text: from __future__ import absolute_import, division, print_function
  
  from collections import OrderedDict
  from functools import partial
  from hashlib import md5
  from operator import getitem
  import inspect
  import pickle
  import os
  import threading
  import uuid
  
  from toolz import merge, groupby, curry, identity
  from toolz.functoolz import Compose
  
  from .compatibility import (
      apply,
      long,
      unicode,
      Iterator,
      is_dataclass,
      dataclass_fields,
      Mapping,
      cPickle,
  )
  from .context import thread_state
  from .core import flatten, quote, get as simple_get
  from .hashing import hash_buffer_hex
  from .utils import Dispatch, ensure_dict
  from . import config, local, threaded
  
  
  __all__ = (
      "DaskMethodsMixin",
      "is_dask_collection",
      "compute",
      "persist",
      "optimize",
      "visualize",
      "tokenize",
      "normalize_token",
  )
  
  
  def is_dask_collection(x):
      """Returns ``True`` if ``x`` is a dask collection"""
      try:
          return x.__dask_graph__() is not None
      except (AttributeError, TypeError):
          return False
  
  
  class DaskMethodsMixin(object):
      """A mixin adding standard dask collection methods"""
  
      __slots__ = ()
  
      def visualize(self, filename="mydask", format=None, optimize_graph=False, **kwargs):
          """Render the computation of this object's task graph using graphviz.
  
          Requires ``graphviz`` to be installed.
  
          Parameters
          ----------
          filename : str or None, optional
              The name (without an extension) of the file to write to disk.  If
              `filename` is None, no file will be written, and we communicate
              with dot using only pipes.
          format : {'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional
              Format in which to write output file.  Default is 'png'.
          optimize_graph : bool, optional
              If True, the graph is optimized before rendering.  Otherwise,
              the graph is displayed as is. Default is False.
          color: {None, 'order'}, optional
              Options to color nodes.  Provide ``cmap=`` keyword for additional
              colormap
          **kwargs
             Additional keyword arguments to forward to ``to_graphviz``.
  
          Examples
          --------
          >>> x.visualize(filename='dask.pdf')  # doctest: +SKIP
          >>> x.visualize(filename='dask.pdf', color='order')  # doctest: +SKIP
  
          Returns
          -------
          result : IPython.diplay.Image, IPython.display.SVG, or None
              See dask.dot.dot_graph for more information.
  
          See Also
          --------
          dask.base.visualize
          dask.dot.dot_graph
  
          Notes
          -----
          For more information on optimization see here:
  
          https://docs.dask.org/en/latest/optimize.html
          """
          return visualize(
              self,
              filename=filename,
              format=format,
              optimize_graph=optimize_graph,
              **kwargs
          )
  
      def persist(self, **kwargs):
          """Persist this dask collection into memory
  
          This turns a lazy Dask collection into a Dask collection with the same
          metadata, but now with the results fully computed or actively computing
          in the background.
  
          The action of function differs significantly depending on the active
          task scheduler.  If the task scheduler supports asynchronous computing,
          such as is the case of the dask.distributed scheduler, then persist
          will return *immediately* and the return value's task graph will
          contain Dask Future objects.  However if the task scheduler only
          supports blocking computation then the call to persist will *block*
          and the return value's task graph will contain concrete Python results.
  
          This function is particularly useful when using distributed systems,
          because the results will be kept in distributed memory, rather than
          returned to the local process as with compute.
  
          Parameters
          ----------
          scheduler : string, optional
              Which scheduler to use like "threads", "synchronous" or "processes".
              If not provided, the default is to check the global settings first,
              and then fall back to the collection defaults.
          optimize_graph : bool, optional
              If True [default], the graph is optimized before computation.
              Otherwise the graph is run as is. This can be useful for debugging.
          **kwargs
              Extra keywords to forward to the scheduler function.
  
