# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy
import os
import torch
import shutil
from pathlib import Path
from typing import Union
from collections import OrderedDict

from transformers import AutoModel

from towhee.operator import NNOperator
from towhee import register
# from towhee.dc2 import accelerate

import warnings
import logging
from transformers import logging as t_logging

log = logging.getLogger('run_op')
warnings.filterwarnings('ignore')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
t_logging.set_verbosity_error()


# @accelerate
class Model:
    def __init__(self, model):
        self.model = model

    def __call__(self, *args, **kwargs):
        outs = self.model(*args, **kwargs, return_dict=True)
        return outs['last_hidden_state']


@register(output_schema=['vec'])
class AutoTransformers(NNOperator):
    """
    NLP embedding operator that uses the pretrained transformers model gathered by huggingface.
    Args:
        model_name (`str`):
            The model name to load a pretrained model from transformers.
        checkpoint_path (`str`):
            The local checkpoint path.
        tokenizer (`object`):
            The tokenizer to tokenize input text as model inputs.
    """

    def __init__(self,
                 model_name: str = None,
                 checkpoint_path: str = None,
                 tokenizer: object = None,
                 device: str = None,
                 norm: bool = False
                 ):
        super().__init__()
        self._device = device
        self.model_name = model_name
        self.user_tokenizer = tokenizer
        self.norm = norm
        self.checkpoint_path = checkpoint_path

        if self.model_name:
            model_list = self.supported_model_names()
            # assert model_name in model_list, f"Invalid model name: {model_name}. Supported model names: {model_list}"
            self.model = Model(self._model)
        else:
            log.warning('The operator is initialized without specified model.')
            pass

    def __call__(self, data: Union[str, list]) -> numpy.ndarray:
        if isinstance(data, str):
            txt = [data]
        else:
            txt = data
        try:
            inputs = self.tokenizer(txt, padding=True, truncation=True, return_tensors='pt').to(self.device)
        except Exception as e:
            log.error(f'Fail to tokenize inputs: {e}')
            raise e
        try:
            outs = self.model(**inputs)
        except Exception as e:
            log.error(f'Invalid input for the model: {self.model_name}')
            raise e
        outs = self.post_proc(outs, inputs)
        if self.norm:
            outs = torch.nn.functional.normalize(outs, )
        features = outs.cpu().detach().numpy()
        if isinstance(data, str):
            features = features.squeeze(0)
        else:
            features = list(features)
        return features

    @property
    def _model(self):
        model = AutoModel.from_pretrained(self.model_name).to(self.device)
        if hasattr(model, 'pooler') and model.pooler:
            model.pooler = None
        if self.checkpoint_path:
            try:
                state_dict = torch.load(self.checkpoint_path, map_location=self.device)
                model.load_state_dict(state_dict)
            except Exception:
                log.error(f'Fail to load weights from {self.checkpoint_path}')
        model.eval()
        return model

    @property
    def device(self):
        if self._device is None:
            if self._device_id < 0:
                self._device = torch.device('cpu')
            else:
                self._device = torch.device(self._device_id)
        return self._device

    @property
    def model_config(self):
        from transformers import AutoConfig
        configs = AutoConfig.from_pretrained(self.model_name)
        return configs

    @property
    def onnx_config(self):
        from transformers.onnx.features import FeaturesManager
        model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
            self._model, feature='default')
        old_config = model_onnx_config(self.model_config)
        onnx_config = {
            'inputs': dict(old_config.inputs),
            'outputs': {'last_hidden_state': old_config.outputs['last_hidden_state']}
        }
        return onnx_config

    @property
    def tokenizer(self):
        from transformers import AutoTokenizer
        try:
            if self.user_tokenizer:
                t = tokenizer
            else:
                t = AutoTokenizer.from_pretrained(self.model_name)
            if not t.pad_token:
                t.pad_token = '[PAD]'
        except Exception as e:
            log.error(f'Fail to load tokenizer.')
            raise e
        return t

    def post_proc(self, token_embeddings, inputs):
        token_embeddings = token_embeddings.to(self.device)
        attention_mask = inputs['attention_mask'].to(self.device)
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        sentence_embs = torch.sum(
            token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
        return sentence_embs

    def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'):
        if output_file == 'default':
            output_file = str(Path(__file__).parent)
            output_file = os.path.join(output_file, 'saved', model_type)
            os.makedirs(output_file, exist_ok=True)
            name = self.model_name.replace('/', '-')
            output_file = os.path.join(output_file, name)
            if model_type in ['pytorch', 'torchscript']:
                output_file = output_file + '.pt'
            elif model_type == 'onnx':
                output_file = output_file + '.onnx'
            else:
                raise AttributeError('Unsupported model_type.')

        dummy_input = 'test sentence'
        inputs = self.tokenizer(dummy_input, padding=True, truncation=True, return_tensors='pt')  # a dictionary
        if model_type == 'pytorch':
            torch.save(self._model, output_file)
        elif model_type == 'torchscript':
            inputs = list(inputs.values())
            try:
                try:
                    jit_model = torch.jit.script(self._model)
                except Exception:
                    jit_model = torch.jit.trace(self._model, inputs, strict=False)
                torch.jit.save(jit_model, output_file)
            except Exception as e:
                log.error(f'Fail to save as torchscript: {e}.')
                raise RuntimeError(f'Fail to save as torchscript: {e}.')
        elif model_type == 'onnx':
            dynamic_axes = {}
            for k, v in self.onnx_config['inputs'].items():
                dynamic_axes[k] = v
            for k, v in self.onnx_config['outputs'].items():
                dynamic_axes[k] = v
            torch.onnx.export(
                self._model,
                tuple(inputs.values()),
                output_file,
                input_names=list(self.onnx_config['inputs'].keys()),
                output_names=list(self.onnx_config['outputs'].keys()),
                dynamic_axes=dynamic_axes,
                opset_version=torch.onnx.constant_folding_opset_versions[-1],
                do_constant_folding=True,
            )
        # todo: elif format == 'tensorrt':
        else:
            log.error(f'Unsupported format "{format}".')
        return Path(output_file).resolve()

    @property
    def supported_formats(self):
        onnxes = self.supported_model_names(format='onnx')
        if self.model_name in onnxes:
            return ['onnx']
        else:
            return ['pytorch']

    @staticmethod
    def supported_model_names(format: str = None):
        full_list = [

        ]
        full_list.sort()
        if format is None:
            model_list = full_list
        elif format == 'pytorch':
            to_remove = []
            assert set(to_remove).issubset(set(full_list))
            model_list = list(set(full_list) - set(to_remove))
        elif format == 'torchscript':
            to_remove = [
            ]
            assert set(to_remove).issubset(set(full_list))
            model_list = list(set(full_list) - set(to_remove))
        elif format == 'onnx':
            to_remove = [
            ]
            assert set(to_remove).issubset(set(full_list))
            model_list = list(set(full_list) - set(to_remove))
        # todo: elif format == 'tensorrt':
        else:
            log.error(f'Invalid format "{format}". Currently supported formats: "pytorch", "torchscript".')
        return model_list