content: """
  Msgpack serializer support for reading and writing pandas data structures
  to disk
  
  portions of msgpack_numpy package, by Lev Givon were incorporated
  into this module (and tests_packers.py)
  
  License
  =======
  
  Copyright (c) 2013, Lev Givon.
  All rights reserved.
  
  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are
  met:
  
  * Redistributions of source code must retain the above copyright
    notice, this list of conditions and the following disclaimer.
  * Redistributions in binary form must reproduce the above
    copyright notice, this list of conditions and the following
    disclaimer in the documentation and/or other materials provided
    with the distribution.
  * Neither the name of Lev Givon nor the names of any
    contributors may be used to endorse or promote products derived
    from this software without specific prior written permission.
  
  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  """
  
  from datetime import datetime, date, timedelta
  from dateutil.parser import parse
  import os
  from textwrap import dedent
  import warnings
  
  import numpy as np
  from pandas import compat
  from pandas.compat import u, u_safe
  
  from pandas.core.dtypes.common import (
      is_categorical_dtype, is_object_dtype,
      needs_i8_conversion, pandas_dtype)
  
  from pandas import (Timestamp, Period, Series, DataFrame,  # noqa
                      Index, MultiIndex, Float64Index, Int64Index,
                      Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT,
                      Categorical, CategoricalIndex)
  from pandas._libs.tslib import NaTType
  from pandas.core.sparse.api import SparseSeries, SparseDataFrame
  from pandas.core.sparse.array import BlockIndex, IntIndex
  from pandas.core.generic import NDFrame
  from pandas.errors import PerformanceWarning
  from pandas.io.common import get_filepath_or_buffer, _stringify_path
  from pandas.core.internals import BlockManager, make_block, _safe_reshape
  import pandas.core.internals as internals
  
  from pandas.io.msgpack import Unpacker as _Unpacker, Packer as _Packer, ExtType
  from pandas.util._move import (
      BadMove as _BadMove,
      move_into_mutable_buffer as _move_into_mutable_buffer,
  )
  
  # check whcih compression libs we have installed
  try:
      import zlib
  
      def _check_zlib():
          pass
  except ImportError:
      def _check_zlib():
          raise ImportError('zlib is not installed')
  
  _check_zlib.__doc__ = dedent(
      """\
      Check if zlib is installed.
  
      Raises
      ------
      ImportError
          Raised when zlib is not installed.
      """,
  )
  
  try:
      import blosc
  
      def _check_blosc():
          pass
  except ImportError:
      def _check_blosc():
          raise ImportError('blosc is not installed')
  
  _check_blosc.__doc__ = dedent(
      """\
      Check if blosc is installed.
  
      Raises
      ------
      ImportError
          Raised when blosc is not installed.
      """,
  )
  
  # until we can pass this into our conversion functions,
  # this is pretty hacky
  compressor = None
  
  
  def to_msgpack(path_or_buf, *args, **kwargs):
      """
      msgpack (serialize) object to input file path
  
      THIS IS AN EXPERIMENTAL LIBRARY and the storage format
      may not be stable until a future release.
  
      Parameters
      ----------
      path_or_buf : string File path, buffer-like, or None
                    if None, return generated string
      args : an object or objects to serialize
      encoding: encoding for unicode objects
      append : boolean whether to append to an existing msgpack
               (default is False)
      compress : type of compressor (zlib or blosc), default to None (no
                 compression)
      """
      global compressor
      compressor = kwargs.pop('compress', None)
      if compressor:
          compressor = u(compressor)
      append = kwargs.pop('append', None)
      if append:
          mode = 'a+b'
      else:
          mode = 'wb'
  
      def writer(fh):
          for a in args:
              fh.write(pack(a, **kwargs))
  
      path_or_buf = _stringify_path(path_or_buf)
      if isinstance(path_or_buf, compat.string_types):
          with open(path_or_buf, mode) as fh:
              writer(fh)
      elif path_or_buf is None:
          buf = compat.BytesIO()
          writer(buf)
          return buf.getvalue()
      else:
          writer(path_or_buf)
  
  
  def read_msgpack(path_or_buf, encoding='utf-8', iterator=False, **kwargs):
      """
      Load msgpack pandas object from the specified
      file path
  
      THIS IS AN EXPERIMENTAL LIBRARY and the storage format
      may not be stable until a future release.
  
