# Path: deep_architect/searchers/common.py
# class Searcher:
#     """Abstract base class from which new searchers should inherit from.
# 
#     A search takes a function that when called returns a new search space, i.e.,
#     a search space where all the hyperparameters have not being specified.
#     Searchers essentially sample a sequence of models in the search space by
#     specifying the hyperparameters sequentially. After the sampled architecture
#     has been evaluated somehow, the state of the searcher can be updated with
#     the performance information, guaranteeing that future architectures
#     are sampled from the search space in a more informed manner.
# 
#     Args:
#         search_space_fn (() -> (dict[str,deep_architect.core.Input], dict[str,deep_architect.core.Output], dict[str,deep_architect.core.Hyperparameter])):
#             Search space function that when called returns a dictionary of
#             inputs, dictionary of outputs, and dictionary of hyperparameters
#             encoding the search space from which models can be sampled by
#             specifying all hyperparameters (i.e., both those arising in the
#             graph part and those in the dictionary of hyperparameters).
#         reset_default_scope_upon_get (bool): Whether to clean the scope upon getting
#             a new search space. Should be ``True`` unless you want to persist models
#             across samples.
#     """
# 
#     def __init__(self, search_space_fn, reset_default_scope_upon_sample=True):
#         x = mo.SearchSpaceFactory(search_space_fn,
#                                   reset_default_scope_upon_sample)
#         self.search_space_fn = x.get_search_space
# 
#     def sample(self):
#         """Returns a model from the search space.
# 
#         Models are encoded via a dictionary of inputs, a dictionary of outputs,
#         and a dictionary of hyperparameters. The forward computation for the
#         model can then be done as all values for the hyperparameters have been
#         chosen.
# 
#         Returns:
#             (dict[str, deep_architect.core.Input], dict[str, deep_architect.core.Output], dict[str, deep_architect.core.Hyperparameter], list[object], dict[str, object]):
#                 Tuple encoding the model sampled from the search space.
#                 The positional arguments have the following semantics:
#                 1: Dictionary of names to inputs of the model.
#                 2: Dictionary of names to outputs of the model.
#                 3: List with list of values that can be to replay the sequence
#                 of values assigned to the hyperparameters, and therefore,
#                 reproduce, given the search space, the model sampled.
#                 4: Searcher evaluation token that is sufficient for the searcher
#                 to update its state when combined with the results of the
#                 evaluation.
#         """
#         raise NotImplementedError
# 
#     def update(self, val, searcher_eval_token):
#         """Updates the state of the searcher based on the searcher token
#         for a particular evaluation and the results of the evaluation.
# 
#         Args:
#             val (object): Result of the evaluation to use to update the state of the searcher.
#             searcher_eval_token (dict[str, object]): Searcher evaluation token
#                 that is sufficient for the searcher to update its state when
#                 combined with the results of the evaluation.
#         """
#         raise NotImplementedError
# 
#     # TODO: needs to be changed to a nicer API. use sparsely.
#     # NOTE: this solution only allows a single saved state per frolder.
#     def save_state(self, folderpath):
#         raise NotImplementedError
# 
#     def load_state(self, folderpath):
#         raise NotImplementedError
# 
# def random_specify_hyperparameter(hyperp):
#     """Choose a random value for an unspecified hyperparameter.
# 
#     The hyperparameter becomes specified after the call.
# 
#     hyperp (deep_architect.core.Hyperparameter): Hyperparameter to specify.
#     """
#     assert not hyperp.has_value_assigned()
# 
#     if isinstance(hyperp, hp.Discrete):
#         v = hyperp.vs[np.random.randint(len(hyperp.vs))]
#         hyperp.assign_value(v)
#     else:
#         raise ValueError
#     return v


import random
import deep_architect.utils as ut
import deep_architect.core as co
import numpy as np
from collections import deque
from deep_architect.searchers.common import Searcher, random_specify_hyperparameter
        new_vs = new_vs[:m_ind + 1]

    inputs, outputs = search_space_fn()
    all_vs = specify_evolution(outputs, mutatable_fn, new_vs)
    return inputs, outputs, new_vs, all_vs


def random_specify_evolution(outputs, mutatable_fn):
    user_vs = []
    all_vs = []
    for h in co.unassigned_independent_hyperparameter_iterator(outputs):
        v = random_specify_hyperparameter(h)
        if mutatable_fn(h):
            user_vs.append(v)
        all_vs.append(v)
    return user_vs, all_vs


def specify_evolution(outputs, mutatable_fn, user_vs):
    vs_idx = 0
    vs = []
    for i, h in enumerate(
            co.unassigned_independent_hyperparameter_iterator(outputs)):
        if mutatable_fn(h):
            if vs_idx >= len(user_vs):
                user_vs.append(h.vs[random.randint(0, len(h.vs) - 1)])
            h.assign_value(user_vs[vs_idx])
            vs.append(user_vs[vs_idx])
            vs_idx += 1
        else:

