# Copyright 2016    Johns Hopkins University (Dan Povey)
#           2016    Vijayaditya Peddinti
#           2017    Google Inc. (vpeddinti@google.com)
#           2017    Vimal Manohar
# Apache 2.0.

""" This module contains the parent class from which all layers are inherited
and some basic layer definitions.
"""

from __future__ import print_function
from __future__ import division
import math
import re
import sys
import libs.nnet3.xconfig.utils as xutils
import libs.common as common_lib


class XconfigLayerBase(object):
    """ A base-class for classes representing layers of xconfig files.
    """

    def __init__(self, first_token, key_to_value, all_layers):
        """
         first_token: first token on the xconfig line, e.g. 'affine-layer'.f
         key_to_value: dictionary with parameter values
             { 'name':'affine1',
               'input':'Append(0, 1, 2, ReplaceIndex(ivector, t, 0))',
               'dim=1024' }.
             The only required and 'special' values that are dealt with directly
             at this level, are 'name' and 'input'. The rest are put in
             self.config and are dealt with by the child classes' init functions.
         all_layers: An array of objects inheriting XconfigLayerBase for all
                    previously parsed layers.
        """

        self.layer_type = first_token
        if 'name' not in key_to_value:
            raise RuntimeError("Expected 'name' to be specified.")
        self.name = key_to_value['name']
        if not xutils.is_valid_line_name(self.name):
            raise RuntimeError("Invalid value: name={0}".format(
                key_to_value['name']))

        # It is possible to have two layers with a same name in 'all_layer', if
        # the layer type for one of them is 'existing'.
        # Layers of type 'existing' are corresponding to the component-node names
        # in the existing model, which we are adding layers to them.
        # 'existing' layers are not presented in any config file, and new layer
        # with the same name can exist in 'all_layers'.
        # e.g. It is possible to have 'output-node' with name 'output' in the
        # existing model, which is added to all_layers using layer type 'existing',
        # and 'output-node' of type 'output-layer' with the same name 'output' in
        # 'all_layers'.
        for prev_layer in all_layers:
            if (self.name == prev_layer.name and
                prev_layer.layer_type is not 'existing'):
                raise RuntimeError("Name '{0}' is used for more than one "
                                   "layer.".format(self.name))

        self.config = {}
        # the following, which should be overridden in the child class, sets
        # default config parameters in self.config.
        self.set_default_configs()
        # The following is not to be reimplemented in child classes;
        # it sets the config values to those specified by the user, and
        # parses any Descriptors.
        self.set_configs(key_to_value, all_layers)
        # This method, sets the derived default config values
        # i.e., config values when not specified can be derived from
        # other values. It can be overridden in the child class.
        self.set_derived_configs()
        # the following, which should be overridden in the child class, checks
        # that the config parameters that have been set are reasonable.
        self.check_configs()


    def set_configs(self, key_to_value, all_layers):
        """ Sets the config variables.
            We broke this code out of __init__ for clarity.
            the child-class constructor will deal with the configuration values
            in a more specific way.
        """

        # First check that there are no keys that don't correspond to any config
        # parameter of this layer, and if so, raise an exception with an
        # informative message saying what configs are allowed.
        for key, value in key_to_value.items():
            if key != 'name':
                if key not in self.config:
                    configs = ' '.join([('{0}->"{1}"'.format(x, y) if isinstance(y, str)
                                         else '{0}->{1}'.format(x, y))
                                        for x, y in self.config.items()])
                    raise RuntimeError("Configuration value {0}={1} was not "
                                       "expected in layer of type {2}; allowed "
                                       "configs with their defaults: {3}"
                                       "" .format(key, value, self.layer_type, configs))

        for key, value in key_to_value.items():
            if key != 'name':
                assert key in self.config  # we checked above.
                self.config[key] = xutils.convert_value_to_type(key,
                                                                type(self.config[key]),
                                                                value)
        self.descriptors = dict()
        self.descriptor_dims = dict()
        # Parse Descriptors and get their dims and their 'final' string form.
        # in self.descriptors[key]
        for key in self.get_input_descriptor_names():
            if key not in self.config:
                raise RuntimeError("{0}: object of type {1} needs to override"
                                   " get_input_descriptor_names()."
                                   "".format(sys.argv[0], str(type(self))))

            descriptor_string = self.config[key]  # input string.
            assert isinstance(descriptor_string, str)
            desc = self.convert_to_descriptor(descriptor_string, all_layers)
            desc_dim = self.get_dim_for_descriptor(desc, all_layers)
            desc_norm_str = desc.str()

            # desc_output_str contains the "final" component names, those that
            # appear in the actual config file (i.e. not names like
            # 'layer.auxiliary_output'); that's how it differs from desc_norm_str.
            # Note: it's possible that the two strings might be the same in
            # many, even most, cases-- it depends whether
            # output_name(self, auxiliary_output)
            # returns self.get_name() + '.' + auxiliary_output
            # when auxiliary_output is not None.
            # That's up to the designer of the layer type.
            desc_output_str = self.get_string_for_descriptor(desc, all_layers)
            self.descriptors[key] = {'string': desc,
                                     'normalized-string': desc_norm_str,
                                     'final-string': desc_output_str,
                                     'dim': desc_dim}

            # the following helps to check the code by parsing it again.
            desc2 = self.convert_to_descriptor(desc_norm_str, all_layers)
            desc_norm_str2 = desc2.str()
            # if the following ever fails we'll have to do some debugging.
            if desc_norm_str != desc_norm_str2:
                raise RuntimeError("Likely code error: '{0}' != '{1}'"
                                   "".format(desc_norm_str, desc_norm_str2))

    def str(self):
        """Converts 'this' to a string which could be printed to
        an xconfig file; in xconfig_to_configs.py we actually expand all the
        lines to strings and write it as xconfig.expanded as a reference
        (so users can see any defaults).
        """

        list_of_entries = ['{0} name={1}'.format(self.layer_type, self.name)]
        for key, value in sorted(self.config.items()):
            if isinstance(value, str) and re.search('=', value):
                # the value is a string that contains an '=' sign, so we need to
                # enclose it in double-quotes, otherwise we woudldn't be able to
                # parse from that output.
                if re.search('"', value):
                    print("Warning: config '{0}={1}' contains both double-quotes "
                          "and equals sign; it will not be possible to parse it "
                          "from the file.".format(key, value), file=sys.stderr)
                list_of_entries.append('{0}="{1}"'.format(key, value))
            else:
                list_of_entries.append('{0}={1}'.format(key, value))

