from transformers import PretrainedConfig, PreTrainedModel, AutoConfig, AutoModel
from transformers.pipelines import PIPELINE_REGISTRY
from huggingface_hub import hf_hub_download

import onnxruntime as ort
import torch
import os

# 1. register AutoConfig
class ONNXBaseConfig(PretrainedConfig):
    model_type = 'onnx-base'

AutoConfig.register('onnx-base', ONNXBaseConfig)

# 2. register AutoModel
class ONNXBaseModel(PreTrainedModel):
    config_class = ONNXBaseConfig
    def __init__(self, config, base_path=None):
        super().__init__(config)
        if base_path:
            model_path = base_path + '/' + config.model_path
            if os.path.exists(model_path):
                self.session = ort.InferenceSession(model_path)

    def forward(self, input=None, **kwargs):
        outs = self.session.run(None, {'input': input})
        return outs

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
        if config.model_path is None:
            config.model_path = 'model.onnx'
        is_local = os.path.isdir(pretrained_model_name_or_path)
        if is_local:
            base_path = pretrained_model_name_or_path
        else:
            config_path = hf_hub_download(repo_id=pretrained_model_name_or_path, filename='config.json')
            base_path = os.path.dirname(config_path)
            hf_hub_download(repo_id=pretrained_model_name_or_path, filename=config.model_path)
        return cls(config, base_path=base_path)

    @property
    def device(self):
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        return torch.device(device)

AutoModel.register(ONNXBaseConfig, ONNXBaseModel)

# 2. register Pipeline
from transformers.pipelines import Pipeline

class ONNXBasePipeline(Pipeline):
    def __init__(self, model, **kwargs):
        self.device_id = kwargs['device']
        super().__init__(model=model, **kwargs)

    def _sanitize_parameters(self, **kwargs):
        return {}, {}, {}

    def preprocess(self, input):
        return {'input': input}

    def _forward(self, model_input):
        with torch.no_grad():
            outputs = self.model(**model_input)
        return outputs

    def postprocess(self, model_outputs):
        return model_outputs

PIPELINE_REGISTRY.register_pipeline(
    task='onnx-base',
    pipeline_class=ONNXBasePipeline,
    pt_model=ONNXBaseModel
)