# Copyright 2015 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.
# ==============================================================================

"""Input layer code (`Input` and `InputLayer`)."""

import tensorflow.compat.v2 as tf

from keras.src import backend
from keras.src.distribute import distributed_training_utils
from keras.src.engine import base_layer
from keras.src.engine import keras_tensor
from keras.src.engine import node as node_module
from keras.src.saving import serialization_lib
from keras.src.saving.legacy.saved_model import layer_serialization
from keras.src.utils import tf_utils
from keras.src.utils import traceback_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export


def _assert_other_arg_none(arg_name, arg):
    if arg is not None:
        raise ValueError(
            "When `type_spec` is not None, all other args "
            "except `name` must be None, "
            "but %s is not None." % arg_name
        )


@keras_export("keras.layers.InputLayer")
class InputLayer(base_layer.Layer):
    """Layer to be used as an entry point into a Network (a graph of layers).

    It can either wrap an existing tensor (pass an `input_tensor` argument)
    or create a placeholder tensor (pass arguments `input_shape`, and
    optionally, `dtype`).

    It is generally recommend to use the Keras Functional model via `Input`,
    (which creates an `InputLayer`) without directly using `InputLayer`.

    When using `InputLayer` with the Keras Sequential model, it can be skipped
    by moving the `input_shape` parameter to the first layer after the
    `InputLayer`.

    This class can create placeholders for `tf.Tensors`, `tf.SparseTensors`, and
    `tf.RaggedTensors` by choosing `sparse=True` or `ragged=True`. Note that
    `sparse` and `ragged` can't be configured to `True` at the same time.
    Usage:

    ```python
    # With explicit InputLayer.
    model = tf.keras.Sequential([
      tf.keras.layers.InputLayer(input_shape=(4,)),
      tf.keras.layers.Dense(8)])
    model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse')
    model.fit(np.zeros((10, 4)),
              np.ones((10, 8)))

    # Without InputLayer and let the first layer to have the input_shape.
    # Keras will add a input for the model behind the scene.
    model = tf.keras.Sequential([
      tf.keras.layers.Dense(8, input_shape=(4,))])
    model.compile(tf.keras.optimizers.RMSprop(0.001), loss='mse')
    model.fit(np.zeros((10, 4)),
              np.ones((10, 8)))
    ```

    Args:
        input_shape: Shape tuple (not including the batch axis), or
            `TensorShape` instance (not including the batch axis).
        batch_size: Optional input batch size (integer or `None`).
        dtype: Optional datatype of the input. When not provided, the Keras
            default `float` type will be used.
        input_tensor: Optional tensor to use as layer input. If set, the layer
            will use the `tf.TypeSpec` of this tensor rather
            than creating a new placeholder tensor.
        sparse: Boolean, whether the placeholder created is meant to be sparse.
            Defaults to `False`.
        ragged: Boolean, whether the placeholder created is meant to be ragged.
            In this case, values of `None` in the `shape` argument represent
            ragged dimensions. For more information about `tf.RaggedTensor`, see
            [this guide](https://www.tensorflow.org/guide/ragged_tensor).
            Defaults to `False`.
        type_spec: A `tf.TypeSpec` object to create Input from. This
            `tf.TypeSpec` represents the entire batch. When provided, all other
            args except name must be `None`.
        name: Optional name of the layer (string).
    """

    @traceback_utils.filter_traceback
    def __init__(
        self,
        input_shape=None,
        batch_size=None,
        dtype=None,
        input_tensor=None,
        sparse=None,
        name=None,
        ragged=None,
        type_spec=None,
        **kwargs,
    ):
        self._init_input_shape = input_shape
        self._init_batch_size = batch_size
        self._init_dtype = dtype
        self._init_sparse = sparse
        self._init_ragged = ragged
        self._init_type_spec = type_spec

        strategy = tf.distribute.get_strategy()
        if (
            strategy
            and batch_size is not None
            and distributed_training_utils.global_batch_size_supported(strategy)
        ):
            if batch_size % strategy.num_replicas_in_sync != 0:
                raise ValueError(
                    "The `batch_size` argument ({}) must be divisible by "
                    "the number of replicas ({})".format(
                        batch_size, strategy.num_replicas_in_sync
                    )
                )
            batch_size = batch_size // strategy.num_replicas_in_sync

        if "batch_input_shape" in kwargs:
            batch_input_shape = kwargs.pop("batch_input_shape")
            if input_shape and batch_input_shape:
                raise ValueError(
                    "Only provide the input_shape OR "
                    "batch_input_shape argument to "
                    "InputLayer, not both at the same time."
                )
            # Set the input shape and batch size from the batch_input_shape.
            # Note that batch_input_shape can be None (unknown rank) or []
            # (scalar), in which case the batch size must be None.
            if batch_input_shape:
                batch_size = batch_input_shape[0]
                input_shape = batch_input_shape[1:]
        if kwargs:
            raise ValueError(
                f"Unrecognized keyword arguments: {list(kwargs.keys())}"
            )

        if sparse and ragged:
            raise ValueError(
                "Cannot set both sparse and ragged to True in a Keras input."
            )