        return ' '.join(list_of_entries)

    def __str__(self):
        return self.str()

    def normalize_descriptors(self):
        """Converts any config variables in self.config which correspond to
        Descriptors, into a 'normalized form' derived from parsing them as
        Descriptors, replacing things like [-1] with the actual layer names,
        and regenerating them as strings.  We stored this when the object was
        initialized, in self.descriptors; this function just copies them back
        to the config.
        """

        for key, desc_str_dict in self.descriptors.items():
            self.config[key] = desc_str_dict['normalized-string']

    def convert_to_descriptor(self, descriptor_string, all_layers):
        """Convenience function intended to be called from child classes,
        converts a string representing a descriptor ('descriptor_string')
        into an object of type Descriptor, and returns it. It needs 'self' and
        'all_layers' (where 'all_layers' is a list of objects of type
        XconfigLayerBase) so that it can work out a list of the names of other
        layers, and get dimensions from them.
        """

        prev_names = xutils.get_prev_names(all_layers, self)
        tokens = xutils.tokenize_descriptor(descriptor_string, prev_names)
        pos = 0
        (descriptor, pos) = xutils.parse_new_descriptor(tokens, pos, prev_names)
        # note: 'pos' should point to the 'end of string' marker
        # that terminates 'tokens'.
        if pos != len(tokens) - 1:
            raise RuntimeError("Parsing Descriptor, saw junk at end: {0}"
                               "".format(' '.join(tokens[pos:-1])))
        return descriptor

    def get_dim_for_descriptor(self, descriptor, all_layers):
        """Returns the dimension of a Descriptor object. This is a convenience
        function used in set_configs.
        """

        layer_to_dim_func = \
                lambda name: xutils.get_dim_from_layer_name(all_layers, self,
                                                            name)
        return descriptor.dim(layer_to_dim_func)

    def get_string_for_descriptor(self, descriptor, all_layers):
        """Returns the 'final' string form of a Descriptor object,
        as could be used in config files. This is a convenience function
        provided for use in child classes;
        """

        layer_to_string_func = \
                lambda name: xutils.get_string_from_layer_name(all_layers,
                                                               self, name)
        return descriptor.config_string(layer_to_string_func)

    def get_name(self):
        """Returns the name of this layer, e.g. 'affine1'.  It does not
        necessarily correspond to a component name.
        """

        return self.name

    ######  Functions that might be overridden by the child class: #####

    def set_default_configs(self):
        """Child classes should override this.
        """

        raise Exception("Child classes must override set_default_configs().")

    def set_derived_configs(self):
        """This is expected to be called after set_configs and before
        check_configs().
        """
        if 'dim' in self.config and self.config['dim'] <= 0:
            self.config['dim'] = self.descriptors['input']['dim']

    def check_configs(self):
        """child classes should override this.
        """

        pass

    def get_input_descriptor_names(self):
        """This function, which may be (but usually will not have to be)
        overridden by child classes, returns a list of names of the input
        descriptors expected by this component. Typically this would just
        return ['input'] as most layers just have one 'input'. However some
        layers might require more inputs (e.g. cell state of previous LSTM layer
        in Highway LSTMs). It is used in the function 'normalize_descriptors()'.
        This implementation will work for layer types whose only
        Descriptor-valued config is 'input'.
        If a child class adds more inputs, or does not have an input
        (e.g. the XconfigInputLayer), it should override this function's
        implementation to something like: `return ['input', 'input2']`
        """

        return ['input']

    def auxiliary_outputs(self):
        """Returns a list of all auxiliary outputs that this layer supports.
        These are either 'None' for the regular output, or a string
        (e.g. 'projection' or 'memory_cell') for any auxiliary outputs that
        the layer might provide.  Most layer types will not need to override
        this.
        """

        return [None]

    def output_name(self, auxiliary_output=None):
        """Called with auxiliary_output is None, this returns the component-node
        name of the principal output of the layer (or if you prefer, the text
        form of a descriptor that gives you such an output; such as
        Append(some_node, some_other_node)).
        The 'auxiliary_output' argument is a text value that is designed for
        extensions to layers that have additional auxiliary outputs.
        For example, to implement a highway LSTM you need the memory-cell of a
        layer, so you might allow auxiliary_output='memory_cell' for such a
        layer type, and it would return the component node or a suitable
        Descriptor: something like 'lstm3.c_t'
        """

        raise Exception("Child classes must override output_name()")

    def output_dim(self, auxiliary_output=None):
        """The dimension that this layer outputs.  The 'auxiliary_output'
        parameter is for layer types which support auxiliary outputs.
        """

        raise Exception("Child classes must override output_dim()")

    def get_full_config(self):
        """This function returns lines destined for the 'full' config format, as
        would be read by the C++ programs. Since the program
        xconfig_to_configs.py writes several config files, this function returns
        a list of pairs of the form (config_file_basename, line),
        e.g. something like
         [  ('init', 'input-node name=input dim=40'),
            ('ref', 'input-node name=input dim=40') ]
        which would be written to config_dir/init.config and config_dir/ref.config.
        """

        raise Exception("Child classes must override get_full_config()")


class XconfigInputLayer(XconfigLayerBase):
    """This class is for lines like
    'input name=input dim=40'
    or
    'input name=ivector dim=100'
    in the config file.
    """
    def __init__(self, first_token, key_to_value, prev_names=None):

        assert first_token == 'input'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):

        self.config = {'dim': -1}

    def check_configs(self):

        if self.config['dim'] <= 0:
            raise RuntimeError("Dimension of input-layer '{0}'"
                               "should be positive.".format(self.name))

    def get_input_descriptor_names(self):

        return []  # there is no 'input' field in self.config.

    def output_name(self, auxiliary_outputs=None):

        # there are no auxiliary outputs as this layer will just pass the input
        assert auxiliary_outputs is None
        return self.name

    def output_dim(self, auxiliary_outputs=None):

        # there are no auxiliary outputs as this layer will just pass the input
        assert auxiliary_outputs is None
        return self.config['dim']

    def get_full_config(self):

        # unlike other layers the input layers need to be printed in
        # 'init.config' (which initializes the neural network prior to the LDA)
        ans = []
        for config_name in ['init', 'ref', 'final']:
            ans.append((config_name,
                        'input-node name={0} dim={1}'.format(self.name,
                                                             self.config['dim'])))
        return ans


class XconfigTrivialOutputLayer(XconfigLayerBase):
    """
    This class is for lines like
    'output name=output input=Append(input@-1, input@0, input@1, ReplaceIndex(ivector, t, 0))'
    This is for outputs that are not really output "layers"
    (there is no affine transform or nonlinearity), they just directly map to an
    output-node in nnet3.

