


"""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,
)

UpperCAmelCase						:		List[str]			= logging.getLogger(__name__)

UpperCAmelCase						:		Dict			= {"facebook/bart-base": BartForConditionalGeneration}
UpperCAmelCase						:		List[str]			= {"facebook/bart-base": BartTokenizer}


def 			_SCREAMING_SNAKE_CASE						() ->						Any:







  '''simple docstring'''


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

  lowercase_								= parser.parse_args()

  return args


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase="cpu"							) ->						int:







  '''simple docstring'''


  lowercase_								= model_dict[model_name].from_pretrained(__lowerCAmelCase							).to(__lowerCAmelCase							)
  lowercase_								= tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase							)

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

  return huggingface_model, tokenizer


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  model.eval()

  lowercase_								= None
  lowercase_								= torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase							)							)

  with torch.no_grad():
    lowercase_								= """My friends are cool but they eat too many carbs."""
    lowercase_								= tokenizer([ARTICLE_TO_SUMMARIZE] ,  max_length=10_24 ,  return_tensors="""pt"""							).to(model.device							)

    lowercase_								= model.generate(
        inputs["""input_ids"""] ,  attention_mask=inputs["""attention_mask"""] ,  num_beams=__lowerCAmelCase ,  max_length=__lowerCAmelCase ,  early_stopping=__lowerCAmelCase ,  decoder_start_token_id=model.config.decoder_start_token_id ,  )

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

    logger.info("""Model exported to {}""".format(__lowerCAmelCase							)							)

    lowercase_								= remove_dup_initializers(os.path.abspath(__lowerCAmelCase							)							)

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

    lowercase_								= onnxruntime.InferenceSession(__lowerCAmelCase							)
    lowercase_								= ort_sess.run(
        __lowerCAmelCase ,  {
            """input_ids""": inputs["""input_ids"""].cpu().numpy(),
            """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
            """num_beams""": np.array(__lowerCAmelCase							),
            """max_length""": np.array(__lowerCAmelCase							),
            """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						() ->						Any:







  '''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 ,  __lowerCAmelCase							)

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

  model.to(__lowerCAmelCase							)

  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(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)


if __name__ == "__main__":
       main()






"""simple docstring"""
from ..utils import DummyObject, requires_backends


class    SCREAMING_SNAKE_CASE__ (							metaclass=__UpperCAmelCase   ):
    lowercase__      =   ["keras_nlp"]

    def __init__(				self			:				Dict ,  *lowerCAmelCase_			:				List[str] ,  **lowerCAmelCase_			:				Tuple):







      """simple docstring"""







      requires_backends(self ,  ["""keras_nlp"""])

