content: """Internal utilties; not for external use
  """
  import contextlib
  import functools
  import itertools
  import os.path
  import re
  import warnings
  from collections import OrderedDict
  from typing import (
      AbstractSet, Any, Callable, Container, Dict, Hashable, Iterable, Iterator,
      Mapping, MutableMapping, MutableSet, Optional, Sequence, Tuple, TypeVar,
      cast)
  
  import numpy as np
  import pandas as pd
  
  from .pycompat import dask_array_type
  
  try:  # Fix typed collections in Python 3.5.0~3.5.2
      from .pycompat import Mapping, MutableMapping, MutableSet  # noqa: F811
  except ImportError:
      pass
  
  
  K = TypeVar('K')
  V = TypeVar('V')
  T = TypeVar('T')
  
  
  def _check_inplace(inplace: Optional[bool], default: bool = False) -> bool:
      if inplace is None:
          inplace = default
      else:
          warnings.warn('The inplace argument has been deprecated and will be '
                        'removed in a future version of xarray.',
                        FutureWarning, stacklevel=3)
  
      return inplace
  
  
  def alias_message(old_name: str, new_name: str) -> str:
      return '%s has been deprecated. Use %s instead.' % (old_name, new_name)
  
  
  def alias_warning(old_name: str, new_name: str, stacklevel: int = 3) -> None:
      warnings.warn(alias_message(old_name, new_name), FutureWarning,
                    stacklevel=stacklevel)
  
  
  def alias(obj: Callable[..., T], old_name: str) -> Callable[..., T]:
      assert isinstance(old_name, str)
  
      @functools.wraps(obj)
      def wrapper(*args, **kwargs):
          alias_warning(old_name, obj.__name__)
          return obj(*args, **kwargs)
      wrapper.__doc__ = alias_message(old_name, obj.__name__)
      return wrapper
  
  
  def _maybe_cast_to_cftimeindex(index: pd.Index) -> pd.Index:
      from ..coding.cftimeindex import CFTimeIndex
  
      if len(index) > 0 and index.dtype == 'O':
          try:
              return CFTimeIndex(index)
          except (ImportError, TypeError):
              return index
      else:
          return index
  
  
  def safe_cast_to_index(array: Any) -> pd.Index:
      """Given an array, safely cast it to a pandas.Index.
  
      If it is already a pandas.Index, return it unchanged.
  
      Unlike pandas.Index, if the array has dtype=object or dtype=timedelta64,
      this function will not attempt to do automatic type conversion but will
      always return an index with dtype=object.
      """
      if isinstance(array, pd.Index):
          index = array
      elif hasattr(array, 'to_index'):
          index = array.to_index()
      else:
          kwargs = {}
          if hasattr(array, 'dtype') and array.dtype.kind == 'O':
              kwargs['dtype'] = object
          index = pd.Index(np.asarray(array), **kwargs)
      return _maybe_cast_to_cftimeindex(index)
  
  
  def multiindex_from_product_levels(levels: Sequence[pd.Index],
                                     names: Optional[Sequence[str]] = None
                                     ) -> pd.MultiIndex:
      """Creating a MultiIndex from a product without refactorizing levels.
  
      Keeping levels the same gives back the original labels when we unstack.
  
      Parameters
      ----------
      levels : sequence of pd.Index
          Values for each MultiIndex level.
      names : optional sequence of objects
          Names for each level.
  
      Returns
      -------
      pandas.MultiIndex
      """
      if any(not isinstance(lev, pd.Index) for lev in levels):
          raise TypeError('levels must be a list of pd.Index objects')
  
      split_labels, levels = zip(*[lev.factorize() for lev in levels])
      labels_mesh = np.meshgrid(*split_labels, indexing='ij')
      labels = [x.ravel() for x in labels_mesh]
      return pd.MultiIndex(levels, labels, sortorder=0, names=names)
  
  
  def maybe_wrap_array(original, new_array):
      """Wrap a transformed array with __array_wrap__ is it can be done safely.
  
      This lets us treat arbitrary functions that take and return ndarray objects
      like ufuncs, as long as they return an array with the same shape.
      """
      # in case func lost array's metadata
      if isinstance(new_array, np.ndarray) and new_array.shape == original.shape:
          return original.__array_wrap__(new_array)
      else:
          return new_array
  
  
  def equivalent(first: T, second: T) -> bool:
      """Compare two objects for equivalence (identity or equality), using
      array_equiv if either object is an ndarray
      """
      # TODO: refactor to avoid circular import
      from . import duck_array_ops
      if isinstance(first, np.ndarray) or isinstance(second, np.ndarray):
          return duck_array_ops.array_equiv(first, second)
      else:
          return ((first is second) or
                  (first == second) or
                  (pd.isnull(first) and pd.isnull(second)))
  
  
  def peek_at(iterable: Iterable[T]) -> Tuple[T, Iterator[T]]:
      """Returns the first value from iterable, as well as a new iterator with
      the same content as the original iterable
      """
      gen = iter(iterable)
      peek = next(gen)
      return peek, itertools.chain([peek], gen)
  
  
  def update_safety_check(first_dict: MutableMapping[K, V],
                          second_dict: Mapping[K, V],
                          compat: Callable[[V, V], bool] = equivalent) -> None:
      """Check the safety of updating one dictionary with another.
  
