1950 lines
111 KiB
Python
1950 lines
111 KiB
Python
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"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.security.fuzzing.py import annotation_types as _atypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar, List, Any
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from typing_extensions import Annotated
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TV_AnonymousHashTable_key_dtype = TypeVar("TV_AnonymousHashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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TV_AnonymousHashTable_value_dtype = TypeVar("TV_AnonymousHashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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def anonymous_hash_table(key_dtype: TV_AnonymousHashTable_key_dtype, value_dtype: TV_AnonymousHashTable_value_dtype, name=None) -> Annotated[Any, _atypes.Resource]:
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r"""Creates a uninitialized anonymous hash table.
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This op creates a new anonymous hash table (as a resource) everytime
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it is executed, with the specified dtype of its keys and values,
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returning the resource handle. Before using the table you will have
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to initialize it. After initialization the table will be
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immutable. The table is anonymous in the sense that it can only be
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accessed by the returned resource handle (e.g. it cannot be looked up
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by a name in a resource manager). The table will be automatically
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deleted when all resource handles pointing to it are gone.
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Args:
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key_dtype: A `tf.DType`. Type of the table keys.
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value_dtype: A `tf.DType`. Type of the table values.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `resource`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "AnonymousHashTable", name, "key_dtype", key_dtype,
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"value_dtype", value_dtype)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return anonymous_hash_table_eager_fallback(
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key_dtype=key_dtype, value_dtype=value_dtype, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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key_dtype = _execute.make_type(key_dtype, "key_dtype")
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value_dtype = _execute.make_type(value_dtype, "value_dtype")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"AnonymousHashTable", key_dtype=key_dtype, value_dtype=value_dtype,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("key_dtype", _op._get_attr_type("key_dtype"), "value_dtype",
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_op._get_attr_type("value_dtype"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"AnonymousHashTable", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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AnonymousHashTable = tf_export("raw_ops.AnonymousHashTable")(_ops.to_raw_op(anonymous_hash_table))
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def anonymous_hash_table_eager_fallback(key_dtype: TV_AnonymousHashTable_key_dtype, value_dtype: TV_AnonymousHashTable_value_dtype, name, ctx) -> Annotated[Any, _atypes.Resource]:
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key_dtype = _execute.make_type(key_dtype, "key_dtype")
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value_dtype = _execute.make_type(value_dtype, "value_dtype")
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_inputs_flat = []
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_attrs = ("key_dtype", key_dtype, "value_dtype", value_dtype)
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_result = _execute.execute(b"AnonymousHashTable", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"AnonymousHashTable", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_AnonymousMutableDenseHashTable_key_dtype = TypeVar("TV_AnonymousMutableDenseHashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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TV_AnonymousMutableDenseHashTable_value_dtype = TypeVar("TV_AnonymousMutableDenseHashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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def anonymous_mutable_dense_hash_table(empty_key: Annotated[Any, TV_AnonymousMutableDenseHashTable_key_dtype], deleted_key: Annotated[Any, TV_AnonymousMutableDenseHashTable_key_dtype], value_dtype: TV_AnonymousMutableDenseHashTable_value_dtype, value_shape=[], initial_num_buckets:int=131072, max_load_factor:float=0.8, name=None) -> Annotated[Any, _atypes.Resource]:
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r"""Creates an empty anonymous mutable hash table that uses tensors as the backing store.
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This op creates a new anonymous mutable hash table (as a resource) everytime
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it is executed, with the specified dtype of its keys and values,
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returning the resource handle. Each value must be a scalar.
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Data can be inserted into the table using
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the insert operations. It does not support the initialization operation.
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It uses "open addressing" with quadratic reprobing to resolve
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collisions.
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The table is anonymous in the sense that it can only be
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accessed by the returned resource handle (e.g. it cannot be looked up
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by a name in a resource manager). The table will be automatically
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deleted when all resource handles pointing to it are gone.
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Args:
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empty_key: A `Tensor`.
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The key used to represent empty key buckets internally. Must not
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be used in insert or lookup operations.
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deleted_key: A `Tensor`. Must have the same type as `empty_key`.
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value_dtype: A `tf.DType`. Type of the table values.
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value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
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The shape of each value.
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initial_num_buckets: An optional `int`. Defaults to `131072`.
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The initial number of hash table buckets. Must be a power
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to 2.
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max_load_factor: An optional `float`. Defaults to `0.8`.
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The maximum ratio between number of entries and number of
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buckets before growing the table. Must be between 0 and 1.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `resource`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "AnonymousMutableDenseHashTable", name, empty_key, deleted_key,
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"value_dtype", value_dtype, "value_shape", value_shape,
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"initial_num_buckets", initial_num_buckets, "max_load_factor",
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max_load_factor)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return anonymous_mutable_dense_hash_table_eager_fallback(
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empty_key, deleted_key, value_dtype=value_dtype,
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value_shape=value_shape, initial_num_buckets=initial_num_buckets,
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max_load_factor=max_load_factor, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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value_dtype = _execute.make_type(value_dtype, "value_dtype")
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if value_shape is None:
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value_shape = []
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value_shape = _execute.make_shape(value_shape, "value_shape")
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if initial_num_buckets is None:
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initial_num_buckets = 131072
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initial_num_buckets = _execute.make_int(initial_num_buckets, "initial_num_buckets")
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if max_load_factor is None:
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max_load_factor = 0.8
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max_load_factor = _execute.make_float(max_load_factor, "max_load_factor")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"AnonymousMutableDenseHashTable", empty_key=empty_key,
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deleted_key=deleted_key,
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value_dtype=value_dtype,
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value_shape=value_shape,
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initial_num_buckets=initial_num_buckets,
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max_load_factor=max_load_factor,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("key_dtype", _op._get_attr_type("key_dtype"), "value_dtype",
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_op._get_attr_type("value_dtype"), "value_shape",
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_op.get_attr("value_shape"), "initial_num_buckets",
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_op._get_attr_int("initial_num_buckets"), "max_load_factor",
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_op.get_attr("max_load_factor"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"AnonymousMutableDenseHashTable", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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AnonymousMutableDenseHashTable = tf_export("raw_ops.AnonymousMutableDenseHashTable")(_ops.to_raw_op(anonymous_mutable_dense_hash_table))
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def anonymous_mutable_dense_hash_table_eager_fallback(empty_key: Annotated[Any, TV_AnonymousMutableDenseHashTable_key_dtype], deleted_key: Annotated[Any, TV_AnonymousMutableDenseHashTable_key_dtype], value_dtype: TV_AnonymousMutableDenseHashTable_value_dtype, value_shape, initial_num_buckets: int, max_load_factor: float, name, ctx) -> Annotated[Any, _atypes.Resource]:
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value_dtype = _execute.make_type(value_dtype, "value_dtype")
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if value_shape is None:
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value_shape = []
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value_shape = _execute.make_shape(value_shape, "value_shape")
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if initial_num_buckets is None:
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initial_num_buckets = 131072
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initial_num_buckets = _execute.make_int(initial_num_buckets, "initial_num_buckets")
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if max_load_factor is None:
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max_load_factor = 0.8
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max_load_factor = _execute.make_float(max_load_factor, "max_load_factor")
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_attr_key_dtype, _inputs_key_dtype = _execute.args_to_matching_eager([empty_key, deleted_key], ctx, [])
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(empty_key, deleted_key) = _inputs_key_dtype
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_inputs_flat = [empty_key, deleted_key]
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_attrs = ("key_dtype", _attr_key_dtype, "value_dtype", value_dtype,
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"value_shape", value_shape, "initial_num_buckets", initial_num_buckets,
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"max_load_factor", max_load_factor)
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_result = _execute.execute(b"AnonymousMutableDenseHashTable", 1,
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inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
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name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"AnonymousMutableDenseHashTable", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_AnonymousMutableHashTable_key_dtype = TypeVar("TV_AnonymousMutableHashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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TV_AnonymousMutableHashTable_value_dtype = TypeVar("TV_AnonymousMutableHashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
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def anonymous_mutable_hash_table(key_dtype: TV_AnonymousMutableHashTable_key_dtype, value_dtype: TV_AnonymousMutableHashTable_value_dtype, name=None) -> Annotated[Any, _atypes.Resource]:
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r"""Creates an empty anonymous mutable hash table.
