350 lines
17 KiB
Python
350 lines
17 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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_DenseCountSparseOutputOutput = collections.namedtuple(
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"DenseCountSparseOutput",
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["output_indices", "output_values", "output_dense_shape"])
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TV_DenseCountSparseOutput_T = TypeVar("TV_DenseCountSparseOutput_T", _atypes.Int32, _atypes.Int64)
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TV_DenseCountSparseOutput_output_type = TypeVar("TV_DenseCountSparseOutput_output_type", _atypes.Float32, _atypes.Float64, _atypes.Int32, _atypes.Int64)
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def dense_count_sparse_output(values: Annotated[Any, TV_DenseCountSparseOutput_T], weights: Annotated[Any, TV_DenseCountSparseOutput_output_type], binary_output: bool, minlength:int=-1, maxlength:int=-1, name=None):
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r"""Performs sparse-output bin counting for a tf.tensor input.
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Counts the number of times each value occurs in the input.
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Args:
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values: A `Tensor`. Must be one of the following types: `int32`, `int64`.
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Tensor containing data to count.
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weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
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A Tensor of the same shape as indices containing per-index weight values. May
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also be the empty tensor if no weights are used.
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binary_output: A `bool`.
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Whether to output the number of occurrences of each value or 1.
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minlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Minimum value to count. Can be set to -1 for no minimum.
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maxlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Maximum value to count. Can be set to -1 for no maximum.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape).
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output_indices: A `Tensor` of type `int64`.
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output_values: A `Tensor`. Has the same type as `weights`.
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output_dense_shape: A `Tensor` of type `int64`.
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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, "DenseCountSparseOutput", name, values, weights, "minlength",
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minlength, "maxlength", maxlength, "binary_output", binary_output)
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_result = _DenseCountSparseOutputOutput._make(_result)
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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 dense_count_sparse_output_eager_fallback(
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values, weights, minlength=minlength, maxlength=maxlength,
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binary_output=binary_output, 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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binary_output = _execute.make_bool(binary_output, "binary_output")
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if minlength is None:
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minlength = -1
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minlength = _execute.make_int(minlength, "minlength")
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if maxlength is None:
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maxlength = -1
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maxlength = _execute.make_int(maxlength, "maxlength")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"DenseCountSparseOutput", values=values, weights=weights,
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binary_output=binary_output,
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minlength=minlength, maxlength=maxlength,
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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 = ("T", _op._get_attr_type("T"), "minlength",
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_op._get_attr_int("minlength"), "maxlength",
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_op._get_attr_int("maxlength"), "binary_output",
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_op._get_attr_bool("binary_output"), "output_type",
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_op._get_attr_type("output_type"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"DenseCountSparseOutput", _inputs_flat, _attrs, _result)
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_result = _DenseCountSparseOutputOutput._make(_result)
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return _result
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DenseCountSparseOutput = tf_export("raw_ops.DenseCountSparseOutput")(_ops.to_raw_op(dense_count_sparse_output))
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def dense_count_sparse_output_eager_fallback(values: Annotated[Any, TV_DenseCountSparseOutput_T], weights: Annotated[Any, TV_DenseCountSparseOutput_output_type], binary_output: bool, minlength: int, maxlength: int, name, ctx):
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binary_output = _execute.make_bool(binary_output, "binary_output")
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if minlength is None:
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minlength = -1
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minlength = _execute.make_int(minlength, "minlength")
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if maxlength is None:
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maxlength = -1
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maxlength = _execute.make_int(maxlength, "maxlength")
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_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [_dtypes.int32, _dtypes.int64, ])
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_attr_output_type, (weights,) = _execute.args_to_matching_eager([weights], ctx, [_dtypes.int32, _dtypes.int64, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [values, weights]
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_attrs = ("T", _attr_T, "minlength", minlength, "maxlength", maxlength,
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"binary_output", binary_output, "output_type", _attr_output_type)
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_result = _execute.execute(b"DenseCountSparseOutput", 3,
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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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"DenseCountSparseOutput", _inputs_flat, _attrs, _result)
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_result = _DenseCountSparseOutputOutput._make(_result)
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return _result
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_RaggedCountSparseOutputOutput = collections.namedtuple(
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"RaggedCountSparseOutput",
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["output_indices", "output_values", "output_dense_shape"])
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TV_RaggedCountSparseOutput_T = TypeVar("TV_RaggedCountSparseOutput_T", _atypes.Int32, _atypes.Int64)
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TV_RaggedCountSparseOutput_output_type = TypeVar("TV_RaggedCountSparseOutput_output_type", _atypes.Float32, _atypes.Float64, _atypes.Int32, _atypes.Int64)
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def ragged_count_sparse_output(splits: Annotated[Any, _atypes.Int64], values: Annotated[Any, TV_RaggedCountSparseOutput_T], weights: Annotated[Any, TV_RaggedCountSparseOutput_output_type], binary_output: bool, minlength:int=-1, maxlength:int=-1, name=None):
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r"""Performs sparse-output bin counting for a ragged tensor input.
