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# Mock Args class, to be replaced with argparse or equivalent later 
class Args:
    def __init__(self, **attrs):
        self.__dict__.update(attrs)

args = Args(**{
    "input_basepath" : "/home/nay/git-repos/w2v2-10min-exps/data/MASS-1h",
    "train_tsv" : "train1h.tsv",
    "valid_tsv" : "dev.tsv",
    "output_basepath" : "/home/nay/librispeech-1h-fairseq/manifest-MASS-1h"

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# NVIDIA official container with CUDA 11.7 (note pytorch-cuda=11.7 in PyTorch install below)
# See https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html
FROM nvcr.io/nvidia/pytorch:22.05-py3

# Update container PyTorch from 1.9 to 1.13.1
RUN conda install pytorch==1.13.1 \
    torchvision==0.14.1 \
    torchaudio==0.13.1 \
    pytorch-cuda=11.7 \
    -c pytorch \

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library(httr)
library(purrr)
library(tibble)

# See also https://bookdown.org/paul/apis_for_social_scientists/reddit-api.html

url <- 'https://www.reddit.com/r/mentalhealth/new.json?t=day&limit=100'

response <- GET(url, user_agent('Extracting data from Reddit'))

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from multiprocessing import Pool
import tqdm
import time

def _foo(my_number):
   square = my_number * my_number
   time.sleep(1)
   return square 

if __name__ == '__main__':

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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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

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import torch
import torchaudio

from torchaudio.models.decoder import ctc_decoder
from typing import List

bundle = torchaudio.pipelines.WAV2VEC2_ASR_LARGE_10M
acoustic_model = bundle.get_model()
acoustic_model.to('cuda')

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def ms_to_hms(start_ms: int) -> str:
    import math
    
    """
    Convert milliseconds to hours-minutes-seconds string format to use within identifiers
    
    e.g. 3990060 ms -> '01h06m30.060'
    """
    
    s, ms = divmod(start_ms, 1000)

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import pandas as pd

from collections import Counter
from tqdm.contrib.concurrent import process_map

def get_vocab(texts_list, ids_list=None):
    
    def sum_counters(counter_list):

        '''

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# Abstract class ('phoneme')
class Consonant:
    # Class attributes assigned when initialised ('realised')
    def __init__(self, place_of_art, manner_of_art):
        self.place_of_art = place_of_art
        self.manner_of_art = manner_of_art
        
    def lenite(self):
        if self.manner_of_art == 'stop':
            self.manner_of_art = 'fricative'

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# Adapted from https://stackoverflow.com/questions/12130883/r-expand-grid-function-in-python
#
# Usage: 
# expand_grid([0, 1], [2,3,4])
#
# Output:
# array([[0, 2],
#       [1, 2],
#       [0, 3],
#       [1, 3],