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This op creates a new anonymous mutable hash table (as a resource) everytime
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it is executed, with the specified dtype of its keys and values,
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returning the resource handle. Each value must be a scalar.
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Data can be inserted into the table using
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|
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the insert operations. It does not support the initialization operation.
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The table is anonymous in the sense that it can only be
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|
|
accessed by the returned resource handle (e.g. it cannot be looked up
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|
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by a name in a resource manager). The table will be automatically
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|
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deleted when all resource handles pointing to it are gone.
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|
|
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Args:
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key_dtype: A `tf.DType`. Type of the table keys.
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value_dtype: A `tf.DType`. Type of the table values.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `resource`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "AnonymousMutableHashTable", name, "key_dtype", key_dtype,
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"value_dtype", value_dtype)
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return _result
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||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return anonymous_mutable_hash_table_eager_fallback(
|
||
|
|
key_dtype=key_dtype, value_dtype=value_dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"AnonymousMutableHashTable", key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("key_dtype", _op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"AnonymousMutableHashTable", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
AnonymousMutableHashTable = tf_export("raw_ops.AnonymousMutableHashTable")(_ops.to_raw_op(anonymous_mutable_hash_table))
|
||
|
|
|
||
|
|
|
||
|
|
def anonymous_mutable_hash_table_eager_fallback(key_dtype: TV_AnonymousMutableHashTable_key_dtype, value_dtype: TV_AnonymousMutableHashTable_value_dtype, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = ("key_dtype", key_dtype, "value_dtype", value_dtype)
|
||
|
|
_result = _execute.execute(b"AnonymousMutableHashTable", 1,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"AnonymousMutableHashTable", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_AnonymousMutableHashTableOfTensors_key_dtype = TypeVar("TV_AnonymousMutableHashTableOfTensors_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_AnonymousMutableHashTableOfTensors_value_dtype = TypeVar("TV_AnonymousMutableHashTableOfTensors_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def anonymous_mutable_hash_table_of_tensors(key_dtype: TV_AnonymousMutableHashTableOfTensors_key_dtype, value_dtype: TV_AnonymousMutableHashTableOfTensors_value_dtype, value_shape=[], name=None) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
r"""Creates an empty anonymous mutable hash table of vector values.
|
||
|
|
|
||
|
|
This op creates a new anonymous mutable hash table (as a resource) everytime
|
||
|
|
it is executed, with the specified dtype of its keys and values,
|
||
|
|
returning the resource handle. Each value must be a vector.
|
||
|
|
Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
The table is anonymous in the sense that it can only be
|
||
|
|
accessed by the returned resource handle (e.g. it cannot be looked up
|
||
|
|
by a name in a resource manager). The table will be automatically
|
||
|
|
deleted when all resource handles pointing to it are gone.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `resource`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "AnonymousMutableHashTableOfTensors", name, "key_dtype",
|
||
|
|
key_dtype, "value_dtype", value_dtype, "value_shape", value_shape)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return anonymous_mutable_hash_table_of_tensors_eager_fallback(
|
||
|
|
key_dtype=key_dtype, value_dtype=value_dtype,
|
||
|
|
value_shape=value_shape, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"AnonymousMutableHashTableOfTensors", key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype,
|
||
|
|
value_shape=value_shape,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("key_dtype", _op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"), "value_shape",
|
||
|
|
_op.get_attr("value_shape"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"AnonymousMutableHashTableOfTensors", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
AnonymousMutableHashTableOfTensors = tf_export("raw_ops.AnonymousMutableHashTableOfTensors")(_ops.to_raw_op(anonymous_mutable_hash_table_of_tensors))
|
||
|
|
|
||
|
|
|
||
|
|
def anonymous_mutable_hash_table_of_tensors_eager_fallback(key_dtype: TV_AnonymousMutableHashTableOfTensors_key_dtype, value_dtype: TV_AnonymousMutableHashTableOfTensors_value_dtype, value_shape, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = ("key_dtype", key_dtype, "value_dtype", value_dtype, "value_shape",
|
||
|
|
value_shape)
|
||
|
|
_result = _execute.execute(b"AnonymousMutableHashTableOfTensors", 1,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"AnonymousMutableHashTableOfTensors", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_HashTable_key_dtype = TypeVar("TV_HashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_HashTable_value_dtype = TypeVar("TV_HashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def hash_table(key_dtype: TV_HashTable_key_dtype, value_dtype: TV_HashTable_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, name=None) -> Annotated[Any, _atypes.String]:
|
||
|
|
r"""Creates a non-initialized hash table.
|
||
|
|
|
||
|
|
This op creates a hash table, specifying the type of its keys and values.
|
||
|
|
Before using the table you will have to initialize it. After initialization the
|
||
|
|
table will be immutable.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
If true and shared_name is empty, the table is shared
|
||
|
|
using the node name.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type mutable `string`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"HashTable", key_dtype=key_dtype, value_dtype=value_dtype,
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"HashTable", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
HashTable = tf_export("raw_ops.HashTable")(_ops.to_raw_op(hash_table))
|
||
|
|
|
||
|
|
|
||
|
|
def hash_table_eager_fallback(key_dtype: TV_HashTable_key_dtype, value_dtype: TV_HashTable_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, name, ctx) -> Annotated[Any, _atypes.String]:
|
||
|
|
raise RuntimeError("hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_HashTableV2_key_dtype = TypeVar("TV_HashTableV2_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_HashTableV2_value_dtype = TypeVar("TV_HashTableV2_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def hash_table_v2(key_dtype: TV_HashTableV2_key_dtype, value_dtype: TV_HashTableV2_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, name=None) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
r"""Creates a non-initialized hash table.
|
||
|
|
|
||
|
|
This op creates a hash table, specifying the type of its keys and values.