      Parameters
      ----------
      path_or_buf : string File path, BytesIO like or string
      encoding: Encoding for decoding msgpack str type
      iterator : boolean, if True, return an iterator to the unpacker
                 (default is False)
  
      Returns
      -------
      obj : type of object stored in file
  
      """
      path_or_buf, _, _ = get_filepath_or_buffer(path_or_buf)
      if iterator:
          return Iterator(path_or_buf)
  
      def read(fh):
          l = list(unpack(fh, encoding=encoding, **kwargs))
          if len(l) == 1:
              return l[0]
          return l
  
      # see if we have an actual file
      if isinstance(path_or_buf, compat.string_types):
  
          try:
              exists = os.path.exists(path_or_buf)
          except (TypeError, ValueError):
              exists = False
  
          if exists:
              with open(path_or_buf, 'rb') as fh:
                  return read(fh)
  
      # treat as a binary-like
      if isinstance(path_or_buf, compat.binary_type):
          fh = None
          try:
              fh = compat.BytesIO(path_or_buf)
              return read(fh)
          finally:
              if fh is not None:
                  fh.close()
  
      # a buffer like
      if hasattr(path_or_buf, 'read') and compat.callable(path_or_buf.read):
          return read(path_or_buf)
  
      raise ValueError('path_or_buf needs to be a string file path or file-like')
  
  
  dtype_dict = {21: np.dtype('M8[ns]'),
                u('datetime64[ns]'): np.dtype('M8[ns]'),
                u('datetime64[us]'): np.dtype('M8[us]'),
                22: np.dtype('m8[ns]'),
                u('timedelta64[ns]'): np.dtype('m8[ns]'),
                u('timedelta64[us]'): np.dtype('m8[us]'),
  
                # this is platform int, which we need to remap to np.int64
                # for compat on windows platforms
                7: np.dtype('int64'),
                'category': 'category'
                }
  
  
  def dtype_for(t):
      """ return my dtype mapping, whether number or name """
      if t in dtype_dict:
          return dtype_dict[t]
      return np.typeDict.get(t, t)
  
  
  c2f_dict = {'complex': np.float64,
              'complex128': np.float64,
              'complex64': np.float32}
  
  # numpy 1.6.1 compat
  if hasattr(np, 'float128'):
      c2f_dict['complex256'] = np.float128
  
  
  def c2f(r, i, ctype_name):
      """
      Convert strings to complex number instance with specified numpy type.
      """
  
      ftype = c2f_dict[ctype_name]
      return np.typeDict[ctype_name](ftype(r) + 1j * ftype(i))
  
  
  def convert(values):
      """ convert the numpy values to a list """
  
      dtype = values.dtype
  
      if is_categorical_dtype(values):
          return values
  
      elif is_object_dtype(dtype):
          return values.ravel().tolist()
  
      if needs_i8_conversion(dtype):
          values = values.view('i8')
      v = values.ravel()
  
      if compressor == 'zlib':
          _check_zlib()
  
          # return string arrays like they are
          if dtype == np.object_:
              return v.tolist()
  
          # convert to a bytes array
          v = v.tostring()
          return ExtType(0, zlib.compress(v))
  
      elif compressor == 'blosc':
          _check_blosc()
  
          # return string arrays like they are
          if dtype == np.object_:
              return v.tolist()
  
          # convert to a bytes array
          v = v.tostring()
          return ExtType(0, blosc.compress(v, typesize=dtype.itemsize))
  