          Returns
          -------
          New dask collections backed by in-memory data
  
          See Also
          --------
          dask.base.persist
          """
          (result,) = persist(self, traverse=False, **kwargs)
          return result
  
      def compute(self, **kwargs):
          """Compute this dask collection
  
          This turns a lazy Dask collection into its in-memory equivalent.
          For example a Dask.array turns into a  :func:`numpy.array` and a Dask.dataframe
          turns into a Pandas dataframe.  The entire dataset must fit into memory
          before calling this operation.
  
          Parameters
          ----------
          scheduler : string, optional
              Which scheduler to use like "threads", "synchronous" or "processes".
              If not provided, the default is to check the global settings first,
              and then fall back to the collection defaults.
          optimize_graph : bool, optional
              If True [default], the graph is optimized before computation.
              Otherwise the graph is run as is. This can be useful for debugging.
          kwargs
              Extra keywords to forward to the scheduler function.
  
          See Also
          --------
          dask.base.compute
          """
          (result,) = compute(self, traverse=False, **kwargs)
          return result
  
      def __await__(self):
          try:
              from distributed import wait, futures_of
          except ImportError:
              raise ImportError(
                  "Using async/await with dask requires the `distributed` package"
              )
          from tornado import gen
  
          @gen.coroutine
          def f():
              if futures_of(self):
                  yield wait(self)
              raise gen.Return(self)
  
          return f().__await__()
  
  
  def compute_as_if_collection(cls, dsk, keys, scheduler=None, get=None, **kwargs):
      """Compute a graph as if it were of type cls.
  
      Allows for applying the same optimizations and default scheduler."""
      schedule = get_scheduler(scheduler=scheduler, cls=cls, get=get)
      dsk2 = optimization_function(cls)(ensure_dict(dsk), keys, **kwargs)
      return schedule(dsk2, keys, **kwargs)
  
  
  def dont_optimize(dsk, keys, **kwargs):
      return dsk
  
  
  def optimization_function(x):
      return getattr(x, "__dask_optimize__", dont_optimize)
  
  
  def collections_to_dsk(collections, optimize_graph=True, **kwargs):
      """
      Convert many collections into a single dask graph, after optimization
      """
      optimizations = kwargs.pop("optimizations", None) or config.get("optimizations", [])
  
      if optimize_graph:
          groups = groupby(optimization_function, collections)
          groups = {opt: _extract_graph_and_keys(val) for opt, val in groups.items()}
  
          for opt in optimizations:
              groups = {k: (opt(dsk, keys), keys) for k, (dsk, keys) in groups.items()}
  
          dsk = merge(
              *map(
                  ensure_dict,
                  [opt(dsk, keys, **kwargs) for opt, (dsk, keys) in groups.items()],
              )
          )
      else:
          dsk, _ = _extract_graph_and_keys(collections)
  
      return dsk
  
  
  def _extract_graph_and_keys(vals):
      """Given a list of dask vals, return a single graph and a list of keys such
      that ``get(dsk, keys)`` is equivalent to ``[v.compute() for v in vals]``."""
      from .highlevelgraph import HighLevelGraph
  
      graphs = [v.__dask_graph__() for v in vals]
      keys = [v.__dask_keys__() for v in vals]
  
      if any(isinstance(graph, HighLevelGraph) for graph in graphs):
          graph = HighLevelGraph.merge(*graphs)
      else:
          graph = merge(*graphs)
  
      return graph, keys
  
  
  def unpack_collections(*args, **kwargs):
      """Extract collections in preparation for compute/persist/etc...
  
      Intended use is to find all collections in a set of (possibly nested)
      python objects, do something to them (compute, etc...), then repackage them
      in equivalent python objects.
  
      Parameters
      ----------
      *args
          Any number of objects. If it is a dask collection, it's extracted and
          added to the list of collections returned. By default, python builtin
          collections are also traversed to look for dask collections (for more
          information see the ``traverse`` keyword).
      traverse : bool, optional
          If True (default), builtin python collections are traversed looking for
          any dask collections they might contain.
  
