



"""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"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase						:		List[Any]			= logging.get_logger(__name__)


UpperCAmelCase						:		List[Any]			= "▁"

UpperCAmelCase						:		Union[str, Any]			= {"vocab_file": "spiece.model"}

UpperCAmelCase						:		List[Any]			= {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCAmelCase						:		Optional[Any]			= {
    "google/reformer-crime-and-punishment": 52_4288,
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   ["input_ids", "attention_mask"]

    def __init__(				self			:				int ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Dict="</s>" ,  lowerCAmelCase_			:				Dict="<unk>" ,  lowerCAmelCase_			:				Dict=[] ,  lowerCAmelCase_			:				Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:				List[str] ,  ):







      """simple docstring"""







      lowercase_								= {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  additional_special_tokens=lowerCAmelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase_ ,  )

      lowercase_								= vocab_file
      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(lowerCAmelCase_)

    @property
    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      return self.sp_model.get_piece_size()

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_								= {self.convert_ids_to_tokens(lowerCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

    def __getstate__(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= self.__dict__.copy()
      lowercase_								= None
      return state

    def __setstate__(				self			:				Optional[Any] ,  lowerCAmelCase_			:				int):







      """simple docstring"""







      lowercase_								= d

      # for backward compatibility
      if not hasattr(self ,  """sp_model_kwargs"""):
        lowercase_								= {}

      lowercase_								= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      return self.sp_model.piece_to_id(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      if index < self.sp_model.get_piece_size():
        lowercase_								= self.sp_model.IdToPiece(lowerCAmelCase_)
      return token

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				Any):







      """simple docstring"""







      lowercase_								= []
      lowercase_								= """"""
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += self.sp_model.decode(lowerCAmelCase_) + token
          lowercase_								= []
        else:
          current_sub_tokens.append(lowerCAmelCase_)
      out_string += self.sp_model.decode(lowerCAmelCase_)
      return out_string.strip()




    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[str] = None):







      """simple docstring"""







      if not os.path.isdir(lowerCAmelCase_):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
        return
      lowercase_								= os.path.join(
          lowerCAmelCase_ ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

      if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_) and os.path.isfile(self.vocab_file):
        copyfile(self.vocab_file ,  lowerCAmelCase_)
      elif not os.path.isfile(self.vocab_file):
        with open(lowerCAmelCase_ ,  """wb""") as fi:
          lowercase_								= self.sp_model.serialized_model_proto()
          fi.write(lowerCAmelCase_)

      return (out_vocab_file,)

