# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  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
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import pynini
from pynini.lib import pynutil

from nemo_text_processing.text_normalization.en.graph_utils import NEMO_NOT_QUOTE, GraphFst
from nemo_text_processing.text_normalization.es.graph_utils import (
    add_cardinal_apocope_fem,
    shift_cardinal_gender,
    strip_cardinal_apocope,
)


class CardinalFst(GraphFst):
    """
	Finite state transducer for verbalizing cardinals
		e.g. cardinal { integer: "dos" } -> "dos"

	Args:
		deterministic: if True will provide a single transduction option,
			for False multiple transduction are generated (used for audio-based normalization)
	"""

    def __init__(self, deterministic: bool = True):
        super().__init__(name="cardinal", kind="verbalize", deterministic=deterministic)
        optional_sign = pynini.closure(pynini.cross("negative: \"true\" ", "menos "), 0, 1)
        self.optional_sign = optional_sign

        integer = pynini.closure(NEMO_NOT_QUOTE, 1)
        self.integer = pynutil.delete(" \"") + integer + pynutil.delete("\"")

        integer = pynutil.delete("integer:") + self.integer

        graph_masc = optional_sign + integer
        graph_fem = shift_cardinal_gender(graph_masc)

        self.graph_masc = pynini.optimize(graph_masc)
        self.graph_fem = pynini.optimize(graph_fem)

        # Adding adjustment for fem gender (choice of gender will be random)
        graph = graph_masc | graph_fem

        if not deterministic:
            # For alternate renderings when apocope is omitted (i.e. cardinal stands alone)
            graph |= strip_cardinal_apocope(graph_masc)
            # "una" will drop to "un" in unique contexts
            graph |= add_cardinal_apocope_fem(graph_fem)

        delete_tokens = self.delete_tokens(graph)
        self.fst = delete_tokens.optimize()