      Returns
      -------
      collections : list
          A list of all dask collections contained in ``args``
      repack : callable
          A function to call on the transformed collections to repackage them as
          they were in the original ``args``.
      """
      traverse = kwargs.pop("traverse", True)
  
      collections = []
      repack_dsk = {}
  
      collections_token = uuid.uuid4().hex
  
      def _unpack(expr):
          if is_dask_collection(expr):
              tok = tokenize(expr)
              if tok not in repack_dsk:
                  repack_dsk[tok] = (getitem, collections_token, len(collections))
                  collections.append(expr)
              return tok
  
          tok = uuid.uuid4().hex
          if not traverse:
              tsk = quote(expr)
          else:
              # Treat iterators like lists
              typ = list if isinstance(expr, Iterator) else type(expr)
              if typ in (list, tuple, set):
                  tsk = (typ, [_unpack(i) for i in expr])
              elif typ in (dict, OrderedDict):
                  tsk = (typ, [[_unpack(k), _unpack(v)] for k, v in expr.items()])
              elif is_dataclass(expr):
                  tsk = (
                      apply,
                      typ,
                      (),
                      (
                          dict,
                          [
                              [f.name, _unpack(getattr(expr, f.name))]
                              for f in dataclass_fields(expr)
                          ],
                      ),
                  )
              else:
                  return expr
  
          repack_dsk[tok] = tsk
          return tok
  
      out = uuid.uuid4().hex
      repack_dsk[out] = (tuple, [_unpack(i) for i in args])
  
      def repack(results):
          dsk = repack_dsk.copy()
          dsk[collections_token] = quote(results)
          return simple_get(dsk, out)
  
      return collections, repack
  
  
  def optimize(*args, **kwargs):
      """Optimize several dask collections at once.
  
      Returns equivalent dask collections that all share the same merged and
      optimized underlying graph. This can be useful if converting multiple
      collections to delayed objects, or to manually apply the optimizations at
      strategic points.
  
      Note that in most cases you shouldn't need to call this method directly.
  
      Parameters
      ----------
      *args : objects
          Any number of objects. If a dask object, its graph is optimized and
          merged with all those of all other dask objects before returning an
          equivalent dask collection. Non-dask arguments are passed through
          unchanged.
      traverse : bool, optional
          By default dask traverses builtin python collections looking for dask
          objects passed to ``optimize``. For large collections this can be
          expensive. If none of the arguments contain any dask objects, set
          ``traverse=False`` to avoid doing this traversal.
      optimizations : list of callables, optional
          Additional optimization passes to perform.
      **kwargs
          Extra keyword arguments to forward to the optimization passes.
  
      Examples
      --------
      >>> import dask.array as da
      >>> a = da.arange(10, chunks=2).sum()
      >>> b = da.arange(10, chunks=2).mean()
      >>> a2, b2 = optimize(a, b)
  
      >>> a2.compute() == a.compute()
      True
      >>> b2.compute() == b.compute()
      True
      """
      collections, repack = unpack_collections(*args, **kwargs)
      if not collections:
          return args
  
      dsk = collections_to_dsk(collections, **kwargs)
      postpersists = [
          a.__dask_postpersist__() if is_dask_collection(a) else (None, a) for a in args
      ]
  
      keys, postpersists = [], []
      for a in collections:
          keys.extend(flatten(a.__dask_keys__()))
          postpersists.append(a.__dask_postpersist__())
  
      return repack([r(dsk, *s) for r, s in postpersists])
  
  
  def compute(*args, **kwargs):
      """Compute several dask collections at once.
  
