# 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 logging
import numpy

from transformers import AutoTokenizer, AutoModel

from towhee.operator import NNOperator
from towhee import register

import warnings

warnings.filterwarnings('ignore')
log = logging.getLogger()


@register(output_schema=['vec'])
class AutoTransformers(NNOperator):
    """
    NLP embedding operator that uses the pretrained transformers model gathered by huggingface.
    Args:
        model_name (`str`):
            Which model to use for the embeddings.
    """

    def __init__(self, model_name: str) -> None:
        super().__init__()
        self.model_name = model_name
        try:
            self.model = AutoModel.from_pretrained(model_name)
        except Exception as e:
            log.error(f'Fail to load model by name: {self.model_name}')
            raise e
        try:
            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        except Exception as e:
            log.error(f'Fail to load tokenizer by name: {self.model_name}')
            raise e

    def __call__(self, txt: str) -> numpy.ndarray:
        try:
            inputs = self.tokenizer(txt, return_tensors="pt")
        except Exception as e:
            log.error(f'Invalid input for the tokenizer: {self.model_name}')
            raise e
        try:
            outs = self.model(**inputs)
        except Exception as e:
            log.error(f'Invalid input for the model: {self.model_name}')
            raise e
        try:
            features = outs.last_hidden_state.squeeze(0)
        except Exception as e:
            log.error(f'Fail to extract features by model: {self.model_name}')
            raise e
        feature_vector = features.detach().numpy()
        return feature_vector