from transformers import AutoTokenizer, AutoModelForSequenceClassification
import datasets
import evaluate


_CITATION = """\
tba
"""

_DESCRIPTION = """\
Negation-aware version of BLEURT metric.
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018) and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations and the CANNOT negation awareness dataset.
"""

_KWARGS_DESCRIPTION = """
Calculates the NegBLEURT scores between references and predictions
Args:
    predictions: list of predictions to score. Each prediction should be a string.
    references: list of references, one for each prediction. Each reference should be a string
    batch_size: batch_size for model inference. Default is 16
Returns:
    negBLEURT: List of NegBLEURT scores for all predictions
Examples:
    >>> negBLEURT = evaluate.load('tum-nlp/negbleurt')
    >>> predictions = ["Ray Charles is a legend.", "Ray Charles isn’t legendary."]
    >>> references = ["Ray Charles is legendary.", "Ray Charles is legendary."]
    >>> results = negBLEURT.compute(predictions=predictions, references=references)
    >>> print(results)
    {'negBLERUT': [0.8409, 0.2601]}
"""

@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class NegBLEURT(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            features=[
                datasets.Features(
                    {
                        "predictions": datasets.Value("string", id="sequence"),
                        "references": datasets.Value("string", id="sequence"),
                    }
                ),
            ],
            codebase_urls=["https://github.com/MiriUll/negation_aware_evaluation"]
        )

    def _download_and_prepare(self, dl_manager):
        model_name = "tum-nlp/NegBLEURT"
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)

    def _compute(
        self, predictions, references, batch_size=16
    ):
        scores_negbleurt = []
        for i in range(0, len(references), batch_size):
            tokenized = self.tokenizer(references[i:i+batch_size], predictions[i:i+batch_size], return_tensors='pt', padding=True, max_length=512, truncation=True)
            scores_negbleurt += self.model(**tokenized).logits.flatten().tolist()
        return {'negBLEURT': scores_negbleurt}