      Parameters
      ----------
      args : object
          Any number of objects. If it is a dask object, it's computed and the
          result is returned. By default, python builtin collections are also
          traversed to look for dask objects (for more information see the
          ``traverse`` keyword). Non-dask arguments are passed through unchanged.
      traverse : bool, optional
          By default dask traverses builtin python collections looking for dask
          objects passed to ``compute``. For large collections this can be
          expensive. If none of the arguments contain any dask objects, set
          ``traverse=False`` to avoid doing this traversal.
      scheduler : string, optional
          Which scheduler to use like "threads", "synchronous" or "processes".
          If not provided, the default is to check the global settings first,
          and then fall back to the collection defaults.
      optimize_graph : bool, optional
          If True [default], the optimizations for each collection are applied
          before computation. Otherwise the graph is run as is. This can be
          useful for debugging.
      kwargs
          Extra keywords to forward to the scheduler function.
  
      Examples
      --------
      >>> import dask.array as da
      >>> a = da.arange(10, chunks=2).sum()
      >>> b = da.arange(10, chunks=2).mean()
      >>> compute(a, b)
      (45, 4.5)
  
      By default, dask objects inside python collections will also be computed:
  
      >>> compute({'a': a, 'b': b, 'c': 1})  # doctest: +SKIP
      ({'a': 45, 'b': 4.5, 'c': 1},)
      """
      traverse = kwargs.pop("traverse", True)
      optimize_graph = kwargs.pop("optimize_graph", True)
  
      collections, repack = unpack_collections(*args, traverse=traverse)
      if not collections:
          return args
  
      schedule = get_scheduler(
          scheduler=kwargs.pop("scheduler", None),
          collections=collections,
          get=kwargs.pop("get", None),
      )
  
      dsk = collections_to_dsk(collections, optimize_graph, **kwargs)
      keys = [x.__dask_keys__() for x in collections]
      postcomputes = [x.__dask_postcompute__() for x in collections]
      results = schedule(dsk, keys, **kwargs)
      return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
  
  
  def visualize(*args, **kwargs):
      """
      Visualize several dask graphs at once.
  
      Requires ``graphviz`` to be installed. All options that are not the dask
      graph(s) should be passed as keyword arguments.
  
      Parameters
      ----------
      dsk : dict(s) or collection(s)
          The dask graph(s) to visualize.
      filename : str or None, optional
          The name (without an extension) of the file to write to disk.  If
          `filename` is None, no file will be written, and we communicate
          with dot using only pipes.
      format : {'png', 'pdf', 'dot', 'svg', 'jpeg', 'jpg'}, optional
          Format in which to write output file.  Default is 'png'.
      optimize_graph : bool, optional
          If True, the graph is optimized before rendering.  Otherwise,
          the graph is displayed as is. Default is False.
      color: {None, 'order'}, optional
          Options to color nodes.  Provide ``cmap=`` keyword for additional
          colormap
      **kwargs
         Additional keyword arguments to forward to ``to_graphviz``.
  
      Examples
      --------
      >>> x.visualize(filename='dask.pdf')  # doctest: +SKIP
      >>> x.visualize(filename='dask.pdf', color='order')  # doctest: +SKIP
  
      Returns
      -------
      result : IPython.diplay.Image, IPython.display.SVG, or None
          See dask.dot.dot_graph for more information.
  
      See Also
      --------
      dask.dot.dot_graph
  
      Notes
      -----
      For more information on optimization see here:
  
      https://docs.dask.org/en/latest/optimize.html
      """
      from dask.dot import dot_graph
  
      filename = kwargs.pop("filename", "mydask")
      optimize_graph = kwargs.pop("optimize_graph", False)
  
      args2 = []
      for arg in args:
          if isinstance(arg, (list, tuple, set)):
              args2.extend(arg)
          else:
              args2.append(arg)
  
      dsks = [arg for arg in args2 if isinstance(arg, Mapping)]
      args3 = [arg for arg in args2 if is_dask_collection(arg)]
  
      dsk = dict(collections_to_dsk(args3, optimize_graph=optimize_graph))
      for d in dsks:
          dsk.update(d)
  