    Parameters of the class, and their defaults:
        input='[-1]'    :   Descriptor giving the input of the layer.
        objective-type=linear   :   the only other choice currently is
            'quadratic', for use in regression problems
        output-delay=0    :  Can be used to shift the frames on the output, equivalent
             to delaying labels by this many frames (positive value increases latency
             in online decoding but may help if you're using unidirectional LSTMs.
    """

    def __init__(self, first_token, key_to_value, prev_names=None):

        assert first_token == 'output'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):

        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        self.config = {'input': '[-1]', 'dim': -1,
                       'objective-type': 'linear',
                       'output-delay': 0}

    def check_configs(self):

        if self.config['objective-type'] != 'linear' and \
                self.config['objective-type'] != 'quadratic':
            raise RuntimeError("In output, objective-type has"
                               " invalid value {0}"
                               "".format(self.config['objective-type']))

    def output_name(self, auxiliary_outputs=None):

        # there are no auxiliary outputs as this layer will just pass the output
        # of the previous layer
        assert auxiliary_outputs is None
        return self.name

    def output_dim(self, auxiliary_outputs=None):

        assert auxiliary_outputs is None
        # note: each value of self.descriptors is (descriptor, dim, normalized-string, output-string).
        return self.descriptors['input']['dim']

    def get_full_config(self):

        # the input layers need to be printed in 'init.config' (which
        # initializes the neural network prior to the LDA), in 'ref.config',
        # which is a version of the config file used for getting left and right
        # context (it doesn't read anything for the LDA-like transform).
        # In 'full.config' we write everything, this is just for reference,
        # and also for cases where we don't use the LDA-like transform.
        ans = []

        # note: each value of self.descriptors is (descriptor, dim,
        # normalized-string, output-string).
        # by 'output-string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        descriptor_final_str = self.descriptors['input']['final-string']
        objective_type = self.config['objective-type']
        output_delay = self.config['output-delay']

        if output_delay != 0:
            descriptor_final_str = (
                'Offset({0}, {1})'.format(descriptor_final_str, output_delay))

        for config_name in ['ref', 'final']:
            ans.append((config_name,
                        'output-node name={0} input={1} '
                        'objective={2}'.format(
                            self.name, descriptor_final_str,
                            objective_type)))
        return ans


class XconfigOutputLayer(XconfigLayerBase):
    """This class is for lines like
    'output-layer name=output dim=4257 input=Append(input@-1, input@0, input@1, ReplaceIndex(ivector, t, 0))'
    By default this includes a log-softmax component.  The parameters are
    initialized to zero, as this empirically tends to be the best approach for output layers.

    Parameters of the class, and their defaults:
        input='[-1]'    :   Descriptor giving the input of the layer.
        dim=None    :   Output dimension of layer, will normally equal the number of pdfs.
        bottleneck-dim=None    :   Bottleneck dimension of layer: if supplied, instead of
                        an affine component we'll have a linear then affine, so a linear
                        bottleneck, with the linear part constrained to be orthonormal.
        include-log-softmax=true    :   setting it to false will omit the
            log-softmax component- useful for chain models.
        objective-type=linear   :   the only other choice currently is
            'quadratic', for use in regression problems
        learning-rate-factor=1.0    :   Learning rate factor for the final
            affine component, multiplies the standard learning rate. normally
            you'll leave this as-is, but for xent regularization output layers
            for chain models you'll want to set
            learning-rate-factor=(0.5/xent_regularize),
            normally learning-rate-factor=5.0 since xent_regularize is
            normally 0.1.
        max-change=1.5 :  Can be used to change the max-change parameter in the
            affine component; this affects how much the matrix can change on each
            iteration.
        l2-regularize=0.0:  Set this to a nonzero value (e.g. 1.0e-05) to
            add l2 regularization on the parameter norm for the affine component.
        output-delay=0    :  Can be used to shift the frames on the output, equivalent
             to delaying labels by this many frames (positive value increases latency
             in online decoding but may help if you're using unidirectional LSTMs.
        ng-affine-options=''  :   Can be used supply non-default options to the affine
             layer (intended for the natural gradient but can be an arbitrary string
             to be added to the config line.  e.g. 'update-period=2'.).
        ng-linear-options=''  :   Options, like ng-affine-options, that are passed to
             the LinearComponent, only in bottleneck layers (i.e. if bottleneck-dim
             is supplied).
    """

    def __init__(self, first_token, key_to_value, prev_names=None):

        assert first_token == 'output-layer'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):

        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        self.config = {'input': '[-1]',
                       'dim': -1,
                       'bottleneck-dim': -1,
                       'orthonormal-constraint': 1.0,
                            # orthonormal-constraint only matters if bottleneck-dim is set.
                       'include-log-softmax': True,
                            # this would be false for chain models
                       'objective-type': 'linear',
                            # see Nnet::ProcessOutputNodeConfigLine in
                            # nnet-nnet.cc for other options
                       'output-delay': 0,
                       'ng-affine-options': '',
                       'ng-linear-options': '',    # only affects bottleneck output layers.

