



"""simple docstring"""




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,
)

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

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


def      lowercase__   (	)   ->	Dict:






      '''simple docstring'''


      lowercase     :					Union[str, Any] 		=      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.' )

      lowercase     :					int 		=      parser.parse_args()

      return args


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase="cpu" )   ->	Any:






      '''simple docstring'''


      lowercase     :					Tuple 		=      model_dict[model_name].from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
      lowercase     :					Optional[Any] 		=      tokenizer_dict[model_name].from_pretrained(UpperCamelCase__ )

      if model_name in ["facebook/bart-base"]:
            lowercase     :					Optional[int] 		=      0
            lowercase     :					Tuple 		=      None
            lowercase     :					Dict 		=      0

      return huggingface_model, tokenizer


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase )   ->	Dict:






      '''simple docstring'''


      model.eval()

      lowercase     :					Optional[Any] 		=      None
      lowercase     :					Optional[Any] 		=      torch.jit.script(BARTBeamSearchGenerator(UpperCamelCase__ ) )

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

            lowercase     :					Dict 		=      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__ ) )

            lowercase     :					List[str] 		=      remove_dup_initializers(os.path.abspath(UpperCamelCase__ ) )

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

            lowercase     :					Any 		=      onnxruntime.InferenceSession(UpperCamelCase__ )
            lowercase     :					Tuple 		=      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      lowercase__   (	)   ->	int:






      '''simple docstring'''


      lowercase     :					Dict 		=      parse_args()
      lowercase     :					int 		=      5
      lowercase     :					Any 		=      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()

      lowercase     :					Union[str, Any] 		=      torch.device(args.device )

      lowercase		,			lowercase     :					Any 		=      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:
            lowercase     :					List[Any] 		=      args.max_length

      if args.num_beams:
            lowercase     :					Tuple 		=      args.num_beams

      if args.output_file_path:
            lowercase     :					List[str] 		=      args.output_file_path
      else:
            lowercase     :					Tuple 		=      'BART.onnx'

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


if __name__ == "__main__":
     main()







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )

       # Save pytorch-model
       print(f"Save PyTorch model to {pytorch_dump_path}"  )
       torch.save(model.state_dict() ,     UpperCamelCase__  )


if __name__ == "__main__":
       a__:      str									=		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





