-bytes-safe]
  
      map_location = validate_map_location(map_location)
      if _extra_files is None:
          _extra_files = {}
  
      cu = torch._C.CompilationUnit()
      if isinstance(f, str) or isinstance(f, pathlib.Path):
          cpp_module = torch._C.import_ir_module(cu, str(f), map_location, _extra_files)
      else:
          cpp_module = torch._C.import_ir_module_from_buffer(
              cu, f.read(), map_location, _extra_files
          )
  
      # TODO: Pretty sure this approach loses ConstSequential status and such
      return wrap_cpp_module(cpp_module)
  
  
  def validate_map_location(map_location=None):
      if isinstance(map_location, str):
          map_location = torch.device(map_location)
      elif not (map_location is None or isinstance(map_location, torch.device)):
          raise ValueError(
              "map_location should be either None, string or torch.device, "
              "but got type: " + str(type(map_location))
          )
  
      if str(map_location).startswith("cuda"):
          validate_cuda_device(map_location)
  
      return map_location
  