


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	:       Optional[int]    	=							logging.getLogger(__name__)

__UpperCAmelCase	:       List[Any]    	=							{"facebook/bart-base": BartForConditionalGeneration}
__UpperCAmelCase	:       int    	=							{"facebook/bart-base": BartTokenizer}







def       A__							(   )       ->   Optional[Any]:
  __snake_case:						Tuple			  =						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.""")

  __snake_case:						List[Any]			  =						parser.parse_args()

  return args







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="cpu")       ->   Dict:
  __snake_case:						List[str]			  =						model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__)

  if model_name in ["facebook/bart-base"]:
    __snake_case:						Any			  =						0
    __snake_case:						Any			  =						None
    __snake_case:						str			  =						0

  return huggingface_model, tokenizer







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Optional[int]:
  model.eval()

  __snake_case:						Tuple			  =						None
  __snake_case:						List[str]			  =						torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE__))

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

    __snake_case:						Tuple			  =						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__))

    __snake_case:						Any			  =						remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE__))

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

    __snake_case:						int			  =						onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE__)
    __snake_case:						Dict			  =						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       A__							(   )       ->   Dict:
  __snake_case:						Dict			  =						parse_args()
  __snake_case:						Any			  =						5
  __snake_case:						List[Any]			  =						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()

  __snake_case:						List[str]			  =						torch.device(args.device)

  __snake_case:						Tuple			  =						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:
    __snake_case:						Any			  =						args.max_length

  if args.num_beams:
    __snake_case:						List[str]			  =						args.num_beams

  if args.output_file_path:
    __snake_case:						Union[str, Any]			  =						args.output_file_path
  else:
    __snake_case:						Tuple			  =						"""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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






