import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, AutoTokenizer, SpeechEncoderDecoderModel, pipeline

from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.models.encoder_decoder.modeling_encoder_decoder import shift_tokens_right
from transformers.modeling_outputs import Seq2SeqLMOutput


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."""

    # From https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german.
    CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
                       "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
                       "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
                       "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
                       "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
    chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
    text = re.sub(chars_to_ignore_regex, "", text.lower())

    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

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

    # load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(args.model_id)

    # load model
    model = Wav2VecGPT2Model.from_pretrained(args.model_id)
    model.config.num_beams = 4

    # load eval pipeline
    if args.device is None:
        args.device = 0 if torch.cuda.is_available() else -1
    asr = pipeline("automatic-speech-recognition", model=model, device=args.device,
                   feature_extractor=feature_extractor, 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"] = normalize_text(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)


class Wav2VecGPT2Model(SpeechEncoderDecoderModel):
    """
    Basically the same as `SpeechEncoderDecoderModel` but position embeddings (initialized with GPT2's position
    embeddings) are added to encoder output
    """
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.encoder_outputs_pos_emb = nn.Embedding(1024, self.decoder.config.hidden_size)
        with torch.no_grad():
            self.encoder_outputs_pos_emb.weight.copy_(self.decoder.transformer.wpe.weight)
        self.enc_to_dec_proj_ln = nn.LayerNorm(self.decoder.config.hidden_size,
                                               eps=self.decoder.config.layer_norm_epsilon)

    def __getattribute__(self, name):
        # Fake class so it is recognized as seq2seq model.
        if name == '__class__':
            return SpeechEncoderDecoderModel
        return SpeechEncoderDecoderModel.__getattribute__(self, name)

    def forward(
        self,
        inputs=None,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        encoder_outputs=None,
        past_key_values=None,
        decoder_inputs_embeds=None,
        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        input_values=None,
        input_features=None,
        return_dict=None,
        **kwargs,
    ):
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}

        kwargs_decoder = {
            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
        }

        if encoder_outputs is None and inputs is None:
            if input_values is not None and input_features is not None:
                raise ValueError("You cannot specify both input_values and input_features at the same time")
            elif input_values is not None:
                inputs = input_values
            elif input_features is not None:
                inputs = input_features
            else:
                raise ValueError("You have to specify either input_values or input_features")

            encoder_outputs = self.encoder(
                inputs,
                attention_mask=attention_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                **kwargs_encoder,
            )

        encoder_hidden_states = encoder_outputs[0]

        # optionally project encoder_hidden_states
        if (
            self.encoder_output_dim != self.decoder.config.hidden_size
            and self.decoder.config.cross_attention_hidden_size is None
        ):
            encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
            encoder_hidden_states += self.encoder_outputs_pos_emb(
                torch.arange(0, encoder_hidden_states.shape[1], device=encoder_hidden_states.device)
            )
            encoder_hidden_states = self.enc_to_dec_proj_ln(encoder_hidden_states)

        # compute correct encoder attention mask
        if attention_mask is not None:
            encoder_attention_mask = self.encoder._get_feature_vector_attention_mask(
                encoder_hidden_states.shape[1], attention_mask
            )
        else:
            encoder_attention_mask = None

        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None):
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            use_cache=use_cache,
            past_key_values=past_key_values,
            return_dict=return_dict,
            **kwargs_decoder,
        )

        # Compute loss independent from decoder (as some shift the logits inside them)
        loss = None
        if labels is not None:
            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))

        if not return_dict:
            if loss is not None:
                return (loss,) + decoder_outputs + encoder_outputs
            else:
                return decoder_outputs + encoder_outputs

        return Seq2SeqLMOutput(
            loss=loss,
            logits=decoder_outputs.logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )


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."
    )
    parser.add_argument(
        "--device",
        type=int,
        default=None,
        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
    )
    args = parser.parse_args()

    main(args)