Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorflow/python/data/ops/take_while_op.py

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2023-06-19 00:49:18 +02:00
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The implementation of `tf.data.Dataset.take_while`."""
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import structured_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_spec
from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
def _take_while(input_dataset, predicate, name=None): # pylint: disable=unused-private-name
"""See `Dataset.take_while()` for details."""
return _TakeWhileDataset(input_dataset, predicate, name=name)
class _TakeWhileDataset(dataset_ops.UnaryUnchangedStructureDataset):
"""A dataset that stops iteration when `predicate` returns false."""
def __init__(self, input_dataset, predicate, name=None):
"""See `take_while()` for details."""
self._input_dataset = input_dataset
wrapped_func = structured_function.StructuredFunctionWrapper(
predicate, self._transformation_name(), dataset=self._input_dataset)
if not wrapped_func.output_structure.is_compatible_with(
tensor_spec.TensorSpec([], dtypes.bool)):
raise ValueError(f"Invalid `predicate`. `predicate` must return a "
f"`tf.bool` scalar tensor but its return type is"
f"{wrapped_func.output_structure}.")
self._predicate = wrapped_func
self._name = name
variant_tensor = ged_ops.take_while_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
other_arguments=self._predicate.function.captured_inputs,
predicate=self._predicate.function,
**self._common_args)
super().__init__(input_dataset, variant_tensor)
def _functions(self):
return [self._predicate]
def _transformation_name(self):
return "Dataset.take_while()"