

'''simple docstring'''





from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)





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,
)

lowercase_             =     logging.getLogger(__name__)

lowercase_             =     {'facebook/bart-base': BartForConditionalGeneration}
lowercase_             =     {'facebook/bart-base': BartTokenizer}
def   a   (    )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =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.'  )

      _lowercase			 =parser.parse_args()

      return args
def   a   (    A__			:     int   ,     A__			:     Optional[int]="cpu"  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =model_dict[model_name].from_pretrained(A__  ).to(A__  )
      _lowercase			 =tokenizer_dict[model_name].from_pretrained(A__  )

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

      return huggingface_model, tokenizer
def   a   (    A__			:     List[str]   ,     A__			:     Optional[Any]   ,     A__			:     List[Any]   ,     A__			:     Dict   ,     A__			:     Tuple  )					->   List[str]:





      """simple docstring"""



      model.eval()

      _lowercase			 =None
      _lowercase			 =torch.jit.script(BARTBeamSearchGenerator(A__  )  )

      with torch.no_grad():
            _lowercase			 ='My friends are cool but they eat too many carbs.'
            _lowercase			 =tokenizer([ARTICLE_TO_SUMMARIZE]   ,     max_length=1024   ,     return_tensors='pt'  ).to(model.device  )

            _lowercase			 =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=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=A__   ,     )

            logger.info('Model exported to {}'.format(A__  )  )

            _lowercase			 =remove_dup_initializers(os.path.abspath(A__  )  )

            logger.info('Deduplicated and optimized model written to {}'.format(A__  )  )

            _lowercase			 =onnxruntime.InferenceSession(A__  )
            _lowercase			 =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   a   (    )					->   int:





      """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   ,     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:
            _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(A__   ,     A__   ,     A__   ,     A__   ,     A__  )


if __name__ == "__main__":
  main()


