code: 
  
  import sacrebleu as scb
  from packaging import version
  
  import datasets
  
  
  _lowerCAmelCase     :			Dict    ="""\
  @inproceedings{post-2018-call,
      title = \"A Call for Clarity in Reporting {BLEU} Scores\",
      author = \"Post, Matt\",
      booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
      month = oct,
      year = \"2018\",
      address = \"Belgium, Brussels\",
      publisher = \"Association for Computational Linguistics\",
      url = \"https://www.aclweb.org/anthology/W18-6319\",
      pages = \"186--191\",
  }
  """
  
  _lowerCAmelCase     :			List[Any]    ="""\
  SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
  Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
  It also knows all the standard test sets and handles downloading, processing, and tokenization for you.
  
  See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
  """
  
  _lowerCAmelCase     :			Tuple    ="""
  Produces BLEU scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
      references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
      smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
          - `'none'`: no smoothing
          - `'floor'`: increment zero counts
          - `'add-k'`: increment num/denom by k for n>1
          - `'exp'`: exponential decay
      smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
      tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
          - `'none'`: No tokenization.
          - `'zh'`: Chinese tokenization.
          - `'13a'`: mimics the `mteval-v13a` script from Moses.
          - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
          - `'char'`: Language-agnostic character-level tokenization.
          - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
      lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
      force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
      use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.
  
  Returns:
      'score': BLEU score,
      'counts': Counts,
      'totals': Totals,
      'precisions': Precisions,
      'bp': Brevity penalty,
      'sys_len': predictions length,
      'ref_len': reference length,
  
  Examples:
  
      Example 1:
          >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions, references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          100.0
  
      Example 2:
          >>> predictions = [\"hello there general kenobi\",
          ...                 \"on our way to ankh morpork\"]
          >>> references = [[\"hello there general kenobi\", \"hello there !\"],
          ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
          >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
          >>> results = sacrebleu.compute(predictions=predictions,
          ...                             references=references)
          >>> print(list(results.keys()))
          ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
          >>> print(round(results[\"score\"], 1))
          39.8
  """
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,_KWARGS_DESCRIPTION		)
  class __UpperCamelCase    (							datasets.Metric		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						if version.parse(scb.__version__	) < version.parse("1.4.12"	):
  							raise ImportWarning(
  							    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
  							    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
  						return datasets.MetricInfo(
  						    description=_DESCRIPTION							,			citation=_CITATION							,			homepage="https://github.com/mjpost/sacreBLEU"							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
  						        {
  						            "predictions": datasets.Value("string"							,			id="sequence"	),
  						            "references": datasets.Sequence(datasets.Value("string"							,			id="sequence"	)							,			id="references"	),
  						        }	)							,			codebase_urls=["https://github.com/mjpost/sacreBLEU"]							,			reference_urls=[
  						        "https://github.com/mjpost/sacreBLEU",
  						        "https://en.wikipedia.org/wiki/BLEU",
  						        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
  						    ]							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__="exp"							,			lowerCamelCase__=None							,			lowerCamelCase__=False							,			lowerCamelCase__=False							,			lowerCamelCase__=None							,			lowerCamelCase__=False							,			):
  						UpperCAmelCase__:  List[str]		       =   len(references[0]	)
  						if any(len(lowerCamelCase__	) != references_per_prediction for refs in references	):
  							raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
  						UpperCAmelCase__:  Tuple		       =   [[refs[i] for refs in references] for i in range(lowerCamelCase__	)]
  						UpperCAmelCase__:  int		       =   scb.corpus_bleu(
  						    lowerCamelCase__							,			lowerCamelCase__							,			smooth_method=lowerCamelCase__							,			smooth_value=lowerCamelCase__							,			force=lowerCamelCase__							,			lowercase=lowerCamelCase__							,			use_effective_order=lowerCamelCase__							,			**({"tokenize": tokenize} if tokenize else {})							,			)
  						UpperCAmelCase__:  Optional[Any]		       =   {
  						    "score": output.score,
  						    "counts": output.counts,
  						    "totals": output.totals,
  						    "precisions": output.precisions,
  						    "bp": output.bp,
  						    "sys_len": output.sys_len,
  						    "ref_len": output.ref_len,
  						}
  						return output_dict
style_context: 
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  _lowerCAmelCase     :			int    ="""1.5.0"""
  
  _lowerCAmelCase     :			Optional[Any]    =["""vision-encoder-decoder"""]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	if is_optimum_available():
  		from optimum.version import __version__ as optimum_version
  
  		UpperCAmelCase__:  Optional[int]		       =   version.parse(SCREAMING_SNAKE_CASE			)
  		if parsed_optimum_version < version.parse(SCREAMING_SNAKE_CASE			):
  			raise RuntimeError(
  			    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
  			    "can upgrade optimum by running: pip install -U optimum[exporters]"			)
  	else:
  		raise RuntimeError(
  		    "transformers.onnx requires optimum to run, you can install the library by running: pip install "
  		    "optimum[exporters]"			)
  	UpperCAmelCase__:  Any		       =   [
  	    sys.executable,
  	    "-m",
  	    "optimum.exporters.onnx",
  	    f"--model {args.model}",
  	    f"--task {args.feature}",
  	    f"--framework {args.framework}" if args.framework is not None else "",
  	    f"{args.output}",
  	]
  	UpperCAmelCase__:  str		       =   subprocess.Popen(" ".join(SCREAMING_SNAKE_CASE			)  ,stdout=subprocess.PIPE  ,shell=SCREAMING_SNAKE_CASE			)
  	proc.wait()
  
  	logger.info(
  	    "The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as "
  	    "transformers.onnx is deprecated, and will be removed in v5. You can find more information here: "
  	    "https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   args.output if args.output.is_file() else args.output.joinpath("model.onnx"			)
  	if not args.output.parent.exists():
  		args.output.parent.mkdir(parents=SCREAMING_SNAKE_CASE			)
  
