# Path: preimage/utils/alphabet.py
# def get_index_to_n_gram(alphabet, n):
#     n_grams = get_n_grams(alphabet, n)
#     indexes = numpy.arange(len(n_grams))
#     index_to_n_gram = dict(zip(indexes, n_grams))
#     return index_to_n_gram
# 
# Path: preimage/exceptions/n_gram.py
# class InvalidNGramLengthError(ValueError):
#     def __init__(self, n, min_n=0):
#         self.n = n
#         self.min_n = min_n
# 
#     def __str__(self):
#         error_message = 'n must be greater than {:d}. Got: n={:d}'.format(self.min_n, self.n)
#         return error_message
# 
# class InvalidYLengthError(ValueError):
#     def __init__(self, n, y_length):
#         self.n = n
#         self.y_length = y_length
# 
#     def __str__(self):
#         error_message = 'y_length must be >= n. Got: y_length={:d}, n={:d}'.format(self.y_length, self.n)
#         return error_message
# 
# class NoThresholdsError(ValueError):
#     def __str__(self):
#         error_message = 'thresholds must be provided when y_length is None'
#         return error_message
# 
# Path: preimage/exceptions/shape.py
# class InvalidShapeError(ValueError):
#     def __init__(self, parameter_name, parameter_shape, valid_shapes):
#         self.parameter_name = parameter_name
#         self.parameter_shape = parameter_shape
#         self.valid_shapes = [str(valid_shape) for valid_shape in valid_shapes]
# 
#     def __str__(self):
#         valid_shapes_string = ' or '.join(self.valid_shapes)
#         error_message = "{} wrong shape: Expected: {} Got: {}".format(self.parameter_name, valid_shapes_string,
#                                                                       str(self.parameter_shape))
#         return error_message


from copy import deepcopy
from preimage.utils.alphabet import get_index_to_n_gram
from preimage.exceptions.n_gram import InvalidNGramLengthError, InvalidYLengthError, NoThresholdsError
from preimage.exceptions.shape import InvalidShapeError
import numpy
        Parameters
        ----------
        n_gram_weights : array, shape=[len(alphabet)**n]
            Weight of each n-gram.
        y_length : int or None
            Length of the string to predict. None if thresholds are used.
        thresholds :array, shape=[len(alphabet)**n] or None
            Threshold value for each n-gram above which the n_gram_weights are rounded to one.
            None if y_length is given.

        Returns
        -------
        y: string
            The predicted string.
        """
        self._verify_weights_length_thresholds(n_gram_weights, y_length, thresholds)
        rounded_weights = self._round_weights(n_gram_weights, thresholds, y_length)
        n_gram_indexes = numpy.where(rounded_weights > 0)[0]
        n_grams = self._get_n_grams_in_selected_indexes(n_gram_indexes, rounded_weights)
        y = self._find_y_corresponding_to_n_grams(n_grams)
        if y_length is not None:
            y = y[0:y_length]
        return y

    def _verify_weights_length_thresholds(self, n_gram_weights, y_length, thresholds):
        if n_gram_weights.shape[0] != self._n_gram_count:
            raise InvalidShapeError('n_gram_weights', n_gram_weights.shape, [(self._n_gram_count,)])
        if y_length is not None and y_length < self.n:
            raise InvalidYLengthError(self.n, y_length)
        if thresholds is not None and thresholds.shape[0] != self._n_gram_count:

# Path: preimage/utils/alphabet.py
# def get_index_to_n_gram(alphabet, n):
#     n_grams = get_n_grams(alphabet, n)
#     indexes = numpy.arange(len(n_grams))
#     index_to_n_gram = dict(zip(indexes, n_grams))
#     return index_to_n_gram
# 
# Path: preimage/exceptions/n_gram.py
# class InvalidNGramLengthError(ValueError):
#     def __init__(self, n, min_n=0):
#         self.n = n
#         self.min_n = min_n
# 
#     def __str__(self):
#         error_message = 'n must be greater than {:d}. Got: n={:d}'.format(self.min_n, self.n)
#         return error_message
# 
# class InvalidYLengthError(ValueError):
#     def __init__(self, n, y_length):
#         self.n = n
#         self.y_length = y_length
# 
#     def __str__(self):
#         error_message = 'y_length must be >= n. Got: y_length={:d}, n={:d}'.format(self.y_length, self.n)
#         return error_message
# 
# class NoThresholdsError(ValueError):
#     def __str__(self):
#         error_message = 'thresholds must be provided when y_length is None'
#         return error_message
# 
# Path: preimage/exceptions/shape.py
# class InvalidShapeError(ValueError):
#     def __init__(self, parameter_name, parameter_shape, valid_shapes):
#         self.parameter_name = parameter_name
#         self.parameter_shape = parameter_shape
#         self.valid_shapes = [str(valid_shape) for valid_shape in valid_shapes]
# 
#     def __str__(self):
#         valid_shapes_string = ' or '.join(self.valid_shapes)
#         error_message = "{} wrong shape: Expected: {} Got: {}".format(self.parameter_name, valid_shapes_string,
#                                                                       str(self.parameter_shape))
#         return error_message


# Path: preimage/inference/euler.py
from copy import deepcopy
from preimage.utils.alphabet import get_index_to_n_gram
from preimage.exceptions.n_gram import InvalidNGramLengthError, InvalidYLengthError, NoThresholdsError
from preimage.exceptions.shape import InvalidShapeError
import numpy

        Parameters
        ----------
        n_gram_weights : array, shape=[len(alphabet)**n]
            Weight of each n-gram.
        y_length : int or None
            Length of the string to predict. None if thresholds are used.
        thresholds :array, shape=[len(alphabet)**n] or None
            Threshold value for each n-gram above which the n_gram_weights are rounded to one.
            None if y_length is given.

        Returns
        -------
        y: string
            The predicted string.
        """
        self._verify_weights_length_thresholds(n_gram_weights, y_length, thresholds)
        rounded_weights = self._round_weights(n_gram_weights, thresholds, y_length)
        n_gram_indexes = numpy.where(rounded_weights > 0)[0]
        n_grams = self._get_n_grams_in_selected_indexes(n_gram_indexes, rounded_weights)
        y = self._find_y_corresponding_to_n_grams(n_grams)
        if y_length is not None:
            y = y[0:y_length]
        return y

    def _verify_weights_length_thresholds(self, n_gram_weights, y_length, thresholds):
        if n_gram_weights.shape[0] != self._n_gram_count:
            raise InvalidShapeError('n_gram_weights', n_gram_weights.shape, [(self._n_gram_count,)])
        if y_length is not None and y_length < self.n:
            raise InvalidYLengthError(self.n, y_length)
        if thresholds is not None and thresholds.shape[0] != self._n_gram_count:

            raise InvalidShapeError('thresholds', thresholds.shape, [(self._n_gram_count,)])