      Raises ValueError if dictionaries have non-compatible values for any key,
      where compatibility is determined by identity (they are the same item) or
      the `compat` function.
  
      Parameters
      ----------
      first_dict, second_dict : dict-like
          All items in the second dictionary are checked against for conflicts
          against items in the first dictionary.
      compat : function, optional
          Binary operator to determine if two values are compatible. By default,
          checks for equivalence.
      """
      for k, v in second_dict.items():
          if k in first_dict and not compat(v, first_dict[k]):
              raise ValueError('unsafe to merge dictionaries without '
                               'overriding values; conflicting key %r' % k)
  
  
  def remove_incompatible_items(first_dict: MutableMapping[K, V],
                                second_dict: Mapping[K, V],
                                compat: Callable[[V, V], bool] = equivalent
                                ) -> None:
      """Remove incompatible items from the first dictionary in-place.
  
      Items are retained if their keys are found in both dictionaries and the
      values are compatible.
  
      Parameters
      ----------
      first_dict, second_dict : dict-like
          Mappings to merge.
      compat : function, optional
          Binary operator to determine if two values are compatible. By default,
          checks for equivalence.
      """
      for k in list(first_dict):
          if k not in second_dict or not compat(first_dict[k], second_dict[k]):
              del first_dict[k]
  
  
  def is_dict_like(value: Any) -> bool:
      return hasattr(value, 'keys') and hasattr(value, '__getitem__')
  
  
  def is_full_slice(value: Any) -> bool:
      return isinstance(value, slice) and value == slice(None)
  
  
  def either_dict_or_kwargs(pos_kwargs: Optional[Mapping[Hashable, T]],
                            kw_kwargs: Mapping[str, T],
                            func_name: str
                            ) -> Mapping[Hashable, T]:
      if pos_kwargs is not None:
          if not is_dict_like(pos_kwargs):
              raise ValueError('the first argument to .%s must be a dictionary'
                               % func_name)
          if kw_kwargs:
              raise ValueError('cannot specify both keyword and positional '
                               'arguments to .%s' % func_name)
          return pos_kwargs
      else:
          # Need an explicit cast to appease mypy due to invariance; see
          # https://github.com/python/mypy/issues/6228
          return cast(Mapping[Hashable, T], kw_kwargs)
  
  
  def is_scalar(value: Any) -> bool:
      """Whether to treat a value as a scalar.
  
      Any non-iterable, string, or 0-D array
      """
      return (
          getattr(value, 'ndim', None) == 0 or
          isinstance(value, (str, bytes)) or not
          isinstance(value, (Iterable, ) + dask_array_type))
  
  
  def is_valid_numpy_dtype(dtype: Any) -> bool:
      try:
          np.dtype(dtype)
      except (TypeError, ValueError):
          return False
      else:
          return True
  
  
  def to_0d_object_array(value: Any) -> np.ndarray:
      """Given a value, wrap it in a 0-D numpy.ndarray with dtype=object.
      """
      result = np.empty((), dtype=object)
      result[()] = value
      return result
  
  
  def to_0d_array(value: Any) -> np.ndarray:
      """Given a value, wrap it in a 0-D numpy.ndarray.
      """
      if np.isscalar(value) or (isinstance(value, np.ndarray) and
                                value.ndim == 0):
          return np.array(value)
      else:
          return to_0d_object_array(value)
  
  
  def dict_equiv(first: Mapping[K, V], second: Mapping[K, V],
                 compat: Callable[[V, V], bool] = equivalent) -> bool:
      """Test equivalence of two dict-like objects. If any of the values are
      numpy arrays, compare them correctly.
  
      Parameters
      ----------
      first, second : dict-like
          Dictionaries to compare for equality
      compat : function, optional
          Binary operator to determine if two values are compatible. By default,
          checks for equivalence.
  
      Returns
      -------
      equals : bool
          True if the dictionaries are equal
      """
      for k in first:
          if k not in second or not compat(first[k], second[k]):
              return False
      for k in second:
          if k not in first:
              return False
      return True
  
  
  def ordered_dict_intersection(first_dict: Mapping[K, V],
                                second_dict: Mapping[K, V],
                                compat: Callable[[V, V], bool] = equivalent
                                ) -> MutableMapping[K, V]:
      """Return the intersection of two dictionaries as a new OrderedDict.
  
