


from __future__ import annotations





def 							A			(  _UpperCAmelCase       :     str					)       ->				list[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     2
			_UpperCAmelCase	 =     []
			while i * i <= n:
						if n % i:
									i += 1
						else:
									n //= i
									factors.append(_UpperCAmelCase					)
			if n > 1:
						factors.append(_UpperCAmelCase					)
			return factors


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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__     = logging.getLogger(__name__)

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





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     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.'					)

			_UpperCAmelCase	 =     parser.parse_args()

			return args





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[Any]="cpu"					)       ->				Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_dict[model_name].from_pretrained(_UpperCAmelCase					).to(_UpperCAmelCase					)
			_UpperCAmelCase	 =     tokenizer_dict[model_name].from_pretrained(_UpperCAmelCase					)

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

			return huggingface_model, tokenizer





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			model.eval()

			_UpperCAmelCase	 =     None
			_UpperCAmelCase	 =     torch.jit.script(BARTBeamSearchGenerator(_UpperCAmelCase					)					)

			with torch.no_grad():
						_UpperCAmelCase	 =     'My friends are cool but they eat too many carbs.'
						_UpperCAmelCase	 =     tokenizer([ARTICLE_TO_SUMMARIZE]							,     max_length=1_024							,     return_tensors='pt'					).to(model.device					)

						_UpperCAmelCase	 =     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					)					)

						_UpperCAmelCase	 =     remove_dup_initializers(os.path.abspath(_UpperCAmelCase					)					)

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

						_UpperCAmelCase	 =     onnxruntime.InferenceSession(_UpperCAmelCase					)
						_UpperCAmelCase	 =     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 							A			(  )       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     parse_args()
			_UpperCAmelCase	 =     5
			_UpperCAmelCase	 =     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()

			_UpperCAmelCase	 =     torch.device(args.device					)

			_UpperCAmelCase ,  _UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     args.max_length

			if args.num_beams:
						_UpperCAmelCase	 =     args.num_beams

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

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


if __name__ == "__main__":
							main()