      color = kwargs.get("color")
  
      if color == "order":
          from .order import order
          import matplotlib.pyplot as plt
  
          o = order(dsk)
          try:
              cmap = kwargs.pop("cmap")
          except KeyError:
              cmap = plt.cm.RdBu
          if isinstance(cmap, str):
              import matplotlib.pyplot as plt
  
              cmap = getattr(plt.cm, cmap)
          mx = max(o.values()) + 1
          colors = {k: _colorize(cmap(v / mx, bytes=True)) for k, v in o.items()}
  
          kwargs["function_attributes"] = {
              k: {"color": v, "label": str(o[k])} for k, v in colors.items()
          }
          kwargs["data_attributes"] = {k: {"color": v} for k, v in colors.items()}
      elif color:
          raise NotImplementedError("Unknown value color=%s" % color)
  
      return dot_graph(dsk, filename=filename, **kwargs)
  
  
  def persist(*args, **kwargs):
      """ Persist multiple Dask collections into memory
  
      This turns lazy Dask collections into Dask collections with the same
      metadata, but now with their results fully computed or actively computing
      in the background.
  
      For example a lazy dask.array built up from many lazy calls will now be a
      dask.array of the same shape, dtype, chunks, etc., but now with all of
      those previously lazy tasks either computed in memory as many small :class:`numpy.array`
      (in the single-machine case) or asynchronously running in the
      background on a cluster (in the distributed case).
  
      This function operates differently if a ``dask.distributed.Client`` exists
      and is connected to a distributed scheduler.  In this case this function
      will return as soon as the task graph has been submitted to the cluster,
      but before the computations have completed.  Computations will continue
      asynchronously in the background.  When using this function with the single
      machine scheduler it blocks until the computations have finished.
  
      When using Dask on a single machine you should ensure that the dataset fits
      entirely within memory.
  
      Examples
      --------
      >>> df = dd.read_csv('/path/to/*.csv')  # doctest: +SKIP
      >>> df = df[df.name == 'Alice']  # doctest: +SKIP
      >>> df['in-debt'] = df.balance < 0  # doctest: +SKIP
      >>> df = df.persist()  # triggers computation  # doctest: +SKIP
  
      >>> df.value().min()  # future computations are now fast  # doctest: +SKIP
      -10
      >>> df.value().max()  # doctest: +SKIP
      100
  
      >>> from dask import persist  # use persist function on multiple collections
      >>> a, b = persist(a, b)  # doctest: +SKIP
  
      Parameters
      ----------
      *args: Dask collections
      scheduler : string, optional
          Which scheduler to use like "threads", "synchronous" or "processes".
          If not provided, the default is to check the global settings first,
          and then fall back to the collection defaults.
      traverse : bool, optional
          By default dask traverses builtin python collections looking for dask
          objects passed to ``persist``. For large collections this can be
          expensive. If none of the arguments contain any dask objects, set
          ``traverse=False`` to avoid doing this traversal.
      optimize_graph : bool, optional
          If True [default], the graph is optimized before computation.
          Otherwise the graph is run as is. This can be useful for debugging.
      **kwargs
          Extra keywords to forward to the scheduler function.
  
      Returns
      -------
      New dask collections backed by in-memory data
      """
      traverse = kwargs.pop("traverse", True)
      optimize_graph = kwargs.pop("optimize_graph", True)
  
      collections, repack = unpack_collections(*args, traverse=traverse)
      if not collections:
          return args
  
      schedule = get_scheduler(
          scheduler=kwargs.pop("scheduler", None), collections=collections
      )
  
      if inspect.ismethod(schedule):
          try:
              from distributed.client import default_client
          except ImportError:
              pass
          else:
              try:
                  client = default_client()
              except ValueError:
                  pass
              else:
                  if client.get == schedule:
                      results = client.persist(
                          collections, optimize_graph=optimize_graph, **kwargs
                      )
                      return repack(results)
  
