




"""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,
)

SCREAMING_SNAKE_CASE_	     =       logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_	     =       {'facebook/bart-base': BartForConditionalGeneration}
SCREAMING_SNAKE_CASE_	     =       {'facebook/bart-base': BartTokenizer}





def      __snake_case (				):


    """simple docstring"""







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

    UpperCamelCase              =	parser.parse_args()

    return args





def      __snake_case (				_lowercase			,_lowercase="cpu"    ):


    """simple docstring"""







    UpperCamelCase              =	model_dict[model_name].from_pretrained(_lowercase    ).to(_lowercase    )
    UpperCamelCase              =	tokenizer_dict[model_name].from_pretrained(_lowercase    )

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

    return huggingface_model, tokenizer





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase    ):


    """simple docstring"""







    model.eval()

    UpperCamelCase              =	None
    UpperCamelCase              =	torch.jit.script(BARTBeamSearchGenerator(_lowercase    )    )

    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=_lowercase			,max_length=_lowercase			,early_stopping=_lowercase			,decoder_start_token_id=model.config.decoder_start_token_id			,)

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

        logger.info('''Model exported to {}'''.format(_lowercase    )    )

        UpperCamelCase              =	remove_dup_initializers(os.path.abspath(_lowercase    )    )

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

        UpperCamelCase              =	onnxruntime.InferenceSession(_lowercase    )
        UpperCamelCase              =	ort_sess.run(
            _lowercase			,{
                '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                '''num_beams''': np.array(_lowercase    ),
                '''max_length''': np.array(_lowercase    ),
                '''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      __snake_case (				):


    """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			,_lowercase    )

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

    model.to(_lowercase    )

    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(_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase    )


if __name__ == "__main__":
  main()






'''simple docstring'''






from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata


A__							:  Dict    =''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):




      class 					UpperCAmelCase	(     tr.AbstractTransform  ):




            def __init__(     self     :					Any     ,     __snake_case     :					str = " "    )							-> Any:
                  _lowerCAmelCase					=				sentence_delimiter




            def 							lowercase__       (     self     :					str     ,     __snake_case     :					str    )							-> Optional[int]:
                  return list(__snake_case    )




            def 							lowercase__       (     self     :					Tuple     ,     __snake_case     :					List[str]    )							-> Union[str, Any]:
                  _lowerCAmelCase					=				[]
                  for sent_idx, sentence in enumerate(__snake_case    ):
                        chars.extend(self.process_string(__snake_case    )    )
                        if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__snake_case    ) - 1:
                              chars.append(self.sentence_delimiter    )
                  return chars




      A__							:  Optional[Any]    =tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      A__							:  Tuple    =tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


A__							:  List[str]    ='''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

A__							:  Optional[int]    ='''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

A__							:  List[str]    ='''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,						_KWARGS_DESCRIPTION  )
class 					UpperCAmelCase	(     datasets.Metric  ):




      def 							lowercase__       (     self     :					Any    )							-> Dict:
            return datasets.MetricInfo(
                description=_DESCRIPTION     ,     citation=_CITATION     ,     inputs_description=_KWARGS_DESCRIPTION     ,     features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""string"""     ,     id="""sequence"""    ),
                        """references""": datasets.Value("""string"""     ,     id="""sequence"""    ),
                    }    )     ,     codebase_urls=["""https://github.com/jitsi/jiwer/"""]     ,     reference_urls=[
                    """https://en.wikipedia.org/wiki/Word_error_rate""",
                    """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
                ]     ,     )




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					List[str]     ,     __snake_case     :					Tuple=False    )							-> List[Any]:
            if concatenate_texts:
                  return jiwer.compute_measures(
                      __snake_case     ,     __snake_case     ,     truth_transform=__snake_case     ,     hypothesis_transform=__snake_case     ,     )["wer"]

            _lowerCAmelCase					=				0
            _lowerCAmelCase					=				0
            for prediction, reference in zip(__snake_case     ,     __snake_case    ):
                  _lowerCAmelCase					=				jiwer.compute_measures(
                      __snake_case     ,     __snake_case     ,     truth_transform=__snake_case     ,     hypothesis_transform=__snake_case     ,     )
                  incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                  total += measures["substitutions"] + measures["deletions"] + measures["hits"]

            return incorrect / total






