




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      ,	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 re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
 from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
 from tensorflow.keras.optimizers import Adam
class 						_A (  tf.keras.optimizers.schedules.LearningRateSchedule				):






  def __init__(  self		:     str				,						_A		:     float				,						_A		:     Callable				,						_A		:     int				,						_A		:     float = 1.0				,						_A		:     str = None				,						)		->   List[str]:






       """simple docstring"""







       super().__init__()
       lowercase :       List[str]       =			initial_learning_rate
       lowercase :       List[str]       =			warmup_steps
       lowercase :       Tuple       =			power
       lowercase :       Any       =			decay_schedule_fn
       lowercase :       Union[str, Any]       =			name






  def __call__(  self		:     str				,						_A		:     Any				)		->   Optional[int]:






       """simple docstring"""







       with tf.name_scope(self.name or '''WarmUp'''				) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            lowercase :       List[Any]       =			tf.cast(_A				,						tf.floataa				)
            lowercase :       Union[str, Any]       =			tf.cast(self.warmup_steps				,						tf.floataa				)
            lowercase :       List[str]       =			global_step_float / warmup_steps_float
            lowercase :       int       =			self.initial_learning_rate * tf.math.pow(_A				,						self.power				)
            return tf.cond(
                global_step_float < warmup_steps_float				,						lambda: warmup_learning_rate				,						lambda: self.decay_schedule_fn(step - self.warmup_steps				)				,						name=_A				,						)






  def  __a      (  self		:     Dict				)		->   List[str]:






       """simple docstring"""







       return {
           "initial_learning_rate": self.initial_learning_rate,
           "decay_schedule_fn": self.decay_schedule_fn,
           "warmup_steps": self.warmup_steps,
           "power": self.power,
           "name": self.name,
       }






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = 0.0			,		__magic_name__ = 0.9			,		__magic_name__ = 0.9_9_9			,		__magic_name__ = 1e-8			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = 0.0			,		__magic_name__ = 1.0			,		__magic_name__ = None			,		)			->							int:





     '''simple docstring'''


     lowercase :       List[Any]       =			tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=__magic_name__			,		decay_steps=num_train_steps - num_warmup_steps			,		end_learning_rate=init_lr * min_lr_ratio			,		power=__magic_name__			,		)
     if num_warmup_steps:
          lowercase :       Optional[int]       =			WarmUp(
              initial_learning_rate=__magic_name__			,		decay_schedule_fn=__magic_name__			,		warmup_steps=__magic_name__			,		)
     if weight_decay_rate > 0.0:
          lowercase :       Optional[Any]       =			AdamWeightDecay(
              learning_rate=__magic_name__			,		weight_decay_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']			,		include_in_weight_decay=__magic_name__			,		)
     else:
          lowercase :       str       =			tf.keras.optimizers.Adam(
              learning_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		)
     # We return the optimizer and the LR scheduler in order to better track the
     # evolution of the LR independently of the optimizer.
     return optimizer, lr_schedule
class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001				,						_A		:     float = 0.9				,						_A		:     float = 0.999				,						_A		:     float = 1E-7				,						_A		:     bool = False				,						_A		:     float = 0.0				,						_A		:     Optional[List[str]] = None				,						_A		:     Optional[List[str]] = None				,						_A		:     str = "AdamWeightDecay"				,						**_A		:     Any				,						)		->   Optional[Any]:






       """simple docstring"""







       super().__init__(_A				,						_A				,						_A				,						_A				,						_A				,						_A				,						**_A				)
       lowercase :       Tuple       =			weight_decay_rate
       lowercase :       List[str]       =			include_in_weight_decay
       lowercase :       Optional[Any]       =			exclude_from_weight_decay






  @classmethod
  def  __a      (  cls		:     Tuple				,						_A		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       Optional[int]       =			{'''WarmUp''': WarmUp}
       return super(_A				,						cls				).from_config(_A				,						custom_objects=_A				)






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






       """simple docstring"""







       super(_A				,						self				)._prepare_local(_A				,						_A				,						_A				)
       lowercase :       List[Any]       =			tf.constant(
           self.weight_decay_rate				,						name='''adam_weight_decay_rate'''				)






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






       """simple docstring"""







       lowercase :       str       =			self._do_use_weight_decay(var.name				)
       if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']				,						use_locking=self._use_locking				,						)
       return tf.no_op()






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






       """simple docstring"""







       lowercase      ,	lowercase :       Tuple       =			list(zip(*_A				)				)
       return super(_A				,						self				).apply_gradients(zip(_A				,						_A				)				,						name=_A				,						**_A				)






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






       """simple docstring"""







       if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

       lowercase :       Any       =			apply_state or {}
       lowercase :       str       =			apply_state.get((var_device, var_dtype)				)
       if coefficients is None:
            lowercase :       List[Any]       =			self._fallback_apply_state(_A				,						_A				)
            lowercase :       List[Any]       =			coefficients

       return coefficients["lr_t"], {"apply_state": apply_state}






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






       """simple docstring"""







       lowercase      ,	lowercase :       List[str]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       Optional[Any]       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_dense(_A				,						_A				,						**_A				)






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






       """simple docstring"""







       lowercase      ,	lowercase :       Optional[int]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       str       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_sparse(_A				,						_A				,						_A				,						**_A				)






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






       """simple docstring"""







       lowercase :       Optional[Any]       =			super().get_config()
       config.update({'''weight_decay_rate''': self.weight_decay_rate}				)
       return config






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






       """simple docstring"""







       if self.weight_decay_rate == 0:
            return False

       if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return True

       if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return False
       return True


class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			[]
       lowercase :       str       =			None






  @property
  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







       if self._accum_steps is None:
            lowercase :       Optional[Any]       =			tf.Variable(
                tf.constant(0				,						dtype=tf.intaa				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)

       return self._accum_steps.value()






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






       """simple docstring"""







       if not self._gradients:
            raise ValueError('''The accumulator should be called first to initialize the gradients'''				)
       return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






  def __call__(  self		:     str				,						_A		:     int				)		->   str:






       """simple docstring"""







       if not self._gradients:
            lowercase :       Optional[Any]       =			self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(_A				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]				)
       if len(_A				) != len(self._gradients				):
            raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(_A				)}"""				)

       for accum_gradient, gradient in zip(self._gradients				,						_A				):
            if accum_gradient is not None and gradient is not None:
                 accum_gradient.assign_add(_A				)

       self._accum_steps.assign_add(1				)






  def  __a      (  self		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       if not self._gradients:
            return
       self._accum_steps.assign(0				)
       for gradient in self._gradients:
            if gradient is not None:
                 gradient.assign(tf.zeros_like(_A				)				)