      dsk = collections_to_dsk(collections, optimize_graph, **kwargs)
      keys, postpersists = [], []
      for a in collections:
          a_keys = list(flatten(a.__dask_keys__()))
          rebuild, state = a.__dask_postpersist__()
          keys.extend(a_keys)
          postpersists.append((rebuild, a_keys, state))
  
      results = schedule(dsk, keys, **kwargs)
      d = dict(zip(keys, results))
      results2 = [r({k: d[k] for k in ks}, *s) for r, ks, s in postpersists]
      return repack(results2)
  
  
  ############
  # Tokenize #
  ############
  
  
  def tokenize(*args, **kwargs):
      """ Deterministic token
  
      >>> tokenize([1, 2, '3'])
      '7d6a880cd9ec03506eee6973ff551339'
  
      >>> tokenize('Hello') == tokenize('Hello')
      True
      """
      if kwargs:
          args = args + (kwargs,)
      return md5(str(tuple(map(normalize_token, args))).encode()).hexdigest()
  
  
  normalize_token = Dispatch()
  normalize_token.register(
      (
          int,
          long,
          float,
          str,
          unicode,
          bytes,
          type(None),
          type,
          slice,
          complex,
          type(Ellipsis),
      ),
      identity,
  )
  
  
  @normalize_token.register(dict)
  def normalize_dict(d):
      return normalize_token(sorted(d.items(), key=str))
  
  
  @normalize_token.register(OrderedDict)
  def normalize_ordered_dict(d):
      return type(d).__name__, normalize_token(list(d.items()))
  
  
  @normalize_token.register(set)
  def normalize_set(s):
      return normalize_token(sorted(s, key=str))
  
  
  @normalize_token.register((tuple, list))
  def normalize_seq(seq):
      return type(seq).__name__, list(map(normalize_token, seq))
  
  
  @normalize_token.register(object)
  def normalize_object(o):
      method = getattr(o, "__dask_tokenize__", None)
      if method is not None:
          return method()
      return normalize_function(o) if callable(o) else uuid.uuid4().hex
  
  
  function_cache = {}
  function_cache_lock = threading.Lock()
  
  
  def normalize_function(func):
      try:
          return function_cache[func]
      except KeyError:
          result = _normalize_function(func)
          if len(function_cache) >= 500:  # clear half of cache if full
              with function_cache_lock:
                  if len(function_cache) >= 500:
                      for k in list(function_cache)[::2]:
                          del function_cache[k]
          function_cache[func] = result
          return result
      except TypeError:  # not hashable
          return _normalize_function(func)
  
  
  def _normalize_function(func):
      if isinstance(func, curry):
          func = func._partial
      if isinstance(func, Compose):
          first = getattr(func, "first", None)
          funcs = reversed((first,) + func.funcs) if first else func.funcs
          return tuple(normalize_function(f) for f in funcs)
      elif isinstance(func, partial):
          args = tuple(normalize_token(i) for i in func.args)
          if func.keywords:
              kws = tuple(
                  (k, normalize_token(v)) for k, v in sorted(func.keywords.items())
              )
          else:
              kws = None
          return (normalize_function(func.func), args, kws)
      else:
          try:
              result = pickle.dumps(func, protocol=0)
              if b"__main__" not in result:  # abort on dynamic functions
                  return result
          except Exception:
              pass
          try:
              import cloudpickle
  
              return cloudpickle.dumps(func, protocol=0)
          except Exception:
              return str(func)
  
  
  @normalize_token.register_lazy("pandas")
  def register_pandas():
      import pandas as pd
  
      @normalize_token.register(pd.Index)
      def normalize_index(ind):
          return [ind.name, normalize_token(ind.values)]
  
      @normalize_token.register(pd.Categorical)
      def normalize_categorical(cat):
          return [
              normalize_token(cat.codes),
              normalize_token(cat.categories),
              cat.ordered,
          ]
  
