import pynini
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from fun_text_processing.text_normalization.de.utils import get_abs_path
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from fun_text_processing.text_normalization.en.graph_utils import (
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DAMO_NOT_QUOTE,
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DAMO_SIGMA,
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GraphFst,
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)
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from pynini.lib import pynutil
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|
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class OrdinalFst(GraphFst):
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"""
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Finite state transducer for verbalizing roman numerals
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e.g. ordinal { integer: "vier" } } -> "vierter"
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-> "viertes" ...
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Args:
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deterministic: if True will provide a single transduction option,
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for False multiple transduction are generated (used for audio-based normalization)
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"""
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def __init__(self, deterministic: bool = True):
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super().__init__(name="ordinal", kind="verbalize", deterministic=deterministic)
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graph_digit = pynini.string_file(get_abs_path("data/ordinals/digit.tsv")).invert()
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graph_ties = pynini.string_file(get_abs_path("data/ordinals/ties.tsv")).invert()
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graph_thousands = pynini.string_file(get_abs_path("data/ordinals/thousands.tsv")).invert()
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graph = (
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pynutil.delete('integer: "') + pynini.closure(DAMO_NOT_QUOTE, 1) + pynutil.delete('"')
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)
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suffixes = pynini.union("ten", "tem", "ter", "tes", "te")
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convert_rest = pynutil.insert(suffixes, weight=0.01)
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self.ordinal_stem = graph_digit | graph_ties | graph_thousands
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suffix = pynini.cdrewrite(
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pynini.closure(self.ordinal_stem, 0, 1) + convert_rest,
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"",
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"[EOS]",
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DAMO_SIGMA,
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).optimize()
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self.graph = pynini.compose(graph, suffix)
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self.suffix = suffix
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delete_tokens = self.delete_tokens(self.graph)
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self.fst = delete_tokens.optimize()
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