EVOLUTION-MANAGER

Edit File: gen_set_ops.py

"""Python wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. Original C++ source file: set_ops.cc """ import collections from tensorflow.python import pywrap_tfe as pywrap_tfe from tensorflow.python.eager import context as _context from tensorflow.python.eager import core as _core from tensorflow.python.eager import execute as _execute from tensorflow.python.framework import dtypes as _dtypes from tensorflow.python.framework import op_def_registry as _op_def_registry from tensorflow.python.framework import ops as _ops from tensorflow.python.framework import op_def_library as _op_def_library from tensorflow.python.util.deprecation import deprecated_endpoints from tensorflow.python.util import dispatch as _dispatch from tensorflow.python.util.tf_export import tf_export from typing import TypeVar _DenseToDenseSetOperationOutput = collections.namedtuple( "DenseToDenseSetOperation", ["result_indices", "result_values", "result_shape"]) def dense_to_dense_set_operation(set1, set2, set_operation, validate_indices=True, name=None): r"""Applies set operation along last dimension of 2 `Tensor` inputs. See SetOperationOp::SetOperationFromContext for values of `set_operation`. Output `result` is a `SparseTensor` represented by `result_indices`, `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` dimension contains the result of `set_operation` applied to the corresponding `[0...n-1]` dimension of `set`. Args: set1: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`. `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. Dimension `n` contains values in a set, duplicates are allowed but ignored. set2: A `Tensor`. Must have the same type as `set1`. `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`. Dimension `n` contains values in a set, duplicates are allowed but ignored. set_operation: A `string`. validate_indices: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (result_indices, result_values, result_shape). result_indices: A `Tensor` of type `int64`. result_values: A `Tensor`. Has the same type as `set1`. result_shape: 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, "DenseToDenseSetOperation", name, set1, set2, "set_operation", set_operation, "validate_indices", validate_indices) _result = _DenseToDenseSetOperationOutput._make(_result) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: return dense_to_dense_set_operation_eager_fallback( set1, set2, set_operation=set_operation, validate_indices=validate_indices, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. # Add nodes to the TensorFlow graph. set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _, _, _op, _outputs = _op_def_library._apply_op_helper( "DenseToDenseSetOperation", set1=set1, set2=set2, set_operation=set_operation, validate_indices=validate_indices, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("set_operation", _op.get_attr("set_operation"), "validate_indices", _op._get_attr_bool("validate_indices"), "T", _op._get_attr_type("T")) _inputs_flat = _op.inputs _execute.record_gradient( "DenseToDenseSetOperation", _inputs_flat, _attrs, _result) _result = _DenseToDenseSetOperationOutput._make(_result) return _result DenseToDenseSetOperation = tf_export("raw_ops.DenseToDenseSetOperation")(_ops.to_raw_op(dense_to_dense_set_operation)) def dense_to_dense_set_operation_eager_fallback(set1, set2, set_operation, validate_indices, name, ctx): set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _attr_T, _inputs_T = _execute.args_to_matching_eager([set1, set2], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ]) (set1, set2) = _inputs_T _inputs_flat = [set1, set2] _attrs = ("set_operation", set_operation, "validate_indices", validate_indices, "T", _attr_T) _result = _execute.execute(b"DenseToDenseSetOperation", 3, inputs=_inputs_flat, attrs=_attrs, ctx=ctx, name=name) if _execute.must_record_gradient(): _execute.record_gradient( "DenseToDenseSetOperation", _inputs_flat, _attrs, _result) _result = _DenseToDenseSetOperationOutput._make(_result) return _result _DenseToSparseSetOperationOutput = collections.namedtuple( "DenseToSparseSetOperation", ["result_indices", "result_values", "result_shape"]) def dense_to_sparse_set_operation(set1, set2_indices, set2_values, set2_shape, set_operation, validate_indices=True, name=None): r"""Applies set operation along last dimension of `Tensor` and `SparseTensor`. See SetOperationOp::SetOperationFromContext for values of `set_operation`. Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same as `set1`. Dimension `n` contains values in a set, duplicates are allowed but ignored. If `validate_indices` is `True`, this op validates the order and range of `set2` indices. Output `result` is a `SparseTensor` represented by `result_indices`, `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` dimension contains the result of `set_operation` applied to the corresponding `[0...n-1]` dimension of `set`. Args: set1: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`. `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`. Dimension `n` contains values in a set, duplicates are allowed but ignored. set2_indices: A `Tensor` of type `int64`. 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major order. set2_values: A `Tensor`. Must have the same type as `set1`. 1D `Tensor`, values of a `SparseTensor`. Must be in row-major order. set2_shape: A `Tensor` of type `int64`. 