# Copyright 2020 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.
# ==============================================================================
"""Utils for creating and loading the Layer metadata for SavedModel.

These are required to retain the original format of the build input shape, since
layers and models may have different build behaviors depending on if the shape
is a list, tuple, or TensorShape. For example, Network.build() will create
separate inputs if the given input_shape is a list, and will create a single
input if the given shape is a tuple.
"""

import collections
import enum
import functools
import json

import numpy as np
import tensorflow.compat.v2 as tf
import wrapt

from keras.src.saving import serialization_lib
from keras.src.saving.legacy import serialization
from keras.src.saving.legacy.saved_model.utils import in_tf_saved_model_scope

# isort: off
from tensorflow.python.framework import type_spec_registry

_EXTENSION_TYPE_SPEC = "_EXTENSION_TYPE_SPEC"


class Encoder(json.JSONEncoder):
    """JSON encoder and decoder that handles TensorShapes and tuples."""

    def default(self, obj):
        """Encodes objects for types that aren't handled by the default
        encoder."""
        if isinstance(obj, tf.TensorShape):
            items = obj.as_list() if obj.rank is not None else None
            return {"class_name": "TensorShape", "items": items}
        return get_json_type(obj)

    def encode(self, obj):
        return super().encode(_encode_tuple(obj))


def _encode_tuple(x):
    if isinstance(x, tuple):
        return {
            "class_name": "__tuple__",
            "items": tuple(_encode_tuple(i) for i in x),
        }
    elif isinstance(x, list):
        return [_encode_tuple(i) for i in x]
    elif isinstance(x, dict):
        return {key: _encode_tuple(value) for key, value in x.items()}
    else:
        return x


def decode(json_string):
    return json.loads(json_string, object_hook=_decode_helper)


def decode_and_deserialize(
    json_string, module_objects=None, custom_objects=None
):
    """Decodes the JSON and deserializes any Keras objects found in the dict."""
    return json.loads(
        json_string,
        object_hook=functools.partial(
            _decode_helper,
            deserialize=True,
            module_objects=module_objects,
            custom_objects=custom_objects,
        ),
    )


def _decode_helper(
    obj, deserialize=False, module_objects=None, custom_objects=None
):
    """A decoding helper that is TF-object aware.

    Args:
      obj: A decoded dictionary that may represent an object.
      deserialize: Boolean, defaults to False. When True, deserializes any Keras
        objects found in `obj`.
      module_objects: A dictionary of built-in objects to look the name up in.
        Generally, `module_objects` is provided by midlevel library
        implementers.
      custom_objects: A dictionary of custom objects to look the name up in.
        Generally, `custom_objects` is provided by the end user.

    Returns:
      The decoded object.
    """
    if isinstance(obj, dict) and "class_name" in obj:
        if obj["class_name"] == "TensorShape":
            return tf.TensorShape(obj["items"])
        elif obj["class_name"] == "TypeSpec":
            return type_spec_registry.lookup(obj["type_spec"])._deserialize(
                _decode_helper(obj["serialized"])
            )
        elif obj["class_name"] == "CompositeTensor":
            spec = obj["spec"]
            tensors = []
            for dtype, tensor in obj["tensors"]:
                tensors.append(
                    tf.constant(tensor, dtype=tf.dtypes.as_dtype(dtype))
                )
            return tf.nest.pack_sequence_as(
                _decode_helper(spec), tensors, expand_composites=True
            )
        elif obj["class_name"] == "__tuple__":
            return tuple(_decode_helper(i) for i in obj["items"])
        elif obj["class_name"] == "__ellipsis__":
            return Ellipsis
        elif deserialize and "__passive_serialization__" in obj:
            # __passive_serialization__ is added by the JSON encoder when
            # encoding an object that has a `get_config()` method.
            try:
                if in_tf_saved_model_scope() or "module" not in obj:
                    return serialization.deserialize_keras_object(
                        obj,
                        module_objects=module_objects,
                        custom_objects=custom_objects,
                    )
                else:
                    return serialization_lib.deserialize_keras_object(
                        obj,
                        module_objects=module_objects,
                        custom_objects=custom_objects,
                    )
            except ValueError:
                pass
        elif obj["class_name"] == "__bytes__":
            return obj["value"].encode("utf-8")
    return obj


def get_json_type(obj):
    """Serializes any object to a JSON-serializable structure.

    Args:
        obj: the object to serialize

    Returns:
        JSON-serializable structure representing `obj`.

    Raises:
        TypeError: if `obj` cannot be serialized.
    """
    # if obj is a serializable Keras class instance
    # e.g. optimizer, layer
    if hasattr(obj, "get_config"):
        serialized = serialization.serialize_keras_object(obj)
        serialized["__passive_serialization__"] = True
        return serialized

    # if obj is any numpy type
    if type(obj).__module__ == np.__name__:
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return obj.item()

    # misc functions (e.g. loss function)
    if callable(obj):
        return obj.__name__

    # if obj is a python 'type'
    if type(obj).__name__ == type.__name__:
        return obj.__name__

    if isinstance(obj, tf.compat.v1.Dimension):
        return obj.value

    if isinstance(obj, tf.TensorShape):
        return obj.as_list()

    if isinstance(obj, tf.DType):
        return obj.name

    if isinstance(obj, collections.abc.Mapping):
        return dict(obj)

    if obj is Ellipsis:
        return {"class_name": "__ellipsis__"}

    if isinstance(obj, wrapt.ObjectProxy):
        return obj.__wrapped__

    if isinstance(obj, tf.TypeSpec):
        try:
            type_spec_name = type_spec_registry.get_name(type(obj))
            return {
                "class_name": "TypeSpec",
                "type_spec": type_spec_name,
                "serialized": obj._serialize(),
            }
        except ValueError:
            raise ValueError(
                f"Unable to serialize {obj} to JSON, because the TypeSpec "
                f"class {type(obj)} has not been registered."
            )
    if isinstance(obj, tf.__internal__.CompositeTensor):
        spec = tf.type_spec_from_value(obj)
        tensors = []
        for tensor in tf.nest.flatten(obj, expand_composites=True):
            tensors.append((tensor.dtype.name, tensor.numpy().tolist()))
        return {
            "class_name": "CompositeTensor",
            "spec": get_json_type(spec),
            "tensors": tensors,
        }

    if isinstance(obj, enum.Enum):
        return obj.value

    if isinstance(obj, bytes):
        return {"class_name": "__bytes__", "value": obj.decode("utf-8")}

    raise TypeError(
        f"Unable to serialize {obj} to JSON. Unrecognized type {type(obj)}."
    )

