# Copyright 2017 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.
# ==============================================================================
"""Utilities for exporting TensorFlow symbols to the API.

Exporting a function or a class:

To export a function or a class use tf_export decorator. For e.g.:
```python
@tf_export('foo', 'bar.foo')
def foo(...):
  ...
```

If a function is assigned to a variable, you can export it by calling
tf_export explicitly. For e.g.:
```python
foo = get_foo(...)
tf_export('foo', 'bar.foo')(foo)
```


Exporting a constant
```python
foo = 1
tf_export('consts.foo').export_constant(__name__, 'foo')
```
"""
from collections.abc import Sequence
import functools
import sys
from typing import Any, NamedTuple, Optional, Protocol, TypeVar

from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect

KERAS_API_NAME = 'keras'
TENSORFLOW_API_NAME = 'tensorflow'

# List of subpackage names used by TensorFlow components. Have to check that
# TensorFlow core repo does not export any symbols under these names.
SUBPACKAGE_NAMESPACES = []


class _Attributes(NamedTuple):
  names: str
  constants: str


# Attribute values must be unique to each API.
API_ATTRS = {
    TENSORFLOW_API_NAME: _Attributes('_tf_api_names', '_tf_api_constants'),
    KERAS_API_NAME: _Attributes('_keras_api_names', '_keras_api_constants'),
}

API_ATTRS_V1 = {
    TENSORFLOW_API_NAME: _Attributes(
        '_tf_api_names_v1', '_tf_api_constants_v1'
    ),
    KERAS_API_NAME: _Attributes(
        '_keras_api_names_v1', '_keras_api_constants_v1'
    ),
}


class InvalidSymbolNameError(Exception):
  """Raised when trying to export symbol as an invalid or unallowed name."""


_NAME_TO_SYMBOL_MAPPING: dict[str, Any] = dict()


def get_symbol_from_name(name: str) -> Optional[Any]:
  return _NAME_TO_SYMBOL_MAPPING.get(name)


def get_canonical_name_for_symbol(
    symbol: Any,
    api_name: str = TENSORFLOW_API_NAME,
    add_prefix_to_v1_names: bool = False,
) -> Optional[str]:
  """Get canonical name for the API symbol.

  Example:
  ```python
  from tensorflow.python.util import tf_export
  cls = tf_export.get_symbol_from_name('keras.optimizers.Adam')

  # Gives `<class 'keras.optimizer_v2.adam.Adam'>`
  print(cls)

  # Gives `keras.optimizers.Adam`
  print(tf_export.get_canonical_name_for_symbol(cls, api_name='keras'))
  ```

  Args:
    symbol: API function or class.
    api_name: API name. Currently, only `tensorflow`.
    add_prefix_to_v1_names: Specifies whether a name available only in V1 should
      be prefixed with compat.v1.

  Returns:
    Canonical name for the API symbol (for e.g. initializers.zeros) if
    canonical name could be determined. Otherwise, returns None.
  """
  if not hasattr(symbol, '__dict__'):
    return None
  api_names_attr = API_ATTRS[api_name].names
  _, undecorated_symbol = tf_decorator.unwrap(symbol)
  if api_names_attr not in undecorated_symbol.__dict__:
    return None
  api_names = getattr(undecorated_symbol, api_names_attr)
  deprecated_api_names = undecorated_symbol.__dict__.get(
      '_tf_deprecated_api_names', []
  )

  canonical_name = get_canonical_name(api_names, deprecated_api_names)
  if canonical_name:
    return canonical_name

  # If there is no V2 canonical name, get V1 canonical name.
  api_names_attr = API_ATTRS_V1[api_name].names
  api_names = getattr(undecorated_symbol, api_names_attr)
  v1_canonical_name = get_canonical_name(api_names, deprecated_api_names)
  if add_prefix_to_v1_names:
    return 'compat.v1.%s' % v1_canonical_name
  return v1_canonical_name


def get_canonical_name(
    api_names: Sequence[str], deprecated_api_names: Sequence[str]
) -> Optional[str]:
  """Get preferred endpoint name.