|
||
|
|
Before using the table you will have to initialize it. After initialization the
|
||
|
|
table will be immutable.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
If true and shared_name is empty, the table is shared
|
||
|
|
using the node name.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `resource`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "HashTableV2", name, "container", container, "shared_name",
|
||
|
|
shared_name, "use_node_name_sharing", use_node_name_sharing,
|
||
|
|
"key_dtype", key_dtype, "value_dtype", value_dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return hash_table_v2_eager_fallback(
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"HashTableV2", key_dtype=key_dtype, value_dtype=value_dtype,
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"HashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
HashTableV2 = tf_export("raw_ops.HashTableV2")(_ops.to_raw_op(hash_table_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def hash_table_v2_eager_fallback(key_dtype: TV_HashTableV2_key_dtype, value_dtype: TV_HashTableV2_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = ("container", container, "shared_name", shared_name,
|
||
|
|
"use_node_name_sharing", use_node_name_sharing, "key_dtype", key_dtype,
|
||
|
|
"value_dtype", value_dtype)
|
||
|
|
_result = _execute.execute(b"HashTableV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"HashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_InitializeTable_Tkey = TypeVar("TV_InitializeTable_Tkey", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_InitializeTable_Tval = TypeVar("TV_InitializeTable_Tval", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def initialize_table(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_InitializeTable_Tkey], values: Annotated[Any, TV_InitializeTable_Tval], name=None):
|
||
|
|
r"""Table initializer that takes two tensors for keys and values respectively.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`.
|
||
|
|
Handle to a table which will be initialized.
|
||
|
|
keys: A `Tensor`. Keys of type Tkey.
|
||
|
|
values: A `Tensor`. Values of type Tval.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("initialize_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"InitializeTable", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
InitializeTable = tf_export("raw_ops.InitializeTable")(_ops.to_raw_op(initialize_table))
|
||
|
|
|
||
|
|
|
||
|
|
def initialize_table_eager_fallback(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_InitializeTable_Tkey], values: Annotated[Any, TV_InitializeTable_Tval], name, ctx):
|
||
|
|
raise RuntimeError("initialize_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
def initialize_table_from_text_file(table_handle: Annotated[Any, _atypes.String], filename: Annotated[Any, _atypes.String], key_index: int, value_index: int, vocab_size:int=-1, delimiter:str="\t", offset:int=0, name=None):
|
||
|
|
r"""Initializes a table from a text file.
|
||
|
|
|
||
|
|
It inserts one key-value pair into the table for each line of the file.
|
||
|
|
The key and value is extracted from the whole line content, elements from the
|
||
|
|
split line based on `delimiter` or the line number (starting from zero).
|
||
|
|
Where to extract the key and value from a line is specified by `key_index` and
|
||
|
|
`value_index`.
|
||
|
|
|
||
|
|
- A value of -1 means use the line number(starting from zero), expects `int64`.
|
||
|
|
- A value of -2 means use the whole line content, expects `string`.
|
||
|
|
- A value >= 0 means use the index (starting at zero) of the split line based
|
||
|
|
on `delimiter`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`.
|
||
|
|
Handle to a table which will be initialized.
|
||
|
|
filename: A `Tensor` of type `string`.
|
||
|
|
Filename of a vocabulary text file.
|
||
|
|
key_index: An `int` that is `>= -2`.
|
||
|
|
Column index in a line to get the table `key` values from.
|
||
|
|
value_index: An `int` that is `>= -2`.
|
||
|
|
Column index that represents information of a line to get the table
|
||
|
|
`value` values from.
|
||
|
|
vocab_size: An optional `int` that is `>= -1`. Defaults to `-1`.
|
||
|
|
Number of elements of the file, use -1 if unknown.
|
||
|
|
delimiter: An optional `string`. Defaults to `"\t"`.
|
||
|
|
Delimiter to separate fields in a line.
|
||
|
|
offset: An optional `int`. Defaults to `0`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("initialize_table_from_text_file op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_index = _execute.make_int(key_index, "key_index")
|
||
|
|
value_index = _execute.make_int(value_index, "value_index")
|
||
|
|
if vocab_size is None:
|
||
|
|
vocab_size = -1
|
||
|
|
vocab_size = _execute.make_int(vocab_size, "vocab_size")
|
||
|
|
if delimiter is None:
|
||
|
|
delimiter = "\t"
|
||
|
|
delimiter = _execute.make_str(delimiter, "delimiter")
|
||
|
|
if offset is None:
|
||
|
|
offset = 0
|
||
|
|
offset = _execute.make_int(offset, "offset")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"InitializeTableFromTextFile", table_handle=table_handle,
|
||
|
|
filename=filename, key_index=key_index,
|
||
|
|
value_index=value_index,
|
||
|
|
vocab_size=vocab_size,
|
||
|
|
delimiter=delimiter, offset=offset,
|
||
|
|
name=name)
|
||
|
|
return _op
|
||
|
|
InitializeTableFromTextFile = tf_export("raw_ops.InitializeTableFromTextFile")(_ops.to_raw_op(initialize_table_from_text_file))
|
||
|
|
|
||
|
|
|
||
|
|
def initialize_table_from_text_file_eager_fallback(table_handle: Annotated[Any, _atypes.String], filename: Annotated[Any, _atypes.String], key_index: int, value_index: int, vocab_size: int, delimiter: str, offset: int, name, ctx):
|
||
|
|
raise RuntimeError("initialize_table_from_text_file op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
def initialize_table_from_text_file_v2(table_handle: Annotated[Any, _atypes.Resource], filename: Annotated[Any, _atypes.String], key_index: int, value_index: int, vocab_size:int=-1, delimiter:str="\t", offset:int=0, name=None):
|
||
|
|
r"""Initializes a table from a text file.
|
||
|
|
|
||
|
|
It inserts one key-value pair into the table for each line of the file.
|
||
|
|
The key and value is extracted from the whole line content, elements from the
|
||
|
|
split line based on `delimiter` or the line number (starting from zero).
|
||
|
|
Where to extract the key and value from a line is specified by `key_index` and
|
||
|
|
`value_index`.
|
||
|
|
|
||
|
|
- A value of -1 means use the line number(starting from zero), expects `int64`.
|
||
|
|
- A value of -2 means use the whole line content, expects `string`.
|
||
|
|
- A value >= 0 means use the index (starting at zero) of the split line based
|
||
|
|
on `delimiter`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`.
|
||
|
|
Handle to a table which will be initialized.
|
||
|
|
filename: A `Tensor` of type `string`.
|
||
|
|
Filename of a vocabulary text file.
|
||
|
|
key_index: An `int` that is `>= -2`.
|
||
|
|
Column index in a line to get the table `key` values from.
|
||
|
|
value_index: An `int` that is `>= -2`.
|
||
|
|
Column index that represents information of a line to get the table
|
||
|
|
`value` values from.
|
||
|
|
vocab_size: An optional `int` that is `>= -1`. Defaults to `-1`.
|
||
|
|
Number of elements of the file, use -1 if unknown.
|
||
|
|
delimiter: An optional `string`. Defaults to `"\t"`.
|
||
|
|
Delimiter to separate fields in a line.