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must be the same as the 1st `n-1` dimensions of `set1`, `result_shape[n]` is the max set size across `n-1` dimensions. set_operation: A `string`. validate_indices: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (result_indices, result_values, result_shape). result_indices: A `Tensor` of type `int64`. result_values: A `Tensor`. Has the same type as `set1`. result_shape: 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, "DenseToSparseSetOperation", name, set1, set2_indices, set2_values, set2_shape, "set_operation", set_operation, "validate_indices", validate_indices) _result = _DenseToSparseSetOperationOutput._make(_result) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: return dense_to_sparse_set_operation_eager_fallback( set1, set2_indices, set2_values, set2_shape, set_operation=set_operation, validate_indices=validate_indices, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. # Add nodes to the TensorFlow graph. set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _, _, _op, _outputs = _op_def_library._apply_op_helper( "DenseToSparseSetOperation", set1=set1, set2_indices=set2_indices, set2_values=set2_values, set2_shape=set2_shape, set_operation=set_operation, validate_indices=validate_indices, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("set_operation", _op.get_attr("set_operation"), "validate_indices", _op._get_attr_bool("validate_indices"), "T", _op._get_attr_type("T")) _inputs_flat = _op.inputs _execute.record_gradient( "DenseToSparseSetOperation", _inputs_flat, _attrs, _result) _result = _DenseToSparseSetOperationOutput._make(_result) return _result DenseToSparseSetOperation = tf_export("raw_ops.DenseToSparseSetOperation")(_ops.to_raw_op(dense_to_sparse_set_operation)) def dense_to_sparse_set_operation_eager_fallback(set1, set2_indices, set2_values, set2_shape, set_operation, validate_indices, name, ctx): set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _attr_T, _inputs_T = _execute.args_to_matching_eager([set1, set2_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ]) (set1, set2_values) = _inputs_T set2_indices = _ops.convert_to_tensor(set2_indices, _dtypes.int64) set2_shape = _ops.convert_to_tensor(set2_shape, _dtypes.int64) _inputs_flat = [set1, set2_indices, set2_values, set2_shape] _attrs = ("set_operation", set_operation, "validate_indices", validate_indices, "T", _attr_T) _result = _execute.execute(b"DenseToSparseSetOperation", 3, inputs=_inputs_flat, attrs=_attrs, ctx=ctx, name=name) if _execute.must_record_gradient(): _execute.record_gradient( "DenseToSparseSetOperation", _inputs_flat, _attrs, _result) _result = _DenseToSparseSetOperationOutput._make(_result) return _result def set_size(set_indices, set_values, set_shape, validate_indices=True, name=None): r"""Number of unique elements along last dimension of input `set`. Input `set` is a `SparseTensor` represented by `set_indices`, `set_values`, and `set_shape`. The last dimension contains values in a set, duplicates are allowed but ignored. If `validate_indices` is `True`, this op validates the order and range of `set` indices. Args: set_indices: A `Tensor` of type `int64`. 2D `Tensor`, indices of a `SparseTensor`. set_values: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`. 1D `Tensor`, values of a `SparseTensor`. set_shape: A `Tensor` of type `int64`. 1D `Tensor`, shape of a `SparseTensor`. validate_indices: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A `Tensor` of type `int32`. """ _ctx = _context._context or _context.context() tld = _ctx._thread_local_data if tld.is_eager: try: _result = pywrap_tfe.TFE_Py_FastPathExecute( _ctx, "SetSize", name, set_indices, set_values, set_shape, "validate_indices", validate_indices) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: return set_size_eager_fallback( set_indices, set_values, set_shape, validate_indices=validate_indices, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. # Add nodes to the TensorFlow graph. if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _, _, _op, _outputs = _op_def_library._apply_op_helper( "SetSize", set_indices=set_indices, set_values=set_values, set_shape=set_shape, validate_indices=validate_indices, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("validate_indices", _op._get_attr_bool("validate_indices"), "T", _op._get_attr_type("T")) _inputs_flat = _op.inputs _execute.record_gradient( "SetSize", _inputs_flat, _attrs, _result) _result, = _result return _result SetSize = tf_export("raw_ops.SetSize")(_ops.to_raw_op(set_size)) def set_size_eager_fallback(set_indices, set_values, set_shape, validate_indices, name, ctx): if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _attr_T, (set_values,) = _execute.args_to_matching_eager([set_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ]) set_indices = _ops.convert_to_tensor(set_indices, _dtypes.int64) set_shape = _ops.convert_to_tensor(set_shape, _dtypes.int64) _inputs_flat = [set_indices, set_values, set_shape] _attrs = ("validate_indices", validate_indices, "T", _attr_T) _result = _execute.execute(b"SetSize", 1, inputs=_inputs_flat, attrs=_attrs, ctx=ctx, name=name) if _execute.must_record_gradient(): _execute.record_gradient( "SetSize", _inputs_flat, _attrs, _result) _result, = _result return _result _SparseToSparseSetOperationOutput = collections.namedtuple( "SparseToSparseSetOperation", ["result_indices", "result_values", "result_shape"]) def