BVh 2dZddlZddlZddlZddlmZddlmZddl m Z ddl m Z ddl m Z ddl mZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddlm"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z)ddl*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1dZ2d Z3dZ4d!Z5d"Z6d#Z7e jpd$e1d%g&Gd'd(e)jrZ:da;ejxZ=d)Z>e1d%g&Gd*d+e/jte)jrej~ej,ZAe jpd$Gd-d.eAZBe1d/g&Gd0d1ejZDGd2d3e+jZFe&je&jeDejje-jdd4e1d5d6ZLe jy)7zPython wrappers for Iterators.N) struct_pb2)ag_ctx)saveable_compat)iterator_autograph) optional_ops)options)nest) structure)context)composite_tensor)dtypes)errors)ops)tensor) tensor_shape) type_spec) type_utils)array_ops_stack)gen_dataset_ops) parsing_ops) string_ops)ragged_string_ops)nested_structure_coder)base)BaseSaverBuilder) deprecation)collections_abc) tf_export aAn unusually high number of `Iterator.get_next()` calls was detected. This often indicates that `Iterator.get_next()` is being called inside a training loop, which will cause gradual slowdown and eventual resource exhaustion. If this is the case, restructure your code to call `next_element = iterator.get_next()` once outside the loop, and use `next_element` as the input to some computation that is invoked inside the loop.zAn unusually high number of `tf.data.Iterator.get_next()` calls was detected. This suggests that the `for elem in dataset: ...` idiom is used within tf.function with AutoGraph disabled. This idiom is only supported when AutoGraph is enabled. iteratorsctjr tjjduStjj }t | SN)r executing_eagerly device_namerget_default_graph _device_functions_outer_to_innerbool) device_stacks W/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/data/ops/iterator_ops.py_device_stack_is_emptyr*UsH  ??  ( (D 00&&(II, ,  ITERATORz data.Iterator)v1cBeZdZdZdZe ddZe ddZedZ ddZ ddZ d Z dd Z eejdd d Zeejdd dZeejdddZedZdZdZy)Iteratorz6Represents the state of iterating through a `Dataset`.c ||_||_|||td|d|d|dtj||||_tj |j |_tj|j |_ tj|j|_ d|_ tjt |jy)aCreates a new iterator from the given iterator resource. Note: Most users will not call this initializer directly, and will instead use `Dataset.make_initializable_iterator()` or `Dataset.make_one_shot_iterator()`. Args: iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the iterator. initializer: A `tf.Operation` that should be run to initialize this iterator. output_types: A (nested) structure of `tf.DType` objects corresponding to each component of an element of this iterator. output_shapes: A (nested) structure of `tf.TensorShape` objects corresponding to each component of an element of this iterator. output_classes: A (nested) structure of Python `type` objects corresponding to each component of an element of this iterator. Raises: TypeError: If `output_types`, `output_shapes`, or `output_classes` is not specified. Nz{All of `output_types`, `output_shapes`, and `output_classes` must be specified to create an iterator. Got `output_types` = z, `output_shapes` = z, `output_classes` = .r)_iterator_resource _initializer ValueErrorr convert_legacy_structure _element_specget_flat_tensor_shapes_flat_tensor_shapesget_flat_tensor_types_flat_tensor_typesriterator_to_string_handle_string_handle_get_next_call_countradd_to_collectionGLOBAL_ITERATORS)selfiterator_resource initializer output_types output_shapesoutput_classess r)__init__zIterator.__init__cs00D#D 5  !  *-.,/0 .1  4 55 #;;m^5D(??  