
"""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"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



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


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