  Args:
    api_names: API names iterable.
    deprecated_api_names: Deprecated API names iterable.

  Returns:
    Returns one of the following in decreasing preference:
    - first non-deprecated endpoint
    - first endpoint
    - None
  """
  non_deprecated_name = next(
      (name for name in api_names if name not in deprecated_api_names), None
  )
  if non_deprecated_name:
    return non_deprecated_name
  if api_names:
    return api_names[0]
  return None


def get_v1_names(symbol: Any) -> Sequence[str]:
  """Get a list of TF 1.* names for this symbol.

  Args:
    symbol: symbol to get API names for.

  Returns:
    List of all API names for this symbol.
  """
  names_v1 = []
  tensorflow_api_attr_v1 = API_ATTRS_V1[TENSORFLOW_API_NAME].names
  keras_api_attr_v1 = API_ATTRS_V1[KERAS_API_NAME].names

  if not hasattr(symbol, '__dict__'):
    return names_v1
  if tensorflow_api_attr_v1 in symbol.__dict__:
    names_v1.extend(getattr(symbol, tensorflow_api_attr_v1))
  if keras_api_attr_v1 in symbol.__dict__:
    names_v1.extend(getattr(symbol, keras_api_attr_v1))
  return names_v1


def get_v2_names(symbol: Any) -> Sequence[str]:
  """Get a list of TF 2.0 names for this symbol.

  Args:
    symbol: symbol to get API names for.

  Returns:
    List of all API names for this symbol.
  """
  names_v2 = []
  tensorflow_api_attr = API_ATTRS[TENSORFLOW_API_NAME].names
  keras_api_attr = API_ATTRS[KERAS_API_NAME].names

  if not hasattr(symbol, '__dict__'):
    return names_v2
  if tensorflow_api_attr in symbol.__dict__:
    names_v2.extend(getattr(symbol, tensorflow_api_attr))
  if keras_api_attr in symbol.__dict__:
    names_v2.extend(getattr(symbol, keras_api_attr))
  return names_v2


def get_v1_constants(module: Any) -> Sequence[str]:
  """Get a list of TF 1.* constants in this module.

  Args:
    module: TensorFlow module.

  Returns:
    List of all API constants under the given module.
  """
  constants_v1 = []
  tensorflow_constants_attr_v1 = API_ATTRS_V1[TENSORFLOW_API_NAME].constants

  if hasattr(module, tensorflow_constants_attr_v1):
    constants_v1.extend(getattr(module, tensorflow_constants_attr_v1))
  return constants_v1


def get_v2_constants(module: Any) -> Sequence[str]:
  """Get a list of TF 2.0 constants in this module.

  Args:
    module: TensorFlow module.

  Returns:
    List of all API constants under the given module.
  """
  constants_v2 = []
  tensorflow_constants_attr = API_ATTRS[TENSORFLOW_API_NAME].constants

  if hasattr(module, tensorflow_constants_attr):
    constants_v2.extend(getattr(module, tensorflow_constants_attr))
  return constants_v2


T = TypeVar('T')


class api_export(object):  # pylint: disable=invalid-name
  """Provides ways to export symbols to the TensorFlow API."""

  _names: Sequence[str]
  _names_v1: Sequence[str]
  _api_name: str

  def __init__(
      self,
      *args: str,
      api_name: str = TENSORFLOW_API_NAME,
      v1: Optional[Sequence[str]] = None,
      allow_multiple_exports: bool = True,  # pylint: disable=unused-argument
  ):
    """Export under the names *args (first one is considered canonical).

    Args:
      *args: API names in dot delimited format.
      api_name: API you want to generate Currently, only `tensorflow`.
      v1: Names for the TensorFlow V1 API. If not set, we will use V2 API names
        both for TensorFlow V1 and V2 APIs.
      allow_multiple_exports: Deprecated.
    """
    self._names = args
    self._names_v1 = v1 if v1 is not None else args
    self._api_name = api_name

    self._validate_symbol_names()

  def _validate_symbol_names(self) -> None:
    """Validate you are exporting symbols under an allowed package.

