
"""simple docstring"""



from functools import lru_cache


@lru_cache
def  _UpperCamelCase	(						_A      )    ->  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()

"""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,
)

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

a					:	int							=		{'''facebook/bart-base''': BartForConditionalGeneration}
a					:	Dict							=		{'''facebook/bart-base''': BartTokenizer}


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   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."""      )

    _UpperCAmelCase          =   parser.parse_args()

    return args


def  _UpperCamelCase	(						_A					,      _A="cpu"      )    ->  Optional[Any]:





    """simple docstring"""

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

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

    return huggingface_model, tokenizer


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    model.eval()

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

    with torch.no_grad():
        _UpperCAmelCase          =   """My friends are cool but they eat too many carbs."""
        _UpperCAmelCase          =   tokenizer([ARTICLE_TO_SUMMARIZE]					,      max_length=1_0_2_4					,      return_tensors="""pt"""      ).to(model.device      )

        _UpperCAmelCase          =   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=1_4					,      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      )      )

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

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

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





    """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					,      _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:
        _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(_A					,      _A					,      _A					,      _A					,      _A      )


if __name__ == "__main__":
 main()