text: # Copyright (c) 2021, 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
  # 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.
  
  # Adapted from https://github.com/google/TextNormalizationCoveringGrammars
  # Russian minimally supervised number grammar.
  
  import pynini
  from nemo_text_processing.text_normalization.en.graph_utils import NEMO_DIGIT, GraphFst
  from pynini.lib import pynutil
  
  
  class OrdinalFst(GraphFst):
      """
      Finite state transducer for classifying cardinals, e.g. 
          "2." -> ordinal { integer: "zwei" } }
          "2tes" -> ordinal { integer: "zwei" } }
  
      Args:
          cardinal: cardinal GraphFst
          deterministic: if True will provide a single transduction option,
              for False multiple transduction are generated (used for audio-based normalization)
      """
  
      def __init__(self, cardinal: GraphFst, deterministic=False):
          super().__init__(name="ordinal", kind="classify", deterministic=deterministic)
  
          cardinal_graph = cardinal.graph
          endings = ["ter", "tes", "tem", "te", "ten"]
          self.graph = (
              (
                  pynini.closure(NEMO_DIGIT | pynini.accep("."))
                  + pynutil.delete(pynutil.add_weight(pynini.union(*endings), weight=0.0001) | pynini.accep("."))
              )
              @ cardinal_graph
          ).optimize()
          final_graph = pynutil.insert("integer: \"") + self.graph + pynutil.insert("\"")
          final_graph = self.add_tokens(final_graph)
          self.fst = final_graph.optimize()
  