#!/usr/bin/env python3
import argparse
import re
from typing import Dict

from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
from transformers import Wav2Vec2CTCTokenizer

class Wav2Vec2WordpieceTokenizer(Wav2Vec2CTCTokenizer):
    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        pad_token="<pad>",
        word_delimiter_token="|",
        do_lower_case=False,
        **kwargs
    ):
        super().__init__(
            vocab_file=vocab_file,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            do_lower_case=do_lower_case,
            word_delimiter_token=word_delimiter_token,
            **kwargs,
        )

        self._create_trie(self.all_special_tokens_extended)
        
    def _tokenize(self, text, **kwargs):
        """
        Converts a string in a sequence of tokens (string), using the tokenizer.
        """
        special_cases = set(['gia', 'qui', 'quy', 'que', 'qua'])
        output_tokens = []
        for token_idx, token in enumerate(text.split()):
            if token in special_cases:
                sub_tokens = [token[:2], token[2:]]
            else:
                end = len(token)
                sub_tokens = []
                while end > 0:
                    start = 0
                    cur_substr = None
                    while start < end:
                        substr = token[start:end]
                        if substr in self.encoder:
                            cur_substr = substr
                            break
                        start += 1
                    if cur_substr is None:
                        sub_tokens.insert(0, self.unk_token)
                        end = start - 1
                    else:
                        sub_tokens.insert(0, cur_substr)
                        end = start
            
            if token_idx > 0:
                output_tokens.append(self.word_delimiter_token)
            output_tokens.extend(sub_tokens)
        return output_tokens
    
    def decode_ids(
        self, 
        token_ids, 
        skip_special_tokens = False, 
        clean_up_tokenization_spaces = True,
        group_tokens: bool = True,
        spaces_between_special_tokens: bool = False,
    ) -> str:
        # For compatible with speechbrain interfaces
        return self.decode(
            token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            group_tokens=group_tokens,
            spaces_between_special_tokens=spaces_between_special_tokens
        )
        
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_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–|]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

    text = re.sub(chars_to_ignore_regex, "", text.lower())

    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
    # note that order is important here!
    token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]

    for t in token_sequences_to_ignore:
        text = " ".join(text.split(t))

    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
    # dataset = dataset.select(range(10))

    # load processor
    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
    sampling_rate = feature_extractor.sampling_rate

    # load tokenizer
    tokenizer = Wav2Vec2WordpieceTokenizer(
        vocab_file = args.model_id + 'vocab.json',
    )

    # resample audio
    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))

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

    # 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, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
    )
    parser.add_argument(
        "--dataset",
        type=str,
        required=True,
        help="Dataset name to evaluate the `model_id`. 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 5 seconds."
    )
    parser.add_argument(
        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
    )
    parser.add_argument(
        "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
    )
    args = parser.parse_args()

    main(args)