D'== D*CC !D !D*D,C,CDr+Nctjtj|}|tjd|}n%tj|t j |}|tjd|}tj||tj|||}|d}tjd|tj|tj|}t|d|||S)a Creates a new, uninitialized `Iterator` with the given structure. This iterator-constructing method can be used to create an iterator that is reusable with many different datasets. The returned iterator is not bound to a particular dataset, and it has no `initializer`. To initialize the iterator, run the operation returned by `Iterator.make_initializer(dataset)`. The following is an example ```python iterator = Iterator.from_structure(tf.int64, tf.TensorShape([])) dataset_range = Dataset.range(10) range_initializer = iterator.make_initializer(dataset_range) dataset_evens = dataset_range.filter(lambda x: x % 2 == 0) evens_initializer = iterator.make_initializer(dataset_evens) # Define a model based on the iterator; in this example, the model_fn # is expected to take scalar tf.int64 Tensors as input (see # the definition of 'iterator' above). prediction, loss = model_fn(iterator.get_next()) # Train for `num_epochs`, where for each epoch, we first iterate over # dataset_range, and then iterate over dataset_evens. for _ in range(num_epochs): # Initialize the iterator to `dataset_range` sess.run(range_initializer) while True: try: pred, loss_val = sess.run([prediction, loss]) except tf.errors.OutOfRangeError: break # Initialize the iterator to `dataset_evens` sess.run(evens_initializer) while True: try: pred, loss_val = sess.run([prediction, loss]) except tf.errors.OutOfRangeError: break ``` Args: output_types: A (nested) structure of `tf.DType` objects corresponding to each component of an element of this dataset. output_shapes: (Optional.) A (nested) structure of `tf.TensorShape` objects corresponding to each component of an element of this dataset. If omitted, each component will have an unconstrainted shape. shared_name: (Optional.) If non-empty, this iterator will be shared under the given name across multiple sessions that share the same devices (e.g. when using a remote server). output_classes: (Optional.) A (nested) structure of Python `type` objects corresponding to each component of an element of this iterator. If omitted, each component is assumed to be of type `tf.Tensor`. Returns: An `Iterator`. Raises: TypeError: If the structures of `output_shapes` and `output_types` are not the same. Nc,tjdSr"r TensorShape_s r)z)Iterator.from_structure..L,,T2r+c"tjSr"rTensorrKs r)rMz)Iterator.from_structure.. FMMr+) container shared_namerCrD)r map_structurer as_dtypemap_structure_up_toras_shapeassert_same_structurer r5r iterator_v2r9r7r/)rCrDrUrEoutput_structurerAs r)from_structurezIterator.from_structuresL%%foo|DL(( 2LBm..|/;/D/D/<>m))*A<Pn|]; 99m^5k'33445EF66   %t\=" $$r+c8tjtj|}|tjd|}n%tj|t j |}|tjd|}tj||tj|||}tj|tj}tj|tj|tj |}t#|d|||S)aCreates a new, uninitialized `Iterator` based on the given handle. This method allows you to define a "feedable" iterator where you can choose between concrete iterators by feeding a value in a `tf.Session.run` call. In that case, `string_handle` would be a `tf.compat.v1.placeholder`, and you would feed it with the value of `tf.data.Iterator.string_handle` in each step. For example, if you had two iterators that marked the current position in a training dataset and a test dataset, you could choose which to use in each step as follows: ```python train_iterator = tf.data.Dataset(...).make_one_shot_iterator() train_iterator_handle = sess.run(train_iterator.string_handle()) test_iterator = tf.data.Dataset(...).make_one_shot_iterator() test_iterator_handle = sess.run(test_iterator.string_handle()) handle = tf.compat.v1.placeholder(tf.string, shape=[]) iterator = tf.data.Iterator.from_string_handle( handle, train_iterator.output_types) next_element = iterator.get_next() loss = f(next_element) train_loss = sess.run(loss, feed_dict={handle: train_iterator_handle}) test_loss = sess.run(loss, feed_dict={handle: test_iterator_handle}) ``` Args: string_handle: A scalar `tf.Tensor` of type `tf.string` that evaluates to a handle produced by the `Iterator.string_handle()` method. output_types: A (nested) structure of `tf.DType` objects corresponding to each component of an element of this dataset. output_shapes: (Optional.) A (nested) structure of `tf.TensorShape` objects corresponding to each component of an element of this dataset. If omitted, each component will have an unconstrainted shape. output_classes: (Optional.) A (nested) structure of Python `type` objects corresponding to each component of an element of this iterator. If omitted, each component is assumed to be of type `tf.Tensor`. Returns: An `Iterator`. Nc,tjdSr"rIrKs r)rMz-Iterator.from_string_handle..%rNr+c"tjSr"rPrKs r)rMz-Iterator.from_string_handle..+rRr+)dtyperCrD)r rVr rWrXrrYrZr r5rconvert_to_tensorstringriterator_from_string_handle_v2r9r7r/) string_handlerCrDrEr\rAs r)from_string_handlezIterator.from_string_handlesd%%foo|DL(( 2LBm..|/;/D/D/<>m))*A<Pn|]; 99m^5))-v}}MM'FF445EF667GHJ %t\=" $$r+cH|j |jStd)zA `tf.Operation` that should be run to initialize this iterator. Returns: A `tf.Operation` that should be run to initialize this iterator Raises: ValueError: If this iterator initializes itself automatically. zThe iterator does not have an initializer. This means it was likely created using `tf.data.Dataset.make_one_shot_iterator()`. For an initializable iterator, use `tf.data.Dataset.make_initializable_iterator()` instead.)r3r4r@s r)rBzIterator.initializer7s3 $     E FFr+c tj|d5}tjd|j}tjd|j}tjd|j}tj |j |tj |j|ttj|jtj|D]%\}}||us td|jd|dttj|j tj|D]&\}} || k7s td|j d |dttj|jtj|D]2\} } | j| rtd |jd |d d d d tj|j5tj |j"|j| cd d d S#1swY^xYw#1swYy xYw)aReturns a `tf.Operation` that initializes this iterator on `dataset`. Args: dataset: A `Dataset` whose `element_spec` if compatible with this iterator. name: (Optional.) A name for the created operation. Returns: A `tf.Operation` that can be run to initialize this iterator on the given `dataset`. Raises: TypeError: If `dataset` and this iterator do not have a compatible `element_spec`. make_initializerc"|jSr"_to_legacy_output_typescomponent_specs r)rMz+Iterator.make_initializer..as!G!G!Ir+c"|jSr"_to_legacy_output_shapesros r)rMz+Iterator.make_initializer..ds!H!H!Jr+c"|jSr"_to_legacy_output_classesros r)rMz+Iterator.make_initializer..gs!I!I!Kr+zExpected output classes z% but got dataset with output classes r1zExpected output types z# but got dataset with output types z'Expected output shapes compatible with z$ but got dataset with output shapes Nname)r name_scoper rV element_specrZrCrDzipflattenrE TypeErroris_compatible_with colocate_withr2r make_iterator_variant_tensor) r@datasetrxdataset_output_typesdataset_output_shapesdataset_output_classesiterator_class dataset_classiterator_dtype dataset_dtypeiterator_shape dataset_shapes r)rkzIterator.make_initializerLsw 0 1$PT"// I    #00 J     $11 K        !2!24HI   !3!35JK+. ,,t** + ,,- .,0J '.-  .()<)<(?@--C,FaIJ J J,/ ,,t(( )4<<8L+M,O> '.- ] *&t'8'8&;<##7":!=> >> ,/ ,,t)) *DLL#-%,&P '.