        if not name:
            prefix = "input"
            name = prefix + "_" + str(backend.get_uid(prefix))

        if not dtype:
            if input_tensor is None:
                dtype = backend.floatx()
            else:
                dtype = backend.dtype(input_tensor)
        elif input_tensor is not None and input_tensor.dtype != dtype:
            raise ValueError(
                "`input_tensor.dtype` differs from `dtype`. Received: "
                f"input_tensor.dtype={input_tensor.dtype} "
                f"but expected dtype={dtype}"
            )
        super().__init__(dtype=dtype, name=name)
        self.built = True
        self.sparse = True if sparse else False
        self.ragged = True if ragged else False
        self.batch_size = batch_size
        self.supports_masking = True

        if isinstance(input_shape, tf.TensorShape):
            input_shape = tuple(input_shape.as_list())
        elif isinstance(input_shape, int):
            input_shape = (input_shape,)

        if type_spec is not None:
            args_that_must_be_none = [
                ("(input_)shape", self._init_input_shape),
                ("batch_size", self._init_batch_size),
                ("dtype", self._init_dtype),
                ("input_tensor", input_tensor),
                ("sparse", self._init_sparse),
                ("ragged", self._init_ragged),
            ]
            for arg_name, arg in args_that_must_be_none:
                _assert_other_arg_none(arg_name, arg)
            if not tf.compat.v1.executing_eagerly_outside_functions():
                raise ValueError(
                    "Creating Keras inputs from a type_spec is only "
                    "supported when eager execution is enabled."
                )
            # Needed for type_spec deserialization since TypeSpec objects
            # are not Keras-native (not automatically deserialized).
            if isinstance(type_spec, dict):
                type_spec = serialization_lib.deserialize_keras_object(
                    type_spec
                )
            input_tensor = keras_tensor.keras_tensor_from_type_spec(type_spec)
            if isinstance(input_tensor, keras_tensor.SparseKerasTensor):
                self.sparse = True
            if isinstance(input_tensor, keras_tensor.RaggedKerasTensor):
                self.ragged = True
            self.is_placeholder = True
            try:
                self._batch_input_shape = tuple(input_tensor.shape.as_list())
            except ValueError:
                # If the shape cannot be represented as a tuple (e.g. unknown
                # rank)
                self._batch_input_shape = None
        elif input_tensor is None:
            if input_shape is not None:
                batch_input_shape = (batch_size,) + tuple(input_shape)
            else:
                batch_input_shape = None
            graph = backend.get_graph()
            with graph.as_default():
                input_tensor = backend.placeholder(
                    shape=batch_input_shape,
                    dtype=dtype,
                    name=self.name,
                    sparse=sparse,
                    ragged=ragged,
                )

            self.is_placeholder = True
            self._batch_input_shape = batch_input_shape
        else:
            if tf.compat.v1.executing_eagerly_outside_functions():
                if not isinstance(input_tensor, keras_tensor.KerasTensor):
                    input_tensor = keras_tensor.keras_tensor_from_tensor(
                        input_tensor
                    )
            else:
                if not tf_utils.is_symbolic_tensor(input_tensor):
                    raise ValueError(
                        "You should not pass an EagerTensor to `Input`. "
                        "For example, instead of creating an "
                        "`InputLayer`, you should instantiate your model "
                        "and directly call it on your input."
                    )
            self.is_placeholder = False
            try:
                self._batch_input_shape = tuple(input_tensor.shape.as_list())
            except ValueError:
                # If the shape cannot be represented as a tuple (e.g. unknown
                # rank)
                self._batch_input_shape = None
        # Create an input node.
        input_tensor._keras_mask = None
        node_module.Node(layer=self, outputs=input_tensor)

        # Store type spec
        if isinstance(input_tensor, keras_tensor.KerasTensor) or (
            tf_utils.is_extension_type(input_tensor)
        ):
            self._type_spec = input_tensor._type_spec
        else:
            self._type_spec = tf.TensorSpec(
                shape=input_tensor.shape,
                dtype=input_tensor.dtype,
                name=self.name,
            )

    def get_config(self):
        if self._init_type_spec is not None:
            config = {"name": self.name, "type_spec": self._init_type_spec}
        else:
            config = {
                "batch_input_shape": self._batch_input_shape,
                "dtype": self.dtype,
                "sparse": self.sparse,
                "ragged": self.ragged,
                "name": self.name,
            }
        return config

    @property
    def _trackable_saved_model_saver(self):
        return layer_serialization.InputLayerSavedModelSaver(self)


@keras_export("keras.Input", "keras.layers.Input")
@traceback_utils.filter_traceback
def Input(
    shape=None,
    batch_size=None,
    name=None,
    dtype=None,
    sparse=None,
    tensor=None,
    ragged=None,
    type_spec=None,
    **kwargs,
):
    """`Input()` is used to instantiate a Keras tensor.

    A Keras tensor is a symbolic tensor-like object, which we augment with
    certain attributes that allow us to build a Keras model just by knowing the
    inputs and outputs of the model.