      Items are retained if their keys are found in both dictionaries and the
      values are compatible.
  
      Parameters
      ----------
      first_dict, second_dict : dict-like
          Mappings to merge.
      compat : function, optional
          Binary operator to determine if two values are compatible. By default,
          checks for equivalence.
  
      Returns
      -------
      intersection : OrderedDict
          Intersection of the contents.
      """
      new_dict = OrderedDict(first_dict)
      remove_incompatible_items(new_dict, second_dict, compat)
      return new_dict
  
  
  class SingleSlotPickleMixin:
      """Mixin class to add the ability to pickle objects whose state is defined
      by a single __slots__ attribute. Only necessary under Python 2.
      """
      def __getstate__(self):
          return getattr(self, self.__slots__[0])
  
      def __setstate__(self, state):
          setattr(self, self.__slots__[0], state)
  
  
  class Frozen(Mapping[K, V], SingleSlotPickleMixin):
      """Wrapper around an object implementing the mapping interface to make it
      immutable. If you really want to modify the mapping, the mutable version is
      saved under the `mapping` attribute.
      """
      __slots__ = ['mapping']
  
      def __init__(self, mapping: Mapping[K, V]):
          self.mapping = mapping
  
      def __getitem__(self, key: K) -> V:
          return self.mapping[key]
  
      def __iter__(self) -> Iterator[K]:
          return iter(self.mapping)
  
      def __len__(self) -> int:
          return len(self.mapping)
  
      def __contains__(self, key: object) -> bool:
          return key in self.mapping
  
      def __repr__(self) -> str:
          return '%s(%r)' % (type(self).__name__, self.mapping)
  
  
  def FrozenOrderedDict(*args, **kwargs) -> Frozen:
      return Frozen(OrderedDict(*args, **kwargs))
  
  
  class SortedKeysDict(MutableMapping[K, V], SingleSlotPickleMixin):
      """An wrapper for dictionary-like objects that always iterates over its
      items in sorted order by key but is otherwise equivalent to the underlying
      mapping.
      """
      __slots__ = ['mapping']
  
      def __init__(self, mapping: Optional[MutableMapping[K, V]] = None):
          self.mapping = {} if mapping is None else mapping
  
      def __getitem__(self, key: K) -> V:
          return self.mapping[key]
  
      def __setitem__(self, key: K, value: V) -> None:
          self.mapping[key] = value
  
      def __delitem__(self, key: K) -> None:
          del self.mapping[key]
  
      def __iter__(self) -> Iterator[K]:
          return iter(sorted(self.mapping))
  
      def __len__(self) -> int:
          return len(self.mapping)
  
      def __contains__(self, key: object) -> bool:
          return key in self.mapping
  
      def __repr__(self) -> str:
          return '%s(%r)' % (type(self).__name__, self.mapping)
  
  
  class OrderedSet(MutableSet[T]):
      """A simple ordered set.
  
      The API matches the builtin set, but it preserves insertion order of
      elements, like an OrderedDict.
      """
      def __init__(self, values: Optional[AbstractSet[T]] = None):
          self._ordered_dict = OrderedDict()  # type: MutableMapping[T, None]
          if values is not None:
              # Disable type checking - both mypy and PyCharm believes that
              # we're altering the type of self in place (see signature of
              # MutableSet.__ior__)
              self |= values  # type: ignore
  
      # Required methods for MutableSet
  
      def __contains__(self, value: object) -> bool:
          return value in self._ordered_dict
  
      def __iter__(self) -> Iterator[T]:
          return iter(self._ordered_dict)
  
      def __len__(self) -> int:
          return len(self._ordered_dict)
  
      def add(self, value: T) -> None:
          self._ordered_dict[value] = None
  
      def discard(self, value: T) -> None:
          del self._ordered_dict[value]
  
      # Additional methods
  
      def update(self, values: AbstractSet[T]) -> None:
          # See comment on __init__ re. type checking
          self |= values  # type: ignore
  
      def __repr__(self) -> str:
          return '%s(%r)' % (type(self).__name__, list(self))
  
  
  class NdimSizeLenMixin:
      """Mixin class that extends a class that defines a ``shape`` property to
      one that also defines ``ndim``, ``size`` and ``__len__``.
      """
      @property
      def ndim(self: Any) -> int:
          return len(self.shape)
  
      @property
      def size(self: Any) -> int:
          # cast to int so that shape = () gives size = 1
          return int(np.prod(self.shape))
  
      def __len__(self: Any) -> int:
          try:
              return self.shape[0]
          except IndexError:
              raise TypeError('len() of unsized object')
  
  
  class NDArrayMixin(NdimSizeLenMixin):
      """Mixin class for making wrappers of N-dimensional arrays that conform to
      the ndarray interface required for the data argument to Variable objects.
  
