




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        _SCREAMING_SNAKE_CASE      (		)						->				Tuple:
						_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case="cpu"       )						->				Optional[int]:
						_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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Any:
						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=1_0_2_4			,     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=1_4			,     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        _SCREAMING_SNAKE_CASE      (		)						->				Union[str, Any]:
						_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()





from typing import Union

import fire
import torch
from tqdm import tqdm




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case = "cpu"			,     snake_case = None       )						->				None:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location=snake_case       )
						for k, v in tqdm(state_dict.items()       ):
												if not isinstance(snake_case			,     torch.Tensor       ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""       )
												_UpperCAmelCase      =			v.half()
						if save_path is None:  # overwrite src_path
												_UpperCAmelCase      =			src_path
						torch.save(snake_case			,     snake_case       )


if __name__ == "__main__":
				fire.Fire(convert)