
"""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,
)

_a														=		logging.getLogger(__name__)

_a														=		{"""facebook/bart-base""": BartForConditionalGeneration}
_a														=		{"""facebook/bart-base""": BartTokenizer}



def 				lowerCamelCase__		(      )							->		str:


					"""simple docstring"""





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

					_UpperCamelCase										=  parser.parse_args()

					return args



def 				lowerCamelCase__		(      __snake_case,					__snake_case="cpu"     )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  model_dict[model_name].from_pretrained(__snake_case     ).to(__snake_case     )
					_UpperCamelCase										=  tokenizer_dict[model_name].from_pretrained(__snake_case     )

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

					return huggingface_model, tokenizer



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		List[Any]:


					"""simple docstring"""





					model.eval()

					_UpperCamelCase										=  None
					_UpperCamelCase										=  torch.jit.script(BARTBeamSearchGenerator(__snake_case     )     )

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

										_UpperCamelCase										=  model.generate(
										    inputs['''input_ids'''],					attention_mask=inputs['''attention_mask'''],					num_beams=__snake_case,					max_length=__snake_case,					early_stopping=__snake_case,					decoder_start_token_id=model.config.decoder_start_token_id,					)

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

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

										_UpperCamelCase										=  remove_dup_initializers(os.path.abspath(__snake_case     )     )

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

										_UpperCamelCase										=  onnxruntime.InferenceSession(__snake_case     )
										_UpperCamelCase										=  ort_sess.run(
										    __snake_case,					{
										        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
										        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
										        '''num_beams''': np.array(__snake_case     ),
										        '''max_length''': np.array(__snake_case     ),
										        '''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__		(      )							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase										=  parse_args()
					_UpperCamelCase										=  5
					_UpperCamelCase										=  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										=  torch.device(args.device     )

					_UpperCamelCase			,  _UpperCamelCase										=  load_model_tokenizer(args.model_name_or_path,					__snake_case     )

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

					model.to(__snake_case     )

					if args.max_length:
										_UpperCamelCase										=  args.max_length

					if args.num_beams:
										_UpperCamelCase										=  args.num_beams

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

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


if __name__ == "__main__":
				main()








"""simple docstring"""





class    _UpperCAmelCase:



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






							'''simple docstring'''
							_UpperCamelCase										=  name
							_UpperCamelCase										=  value
							_UpperCamelCase										=  weight



		def __repr__( self)	->	List[str]:






							'''simple docstring'''
							return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							return self.value



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return self.name



		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.weight




		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.value / self.weight





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  []
					for i in range(len(__snake_case     )     ):
										menu.append(Things(name[i],					value[i],					weight[i]     )     )
					return menu



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase										=  sorted(__snake_case,					key=__snake_case,					reverse=__snake_case     )
					_UpperCamelCase										=  []
					_UpperCamelCase			,  _UpperCamelCase										=  0.0, 0.0
					for i in range(len(__snake_case     )     ):
										if (total_cost + items_copy[i].get_weight()) <= max_cost:
															result.append(items_copy[i]     )
															total_cost += items_copy[i].get_weight()
															total_value += items_copy[i].get_value()
					return (result, total_value)



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""







if __name__ == "__main__":
				import doctest

				doctest.testmod()






