"""HuggingFace Inference API handler for text normalization.

This enables the model to work with the HuggingFace Inference API
and the `text2text-generation` pipeline.
"""

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline


class EndpointHandler:
    def __init__(self, path: str = ""):
        self.model = AutoModelForSeq2SeqLM.from_pretrained(path)
        self.tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")
        self.model.eval()

    def __call__(self, data):
        """Handle inference request.

        Expected input format:
            {"inputs": "<de> Das kostet 12,50 €."}
        or:
            {"inputs": "Das kostet 12,50 €.", "parameters": {"language": "de"}}
        """
        inputs = data.get("inputs", "")
        params = data.get("parameters", {})

        # If language is passed separately, add the prefix
        if not inputs.startswith("<") and "language" in params:
            inputs = f"<{params['language']}> {inputs}"

        tokenized = self.tokenizer(
            inputs, return_tensors="pt", max_length=512, truncation=True
        )

        import torch

        with torch.no_grad():
            output = self.model.generate(
                **tokenized,
                max_new_tokens=params.get("max_new_tokens", 512),
                num_beams=params.get("num_beams", 1),
            )

        result = self.tokenizer.decode(output[0], skip_special_tokens=True)
        return [{"generated_text": result}]