    For instance, if `a`, `b` and `c` are Keras tensors,
    it becomes possible to do:
    `model = Model(input=[a, b], output=c)`

    Args:
        shape: A shape tuple (integers), not including the batch size.
            For instance, `shape=(32,)` indicates that the expected input
            will be batches of 32-dimensional vectors. Elements of this tuple
            can be None; 'None' elements represent dimensions where the shape is
            not known.
        batch_size: optional static batch size (integer).
        name: An optional name string for the layer.
            Should be unique in a model (do not reuse the same name twice).
            It will be autogenerated if it isn't provided.
        dtype: The data type expected by the input, as a string
            (`float32`, `float64`, `int32`...)
        sparse: A boolean specifying whether the placeholder to be created is
            sparse. Only one of 'ragged' and 'sparse' can be True. Note that,
            if `sparse` is False, sparse tensors can still be passed into the
            input - they will be densified with a default value of 0.
        tensor: Optional existing tensor to wrap into the `Input` layer.
            If set, the layer will use the `tf.TypeSpec` of this tensor rather
            than creating a new placeholder tensor.
        ragged: A boolean specifying whether the placeholder to be created is
            ragged. Only one of 'ragged' and 'sparse' can be True. In this case,
            values of 'None' in the 'shape' argument represent ragged
            dimensions.  For more information about RaggedTensors, see
            [this guide](https://www.tensorflow.org/guide/ragged_tensor).
        type_spec: A `tf.TypeSpec` object to create the input placeholder from.
            When provided, all other args except name must be None.
        **kwargs: deprecated arguments support. Supports `batch_shape` and
            `batch_input_shape`.

    Returns:
      A `tensor`.

    Example:

    ```python
    # this is a logistic regression in Keras
    x = Input(shape=(32,))
    y = Dense(16, activation='softmax')(x)
    model = Model(x, y)
    ```

    Note that even if eager execution is enabled,
    `Input` produces a symbolic tensor-like object (i.e. a placeholder).
    This symbolic tensor-like object can be used with lower-level
    TensorFlow ops that take tensors as inputs, as such:

    ```python
    x = Input(shape=(32,))
    y = tf.square(x)  # This op will be treated like a layer
    model = Model(x, y)
    ```

    (This behavior does not work for higher-order TensorFlow APIs such as
    control flow and being directly watched by a `tf.GradientTape`).

    However, the resulting model will not track any variables that were
    used as inputs to TensorFlow ops. All variable usages must happen within
    Keras layers to make sure they will be tracked by the model's weights.

    The Keras Input can also create a placeholder from an arbitrary
    `tf.TypeSpec`, e.g:

    ```python
    x = Input(type_spec=tf.RaggedTensorSpec(shape=[None, None],
                                            dtype=tf.float32, ragged_rank=1))
    y = x.values
    model = Model(x, y)
    ```
    When passing an arbitrary `tf.TypeSpec`, it must represent the signature of
    an entire batch instead of just one example.

    Raises:
      ValueError: If both `sparse` and `ragged` are provided.
      ValueError: If both `shape` and (`batch_input_shape` or `batch_shape`) are
        provided.
      ValueError: If `shape`, `tensor` and `type_spec` are None.
      ValueError: If arguments besides `type_spec` are non-None while
        `type_spec` is passed.
      ValueError: if any unrecognized parameters are provided.
    """
    if sparse and ragged:
        raise ValueError(
            "Cannot set both `sparse` and `ragged` to `True` in a "
            "Keras `Input`."
        )

    has_spec_name = (
        name is None and type_spec is not None and hasattr(type_spec, "name")
    )

    if has_spec_name:
        name = type_spec.name

    input_layer_config = {
        "name": name,
        "dtype": dtype,
        "sparse": sparse,
        "ragged": ragged,
        "input_tensor": tensor,
        "type_spec": type_spec,
    }

    batch_input_shape = kwargs.pop(
        "batch_input_shape", kwargs.pop("batch_shape", None)
    )
    if shape is not None and batch_input_shape is not None:
        raise ValueError(
            "Only provide the `shape` OR `batch_input_shape` argument "
            "to Input, not both at the same time."
        )
    if (
        batch_input_shape is None
        and shape is None
        and tensor is None
        and type_spec is None
    ):
        raise ValueError(
            "Please provide to Input a `shape` "
            "or a `tensor` or a `type_spec` argument. Note that "
            "`shape` does not include the batch "
            "dimension."
        )
    if kwargs:
        raise ValueError(
            f"Unrecognized keyword arguments: {list(kwargs.keys())}"
        )

    if batch_input_shape:
        shape = batch_input_shape[1:]
        input_layer_config.update({"batch_input_shape": batch_input_shape})
    else:
        input_layer_config.update(
            {"batch_size": batch_size, "input_shape": shape}
        )
    input_layer = InputLayer(**input_layer_config)

    # Return tensor including `_keras_history`.
    # Note that in this case train_output and test_output are the same pointer.
    outputs = input_layer._inbound_nodes[0].outputs
    if isinstance(outputs, list) and len(outputs) == 1:
        output = outputs[0]
    else:
        output = outputs
    if has_spec_name and hasattr(output, "_name"):
        output._name = input_layer.name
    return output

