





"""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,
)

__A											=		logging.getLogger(__name__)

__A											=		{"""facebook/bart-base""": BartForConditionalGeneration}
__A											=		{"""facebook/bart-base""": BartTokenizer}
def  __A   ()					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.'			)
  parser.add_argument(
      '--validation_file'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=_SCREAMING_SNAKE_CASE     ,   help='A csv or a json file containing the validation data.'			)
  parser.add_argument(
      '--max_length'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=5     ,   help='The maximum total input sequence length after tokenization.'     ,   )
  parser.add_argument(
      '--num_beams'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=_SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   help='Path to pretrained model or model identifier from huggingface.co/models.'     ,   required=_SCREAMING_SNAKE_CASE     ,   )
  parser.add_argument(
      '--config_name'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=_SCREAMING_SNAKE_CASE     ,   help='Pretrained config name or path if not the same as model_name'     ,   )
  parser.add_argument(
      '--device'     ,   type=_SCREAMING_SNAKE_CASE     ,   default='cpu'     ,   help='Device where the model will be run'     ,   )
  parser.add_argument('--output_file_path'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=_SCREAMING_SNAKE_CASE     ,   help='Where to store the final ONNX file.'			)

  lowerCAmelCase__			:Optional[int]											=							parser.parse_args()

  return args
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE="cpu"			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							model_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							tokenizer_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE			)

  if model_name in ["facebook/bart-base"]:
    lowerCAmelCase__			:List[str]											=							0
    lowerCAmelCase__			:Optional[int]											=							None
    lowerCAmelCase__			:Optional[int]											=							0

  return huggingface_model, tokenizer
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->List[str]:





  """simple docstring"""


  model.eval()

  lowerCAmelCase__			:str											=							None
  lowerCAmelCase__			:List[Any]											=							torch.jit.script(BARTBeamSearchGenerator(_SCREAMING_SNAKE_CASE			)			)

  with torch.no_grad():
    lowerCAmelCase__			:Dict											=							'My friends are cool but they eat too many carbs.'
    lowerCAmelCase__			:Tuple											=							tokenizer([ARTICLE_TO_SUMMARIZE]     ,   max_length=1024     ,   return_tensors='pt'			).to(model.device			)

    lowerCAmelCase__			:Union[str, Any]											=							model.generate(
        inputs['input_ids']     ,   attention_mask=inputs['attention_mask']     ,   num_beams=_SCREAMING_SNAKE_CASE     ,   max_length=_SCREAMING_SNAKE_CASE     ,   early_stopping=_SCREAMING_SNAKE_CASE     ,   decoder_start_token_id=model.config.decoder_start_token_id     ,   )

    torch.onnx.export(
        _SCREAMING_SNAKE_CASE     ,   (
            inputs['input_ids'],
            inputs['attention_mask'],
            num_beams,
            max_length,
            model.config.decoder_start_token_id,
        )     ,   _SCREAMING_SNAKE_CASE     ,   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=_SCREAMING_SNAKE_CASE     ,   )

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

    lowerCAmelCase__			:Tuple											=							remove_dup_initializers(os.path.abspath(_SCREAMING_SNAKE_CASE			)			)

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

    lowerCAmelCase__			:Union[str, Any]											=							onnxruntime.InferenceSession(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Optional[Any]											=							ort_sess.run(
        _SCREAMING_SNAKE_CASE     ,   {
            'input_ids': inputs['input_ids'].cpu().numpy(),
            'attention_mask': inputs['attention_mask'].cpu().numpy(),
            'num_beams': np.array(_SCREAMING_SNAKE_CASE			),
            'max_length': np.array(_SCREAMING_SNAKE_CASE			),
            '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  __A   ()					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:List[Any]											=							parse_args()
  lowerCAmelCase__			:int											=							5
  lowerCAmelCase__			:List[str]											=							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()

  lowerCAmelCase__			:Tuple											=							torch.device(args.device			)

  lowerCAmelCase__							,				lowerCAmelCase__			:str											=							load_model_tokenizer(args.model_name_or_path     ,   _SCREAMING_SNAKE_CASE			)

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

  model.to(_SCREAMING_SNAKE_CASE			)

  if args.max_length:
    lowerCAmelCase__			:Union[str, Any]											=							args.max_length

  if args.num_beams:
    lowerCAmelCase__			:List[str]											=							args.num_beams

  if args.output_file_path:
    lowerCAmelCase__			:Tuple											=							args.output_file_path
  else:
    lowerCAmelCase__			:Optional[Any]											=							'BART.onnx'

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


if __name__ == "__main__":
  main()







"""simple docstring"""





import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad






class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							1_0






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							[1, 2, 3, 4]
         lowerCAmelCase__			:Tuple											=							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
         lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							''
         lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							(
             'It was the year of Our Lord one thousand seven hundred and '
             'seventy-five\n\nSpiritual revelations were conceded to England '
             'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
         )
         lowerCAmelCase__							,				lowerCAmelCase__			:str											=							process_story(__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							[
             'It was the year of Our Lord one thousand seven hundred and seventy-five.',
             'Spiritual revelations were conceded to England at that favoured period, as at this.',
         ]
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							['It was the best of times.']
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							torch.tensor([1, 2, 3, 4]      )
         lowerCAmelCase__			:List[str]											=							torch.tensor([1, 1, 1, 1]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 0      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]      )
         lowerCAmelCase__			:Optional[int]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 2_3      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([8, 2, 3, 4, 1, 1, 1]      )
         lowerCAmelCase__			:Optional[Any]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 1      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							1_0_1
         lowerCAmelCase__			:str											=							torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]      )
         lowerCAmelCase__			:Any											=							torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]      )

         lowerCAmelCase__			:List[Any]											=							compute_token_type_ids(__UpperCAmelCase    , __UpperCAmelCase      )
         np.testing.assert_array_equal(__UpperCAmelCase    , __UpperCAmelCase      )
