






'''simple docstring'''





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


__A				:				Optional[Any]					=		"1.5.0"

__A				:				Union[str, Any]					=		["vision-encoder-decoder"]






def  lowerCamelCase_  (     lowercase__):





 if is_optimum_available():
  from optimum.version import __version__ as optimum_version

  lowerCamelCase__	=  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]")
 lowerCamelCase__	=  [
     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}''',
 ]
 lowerCamelCase__	=  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  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  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
 lowerCamelCase__	=  FeaturesManager.get_model_from_feature(
     args.feature					,  args.model					,  framework=args.framework					,  cache_dir=args.cache_dir)

 lowerCamelCase__	=  FeaturesManager.check_supported_model_or_raise(SCREAMING_SNAKE_CASE_					,  feature=args.feature)
 lowerCamelCase__	=  model_onnx_config(model.config)

 if model_kind in ENCODER_DECODER_MODELS:
  lowerCamelCase__	=  model.get_encoder()
  lowerCamelCase__	=  model.get_decoder()

  lowerCamelCase__	=  onnx_config.get_encoder_config(encoder_model.config)
  lowerCamelCase__	=  onnx_config.get_decoder_config(
      encoder_model.config					,  decoder_model.config					,  feature=args.feature)

  if args.opset is None:
   lowerCamelCase__	=  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.''')

  lowerCamelCase__	=  AutoFeatureExtractor.from_pretrained(args.model)

  lowerCamelCase__	=  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					,  )

  lowerCamelCase__	=  AutoTokenizer.from_pretrained(args.model)

  lowerCamelCase__	=  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":
   lowerCamelCase__	=  get_preprocessor(args.model)
  elif args.preprocessor == "tokenizer":
   lowerCamelCase__	=  AutoTokenizer.from_pretrained(args.model)
  elif args.preprocessor == "image_processor":
   lowerCamelCase__	=  AutoImageProcessor.from_pretrained(args.model)
  elif args.preprocessor == "feature_extractor":
   lowerCamelCase__	=  AutoFeatureExtractor.from_pretrained(args.model)
  elif args.preprocessor == "processor":
   lowerCamelCase__	=  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:
   lowerCamelCase__	=  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.''')

  lowerCamelCase__	=  export(
      SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  args.opset					,  args.output					,  )

  if args.atol is None:
   lowerCamelCase__	=  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  lowerCamelCase_  (     ):
 lowerCamelCase__	=  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."
     )					,  )

 lowerCamelCase__	=  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__":
 __A				:				Any					=		logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
 logger.setLevel(logging.INFO)
 main()





from typing import List

from packaging import version
from sklearn.metrics import fa_score

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


# TODO(Patrick/Anton)
__UpperCAmelCase			:			Union[str, Any]			      =					"\\n"

__UpperCAmelCase			:			Optional[Any]			      =					"\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n"

__UpperCAmelCase			:			str			      =					"\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing 'bleu'.\n        Keywords include Dict can be one of 'smooth_method', 'smooth_value', 'force', 'lowercase',\n        'tokenize', 'use_effective_order'.\n    wer_kwargs: optional dict of keywords to be passed when computing 'wer' and 'cer'.\n        Keywords include 'concatenate_texts'.\nReturns: depending on the XTREME-S task, one or several of:\n    \"accuracy\": Accuracy - for 'fleurs-lang_id', 'minds14'\n    \"f1\": F1 score - for 'minds14'\n    \"wer\": Word error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"cer\": Character error rate - for 'mls', 'fleurs-asr', 'voxpopuli', 'babel'\n    \"bleu\": BLEU score according to the `sacrebleu` metric - for 'covost2'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'mls')  # 'mls', 'voxpopuli', 'fleurs-asr' or 'babel'\n    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]\n    >>> predictions = [\"it's sunny\", \"paper pen are essential\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'wer': 0.56, 'cer': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'covost2')\n    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]\n    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'bleu': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'fleurs-lang_id')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'accuracy': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric('xtreme_s', 'minds14')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {'f1': 0.58, 'accuracy': 0.6}\n"

