





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,
)

SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

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



def        __lowercase       (				)			->       Dict:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						parser.parse_args()

      return args



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						model_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE     ).to(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						tokenizer_dict[model_name].from_pretrained(_SCREAMING_SNAKE_CASE     )

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

      return huggingface_model, tokenizer



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


      '''simple docstring'''


      model.eval()

      SCREAMING_SNAKE_CASE		=						None
      SCREAMING_SNAKE_CASE		=						torch.jit.script(BARTBeamSearchGenerator(_SCREAMING_SNAKE_CASE     )     )

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

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

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

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

            SCREAMING_SNAKE_CASE		=						remove_dup_initializers(os.path.abspath(_SCREAMING_SNAKE_CASE     )     )

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

            SCREAMING_SNAKE_CASE		=						onnxruntime.InferenceSession(_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						ort_sess.run(
                _SCREAMING_SNAKE_CASE			,						{
                    """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                    """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                    """num_beams""": np.array(_SCREAMING_SNAKE_CASE     ),
                    """max_length""": np.array(_SCREAMING_SNAKE_CASE     ),
                    """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        __lowercase       (				)			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						parse_args()
      SCREAMING_SNAKE_CASE		=						5
      SCREAMING_SNAKE_CASE		=						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()

      SCREAMING_SNAKE_CASE		=						torch.device(args.device     )

      SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						load_model_tokenizer(args.model_name_or_path			,						_SCREAMING_SNAKE_CASE     )

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

      model.to(_SCREAMING_SNAKE_CASE     )

      if args.max_length:
            SCREAMING_SNAKE_CASE		=						args.max_length

      if args.num_beams:
            SCREAMING_SNAKE_CASE		=						args.num_beams

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

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


if __name__ == "__main__":
      main()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