      # ndarray (on original dtype)
      return ExtType(0, v.tostring())
  
  
  def unconvert(values, dtype, compress=None):
  
      as_is_ext = isinstance(values, ExtType) and values.code == 0
  
      if as_is_ext:
          values = values.data
  
      if is_categorical_dtype(dtype):
          return values
  
      elif is_object_dtype(dtype):
          return np.array(values, dtype=object)
  
      dtype = pandas_dtype(dtype).base
  
      if not as_is_ext:
          values = values.encode('latin1')
  
      if compress:
          if compress == u'zlib':
              _check_zlib()
              decompress = zlib.decompress
          elif compress == u'blosc':
              _check_blosc()
              decompress = blosc.decompress
          else:
              raise ValueError("compress must be one of 'zlib' or 'blosc'")
  
          try:
              return np.frombuffer(
                  _move_into_mutable_buffer(decompress(values)),
                  dtype=dtype,
              )
          except _BadMove as e:
              # Pull the decompressed data off of the `_BadMove` exception.
              # We don't just store this in the locals because we want to
              # minimize the risk of giving users access to a `bytes` object
              # whose data is also given to a mutable buffer.
              values = e.args[0]
              if len(values) > 1:
                  # The empty string and single characters are memoized in many
                  # string creating functions in the capi. This case should not
                  # warn even though we need to make a copy because we are only
                  # copying at most 1 byte.
                  warnings.warn(
                      'copying data after decompressing; this may mean that'
                      ' decompress is caching its result',
                      PerformanceWarning,
                  )
                  # fall through to copying `np.fromstring`
  
      # Copy the string into a numpy array.
      return np.fromstring(values, dtype=dtype)
  
  
  def encode(obj):
      """
      Data encoder
      """
      tobj = type(obj)
      if isinstance(obj, Index):
          if isinstance(obj, RangeIndex):
              return {u'typ': u'range_index',
                      u'klass': u(obj.__class__.__name__),
                      u'name': getattr(obj, 'name', None),
                      u'start': getattr(obj, '_start', None),
                      u'stop': getattr(obj, '_stop', None),
                      u'step': getattr(obj, '_step', None)}
          elif isinstance(obj, PeriodIndex):
              return {u'typ': u'period_index',
                      u'klass': u(obj.__class__.__name__),
                      u'name': getattr(obj, 'name', None),
                      u'freq': u_safe(getattr(obj, 'freqstr', None)),
                      u'dtype': u(obj.dtype.name),
                      u'data': convert(obj.asi8),
                      u'compress': compressor}
          elif isinstance(obj, DatetimeIndex):
              tz = getattr(obj, 'tz', None)
  
              # store tz info and data as UTC
              if tz is not None:
                  tz = u(tz.zone)
                  obj = obj.tz_convert('UTC')
              return {u'typ': u'datetime_index',
                      u'klass': u(obj.__class__.__name__),
                      u'name': getattr(obj, 'name', None),
                      u'dtype': u(obj.dtype.name),
                      u'data': convert(obj.asi8),
                      u'freq': u_safe(getattr(obj, 'freqstr', None)),
                      u'tz': tz,
                      u'compress': compressor}
          elif isinstance(obj, MultiIndex):
              return {u'typ': u'multi_index',
                      u'klass': u(obj.__class__.__name__),
                      u'names': getattr(obj, 'names', None),
                      u'dtype': u(obj.dtype.name),
                      u'data': convert(obj.values),
                      u'compress': compressor}
          else:
              return {u'typ': u'index',
                      u'klass': u(obj.__class__.__name__),
                      u'name': getattr(obj, 'name', None),
                      u'dtype': u(obj.dtype.name),
                      u'data': convert(obj.values),
                      u'compress': compressor}
  
      elif isinstance(obj, Categorical):
          return {u'typ': u'category',
                  u'klass': u(obj.__class__.__name__),
                  u'name': getattr(obj, 'name', None),
                  u'codes': obj.codes,
                  u'categories': obj.categories,
                  u'ordered': obj.ordered,
                  u'compress': compressor}
  
      elif isinstance(obj, Series):
          if isinstance(obj, SparseSeries):
              raise NotImplementedError(
                  'msgpack sparse series is not implemented'
              )
              # d = {'typ': 'sparse_series',
              #     'klass': obj.__class__.__name__,
              #     'dtype': obj.dtype.name,
              #     'index': obj.index,
              #     'sp_index': obj.sp_index,
              #     'sp_values': convert(obj.sp_values),
              #     'compress': compressor}
              # for f in ['name', 'fill_value', 'kind']:
              #    d[f] = getattr(obj, f, None)
              # return d
          else:
              return {u'typ': u'series',
                      u'klass': u(obj.__class__.__name__),
                      u'name': getattr(obj, 'name', None),
                      u'index': obj.index,
                      u'dtype': u(obj.dtype.name),
                      u'data': convert(obj.values),
                      u'compress': compressor}
      elif issubclass(tobj, NDFrame):
          if isinstance(obj, SparseDataFrame):
              raise NotImplementedError(
                  'msgpack sparse frame is not implemented'
              )
              # d = {'typ': 'sparse_dataframe',
              #     'klass': obj.__class__.__name__,
              #     'columns': obj.columns}
              # for f in ['default_fill_value', 'default_kind']:
              #    d[f] = getattr(obj, f, None)
              # d['data'] = dict([(name, ss)
              #                 for name, ss in compat.iteritems(obj)])
              # return d
          else:
  
