





import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

a__:      List[str]									=		logging.getLogger(__name__)

a__:      Optional[int]									=		{'facebook/bart-base': BartForConditionalGeneration}
a__:      List[Any]									=		{'facebook/bart-base': BartTokenizer}


def 	UpperCamelCase__(       )->Dict:
       A__											=						argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''  )
       parser.add_argument(
           '''--validation_file''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='''A csv or a json file containing the validation data.'''  )
       parser.add_argument(
           '''--max_length''' ,     type=UpperCamelCase__ ,     default=5 ,     help='''The maximum total input sequence length after tokenization.''' ,     )
       parser.add_argument(
           '''--num_beams''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help=(
               '''Number of beams to use for evaluation. This argument will be '''
               '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
           ) ,     )
       parser.add_argument(
           '''--model_name_or_path''' ,     type=UpperCamelCase__ ,     help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,     required=UpperCamelCase__ ,     )
       parser.add_argument(
           '''--config_name''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='''Pretrained config name or path if not the same as model_name''' ,     )
       parser.add_argument(
           '''--device''' ,     type=UpperCamelCase__ ,     default='''cpu''' ,     help='''Device where the model will be run''' ,     )
       parser.add_argument('''--output_file_path''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     help='''Where to store the final ONNX file.'''  )

       A__											=						parser.parse_args()

       return args


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Dict="cpu"  )->Any:
       A__											=						model_dict[model_name].from_pretrained(UpperCamelCase__  ).to(UpperCamelCase__  )
       A__											=						tokenizer_dict[model_name].from_pretrained(UpperCamelCase__  )

       if model_name in ["facebook/bart-base"]:
              A__											=						0
              A__											=						None
              A__											=						0

       return huggingface_model, tokenizer


def 	UpperCamelCase__(       UpperCamelCase__			:       Dict ,     UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       Tuple  )->Dict:
       model.eval()

       A__											=						None
       A__											=						torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase__  )  )

       with torch.no_grad():
              A__											=						'''My friends are cool but they eat too many carbs.'''
              A__											=						tokenizer([ARTICLE_TO_SUMMARIZE] ,     max_length=10_24 ,     return_tensors='''pt'''  ).to(model.device  )

              A__											=						model.generate(
                  inputs['''input_ids'''] ,     attention_mask=inputs['''attention_mask'''] ,     num_beams=UpperCamelCase__ ,     max_length=UpperCamelCase__ ,     early_stopping=UpperCamelCase__ ,     decoder_start_token_id=model.config.decoder_start_token_id ,     )

              torch.onnx.export(
                  UpperCamelCase__ ,     (
                      inputs['''input_ids'''],
                      inputs['''attention_mask'''],
                      num_beams,
                      max_length,
                      model.config.decoder_start_token_id,
                  ) ,     UpperCamelCase__ ,     opset_version=14 ,     input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id'''] ,     output_names=['''output_ids'''] ,     dynamic_axes={
                      '''input_ids''': {0: '''batch''', 1: '''seq'''},
                      '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
                  } ,     example_outputs=UpperCamelCase__ ,     )

              logger.info('''Model exported to {}'''.format(UpperCamelCase__  )  )

              A__											=						remove_dup_initializers(os.path.abspath(UpperCamelCase__  )  )

              logger.info('''Deduplicated and optimized model written to {}'''.format(UpperCamelCase__  )  )

              A__											=						onnxruntime.InferenceSession(UpperCamelCase__  )
              A__											=						ort_sess.run(
                  UpperCamelCase__ ,     {
                      '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                      '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                      '''num_beams''': np.array(UpperCamelCase__  ),
                      '''max_length''': np.array(UpperCamelCase__  ),
                      '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id  ),
                  } ,     )

              np.testing.assert_allclose(summary_ids.cpu().numpy() ,     ort_out[0] ,     rtol=1e-3 ,     atol=1e-3  )

              logger.info('''Model outputs from torch and ONNX Runtime are similar.'''  )
              logger.info('''Success.'''  )


def 	UpperCamelCase__(       )->int:
       A__											=						parse_args()
       A__											=						5
       A__											=						4

       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,     datefmt='''%m/%d/%Y %H:%M:%S''' ,     level=logging.INFO ,     )

       logger.setLevel(logging.INFO  )
       transformers.utils.logging.set_verbosity_error()

       A__											=						torch.device(args.device  )

       A__					,	A__											=						load_model_tokenizer(args.model_name_or_path ,     UpperCamelCase__  )

       if model.config.decoder_start_token_id is None:
              raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''  )

       model.to(UpperCamelCase__  )

       if args.max_length:
              A__											=						args.max_length

       if args.num_beams:
              A__											=						args.num_beams

       if args.output_file_path:
              A__											=						args.output_file_path
       else:
              A__											=						'''BART.onnx'''

       logger.info('''Exporting model to ONNX'''  )
       export_and_validate_model(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )


if __name__ == "__main__":
       main()












import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a__:      List[str]									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''sequence-classification'''



 def __init__(							self,__lowerCamelCase ):
        if type(__lowerCamelCase ) == dict:
               A__											=						Namespace(**__lowerCamelCase )
        A__											=						glue_output_modes[hparams.task]
        A__											=						glue_tasks_num_labels[hparams.task]

        super().__init__(__lowerCamelCase,__lowerCamelCase,self.mode )



