# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pynini
from fun_text_processing.inverse_text_normalization.ko.utils import get_abs_path
from fun_text_processing.inverse_text_normalization.ko.graph_utils import (
    DAMO_DIGIT,
    DAMO_NOT_SPACE,
    DAMO_SIGMA,
    GraphFst,
    convert_space,
    delete_extra_space,
    delete_space,
    get_singulars,
    insert_space,
)
from pynini.lib import pynutil


class MoneyFst(GraphFst):
    """
    Finite state transducer for classifying money
        e.g. twelve dollars and five cents -> money { integer_part: "12" fractional_part: 05 currency: "$" }

    Args:
        cardinal: CardinalFst
        decimal: DecimalFst
    """

    def __init__(self, cardinal: GraphFst, decimal: GraphFst):
        super().__init__(name="money", kind="classify")
        # quantity, integer_part, fractional_part, currency

        cardinal_graph = cardinal.graph_no_exception
        decimal_graph = decimal.final_graph_wo_negative

        unit = pynini.string_file(get_abs_path("data/currency.tsv")).invert()

        graph_unit = pynutil.insert("currency: \"") + unit + pynutil.insert("\"")

        graph_integer = (
            pynutil.insert("integer_part: \"")
            + cardinal_graph
            + pynutil.insert("\"")
            + delete_extra_space
            + graph_unit
        )

        graph_decimal = decimal_graph + pynutil.insert(" ") + graph_unit

        final_graph = graph_integer | graph_decimal

        final_graph = self.add_tokens(final_graph)
        self.fst = final_graph.optimize()