              data = obj._data
              if not data.is_consolidated():
                  data = data.consolidate()
  
              # the block manager
              return {u'typ': u'block_manager',
                      u'klass': u(obj.__class__.__name__),
                      u'axes': data.axes,
                      u'blocks': [{u'locs': b.mgr_locs.as_array,
                                   u'values': convert(b.values),
                                   u'shape': b.values.shape,
                                   u'dtype': u(b.dtype.name),
                                   u'klass': u(b.__class__.__name__),
                                   u'compress': compressor} for b in data.blocks]
                      }
  
      elif isinstance(obj, (datetime, date, np.datetime64, timedelta,
                            np.timedelta64, NaTType)):
          if isinstance(obj, Timestamp):
              tz = obj.tzinfo
              if tz is not None:
                  tz = u(tz.zone)
              freq = obj.freq
              if freq is not None:
                  freq = u(freq.freqstr)
              return {u'typ': u'timestamp',
                      u'value': obj.value,
                      u'freq': freq,
                      u'tz': tz}
          if isinstance(obj, NaTType):
              return {u'typ': u'nat'}
          elif isinstance(obj, np.timedelta64):
              return {u'typ': u'timedelta64',
                      u'data': obj.view('i8')}
          elif isinstance(obj, timedelta):
              return {u'typ': u'timedelta',
                      u'data': (obj.days, obj.seconds, obj.microseconds)}
          elif isinstance(obj, np.datetime64):
              return {u'typ': u'datetime64',
                      u'data': u(str(obj))}
          elif isinstance(obj, datetime):
              return {u'typ': u'datetime',
                      u'data': u(obj.isoformat())}
          elif isinstance(obj, date):
              return {u'typ': u'date',
                      u'data': u(obj.isoformat())}
          raise Exception("cannot encode this datetimelike object: %s" % obj)
      elif isinstance(obj, Period):
          return {u'typ': u'period',
                  u'ordinal': obj.ordinal,
                  u'freq': u(obj.freq)}
      elif isinstance(obj, BlockIndex):
          return {u'typ': u'block_index',
                  u'klass': u(obj.__class__.__name__),
                  u'blocs': obj.blocs,
                  u'blengths': obj.blengths,
                  u'length': obj.length}
      elif isinstance(obj, IntIndex):
          return {u'typ': u'int_index',
                  u'klass': u(obj.__class__.__name__),
                  u'indices': obj.indices,
                  u'length': obj.length}
      elif isinstance(obj, np.ndarray):
          return {u'typ': u'ndarray',
                  u'shape': obj.shape,
                  u'ndim': obj.ndim,
                  u'dtype': u(obj.dtype.name),
                  u'data': convert(obj),
                  u'compress': compressor}
      elif isinstance(obj, np.number):
          if np.iscomplexobj(obj):
              return {u'typ': u'np_scalar',
                      u'sub_typ': u'np_complex',
                      u'dtype': u(obj.dtype.name),
                      u'real': u(obj.real.__repr__()),
                      u'imag': u(obj.imag.__repr__())}
          else:
              return {u'typ': u'np_scalar',
                      u'dtype': u(obj.dtype.name),
                      u'data': u(obj.__repr__())}
      elif isinstance(obj, complex):
          return {u'typ': u'np_complex',
                  u'real': u(obj.real.__repr__()),
                  u'imag': u(obj.imag.__repr__())}
  
      return obj
  
  
  def decode(obj):
      """
      Decoder for deserializing numpy data types.
      """
  
