from pathlib import Path

from ...globals import VOCAB_SIZE, FIXED_IMG_SIZE, IMG_CHANNELS, MAX_TOKEN_SIZE

from transformers import RobertaTokenizerFast, VisionEncoderDecoderModel, VisionEncoderDecoderConfig


class TexTeller(VisionEncoderDecoderModel):
    REPO_NAME = 'OleehyO/TexTeller'

    def __init__(self):
        config = VisionEncoderDecoderConfig.from_pretrained(
            Path(__file__).resolve().parent / "config.json"
        )
        config.encoder.image_size = FIXED_IMG_SIZE
        config.encoder.num_channels = IMG_CHANNELS
        config.decoder.vocab_size = VOCAB_SIZE
        config.decoder.max_position_embeddings = MAX_TOKEN_SIZE

        super().__init__(config=config)

    @classmethod
    def from_pretrained(cls, model_path: str = None, use_onnx=False, onnx_provider=None):
        if model_path is None or model_path == 'default':
            if not use_onnx:
                return VisionEncoderDecoderModel.from_pretrained(cls.REPO_NAME)
            else:
                from optimum.onnxruntime import ORTModelForVision2Seq

                use_gpu = True if onnx_provider == 'cuda' else False
                return ORTModelForVision2Seq.from_pretrained(
                    cls.REPO_NAME,
                    provider="CUDAExecutionProvider" if use_gpu else "CPUExecutionProvider",
                )
        model_path = Path(model_path).resolve()
        return VisionEncoderDecoderModel.from_pretrained(str(model_path))

    @classmethod
    def get_tokenizer(cls, tokenizer_path: str = None) -> RobertaTokenizerFast:
        if tokenizer_path is None or tokenizer_path == 'default':
            return RobertaTokenizerFast.from_pretrained(cls.REPO_NAME)
        tokenizer_path = Path(tokenizer_path).resolve()
        return RobertaTokenizerFast.from_pretrained(str(tokenizer_path))