



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,
)

lowercase_													=      logging.getLogger(__name__)

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






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

					__lowerCAmelCase						 =parser.parse_args()

					return args






def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     List[Any]="cpu"						)	->    Dict:
					__lowerCAmelCase						 =model_dict[model_name].from_pretrained(__lowerCamelCase						).to(__lowerCamelCase						)
					__lowerCAmelCase						 =tokenizer_dict[model_name].from_pretrained(__lowerCamelCase						)

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

					return huggingface_model, tokenizer






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Dict					, __lowerCamelCase						:     str						)	->    Dict:
					model.eval()

					__lowerCAmelCase						 =None
					__lowerCAmelCase						 =torch.jit.script(BARTBeamSearchGenerator(__lowerCamelCase						)						)

					with torch.no_grad():
										__lowerCAmelCase						 ="""My friends are cool but they eat too many carbs."""
										__lowerCAmelCase						 =tokenizer([ARTICLE_TO_SUMMARIZE]					, max_length=1024					, return_tensors="""pt"""						).to(model.device						)

										__lowerCAmelCase						 =model.generate(
										    inputs["""input_ids"""]					, attention_mask=inputs["""attention_mask"""]					, num_beams=__lowerCamelCase					, max_length=__lowerCamelCase					, early_stopping=__lowerCamelCase					, decoder_start_token_id=model.config.decoder_start_token_id					, )

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

										logger.info("""Model exported to {}""".format(__lowerCamelCase						)						)

										__lowerCAmelCase						 =remove_dup_initializers(os.path.abspath(__lowerCamelCase						)						)

										logger.info("""Deduplicated and optimized model written to {}""".format(__lowerCamelCase						)						)

										__lowerCAmelCase						 =onnxruntime.InferenceSession(__lowerCamelCase						)
										__lowerCAmelCase						 =ort_sess.run(
										    __lowerCamelCase					, {
										        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
										        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
										        """num_beams""": np.array(__lowerCamelCase						),
										        """max_length""": np.array(__lowerCamelCase						),
										        """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		(	)	->    Any:
					__lowerCAmelCase						 =parse_args()
					__lowerCAmelCase						 =5
					__lowerCAmelCase						 =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()

					__lowerCAmelCase						 =torch.device(args.device						)

					__lowerCAmelCase ,						__lowerCAmelCase						 =load_model_tokenizer(args.model_name_or_path					, __lowerCamelCase						)

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

					model.to(__lowerCamelCase						)

					if args.max_length:
										__lowerCAmelCase						 =args.max_length

					if args.num_beams:
										__lowerCAmelCase						 =args.num_beams

					if args.output_file_path:
										__lowerCAmelCase						 =args.output_file_path
					else:
										__lowerCAmelCase						 ="""BART.onnx"""

					logger.info("""Exporting model to ONNX"""						)
					export_and_validate_model(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)


if __name__ == "__main__":
				main()









def        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    bool:
					if number < 0:
										raise ValueError("""number must not be negative"""						)
					return number & (number - 1) == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




