





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,
)

snake_case 				=       logging.getLogger(__name__)

snake_case 				=       {"""facebook/bart-base""": BartForConditionalGeneration}
snake_case 				=       {"""facebook/bart-base""": BartTokenizer}

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

  _lowercase							= parser.parse_args()

  return args

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Any="cpu"     )      ->						Dict:
  _lowercase							= model_dict[model_name].from_pretrained(snake_case__     ).to(snake_case__     )
  _lowercase							= tokenizer_dict[model_name].from_pretrained(snake_case__     )

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

  return huggingface_model, tokenizer

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :str			,							snake_case__  :int			,							snake_case__  :Dict			,							snake_case__  :Optional[Any]     )      ->						Dict:
  model.eval()

  _lowercase							= None
  _lowercase							= torch.jit.script(BARTBeamSearchGenerator(snake_case__     )     )

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

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

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

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

    _lowercase							= remove_dup_initializers(os.path.abspath(snake_case__     )     )

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

    _lowercase							= onnxruntime.InferenceSession(snake_case__     )
    _lowercase							= ort_sess.run(
        snake_case__			,							{
            'input_ids': inputs['input_ids'].cpu().numpy(),
            'attention_mask': inputs['attention_mask'].cpu().numpy(),
            'num_beams': np.array(snake_case__     ),
            'max_length': np.array(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 							SCREAMING_SNAKE_CASE__       (  )      ->						Tuple:
  _lowercase							= parse_args()
  _lowercase							= 5
  _lowercase							= 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							= torch.device(args.device     )

  _lowercase						,		_lowercase							= load_model_tokenizer(args.model_name_or_path			,							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(snake_case__     )

  if args.max_length:
    _lowercase							= args.max_length

  if args.num_beams:
    _lowercase							= args.num_beams

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

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


if __name__ == "__main__":
    main()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
      import torch

      from transformers.generation import DisjunctiveConstraint





@require_torch
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[Any]:
         # For consistency across different places the DisjunctiveConstraint is called,
         # dc.token_ids is a list of integers. It is also initialized only by integers.

         _lowercase							= [[1, 2, 4], [1, 2, 3, 4]]
         _lowercase							= DisjunctiveConstraint(__A  )
         self.assertTrue(isinstance(dc.token_ids						,__A  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

         with self.assertRaises(__A  ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					str:
         # We can't have constraints that are complete subsets of another. This leads to a preverse
         # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
         # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
         # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
         # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
         _lowercase							= [[1, 2], [1, 2, 3, 4]]

         with self.assertRaises(__A  ):
           DisjunctiveConstraint(__A  )  # fails here







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= [[1, 2, 3], [1, 2, 4]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         _lowercase							= stepped is True and completed is False and reset is False
         self.assertTrue(__A  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(3  )
         _lowercase							= stepped is True and completed is True and reset is False
         self.assertTrue(__A  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 3]  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

         _lowercase							= DisjunctiveConstraint(__A  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(4  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.current_seq == [1, 2, 4]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

         dc.reset()

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(1  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 3  )
         self.assertTrue(dc.current_seq == [1]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(2  )
         self.assertTrue(not dc.completed  )
         self.assertTrue(dc.remaining() == 2  )
         self.assertTrue(dc.current_seq == [1, 2]  )

         _lowercase						,		_lowercase						,		_lowercase							= dc.update(5  )
         self.assertTrue(dc.completed  )  # Completed!
         self.assertTrue(dc.remaining() == 0  )
         self.assertTrue(dc.current_seq == [1, 2, 5]  )