text: # Copyright 2018 The TensorFlow Probability Authors.
  #
  # 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.
  # ============================================================================
  """Experimental Numpy backend."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import functools
  
  import numpy as np
  
  # pylint: disable=unused-import
  from tensorflow_probability.python.internal.backend.numpy import __internal__
  from tensorflow_probability.python.internal.backend.numpy import _utils as utils
  from tensorflow_probability.python.internal.backend.numpy import bitwise
  from tensorflow_probability.python.internal.backend.numpy import config
  from tensorflow_probability.python.internal.backend.numpy import debugging
  from tensorflow_probability.python.internal.backend.numpy import errors
  from tensorflow_probability.python.internal.backend.numpy import keras
  from tensorflow_probability.python.internal.backend.numpy import linalg
  from tensorflow_probability.python.internal.backend.numpy import nest
  from tensorflow_probability.python.internal.backend.numpy import nn
  from tensorflow_probability.python.internal.backend.numpy import numpy_logging as logging
  from tensorflow_probability.python.internal.backend.numpy import numpy_math as math
  from tensorflow_probability.python.internal.backend.numpy import numpy_signal as signal
  from tensorflow_probability.python.internal.backend.numpy import random_generators as random
  from tensorflow_probability.python.internal.backend.numpy import raw_ops
  from tensorflow_probability.python.internal.backend.numpy import sets_lib as sets
  from tensorflow_probability.python.internal.backend.numpy import sparse_lib as sparse
  from tensorflow_probability.python.internal.backend.numpy import test_lib as test
  from tensorflow_probability.python.internal.backend.numpy.control_flow import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.dtype import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.functional_ops import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.gen.tensor_shape import TensorShape
  from tensorflow_probability.python.internal.backend.numpy.misc import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.numpy_array import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.ops import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.tensor_array_ops import TensorArray
  # pylint: enable=unused-import
  
  
  JAX_MODE = False
  
  
  Assert = debugging.Assert
  
  
  def _function(func=None, input_signature=None, autograph=True,  # pylint: disable=unused-argument
                experimental_autograph_options=None,  # pylint: disable=unused-argument
                experimental_relax_shapes=False, jit_compile=None):  # pylint: disable=unused-argument
    """Like `tf.function`, for JAX."""
    transform = lambda fn: fn
    if jit_compile:
      if JAX_MODE:
        from jax import jit  # pylint: disable=g-import-not-at-top
  
        def non_jittable(arg):
          # Use static args for callables and for bools, which will sometimes
          # be used in a `if` block and fail if they are tracers.
          return (arg is not None and
                  (callable(arg) or np.asarray(arg).dtype == np.bool))
  
        def jit_decorator(f):
          cache = {}
  
          def jit_wrapper(*args, **kwargs):
  
            @functools.wraps(f)
            def unflatten_f(*args_flat):
              unflat_args, unflat_kwargs = nest.pack_sequence_as(
                  (args, kwargs), args_flat)
              return f(*unflat_args, **unflat_kwargs)
  
            args_flat = nest.flatten((args, kwargs))
            static_argnums = tuple(
                i for (i, arg) in enumerate(args_flat) if non_jittable(arg))
            cache_key = (static_argnums, len(args), tuple(kwargs.keys()))
            if cache.get(cache_key, None) is None:
              cache[cache_key] = jit(unflatten_f, static_argnums=static_argnums)
            return cache[cache_key](*args_flat)
  
          return jit_wrapper
  
        transform = jit_decorator
      else:
  
        # The decoration will succeed, but calling such a function will fail. This
        # allows us to have jitted top-level functions in a module, as long as
        # they aren't called in Numpy mode.
        def decorator(f):
          @functools.wraps(f)
          def wrapped_f(*args, **kwargs):
            raise NotImplementedError('Could not find compiler: Numpy only.')
          return wrapped_f
  
        transform = decorator
    # This code path is for the `foo = tf.function(foo, ...)` use case.
    if func is not None:
      return transform(func)
    # This code path is for the following use case:
    #   @tf.function(...)
    #   def foo(...):
    #      ...
    # This case is equivalent to `foo = tf.function(...)(foo)`.
    return transform
  
  
  class _SingleReplicaContext(object):
    """Dummy replica context for numpy."""
  
    @property
    def replica_id_in_sync_group(self):
      if JAX_MODE:
        raise NotImplementedError
      return 0
  
    @property
    def num_replicas_in_sync(self):
      if JAX_MODE:
        raise NotImplementedError
      return 1
  
  
  # --- Begin Public Functions --------------------------------------------------
  
  
  compat = collections.namedtuple('compat', 'dimension_value')(
      lambda dim: None if dim is None else int(dim))
  
  distribute = collections.namedtuple('distribute', 'get_replica_context')(
      _SingleReplicaContext)
  
  function = utils.copy_docstring(
      'tf.function',
      _function)
  
  eye = linalg.eye
  matmul = linalg.matmul
  
  del collections, utils
  