                       # The following are just passed through to the affine
                       # component, and (in the bottleneck case) the linear
                       # component.
                       'learning-rate-factor': '',  # effective default: 1.0
                       'l2-regularize': '',         # effective default: 0.0
                       'max-change': 1.5,

                       # The following are passed through to the affine component only.
                       # It tends to be beneficial to initialize the output layer with
                       # zero values, unlike the hidden layers.
                       'param-stddev': 0.0,
                       'bias-stddev': 0.0,
                      }

    def check_configs(self):

        if self.config['dim'] <= -1:
            raise RuntimeError("In output-layer, dim has invalid value {0}"
                               "".format(self.config['dim']))

        if self.config['objective-type'] != 'linear' and \
                self.config['objective-type'] != 'quadratic':
            raise RuntimeError("In output-layer, objective-type has"
                               " invalid value {0}"
                               "".format(self.config['objective-type']))

        if self.config['orthonormal-constraint'] <= 0.0:
            raise RuntimeError("output-layer does not support negative (floating) "
                               "orthonormal constraint; use a separate linear-component "
                               "followed by batchnorm-component.")

    def auxiliary_outputs(self):

        auxiliary_outputs = ['affine']
        if self.config['include-log-softmax']:
            auxiliary_outputs.append('log-softmax')

        return auxiliary_outputs

    def output_name(self, auxiliary_output=None):

        if auxiliary_output is None:
            # Note: nodes of type output-node in nnet3 may not be accessed in
            # Descriptors, so calling this with auxiliary_outputs=None doesn't
            # make sense.
            raise RuntimeError("Outputs of output-layer may not be used by other"
                               " layers")

        if auxiliary_output in self.auxiliary_outputs():
            return '{0}.{1}'.format(self.name, auxiliary_output)
        else:
            raise RuntimeError("Unknown auxiliary output name {0}"
                               "".format(auxiliary_output))

    def output_dim(self, auxiliary_output=None):

        if auxiliary_output is None:
            # Note: nodes of type output-node in nnet3 may not be accessed in
            # Descriptors, so calling this with auxiliary_outputs=None doesn't
            # make sense.
            raise RuntimeError("Outputs of output-layer may not be used by other"
                               " layers")
        return self.config['dim']

    def get_full_config(self):
        ans = []
        config_lines = self._generate_config()

        for line in config_lines:
            for config_name in ['ref', 'final']:
                # we do not support user specified matrices in LSTM initialization
                # so 'ref' and 'final' configs are the same.
                ans.append((config_name, line))
        return ans


    def _generate_config(self):

        configs = []

        # note: each value of self.descriptors is (descriptor, dim,
        # normalized-string, output-string).
        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        descriptor_final_string = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']
        output_dim = self.config['dim']
        bottleneck_dim = self.config['bottleneck-dim']
        objective_type = self.config['objective-type']
        include_log_softmax = self.config['include-log-softmax']
        output_delay = self.config['output-delay']

        affine_options = self.config['ng-affine-options']
        for opt in [ 'learning-rate-factor', 'l2-regularize', 'max-change',
                     'param-stddev', 'bias-stddev' ]:
            if self.config[opt] != '':
                affine_options += ' {0}={1}'.format(opt, self.config[opt])

        cur_node = descriptor_final_string
        cur_dim = input_dim

        if bottleneck_dim >= 0:
            if bottleneck_dim == 0 or bottleneck_dim >= input_dim or bottleneck_dim >= output_dim:
                raise RuntimeError("Bottleneck dim has value that does not make sense: {0}".format(
                    bottleneck_dim))
            # This is the bottleneck case (it doesn't necessarily imply we
            # will be using the features from the bottleneck; it's just a factorization
            # of the matrix into two pieces without a nonlinearity in between).
            # We don't include the l2-regularize option because it's useless
            # given the orthonormality constraint.
            linear_options = self.config['ng-linear-options']
            for opt in [ 'learning-rate-factor', 'l2-regularize', 'max-change' ]:
                if self.config[opt] != '':
                    linear_options += ' {0}={1}'.format(opt, self.config[opt])


            # note: by default the LinearComponent uses natural gradient.
            line = ('component name={0}.linear type=LinearComponent '
                    'orthonormal-constraint={1} param-stddev={2} '
                    'input-dim={3} output-dim={4} max-change=0.75 {5}'
                    ''.format(self.name, self.config['orthonormal-constraint'],
                              self.config['orthonormal-constraint'] / math.sqrt(input_dim),
                              input_dim, bottleneck_dim, linear_options))
            configs.append(line)
            line = ('component-node name={0}.linear component={0}.linear input={1}'
                    ''.format(self.name, cur_node))
            configs.append(line)
            cur_node = '{0}.linear'.format(self.name)
            cur_dim = bottleneck_dim


        line = ('component name={0}.affine'
                ' type=NaturalGradientAffineComponent'
                ' input-dim={1} output-dim={2} {3}'
                ''.format(self.name, cur_dim, output_dim, affine_options))
        configs.append(line)
        line = ('component-node name={0}.affine'
                ' component={0}.affine input={1}'
                ''.format(self.name, cur_node))
        configs.append(line)
        cur_node = '{0}.affine'.format(self.name)

        if include_log_softmax:
            line = ('component name={0}.log-softmax'
                    ' type=LogSoftmaxComponent dim={1}'
                    ''.format(self.name, output_dim))
            configs.append(line)

            line = ('component-node name={0}.log-softmax'
                    ' component={0}.log-softmax input={1}'
                    ''.format(self.name, cur_node))
            configs.append(line)
            cur_node = '{0}.log-softmax'.format(self.name)

        if output_delay != 0:
            cur_node = 'Offset({0}, {1})'.format(cur_node, output_delay)

        line = ('output-node name={0} input={1} '
                'objective={2}'.format(
                    self.name, cur_node, objective_type))
        configs.append(line)
        return configs


class XconfigBasicLayer(XconfigLayerBase):
    """This class is for parsing lines like
     'relu-renorm-layer name=layer1 dim=1024 input=Append(-3,0,3)'
    or:
     'sigmoid-layer name=layer1 dim=1024 input=Append(-3,0,3)'
    which specify addition of an affine component and a sequence of non-linearities.
    Here, the name of the layer itself dictates the sequence of nonlinearities
    that are applied after the affine component; the name should contain some
    combination of 'relu', 'renorm', 'sigmoid' and 'tanh',
    and these nonlinearities will be added along with the affine component.

    The dimension specified is the output dim; the input dim is worked out from the input descriptor.
    This class supports only nonlinearity types that do not change the dimension; we can create
    another layer type to enable the use p-norm and similar dimension-reducing nonlinearities.

