Do not use or import the ABCs from `collections`, this is deprecated since Python 3.3, and in 3.9 it will stop working. These ABCs have moved to 'collections.abc' and should be used from this package.

PiperOrigin-RevId: 340678321

Instead insert a `tf.function` decorator before (underneath) the
`tff.tf_computation` decorator, and enter a `tf.init_scope` context during
`tf.Variable` creation. The `tf.init_scope` hoists variable creation out of the
`tf.function` to the nearest graph scope (provided by `tff.tf_comptuation`),
which would otherwise result in an error from the the `tf.function` wrapper.

More details on `tf.Variable` in `tf.function`:
https://www.tensorflow.org/guide/function#variables

Longer term, it may be nice to fold this automatic wrapping into the
`tff.tf_computation` decorator, or re-introduce as `tff.tf2_computation`,
however this requires all users to know they must use `tf.init_scope` during
variable creation. Inside TFF packages (e.g.  `tff.learning`) we can handle
this for them, but its an extra point of concern for custom algorithm writers.

PiperOrigin-RevId: 337935371

Fixes pytype mismatch in optimizer_utils, adding extra typing info in federated_averaging and federated_sgd.

PiperOrigin-RevId: 336957857

the `tff.templates.AggregationProcess` used with `tff.learning`.

For now allow usage of either `tff.templates.MeasuredProcess` via the
`aggregation_process` parameter, or a `tff.aggregators.Factory` via the
`model_update_aggregation_factory` parameter, but not both.

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Otherwise writers can potentially chose key lookup (e.g. OrderedDict) or
attribute lookup (attr.s, namedtuple) objects that both adhere to the TFF type
specification but must be handled different by library writers.

PiperOrigin-RevId: 329962347

instead of the package import of `core`.

This will greatly help Bazel prune the build graph, avoiding to need to build,
test or run the entire tree for small changes.

PiperOrigin-RevId: 324953948

PiperOrigin-RevId: 323045971

Previously, NamedTupleTypeWithPyContainerType was often lost and turned into
a NamedTupleType without the container. This resulted in users being given
AnonymousTuples when a more specific container should have been returned.

This change fixes a large number of sites where container types were lost, and
adjusts usage sites as appropriate, including the removal of `from_tff_result`
functions.

This change also makes the `__eq__` function for
`NamedTupleTypeWithPyContainerType` require equivalent container types, rather
than just equivalent field structure. Call sites that wished to compare only
field structure are adjusted to use `Type.{is, check}_equivalent_to`.

PiperOrigin-RevId: 321883012

PiperOrigin-RevId: 321431928

Introduce a new `weights_type_from_model` helper function for creating `tff.Type` from a `tff.learning.Model`.

PiperOrigin-RevId: 318097117

Update `build_federated_averaging_process` and `build_federated_sgd_process` to return the full metrics suite.

After this change, users of `tff.learning.build_federated_averaging_process`
(and the FedSGD version) will receives a named tuple of three groups of metrics: broadcast, train, and aggregation.

- `train` are the same metrics that were previously returned.
- `broadcast` and `aggregation` are metrics for the broadcast and aggregatin
  process respectively. For example, an aggregation process might report
  metrics about the client updates and the aggregated update (e.g. the global
  norms).

Previously these methods only extracted the training metrics to retain
historical behavior. This change updates the methods and callsites to return
the full metrics set (including those from the broadcast and aggregation
processes).

PiperOrigin-RevId: 317888544

Extend the interface of `model_delta_optimizer_process` to accept the new `tff.templates.MeasuredProcess` for broadcast and aggregate functions.

Changes to `model_delta_optimizer_process`:
- Wrap the previous `StatefulFn` arguments in `tff.templates.MeasuredProcess`
  and only use `MeasuredProcess` internally.
- Change the metrics out put to be a namespace separated metrics concatenation:
  `train`, `broadcast` and `aggregation`.

Changes to dependents on `model_delta_optimizer_process`:
-  Change `bulid_federated_averaging_process` and `build_federated_sgd_process`
   to only output the `train` metrics to retain current behavior.
-  Update clipping mean to zip its state on return. Future CLs will convert
   this to a `MeasuredProcess`.
-  Remove default specification of broadcast in robust_federated_aggregation
   reserach.

PiperOrigin-RevId: 317305758

This in in advance of accepting a `tff.utils.MeasuredProcess` for the broadcast
and aggregate computations, which will allow easier composability of metrics
into the final output.

PiperOrigin-RevId: 315414970

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PiperOrigin-RevId: 313419009

PiperOrigin-RevId: 306446003

Migrates `cmp` arguments in attr.s() to `eq` and `order` arguments:
1. If `cmp=False`, replacing it with `eq=False`.
2. If `cmp=True`, replacing it with `eq=True, order=True`.

PiperOrigin-RevId: 294921678

Make `tff.learning.state_with_new_model_weights` consist with previous changes to make ModelWeights unordered.

PiperOrigin-RevId: 294256825

PiperOrigin-RevId: 293243832

Remove a stale comment, we plan to keep federated_map to make it easier to e.g. support tf.functions directly.

PiperOrigin-RevId: 293062407

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PiperOrigin-RevId: 285116495

de-duplication strategy in non-eager mode.

TF, under certain circumstances, prepends `_#` numbers to variables with the
same name. This appears to happen in non-eager mode and in some occasions with
Keras. TFF previously used these names for the type signature, which would
cause problems when the same model was created more than once in the same graph
context.

Instead, rely on the ordering of the sequence of variables contained in the
model and use the unnamed structure for types.

PiperOrigin-RevId: 285032395

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Convert usages of `tf.assign(variable, tesnor)` to `variable.assign(tensor)` to be TF2.0 compatible.

PiperOrigin-RevId: 253102645

- Move `tf_computation` definitions outside the `federated_computations`, they do not need to be nested.
- Making `tf_computation` naming consistent with prefixing by `tf_`

PiperOrigin-RevId: 252691185

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