





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,
)

UpperCamelCase__											= logging.getLogger(__name__)

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





def     _UpperCamelCase     ():






							"""simple docstring"""





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

							UpperCamelCase__				     =			parser.parse_args()

							return args





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Any="cpu"       ):






							"""simple docstring"""





							UpperCamelCase__				     =			model_dict[model_name].from_pretrained(a__       ).to(a__       )
							UpperCamelCase__				     =			tokenizer_dict[model_name].from_pretrained(a__       )

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

							return huggingface_model, tokenizer





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :List[Any]   ,			a__   :Any   ,			a__   :Dict   ,			a__   :List[str]       ):






							"""simple docstring"""





							model.eval()

							UpperCamelCase__				     =			None
							UpperCamelCase__				     =			torch.jit.script(BARTBeamSearchGenerator(a__       )       )

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

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

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

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

														UpperCamelCase__				     =			remove_dup_initializers(os.path.abspath(a__       )       )

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

														UpperCamelCase__				     =			onnxruntime.InferenceSession(a__       )
														UpperCamelCase__				     =			ort_sess.run(
														    a__   ,			{
														        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
														        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
														        """num_beams""": np.array(a__       ),
														        """max_length""": np.array(a__       ),
														        """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     _UpperCamelCase     ():






							"""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   ,			a__       )

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

							model.to(a__       )

							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(a__   ,			a__   ,			a__   ,			a__   ,			a__       )


if __name__ == "__main__":
					main()








from typing import Any





def     _UpperCamelCase     (a__   :list       ):






							"""simple docstring"""





							if not input_list:
														return []
							UpperCamelCase__				     =			[input_list.count(a__       ) for value in input_list]
							UpperCamelCase__				     =			max(a__       )  # Gets the maximum count in the input list.
							# Gets values of modes
							return sorted({input_list[i] for i, value in enumerate(a__       ) if value == y}       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