      @normalize_token.register(pd.Series)
      def normalize_series(s):
          return [
              s.name,
              s.dtype,
              normalize_token(s._data.blocks[0].values),
              normalize_token(s.index),
          ]
  
      @normalize_token.register(pd.DataFrame)
      def normalize_dataframe(df):
          data = [block.values for block in df._data.blocks]
          data += [df.columns, df.index]
          return list(map(normalize_token, data))
  
  
  @normalize_token.register_lazy("numpy")
  def register_numpy():
      import numpy as np
  
      @normalize_token.register(np.ndarray)
      def normalize_array(x):
          if not x.shape:
              return (str(x), x.dtype)
          if hasattr(x, "mode") and getattr(x, "filename", None):
              if hasattr(x.base, "ctypes"):
                  offset = (
                      x.ctypes.get_as_parameter().value
                      - x.base.ctypes.get_as_parameter().value
                  )
              elif hasattr(x, "offset"):
                  offset = getattr(x, "offset")
              else:
                  offset = 0  # root memmap's have mmap object as base
              return (
                  x.filename,
                  os.path.getmtime(x.filename),
                  x.dtype,
                  x.shape,
                  x.strides,
                  offset,
              )
          if x.dtype.hasobject:
              try:
                  try:
                      # string fast-path
                      data = hash_buffer_hex(
                          "-".join(x.flat).encode(
                              encoding="utf-8", errors="surrogatepass"
                          )
                      )
                  except UnicodeDecodeError:
                      # bytes fast-path
                      data = hash_buffer_hex(b"-".join(x.flat))
              except (TypeError, UnicodeDecodeError):
                  # object data w/o fast-path, use fast cPickle
                  try:
                      data = hash_buffer_hex(cPickle.dumps(x, cPickle.HIGHEST_PROTOCOL))
                  except Exception:
                      # pickling not supported, use UUID4-based fallback
                      data = uuid.uuid4().hex
          else:
              try:
                  data = hash_buffer_hex(x.ravel(order="K").view("i1"))
              except (BufferError, AttributeError, ValueError):
                  data = hash_buffer_hex(x.copy().ravel(order="K").view("i1"))
          return (data, x.dtype, x.shape, x.strides)
  
      @normalize_token.register(np.matrix)
      def normalize_matrix(x):
          return type(x).__name__, normalize_array(x.view(type=np.ndarray))
  
      normalize_token.register(np.dtype, repr)
      normalize_token.register(np.generic, repr)
  
      @normalize_token.register(np.ufunc)
      def normalize_ufunc(x):
          try:
              name = x.__name__
              if getattr(np, name) is x:
                  return "np." + name
          except AttributeError:
              return normalize_function(x)
  
  
  @normalize_token.register_lazy("scipy")
  def register_scipy():
      import scipy.sparse as sp
  
      def normalize_sparse_matrix(x, attrs):
          return (
              type(x).__name__,
              normalize_seq((normalize_token(getattr(x, key)) for key in attrs)),
          )
  
      for cls, attrs in [
          (sp.dia_matrix, ("data", "offsets", "shape")),
          (sp.bsr_matrix, ("data", "indices", "indptr", "blocksize", "shape")),
          (sp.coo_matrix, ("data", "row", "col", "shape")),
          (sp.csr_matrix, ("data", "indices", "indptr", "shape")),
          (sp.csc_matrix, ("data", "indices", "indptr", "shape")),
          (sp.lil_matrix, ("data", "rows", "shape")),
      ]:
          normalize_token.register(cls, partial(normalize_sparse_matrix, attrs=attrs))
  