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        return self.model(**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__											=						outputs[0]

        A__											=						self.trainer.lr_schedulers[0]['''scheduler''']
        A__											=						{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}



 def 				UpperCamelCase			(							self ):
        A__											=						self.hparams
        A__											=						processors[args.task]()
        A__											=						processor.get_labels()

        for mode in ["train", "dev"]:
               A__											=						self._feature_file(__lowerCamelCase )
               if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                      logger.info('''Loading features from cached file %s''',__lowerCamelCase )
               else:
                      logger.info('''Creating features from dataset file at %s''',args.data_dir )
                      A__											=						(
                          processor.get_dev_examples(args.data_dir )
                          if mode == '''dev'''
                          else processor.get_train_examples(args.data_dir )
                      )
                      A__											=						convert_examples_to_features(
                          __lowerCamelCase,self.tokenizer,max_length=args.max_seq_length,label_list=self.labels,output_mode=args.glue_output_mode,)
                      logger.info('''Saving features into cached file %s''',__lowerCamelCase )
                      torch.save(__lowerCamelCase,__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = False ):
        A__											=						'''dev''' if mode == '''test''' else mode

        A__											=						self._feature_file(__lowerCamelCase )
        logger.info('''Loading features from cached file %s''',__lowerCamelCase )
        A__											=						torch.load(__lowerCamelCase )
        A__											=						torch.tensor([f.input_ids for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.attention_mask for f in features],dtype=torch.long )
        A__											=						torch.tensor([f.token_type_ids for f in features],dtype=torch.long )
        if self.hparams.glue_output_mode == "classification":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.long )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						torch.tensor([f.label for f in features],dtype=torch.float )

        return DataLoader(
            TensorDataset(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ),batch_size=__lowerCamelCase,shuffle=__lowerCamelCase,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
               A__											=						batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

        A__											=						self(**__lowerCamelCase )
        A__					,	A__											=						outputs[:2]
        A__											=						logits.detach().cpu().numpy()
        A__											=						inputs['''labels'''].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						torch.stack([x['''val_loss'''] for x in outputs] ).mean().detach().cpu().item()
        A__											=						np.concatenate([x['''pred'''] for x in outputs],axis=0 )

        if self.hparams.glue_output_mode == "classification":
               A__											=						np.argmax(__lowerCamelCase,axis=1 )
        elif self.hparams.glue_output_mode == "regression":
               A__											=						np.squeeze(__lowerCamelCase )

        A__											=						np.concatenate([x['''target'''] for x in outputs],axis=0 )
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]
        A__											=						[[] for _ in range(out_label_ids.shape[0] )]

        A__											=						{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task,__lowerCamelCase,__lowerCamelCase )}

        A__											=						dict(results.items() )
        A__											=						results
        return ret, preds_list, out_label_list



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__					,	A__					,	A__											=						self._eval_end(__lowerCamelCase )
        A__											=						ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




 @staticmethod
 def 				UpperCamelCase			(							__lowerCamelCase,__lowerCamelCase ):
        BaseTransformer.add_model_specific_args(__lowerCamelCase,__lowerCamelCase )
        parser.add_argument(
            '''--max_seq_length''',default=128,type=__lowerCamelCase,help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            ),)

        parser.add_argument(
            '''--task''',default='''''',type=__lowerCamelCase,required=__lowerCamelCase,help='''The GLUE task to run''',)
        parser.add_argument(
            '''--gpus''',default=0,type=__lowerCamelCase,help='''The number of GPUs allocated for this, it is by default 0 meaning none''',)

        parser.add_argument(
            '''--overwrite_cache''',action='''store_true''',help='''Overwrite the cached training and evaluation sets''' )

        return parser


def 	UpperCamelCase__(       )->Any:
       A__											=						argparse.ArgumentParser()
       add_generic_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						GLUETransformer.add_model_specific_args(UpperCamelCase__ ,     os.getcwd()  )
       A__											=						parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              A__											=						os.path.join(
                  '''./results''' ,     f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'  )}" ,     )
              os.makedirs(args.output_dir  )

       A__											=						GLUETransformer(UpperCamelCase__  )
       A__											=						generic_train(UpperCamelCase__ ,     UpperCamelCase__  )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              A__											=						sorted(glob.glob(os.path.join(args.output_dir ,     '''checkpoint-epoch=*.ckpt'''  ) ,     recursive=UpperCamelCase__  )  )
              A__											=						model.load_from_checkpoint(checkpoints[-1]  )
              return trainer.test(UpperCamelCase__  )


if __name__ == "__main__":
       main()