|
||
|
|
offset: An optional `int`. Defaults to `0`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "InitializeTableFromTextFileV2", name, table_handle, filename,
|
||
|
|
"key_index", key_index, "value_index", value_index, "vocab_size",
|
||
|
|
vocab_size, "delimiter", delimiter, "offset", offset)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return initialize_table_from_text_file_v2_eager_fallback(
|
||
|
|
table_handle, filename, key_index=key_index,
|
||
|
|
value_index=value_index, vocab_size=vocab_size, delimiter=delimiter,
|
||
|
|
offset=offset, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_index = _execute.make_int(key_index, "key_index")
|
||
|
|
value_index = _execute.make_int(value_index, "value_index")
|
||
|
|
if vocab_size is None:
|
||
|
|
vocab_size = -1
|
||
|
|
vocab_size = _execute.make_int(vocab_size, "vocab_size")
|
||
|
|
if delimiter is None:
|
||
|
|
delimiter = "\t"
|
||
|
|
delimiter = _execute.make_str(delimiter, "delimiter")
|
||
|
|
if offset is None:
|
||
|
|
offset = 0
|
||
|
|
offset = _execute.make_int(offset, "offset")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"InitializeTableFromTextFileV2", table_handle=table_handle,
|
||
|
|
filename=filename,
|
||
|
|
key_index=key_index,
|
||
|
|
value_index=value_index,
|
||
|
|
vocab_size=vocab_size,
|
||
|
|
delimiter=delimiter, offset=offset,
|
||
|
|
name=name)
|
||
|
|
return _op
|
||
|
|
InitializeTableFromTextFileV2 = tf_export("raw_ops.InitializeTableFromTextFileV2")(_ops.to_raw_op(initialize_table_from_text_file_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def initialize_table_from_text_file_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], filename: Annotated[Any, _atypes.String], key_index: int, value_index: int, vocab_size: int, delimiter: str, offset: int, name, ctx):
|
||
|
|
key_index = _execute.make_int(key_index, "key_index")
|
||
|
|
value_index = _execute.make_int(value_index, "value_index")
|
||
|
|
if vocab_size is None:
|
||
|
|
vocab_size = -1
|
||
|
|
vocab_size = _execute.make_int(vocab_size, "vocab_size")
|
||
|
|
if delimiter is None:
|
||
|
|
delimiter = "\t"
|
||
|
|
delimiter = _execute.make_str(delimiter, "delimiter")
|
||
|
|
if offset is None:
|
||
|
|
offset = 0
|
||
|
|
offset = _execute.make_int(offset, "offset")
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
filename = _ops.convert_to_tensor(filename, _dtypes.string)
|
||
|
|
_inputs_flat = [table_handle, filename]
|
||
|
|
_attrs = ("key_index", key_index, "value_index", value_index, "vocab_size",
|
||
|
|
vocab_size, "delimiter", delimiter, "offset", offset)
|
||
|
|
_result = _execute.execute(b"InitializeTableFromTextFileV2", 0,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
_result = None
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_InitializeTableV2_Tkey = TypeVar("TV_InitializeTableV2_Tkey", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_InitializeTableV2_Tval = TypeVar("TV_InitializeTableV2_Tval", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def initialize_table_v2(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_InitializeTableV2_Tkey], values: Annotated[Any, TV_InitializeTableV2_Tval], name=None):
|
||
|
|
r"""Table initializer that takes two tensors for keys and values respectively.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`.
|
||
|
|
Handle to a table which will be initialized.
|
||
|
|
keys: A `Tensor`. Keys of type Tkey.
|
||
|
|
values: A `Tensor`. Values of type Tval.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "InitializeTableV2", name, table_handle, keys, values)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return initialize_table_v2_eager_fallback(
|
||
|
|
table_handle, keys, values, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"InitializeTableV2", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
InitializeTableV2 = tf_export("raw_ops.InitializeTableV2")(_ops.to_raw_op(initialize_table_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def initialize_table_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_InitializeTableV2_Tkey], values: Annotated[Any, TV_InitializeTableV2_Tval], name, ctx):
|
||
|
|
_attr_Tkey, (keys,) = _execute.args_to_matching_eager([keys], ctx, [])
|
||
|
|
_attr_Tval, (values,) = _execute.args_to_matching_eager([values], ctx, [])
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle, keys, values]
|
||
|
|
_attrs = ("Tkey", _attr_Tkey, "Tval", _attr_Tval)
|
||
|
|
_result = _execute.execute(b"InitializeTableV2", 0, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
_result = None
|
||
|
|
return _result
|
||
|
|
|
||
|
|
_LookupTableExportOutput = collections.namedtuple(
|
||
|
|
"LookupTableExport",
|
||
|
|
["keys", "values"])
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableExport_Tkeys = TypeVar("TV_LookupTableExport_Tkeys", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableExport_Tvalues = TypeVar("TV_LookupTableExport_Tvalues", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_export(table_handle: Annotated[Any, _atypes.String], Tkeys: TV_LookupTableExport_Tkeys, Tvalues: TV_LookupTableExport_Tvalues, name=None):
|
||
|
|
r"""Outputs all keys and values in the table.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`. Handle to the table.
|
||
|
|
Tkeys: A `tf.DType`.
|
||
|
|
Tvalues: A `tf.DType`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (keys, values).
|
||
|
|
|
||
|
|
keys: A `Tensor` of type `Tkeys`.
|
||
|
|
values: A `Tensor` of type `Tvalues`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("lookup_table_export op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
Tkeys = _execute.make_type(Tkeys, "Tkeys")
|
||
|
|
Tvalues = _execute.make_type(Tvalues, "Tvalues")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableExport", table_handle=table_handle, Tkeys=Tkeys,
|
||
|
|
Tvalues=Tvalues, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("Tkeys", _op._get_attr_type("Tkeys"), "Tvalues",
|
||
|
|
_op._get_attr_type("Tvalues"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableExport", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _LookupTableExportOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableExport = tf_export("raw_ops.LookupTableExport")(_ops.to_raw_op(lookup_table_export))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_export_eager_fallback(table_handle: Annotated[Any, _atypes.String], Tkeys: TV_LookupTableExport_Tkeys, Tvalues: TV_LookupTableExport_Tvalues, name, ctx):
|
||
|
|
raise RuntimeError("lookup_table_export op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
_LookupTableExportV2Output = collections.namedtuple(
|
||
|
|
"LookupTableExportV2",
|
||
|
|
["keys", "values"])
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableExportV2_Tkeys = TypeVar("TV_LookupTableExportV2_Tkeys", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableExportV2_Tvalues = TypeVar("TV_LookupTableExportV2_Tvalues", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_export_v2(table_handle: Annotated[Any, _atypes.Resource], Tkeys: TV_LookupTableExportV2_Tkeys, Tvalues: TV_LookupTableExportV2_Tvalues, name=None):
|
||
|
|
r"""Outputs all keys and values in the table.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
Tkeys: A `tf.DType`.
|
||
|
|
Tvalues: A `tf.DType`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A tuple of `Tensor` objects (keys, values).
|
||
|
|
|
||
|
|
keys: A `Tensor` of type `Tkeys`.
