# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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/keithito/tacotron """

'''
Cleaners are transformations that run over the input text at both training and eval time.

Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use:
    1. "english_cleaners" for English text
    2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
         the Unidecode library (https://pypi.python.org/pypi/Unidecode)
    3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
         the symbols in symbols.py to match your data).
'''

import re
from unidecode import unidecode
from .numerical import normalize_numbers
from .acronyms import normalize_acronyms, spell_acronyms
from .datestime import normalize_datestime
from .letters_and_numbers import normalize_letters_and_numbers
from .abbreviations import normalize_abbreviations


# Regular expression matching whitespace:
_whitespace_re = re.compile(r'\s+')


def expand_abbreviations(text):
    return normalize_abbreviations(text)


def expand_numbers(text):
    return normalize_numbers(text)


def expand_acronyms(text):
    return normalize_acronyms(text)


def expand_datestime(text):
    return normalize_datestime(text)


def expand_letters_and_numbers(text):
    return normalize_letters_and_numbers(text)


def lowercase(text):
    return text.lower()


def collapse_whitespace(text):
    return re.sub(_whitespace_re, ' ', text)


def separate_acronyms(text):
    text = re.sub(r"([0-9]+)([a-zA-Z]+)", r"\1 \2", text)
    text = re.sub(r"([a-zA-Z]+)([0-9]+)", r"\1 \2", text)
    return text


def convert_to_ascii(text):
    return unidecode(text)


def basic_cleaners(text):
    '''Basic pipeline that collapses whitespace without transliteration.'''
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def transliteration_cleaners(text):
    '''Pipeline for non-English text that transliterates to ASCII.'''
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners(text):
    '''Pipeline for English text, with number and abbreviation expansion.'''
    text = convert_to_ascii(text)
    text = lowercase(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = collapse_whitespace(text)
    return text


def english_cleaners_v2(text):
    text = convert_to_ascii(text)
    text = expand_datestime(text)
    text = expand_letters_and_numbers(text)
    text = expand_numbers(text)
    text = expand_abbreviations(text)
    text = spell_acronyms(text)
    text = lowercase(text)
    text = collapse_whitespace(text)
    # compatibility with basic_english symbol set
    text = re.sub(r'/+', ' ', text)
    return text