



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,
)

_snake_case		=  logging.getLogger(__name__)

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






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    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     :							List[str]    =    parser.parse_args()

							return args






def 				A				(					_lowerCamelCase				, _lowerCamelCase="cpu"  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    model_dict[model_name].from_pretrained(_lowerCamelCase  ).to(_lowerCamelCase  )
							_lowerCAmelCase     :							List[Any]    =    tokenizer_dict[model_name].from_pretrained(_lowerCamelCase  )

							if model_name in ["facebook/bart-base"]:
														_lowerCAmelCase     :							int    =    0
														_lowerCAmelCase     :							int    =    None
														_lowerCAmelCase     :							Any    =    0

							return huggingface_model, tokenizer






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							model.eval()

							_lowerCAmelCase     :							Optional[int]    =    None
							_lowerCAmelCase     :							Optional[Any]    =    torch.jit.script(BARTBeamSearchGenerator(_lowerCamelCase  )  )

							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    "My friends are cool but they eat too many carbs."
														_lowerCAmelCase     :							List[Any]    =    tokenizer([ARTICLE_TO_SUMMARIZE]				, max_length=1_024				, return_tensors="pt"  ).to(model.device  )

														_lowerCAmelCase     :							Optional[Any]    =    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     :							Union[str, Any]    =    remove_dup_initializers(os.path.abspath(_lowerCamelCase  )  )

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

														_lowerCAmelCase     :							Optional[int]    =    onnxruntime.InferenceSession(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    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 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    parse_args()
							_lowerCAmelCase     :							Dict    =    5
							_lowerCAmelCase     :							Tuple    =    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     :							int    =    torch.device(args.device  )

							_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    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     :							List[Any]    =    args.max_length

							if args.num_beams:
														_lowerCAmelCase     :							Any    =    args.num_beams

							if args.output_file_path:
														_lowerCAmelCase     :							Union[str, Any]    =    args.output_file_path
							else:
														_lowerCAmelCase     :							Any    =    "BART.onnx"

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


if __name__ == "__main__":
						main()





def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if density <= 0:
														raise ValueError("Impossible fluid density"  )
							if bulk_modulus <= 0:
														raise ValueError("Impossible bulk modulus"  )

							return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()