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Counts the number of times each value occurs in the input.
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Args:
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splits: A `Tensor` of type `int64`.
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Tensor containing the row splits of the ragged tensor to count.
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values: A `Tensor`. Must be one of the following types: `int32`, `int64`.
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Tensor containing values of the sparse tensor to count.
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weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
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A Tensor of the same shape as indices containing per-index weight values.
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May also be the empty tensor if no weights are used.
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binary_output: A `bool`.
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Whether to output the number of occurrences of each value or 1.
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minlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Minimum value to count. Can be set to -1 for no minimum.
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maxlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Maximum value to count. Can be set to -1 for no maximum.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape).
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output_indices: A `Tensor` of type `int64`.
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output_values: A `Tensor`. Has the same type as `weights`.
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output_dense_shape: A `Tensor` of type `int64`.
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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, "RaggedCountSparseOutput", name, splits, values, weights,
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"minlength", minlength, "maxlength", maxlength, "binary_output",
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binary_output)
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_result = _RaggedCountSparseOutputOutput._make(_result)
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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 ragged_count_sparse_output_eager_fallback(
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splits, values, weights, minlength=minlength, maxlength=maxlength,
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binary_output=binary_output, 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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binary_output = _execute.make_bool(binary_output, "binary_output")
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if minlength is None:
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minlength = -1
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minlength = _execute.make_int(minlength, "minlength")
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if maxlength is None:
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maxlength = -1
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maxlength = _execute.make_int(maxlength, "maxlength")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"RaggedCountSparseOutput", splits=splits, values=values,
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weights=weights,
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binary_output=binary_output,
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minlength=minlength, maxlength=maxlength,
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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 = ("T", _op._get_attr_type("T"), "minlength",
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_op._get_attr_int("minlength"), "maxlength",
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_op._get_attr_int("maxlength"), "binary_output",
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_op._get_attr_bool("binary_output"), "output_type",
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_op._get_attr_type("output_type"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"RaggedCountSparseOutput", _inputs_flat, _attrs, _result)
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_result = _RaggedCountSparseOutputOutput._make(_result)
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return _result
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RaggedCountSparseOutput = tf_export("raw_ops.RaggedCountSparseOutput")(_ops.to_raw_op(ragged_count_sparse_output))
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def ragged_count_sparse_output_eager_fallback(splits: Annotated[Any, _atypes.Int64], values: Annotated[Any, TV_RaggedCountSparseOutput_T], weights: Annotated[Any, TV_RaggedCountSparseOutput_output_type], binary_output: bool, minlength: int, maxlength: int, name, ctx):
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binary_output = _execute.make_bool(binary_output, "binary_output")
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if minlength is None:
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minlength = -1
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minlength = _execute.make_int(minlength, "minlength")
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if maxlength is None:
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maxlength = -1
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maxlength = _execute.make_int(maxlength, "maxlength")
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_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [_dtypes.int32, _dtypes.int64, ])
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_attr_output_type, (weights,) = _execute.args_to_matching_eager([weights], ctx, [_dtypes.int32, _dtypes.int64, _dtypes.float32, _dtypes.float64, ])
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splits = _ops.convert_to_tensor(splits, _dtypes.int64)
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_inputs_flat = [splits, values, weights]
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_attrs = ("T", _attr_T, "minlength", minlength, "maxlength", maxlength,
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"binary_output", binary_output, "output_type", _attr_output_type)
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_result = _execute.execute(b"RaggedCountSparseOutput", 3,
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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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"RaggedCountSparseOutput", _inputs_flat, _attrs, _result)
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_result = _RaggedCountSparseOutputOutput._make(_result)
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return _result
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_SparseCountSparseOutputOutput = collections.namedtuple(
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"SparseCountSparseOutput",
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["output_indices", "output_values", "output_dense_shape"])
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TV_SparseCountSparseOutput_T = TypeVar("TV_SparseCountSparseOutput_T", _atypes.Int32, _atypes.Int64)
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TV_SparseCountSparseOutput_output_type = TypeVar("TV_SparseCountSparseOutput_output_type", _atypes.Float32, _atypes.Float64, _atypes.Int32, _atypes.Int64)
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def sparse_count_sparse_output(indices: Annotated[Any, _atypes.Int64], values: Annotated[Any, TV_SparseCountSparseOutput_T], dense_shape: Annotated[Any, _atypes.Int64], weights: Annotated[Any, TV_SparseCountSparseOutput_output_type], binary_output: bool, minlength:int=-1, maxlength:int=-1, name=None):
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r"""Performs sparse-output bin counting for a sparse tensor input.