      typ = obj.get(u'typ')
      if typ is None:
          return obj
      elif typ == u'timestamp':
          freq = obj[u'freq'] if 'freq' in obj else obj[u'offset']
          return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq)
      elif typ == u'nat':
          return NaT
      elif typ == u'period':
          return Period(ordinal=obj[u'ordinal'], freq=obj[u'freq'])
      elif typ == u'index':
          dtype = dtype_for(obj[u'dtype'])
          data = unconvert(obj[u'data'], dtype,
                           obj.get(u'compress'))
          return globals()[obj[u'klass']](data, dtype=dtype, name=obj[u'name'])
      elif typ == u'range_index':
          return globals()[obj[u'klass']](obj[u'start'],
                                          obj[u'stop'],
                                          obj[u'step'],
                                          name=obj[u'name'])
      elif typ == u'multi_index':
          dtype = dtype_for(obj[u'dtype'])
          data = unconvert(obj[u'data'], dtype,
                           obj.get(u'compress'))
          data = [tuple(x) for x in data]
          return globals()[obj[u'klass']].from_tuples(data, names=obj[u'names'])
      elif typ == u'period_index':
          data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
          d = dict(name=obj[u'name'], freq=obj[u'freq'])
          return globals()[obj[u'klass']]._from_ordinals(data, **d)
      elif typ == u'datetime_index':
          data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
          d = dict(name=obj[u'name'], freq=obj[u'freq'], verify_integrity=False)
          result = globals()[obj[u'klass']](data, **d)
          tz = obj[u'tz']
  
          # reverse tz conversion
          if tz is not None:
              result = result.tz_localize('UTC').tz_convert(tz)
          return result
  
      elif typ == u'category':
          from_codes = globals()[obj[u'klass']].from_codes
          return from_codes(codes=obj[u'codes'],
                            categories=obj[u'categories'],
                            ordered=obj[u'ordered'])
  
      elif typ == u'series':
          dtype = dtype_for(obj[u'dtype'])
          pd_dtype = pandas_dtype(dtype)
  
          index = obj[u'index']
          result = globals()[obj[u'klass']](unconvert(obj[u'data'], dtype,
                                                      obj[u'compress']),
                                            index=index,
                                            dtype=pd_dtype,
                                            name=obj[u'name'])
          return result
  
      elif typ == u'block_manager':
          axes = obj[u'axes']
  
          def create_block(b):
              values = _safe_reshape(unconvert(
                  b[u'values'], dtype_for(b[u'dtype']),
                  b[u'compress']), b[u'shape'])
  
              # locs handles duplicate column names, and should be used instead
              # of items; see GH 9618
              if u'locs' in b:
                  placement = b[u'locs']
              else:
                  placement = axes[0].get_indexer(b[u'items'])
              return make_block(values=values,
                                klass=getattr(internals, b[u'klass']),
                                placement=placement,
                                dtype=b[u'dtype'])
  
          blocks = [create_block(b) for b in obj[u'blocks']]
          return globals()[obj[u'klass']](BlockManager(blocks, axes))
      elif typ == u'datetime':
          return parse(obj[u'data'])
      elif typ == u'datetime64':
          return np.datetime64(parse(obj[u'data']))
      elif typ == u'date':
          return parse(obj[u'data']).date()
      elif typ == u'timedelta':
          return timedelta(*obj[u'data'])
      elif typ == u'timedelta64':
          return np.timedelta64(int(obj[u'data']))
      # elif typ == 'sparse_series':
      #    dtype = dtype_for(obj['dtype'])
      #    return globals()[obj['klass']](
      #        unconvert(obj['sp_values'], dtype, obj['compress']),
      #        sparse_index=obj['sp_index'], index=obj['index'],
      #        fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name'])
      # elif typ == 'sparse_dataframe':
      #    return globals()[obj['klass']](
      #        obj['data'], columns=obj['columns'],
      #        default_fill_value=obj['default_fill_value'],
      #        default_kind=obj['default_kind']
      #    )
      # elif typ == 'sparse_panel':
      #    return globals()[obj['klass']](
      #        obj['data'], items=obj['items'],
      #        default_fill_value=obj['default_fill_value'],
      #        default_kind=obj['default_kind'])
      elif typ == u'block_index':
          return globals()[obj[u'klass']](obj[u'length'], obj[u'blocs'],
                                          obj[u'blengths'])
      elif typ == u'int_index':
          return globals()[obj[u'klass']](obj[u'length'], obj[u'indices'])
      elif typ == u'ndarray':
          return unconvert(obj[u'data'], np.typeDict[obj[u'dtype']],
                           obj.get(u'compress')).reshape(obj[u'shape'])
      elif typ == u'np_scalar':
          if obj.get(u'sub_typ') == u'np_complex':
              return c2f(obj[u'real'], obj[u'imag'], obj[u'dtype'])
          else:
              dtype = dtype_for(obj[u'dtype'])
              try:
                  return dtype(obj[u'data'])
              except:
                  return dtype.type(obj[u'data'])
      elif typ == u'np_complex':
          return complex(obj[u'real'] + u'+' + obj[u'imag'] + u'j')
      elif isinstance(obj, (dict, list, set)):
          return obj
      else:
          return obj
  