    See other configuration values below.

    Parameters of the class, and their defaults:
      input='[-1]'             [Descriptor giving the input of the layer.]
      dim=-1                   [Output dimension of layer, e.g. 1024]
      bottleneck-dim=-1        [If you set this, a linear bottleneck is added, so
                                we project to first bottleneck-dim then to dim.  The
                                first of the two matrices is constrained to be
                                orthonormal.]
      self-repair-scale=1.0e-05  [Affects relu, sigmoid and tanh layers.]
      learning-rate-factor=1.0   [This can be used to make the affine component
                                  train faster or slower].
      add-log-stddev=False     [If true, the log of the stddev of the output of
                                renorm layer is appended as an
                                additional dimension of the layer's output]
      l2-regularize=0.0       [Set this to a nonzero value (e.g. 1.0e-05) to
                               add l2 regularization on the parameter norm for
                                this component.
    """
    def __init__(self, first_token, key_to_value, prev_names=None):
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):

        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        self.config = {'input': '[-1]',
                       'dim': -1,
                       'bottleneck-dim': -1,  # Deprecated!  Use tdnnf-layer for
                                              # factorized TDNNs, or prefinal-layer
                                              # for bottlenecks just before the output.
                       'self-repair-scale': 1.0e-05,
                       'target-rms': 1.0,
                       'ng-affine-options': '',
                       'ng-linear-options': '',    # only affects bottleneck layers.
                       'dropout-proportion': 0.5,  # dropout-proportion only
                                                   # affects layers with
                                                   # 'dropout' in the name
                       'dropout-per-dim': False,  # if dropout-per-dim=true, the dropout
                                                  # mask is shared across time.
                       'dropout-per-dim-continuous':  False, # if you set this, it's
                                                    # like dropout-per-dim but with a
                                                    # continuous-valued (not zero-one) mask.
                       'add-log-stddev': False,
                       # the following are not really inspected by this level of
                       # code, just passed through to the affine component if
                       # their value is not ''.
                       'bias-stddev': '',
                       'l2-regularize': '',
                       'learning-rate-factor': '',
                       'max-change': 0.75 }

    def check_configs(self):
        if self.config['dim'] < 0:
            raise RuntimeError("dim has invalid value {0}".format(self.config['dim']))
        b = self.config['bottleneck-dim']
        if b >= 0 and (b >= self.config['dim'] or b == 0):
            raise RuntimeError("bottleneck-dim has an invalid value {0}".format(b))

        if self.config['self-repair-scale'] < 0.0 or self.config['self-repair-scale'] > 1.0:
            raise RuntimeError("self-repair-scale has invalid value {0}"
                               .format(self.config['self-repair-scale']))
        if self.config['target-rms'] < 0.0:
            raise RuntimeError("target-rms has invalid value {0}"
                               .format(self.config['target-rms']))
        if (self.config['learning-rate-factor'] != '' and
            self.config['learning-rate-factor'] <= 0.0):
            raise RuntimeError("learning-rate-factor has invalid value {0}"
                               .format(self.config['learning-rate-factor']))

    def output_name(self, auxiliary_output=None):
        # at a later stage we might want to expose even the pre-nonlinearity
        # vectors
        assert auxiliary_output is None

        split_layer_name = self.layer_type.split('-')
        assert split_layer_name[-1] == 'layer'
        last_nonlinearity = split_layer_name[-2]
        # return something like: layer3.renorm
        return '{0}.{1}'.format(self.name, last_nonlinearity)

    def output_dim(self, auxiliary_output=None):
        output_dim = self.config['dim']
        # If not set, the output-dim defaults to the input-dim.
        if output_dim <= 0:
            self.config['dim'] = self.descriptors['input']['dim']

        return output_dim

    def get_full_config(self):
        ans = []
        config_lines = self._generate_config()

        for line in config_lines:
            for config_name in ['ref', 'final']:
                # we do not support user specified matrices in this layer
                # so 'ref' and 'final' configs are the same.
                ans.append((config_name, line))
        return ans

    def _generate_config(self):
        split_layer_name = self.layer_type.split('-')
        assert split_layer_name[-1] == 'layer'
        nonlinearities = split_layer_name[:-1]

        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        input_desc = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']

        # the child classes e.g. tdnn might want to process the input
        # before adding the other components

        return self._add_components(input_desc, input_dim, nonlinearities)

    def _add_components(self, input_desc, input_dim, nonlinearities):
        output_dim = self.output_dim()
        self_repair_scale = self.config['self-repair-scale']
        target_rms = self.config['target-rms']

        affine_options = self.config['ng-affine-options']
        for opt_name in [ 'max-change', 'learning-rate-factor',
                          'bias-stddev', 'l2-regularize' ]:
            value = self.config[opt_name]
            if value != '':
                affine_options += ' {0}={1}'.format(opt_name, value)

        # The output of the affine component needs to have one dimension fewer in order to
        # get the required output dim, if the final 'renorm' component has 'add-log-stddev' set
        # (since in that case it increases the dimension by one).
        if self.config['add-log-stddev']:
            output_dim -= 1
            if not self.layer_type.split('-')[-2] == "renorm":
                raise RuntimeError("add-log-stddev cannot be true unless "
                                   "there is a final 'renorm' component.")

        configs = []
        cur_dim = input_dim
        cur_node = input_desc

        # First the affine node (or linear then affine, if bottleneck).
        if self.config['bottleneck-dim'] > 0:
            # The 'bottleneck-dim' option is deprecated and may eventually be
            # removed.  Best to use tdnnf-layer if you want factorized TDNNs.

