# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

from inverse_text_normalization.kn.data_loader_utils import get_abs_path
from inverse_text_normalization.kn.graph_utils import GraphFst, convert_space

try:
    import pynini
    from pynini.lib import pynutil

    PYNINI_AVAILABLE = True
except (ModuleNotFoundError, ImportError):
    PYNINI_AVAILABLE = False

# from inverse_text_normalization.lang_params import LANG
# lang_data_path = f'inverse_text_normalization/data/{LANG}_data/'
lang_data_path = 'data/'

class WhiteListFst(GraphFst):
    """
    Finite state transducer for classifying whitelist
        e.g. misses -> tokens { name: "mrs." }
    This class has highest priority among all classifiers and loads lookup table from "data/whitelist.tsv"
    """

    def __init__(self):
        super().__init__(name="whitelist", kind="classify")

        whitelist = pynini.string_file(get_abs_path(lang_data_path+"whitelist.tsv")).invert()
        graph = pynutil.insert("name: \"") + convert_space(whitelist) + pynutil.insert("\"")
        self.fst = graph.optimize()