|
||
|
|
values: A `Tensor` of type `Tvalues`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableExportV2", name, table_handle, "Tkeys", Tkeys,
|
||
|
|
"Tvalues", Tvalues)
|
||
|
|
_result = _LookupTableExportV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_export_v2_eager_fallback(
|
||
|
|
table_handle, Tkeys=Tkeys, Tvalues=Tvalues, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
Tkeys = _execute.make_type(Tkeys, "Tkeys")
|
||
|
|
Tvalues = _execute.make_type(Tvalues, "Tvalues")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableExportV2", table_handle=table_handle, Tkeys=Tkeys,
|
||
|
|
Tvalues=Tvalues, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("Tkeys", _op._get_attr_type("Tkeys"), "Tvalues",
|
||
|
|
_op._get_attr_type("Tvalues"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableExportV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _LookupTableExportV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableExportV2 = tf_export("raw_ops.LookupTableExportV2")(_ops.to_raw_op(lookup_table_export_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_export_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], Tkeys: TV_LookupTableExportV2_Tkeys, Tvalues: TV_LookupTableExportV2_Tvalues, name, ctx):
|
||
|
|
Tkeys = _execute.make_type(Tkeys, "Tkeys")
|
||
|
|
Tvalues = _execute.make_type(Tvalues, "Tvalues")
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle]
|
||
|
|
_attrs = ("Tkeys", Tkeys, "Tvalues", Tvalues)
|
||
|
|
_result = _execute.execute(b"LookupTableExportV2", 2, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableExportV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _LookupTableExportV2Output._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableFind_Tin = TypeVar("TV_LookupTableFind_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableFind_Tout = TypeVar("TV_LookupTableFind_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_find(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableFind_Tin], default_value: Annotated[Any, TV_LookupTableFind_Tout], name=None) -> Annotated[Any, TV_LookupTableFind_Tout]:
|
||
|
|
r"""Looks up keys in a table, outputs the corresponding values.
|
||
|
|
|
||
|
|
The tensor `keys` must of the same type as the keys of the table.
|
||
|
|
The output `values` is of the type of the table values.
|
||
|
|
|
||
|
|
The scalar `default_value` is the value output for keys not present in the
|
||
|
|
table. It must also be of the same type as the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
default_value: A `Tensor`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `default_value`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("lookup_table_find op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableFind", table_handle=table_handle, keys=keys,
|
||
|
|
default_value=default_value, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("Tin", _op._get_attr_type("Tin"), "Tout",
|
||
|
|
_op._get_attr_type("Tout"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableFind", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableFind = tf_export("raw_ops.LookupTableFind")(_ops.to_raw_op(lookup_table_find))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_find_eager_fallback(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableFind_Tin], default_value: Annotated[Any, TV_LookupTableFind_Tout], name, ctx) -> Annotated[Any, TV_LookupTableFind_Tout]:
|
||
|
|
raise RuntimeError("lookup_table_find op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_LookupTableFindV2_Tin = TypeVar("TV_LookupTableFindV2_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableFindV2_Tout = TypeVar("TV_LookupTableFindV2_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_find_v2(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableFindV2_Tin], default_value: Annotated[Any, TV_LookupTableFindV2_Tout], name=None) -> Annotated[Any, TV_LookupTableFindV2_Tout]:
|
||
|
|
r"""Looks up keys in a table, outputs the corresponding values.
|
||
|
|
|
||
|
|
The tensor `keys` must of the same type as the keys of the table.
|
||
|
|
The output `values` is of the type of the table values.
|
||
|
|
|
||
|
|
The scalar `default_value` is the value output for keys not present in the
|
||
|
|
table. It must also be of the same type as the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
default_value: A `Tensor`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `default_value`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableFindV2", name, table_handle, keys, default_value)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_find_v2_eager_fallback(
|
||
|
|
table_handle, keys, default_value, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableFindV2", table_handle=table_handle, keys=keys,
|
||
|
|
default_value=default_value, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("Tin", _op._get_attr_type("Tin"), "Tout",
|
||
|
|
_op._get_attr_type("Tout"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableFindV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableFindV2 = tf_export("raw_ops.LookupTableFindV2")(_ops.to_raw_op(lookup_table_find_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_find_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableFindV2_Tin], default_value: Annotated[Any, TV_LookupTableFindV2_Tout], name, ctx) -> Annotated[Any, TV_LookupTableFindV2_Tout]:
|
||
|
|
_attr_Tin, (keys,) = _execute.args_to_matching_eager([keys], ctx, [])
|
||
|
|
_attr_Tout, (default_value,) = _execute.args_to_matching_eager([default_value], ctx, [])
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle, keys, default_value]
|
||
|
|
_attrs = ("Tin", _attr_Tin, "Tout", _attr_Tout)
|
||
|
|
_result = _execute.execute(b"LookupTableFindV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableFindV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableImport_Tin = TypeVar("TV_LookupTableImport_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableImport_Tout = TypeVar("TV_LookupTableImport_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_import(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableImport_Tin], values: Annotated[Any, TV_LookupTableImport_Tout], name=None):
|
||
|
|
r"""Replaces the contents of the table with the specified keys and values.
|
||
|
|
|
||
|
|
The tensor `keys` must be of the same type as the keys of the table.
|
||
|
|
The tensor `values` must be of the type of the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
values: A `Tensor`. Values to associate with keys.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("lookup_table_import op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableImport", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
LookupTableImport = tf_export("raw_ops.LookupTableImport")(_ops.to_raw_op(lookup_table_import))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_import_eager_fallback(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableImport_Tin], values: Annotated[Any, TV_LookupTableImport_Tout], name, ctx):
|
||
|
|
raise RuntimeError("lookup_table_import op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_LookupTableImportV2_Tin = TypeVar("TV_LookupTableImportV2_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableImportV2_Tout = TypeVar("TV_LookupTableImportV2_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_import_v2(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableImportV2_Tin], values: Annotated[Any, TV_LookupTableImportV2_Tout], name=None):
|
||
|
|
r"""Replaces the contents of the table with the specified keys and values.
|
||
|
|
|
||
|
|
The tensor `keys` must be of the same type as the keys of the table.
|
||
|
|
The tensor `values` must be of the type of the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
values: A `Tensor`. Values to associate with keys.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableImportV2", name, table_handle, keys, values)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_import_v2_eager_fallback(
|
||
|
|
table_handle, keys, values, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableImportV2", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
LookupTableImportV2 = tf_export("raw_ops.LookupTableImportV2")(_ops.to_raw_op(lookup_table_import_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_import_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableImportV2_Tin], values: Annotated[Any, TV_LookupTableImportV2_Tout], name, ctx):
|
||
|
|
_attr_Tin, (keys,) = _execute.args_to_matching_eager([keys], ctx, [])
|
||
|
|
_attr_Tout, (values,) = _execute.args_to_matching_eager([values], ctx, [])
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle, keys, values]
|
||
|
|
_attrs = ("Tin", _attr_Tin, "Tout", _attr_Tout)
|
||
|
|
_result = _execute.execute(b"LookupTableImportV2", 0, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
_result = None
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableInsert_Tin = TypeVar("TV_LookupTableInsert_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableInsert_Tout = TypeVar("TV_LookupTableInsert_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_insert(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableInsert_Tin], values: Annotated[Any, TV_LookupTableInsert_Tout], name=None):
|
||
|
|
r"""Updates the table to associates keys with values.