            # This is the bottleneck case (it doesn't necessarily imply we
            # will be using the features from the bottleneck; it's just a factorization
            # of the matrix into two pieces without a nonlinearity in between).
            # We don't include the l2-regularize option because it's useless
            # given the orthonormality constraint.
            linear_options = self.config['ng-linear-options']
            for opt_name in [ 'max-change', 'learning-rate-factor' ]:
                value = self.config[opt_name]
                if value != '':
                    linear_options += ' {0}={1}'.format(opt_name, value)

            bottleneck_dim = self.config['bottleneck-dim']
            # note: by default the LinearComponent uses natural gradient.
            line = ('component name={0}.linear type=LinearComponent '
                    'input-dim={1} orthonormal-constraint=1.0 output-dim={2} {3}'
                    ''.format(self.name, input_dim, bottleneck_dim, linear_options))
            configs.append(line)
            line = ('component-node name={0}.linear component={0}.linear input={1}'
                    ''.format(self.name, cur_node))
            configs.append(line)
            cur_node = '{0}.linear'.format(self.name)
            cur_dim = bottleneck_dim


        line = ('component name={0}.affine type=NaturalGradientAffineComponent'
                ' input-dim={1} output-dim={2} {3}'
                ''.format(self.name, cur_dim, output_dim, affine_options))
        configs.append(line)
        line = ('component-node name={0}.affine component={0}.affine input={1}'
                ''.format(self.name, cur_node))
        configs.append(line)
        cur_node = '{0}.affine'.format(self.name)

        for i, nonlinearity in enumerate(nonlinearities):
            if nonlinearity == 'relu':
                line = ('component name={0}.{1} type=RectifiedLinearComponent dim={2}'
                        ' self-repair-scale={3}'
                        ''.format(self.name, nonlinearity, output_dim,
                                  self_repair_scale))

            elif nonlinearity == 'sigmoid':
                line = ('component name={0}.{1}'
                        ' type=SigmoidComponent dim={2}'
                        ' self-repair-scale={3}'
                        ''.format(self.name, nonlinearity, output_dim,
                                  self_repair_scale))

            elif nonlinearity == 'tanh':
                line = ('component name={0}.{1}'
                        ' type=TanhComponent dim={2}'
                        ' self-repair-scale={3}'
                        ''.format(self.name, nonlinearity, output_dim,
                                  self_repair_scale))

            elif nonlinearity == 'renorm':
                add_log_stddev = "false"
                if i == len(nonlinearities) - 1:
                    add_log_stddev = ("true" if self.config['add-log-stddev']
                                      else "false")
                line = ('component name={0}.{1}'
                        ' type=NormalizeComponent dim={2}'
                        ' target-rms={3}'
                        ' add-log-stddev={4}'
                        ''.format(self.name, nonlinearity, output_dim,
                                  target_rms, add_log_stddev))

            elif nonlinearity == 'batchnorm':
                line = ('component name={0}.{1}'
                        ' type=BatchNormComponent dim={2} target-rms={3}'
                        ''.format(self.name, nonlinearity, output_dim,
                                  target_rms))

            elif nonlinearity == 'so':
                line = ('component name={0}.{1}'
                        ' type=ScaleAndOffsetComponent dim={2} max-change=0.5 '
                        ''.format(self.name, nonlinearity, output_dim))

            elif nonlinearity == 'dropout':
                if not (self.config['dropout-per-dim'] or
                        self.config['dropout-per-dim-continuous']):
                    line = ('component name={0}.{1} type=DropoutComponent '
                            'dim={2} dropout-proportion={3}'.format(
                                self.name, nonlinearity, output_dim,
                                self.config['dropout-proportion']))
                else:
                    continuous_opt='continuous=true' if self.config['dropout-per-dim-continuous'] else ''

                    line = ('component name={0}.dropout type=GeneralDropoutComponent '
                            'dim={1} dropout-proportion={2} {3}'.format(
                                self.name, output_dim, self.config['dropout-proportion'],
                                continuous_opt))
            else:
                raise RuntimeError("Unknown nonlinearity type: {0}"
                                   .format(nonlinearity))

            configs.append(line)
            line = ('component-node name={0}.{1}'
                    ' component={0}.{1} input={2}'
                    ''.format(self.name, nonlinearity, cur_node))

            configs.append(line)
            cur_node = '{0}.{1}'.format(self.name, nonlinearity)
        return configs


class XconfigFixedAffineLayer(XconfigLayerBase):
    """
    This class is for lines like
     'fixed-affine-layer name=lda input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0)) affine-transform-file=foo/bar/lda.mat'

    The output dimension of the layer may be specified via 'dim=xxx', but if not specified,
    the dimension defaults to the same as the input.  Note: we don't attempt to read that
    file at the time the config is created, because in the recipes, that file is created
    after the config files.

    See other configuration values below.

    Parameters of the class, and their defaults:
      input='[-1]'             [Descriptor giving the input of the layer.]
      dim=None                   [Output dimension of layer; defaults to the same as the input dim.]
      affine-transform-file='' [Must be specified.]
      delay=0                  [Optional delay for the output-node in init.config]
    """
    def __init__(self, first_token, key_to_value, prev_names=None):
        assert first_token == 'fixed-affine-layer'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):
        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        self.config = {'input': '[-1]',
                       'dim': -1,
                       'affine-transform-file': '',
                       'delay': 0,
                       'write-init-config': True}

    def check_configs(self):
        if self.config['affine-transform-file'] is None:
            raise RuntimeError("affine-transform-file must be set.")

    def output_name(self, auxiliary_output=None):
        # Fixed affine layer computes only one vector, there are no intermediate
        # vectors.
        assert auxiliary_output is None
        return self.name

    def output_dim(self, auxiliary_output=None):
        output_dim = self.config['dim']
        # If not set, the output-dim defaults to the input-dim.
        if output_dim <= 0:
            output_dim = self.descriptors['input']['dim']
        return output_dim

    def get_full_config(self):
        ans = []

        # note: each value of self.descriptors is (descriptor, dim,
        # normalized-string, output-string).
        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        descriptor_final_string = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']
        output_dim = self.output_dim()
        transform_file = self.config['affine-transform-file']

        if self.config['write-init-config']:
            if self.config['delay'] != 0:
                line = 'component name={0}.delayed type=NoOpComponent dim={1}'.format(self.name, input_dim)
                ans.append(('init', line))
                line = 'component-node name={0}.delayed component={0}.delayed input={1}'.format(self.name, descriptor_final_string)
                ans.append(('init', line))
                line = 'output-node name=output input=Offset({0}.delayed, {1})'.format(self.name, self.config['delay'])
                ans.append(('init', line))
            else:
                # to init.config we write an output-node with the name 'output' and
                # with a Descriptor equal to the descriptor that's the input to this
                # layer.  This will be used to accumulate stats to learn the LDA transform.
                line = 'output-node name=output input={0}'.format(descriptor_final_string)
                ans.append(('init', line))