-00?78J8J7MN44I3LAOP P P=$PN  422 3G  * *  ! !4#:#:GGGO$P$PNGGs,C*IA$I'A0II,III(c|xjdz c_|jtkDrtjtt j |j5tj|j|j|j|}tj|j|cdddS#1swYyxYw)aReturns the next element. In graph mode, you should typically call this method *once* and use its result as the input to another computation. A typical loop will then call `tf.Session.run` on the result of that computation. The loop will terminate when the `Iterator.get_next()` operation raises `tf.errors.OutOfRangeError`. The following skeleton shows how to use this method when building a training loop: ```python dataset = ... # A `tf.data.Dataset` object. iterator = dataset.make_initializable_iterator() next_element = iterator.get_next() # Build a TensorFlow graph that does something with each element. loss = model_function(next_element) optimizer = ... # A `tf.compat.v1.train.Optimizer` object. train_op = optimizer.minimize(loss) with tf.compat.v1.Session() as sess: try: while True: sess.run(train_op) except tf.errors.OutOfRangeError: pass ``` NOTE: It is legitimate to call `Iterator.get_next()` multiple times, e.g. when you are distributing different elements to multiple devices in a single step. However, a common pitfall arises when users call `Iterator.get_next()` in each iteration of their training loop. `Iterator.get_next()` adds ops to the graph, and executing each op allocates resources (including threads); as a consequence, invoking it in every iteration of a training loop causes slowdown and eventual resource exhaustion. To guard against this outcome, we log a warning when the number of uses crosses a fixed threshold of suspiciousness. Args: name: (Optional.) A name for the created operation. Returns: A (nested) structure of values matching `tf.data.Iterator.element_spec`. )rCrDrxN)r=GET_NEXT_CALL_WARNING_THRESHOLDwarningswarnGET_NEXT_CALL_WARNING_MESSAGErrr2riterator_get_nextr:r8r from_tensor_listr6)r@rxflat_rets r)get_nextzIterator.get_nextsX "   #BBmm12  422 3F 22  ! !..00 h  ' '(:(:H EFFFs !ACC c `tj|j5tjt j |jtj|jtj|j|jcdddS#1swYyxYwNrb rrr2r _OptionalImplriterator_get_next_as_optionalr r9rzr7ris r)get_next_as_optionalzIterator.get_next_as_optional  422 3:  ' '  7 7%%$::4;L;LM%<<##% &(,'8'8 :::: A:B$$B-c`| |jStj|j|S)zReturns a string-valued `tf.Tensor` that represents this iterator. Args: name: (Optional.) A name for the created operation. Returns: A scalar `tf.Tensor` of type `tf.string`. rw)r<rr;r2)r@rxs r)rfzIterator.string_handles3 |    6 6  ! !..r+5Use `tf.compat.v1.data.get_output_classes(iterator)`.cDtjd|jS)Returns the class of each component of an element of this iterator. The expected values are `tf.Tensor` and `tf.sparse.SparseTensor`. Returns: A (nested) structure of Python `type` objects corresponding to each component of an element of this dataset. c"|jSr"ruros r)rMz)Iterator.output_classes..~GGIr+r rVr6ris r)rEzIterator.output_classes#   I  r+4Use `tf.compat.v1.data.get_output_shapes(iterator)`.cDtjd|jS)Returns the shape of each component of an element of this iterator. Returns: A (nested) structure of `tf.TensorShape` objects corresponding to each component of an element of this dataset. c"|jSr"rrros r)rMz(Iterator.output_shapes..~FFHr+rris r)rDzIterator.output_shapes#   H  r+3Use `tf.compat.v1.data.get_output_types(iterator)`.cDtjd|jS)Returns the type of each component of an element of this iterator. Returns: A (nested) structure of `tf.DType` objects corresponding to each component of an element of this dataset. c"|jSr"rmros r)rMz'Iterator.output_types..