# 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
from pathlib import Path
from transformers import AutoTokenizer, AutoModel

from towhee.operator import NNOperator
from towhee import register

import warnings

warnings.filterwarnings('ignore')


@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 = "bert-base-uncased", device=None) -> None:
        super().__init__()
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.device = device
        self.model_name = model_name
        try:
            self.model = AutoModel.from_pretrained(model_name).to(self.device)
            self.model.eval()
        except Exception as e:
            model_list = self.supported_model_names()
            if model_name not in model_list:
                log.error(f"Invalid model name: {model_name}. Supported model names: {model_list}")
            else:
                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").to(self.device)
        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
        vec = features.cpu().detach().numpy()
        return vec

    def save_model(self, format: str = 'pytorch', path: str = 'default'):
        if path == 'default':
            path = str(Path(__file__).parent)
            path = os.path.join(path, 'saved', format)
            os.makedirs(path, exist_ok=True)
            name = self.model_name.replace('/', '-')
            path = os.path.join(path, name)
        inputs = self.tokenizer('[CLS]', return_tensors='pt')  # a dictionary
        if format == 'pytorch':
            path = path + '.pt'
            torch.save(self.model, path)
        elif format == 'torchscript':
            path = path + '.pt'
            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, path)
            except Exception as e:
                log.error(f'Fail to save as torchscript: {e}.')
                raise RuntimeError(f'Fail to save as torchscript: {e}.')
        elif format == 'onnx':
            path = path + '.onnx'
            try:
                torch.onnx.export(self.model,
                                  tuple(inputs.values()),
                                  path,
                                  input_names=list(inputs.keys()),
                                  output_names=["last_hidden_state"],
                                  dynamic_axes={
                                      "input_ids": {0: "batch_size", 1: "input_length"},
                                      "token_type_ids": {0: "batch_size", 1: "input_length"},
                                      "attention_mask": {0: "batch_size", 1: "input_length"},
                                      "last_hidden_state": {0: "batch_size"},
                                  },
                                  opset_version=13,
                                  do_constant_folding=True,
                                  # enable_onnx_checker=True,
                                  )
            except Exception as e:
                print(e, '\nTrying with 2 outputs...')
                torch.onnx.export(self.model,
                                  tuple(inputs.values()),
                                  path,
                                  input_names=["input_ids", "token_type_ids", "attention_mask"],  # list(inputs.keys())
                                  output_names=["last_hidden_state", "pooler_output"],
                                  opset_version=13,
                                  dynamic_axes={
                                      "input_ids": {0: "batch_size", 1: "input_length"},
                                      "token_type_ids": {0: "batch_size", 1: "input_length"},
                                      "attention_mask": {0: "batch_size", 1: "input_length"},
                                      "last_hidden_state": {0: "batch_size"},
                                      "pooler_outputs": {0: "batch_size"}
                                  })
        # todo: elif format == 'tensorrt':
        else:
            log.error(f'Unsupported format "{format}".')