        # write the 'real' component to final.config
        line = 'component name={0} type=FixedAffineComponent matrix={1}'.format(
            self.name, transform_file)
        ans.append(('final', line))
        # write a random version of the component, with the same dims, to ref.config
        line = 'component name={0} type=FixedAffineComponent input-dim={1} output-dim={2}'.format(
            self.name, input_dim, output_dim)
        ans.append(('ref', line))
        # the component-node gets written to final.config and ref.config.
        line = 'component-node name={0} component={0} input={1}'.format(
            self.name, descriptor_final_string)
        ans.append(('final', line))
        ans.append(('ref', line))
        return ans


class XconfigAffineLayer(XconfigLayerBase):
    """
    This class is for lines like
     'affine-layer name=affine input=Append(-2,-1,0,1,2,ReplaceIndex(ivector, t, 0))'

    The output dimension of the layer may be specified via 'dim=xxx', but if not specified,
    the dimension defaults to the same as the input.  Note: we don't attempt to read that
    file at the time the config is created, because in the recipes, that file is created
    after the config files.

    See other configuration values below.

    Parameters of the class, and their defaults:
      input='[-1]'             [Descriptor giving the input of the layer.]
      dim=None                 [Output dimension of layer; defaults to the same as the input dim.]

      l2-regularize=0.0       [Set this to a nonzero value (e.g. 1.0e-05) to
                               add l2 regularization on the parameter norm
                               for the affine component.]
    """

    def __init__(self, first_token, key_to_value, prev_names=None):
        assert first_token == 'affine-layer'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):
        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        # use None for optional parameters as we want to default to the C++ defaults
        # C++ component provides more options but I will just expose these for now
        # Note : The type of the parameter is determined based on the value assigned
        #        so please use decimal point if your parameter is a float
        self.config = {'input': '[-1]',
                       'dim': -1,
                       'param-stddev': -1.0,  # this has to be initialized to 1/sqrt(input_dim)
                       'bias-stddev': 1.0,
                       'bias-mean': 0.0,
                       'max-change': 0.75,
                       'l2-regularize': 0.0,
                       'learning-rate-factor': 1.0,
                       'ng-affine-options': ''}

    def set_derived_configs(self):
        super(XconfigAffineLayer, self).set_derived_configs()
        if self.config['param-stddev'] < 0:
            self.config['param-stddev'] = 1.0 / math.sqrt(self.descriptors['input']['dim'])

    def check_configs(self):
        if self.config['dim'] <= 0:
            raise RuntimeError("dim specified is invalid")

    def output_name(self, auxiliary_output=None):
        # affine layer computes only one vector, there are no intermediate
        # vectors.
        assert auxiliary_output is None
        return self.name

    def output_dim(self, auxiliary_output=None):
        output_dim = self.config['dim']
        # If not set, the output-dim defaults to the input-dim.
        if output_dim <= 0:
            output_dim = self.descriptors['input']['dim']

        return output_dim

    def get_full_config(self):
        ans = []

        # note: each value of self.descriptors is (descriptor, dim,
        # normalized-string, output-string).
        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        descriptor_final_string = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']
        output_dim = self.output_dim()

        option_string = ''
        for key in ['param-stddev', 'bias-stddev', 'bias-mean', 'max-change',
                    'l2-regularize']:
            option_string += ' {0}={1}'.format(key, self.config[key])
        option_string += self.config['ng-affine-options']

        conf_lines = []
        # write the 'real' component to final.config
        conf_lines.append('component name={n} type=NaturalGradientAffineComponent '
                          'input-dim={i} output-dim={o} {opts}'.format(n=self.name,
                                                                       i=input_dim,
                                                                       o=output_dim,
                                                                       opts=option_string))
        # the component-node gets written to final.config and ref.config.
        conf_lines.append('component-node name={0} component={0} input={1}'.format(self.name,
                                                                                   descriptor_final_string))

        # the config is same for both final and ref configs
        for conf_name in ['final', 'ref']:
            for line in conf_lines:
                ans.append((conf_name, line))
        return ans


class XconfigIdctLayer(XconfigLayerBase):
    """
    This class is for lines like
     'idct-layer name=idct dim=40 cepstral-lifter=22 affine-transform-file=foo/bar/idct.mat'

    This is used to convert input MFCC-features to Filterbank featurs. The
    affine transformation is written out to the file specified via
    'affine-transform-file=xxx'.
    The output dimension of the layer may be specified via 'dim=xxx', but if not specified,
    the dimension defaults to the same as the input.

    See other configuration values below.

    Parameters of the class, and their defaults:
      input='[-1]'             [Descriptor giving the input of the layer.]
      dim=None                   [Output dimension of layer; defaults to the same as the input dim.]
      cepstral-lifter=22       [Apply liftering co-efficient.]
      affine-transform-file='' [Must be specified.]
      include-in-init=false     [You should set this to true if this precedes a
                                `fixed-affine-layer` that is to be initialized
                                 via LDA]
    """
    def __init__(self, first_token, key_to_value, prev_names=None):
        assert first_token == 'idct-layer'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):
        # note: self.config['input'] is a descriptor, '[-1]' means output
        # the most recent layer.
        self.config = {'input': '[-1]',
                       'dim': -1,
                       'cepstral-lifter': 22.0,
                       'affine-transform-file': '',
                       'include-in-init': False}

    def check_configs(self):
        if self.config['affine-transform-file'] is None:
            raise RuntimeError("affine-transform-file must be set.")

    def output_name(self, auxiliary_output=None):
        # Fixed affine layer computes only one vector, there are no intermediate
        # vectors.
        assert auxiliary_output is None
        return self.name

    def output_dim(self, auxiliary_output=None):
        output_dim = self.config['dim']
        # If not set, the output-dim defaults to the input-dim.
        if output_dim <= 0:
            output_dim = self.descriptors['input']['dim']
        return output_dim

    def get_full_config(self):
        ans = []
        config_lines = self._generate_config()
        for line in config_lines:
            for config_name in ['ref', 'final']:
                # we do not support user specified matrices in this layer
                # so 'ref' and 'final' configs are the same.
                ans.append((config_name, line))
            if self.config['include-in-init']:
                ans.append(('init', line))
        return ans


    def _generate_config(self):