    We need to ensure things exported by tf_export, etc.
    export symbols under disjoint top-level package names.

    For TensorFlow, we check that it does not export anything under subpackage
    names used by components (keras, etc.).

    For each component, we check that it exports everything under its own
    subpackage.

    Raises:
      InvalidSymbolNameError: If you try to export symbol under disallowed name.
    """
    all_symbol_names = set(self._names) | set(self._names_v1)
    if self._api_name == TENSORFLOW_API_NAME:
      for subpackage in SUBPACKAGE_NAMESPACES:
        if any(n.startswith(subpackage) for n in all_symbol_names):
          raise InvalidSymbolNameError(
              '@tf_export is not allowed to export symbols under %s.*'
              % (subpackage)
          )
    else:
      if not all(n.startswith(self._api_name) for n in all_symbol_names):
        raise InvalidSymbolNameError(
            'Can only export symbols under package name of component.'
        )

  def __call__(self, func: T) -> T:
    """Calls this decorator.

    Args:
      func: decorated symbol (function or class).

    Returns:
      The input function with _tf_api_names attribute set.
    """
    api_names_attr = API_ATTRS[self._api_name].names
    api_names_attr_v1 = API_ATTRS_V1[self._api_name].names

    _, undecorated_func = tf_decorator.unwrap(func)
    self.set_attr(undecorated_func, api_names_attr, self._names)
    self.set_attr(undecorated_func, api_names_attr_v1, self._names_v1)

    for name in self._names:
      _NAME_TO_SYMBOL_MAPPING[name] = func
    for name_v1 in self._names_v1:
      _NAME_TO_SYMBOL_MAPPING['compat.v1.%s' % name_v1] = func

    return func

  def set_attr(
      self, func: Any, api_names_attr: str, names: Sequence[str]
  ) -> None:
    setattr(func, api_names_attr, names)

  def export_constant(self, module_name: str, name: str) -> None:
    """Store export information for constants/string literals.

    Export information is stored in the module where constants/string literals
    are defined.

    e.g.
    ```python
    foo = 1
    bar = 2
    tf_export("consts.foo").export_constant(__name__, 'foo')
    tf_export("consts.bar").export_constant(__name__, 'bar')
    ```

    Args:
      module_name: (string) Name of the module to store constant at.
      name: (string) Current constant name.
    """
    module = sys.modules[module_name]
    api_constants_attr = API_ATTRS[self._api_name].constants
    api_constants_attr_v1 = API_ATTRS_V1[self._api_name].constants

    if not hasattr(module, api_constants_attr):
      setattr(module, api_constants_attr, [])
    # pylint: disable=protected-access
    getattr(module, api_constants_attr).append((self._names, name))

    if not hasattr(module, api_constants_attr_v1):
      setattr(module, api_constants_attr_v1, [])
    getattr(module, api_constants_attr_v1).append((self._names_v1, name))


def kwarg_only(f: Any) -> Any:
  """A wrapper that throws away all non-kwarg arguments."""
  f_argspec = tf_inspect.getfullargspec(f)

  def wrapper(*args, **kwargs):
    if args:
      raise TypeError(
          '{f} only takes keyword args (possible keys: {kwargs}). '
          'Please pass these args as kwargs instead.'.format(
              f=f.__name__, kwargs=f_argspec.args
          )
      )
    return f(**kwargs)

  return tf_decorator.make_decorator(f, wrapper, decorator_argspec=f_argspec)


class ExportType(Protocol):

  def __call__(
      self,
      *v2: str,
      v1: Optional[Sequence[str]] = None,
      allow_multiple_exports: bool = True,  # Deprecated, no-op
  ) -> api_export:
    ...


tf_export: ExportType = functools.partial(
    api_export, api_name=TENSORFLOW_API_NAME
)
keras_export: ExportType = functools.partial(
    api_export, api_name=KERAS_API_NAME
)