    @staticmethod
    def supported_model_names(format: str = None):
        full_list = [
            "bert-large-uncased",
            "bert-base-cased",
            "bert-large-cased",
            "bert-base-multilingual-uncased",
            "bert-base-multilingual-cased",
            "bert-base-chinese",
            "bert-base-german-cased",
            "bert-large-uncased-whole-word-masking",
            "bert-large-cased-whole-word-masking",
            "bert-large-uncased-whole-word-masking-finetuned-squad",
            "bert-large-cased-whole-word-masking-finetuned-squad",
            "bert-base-cased-finetuned-mrpc",
            "bert-base-german-dbmdz-cased",
            "bert-base-german-dbmdz-uncased",
            "cl-tohoku/bert-base-japanese-whole-word-masking",
            "cl-tohoku/bert-base-japanese-char",
            "cl-tohoku/bert-base-japanese-char-whole-word-masking",
            "TurkuNLP/bert-base-finnish-cased-v1",
            "TurkuNLP/bert-base-finnish-uncased-v1",
            "wietsedv/bert-base-dutch-cased",
            "google/bigbird-roberta-base",
            "google/bigbird-roberta-large",
            "google/bigbird-base-trivia-itc",
            "albert-base-v1",
            "albert-large-v1",
            "albert-xlarge-v1",
            "albert-xxlarge-v1",
            "albert-base-v2",
            "albert-large-v2",
            "albert-xlarge-v2",
            "albert-xxlarge-v2",
            "facebook/bart-large",
            "google/bert_for_seq_generation_L-24_bbc_encoder",
            "google/bigbird-pegasus-large-arxiv",
            "google/bigbird-pegasus-large-pubmed",
            "google/bigbird-pegasus-large-bigpatent",
            "google/canine-s",
            "google/canine-c",
            "YituTech/conv-bert-base",
            "YituTech/conv-bert-medium-small",
            "YituTech/conv-bert-small",
            "ctrl",
            "microsoft/deberta-base",
            "microsoft/deberta-large",
            "microsoft/deberta-xlarge",
            "microsoft/deberta-base-mnli",
            "microsoft/deberta-large-mnli",
            "microsoft/deberta-xlarge-mnli",
            "distilbert-base-uncased",
            "distilbert-base-uncased-distilled-squad",
            "distilbert-base-cased",
            "distilbert-base-cased-distilled-squad",
            "distilbert-base-german-cased",
            "distilbert-base-multilingual-cased",
            "distilbert-base-uncased-finetuned-sst-2-english",
            "google/electra-small-generator",
            "google/electra-base-generator",
            "google/electra-large-generator",
            "google/electra-small-discriminator",
            "google/electra-base-discriminator",
            "google/electra-large-discriminator",
            "google/fnet-base",
            "google/fnet-large",
            "facebook/wmt19-ru-en",
            "funnel-transformer/small",
            "funnel-transformer/small-base",
            "funnel-transformer/medium",
            "funnel-transformer/medium-base",
            "funnel-transformer/intermediate",
            "funnel-transformer/intermediate-base",
            "funnel-transformer/large",
            "funnel-transformer/large-base",
            "funnel-transformer/xlarge-base",
            "funnel-transformer/xlarge",
            "gpt2",
            "gpt2-medium",
            "gpt2-large",
            "gpt2-xl",
            "distilgpt2",
            "EleutherAI/gpt-neo-1.3B",
            "EleutherAI/gpt-j-6B",
            "kssteven/ibert-roberta-base",
            "allenai/led-base-16384",
            "google/mobilebert-uncased",
            "microsoft/mpnet-base",
            "uw-madison/nystromformer-512",
            "openai-gpt",
            "google/reformer-crime-and-punishment",
            "tau/splinter-base",
            "tau/splinter-base-qass",
            "tau/splinter-large",
            "tau/splinter-large-qass",
            "squeezebert/squeezebert-uncased",
            "squeezebert/squeezebert-mnli",
            "squeezebert/squeezebert-mnli-headless",
            "transfo-xl-wt103",
            "xlm-mlm-en-2048",
            "xlm-mlm-ende-1024",
            "xlm-mlm-enfr-1024",
            "xlm-mlm-enro-1024",
            "xlm-mlm-tlm-xnli15-1024",
            "xlm-mlm-xnli15-1024",
            "xlm-clm-enfr-1024",
            "xlm-clm-ende-1024",
            "xlm-mlm-17-1280",
            "xlm-mlm-100-1280",
            "xlm-roberta-base",
            "xlm-roberta-large",
            "xlm-roberta-large-finetuned-conll02-dutch",