        # note: each value of self.descriptors is (descriptor, dim,
        # normalized-string, output-string).
        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        descriptor_final_string = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']
        output_dim = self.output_dim()
        transform_file = self.config['affine-transform-file']

        idct_mat = common_lib.compute_idct_matrix(
            input_dim, output_dim, self.config['cepstral-lifter'])
        # append a zero column to the matrix, this is the bias of the fixed
        # affine component
        for n in range(0, output_dim):
            idct_mat[n].append(0)
        common_lib.write_kaldi_matrix(transform_file, idct_mat)

        configs = []

        # write the 'real' component to final.config
        line = 'component name={0} type=FixedAffineComponent matrix={1}'.format(
            self.name, transform_file)
        configs.append(line)
        line = 'component-node name={0} component={0} input={1}'.format(
            self.name, descriptor_final_string)
        configs.append(line)
        return configs


class XconfigExistingLayer(XconfigLayerBase):
    """
    This class is used to internally convert component-nodes in an existing
    model into lines like
    'existing name=tdnn1.affine dim=40'.

    Layers of this type are not presented in any actual xconfig or config
    files, but are created internally for all component nodes
    in an existing neural net model to use as input to other layers in xconfig.
    (i.e. get_model_component_info function, which is called in
     steps/nnet3/xconfig_to_configs.py, parses the name and
     dimension of component-nodes used in the existing model
     using the nnet3-info and returns a list of 'existing' layers.)

    This class is useful in cases like transferring existing model
    and using {input, output, component}-nodes in this model as
    input to new layers.
    """

    def __init__(self, first_token, key_to_value, prev_names=None):

        assert first_token == 'existing'
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)


    def set_default_configs(self):
        self.config = { 'dim': -1}

    def check_configs(self):
        if self.config['dim'] <= 0:
            raise RuntimeError("Dimension of existing-layer '{0}'"
                                "should be positive.".format(self.name))

    def get_input_descriptor_names(self):
        return []  # there is no 'input' field in self.config.

    def output_name(self, auxiliary_outputs=None):
        # there are no auxiliary outputs as this layer will just pass the input
        assert auxiliary_outputs is None
        return self.name

    def output_dim(self, auxiliary_outputs=None):
        # there are no auxiliary outputs as this layer will just pass the input
        assert auxiliary_outputs is None
        return self.config['dim']

    def get_full_config(self):
        # unlike other layers the existing layers should not to be printed in
        # any '*.config'
        ans = []
        return ans


class XconfigSpecAugmentLayer(XconfigLayerBase):
    """This class is for parsing lines like
     'spec-augment-layer name=spec-augment freq-max-proportion=0.5 time-zeroed-proportion=0.2 time-mask-max-frames=10'

    which will produce a component of type GeneralDropoutComponent (to do the
    frequency-domain part) and then one of type SpecaugmentTimeMaskComponent (to
    do the time part).

    Parameters of the class, and their defaults:
      input='[-1]'             [Descriptor giving the input of the layer.]
      freq-max-proportion=0.5  [The maximum proportion of the frequency space that
                                might be zeroed out]
      time-zeroed-proportion=0.2  [The proportion of time frames that will be zeroed
                                  out]
      time-mask-max-frames=20   [The maximum length of a zeroed region in the time
                                axis, in frames.]
      include-in-init=false     [You should set this to true if this precedes a
                                `fixed-affine-layer` that is to be initialized
                                 via LDA]
    """
    def __init__(self, first_token, key_to_value, prev_names=None):
        XconfigLayerBase.__init__(self, first_token, key_to_value, prev_names)

    def set_default_configs(self):
        self.config = {'input': '[-1]',
                       'freq-max-proportion': 0.5,
                       'time-zeroed-proportion': 0.2,
                       'time-mask-max-frames': 20,
                       'include-in-init': False}


    def check_configs(self):
        assert (self.config['freq-max-proportion'] > 0.0 and self.config['freq-max-proportion'] < 1.0
                and self.config['time-zeroed-proportion'] > 0.0 and self.config['time-zeroed-proportion'] < 1.0
                and self.config['time-mask-max-frames'] >= 1)


    def output_name(self, auxiliary_output=None):
        assert auxiliary_output is None
        return '{0}.time-mask'.format(self.name)

    def output_dim(self, auxiliary_output=None):
        assert auxiliary_output is None
        input_dim = self.descriptors['input']['dim']
        return input_dim

    def get_full_config(self):
        ans = []
        config_lines = self._generate_config()

        for line in config_lines:
            for config_name in ['ref', 'final']:
                # we do not support user specified matrices in this layer
                # so 'ref' and 'final' configs are the same.
                ans.append((config_name, line))
            if self.config['include-in-init']:
                ans.append(('init', line))
        return ans

    def _generate_config(self):
        # by 'descriptor_final_string' we mean a string that can appear in
        # config-files, i.e. it contains the 'final' names of nodes.
        input_desc = self.descriptors['input']['final-string']
        input_dim = self.descriptors['input']['dim']
        freq_max_proportion = self.config['freq-max-proportion']
        time_zeroed_proportion = self.config['time-zeroed-proportion']
        time_mask_max_frames = self.config['time-mask-max-frames']

        configs = []
        line = ('component name={0}.freq-mask type=GeneralDropoutComponent dim={1} specaugment-max-proportion={2}'.format(
            self.name, input_dim, freq_max_proportion))
        configs.append(line)
        line = ('component-node name={0}.freq-mask component={0}.freq-mask input={1}'.format(
            self.name, input_desc))
        configs.append(line)
        line = ('component name={0}.time-mask type=SpecAugmentTimeMaskComponent dim={1} '
                'zeroed-proportion={2} time-mask-max-frames={3}'.format(
                    self.name, input_dim, time_zeroed_proportion, time_mask_max_frames))
        configs.append(line)
        line = ('component-node name={0}.time-mask component={0}.time-mask input={0}.freq-mask'.format(
            self.name))
        configs.append(line)
        return configs


def test_layers():
    # for some config lines that should be printed the same way as they
    # are read, check that this is the case.
    for x in ['input name=input dim=30']:
        assert str(config_line_to_object(x, [])) == x