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Counts the number of times each value occurs in the input.
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Args:
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indices: A `Tensor` of type `int64`.
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Tensor containing the indices of the sparse tensor to count.
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values: A `Tensor`. Must be one of the following types: `int32`, `int64`.
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Tensor containing values of the sparse tensor to count.
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dense_shape: A `Tensor` of type `int64`.
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Tensor containing the dense shape of the sparse tensor to count.
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weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
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A Tensor of the same shape as indices containing per-index weight values.
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May also be the empty tensor if no weights are used.
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binary_output: A `bool`.
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Whether to output the number of occurrences of each value or 1.
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minlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Minimum value to count. Can be set to -1 for no minimum.
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maxlength: An optional `int` that is `>= -1`. Defaults to `-1`.
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Maximum value to count. Can be set to -1 for no maximum.
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name: A name for the operation (optional).
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Returns:
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A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape).
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output_indices: A `Tensor` of type `int64`.
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output_values: A `Tensor`. Has the same type as `weights`.
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output_dense_shape: A `Tensor` of type `int64`.
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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, "SparseCountSparseOutput", name, indices, values, dense_shape,
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weights, "minlength", minlength, "maxlength", maxlength,
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"binary_output", binary_output)
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_result = _SparseCountSparseOutputOutput._make(_result)
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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 sparse_count_sparse_output_eager_fallback(
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indices, values, dense_shape, weights, minlength=minlength,
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maxlength=maxlength, binary_output=binary_output, name=name,
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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.
|
||
|
|
binary_output = _execute.make_bool(binary_output, "binary_output")
|
||
|
|
if minlength is None:
|
||
|
|
minlength = -1
|
||
|
|
minlength = _execute.make_int(minlength, "minlength")
|
||
|
|
if maxlength is None:
|
||
|
|
maxlength = -1
|
||
|
|
maxlength = _execute.make_int(maxlength, "maxlength")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"SparseCountSparseOutput", indices=indices, values=values,
|
||
|
|
dense_shape=dense_shape, weights=weights,
|
||
|
|
binary_output=binary_output,
|
||
|
|
minlength=minlength, maxlength=maxlength,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"), "minlength",
|
||
|
|
_op._get_attr_int("minlength"), "maxlength",
|
||
|
|
_op._get_attr_int("maxlength"), "binary_output",
|
||
|
|
_op._get_attr_bool("binary_output"), "output_type",
|
||
|
|
_op._get_attr_type("output_type"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"SparseCountSparseOutput", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _SparseCountSparseOutputOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|
||
|
|
SparseCountSparseOutput = tf_export("raw_ops.SparseCountSparseOutput")(_ops.to_raw_op(sparse_count_sparse_output))
|
||
|
|
|
||
|
|
|
||
|
|
def sparse_count_sparse_output_eager_fallback(indices: Annotated[Any, _atypes.Int64], values: Annotated[Any, TV_SparseCountSparseOutput_T], dense_shape: Annotated[Any, _atypes.Int64], weights: Annotated[Any, TV_SparseCountSparseOutput_output_type], binary_output: bool, minlength: int, maxlength: int, name, ctx):
|
||
|
|
binary_output = _execute.make_bool(binary_output, "binary_output")
|
||
|
|
if minlength is None:
|
||
|
|
minlength = -1
|
||
|
|
minlength = _execute.make_int(minlength, "minlength")
|
||
|
|
if maxlength is None:
|
||
|
|
maxlength = -1
|
||
|
|
maxlength = _execute.make_int(maxlength, "maxlength")
|
||
|
|
_attr_T, (values,) = _execute.args_to_matching_eager([values], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_attr_output_type, (weights,) = _execute.args_to_matching_eager([weights], ctx, [_dtypes.int32, _dtypes.int64, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
indices = _ops.convert_to_tensor(indices, _dtypes.int64)
|
||
|
|
dense_shape = _ops.convert_to_tensor(dense_shape, _dtypes.int64)
|
||
|
|
_inputs_flat = [indices, values, dense_shape, weights]
|
||
|
|
_attrs = ("T", _attr_T, "minlength", minlength, "maxlength", maxlength,
|
||
|
|
"binary_output", binary_output, "output_type", _attr_output_type)
|
||
|
|
_result = _execute.execute(b"SparseCountSparseOutput", 3,
|
||
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
||
|
|
name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"SparseCountSparseOutput", _inputs_flat, _attrs, _result)
|
||
|
|
_result = _SparseCountSparseOutputOutput._make(_result)
|
||
|
|
return _result
|
||
|
|
|