#!/usr/bin/env python3
from datasets import load_dataset, load_metric, Audio, Dataset
from transformers import pipeline, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2ForCTC
import os
import re
import argparse
import unicodedata
from typing import Dict


def log_results(result: Dataset, args: Dict[str, str]):
    """ DO NOT CHANGE. This function computes and logs the result metrics. """

    log_outputs = args.log_outputs
    dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])

    # load metric
    wer = load_metric("wer")
    cer = load_metric("cer")

    # compute metrics
    wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
    cer_result = cer.compute(references=result["target"], predictions=result["prediction"])

    # print & log results
    result_str = (
        f"WER: {wer_result}\n"
        f"CER: {cer_result}"
    )
    print(result_str)

    with open(f"{dataset_id}_eval_results.txt", "w") as f:
        f.write(result_str)

    # log all results in text file. Possibly interesting for analysis
    if log_outputs is not None:
        pred_file = f"log_{dataset_id}_predictions.txt"
        target_file = f"log_{dataset_id}_targets.txt"

        with open(pred_file, "w") as p, open(target_file, "w") as t:

            # mapping function to write output
            def write_to_file(batch, i):
                p.write(f"{i}" + "\n")
                p.write(batch["prediction"] + "\n")
                t.write(f"{i}" + "\n")
                t.write(batch["target"] + "\n")

            result.map(write_to_file, with_indices=True)


def normalize_text(text: str) -> str:
    """ DO ADAPT FOR YOUR USE CASE. this function normalizes the target text. """


    CHARS = {
    'ü': 'ue',
    'ö': 'oe',
    'ï': 'i',
    'ë': 'e',
    'ä': 'ae',
    'ã': 'a',
    'à': 'á',
    'ø': 'o',
    'è': 'é',
    'ê': 'é',
    'å': 'ó',
    'î': 'i',
    'ñ': 'ň',
    'ç': 's',
    'ľ': 'l',
    'ż': 'ž',
    'ł': 'w',
    'ć': 'č',
    'þ': 't',
    'ß': 'ss',
    'ę': 'en',
    'ą': 'an',
    'æ': 'ae',
  }

    def replace_chars(sentence):
      result = ''
      for ch in sentence:
        new = CHARS[ch] if ch in CHARS else ch
        result += new

      return result
    
    chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\�\–\'\`\«\»\—\’\…]'

    text = text.lower()
    # normalize non-standard (stylized) unicode characters
    text = unicodedata.normalize('NFKC', text)
    # remove punctuation
    text = re.sub(chars_to_ignore_regex, "", text)
    text = replace_chars(text)

    # Let's also make sure we split on all kinds of newlines, spaces, etc...
    text = " ".join(text.split())

    return text


def main(args):
    # load dataset
    dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)

    # for testing: only process the first two examples as a test
    if args.limit:
        dataset = dataset.select(range(limit))

   
    asr = None
    feature_extractor = None
    
    if not args.model_id and not args.path:
        raise RuntimeError('No model given!')
        
    if not args.model_id:
        model = Wav2Vec2ForCTC.from_pretrained(args.path)
        tokenizer = AutoTokenizer.from_pretrained(args.path)
        feature_extractor = AutoFeatureExtractor.from_pretrained(args.path)

        # load eval pipeline
        asr = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)

    else:
        feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
        asr = pipeline("automatic-speech-recognition", model=args.model_id)
        
    # map function to decode audio
    def map_to_pred(batch):
        prediction = asr(batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s)

        batch["prediction"] = prediction["text"]
        batch["target"] = normalize_text(batch["sentence"])
        return batch

    # load processor
    sampling_rate = feature_extractor.sampling_rate

    # resample audio
    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
 
# run inference on all examples
    result = dataset.map(map_to_pred, remove_columns=dataset.column_names)

    # compute and log_results
    # do not change function below
    log_results(result, args)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--model_id", type=str, help="Model identifier. Should be loadable with 🤗 Transformers", default=''
    )
    parser.add_argument(
        "--dataset", type=str, required=True, help="Dataset name to evaluate the model. Should be loadable with 🤗 Datasets"
    )
    parser.add_argument(
        "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
    )
    parser.add_argument(
        "--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`"
    )
    parser.add_argument(
        "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to None. For long audio files a good value would be 5.0 seconds."
    )
    parser.add_argument(
        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to None. For long audio files a good value would be 1.0 seconds."
    )
    parser.add_argument(
        "--log_outputs", action='store_true', help="If defined, write outputs to log file for analysis."
    )
    parser.add_argument(
        "--path", type=str, help="If set and model_id is not set, use local model from this path.", default=''
    )
    parser.add_argument(
        "--limit", type=int, help="Not required. If greater than zero, select a subset of this size from the dataset.", default=0
    )
    args = parser.parse_args()

    main(args)