from __future__ import annotations

import os
from collections.abc import Sequence

from speech_recognition import PortableNamedTemporaryFile
from speech_recognition.audio import AudioData
from speech_recognition.exceptions import RequestError, UnknownValueError

AcousticParametersDirectoryPath = str
LanguageModelFilePath = str
PhonemeDictionaryFilePath = str
SphinxDataFilePaths = tuple[AcousticParametersDirectoryPath, LanguageModelFilePath, PhonemeDictionaryFilePath]

Keyword = str
Sensitivity = float
KeywordEntry = tuple[Keyword, Sensitivity]


def recognize(
    recognizer,
    audio_data: AudioData,
    language: str | SphinxDataFilePaths = "en-US",
    keyword_entries: Sequence[KeywordEntry] | None = None,
    grammar: str | None = None,
    show_all: bool = False,
):
    """
    Performs speech recognition on ``audio_data`` (an ``AudioData`` instance), using CMU Sphinx.

    The recognition language is determined by ``language``, an RFC5646 language tag like ``"en-US"`` or ``"en-GB"``, defaulting to US English. Out of the box, only ``en-US`` is supported. See `Notes on using `PocketSphinx <https://github.com/Uberi/speech_recognition/blob/master/reference/pocketsphinx.rst>`__ for information about installing other languages. This document is also included under ``reference/pocketsphinx.rst``. The ``language`` parameter can also be a tuple of filesystem paths, of the form ``(acoustic_parameters_directory, language_model_file, phoneme_dictionary_file)`` - this allows you to load arbitrary Sphinx models.

    If specified, the keywords to search for are determined by ``keyword_entries``, an iterable of tuples of the form ``(keyword, sensitivity)``, where ``keyword`` is a phrase, and ``sensitivity`` is how sensitive to this phrase the recognizer should be, on a scale of 0 (very insensitive, more false negatives) to 1 (very sensitive, more false positives) inclusive. If not specified or ``None``, no keywords are used and Sphinx will simply transcribe whatever words it recognizes. Specifying ``keyword_entries`` is more accurate than just looking for those same keywords in non-keyword-based transcriptions, because Sphinx knows specifically what sounds to look for.

    Sphinx can also handle FSG or JSGF grammars. The parameter ``grammar`` expects a path to the grammar file. Note that if a JSGF grammar is passed, an FSG grammar will be created at the same location to speed up execution in the next run. If ``keyword_entries`` are passed, content of ``grammar`` will be ignored.

    Returns the most likely transcription if ``show_all`` is false (the default). Otherwise, returns the Sphinx ``pocketsphinx.pocketsphinx.Decoder`` object resulting from the recognition.

    Raises a ``speech_recognition.UnknownValueError`` exception if the speech is unintelligible. Raises a ``speech_recognition.RequestError`` exception if there are any issues with the Sphinx installation.
    """
    # TODO Move this validation into KeywordEntry initialization
    assert keyword_entries is None or all(isinstance(keyword, (type(""), type(u""))) and 0 <= sensitivity <= 1 for keyword, sensitivity in keyword_entries), "``keyword_entries`` must be ``None`` or a list of pairs of strings and numbers between 0 and 1"

    try:
        from pocketsphinx import FsgModel, Jsgf, pocketsphinx
    except ImportError:
        raise RequestError("missing PocketSphinx module: ensure that PocketSphinx is set up correctly.")

    if isinstance(language, str):  # directory containing language data
        language_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.realpath(__file__))), "pocketsphinx-data", language)
        if not os.path.isdir(language_directory):
            raise RequestError("missing PocketSphinx language data directory: \"{}\"".format(language_directory))
        acoustic_parameters_directory = os.path.join(language_directory, "acoustic-model")
        language_model_file = os.path.join(language_directory, "language-model.lm.bin")
        phoneme_dictionary_file = os.path.join(language_directory, "pronounciation-dictionary.dict")
    else:  # 3-tuple of Sphinx data file paths
        acoustic_parameters_directory, language_model_file, phoneme_dictionary_file = language
    if not os.path.isdir(acoustic_parameters_directory):
        raise RequestError("missing PocketSphinx language model parameters directory: \"{}\"".format(acoustic_parameters_directory))
    if not os.path.isfile(language_model_file):
        raise RequestError("missing PocketSphinx language model file: \"{}\"".format(language_model_file))
    if not os.path.isfile(phoneme_dictionary_file):
        raise RequestError("missing PocketSphinx phoneme dictionary file: \"{}\"".format(phoneme_dictionary_file))

    # create decoder object
    config = pocketsphinx.Config()
    config.set_string("-hmm", acoustic_parameters_directory)  # set the path of the hidden Markov model (HMM) parameter files
    config.set_string("-lm", language_model_file)
    config.set_string("-dict", phoneme_dictionary_file)
    config.set_string("-logfn", os.devnull)  # disable logging (logging causes unwanted output in terminal)
    decoder = pocketsphinx.Decoder(config)

    # obtain audio data
    raw_data = audio_data.get_raw_data(convert_rate=16000, convert_width=2)  # the included language models require audio to be 16-bit mono 16 kHz in little-endian format

    # obtain recognition results
    if keyword_entries is not None:  # explicitly specified set of keywords
        with PortableNamedTemporaryFile("w") as f:
            # generate a keywords file - Sphinx documentation recommendeds sensitivities between 1e-50 and 1e-5
            f.writelines("{} /1e{}/\n".format(keyword, 100 * sensitivity - 110) for keyword, sensitivity in keyword_entries)
            f.flush()

            # perform the speech recognition with the keywords file (this is inside the context manager so the file isn;t deleted until we're done)
            decoder.add_kws("keywords", f.name)
            decoder.activate_search("keywords")
    elif grammar is not None:  # a path to a FSG or JSGF grammar
        if not os.path.exists(grammar):
            raise ValueError("Grammar '{0}' does not exist.".format(grammar))
        grammar_path = os.path.abspath(os.path.dirname(grammar))
        grammar_name = os.path.splitext(os.path.basename(grammar))[0]
        fsg_path = "{0}/{1}.fsg".format(grammar_path, grammar_name)
        if not os.path.exists(fsg_path):  # create FSG grammar if not available
            jsgf = Jsgf(grammar)
            rule = jsgf.get_rule("{0}.{0}".format(grammar_name))
            fsg = jsgf.build_fsg(rule, decoder.get_logmath(), 7.5)
            fsg.writefile(fsg_path)
        else:
            fsg = FsgModel(fsg_path, decoder.get_logmath(), 7.5)
        decoder.set_fsg(grammar_name, fsg)
        decoder.set_search(grammar_name)

    decoder.start_utt()  # begin utterance processing
    decoder.process_raw(raw_data, False, True)  # process audio data with recognition enabled (no_search = False), as a full utterance (full_utt = True)
    decoder.end_utt()  # stop utterance processing

    if show_all: return decoder

    # return results
    hypothesis = decoder.hyp()
    if hypothesis is not None: return hypothesis.hypstr
    raise UnknownValueError()  # no transcriptions available