





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,
)

_A :   Optional[Any]  			=  logging.getLogger(__name__)

_A :   Optional[Any]  			=  {'facebook/bart-base': BartForConditionalGeneration}
_A :   Any  			=  {'facebook/bart-base': BartTokenizer}



def 	_a						(   )    ->    Optional[int]:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''					)
   parser.add_argument(
       '''--validation_file'''      ,  type=UpperCAmelCase      ,  default=UpperCAmelCase      ,  help='''A csv or a json file containing the validation data.'''					)
   parser.add_argument(
       '''--max_length'''      ,  type=UpperCAmelCase      ,  default=5      ,  help='''The maximum total input sequence length after tokenization.'''      ,  )
   parser.add_argument(
       '''--num_beams'''      ,  type=UpperCAmelCase      ,  default=UpperCAmelCase      ,  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=UpperCAmelCase      ,  help='''Path to pretrained model or model identifier from huggingface.co/models.'''      ,  required=UpperCAmelCase      ,  )
   parser.add_argument(
       '''--config_name'''      ,  type=UpperCAmelCase      ,  default=UpperCAmelCase      ,  help='''Pretrained config name or path if not the same as model_name'''      ,  )
   parser.add_argument(
       '''--device'''      ,  type=UpperCAmelCase      ,  default='''cpu'''      ,  help='''Device where the model will be run'''      ,  )
   parser.add_argument('''--output_file_path'''      ,  type=UpperCAmelCase      ,  default=UpperCAmelCase      ,  help='''Where to store the final ONNX file.'''					)

   lowerCamelCase__       :						Any        =							parser.parse_args()

   return args



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase="cpu"					)    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							model_dict[model_name].from_pretrained(UpperCAmelCase					).to(UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							tokenizer_dict[model_name].from_pretrained(UpperCAmelCase					)

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

   return huggingface_model, tokenizer



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   model.eval()

   lowerCamelCase__       :						Dict        =							None
   lowerCamelCase__       :						Dict        =							torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase					)					)

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

      lowerCamelCase__       :						Dict        =							model.generate(
          inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  num_beams=UpperCAmelCase      ,  max_length=UpperCAmelCase      ,  early_stopping=UpperCAmelCase      ,  decoder_start_token_id=model.config.decoder_start_token_id      ,  )

      torch.onnx.export(
          UpperCAmelCase      ,  (
              inputs['''input_ids'''],
              inputs['''attention_mask'''],
              num_beams,
              max_length,
              model.config.decoder_start_token_id,
          )      ,  UpperCAmelCase      ,  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=UpperCAmelCase      ,  )

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

      lowerCamelCase__       :						int        =							remove_dup_initializers(os.path.abspath(UpperCAmelCase					)					)

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

      lowerCamelCase__       :						Optional[Any]        =							onnxruntime.InferenceSession(UpperCAmelCase					)
      lowerCamelCase__       :						str        =							ort_sess.run(
          UpperCAmelCase      ,  {
              '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
              '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
              '''num_beams''': np.array(UpperCAmelCase					),
              '''max_length''': np.array(UpperCAmelCase					),
              '''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						(   )    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							parse_args()
   lowerCamelCase__       :						str        =							5
   lowerCamelCase__       :						str        =							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()

   lowerCamelCase__       :						Tuple        =							torch.device(args.device					)

   lowerCamelCase__       :						str        =							load_model_tokenizer(args.model_name_or_path      ,  UpperCAmelCase					)

   if model.config.decoder_start_token_id is None:
      raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''					)

   model.to(UpperCAmelCase					)

   if args.max_length:
      lowerCamelCase__       :						Union[str, Any]        =							args.max_length

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

   if args.output_file_path:
      lowerCamelCase__       :						int        =							args.output_file_path
   else:
      lowerCamelCase__       :						Optional[Any]        =							'''BART.onnx'''

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


if __name__ == "__main__":
       main()










import os
from pathlib import Path



def 	_a						(   )    ->    Tuple:







   """simple docstring"""
   from torch.utils.cpp_extension import load

   lowerCamelCase__       :						List[Any]        =							Path(UpperCAmelCase					).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
   lowerCamelCase__       :						Any        =							[
       root / filename
       for filename in [
           '''vision.cpp''',
           os.path.join('''cpu'''      ,  '''ms_deform_attn_cpu.cpp'''					),
           os.path.join('''cuda'''      ,  '''ms_deform_attn_cuda.cu'''					),
       ]
   ]

   load(
       '''MultiScaleDeformableAttention'''      ,  UpperCAmelCase      ,  with_cuda=UpperCAmelCase      ,  extra_include_paths=[str(UpperCAmelCase					)]      ,  extra_cflags=['''-DWITH_CUDA=1''']      ,  extra_cuda_cflags=[
           '''-DCUDA_HAS_FP16=1''',
           '''-D__CUDA_NO_HALF_OPERATORS__''',
           '''-D__CUDA_NO_HALF_CONVERSIONS__''',
           '''-D__CUDA_NO_HALF2_OPERATORS__''',
       ]      ,  )

   import MultiScaleDeformableAttention as MSDA

   return MSDA



