from keras.src import backend
from keras.src.utils.module_utils import tensorflow as tf


def get_tensor_spec(t, dynamic_batch=False, name=None):
    """Returns a `TensorSpec` given a single `Tensor` or `TensorSpec`."""
    if isinstance(t, tf.TypeSpec):
        spec = t
    elif isinstance(t, tf.__internal__.CompositeTensor):
        # Check for ExtensionTypes
        spec = t._type_spec
    elif hasattr(t, "shape") and hasattr(t, "dtype"):
        spec = tf.TensorSpec(shape=t.shape, dtype=t.dtype, name=name)
    else:
        return None  # Allow non-Tensors to pass through.

    if not dynamic_batch:
        return spec

    shape = spec.shape
    if shape.rank is None or shape.rank == 0:
        return spec

    shape_list = shape.as_list()
    shape_list[0] = None
    shape = tf.TensorShape(shape_list)
    spec._shape = shape
    return spec


def ensure_tensor(inputs, dtype=None):
    """Ensures the input is a Tensor, SparseTensor or RaggedTensor."""
    if not isinstance(inputs, (tf.Tensor, tf.SparseTensor, tf.RaggedTensor)):
        if backend.backend() == "torch" and backend.is_tensor(inputs):
            # Plain `np.asarray()` conversion fails with PyTorch.
            inputs = backend.convert_to_numpy(inputs)
        inputs = tf.convert_to_tensor(inputs, dtype)
    if dtype is not None and inputs.dtype != dtype:
        inputs = tf.cast(inputs, dtype)
    return inputs


def is_ragged_tensor(x):
    return "ragged_tensor.RaggedTensor" in str(type(x))
from keras.src import backend
from keras.src.utils.module_utils import tensorflow as tf


def get_tensor_spec(t, dynamic_batch=False, name=None):
    """Returns a `TensorSpec` given a single `Tensor` or `TensorSpec`."""
    if isinstance(t, tf.TypeSpec):
        spec = t
    elif isinstance(t, tf.__internal__.CompositeTensor):
        # Check for ExtensionTypes
        spec = t._type_spec
    elif hasattr(t, "shape") and hasattr(t, "dtype"):
        spec = tf.TensorSpec(shape=t.shape, dtype=t.dtype, name=name)
    else:
        return None  # Allow non-Tensors to pass through.

    if not dynamic_batch:
        return spec

    shape = spec.shape
    if shape.rank is None or shape.rank == 0:
        return spec

    shape_list = shape.as_list()
    shape_list[0] = None
    shape = tf.TensorShape(shape_list)
    spec._shape = shape
    return spec


def ensure_tensor(inputs, dtype=None):
    """Ensures the input is a Tensor, SparseTensor or RaggedTensor."""
    if not isinstance(inputs, (tf.Tensor, tf.SparseTensor, tf.RaggedTensor)):
        if backend.backend() == "torch" and backend.is_tensor(inputs):
            # Plain `np.asarray()` conversion fails with PyTorch.
            inputs = backend.convert_to_numpy(inputs)
        inputs = tf.convert_to_tensor(inputs, dtype)
    if dtype is not None and inputs.dtype != dtype:
        inputs = tf.cast(inputs, dtype)
    return inputs
