# 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

    References
    ----------
    .. [1] Cortes, Corinna, Mehryar Mohri, and Jason Weston. "A general regression framework for learning
       string-to-string mappings." Predicting Structured Data (2007): 143-168.
    """

    def __init__(self, alphabet, n, min_length=1, is_merging_path=True):
        """Initialize the eulerian path algorithm

        Parameters
        ----------
        alphabet : list
            list of letters.
        n : int
            n-gram length.
        min_length :int
            Minimum length of the predicted string.
        is_merging_path : bool
            True if merges path when the graph is not connected, False otherwise (choose the longest path instead).
        """
        self.n = int(n)
        self.is_merging_path = is_merging_path
        self.min_n_gram_count = 1 if min_length <= self.n else min_length - self.n + 1
        self._index_to_n_gram = get_index_to_n_gram(alphabet, self.n)
        self._n_gram_count = len(alphabet) ** self.n
        self._verify_n(self.n)

    def _verify_n(self, n):
        if n <= 1:

# 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


    References
    ----------
    .. [1] Cortes, Corinna, Mehryar Mohri, and Jason Weston. "A general regression framework for learning
       string-to-string mappings." Predicting Structured Data (2007): 143-168.
    """

    def __init__(self, alphabet, n, min_length=1, is_merging_path=True):
        """Initialize the eulerian path algorithm

        Parameters
        ----------
        alphabet : list
            list of letters.
        n : int
            n-gram length.
        min_length :int
            Minimum length of the predicted string.
        is_merging_path : bool
            True if merges path when the graph is not connected, False otherwise (choose the longest path instead).
        """
        self.n = int(n)
        self.is_merging_path = is_merging_path
        self.min_n_gram_count = 1 if min_length <= self.n else min_length - self.n + 1
        self._index_to_n_gram = get_index_to_n_gram(alphabet, self.n)
        self._n_gram_count = len(alphabet) ** self.n
        self._verify_n(self.n)

    def _verify_n(self, n):
        if n <= 1:

            raise InvalidNGramLengthError(n, 1)