  
  def pack(o, default=encode,
           encoding='utf-8', unicode_errors='strict', use_single_float=False,
           autoreset=1, use_bin_type=1):
      """
      Pack an object and return the packed bytes.
      """
  
      return Packer(default=default, encoding=encoding,
                    unicode_errors=unicode_errors,
                    use_single_float=use_single_float,
                    autoreset=autoreset,
                    use_bin_type=use_bin_type).pack(o)
  
  
  def unpack(packed, object_hook=decode,
             list_hook=None, use_list=False, encoding='utf-8',
             unicode_errors='strict', object_pairs_hook=None,
             max_buffer_size=0, ext_hook=ExtType):
      """
      Unpack a packed object, return an iterator
      Note: packed lists will be returned as tuples
      """
  
      return Unpacker(packed, object_hook=object_hook,
                      list_hook=list_hook,
                      use_list=use_list, encoding=encoding,
                      unicode_errors=unicode_errors,
                      object_pairs_hook=object_pairs_hook,
                      max_buffer_size=max_buffer_size,
                      ext_hook=ext_hook)
  
  
  class Packer(_Packer):
  
      def __init__(self, default=encode,
                   encoding='utf-8',
                   unicode_errors='strict',
                   use_single_float=False,
                   autoreset=1,
                   use_bin_type=1):
          super(Packer, self).__init__(default=default,
                                       encoding=encoding,
                                       unicode_errors=unicode_errors,
                                       use_single_float=use_single_float,
                                       autoreset=autoreset,
                                       use_bin_type=use_bin_type)
  
  
  class Unpacker(_Unpacker):
  
      def __init__(self, file_like=None, read_size=0, use_list=False,
                   object_hook=decode,
                   object_pairs_hook=None, list_hook=None, encoding='utf-8',
                   unicode_errors='strict', max_buffer_size=0, ext_hook=ExtType):
          super(Unpacker, self).__init__(file_like=file_like,
                                         read_size=read_size,
                                         use_list=use_list,
                                         object_hook=object_hook,
                                         object_pairs_hook=object_pairs_hook,
                                         list_hook=list_hook,
                                         encoding=encoding,
                                         unicode_errors=unicode_errors,
                                         max_buffer_size=max_buffer_size,
                                         ext_hook=ext_hook)
  
  
  class Iterator(object):
  
      """ manage the unpacking iteration,
          close the file on completion """
  
      def __init__(self, path, **kwargs):
          self.path = path
          self.kwargs = kwargs
  
      def __iter__(self):
  
          needs_closing = True
          try:
  
              # see if we have an actual file
              if isinstance(self.path, compat.string_types):
  
                  try:
                      path_exists = os.path.exists(self.path)
                  except TypeError:
                      path_exists = False
  
                  if path_exists:
                      fh = open(self.path, 'rb')
                  else:
                      fh = compat.BytesIO(self.path)
  
              else:
  
                  if not hasattr(self.path, 'read'):
                      fh = compat.BytesIO(self.path)
  
                  else:
  
                      # a file-like
                      needs_closing = False
                      fh = self.path
  
              unpacker = unpack(fh)
              for o in unpacker:
                  yield o
          finally:
              if needs_closing:
                  fh.close()
  