|
||
|
|
|
||
|
|
The tensor `keys` must be of the same type as the keys of the table.
|
||
|
|
The tensor `values` must be of the type of the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
values: A `Tensor`. Values to associate with keys.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("lookup_table_insert op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableInsert", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
LookupTableInsert = tf_export("raw_ops.LookupTableInsert")(_ops.to_raw_op(lookup_table_insert))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_insert_eager_fallback(table_handle: Annotated[Any, _atypes.String], keys: Annotated[Any, TV_LookupTableInsert_Tin], values: Annotated[Any, TV_LookupTableInsert_Tout], name, ctx):
|
||
|
|
raise RuntimeError("lookup_table_insert op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_LookupTableInsertV2_Tin = TypeVar("TV_LookupTableInsertV2_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_LookupTableInsertV2_Tout = TypeVar("TV_LookupTableInsertV2_Tout", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_insert_v2(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableInsertV2_Tin], values: Annotated[Any, TV_LookupTableInsertV2_Tout], name=None):
|
||
|
|
r"""Updates the table to associates keys with values.
|
||
|
|
|
||
|
|
The tensor `keys` must be of the same type as the keys of the table.
|
||
|
|
The tensor `values` must be of the type of the table values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys to look up.
|
||
|
|
values: A `Tensor`. Values to associate with keys.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableInsertV2", name, table_handle, keys, values)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_insert_v2_eager_fallback(
|
||
|
|
table_handle, keys, values, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableInsertV2", table_handle=table_handle, keys=keys,
|
||
|
|
values=values, name=name)
|
||
|
|
return _op
|
||
|
|
LookupTableInsertV2 = tf_export("raw_ops.LookupTableInsertV2")(_ops.to_raw_op(lookup_table_insert_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_insert_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableInsertV2_Tin], values: Annotated[Any, TV_LookupTableInsertV2_Tout], name, ctx):
|
||
|
|
_attr_Tin, (keys,) = _execute.args_to_matching_eager([keys], ctx, [])
|
||
|
|
_attr_Tout, (values,) = _execute.args_to_matching_eager([values], ctx, [])
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle, keys, values]
|
||
|
|
_attrs = ("Tin", _attr_Tin, "Tout", _attr_Tout)
|
||
|
|
_result = _execute.execute(b"LookupTableInsertV2", 0, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
_result = None
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_LookupTableRemoveV2_Tin = TypeVar("TV_LookupTableRemoveV2_Tin", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def lookup_table_remove_v2(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableRemoveV2_Tin], name=None):
|
||
|
|
r"""Removes keys and its associated values from a table.
|
||
|
|
|
||
|
|
The tensor `keys` must of the same type as the keys of the table. Keys not
|
||
|
|
already in the table are silently ignored.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
keys: A `Tensor`. Any shape. Keys of the elements to remove.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The created Operation.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableRemoveV2", name, table_handle, keys)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_remove_v2_eager_fallback(
|
||
|
|
table_handle, keys, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableRemoveV2", table_handle=table_handle, keys=keys,
|
||
|
|
name=name)
|
||
|
|
return _op
|
||
|
|
LookupTableRemoveV2 = tf_export("raw_ops.LookupTableRemoveV2")(_ops.to_raw_op(lookup_table_remove_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_remove_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], keys: Annotated[Any, TV_LookupTableRemoveV2_Tin], name, ctx):
|
||
|
|
_attr_Tin, (keys,) = _execute.args_to_matching_eager([keys], ctx, [])
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle, keys]
|
||
|
|
_attrs = ("Tin", _attr_Tin)
|
||
|
|
_result = _execute.execute(b"LookupTableRemoveV2", 0, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
_result = None
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_size(table_handle: Annotated[Any, _atypes.String], name=None) -> Annotated[Any, _atypes.Int64]:
|
||
|
|
r"""Computes the number of elements in the given table.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type mutable `string`. Handle to the table.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `int64`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("lookup_table_size op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableSize", table_handle=table_handle, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ()
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableSize", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableSize = tf_export("raw_ops.LookupTableSize")(_ops.to_raw_op(lookup_table_size))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_size_eager_fallback(table_handle: Annotated[Any, _atypes.String], name, ctx) -> Annotated[Any, _atypes.Int64]:
|
||
|
|
raise RuntimeError("lookup_table_size op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
def lookup_table_size_v2(table_handle: Annotated[Any, _atypes.Resource], name=None) -> Annotated[Any, _atypes.Int64]:
|
||
|
|
r"""Computes the number of elements in the given table.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
table_handle: A `Tensor` of type `resource`. Handle to the table.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `int64`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "LookupTableSizeV2", name, table_handle)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return lookup_table_size_v2_eager_fallback(
|
||
|
|
table_handle, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"LookupTableSizeV2", table_handle=table_handle, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ()
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableSizeV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
LookupTableSizeV2 = tf_export("raw_ops.LookupTableSizeV2")(_ops.to_raw_op(lookup_table_size_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def lookup_table_size_v2_eager_fallback(table_handle: Annotated[Any, _atypes.Resource], name, ctx) -> Annotated[Any, _atypes.Int64]:
|
||
|
|
table_handle = _ops.convert_to_tensor(table_handle, _dtypes.resource)
|
||
|
|
_inputs_flat = [table_handle]
|
||
|
|
_attrs = None
|
||
|
|
_result = _execute.execute(b"LookupTableSizeV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"LookupTableSizeV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_MutableDenseHashTable_key_dtype = TypeVar("TV_MutableDenseHashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableDenseHashTable_value_dtype = TypeVar("TV_MutableDenseHashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_dense_hash_table(empty_key: Annotated[Any, TV_MutableDenseHashTable_key_dtype], value_dtype: TV_MutableDenseHashTable_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, value_shape=[], initial_num_buckets:int=131072, max_load_factor:float=0.8, name=None) -> Annotated[Any, _atypes.String]:
|
||
|
|
r"""Creates an empty hash table that uses tensors as the backing store.
|
||
|
|
|
||
|
|
It uses "open addressing" with quadratic reprobing to resolve
|
||
|
|
collisions.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a scalar. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
empty_key: A `Tensor`.
|
||
|
|
The key used to represent empty key buckets internally. Must not
|
||
|
|
be used in insert or lookup operations.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
|
||
|
|
The shape of each value.
|
||
|
|
initial_num_buckets: An optional `int`. Defaults to `131072`.
|
||
|
|
The initial number of hash table buckets. Must be a power
|
||
|
|
to 2.
|
||
|
|
max_load_factor: An optional `float`. Defaults to `0.8`.
