


'''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,
)

lowercase							=logging.getLogger(__name__)

lowercase							={'facebook/bart-base': BartForConditionalGeneration}
lowercase							={'facebook/bart-base': BartTokenizer}





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   =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.'							)

   _UpperCAmelCase       :       str							   =parser.parse_args()

   return args





def lowerCamelCase__      (						__lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							Dict="cpu"							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =model_dict[model_name].from_pretrained(__lowerCamelCase							).to(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =tokenizer_dict[model_name].from_pretrained(__lowerCamelCase							)

   if model_name in ["facebook/bart-base"]:
      _UpperCAmelCase       :       List[Any]							   =0
      _UpperCAmelCase       :       Optional[int]							   =None
      _UpperCAmelCase       :       int							   =0

   return huggingface_model, tokenizer





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   model.eval()

   _UpperCAmelCase       :       Optional[int]							   =None
   _UpperCAmelCase       :       List[str]							   =torch.jit.script(BARTBeamSearchGenerator(__lowerCamelCase							)							)

   with torch.no_grad():
      _UpperCAmelCase       :       str							   ='My friends are cool but they eat too many carbs.'
      _UpperCAmelCase       :       List[str]							   =tokenizer([ARTICLE_TO_SUMMARIZE]	,   max_length=1_0_2_4	,   return_tensors='pt'							).to(model.device							)

      _UpperCAmelCase       :       Any							   =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=1_4	,   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							)							)

      _UpperCAmelCase       :       Optional[Any]							   =remove_dup_initializers(os.path.abspath(__lowerCamelCase							)							)

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

      _UpperCAmelCase       :       Union[str, Any]							   =onnxruntime.InferenceSession(__lowerCamelCase							)
      _UpperCAmelCase       :       Optional[int]							   =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 lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =parse_args()
   _UpperCAmelCase       :       str							   =5
   _UpperCAmelCase       :       int							   =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()

   _UpperCAmelCase       :       Dict							   =torch.device(args.device							)

   _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[Any]							   =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:
      _UpperCAmelCase       :       int							   =args.max_length

   if args.num_beams:
      _UpperCAmelCase       :       List[Any]							   =args.num_beams

   if args.output_file_path:
      _UpperCAmelCase       :       str							   =args.output_file_path
   else:
      _UpperCAmelCase       :       Tuple							   ='BART.onnx'

   logger.info('Exporting model to ONNX'							)
   export_and_validate_model(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 main()





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


lowercase							=logging.get_logger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							*snake_case  ,							**snake_case)						->  None:




          '''simple docstring'''
          warnings.warn(
              'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
              ' Please use DeformableDetrImageProcessor instead.'  ,							snake_case  ,							)
          super().__init__(*snake_case  ,							**snake_case)

