

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase_             =     {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['LayoutLMv2TokenizerFast']

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     ['LayoutLMv2FeatureExtractor']
  lowercase_             =     ['LayoutLMv2ImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_             =     [
      'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
      'LayoutLMv2ForQuestionAnswering',
      'LayoutLMv2ForSequenceClassification',
      'LayoutLMv2ForTokenClassification',
      'LayoutLMv2Layer',
      'LayoutLMv2Model',
      'LayoutLMv2PreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
  from .processing_layoutlmva import LayoutLMvaProcessor
  from .tokenization_layoutlmva import LayoutLMvaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlmva import (
        LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaLayer,
        LayoutLMvaModel,
        LayoutLMvaPreTrainedModel,
    )
else:
  import sys

  lowercase_             =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





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()


