






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,
)

_SCREAMING_SNAKE_CASE					:  Tuple             =      logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {'''facebook/bart-base''': BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {'''facebook/bart-base''': BartTokenizer}
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   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.'''    )

     SCREAMING_SNAKE_CASE__   =   parser.parse_args()

     return args
def     UpperCAmelCase_       (						_A				,		_A="cpu"    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   model_dict[model_name].from_pretrained(_A    ).to(_A    )
     SCREAMING_SNAKE_CASE__   =   tokenizer_dict[model_name].from_pretrained(_A    )

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

     return huggingface_model, tokenizer
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     model.eval()

     SCREAMING_SNAKE_CASE__   =   None
     SCREAMING_SNAKE_CASE__   =   torch.jit.script(BARTBeamSearchGenerator(_A    )    )

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

          SCREAMING_SNAKE_CASE__   =   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    )    )

          SCREAMING_SNAKE_CASE__   =   remove_dup_initializers(os.path.abspath(_A    )    )

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

          SCREAMING_SNAKE_CASE__   =   onnxruntime.InferenceSession(_A    )
          SCREAMING_SNAKE_CASE__   =   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     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   parse_args()
     SCREAMING_SNAKE_CASE__   =   5
     SCREAMING_SNAKE_CASE__   =   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()

     SCREAMING_SNAKE_CASE__   =   torch.device(args.device    )

     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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:
          SCREAMING_SNAKE_CASE__   =   args.max_length

     if args.num_beams:
          SCREAMING_SNAKE_CASE__   =   args.num_beams

     if args.output_file_path:
          SCREAMING_SNAKE_CASE__   =   args.output_file_path
     else:
          SCREAMING_SNAKE_CASE__   =   '''BART.onnx'''

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


if __name__ == "__main__":
       main()





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Tuple             =      {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "transfo-xl"
  a									= ["mems"]
  a									= {
      "n_token": "vocab_size",
      "hidden_size": "d_model",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Any     ,       __lowerCamelCase						:		int=26_7735     ,       __lowerCamelCase						:		Any=[2_0000, 4_0000, 20_0000]     ,       __lowerCamelCase						:		Dict=1024     ,       __lowerCamelCase						:		Optional[int]=1024     ,       __lowerCamelCase						:		List[Any]=16     ,       __lowerCamelCase						:		Union[str, Any]=64     ,       __lowerCamelCase						:		Dict=4096     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Dict=False     ,       __lowerCamelCase						:		Tuple=18     ,       __lowerCamelCase						:		Optional[int]=1600     ,       __lowerCamelCase						:		str=1000     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		List[Any]=0     ,       __lowerCamelCase						:		int=-1     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		str="normal"     ,       __lowerCamelCase						:		List[str]=0.01     ,       __lowerCamelCase						:		Any=0.01     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		List[str]=1e-5     ,       __lowerCamelCase						:		Union[str, Any]=0     ,       **__lowerCamelCase						:		int     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   []
       self.cutoffs.extend(__lowerCamelCase )
       if proj_share_all_but_first:
            SCREAMING_SNAKE_CASE__   =   [False] + [True] * len(self.cutoffs )
       else:
            SCREAMING_SNAKE_CASE__   =   [False] + [False] * len(self.cutoffs )
       SCREAMING_SNAKE_CASE__   =   d_model
       SCREAMING_SNAKE_CASE__   =   d_embed
       SCREAMING_SNAKE_CASE__   =   d_head
       SCREAMING_SNAKE_CASE__   =   d_inner
       SCREAMING_SNAKE_CASE__   =   div_val
       SCREAMING_SNAKE_CASE__   =   pre_lnorm
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   mem_len
       SCREAMING_SNAKE_CASE__   =   same_length
       SCREAMING_SNAKE_CASE__   =   attn_type
       SCREAMING_SNAKE_CASE__   =   clamp_len
       SCREAMING_SNAKE_CASE__   =   sample_softmax
       SCREAMING_SNAKE_CASE__   =   adaptive
       SCREAMING_SNAKE_CASE__   =   dropout
       SCREAMING_SNAKE_CASE__   =   dropatt
       SCREAMING_SNAKE_CASE__   =   untie_r
       SCREAMING_SNAKE_CASE__   =   init
       SCREAMING_SNAKE_CASE__   =   init_range
       SCREAMING_SNAKE_CASE__   =   proj_init_std
       SCREAMING_SNAKE_CASE__   =   init_std
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       super().__init__(eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )






  @property
  def 				lowercase_		(							self						:		str )		->							Dict:
       # Message copied from Transformer-XL documentation
       logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1







  @max_position_embeddings.setter
  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Any )		->							List[Any]:
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


