





"""simple docstring"""
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,
)

lowerCamelCase_								=		logging.getLogger(__name__)

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



def      __lowerCamelCase   (							)			->      List[Any]:
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  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.'''							)

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

      return args



def      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		List[Any]="cpu"							)			->      str:
      __SCREAMING_SNAKE_CASE   :Tuple						=  model_dict[model_name].from_pretrained(a_							).to(a_							)
      __SCREAMING_SNAKE_CASE   :Any						=  tokenizer_dict[model_name].from_pretrained(a_							)

      if model_name in ["facebook/bart-base"]:
            __SCREAMING_SNAKE_CASE   :Dict						=  0
            __SCREAMING_SNAKE_CASE   :List[Any]						=  None
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  0

      return huggingface_model, tokenizer



def      __lowerCamelCase   (							a_     :		Dict ,   a_     :		List[Any] ,   a_     :		Tuple ,   a_     :		List[Any] ,   a_     :		Tuple							)			->      Optional[int]:
      model.eval()

      __SCREAMING_SNAKE_CASE   :int						=  None
      __SCREAMING_SNAKE_CASE   :Tuple						=  torch.jit.script(BARTBeamSearchGenerator(a_							)							)

      with torch.no_grad():
            __SCREAMING_SNAKE_CASE   :List[Any]						=  '''My friends are cool but they eat too many carbs.'''
            __SCREAMING_SNAKE_CASE   :Any						=  tokenizer([ARTICLE_TO_SUMMARIZE] ,   max_length=10_24 ,   return_tensors='''pt'''							).to(model.device							)

            __SCREAMING_SNAKE_CASE   :int						=  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_							)							)

            __SCREAMING_SNAKE_CASE   :str						=  remove_dup_initializers(os.path.abspath(a_							)							)

            logger.info('''Deduplicated and optimized model written to {}'''.format(a_							)							)

            __SCREAMING_SNAKE_CASE   :Optional[Any]						=  onnxruntime.InferenceSession(a_							)
            __SCREAMING_SNAKE_CASE   :Tuple						=  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      __lowerCamelCase   (							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :int						=  parse_args()
      __SCREAMING_SNAKE_CASE   :List[str]						=  5
      __SCREAMING_SNAKE_CASE   :str						=  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   :Union[str, Any]						=  torch.device(args.device							)

      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :int						=  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:
            __SCREAMING_SNAKE_CASE   :List[str]						=  args.max_length

      if args.num_beams:
            __SCREAMING_SNAKE_CASE   :Tuple						=  args.num_beams

      if args.output_file_path:
            __SCREAMING_SNAKE_CASE   :List[str]						=  args.output_file_path
      else:
            __SCREAMING_SNAKE_CASE   :List[str]						=  '''BART.onnx'''

      logger.info('''Exporting model to ONNX'''							)
      export_and_validate_model(a_ ,   a_ ,   a_ ,   a_ ,   a_							)


if __name__ == "__main__":
  main()






"""simple docstring"""
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''' ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42 ,   dataset_name='''my_dataset'''							)}							),
        SplitDict({'''train''': SplitInfo(name='''train''' ,   num_bytes=13_37 ,   num_examples=42							)}							),
        SplitDict({'''train''': SplitInfo()}							),
    ] ,   )
def      __lowerCamelCase   (							a_     :		SplitDict							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  split_dict._to_yaml_list()
      assert len(a_							) == len(a_							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  SplitDict._from_yaml_list(a_							)
      for split_name, split_info in split_dict.items():
            # dataset_name field is deprecated, and is therefore not part of the YAML dump
            __SCREAMING_SNAKE_CASE   :int						=  None
            # the split name of split_dict takes over the name of the split info object
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  split_name
      assert split_dict == reloaded







@pytest.mark.parametrize(
    '''split_info''' ,   [SplitInfo(), SplitInfo(dataset_name=a_							), SplitInfo(dataset_name='''my_dataset'''							)]							)
def      __lowerCamelCase   (							a_     :		Dict							)			->      Any:
      # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
      # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  asdict(SplitDict({'''train''': split_info}							)							)
      assert "dataset_name" in split_dict_asdict["train"]
      assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name