EVOLUTION-MANAGER

Edit File: generic_utils.py

"""Python utilities required by Keras."""

import inspect
import sys

from tensorflow.keras.utils import CustomObjectScope
from tensorflow.keras.utils import custom_object_scope
from tensorflow.keras.utils import get_custom_objects
from tensorflow.keras.utils import serialize_keras_object
from tensorflow.keras.utils import deserialize_keras_object
from tensorflow.keras.utils import Progbar

def to_list(x, allow_tuple=False):
    """Normalizes a list/tensor into a list.

If a tensor is passed, we return
    a list of size 1 containing the tensor.

# Arguments
        x: target object to be normalized.
        allow_tuple: If False and x is a tuple,
            it will be converted into a list
            with a single element (the tuple).
            Else converts the tuple to a list.

# Returns
        A list.
    """
    if isinstance(x, list):
        return x
    if allow_tuple and isinstance(x, tuple):
        return list(x)
    return [x]

def unpack_singleton(x):
    """Gets the first element if the iterable has only one value.

Otherwise return the iterable.

# Argument
        x: A list or tuple.

# Returns
        The same iterable or the first element.
    """
    if len(x) == 1:
        return x[0]
    return x

def object_list_uid(object_list):
    object_list = to_list(object_list)
    return ', '.join((str(abs(id(x))) for x in object_list))

def is_all_none(iterable_or_element):
    iterable = to_list(iterable_or_element, allow_tuple=True)
    for element in iterable:
        if element is not None:
            return False
    return True

def slice_arrays(arrays, start=None, stop=None):
    """Slices an array or list of arrays.

This takes an array-like, or a list of
    array-likes, and outputs:
        - arrays[start:stop] if `arrays` is an array-like
        - [x[start:stop] for x in arrays] if `arrays` is a list

Can also work on list/array of indices: `_slice_arrays(x, indices)`

# Arguments
        arrays: Single array or list of arrays.
        start: can be an integer index (start index)
            or a list/array of indices
        stop: integer (stop index); should be None if
            `start` was a list.

# Returns
        A slice of the array(s).
    """
    if arrays is None:
        return [None]
    elif isinstance(arrays, list):
        if hasattr(start, '__len__'):
            # hdf5 datasets only support list objects as indices
            if hasattr(start, 'shape'):
                start = start.tolist()
            return [None if x is None else x[start] for x in arrays]
        else:
            return [None if x is None else x[start:stop] for x in arrays]
    else:
        if hasattr(start, '__len__'):
            if hasattr(start, 'shape'):
                start = start.tolist()
            return arrays[start]
        elif hasattr(start, '__getitem__'):
            return arrays[start:stop]
        else:
            return [None]

def transpose_shape(shape, target_format, spatial_axes):
    """Converts a tuple or a list to the correct `data_format`.

It does so by switching the positions of its elements.

# Arguments
        shape: Tuple or list, often representing shape,
            corresponding to `'channels_last'`.
        target_format: A string, either `'channels_first'` or `'channels_last'`.
        spatial_axes: A tuple of integers.
            Correspond to the indexes of the spatial axes.
            For example, if you pass a shape
            representing (batch_size, timesteps, rows, cols, channels),
            then `spatial_axes=(2, 3)`.

# Returns
        A tuple or list, with the elements permuted according
        to `target_format`.

# Example
    ```python
        >>> from keras.utils.generic_utils import transpose_shape
        >>> transpose_shape((16, 128, 128, 32),'channels_first', spatial_axes=(1, 2))
        (16, 32, 128, 128)
        >>> transpose_shape((16, 128, 128, 32), 'channels_last', spatial_axes=(1, 2))
        (16, 128, 128, 32)
        >>> transpose_shape((128, 128, 32), 'channels_first', spatial_axes=(0, 1))
        (32, 128, 128)
    ```

# Raises
        ValueError: if `value` or the global `data_format` invalid.
    """
    if target_format == 'channels_first':
        new_values = shape[:spatial_axes[0]]
        new_values += (shape[-1],)
        new_values += tuple(shape[x] for x in spatial_axes)

if isinstance(shape, list):
            return list(new_values)
        return new_values
    elif target_format == 'channels_last':
        return shape
    else:
        raise ValueError('The `data_format` argument must be one of '
                         '"channels_first", "channels_last". Received: ' +
                         str(target_format))

def check_for_unexpected_keys(name, input_dict, expected_values):
    unknown = set(input_dict.keys()).difference(expected_values)
    if unknown:
        raise ValueError('Unknown entries in {} dictionary: {}. Only expected '
                         'following keys: {}'.format(name, list(unknown),
                                                     expected_values))

def has_arg(fn, name, accept_all=False):
    """Checks if a callable accepts a given keyword argument.
    For Python 2, checks if there is an argument with the given name.
    For Python 3, checks if there is an argument with the given name, and
    also whether this argument can be called with a keyword (i.e. if it is
    not a positional-only argument).
    # Arguments
        fn: Callable to inspect.
        name: Check if `fn` can be called with `name` as a keyword argument.
        accept_all: What to return if there is no parameter called `name`
                    but the function accepts a `**kwargs` argument.
    # Returns
        bool, whether `fn` accepts a `name` keyword argument.
    """
    if sys.version_info < (3,):
        arg_spec = inspect.getargspec(fn)
        if accept_all and arg_spec.keywords is not None:
            return True
        return (name in arg_spec.args)
    elif sys.version_info < (3, 3):
        arg_spec = inspect.getfullargspec(fn)
        if accept_all and arg_spec.varkw is not None:
            return True
        return (name in arg_spec.args or
                name in arg_spec.kwonlyargs)
    else:
        signature = inspect.signature(fn)
        parameter = signature.parameters.get(name)
        if parameter is None:
            if accept_all:
                for param in signature.parameters.values():
                    if param.kind == inspect.Parameter.VAR_KEYWORD:
                        return True
            return False
        return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD,
                                   inspect.Parameter.KEYWORD_ONLY))