# Path: tupa/classifiers/nn/constants.py
# ACTIVATIONS = {
#     "cube": "cube",
#     "tanh": "tanh",
#     "sigmoid": "logistic",
#     "relu": "rectify",
# }
# 
# INITIALIZERS = {
#     # "saxe": "SaxeInitializer",
#     "glorot_uniform": "GlorotInitializer",
#     "normal": "NormalInitializer",
# }
# 
# class CategoricalParameter:
#     def __init__(self, values, string):
#         self._value = self._string = None
#         self.values = values
#         self.string = string
# 
#     def __call__(self):
#         import dynet as dy
#         return getattr(dy, self._value)
# 
#     @property
#     def string(self):
#         return self._string
# 
#     @string.setter
#     def string(self, s):
#         self._string = s
#         self._value = self.values.get(s)
# 
#     def __str__(self):
#         return self._string
# 
# Path: tupa/classifiers/nn/sub_model.py
# class SubModel:
#     def __init__(self, params=None, save_path=(), shared=False, copy_shared=False):
#         self.params = OrderedDict() if params is None else params  # string (param identifier) -> parameter
#         self.save_path = save_path
#         self.shared = shared
#         self.copy_shared = copy_shared
# 
#     def save_sub_model(self, d, *args):
#         self.get_sub_dict(d).update(args + (("param_keys", list(self.params.keys())),))
#         return list(self.params.values())
# 
#     def load_sub_model(self, d, *args, load_path=None):
#         d = self.get_sub_dict(d, load_path=load_path)
#         param_keys = d.get("param_keys", ())
#         assert len(param_keys) <= len(args), "%s loaded values: expected %d, got %d" % ("/".join(self.save_path),
#                                                                                         len(param_keys), len(args))
#         self.params.clear()
#         self.params.update(zip(param_keys, args))
#         return d
# 
#     def get_sub_dict(self, d, load_path=None):
#         for element in load_path or self.save_path:
#             d = d.setdefault(element, OrderedDict())
#         return d
# 
#     def params_str(self):
#         return "/".join(self.save_path) + (": " if self.save_path else "") + ", ".join(self.params.keys())
# 
#     def invalidate_caches(self):
#         for model in self.sub_models():
#             model.invalidate_caches()
# 
#     def sub_models(self):
#         return ()
# 
# Path: tupa/classifiers/nn/util.py
# def randomize_orthonormal(*parameters, activation=None):  # Saxe et al., 2014 (https://arxiv.org/abs/1312.6120)
#     for param in parameters:
#         shape = param.shape()
#         if len(shape) == 2 and shape[0] == shape[1] > 1:
#             init, _, _ = np.linalg.svd(np.random.randn(*shape))
#             if str(activation) == "relu":
#                 init *= np.sqrt(2)
#             param.set_value(init)


from itertools import groupby
from .constants import ACTIVATIONS, INITIALIZERS, CategoricalParameter
from .sub_model import SubModel
from .util import randomize_orthonormal
import dynet as dy
import re



MLP_PARAM_PATTERN = re.compile(r"[Wb]\d+")


class MultilayerPerceptron(SubModel):
    def __init__(self, config, args, model, layers=None, layer_dim=None, output_dim=None, num_labels=None, params=None,
                 **kwargs):
        super().__init__(params=params, **kwargs)
        self.config = config
        self.args = args
        self.model = model
        self.layers = self.args.layers if layers is None else layers
        self.layer_dim = self.args.layer_dim if layer_dim is None else layer_dim
        self.output_dim = self.args.output_dim if output_dim is None else output_dim

# Path: tupa/classifiers/nn/constants.py
# ACTIVATIONS = {
#     "cube": "cube",
#     "tanh": "tanh",
#     "sigmoid": "logistic",
#     "relu": "rectify",
# }
# 
# INITIALIZERS = {
#     # "saxe": "SaxeInitializer",
#     "glorot_uniform": "GlorotInitializer",
#     "normal": "NormalInitializer",
# }
# 
# class CategoricalParameter:
#     def __init__(self, values, string):
#         self._value = self._string = None
#         self.values = values
#         self.string = string
# 
#     def __call__(self):
#         import dynet as dy
#         return getattr(dy, self._value)
# 
#     @property
#     def string(self):
#         return self._string
# 
#     @string.setter
#     def string(self, s):
#         self._string = s
#         self._value = self.values.get(s)
# 
#     def __str__(self):
#         return self._string
# 
# Path: tupa/classifiers/nn/sub_model.py
# class SubModel:
#     def __init__(self, params=None, save_path=(), shared=False, copy_shared=False):
#         self.params = OrderedDict() if params is None else params  # string (param identifier) -> parameter
#         self.save_path = save_path
#         self.shared = shared
#         self.copy_shared = copy_shared
# 
#     def save_sub_model(self, d, *args):
#         self.get_sub_dict(d).update(args + (("param_keys", list(self.params.keys())),))
#         return list(self.params.values())
# 
#     def load_sub_model(self, d, *args, load_path=None):
#         d = self.get_sub_dict(d, load_path=load_path)
#         param_keys = d.get("param_keys", ())
#         assert len(param_keys) <= len(args), "%s loaded values: expected %d, got %d" % ("/".join(self.save_path),
#                                                                                         len(param_keys), len(args))
#         self.params.clear()
#         self.params.update(zip(param_keys, args))
#         return d
# 
#     def get_sub_dict(self, d, load_path=None):
#         for element in load_path or self.save_path:
#             d = d.setdefault(element, OrderedDict())
#         return d
# 
#     def params_str(self):
#         return "/".join(self.save_path) + (": " if self.save_path else "") + ", ".join(self.params.keys())
# 
#     def invalidate_caches(self):
#         for model in self.sub_models():
#             model.invalidate_caches()
# 
#     def sub_models(self):
#         return ()
# 
# Path: tupa/classifiers/nn/util.py
# def randomize_orthonormal(*parameters, activation=None):  # Saxe et al., 2014 (https://arxiv.org/abs/1312.6120)
#     for param in parameters:
#         shape = param.shape()
#         if len(shape) == 2 and shape[0] == shape[1] > 1:
#             init, _, _ = np.linalg.svd(np.random.randn(*shape))
#             if str(activation) == "relu":
#                 init *= np.sqrt(2)
#             param.set_value(init)


# Path: tupa/classifiers/nn/mlp.py
from itertools import groupby
from .constants import ACTIVATIONS, INITIALIZERS, CategoricalParameter
from .sub_model import SubModel
from .util import randomize_orthonormal
import dynet as dy
import re




MLP_PARAM_PATTERN = re.compile(r"[Wb]\d+")


class MultilayerPerceptron(SubModel):
    def __init__(self, config, args, model, layers=None, layer_dim=None, output_dim=None, num_labels=None, params=None,
                 **kwargs):
        super().__init__(params=params, **kwargs)
        self.config = config
        self.args = args
        self.model = model
        self.layers = self.args.layers if layers is None else layers
        self.layer_dim = self.args.layer_dim if layer_dim is None else layer_dim
        self.output_dim = self.args.output_dim if output_dim is None else output_dim

        self.activation = CategoricalParameter(ACTIVATIONS, self.args.activation)