# Copyright (c) Alibaba, Inc. and its affiliates.
from collections import OrderedDict
from typing import Dict

import torch
from transformers import BertTokenizer

from modelscope.metainfo import Models
from modelscope.models.base import Tensor, TorchModel
from modelscope.models.builder import MODELS
from modelscope.models.nlp.gpt3 import GPT3Model
from modelscope.utils.constant import Tasks
from modelscope.utils.hub import read_config

__all__ = ['GPT3ForTextGeneration']


@MODELS.register_module(Tasks.text_generation, module_name=Models.gpt3)
class GPT3ForTextGeneration(TorchModel):

    def __init__(self, model_dir: str, *args, **kwargs):
        """initialize the text generation model from the `model_dir` path.

        Args:
            model_dir (str): the model path.
        """
        super().__init__(model_dir, *args, **kwargs)

        # Temporarily compatible with DistributedGPT3 and GPT3Model,
        # the base/large model based on GPT3Model will be replaced in the future,
        # and GPT3Model will be deprecated
        if 'megatron' in read_config(model_dir):
            from modelscope.models.nlp import DistributedGPT3
            self.model = DistributedGPT3(model_dir, **kwargs)
        else:
            self.model = GPT3Model.from_pretrained(model_dir)
            self.tokenizer = BertTokenizer.from_pretrained(model_dir)

    def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
        """return the result by the model

        Args:
            input (Dict[str, Tensor]): the preprocessed data

        Returns:
            Dict[str, Tensor]: results

        Example:
            >>> {
            >>>     'logits': Tensor([[0.54, 0.32...])]), # logits
            >>> }
        """
        return self.model(**input)

    def generate(self, inputs: Dict[str, Tensor],
                 **kwargs) -> Dict[str, Tensor]:
        if not isinstance(self.model, GPT3Model):
            return self.model.generate(**inputs, **kwargs)

        tokens = inputs['input_ids']
        lengths = self._get_length(inputs['attention_mask'])
        return self.model.generate(tokens, prompt_length=lengths, **kwargs)

    @staticmethod
    def _get_length(attention_mask: torch.Tensor) -> Tensor:
        return attention_mask.sum(-1) - 1

    def save_pretrained(self, *args, **kwargs):
        if not isinstance(self.model, GPT3Model):
            return self.model.save_pretrained(*args, **kwargs)
        return super().save_pretrained(*args, **kwargs)

    def state_dict(self, destination=None, prefix='', keep_vars=False):
        return self.model.state_dict(destination, prefix, keep_vars)

    def load_state_dict(self,
                        state_dict: 'OrderedDict[str, Tensor]',
                        strict: bool = True):
        return self.model.load_state_dict(state_dict, strict)