            "xlm-roberta-large-finetuned-conll02-spanish",
            "xlm-roberta-large-finetuned-conll03-english",
            "xlm-roberta-large-finetuned-conll03-german",
            "xlnet-base-cased",
            "xlnet-large-cased",
            "uw-madison/yoso-4096",
            "microsoft/deberta-v2-xlarge",
            "microsoft/deberta-v2-xxlarge",
            "microsoft/deberta-v2-xlarge-mnli",
            "microsoft/deberta-v2-xxlarge-mnli",
            "flaubert/flaubert_small_cased",
            "flaubert/flaubert_base_uncased",
            "flaubert/flaubert_base_cased",
            "flaubert/flaubert_large_cased",
            "camembert-base",
            "Musixmatch/umberto-commoncrawl-cased-v1",
            "Musixmatch/umberto-wikipedia-uncased-v1",
        ]
        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 = [
                'EleutherAI/gpt-j-6B',
                'EleutherAI/gpt-neo-1.3B',
                'allenai/led-base-16384',
                'ctrl',
                'distilgpt2',
                'facebook/bart-large',
                'google/bigbird-pegasus-large-arxiv',
                'google/bigbird-pegasus-large-bigpatent',
                'google/bigbird-pegasus-large-pubmed',
                'google/canine-c',
                'google/canine-s',
                'google/reformer-crime-and-punishment',
                'gpt2',
                'gpt2-large',
                'gpt2-medium',
                'gpt2-xl',
                'microsoft/deberta-base',
                'microsoft/deberta-base-mnli',
                'microsoft/deberta-large',
                'microsoft/deberta-large-mnli',
                'microsoft/deberta-xlarge',
                'microsoft/deberta-xlarge-mnli',
                'openai-gpt',
                'transfo-xl-wt103',
                'uw-madison/yoso-4096',
                'xlm-clm-ende-1024',
                'xlm-clm-enfr-1024',
                'xlm-mlm-100-1280',
                'xlm-mlm-17-1280',
                'xlm-mlm-en-2048',
                'xlm-mlm-ende-1024',
                'xlm-mlm-enfr-1024',
                'xlm-mlm-enro-1024',
                'xlm-mlm-tlm-xnli15-1024',
                'xlm-mlm-xnli15-1024',
                'xlnet-base-cased',
                'xlnet-large-cased',
                'microsoft/deberta-v2-xlarge',
                'microsoft/deberta-v2-xxlarge',
                'microsoft/deberta-v2-xlarge-mnli',
                'microsoft/deberta-v2-xxlarge-mnli',
                'flaubert/flaubert_small_cased',
                'flaubert/flaubert_base_uncased',
                'flaubert/flaubert_base_cased',
                'flaubert/flaubert_large_cased'
            ]
            assert set(to_remove).issubset(set(full_list))
            model_list = list(set(full_list) - set(to_remove))
        elif format == 'onnx':
            to_remove = [
                'allenai/led-base-16384',
                'ctrl',
                'distilgpt2',
                'EleutherAI/gpt-j-6B',
                'EleutherAI/gpt-neo-1.3B',
                'funnel-transformer/large',
                'funnel-transformer/medium',
                'funnel-transformer/small',
                'funnel-transformer/xlarge',
                'google/bigbird-pegasus-large-arxiv',
                'google/bigbird-pegasus-large-bigpatent',
                'google/bigbird-pegasus-large-pubmed',
                'google/fnet-base',
                'google/fnet-large',
                'google/reformer-crime-and-punishment',
                'gpt2',
                'gpt2-large',
                'gpt2-medium',
                'gpt2-xl',
                'microsoft/deberta-v2-xlarge',
                'microsoft/deberta-v2-xlarge-mnli',
                'microsoft/deberta-v2-xxlarge',
                'microsoft/deberta-v2-xxlarge-mnli',
                'microsoft/deberta-xlarge',
                'microsoft/deberta-xlarge-mnli',
                'openai-gpt',
                'transfo-xl-wt103',
                'uw-madison/yoso-4096',
                'xlm-mlm-100-1280',
                'xlm-mlm-17-1280',
                'xlm-mlm-en-2048',
                'xlm-roberta-large',
                'xlm-roberta-large-finetuned-conll02-dutch',
                'xlm-roberta-large-finetuned-conll02-spanish',
                'xlm-roberta-large-finetuned-conll03-english',
                'xlm-roberta-large-finetuned-conll03-german',
                'xlnet-base-cased',
                'xlnet-large-cased'
            ]
            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