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,
)

lowerCAmelCase_						=			logging.getLogger(__name__)

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






def  snake_case(    )			->							int:





     '''simple docstring'''


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

     lowercase :       str       =			parser.parse_args()

     return args






def  snake_case(    __magic_name__			,		__magic_name__="cpu"   )			->							Dict:





     '''simple docstring'''


     lowercase :       int       =			model_dict[model_name].from_pretrained(__magic_name__   ).to(__magic_name__   )
     lowercase :       Tuple       =			tokenizer_dict[model_name].from_pretrained(__magic_name__   )

     if model_name in ["facebook/bart-base"]:
          lowercase :       Union[str, Any]       =			0
          lowercase :       Tuple       =			None
          lowercase :       Optional[Any]       =			0

     return huggingface_model, tokenizer






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     model.eval()

     lowercase :       Optional[Any]       =			None
     lowercase :       Union[str, Any]       =			torch.jit.script(BARTBeamSearchGenerator(__magic_name__   )   )

     with torch.no_grad():
          lowercase :       Dict       =			'''My friends are cool but they eat too many carbs.'''
          lowercase :       List[Any]       =			tokenizer([ARTICLE_TO_SUMMARIZE]			,		max_length=10_24			,		return_tensors='''pt'''   ).to(model.device   )

          lowercase :       List[Any]       =			model.generate(
              inputs['''input_ids''']			,		attention_mask=inputs['''attention_mask''']			,		num_beams=__magic_name__			,		max_length=__magic_name__			,		early_stopping=__magic_name__			,		decoder_start_token_id=model.config.decoder_start_token_id			,		)

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

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

          lowercase :       Optional[int]       =			remove_dup_initializers(os.path.abspath(__magic_name__   )   )

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

          lowercase :       Dict       =			onnxruntime.InferenceSession(__magic_name__   )
          lowercase :       str       =			ort_sess.run(
              __magic_name__			,		{
                  '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                  '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                  '''num_beams''': np.array(__magic_name__   ),
                  '''max_length''': np.array(__magic_name__   ),
                  '''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  snake_case(    )			->							Any:





     '''simple docstring'''


     lowercase :       str       =			parse_args()
     lowercase :       str       =			5
     lowercase :       List[Any]       =			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 :       Optional[Any]       =			torch.device(args.device   )

     lowercase :       Optional[int]       =			load_model_tokenizer(args.model_name_or_path			,		__magic_name__   )

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

     model.to(__magic_name__   )

     if args.max_length:
          lowercase :       List[Any]       =			args.max_length

     if args.num_beams:
          lowercase :       Dict       =			args.num_beams

     if args.output_file_path:
          lowercase :       int       =			args.output_file_path
     else:
          lowercase :       List[str]       =			'''BART.onnx'''

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


if __name__ == "__main__":
 main()





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_						=			{
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCAmelCase_						=			{
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






  def __init__(  self		:     Any				,						_A		:     Optional[int]				,						_A		:     List[str]="<s>"				,						_A		:     Tuple="</s>"				,						_A		:     Dict="</s>"				,						_A		:     Dict="<s>"				,						_A		:     List[str]="<unk>"				,						_A		:     str="<pad>"				,						_A		:     Any="<mask>"				,						_A		:     Optional[Dict[str, Any]] = None				,						**_A		:     Union[str, Any]				,						)		->   None:






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[Any]       =			{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=_A				,						eos_token=_A				,						unk_token=_A				,						sep_token=_A				,						cls_token=_A				,						pad_token=_A				,						mask_token=_A				,						sp_model_kwargs=self.sp_model_kwargs				,						**_A				,						)

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

       lowercase :       Optional[Any]       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       lowercase :       Tuple       =			len(self.sp_model				) - 1
       lowercase :       Any       =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def  __a      (  self		:     Tuple				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase :       Any       =			[self.cls_token_id]
       lowercase :       int       =			[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """simple docstring"""







       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_A				,						token_ids_a=_A				,						already_has_special_tokens=_A				)

       if token_ids_a is None:
            return [1] + ([0] * len(_A				)) + [1]
       return [1] + ([0] * len(_A				)) + [1, 1] + ([0] * len(_A				)) + [1]






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       List[str]       =			[self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep				) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]






  @property
  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{self.convert_ids_to_tokens(_A				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab






  def  __a      (  self		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[int]				,						_A		:     str				)		->   Dict:






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """simple docstring"""







       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(_A				)






  def  __a      (  self		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       str       =			False
       for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                 if not prev_is_special:
                      out_string += " "
                 out_string += self.sp_model.decode(_A				) + token
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def __getstate__(  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			self.__dict__.copy()
       lowercase :       Any       =			None
       return state






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

       # for backward compatibility
       if not hasattr(self				,						'''sp_model_kwargs'''				):
            lowercase :       Union[str, Any]       =			{}

       lowercase :       List[Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(self.vocab_file				)






  def  __a      (  self		:     str				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







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

       if os.path.abspath(self.vocab_file				) != os.path.abspath(_A				) and os.path.isfile(self.vocab_file				):
            copyfile(self.vocab_file				,						_A				)
       elif not os.path.isfile(self.vocab_file				):
            with open(_A				,						'''wb'''				) as fi:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)