|
||
|
|
The maximum ratio between number of entries and number of
|
||
|
|
buckets before growing the table. Must be between 0 and 1.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type mutable `string`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("mutable_dense_hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
if initial_num_buckets is None:
|
||
|
|
initial_num_buckets = 131072
|
||
|
|
initial_num_buckets = _execute.make_int(initial_num_buckets, "initial_num_buckets")
|
||
|
|
if max_load_factor is None:
|
||
|
|
max_load_factor = 0.8
|
||
|
|
max_load_factor = _execute.make_float(max_load_factor, "max_load_factor")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableDenseHashTable", empty_key=empty_key, value_dtype=value_dtype,
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
value_shape=value_shape,
|
||
|
|
initial_num_buckets=initial_num_buckets,
|
||
|
|
max_load_factor=max_load_factor, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"), "value_shape",
|
||
|
|
_op.get_attr("value_shape"), "initial_num_buckets",
|
||
|
|
_op._get_attr_int("initial_num_buckets"), "max_load_factor",
|
||
|
|
_op.get_attr("max_load_factor"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableDenseHashTable", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableDenseHashTable = tf_export("raw_ops.MutableDenseHashTable")(_ops.to_raw_op(mutable_dense_hash_table))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_dense_hash_table_eager_fallback(empty_key: Annotated[Any, TV_MutableDenseHashTable_key_dtype], value_dtype: TV_MutableDenseHashTable_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, value_shape, initial_num_buckets: int, max_load_factor: float, name, ctx) -> Annotated[Any, _atypes.String]:
|
||
|
|
raise RuntimeError("mutable_dense_hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_MutableDenseHashTableV2_key_dtype = TypeVar("TV_MutableDenseHashTableV2_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableDenseHashTableV2_value_dtype = TypeVar("TV_MutableDenseHashTableV2_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_dense_hash_table_v2(empty_key: Annotated[Any, TV_MutableDenseHashTableV2_key_dtype], deleted_key: Annotated[Any, TV_MutableDenseHashTableV2_key_dtype], value_dtype: TV_MutableDenseHashTableV2_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, value_shape=[], initial_num_buckets:int=131072, max_load_factor:float=0.8, name=None) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
r"""Creates an empty hash table that uses tensors as the backing store.
|
||
|
|
|
||
|
|
It uses "open addressing" with quadratic reprobing to resolve
|
||
|
|
collisions.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a scalar. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
empty_key: A `Tensor`.
|
||
|
|
The key used to represent empty key buckets internally. Must not
|
||
|
|
be used in insert or lookup operations.
|
||
|
|
deleted_key: A `Tensor`. Must have the same type as `empty_key`.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
|
||
|
|
The shape of each value.
|
||
|
|
initial_num_buckets: An optional `int`. Defaults to `131072`.
|
||
|
|
The initial number of hash table buckets. Must be a power
|
||
|
|
to 2.
|
||
|
|
max_load_factor: An optional `float`. Defaults to `0.8`.
|
||
|
|
The maximum ratio between number of entries and number of
|
||
|
|
buckets before growing the table. Must be between 0 and 1.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `resource`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "MutableDenseHashTableV2", name, empty_key, deleted_key,
|
||
|
|
"container", container, "shared_name", shared_name,
|
||
|
|
"use_node_name_sharing", use_node_name_sharing, "value_dtype",
|
||
|
|
value_dtype, "value_shape", value_shape, "initial_num_buckets",
|
||
|
|
initial_num_buckets, "max_load_factor", max_load_factor)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return mutable_dense_hash_table_v2_eager_fallback(
|
||
|
|
empty_key, deleted_key, container=container,
|
||
|
|
shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
value_dtype=value_dtype, value_shape=value_shape,
|
||
|
|
initial_num_buckets=initial_num_buckets,
|
||
|
|
max_load_factor=max_load_factor, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
if initial_num_buckets is None:
|
||
|
|
initial_num_buckets = 131072
|
||
|
|
initial_num_buckets = _execute.make_int(initial_num_buckets, "initial_num_buckets")
|
||
|
|
if max_load_factor is None:
|
||
|
|
max_load_factor = 0.8
|
||
|
|
max_load_factor = _execute.make_float(max_load_factor, "max_load_factor")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableDenseHashTableV2", empty_key=empty_key,
|
||
|
|
deleted_key=deleted_key,
|
||
|
|
value_dtype=value_dtype,
|
||
|
|
container=container,
|
||
|
|
shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
value_shape=value_shape,
|
||
|
|
initial_num_buckets=initial_num_buckets,
|
||
|
|
max_load_factor=max_load_factor, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"), "value_shape",
|
||
|
|
_op.get_attr("value_shape"), "initial_num_buckets",
|
||
|
|
_op._get_attr_int("initial_num_buckets"), "max_load_factor",
|
||
|
|
_op.get_attr("max_load_factor"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableDenseHashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableDenseHashTableV2 = tf_export("raw_ops.MutableDenseHashTableV2")(_ops.to_raw_op(mutable_dense_hash_table_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_dense_hash_table_v2_eager_fallback(empty_key: Annotated[Any, TV_MutableDenseHashTableV2_key_dtype], deleted_key: Annotated[Any, TV_MutableDenseHashTableV2_key_dtype], value_dtype: TV_MutableDenseHashTableV2_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, value_shape, initial_num_buckets: int, max_load_factor: float, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
if initial_num_buckets is None:
|
||
|
|
initial_num_buckets = 131072
|
||
|
|
initial_num_buckets = _execute.make_int(initial_num_buckets, "initial_num_buckets")
|
||
|
|
if max_load_factor is None:
|
||
|
|
max_load_factor = 0.8
|
||
|
|
max_load_factor = _execute.make_float(max_load_factor, "max_load_factor")
|
||
|
|
_attr_key_dtype, _inputs_key_dtype = _execute.args_to_matching_eager([empty_key, deleted_key], ctx, [])
|
||
|
|
(empty_key, deleted_key) = _inputs_key_dtype
|
||
|
|
_inputs_flat = [empty_key, deleted_key]
|
||
|
|
_attrs = ("container", container, "shared_name", shared_name,
|
||
|
|
"use_node_name_sharing", use_node_name_sharing, "key_dtype",
|
||
|
|
_attr_key_dtype, "value_dtype", value_dtype, "value_shape", value_shape,
|
||
|
|
"initial_num_buckets", initial_num_buckets, "max_load_factor",
|
||
|
|
max_load_factor)
|
||
|
|
_result = _execute.execute(b"MutableDenseHashTableV2", 1,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableDenseHashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_MutableHashTable_key_dtype = TypeVar("TV_MutableHashTable_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableHashTable_value_dtype = TypeVar("TV_MutableHashTable_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_hash_table(key_dtype: TV_MutableHashTable_key_dtype, value_dtype: TV_MutableHashTable_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, name=None) -> Annotated[Any, _atypes.String]:
|
||
|
|
r"""Creates an empty hash table.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a scalar. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
If true and shared_name is empty, the table is shared
|
||
|
|
using the node name.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type mutable `string`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("mutable_hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableHashTable", key_dtype=key_dtype, value_dtype=value_dtype,
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTable", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableHashTable = tf_export("raw_ops.MutableHashTable")(_ops.to_raw_op(mutable_hash_table))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_hash_table_eager_fallback(key_dtype: TV_MutableHashTable_key_dtype, value_dtype: TV_MutableHashTable_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, name, ctx) -> Annotated[Any, _atypes.String]:
|
||
|
|
raise RuntimeError("mutable_hash_table op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_MutableHashTableOfTensors_key_dtype = TypeVar("TV_MutableHashTableOfTensors_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableHashTableOfTensors_value_dtype = TypeVar("TV_MutableHashTableOfTensors_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_hash_table_of_tensors(key_dtype: TV_MutableHashTableOfTensors_key_dtype, value_dtype: TV_MutableHashTableOfTensors_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, value_shape=[], name=None) -> Annotated[Any, _atypes.String]:
|
||
|
|
r"""Creates an empty hash table.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a vector. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type mutable `string`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
raise RuntimeError("mutable_hash_table_of_tensors op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableHashTableOfTensors", key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype,
|
||
|
|
container=container,
|
||
|
|
shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
value_shape=value_shape, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"), "value_shape",
|
||
|
|
_op.get_attr("value_shape"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTableOfTensors", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableHashTableOfTensors = tf_export("raw_ops.MutableHashTableOfTensors")(_ops.to_raw_op(mutable_hash_table_of_tensors))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_hash_table_of_tensors_eager_fallback(key_dtype: TV_MutableHashTableOfTensors_key_dtype, value_dtype: TV_MutableHashTableOfTensors_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, value_shape, name, ctx) -> Annotated[Any, _atypes.String]:
|
||
|
|
raise RuntimeError("mutable_hash_table_of_tensors op does not support eager execution. Arg 'table_handle' is a ref.")
