# 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.

import os
from argparse import ArgumentParser
from time import perf_counter
from typing import List

from nemo_text_processing.text_normalization.data_loader_utils import load_file, write_file
from nemo_text_processing.text_normalization.en.graph_utils import INPUT_CASED, INPUT_LOWER_CASED
from nemo_text_processing.text_normalization.normalize import Normalizer
from nemo_text_processing.text_normalization.token_parser import TokenParser


class InverseNormalizer(Normalizer):
    """
    Inverse normalizer that converts text from spoken to written form. Useful for ASR postprocessing.
    Input is expected to have no punctuation outside of approstrophe (') and dash (-) and be lower cased.

    Args:
        input_case: Input text capitalization, set to 'cased' if text contains capital letters.
            This flag affects normalization rules applied to the text. Note, `lower_cased` won't lower case input.
        lang: language specifying the ITN
        whitelist: path to a file with whitelist replacements. (each line of the file: written_form\tspoken_form\n),
            e.g. nemo_text_processing/inverse_text_normalization/en/data/whitelist.tsv
        cache_dir: path to a dir with .far grammar file. Set to None to avoid using cache.
        overwrite_cache: set to True to overwrite .far files
        max_number_of_permutations_per_split: a maximum number
            of permutations which can be generated from input sequence of tokens.
    """

    def __init__(
        self,
        input_case: str = INPUT_LOWER_CASED,
        lang: str = "en",
        whitelist: str = None,
        cache_dir: str = None,
        overwrite_cache: bool = False,
        max_number_of_permutations_per_split: int = 729,
    ):

        assert input_case in ["lower_cased", "cased"]

        if lang == 'en':  # English
            from nemo_text_processing.inverse_text_normalization.en.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.en.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )

        elif lang == 'es':  # Spanish (Espanol)
            from nemo_text_processing.inverse_text_normalization.es.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.es.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )

        elif lang == 'pt':  # Portuguese (Português)
            from nemo_text_processing.inverse_text_normalization.pt.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.pt.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )

        elif lang == 'ru':  # Russian (Russkiy Yazyk)
            from nemo_text_processing.inverse_text_normalization.ru.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.ru.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )

        elif lang == 'de':  # German (Deutsch)
            from nemo_text_processing.inverse_text_normalization.de.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.de.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'fr':  # French (Français)
            from nemo_text_processing.inverse_text_normalization.fr.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.fr.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'sv':  # Swedish (Svenska)
            from nemo_text_processing.inverse_text_normalization.sv.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.sv.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'vi':  # Vietnamese (Tiếng Việt)
            from nemo_text_processing.inverse_text_normalization.vi.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.vi.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'ar':  # Arabic
            from nemo_text_processing.inverse_text_normalization.ar.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.ar.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'es_en':  # Arabic
            from nemo_text_processing.inverse_text_normalization.es_en.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.es_en.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'zh':  # Mandarin
            from nemo_text_processing.inverse_text_normalization.zh.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.zh.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'mr':  # Marathi
            from nemo_text_processing.inverse_text_normalization.mr.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.mr.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'hy':
            from nemo_text_processing.inverse_text_normalization.hy.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.hy.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )
        elif lang == 'ja':  # Japanese
            from nemo_text_processing.inverse_text_normalization.ja.taggers.tokenize_and_classify import ClassifyFst
            from nemo_text_processing.inverse_text_normalization.ja.verbalizers.verbalize_final import (
                VerbalizeFinalFst,
            )

        self.tagger = ClassifyFst(
            cache_dir=cache_dir, whitelist=whitelist, overwrite_cache=overwrite_cache, input_case=input_case
        )
        self.verbalizer = VerbalizeFinalFst()
        self.parser = TokenParser()
        self.lang = lang
        self.max_number_of_permutations_per_split = max_number_of_permutations_per_split

    def inverse_normalize_list(self, texts: List[str], verbose=False) -> List[str]:
        """
        NeMo inverse text normalizer

        Args:
            texts: list of input strings
            verbose: whether to print intermediate meta information

        Returns converted list of input strings
        """
        return self.normalize_list(texts=texts, verbose=verbose)

    def inverse_normalize(self, text: str, verbose: bool) -> str:
        """
        Main function. Inverse normalizes tokens from spoken to written form
            e.g. twelve kilograms -> 12 kg

        Args:
            text: string that may include semiotic classes
            verbose: whether to print intermediate meta information

        Returns: written form
        """
        return self.normalize(text=text, verbose=verbose)


def parse_args():
    parser = ArgumentParser()
    input = parser.add_mutually_exclusive_group()
    input.add_argument("--text", dest="input_string", help="input string", type=str)
    input.add_argument("--input_file", dest="input_file", help="input file path", type=str)
    parser.add_argument('--output_file', dest="output_file", help="output file path", type=str)
    parser.add_argument(
        "--language",
        help="language",
        choices=['en', 'de', 'es', 'pt', 'ru', 'fr', 'sv', 'vi', 'ar', 'es_en', 'zh', 'hy', 'mr', 'ja'],
        default="en",
        type=str,
    )
    parser.add_argument(
        "--input_case",
        help="Input text capitalization, set to 'cased' if text contains capital letters."
        "This flag affects normalization rules applied to the text. Note, `lower_cased` won't lower case input.",
        choices=[INPUT_CASED, INPUT_LOWER_CASED],
        default=INPUT_LOWER_CASED,
        type=str,
    )
    parser.add_argument(
        "--whitelist",
        help="Path to a file with with whitelist replacements," "e.g., inverse_normalization/en/data/whitelist.tsv",
        default=None,
        type=str,
    )
    parser.add_argument("--verbose", help="print info for debugging", action='store_true')
    parser.add_argument("--overwrite_cache", help="set to True to re-create .far grammar files", action="store_true")
    parser.add_argument(
        "--cache_dir",
        help="path to a dir with .far grammar file. Set to None to avoid using cache",
        default=None,
        type=str,
    )
    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()

    whitelist = os.path.abspath(args.whitelist) if args.whitelist else None
    start_time = perf_counter()
    inverse_normalizer = InverseNormalizer(
        input_case=args.input_case,
        lang=args.language,
        cache_dir=args.cache_dir,
        overwrite_cache=args.overwrite_cache,
        whitelist=whitelist,
    )
    print(f'Time to generate graph: {round(perf_counter() - start_time, 2)} sec')

    if args.input_string:
        print(inverse_normalizer.inverse_normalize(args.input_string, verbose=args.verbose))
    elif args.input_file:
        print("Loading data: " + args.input_file)
        data = load_file(args.input_file)

        print("- Data: " + str(len(data)) + " sentences")
        prediction = inverse_normalizer.inverse_normalize_list(data, verbose=args.verbose)
        if args.output_file:
            write_file(args.output_file, prediction)
            print(f"- Denormalized. Writing out to {args.output_file}")
        else:
            print(prediction)