      @normalize_token.register(sp.dok_matrix)
      def normalize_dok_matrix(x):
          return type(x).__name__, normalize_token(sorted(x.items()))
  
  
  def _colorize(t):
      """ Convert (r, g, b) triple to "#RRGGBB" string
  
      For use with ``visualize(color=...)``
  
      Examples
      --------
      >>> _colorize((255, 255, 255))
      '#FFFFFF'
      >>> _colorize((0, 32, 128))
      '#002080'
      """
      t = t[:3]
      i = sum(v * 256 ** (len(t) - i - 1) for i, v in enumerate(t))
      h = hex(int(i))[2:].upper()
      h = "0" * (6 - len(h)) + h
      return "#" + h
  
  
  named_schedulers = {
      "sync": local.get_sync,
      "synchronous": local.get_sync,
      "single-threaded": local.get_sync,
      "threads": threaded.get,
      "threading": threaded.get,
  }
  
  try:
      from dask import multiprocessing as dask_multiprocessing
  except ImportError:
      pass
  else:
      named_schedulers.update(
          {
              "processes": dask_multiprocessing.get,
              "multiprocessing": dask_multiprocessing.get,
          }
      )
  
  
  get_err_msg = """
  The get= keyword has been removed.
  
  Please use the scheduler= keyword instead with the name of
  the desired scheduler like 'threads' or 'processes'
  
      x.compute(scheduler='single-threaded')
      x.compute(scheduler='threads')
      x.compute(scheduler='processes')
  
  or with a function that takes the graph and keys
  
      x.compute(scheduler=my_scheduler_function)
  
  or with a Dask client
  
      x.compute(scheduler=client)
  """.strip()
  
  
  def get_scheduler(get=None, scheduler=None, collections=None, cls=None):
      """ Get scheduler function
  
      There are various ways to specify the scheduler to use:
  
      1.  Passing in scheduler= parameters
      2.  Passing these into global confiuration
      3.  Using defaults of a dask collection
  
      This function centralizes the logic to determine the right scheduler to use
      from those many options
      """
      if get:
          raise TypeError(get_err_msg)
  
      if scheduler is not None:
          if callable(scheduler):
              return scheduler
          elif "Client" in type(scheduler).__name__ and hasattr(scheduler, "get"):
              return scheduler.get
          elif scheduler.lower() in named_schedulers:
              return named_schedulers[scheduler.lower()]
          elif scheduler.lower() in ("dask.distributed", "distributed"):
              from distributed.worker import get_client
  
              return get_client().get
          elif scheduler.lower() in ["processes", "multiprocessing"]:
              raise ValueError(
                  "Please install cloudpickle to use the '%s' scheduler." % scheduler
              )
          else:
              raise ValueError(
                  "Expected one of [distributed, %s]"
                  % ", ".join(sorted(named_schedulers))
              )
          # else:  # try to connect to remote scheduler with this name
          #     return get_client(scheduler).get
  
      if config.get("scheduler", None):
          return get_scheduler(scheduler=config.get("scheduler", None))
  
      if config.get("get", None):
          raise ValueError(get_err_msg)
  
      if getattr(thread_state, "key", False):
          from distributed.worker import get_worker
  
          return get_worker().client.get
  
      if cls is not None:
          return cls.__dask_scheduler__
  
      if collections:
          collections = [c for c in collections if c is not None]
      if collections:
          get = collections[0].__dask_scheduler__
          if not all(c.__dask_scheduler__ == get for c in collections):
              raise ValueError(
                  "Compute called on multiple collections with "
                  "differing default schedulers. Please specify a "
                  "scheduler=` parameter explicitly in compute or "
                  "globally with `dask.config.set`."
              )
          return get
  
      return None
  
  
  def wait(x, timeout=None, return_when="ALL_COMPLETED"):
      """ Wait until computation has finished
  
      This is a compatibility alias for ``dask.distributed.wait``.
      If it is applied onto Dask collections without Dask Futures or if Dask
      distributed is not installed then it is a no-op
      """
      try:
          from distributed import wait
  
          return wait(x, timeout=timeout, return_when=return_when)
      except (ImportError, ValueError):
          return x
  