import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers.generation import LogitsProcessor


class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
    def __init__(self, penalty: float, model):
        last_bias = model.classifier.nonlinearity[-1].bias.data
        last_bias = torch.nn.functional.log_softmax(last_bias)
        self.penalty = penalty * (last_bias - last_bias.max())

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
        penalized_score = torch.gather(scores + self.penalty.unsqueeze(0).to(input_ids.device), 1, input_ids).to(scores.dtype)
        scores.scatter_(1, input_ids, penalized_score)
        return scores


class Translator:
    def __init__(self, model_path="ltg/nort5-base-en-no-translation", device="cpu"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.cls_index = self.tokenizer.convert_tokens_to_ids("[CLS]")
        self.sep_index = self.tokenizer.convert_tokens_to_ids("[SEP]")
        self.eos_index = self.tokenizer.convert_tokens_to_ids("[EOS]")
        self.pad_index = self.tokenizer.convert_tokens_to_ids("[PAD]")
        self.eng_index = self.tokenizer.convert_tokens_to_ids(">>eng<<")
        self.nob_index = self.tokenizer.convert_tokens_to_ids(">>nob<<")
        self.nno_index = self.tokenizer.convert_tokens_to_ids(">>nno<<")

        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path, trust_remote_code=True)

        self.device = device
        print(f"SYSTEM: Running on {self.device}", flush=True)

        self.model = self.model.to(device)
        self.model.eval()

        print(f"Sucessfully loaded the model to the memory")

        self.LANGUAGE_IDS = {
            "en": self.eng_index,
            "nb": self.nob_index,
            "nn": self.nno_index
        }

    def __call__(self, source, source_language, target_language):
        source = [s.strip() for s in source.split('\n')]
        source_subwords = self.tokenizer(source).input_ids
        source_subwords = [[self.cls_index, self.LANGUAGE_IDS[target_language], self.LANGUAGE_IDS[source_language]] + s + [self.sep_index] for s in source_subwords]
        source_subwords = [torch.tensor(s) for s in source_subwords]
        source_subwords = torch.nn.utils.rnn.pad_sequence(source_subwords, batch_first=True, padding_value=self.pad_index)
        source_subwords = source_subwords[:, :512].to(self.device)

        def generate(model, **kwargs):
            with torch.inference_mode():
                with torch.autocast(enabled=self.device != "cpu", device_type="cuda", dtype=torch.bfloat16):
                    return model.generate(**kwargs)

        generate_kwargs = dict(
            input_ids=source_subwords,
            attention_mask=(source_subwords != self.pad_index).long(),
            max_new_tokens = 512-1,
            num_beams=8,
            length_penalty=1.6,
            early_stopping=True,
            do_sample=False,
            use_cache=True,
            logits_processor=[RepetitionPenaltyLogitsProcessor(0.5, self.model), transformers.LogitNormalization()]
        )
        output = generate(self.model, **generate_kwargs).tolist()
        paragraphs = [self.tokenizer.decode(c, skip_special_tokens=True).strip() for c in output]
        translation = '\n'.join(paragraphs)

        return translation


if __name__ == "__main__":

    translator = Translator()

    en_text = "How are you feeling right now? Better?"
    no_text = translator(en_text, "en", "nb")

    print(en_text)
    print(no_text)