  	# Allocate the model
  	UpperCAmelCase__:  int		       =   FeaturesManager.get_model_from_feature(
  	    args.feature  ,args.model  ,framework=args.framework  ,cache_dir=args.cache_dir			)
  
  	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE  ,feature=args.feature			)
  	UpperCAmelCase__:  str		       =   model_onnx_config(model.config			)
  
  	if model_kind in ENCODER_DECODER_MODELS:
  		UpperCAmelCase__:  List[str]		       =   model.get_encoder()
  		UpperCAmelCase__:  Optional[int]		       =   model.get_decoder()
  
  		UpperCAmelCase__:  List[str]		       =   onnx_config.get_encoder_config(encoder_model.config			)
  		UpperCAmelCase__:  Any		       =   onnx_config.get_decoder_config(
  		    encoder_model.config  ,decoder_model.config  ,feature=args.feature			)
  
  		if args.opset is None:
  			UpperCAmelCase__:  Tuple		       =   max(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)
  
  		if args.opset < min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			):
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
  			    f" {min(encoder_onnx_config.default_onnx_opset  ,decoder_onnx_config.default_onnx_opset			)} is required."			)
  
  		UpperCAmelCase__:  str		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("encoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,)
  
  		UpperCAmelCase__:  Dict		       =   AutoTokenizer.from_pretrained(args.model			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,)
  
  		validate_model_outputs(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output.parent.joinpath("decoder_model.onnx"			)  ,SCREAMING_SNAKE_CASE  ,args.atol if args.atol else decoder_onnx_config.atol_for_validation  ,)
  		logger.info(
  		    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"
  		    f" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"			)
  
  	else:
  		# Instantiate the appropriate preprocessor
  		if args.preprocessor == "auto":
  			UpperCAmelCase__:  str		       =   get_preprocessor(args.model			)
  		elif args.preprocessor == "tokenizer":
  			UpperCAmelCase__:  Any		       =   AutoTokenizer.from_pretrained(args.model			)
  		elif args.preprocessor == "image_processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoImageProcessor.from_pretrained(args.model			)
  		elif args.preprocessor == "feature_extractor":
  			UpperCAmelCase__:  int		       =   AutoFeatureExtractor.from_pretrained(args.model			)
  		elif args.preprocessor == "processor":
  			UpperCAmelCase__:  Optional[int]		       =   AutoProcessor.from_pretrained(args.model			)
  		else:
  			raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"			)
  
  		# Ensure the requested opset is sufficient
  		if args.opset is None:
  			UpperCAmelCase__:  List[str]		       =   onnx_config.default_onnx_opset
  
  		if args.opset < onnx_config.default_onnx_opset:
  			raise ValueError(
  			    f"Opset {args.opset} is not sufficient to export {model_kind}. "
  			    f"At least  {onnx_config.default_onnx_opset} is required."			)
  
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   export(
  		    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.opset  ,args.output  ,)
  
  		if args.atol is None:
  			UpperCAmelCase__:  List[Any]		       =   onnx_config.atol_for_validation
  
  		validate_model_outputs(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,args.output  ,SCREAMING_SNAKE_CASE  ,args.atol			)
  		logger.info(f"All good, model saved at: {args.output.as_posix()}"			)
  		warnings.warn(
  		    "The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"
  		    " using optimum.exporters.onnx in future. You can find more information here:"
  		    " https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."  ,SCREAMING_SNAKE_CASE  ,)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  str		       =   ArgumentParser("Hugging Face Transformers ONNX exporter"			)
  	parser.add_argument(
  	    "-m"  ,"--model"  ,type=SCREAMING_SNAKE_CASE  ,required=SCREAMING_SNAKE_CASE  ,help="Model ID on huggingface.co or path on disk to load model from."			)
  	parser.add_argument(
  	    "--feature"  ,default="default"  ,help="The type of features to export the model with."  ,)
  	parser.add_argument("--opset"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="ONNX opset version to export the model with."			)
  	parser.add_argument(
  	    "--atol"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Absolute difference tolerance when validating the model."			)
  	parser.add_argument(
  	    "--framework"  ,type=SCREAMING_SNAKE_CASE  ,choices=["pt", "tf"]  ,default=SCREAMING_SNAKE_CASE  ,help=(
  	        "The framework to use for the ONNX export."
  	        " If not provided, will attempt to use the local checkpoint's original framework"
  	        " or what is available in the environment."
  	    )  ,)
  	parser.add_argument("output"  ,type=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store generated ONNX model."			)
  	parser.add_argument("--cache_dir"  ,type=SCREAMING_SNAKE_CASE  ,default=SCREAMING_SNAKE_CASE  ,help="Path indicating where to store cache."			)
  	parser.add_argument(
  	    "--preprocessor"  ,type=SCREAMING_SNAKE_CASE  ,choices=["auto", "tokenizer", "feature_extractor", "image_processor", "processor"]  ,default="auto"  ,help="Which type of preprocessor to use. 'auto' tries to automatically detect it."  ,)
  	parser.add_argument(
  	    "--export_with_transformers"  ,action="store_true"  ,help=(
  	        "Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be "
  	        "useful when exporting a model supported in transformers but not in optimum, otherwise it is not "
  	        "recommended."
  	    )  ,)
  
  	UpperCAmelCase__:  Optional[int]		       =   parser.parse_args()
  	if args.export_with_transformers or not is_optimum_available():
  		export_with_transformers(SCREAMING_SNAKE_CASE			)
  	else:
  		export_with_optimum(SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			int    =logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  					logger.setLevel(logging.INFO)
  					main()