|
||
|
|
|
||
|
|
TV_MutableHashTableOfTensorsV2_key_dtype = TypeVar("TV_MutableHashTableOfTensorsV2_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableHashTableOfTensorsV2_value_dtype = TypeVar("TV_MutableHashTableOfTensorsV2_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_hash_table_of_tensors_v2(key_dtype: TV_MutableHashTableOfTensorsV2_key_dtype, value_dtype: TV_MutableHashTableOfTensorsV2_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, value_shape=[], name=None) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
r"""Creates an empty hash table.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a vector. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
value_shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `resource`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "MutableHashTableOfTensorsV2", name, "container", container,
|
||
|
|
"shared_name", shared_name, "use_node_name_sharing",
|
||
|
|
use_node_name_sharing, "key_dtype", key_dtype, "value_dtype",
|
||
|
|
value_dtype, "value_shape", value_shape)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return mutable_hash_table_of_tensors_v2_eager_fallback(
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype, value_shape=value_shape, name=name,
|
||
|
|
ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableHashTableOfTensorsV2", key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype,
|
||
|
|
container=container,
|
||
|
|
shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
value_shape=value_shape, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"), "value_shape",
|
||
|
|
_op.get_attr("value_shape"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTableOfTensorsV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableHashTableOfTensorsV2 = tf_export("raw_ops.MutableHashTableOfTensorsV2")(_ops.to_raw_op(mutable_hash_table_of_tensors_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_hash_table_of_tensors_v2_eager_fallback(key_dtype: TV_MutableHashTableOfTensorsV2_key_dtype, value_dtype: TV_MutableHashTableOfTensorsV2_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, value_shape, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
if value_shape is None:
|
||
|
|
value_shape = []
|
||
|
|
value_shape = _execute.make_shape(value_shape, "value_shape")
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = ("container", container, "shared_name", shared_name,
|
||
|
|
"use_node_name_sharing", use_node_name_sharing, "key_dtype", key_dtype,
|
||
|
|
"value_dtype", value_dtype, "value_shape", value_shape)
|
||
|
|
_result = _execute.execute(b"MutableHashTableOfTensorsV2", 1,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTableOfTensorsV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_MutableHashTableV2_key_dtype = TypeVar("TV_MutableHashTableV2_key_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
TV_MutableHashTableV2_value_dtype = TypeVar("TV_MutableHashTableV2_value_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def mutable_hash_table_v2(key_dtype: TV_MutableHashTableV2_key_dtype, value_dtype: TV_MutableHashTableV2_value_dtype, container:str="", shared_name:str="", use_node_name_sharing:bool=False, name=None) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
r"""Creates an empty hash table.
|
||
|
|
|
||
|
|
This op creates a mutable hash table, specifying the type of its keys and
|
||
|
|
values. Each value must be a scalar. Data can be inserted into the table using
|
||
|
|
the insert operations. It does not support the initialization operation.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
key_dtype: A `tf.DType`. Type of the table keys.
|
||
|
|
value_dtype: A `tf.DType`. Type of the table values.
|
||
|
|
container: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is placed in the given container.
|
||
|
|
Otherwise, a default container is used.
|
||
|
|
shared_name: An optional `string`. Defaults to `""`.
|
||
|
|
If non-empty, this table is shared under the given name across
|
||
|
|
multiple sessions.
|
||
|
|
use_node_name_sharing: An optional `bool`. Defaults to `False`.
|
||
|
|
If true and shared_name is empty, the table is shared
|
||
|
|
using the node name.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `resource`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "MutableHashTableV2", name, "container", container,
|
||
|
|
"shared_name", shared_name, "use_node_name_sharing",
|
||
|
|
use_node_name_sharing, "key_dtype", key_dtype, "value_dtype",
|
||
|
|
value_dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return mutable_hash_table_v2_eager_fallback(
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing, key_dtype=key_dtype,
|
||
|
|
value_dtype=value_dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"MutableHashTableV2", key_dtype=key_dtype, value_dtype=value_dtype,
|
||
|
|
container=container, shared_name=shared_name,
|
||
|
|
use_node_name_sharing=use_node_name_sharing,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("container", _op.get_attr("container"), "shared_name",
|
||
|
|
_op.get_attr("shared_name"), "use_node_name_sharing",
|
||
|
|
_op._get_attr_bool("use_node_name_sharing"), "key_dtype",
|
||
|
|
_op._get_attr_type("key_dtype"), "value_dtype",
|
||
|
|
_op._get_attr_type("value_dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
MutableHashTableV2 = tf_export("raw_ops.MutableHashTableV2")(_ops.to_raw_op(mutable_hash_table_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def mutable_hash_table_v2_eager_fallback(key_dtype: TV_MutableHashTableV2_key_dtype, value_dtype: TV_MutableHashTableV2_value_dtype, container: str, shared_name: str, use_node_name_sharing: bool, name, ctx) -> Annotated[Any, _atypes.Resource]:
|
||
|
|
key_dtype = _execute.make_type(key_dtype, "key_dtype")
|
||
|
|
value_dtype = _execute.make_type(value_dtype, "value_dtype")
|
||
|
|
if container is None:
|
||
|
|
container = ""
|
||
|
|
container = _execute.make_str(container, "container")
|
||
|
|
if shared_name is None:
|
||
|
|
shared_name = ""
|
||
|
|
shared_name = _execute.make_str(shared_name, "shared_name")
|
||
|
|
if use_node_name_sharing is None:
|
||
|
|
use_node_name_sharing = False
|
||
|
|
use_node_name_sharing = _execute.make_bool(use_node_name_sharing, "use_node_name_sharing")
|
||
|
|
_inputs_flat = []
|
||
|
|
_attrs = ("container", container, "shared_name", shared_name,
|
||
|
|
"use_node_name_sharing", use_node_name_sharing, "key_dtype", key_dtype,
|
||
|
|
"value_dtype", value_dtype)
|
||
|
|
_result = _execute.execute(b"MutableHashTableV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"MutableHashTableV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|