



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

			lowercase__					  =				parser.parse_args()

			return args



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_="cpu"  ):
			lowercase__					  =				model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_  )

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

			return huggingface_model, tokenizer



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			model.eval()

			lowercase__					  =				None
			lowercase__					  =				torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_  )  )

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

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

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

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

						lowercase__					  =				remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_  )  )

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

						lowercase__					  =				onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				ort_sess.run(
						    SCREAMING_SNAKE_CASE_						,  {
						        "input_ids": inputs["input_ids"].cpu().numpy(),
						        "attention_mask": inputs["attention_mask"].cpu().numpy(),
						        "num_beams": np.array(SCREAMING_SNAKE_CASE_  ),
						        "max_length": np.array(SCREAMING_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 (     ):
			lowercase__					  =				parse_args()
			lowercase__					  =				5
			lowercase__					  =				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()

			lowercase__					  =				torch.device(args.device  )

			lowercase__  ,       lowercase__					  =				load_model_tokenizer(args.model_name_or_path						,  SCREAMING_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(SCREAMING_SNAKE_CASE_  )

			if args.max_length:
						lowercase__					  =				args.max_length

			if args.num_beams:
						lowercase__					  =				args.num_beams

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

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


if __name__ == "__main__":
							main()









import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						Optional[Any],					__lowercase					:						str = "▁",					__lowercase					:						bool = True,					__lowercase					:						Union[str, AddedToken] = "<unk>",					__lowercase					:						Union[str, AddedToken] = "</s>",					__lowercase					:						Union[str, AddedToken] = "<pad>",					):
							lowercase__					  =				{
							    "pad": {"id": 0, "token": pad_token},
							    "eos": {"id": 1, "token": eos_token},
							    "unk": {"id": 2, "token": unk_token},
							}

							lowercase__					  =				[None] * len(self.special_tokens						)
							for token_dict in self.special_tokens.values():
										lowercase__					  =				token_dict["token"]

							lowercase__					  =				Tokenizer(Unigram()						)

							lowercase__					  =				normalizers.Sequence(
							    [
							        normalizers.Nmt(),
							        normalizers.NFKC(),
							        normalizers.Replace(Regex(" {2,}"						),					" "						),
							        normalizers.Lowercase(),
							    ]						)
							lowercase__					  =				pre_tokenizers.Sequence(
							    [
							        pre_tokenizers.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						),
							        pre_tokenizers.Digits(individual_digits=__lowercase						),
							        pre_tokenizers.Punctuation(),
							    ]						)
							lowercase__					  =				decoders.Metaspace(replacement=__lowercase,					add_prefix_space=__lowercase						)

							lowercase__					  =				TemplateProcessing(
							    single=F'''$A {self.special_tokens["eos"]["token"]}''',					special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],					)

							lowercase__					  =				{
							    "model": "SentencePieceUnigram",
							    "replacement": replacement,
							    "add_prefix_space": add_prefix_space,
							}

							super().__init__(__lowercase,					__lowercase						)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, List[str]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				[files]
							self._tokenizer.train(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[Iterator[str], Iterator[Iterator[str]]],					__lowercase					:						int = 8000,					__lowercase					:						bool = True,					):
							lowercase__					  =				trainers.UnigramTrainer(
							    vocab_size=__lowercase,					special_tokens=self.special_tokens_list,					show_progress=__lowercase,					)

							self._tokenizer.train_from_iterator(__lowercase,					trainer=__lowercase						)

							self.add_unk_id()




				def 							A__      (						self					:						str						):
							lowercase__					  =				json.loads(self._tokenizer.to_str()						)

							lowercase__					  =				self.special_tokens["unk"]["id"]

							lowercase__					  =				Tokenizer.from_str(json.dumps(__lowercase						)						)