# Path: deep_architect/searchers/common.py
# class Searcher:
#     """Abstract base class from which new searchers should inherit from.
# 
#     A search takes a function that when called returns a new search space, i.e.,
#     a search space where all the hyperparameters have not being specified.
#     Searchers essentially sample a sequence of models in the search space by
#     specifying the hyperparameters sequentially. After the sampled architecture
#     has been evaluated somehow, the state of the searcher can be updated with
#     the performance information, guaranteeing that future architectures
#     are sampled from the search space in a more informed manner.
# 
#     Args:
#         search_space_fn (() -> (dict[str,deep_architect.core.Input], dict[str,deep_architect.core.Output], dict[str,deep_architect.core.Hyperparameter])):
#             Search space function that when called returns a dictionary of
#             inputs, dictionary of outputs, and dictionary of hyperparameters
#             encoding the search space from which models can be sampled by
#             specifying all hyperparameters (i.e., both those arising in the
#             graph part and those in the dictionary of hyperparameters).
#         reset_default_scope_upon_get (bool): Whether to clean the scope upon getting
#             a new search space. Should be ``True`` unless you want to persist models
#             across samples.
#     """
# 
#     def __init__(self, search_space_fn, reset_default_scope_upon_sample=True):
#         x = mo.SearchSpaceFactory(search_space_fn,
#                                   reset_default_scope_upon_sample)
#         self.search_space_fn = x.get_search_space
# 
#     def sample(self):
#         """Returns a model from the search space.
# 
#         Models are encoded via a dictionary of inputs, a dictionary of outputs,
#         and a dictionary of hyperparameters. The forward computation for the
#         model can then be done as all values for the hyperparameters have been
#         chosen.
# 
#         Returns:
#             (dict[str, deep_architect.core.Input], dict[str, deep_architect.core.Output], dict[str, deep_architect.core.Hyperparameter], list[object], dict[str, object]):
#                 Tuple encoding the model sampled from the search space.
#                 The positional arguments have the following semantics:
#                 1: Dictionary of names to inputs of the model.
#                 2: Dictionary of names to outputs of the model.
#                 3: List with list of values that can be to replay the sequence
#                 of values assigned to the hyperparameters, and therefore,
#                 reproduce, given the search space, the model sampled.
#                 4: Searcher evaluation token that is sufficient for the searcher
#                 to update its state when combined with the results of the
#                 evaluation.
#         """
#         raise NotImplementedError
# 
#     def update(self, val, searcher_eval_token):
#         """Updates the state of the searcher based on the searcher token
#         for a particular evaluation and the results of the evaluation.
# 
#         Args:
#             val (object): Result of the evaluation to use to update the state of the searcher.
#             searcher_eval_token (dict[str, object]): Searcher evaluation token
#                 that is sufficient for the searcher to update its state when
#                 combined with the results of the evaluation.
#         """
#         raise NotImplementedError
# 
#     # TODO: needs to be changed to a nicer API. use sparsely.
#     # NOTE: this solution only allows a single saved state per frolder.
#     def save_state(self, folderpath):
#         raise NotImplementedError
# 
#     def load_state(self, folderpath):
#         raise NotImplementedError
# 
# def random_specify_hyperparameter(hyperp):
#     """Choose a random value for an unspecified hyperparameter.
# 
#     The hyperparameter becomes specified after the call.
# 
#     hyperp (deep_architect.core.Hyperparameter): Hyperparameter to specify.
#     """
#     assert not hyperp.has_value_assigned()
# 
#     if isinstance(hyperp, hp.Discrete):
#         v = hyperp.vs[np.random.randint(len(hyperp.vs))]
#         hyperp.assign_value(v)
#     else:
#         raise ValueError
#     return v


# Path: deep_architect/searchers/regularized_evolution.py
import random
import deep_architect.utils as ut
import deep_architect.core as co
import numpy as np
from collections import deque
from deep_architect.searchers.common import Searcher, random_specify_hyperparameter

        new_vs = new_vs[:m_ind + 1]

    inputs, outputs = search_space_fn()
    all_vs = specify_evolution(outputs, mutatable_fn, new_vs)
    return inputs, outputs, new_vs, all_vs


def random_specify_evolution(outputs, mutatable_fn):
    user_vs = []
    all_vs = []
    for h in co.unassigned_independent_hyperparameter_iterator(outputs):
        v = random_specify_hyperparameter(h)
        if mutatable_fn(h):
            user_vs.append(v)
        all_vs.append(v)
    return user_vs, all_vs


def specify_evolution(outputs, mutatable_fn, user_vs):
    vs_idx = 0
    vs = []
    for i, h in enumerate(
            co.unassigned_independent_hyperparameter_iterator(outputs)):
        if mutatable_fn(h):
            if vs_idx >= len(user_vs):
                user_vs.append(h.vs[random.randint(0, len(h.vs) - 1)])
            h.assign_value(user_vs[vs_idx])
            vs.append(user_vs[vs_idx])
            vs_idx += 1
        else:

            v = random_specify_hyperparameter(h)