sparse_to_sparse_set_operation(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation, validate_indices=True, name=None): r"""Applies set operation along last dimension of 2 `SparseTensor` inputs. See SetOperationOp::SetOperationFromContext for values of `set_operation`. If `validate_indices` is `True`, `SparseToSparseSetOperation` validates the order and range of `set1` and `set2` indices. Input `set1` is a `SparseTensor` represented by `set1_indices`, `set1_values`, and `set1_shape`. For `set1` ranked `n`, 1st `n-1` dimensions must be the same as `set2`. Dimension `n` contains values in a set, duplicates are allowed but ignored. Input `set2` is a `SparseTensor` represented by `set2_indices`, `set2_values`, and `set2_shape`. For `set2` ranked `n`, 1st `n-1` dimensions must be the same as `set1`. Dimension `n` contains values in a set, duplicates are allowed but ignored. If `validate_indices` is `True`, this op validates the order and range of `set1` and `set2` indices. Output `result` is a `SparseTensor` represented by `result_indices`, `result_values`, and `result_shape`. For `set1` and `set2` ranked `n`, this has rank `n` and the same 1st `n-1` dimensions as `set1` and `set2`. The `nth` dimension contains the result of `set_operation` applied to the corresponding `[0...n-1]` dimension of `set`. Args: set1_indices: A `Tensor` of type `int64`. 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major order. set1_values: A `Tensor`. Must be one of the following types: `int8`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `string`. 1D `Tensor`, values of a `SparseTensor`. Must be in row-major order. set1_shape: A `Tensor` of type `int64`. 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must be the same as `set2_shape[0...n-1]`, `set1_shape[n]` is the max set size across `0...n-1` dimensions. set2_indices: A `Tensor` of type `int64`. 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major order. set2_values: A `Tensor`. Must have the same type as `set1_values`. 1D `Tensor`, values of a `SparseTensor`. Must be in row-major order. set2_shape: A `Tensor` of type `int64`. 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must be the same as `set1_shape[0...n-1]`, `set2_shape[n]` is the max set size across `0...n-1` dimensions. set_operation: A `string`. validate_indices: An optional `bool`. Defaults to `True`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (result_indices, result_values, result_shape). result_indices: A `Tensor` of type `int64`. result_values: A `Tensor`. Has the same type as `set1_values`. result_shape: 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, "SparseToSparseSetOperation", name, set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, "set_operation", set_operation, "validate_indices", validate_indices) _result = _SparseToSparseSetOperationOutput._make(_result) return _result except _core._NotOkStatusException as e: _ops.raise_from_not_ok_status(e, name) except _core._FallbackException: pass try: return sparse_to_sparse_set_operation_eager_fallback( set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation=set_operation, validate_indices=validate_indices, name=name, ctx=_ctx) except _core._SymbolicException: pass # Add nodes to the TensorFlow graph. # Add nodes to the TensorFlow graph. set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _, _, _op, _outputs = _op_def_library._apply_op_helper( "SparseToSparseSetOperation", set1_indices=set1_indices, set1_values=set1_values, set1_shape=set1_shape, set2_indices=set2_indices, set2_values=set2_values, set2_shape=set2_shape, set_operation=set_operation, validate_indices=validate_indices, name=name) _result = _outputs[:] if _execute.must_record_gradient(): _attrs = ("set_operation", _op.get_attr("set_operation"), "validate_indices", _op._get_attr_bool("validate_indices"), "T", _op._get_attr_type("T")) _inputs_flat = _op.inputs _execute.record_gradient( "SparseToSparseSetOperation", _inputs_flat, _attrs, _result) _result = _SparseToSparseSetOperationOutput._make(_result) return _result SparseToSparseSetOperation = tf_export("raw_ops.SparseToSparseSetOperation")(_ops.to_raw_op(sparse_to_sparse_set_operation)) def sparse_to_sparse_set_operation_eager_fallback(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation, validate_indices, name, ctx): set_operation = _execute.make_str(set_operation, "set_operation") if validate_indices is None: validate_indices = True validate_indices = _execute.make_bool(validate_indices, "validate_indices") _attr_T, _inputs_T = _execute.args_to_matching_eager([set1_values, set2_values], ctx, [_dtypes.int8, _dtypes.int16, _dtypes.int32, _dtypes.int64, _dtypes.uint8, _dtypes.uint16, _dtypes.string, ]) (set1_values, set2_values) = _inputs_T set1_indices = _ops.convert_to_tensor(set1_indices, _dtypes.int64) set1_shape = _ops.convert_to_tensor(set1_shape, _dtypes.int64) set2_indices = _ops.convert_to_tensor(set2_indices, _dtypes.int64) set2_shape = _ops.convert_to_tensor(set2_shape, _dtypes.int64) _inputs_flat = [set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape] _attrs = ("set_operation", set_operation, "validate_indices", validate_indices, "T", _attr_T) _result = _execute.execute(b"SparseToSparseSetOperation", 3, inputs=_inputs_flat, attrs=_attrs, ctx=ctx, name=name) if _execute.must_record_gradient(): _execute.record_gradient( "SparseToSparseSetOperation", _inputs_flat, _attrs, _result) _result = _SparseToSparseSetOperationOutput._make(_result) return _result