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     :  Optional[int]					     =       logging.getLogger(__name__)

A     :  Any					     =       {"facebook/bart-base": BartForConditionalGeneration}
A     :  Tuple					     =       {"facebook/bart-base": BartTokenizer}


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

						SCREAMING_SNAKE_CASE_          =  parser.parse_args()

						return args


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

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

						return huggingface_model, tokenizer


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
						model.eval()

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

						with torch.no_grad():
												SCREAMING_SNAKE_CASE_          =  "My friends are cool but they eat too many carbs."
												SCREAMING_SNAKE_CASE_          =  tokenizer([ARTICLE_TO_SUMMARIZE]     ,      max_length=1_0_2_4     ,      return_tensors="pt"				).to(model.device				)

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

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

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

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

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

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

						SCREAMING_SNAKE_CASE_          =  torch.device(args.device				)

						SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  load_model_tokenizer(args.model_name_or_path     ,      __UpperCamelCase				)

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

						model.to(__UpperCamelCase				)

						if args.max_length:
												SCREAMING_SNAKE_CASE_          =  args.max_length

						if args.num_beams:
												SCREAMING_SNAKE_CASE_          =  args.num_beams

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

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


if __name__ == "__main__":
						main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A     :  Dict					     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Optional[Any]					     =       ["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      A     :  Optional[int]					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


