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# Copyright 2024 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.
"""An abstraction that NLP models define input pipelines."""
import abc
from typing import Optional
import tensorflow as tf, tf_keras
class DataLoader(metaclass=abc.ABCMeta):
"""An abstract class defining the APIs for tf.data input pipeline."""
@abc.abstractmethod
def load(
self,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Implements DataLoader load method.
Builds the entire input pipeline inside the load method. Users can define
states inside the DataLoader class and returns a tf.data dataset
object.
Args:
input_context: This is a context class that is passed to the user's input
function and contains information about the compute replicas and input
pipelines. This object is used for multi-host inputs and passed by the
distribution strategy.
Returns:
A per-host tf.data dataset. Note that, we usually create the distributed
dataset through the load method, so we should not directly return a
distributed dataset here.
"""
pass
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