      A subclass should set the `array` property and override one or more of
      `dtype`, `shape` and `__getitem__`.
      """
      @property
      def dtype(self: Any) -> np.dtype:
          return self.array.dtype
  
      @property
      def shape(self: Any) -> Tuple[int]:
          return self.array.shape
  
      def __getitem__(self: Any, key):
          return self.array[key]
  
      def __repr__(self: Any) -> str:
          return '%s(array=%r)' % (type(self).__name__, self.array)
  
  
  class ReprObject:
      """Object that prints as the given value, for use with sentinel values.
      """
      def __init__(self, value: str):
          self._value = value
  
      def __repr__(self) -> str:
          return self._value
  
  
  @contextlib.contextmanager
  def close_on_error(f):
      """Context manager to ensure that a file opened by xarray is closed if an
      exception is raised before the user sees the file object.
      """
      try:
          yield
      except Exception:
          f.close()
          raise
  
  
  def is_remote_uri(path: str) -> bool:
      return bool(re.search(r'^https?\://', path))
  
  
  def is_grib_path(path: str) -> bool:
      _, ext = os.path.splitext(path)
      return ext in ['.grib', '.grb', '.grib2', '.grb2']
  
  
  def is_uniform_spaced(arr, **kwargs) -> bool:
      """Return True if values of an array are uniformly spaced and sorted.
  
      >>> is_uniform_spaced(range(5))
      True
      >>> is_uniform_spaced([-4, 0, 100])
      False
  
      kwargs are additional arguments to ``np.isclose``
      """
      arr = np.array(arr, dtype=float)
      diffs = np.diff(arr)
      return bool(np.isclose(diffs.min(), diffs.max(), **kwargs))
  
  
  def hashable(v: Any) -> bool:
      """Determine whether `v` can be hashed.
      """
      try:
          hash(v)
      except TypeError:
          return False
      return True
  
  
  def not_implemented(*args, **kwargs):
      return NotImplemented
  
  
  def decode_numpy_dict_values(attrs: Mapping[K, V]) -> Dict[K, V]:
      """Convert attribute values from numpy objects to native Python objects,
      for use in to_dict
      """
      attrs = dict(attrs)
      for k, v in attrs.items():
          if isinstance(v, np.ndarray):
              attrs[k] = v.tolist()
          elif isinstance(v, np.generic):
              attrs[k] = v.item()
      return attrs
  
  
  def ensure_us_time_resolution(val):
      """Convert val out of numpy time, for use in to_dict.
      Needed because of numpy bug GH#7619"""
      if np.issubdtype(val.dtype, np.datetime64):
          val = val.astype('datetime64[us]')
      elif np.issubdtype(val.dtype, np.timedelta64):
          val = val.astype('timedelta64[us]')
      return val
  
  
  class HiddenKeyDict(MutableMapping[K, V]):
      """Acts like a normal dictionary, but hides certain keys.
      """
      # ``__init__`` method required to create instance from class.
  
      def __init__(self, data: MutableMapping[K, V], hidden_keys: Iterable[K]):
          self._data = data
          self._hidden_keys = frozenset(hidden_keys)
  
      def _raise_if_hidden(self, key: K) -> None:
          if key in self._hidden_keys:
              raise KeyError('Key `%r` is hidden.' % key)
  
      # The next five methods are requirements of the ABC.
      def __setitem__(self, key: K, value: V) -> None:
          self._raise_if_hidden(key)
          self._data[key] = value
  
      def __getitem__(self, key: K) -> V:
          self._raise_if_hidden(key)
          return self._data[key]
  
      def __delitem__(self, key: K) -> None:
          self._raise_if_hidden(key)
          del self._data[key]
  
      def __iter__(self) -> Iterator[K]:
          for k in self._data:
              if k not in self._hidden_keys:
                  yield k
  
      def __len__(self) -> int:
          num_hidden = len(self._hidden_keys & self._data.keys())
          return len(self._data) - num_hidden
  
  
  def get_temp_dimname(dims: Container[Hashable], new_dim: Hashable) -> Hashable:
      """ Get an new dimension name based on new_dim, that is not used in dims.
      If the same name exists, we add an underscore(s) in the head.
  
      Example1:
          dims: ['a', 'b', 'c']
          new_dim: ['_rolling']
          -> ['_rolling']
      Example2:
          dims: ['a', 'b', 'c', '_rolling']
          new_dim: ['_rolling']
          -> ['__rolling']
      """
      while new_dim in dims:
          new_dim = '_' + str(new_dim)
      return new_dim
  