

'''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,
)

lowerCAmelCase__      		=			logging.getLogger(__name__)

lowerCAmelCase__      		=			{'''facebook/bart-base''': BartForConditionalGeneration}
lowerCAmelCase__      		=			{'''facebook/bart-base''': BartTokenizer}


def 	_A  (					):






 """simple docstring"""



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

 __lowercase													=				parser.parse_args()

 return args


def 	_A  (					A__  ,       A__="cpu"   ):






 """simple docstring"""



 __lowercase													=				model_dict[model_name].from_pretrained(A__   ).to(A__   )
 __lowercase													=				tokenizer_dict[model_name].from_pretrained(A__   )

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

 return huggingface_model, tokenizer


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 model.eval()

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

 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=A__  ,       max_length=A__  ,       early_stopping=A__  ,       decoder_start_token_id=model.config.decoder_start_token_id  ,       )

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

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

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

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

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






 """simple docstring"""



 __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  ,       A__   )

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

 model.to(A__   )

 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(A__  ,       A__  ,       A__  ,       A__  ,       A__   )


if __name__ == "__main__":
 main()




'''simple docstring'''






def 	_A  (					A__   ):






 """simple docstring"""



 if not nums:  # Makes sure that the list is not empty
  raise ValueError('''List is empty'''   )

 __lowercase													=				sum(A__   ) / len(A__   )  # Calculate the average
 return sum(abs(x - average   ) for x in nums   ) / len(A__   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