~EEGr+rris r)rCzIterator.output_types#   G  r+c|jS)aVThe type specification of an element of this iterator. For more information, read [this guide](https://www.tensorflow.org/guide/data#dataset_structure). Returns: A (nested) structure of `tf.TypeSpec` objects matching the structure of an element of this iterator and specifying the type of individual components. r6ris r)rzzIterator.element_specs   r+ctj|jtjj j }d|iSN_STATE)rserialize_iteratorr2 options_libExternalStatePolicyFAILvaluer@serialized_iterators r)_serialize_to_tensorszIterator._serialize_to_tensorss?)<< '',,224 ) **r+ctj|j5tj|j|dgcdddS#1swYyxYwrrrr2rdeserialize_iteratorr@restored_tensorss r)_restore_from_tensorszIterator._restore_from_tensorsT  422 3@22  ! !#3H#=?@@@@ $AANNN)NNr")__name__ __module__ __qualname____doc__rF staticmethodr]rgpropertyrBrkrrrfr deprecatedrErDrCrzrrr+r)r/r/^s-?-E^#'!%$([$[$z(,(,D$D$L F F(:Gx8Ft : . ; CE E   ; BD D   ; AC C      +@r+r/czt5t}tdz addddj|S#1swYxYw)Nrz{}{}) _uid_lock _uid_counterformat)prefixuids r)_generate_shared_namer(s< CAL vs ## s1:c|eZdZdZej dZejdZejdZ y) IteratorBaseaRepresents an iterator of a `tf.data.Dataset`. `tf.data.Iterator` is the primary mechanism for enumerating elements of a `tf.data.Dataset`. It supports the Python Iterator protocol, which means it can be iterated over using a for-loop: >>> dataset = tf.data.Dataset.range(2) >>> for element in dataset: ... print(element) tf.Tensor(0, shape=(), dtype=int64) tf.Tensor(1, shape=(), dtype=int64) or by fetching individual elements explicitly via `get_next()`: >>> dataset = tf.data.Dataset.range(2) >>> iterator = iter(dataset) >>> print(iterator.get_next()) tf.Tensor(0, shape=(), dtype=int64) >>> print(iterator.get_next()) tf.Tensor(1, shape=(), dtype=int64) In addition, non-raising iteration is supported via `get_next_as_optional()`, which returns the next element (if available) wrapped in a `tf.experimental.Optional`. >>> dataset = tf.data.Dataset.from_tensors(42) >>> iterator = iter(dataset) >>> optional = iterator.get_next_as_optional() >>> print(optional.has_value()) tf.Tensor(True, shape=(), dtype=bool) >>> optional = iterator.get_next_as_optional() >>> print(optional.has_value()) tf.Tensor(False, shape=(), dtype=bool) ctd)aThe type specification of an element of this iterator. >>> dataset = tf.data.Dataset.from_tensors(42) >>> iterator = iter(dataset) >>> iterator.element_spec tf.TensorSpec(shape=(), dtype=tf.int32, name=None) For more information, read [this guide](https://www.tensorflow.org/guide/data#dataset_structure). Returns: A (nested) structure of `tf.TypeSpec` objects matching the structure of an element of this iterator, specifying the type of individual components. zIterator.element_specNotImplementedErrorris r)rzzIteratorBase.element_specYs 5 66r+ctd)ayReturns the next element. >>> dataset = tf.data.Dataset.from_tensors(42) >>> iterator = iter(dataset) >>> print(iterator.get_next()) tf.Tensor(42, shape=(), dtype=int32) Returns: A (nested) structure of values matching `tf.data.Iterator.element_spec`. Raises: `tf.errors.OutOfRangeError`: If the end of the iterator has been reached. zIterator.get_next()rris r)rzIteratorBase.get_nextks 3 44r+ctd)aReturns the next element wrapped in `tf.experimental.Optional`. If the iterator has reached the end of the sequence, the returned `tf.experimental.Optional` will have no value. >>> dataset = tf.data.Dataset.from_tensors(42) >>> iterator = iter(dataset) >>> optional = iterator.get_next_as_optional() >>> print(optional.has_value()) tf.Tensor(True, shape=(), dtype=bool) >>> print(optional.get_value()) tf.Tensor(42, shape=(), dtype=int32) >>> optional = iterator.get_next_as_optional() >>> print(optional.has_value()) tf.Tensor(False, shape=(), dtype=bool) Returns: A `tf.experimental.Optional` object representing the next element. zIterator.get_next_as_optional()rris r)rz!IteratorBase.get_next_as_optional|s* ? @@r+N) rrrrabcabstractpropertyrzabstractmethodrrrr+r)rr0sX !F77"55 AAr+r) metaclasscFeZdZdZdfd ZdZdZdZdZdZ d Z e d Z d Z e ejdd d Ze ejdddZe ejdddZe dZdZdZdZdZdZdZxZS) OwnedIteratora^An iterator producing tf.Tensor objects from a tf.data.Dataset. The iterator resource created through `OwnedIterator` is owned by the Python object and the life time of the underlying resource is tied to the life time of the `OwnedIterator` object. This makes `OwnedIterator` appropriate for use in eager mode and inside of tf.functions. Nchtt| |v|| td||_t j |j|_t j|j|_ ||_ |\|_ d|_ y|| td|j|d|_ y)a'Creates a new iterator from the given dataset. If `dataset` is not specified, the iterator will be created from the given tensor components and element structure. In particular, the alternative for constructing the iterator is used when the iterator is reconstructed from it `CompositeTensor` representation. Args: dataset: A `tf.data.Dataset` object. components: Tensor components to construct the iterator from. element_spec: A (nested) structure of `TypeSpec` objects that represents the type specification of elements of the iterator. Raises: ValueError: If `dataset` is not provided and either `components` or `element_spec` is not provided. Or `dataset` is provided and either `components` and `element_spec` is provided. NzWWhen `dataset` is not provided, both `components` and `element_spec` must be specified.zRWhen `dataset` is provided, `element_spec` and `components` must not be specified.r)superrrFr4r6r r9_flat_output_typesr7_flat_output_shapes _componentsr2_create_iteratorr=)r@r componentsrz __class__s r)rFzOwnedIterator.__init__s& -')   4 01 1(d ) ? ?   !d!*!A!A   "d#d!+d!"D  L$< ! ! G$ !Dr+c.|j}||_|j}|j|_t j |j|_t j|j|_ tj|5tj|j|j|_tj st#j$|j}t'|j(dj(dj(t'|jk(sJ|jj*j-|tj.||jdddy#1swYyxYw)Nrbr)_apply_debug_options_datasetrrzr6r r9rr7rrrranonymous_iterator_v3r2r r#riterator_full_type_from_speclenargsopexperimental_set_typer)r@r ds_variantfulltypes r)rzOwnedIterator._create_iteratorsU**,G DM((J --D'== D(??  D  : &I  / /22 44 6  & & (::    8==#((+001S  # #6%% %% ""88B##J0G0GH'IIIs C0F  Fc|Sr"rris r)__iter__zOwnedIterator.__iter__s Kr+c"|jSr")__next__ris r)nextzOwnedIterator.nexts ==?r+ctjj}|tjjk(}t j s9|r7|xjdz c_|jtkDrttt j stj|j5tj|j|j |j"}t%j&|j(|cdddSt j*t j,5tj|j|j |j"} |j(j/|cdddS#1swYxYw#t0$r,t%j&|j(|cYcdddSwxYw#1swYyxYw)Nrrb) autograph_ctxcontrol_status_ctxstatusStatusDISABLEDr r#r=GET_NEXT_CALL_ERROR_THRESHOLDr4GET_NEXT_CALL_ERROR_MESSAGErrr2rrrrr from_compatible_tensor_listr6execution_modeSYNC_from_compatible_tensor_listAttributeError)r@autograph_statusautograph_disabledrets r)_next_internalzOwnedIterator._next_internals$779@@)]-A-A-J-JJ  $ $ &+= 1$ " "%B B455  $ $ &   T44 5N//  # #0022444T5G5GM NN    - N  - -  ! !..00 2c N!!>>sC N NNN$N44T5G5GMM N NN N Ns7.Krr+rris r)rEzOwnedIterator.output_classes>rr+rcDtjd|jS)rc"|jSr"rrros r)rMz-OwnedIterator.output_shapes..Yrr+rris r)rDzOwnedIterator.output_shapesNrr+rcDtjd|jS)rc"|jSr"rmros r)rMz,OwnedIterator.output_types..grr+rris r)rCzOwnedIterator.output_types\rr+c|jSr"rris r)rzzOwnedIterator.element_specjs   r+c"|jSr")r ris r)rzOwnedIterator.get_nextns     r+c `tj|j5tjt j |jtj|jtj|j|jcdddS#1swYyxYwrrris r)rz"OwnedIterator.get_next_as_optionalqrrcd}|jrt|jjjrPtj|j |jjjj }d|iStj|j tjjj }d|iSr) rrrrrr2rrrrrs r)rz#OwnedIterator._serialize_to_tensors|s  BB+>>  ! ! --   ! D D J JL ) **,>>  ! !  ) ) . . 4 46 ) **r+ctj|j5tj|j|dgcdddS#1swYyxYwrrrs r)rz#OwnedIterator._restore_from_tensorsrrc||vrJ|j%t|j|j||<nt|j||<|j }||j |y)z3Implements checkpointing protocols for `Trackable`.N)rrz)r)rrrr6rr)r@ object_map serializeds r)_copy_trackable_to_cpuz$OwnedIterator._copy_trackable_to_cpuso :  (D4D4D6:6H6HJ 4)? 4++-Jt**:6r+c|jSr")r%)r@rLs r)__tf_tracing_type__z!OwnedIterator.__tf_tracing_type__s ??r+r)rrrrrFrrrr rr!rr%rrrrErDrCrzrrrrr7r9 __classcell__rs@r)rrs)"V#IJN>,  + +  ; CE E   ; BD D   ; AC C    ! : +@ 7r+rzdata.IteratorSpecc^eZdZdZdgZdZedZdZedZ dZ dZ e d Z y ) r$aType specification for `tf.data.Iterator`. For instance, `tf.data.IteratorSpec` can be used to define a tf.function that takes `tf.data.Iterator` as an input argument: >>> @tf.function(input_signature=[tf.data.IteratorSpec( ... tf.TensorSpec(shape=(), dtype=tf.int32, name=None))]) ... def square(iterator): ... x = iterator.get_next() ... return x * x >>> dataset = tf.data.Dataset.from_tensors(5) >>> iterator = iter(dataset) >>> print(square(iterator)) tf.Tensor(25, shape=(), dtype=int32) Attributes: element_spec: A (nested) structure of `tf.TypeSpec` objects that represents the type specification of the iterator elements. r6c||_yr"r)r@rzs r)rFzIteratorSpec.__init__s %Dr+ctSr")rris r) value_typezIteratorSpec.value_types r+c|jfSr"rris r) _serializezIteratorSpec._serializes     r+cLtjgtjfSr")r TensorSpecr resourceris r)_component_specszIteratorSpec._component_specss   b&// 2 44r+c|jfSr")r2)r@rs r)_to_componentszIteratorSpec._to_componentss  $ $ &&r+c2td||jS)N)rrrz)rr6)r@rs r)_from_componentszIteratorSpec._from_componentss '' ))r+c,t|jSr"r#)rs r) from_valuezIteratorSpec.from_values ** ++r+N)rrrr __slots__rFrr?rArErGrIrrKrr+r)r$r$sa()&  ! 5 5') ,,r+r$cTeZdZdZej j ffd ZdZxZ S)_IteratorSaveablez3SaveableObject for saving/restoring iterator state.ctj||j}tj|d|dz|j g}t t|#|||y)N)rrSr)device) rrrrSaveSpecrPrrNrF)r@rArxrrspecsrs r)rFz_IteratorSaveable.__init__sg *<<1F1L1LN !!   8O$++  - E T+,=udKr+ctj|j5tj|j|dcdddS#1swYyxYw)Nr)rrrrr)r@rrestored_shapess r)restorez_IteratorSaveable.restoresG  477 #P  1 1$'';KA;N OPPPs #A  A) rrrrrrrrFrUr:r;s@r)rNrNs#; (;;@@ L Pr+rNz6Use `tf.data.Iterator.get_next_as_optional()` instead.z&data.experimental.get_next_as_optionalc"|jS)a|Returns a `tf.experimental.Optional` with the next element of the iterator. If the iterator has reached the end of the sequence, the returned `tf.experimental.Optional` will have no value. Args: iterator: A `tf.data.Iterator`. Returns: A `tf.experimental.Optional` object which either contains the next element of the iterator (if it exists) or no value. )r)iterators r)rrs  & & ((r+)Nrr threadingrtensorflow.core.protobufr tensorflow.python.autograph.corerrtensorflow.python.checkpointrtensorflow.python.data.opsrrrrtensorflow.python.data.utilr r tensorflow.python.eagerr tensorflow.python.frameworkr r rrrrrrtensorflow.python.opsrrrrtensorflow.python.ops.raggedrtensorflow.python.saved_modelrtensorflow.python.trackabler trackable tensorflow.python.training.saverrtensorflow.python.utilrtensorflow.python.util.compatr tensorflow.python.util.tf_exportrrrrrr?r*legacy_saveable_name Trackabler/rLockrrCompositeTensorABCMetarrTypeSpecr$SaveableObjectrNregister_codecBuiltInTypeSpecCodec TypeSpecProtoDATA_ITERATOR_SPECrrregister_overridesrr+r)rus(% /D893=,1+8..+.41211-,:@9=.96#%!#! &%%j1  A@y""A@!2A@H INN  $ ?r"`A $$kk `A#`AF&%%j1ILI2IX 2&0,9%%0,'0,hP(77P0&%%///j..AA BD 34 )5D ) &%%'r+