'''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,
)

__lowercase        =    logging.getLogger(__name__)

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






def 							snake_case__				(     )       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    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."""					)

		lowerCAmelCase     =    parser.parse_args()

		return args






def 							snake_case__				(     _A:   int ,  _A:   Union[str, Any]="cpu"					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    model_dict[model_name].from_pretrained(_A					).to(_A					)
		lowerCAmelCase     =    tokenizer_dict[model_name].from_pretrained(_A					)

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

		return huggingface_model, tokenizer






def 							snake_case__				(     _A:   Optional[Any] ,  _A:   Tuple ,  _A:   Dict ,  _A:   Tuple ,  _A:   Dict					)       ->      List[Any]:

		'''simple docstring'''


		model.eval()

		lowerCAmelCase     =    None
		lowerCAmelCase     =    torch.jit.script(BARTBeamSearchGenerator(_A					)					)

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

				lowerCAmelCase     =    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					)					)

				lowerCAmelCase     =    remove_dup_initializers(os.path.abspath(_A					)					)

				logger.info("""Deduplicated and optimized model written to {}""".format(_A					)					)

				lowerCAmelCase     =    onnxruntime.InferenceSession(_A					)
				lowerCAmelCase     =    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 							snake_case__				(     )       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    parse_args()
		lowerCAmelCase     =    5
		lowerCAmelCase     =    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     =    torch.device(args.device					)

		lowerCAmelCase   ,	lowerCAmelCase     =    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:
				lowerCAmelCase     =    args.max_length

		if args.num_beams:
				lowerCAmelCase     =    args.num_beams

		if args.output_file_path:
				lowerCAmelCase     =    args.output_file_path
		else:
				lowerCAmelCase     =    """BART.onnx"""

		logger.info("""Exporting model to ONNX"""					)
		export_and_validate_model(_A ,  _A ,  _A ,  _A ,  _A					)


if __name__ == "__main__":
						main()





'''simple docstring'''




from functools import lru_cache






@lru_cache
def 							snake_case__				(     _A:   int					)       ->      int:

		'''simple docstring'''


		if num < 0:
				raise ValueError("""Number should not be negative."""					)

		return 1 if num in (0, 1) else num * factorial(num - 1					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