__UpperCAmelCase			:			Union[str, Any]			      =					["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__UpperCAmelCase			:			List[str]			      =					""

try:
  from jiwer import transforms as tr

  __UpperCAmelCase			:			Optional[int]			      =					True
except ImportError:
  __UpperCAmelCase			:			str			      =					False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):



  class  UpperCAmelCase_						(				tr.AbstractTransform):
         '''simple docstring'''






         def __init__(  self			,       __SCREAMING_SNAKE_CASE = " "    ):




                """simple docstring"""




                UpperCamelCase     :						List[str] 						=		sentence_delimiter






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                return list(__SCREAMING_SNAKE_CASE    )






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                UpperCamelCase     :						Dict 						=		[]
                for sent_idx, sentence in enumerate(__SCREAMING_SNAKE_CASE    ):
                       chars.extend(self.process_string(__SCREAMING_SNAKE_CASE    )    )
                       if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__SCREAMING_SNAKE_CASE    ) - 1:
                              chars.append(self.sentence_delimiter    )
                return chars




  __UpperCAmelCase			:			Optional[Any]			      =					tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
elif _jiwer_available:
  __UpperCAmelCase			:			int			      =					tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )
else:
  __UpperCAmelCase			:			List[Any]			      =					None


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





       return float((preds == labels).mean()							)


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       return {
           "f1": float(fa_score(y_true=SCREAMING_SNAKE_CASE_				,      y_pred=SCREAMING_SNAKE_CASE_				,      average='''macro'''							)							),
           "accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							),
       }


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]="exp"				,      SCREAMING_SNAKE_CASE_   :		Any=None				,      SCREAMING_SNAKE_CASE_   :		Any=False				,      SCREAMING_SNAKE_CASE_   :		List[str]=False				,      SCREAMING_SNAKE_CASE_   :		Dict=None				,      SCREAMING_SNAKE_CASE_   :		Tuple=False				,      ):






       """simple docstring"""





       UpperCamelCase     :						str 						=		[[label] for label in labels]
       UpperCamelCase     :						int 						=		list(SCREAMING_SNAKE_CASE_							)
       try:
              import sacrebleu as scb
       except ImportError:
              raise ValueError(
                  '''sacrebleu has to be installed in order to apply the bleu metric for covost2.'''
                  '''You can install it via `pip install sacrebleu`.'''							)

       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"`.'''							)

       UpperCamelCase     :						Optional[int] 						=		len(labels[0]							)
       if any(len(SCREAMING_SNAKE_CASE_							) != references_per_prediction for refs in labels							):
              raise ValueError('''Sacrebleu requires the same number of references for each prediction'''							)
       UpperCamelCase     :						Optional[Any] 						=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE_							)]
       UpperCamelCase     :						Optional[Any] 						=		scb.corpus_bleu(
           SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      smooth_method=SCREAMING_SNAKE_CASE_				,      smooth_value=SCREAMING_SNAKE_CASE_				,      force=SCREAMING_SNAKE_CASE_				,      lowercase=SCREAMING_SNAKE_CASE_				,      use_effective_order=SCREAMING_SNAKE_CASE_				,      **({'''tokenize''': tokenize} if tokenize else {})				,      )
       return {"bleu": output.score}


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """simple docstring"""





       try:
              from jiwer import compute_measures
       except ImportError:
              raise ValueError(
                  F"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
                  '''You can install it via `pip install jiwer`.'''							)

       if concatenate_texts:
              UpperCamelCase     :						List[Any] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)['''wer''']

              UpperCamelCase     :						List[str] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      truth_transform=SCREAMING_SNAKE_CASE_				,      hypothesis_transform=SCREAMING_SNAKE_CASE_							)['''wer''']
              return {"wer": wer, "cer": cer}
       else:

              def compute_score(SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]="wer"							):
                     UpperCamelCase     :						int 						=		0
                     UpperCamelCase     :						Union[str, Any] 						=		0
                     for prediction, reference in zip(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							):
                            if score_type == "wer":
                                   UpperCamelCase     :						Optional[int] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)
                            elif score_type == "cer":
                                   UpperCamelCase     :						Union[str, Any] 						=		compute_measures(
                                       SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      truth_transform=SCREAMING_SNAKE_CASE_				,      hypothesis_transform=SCREAMING_SNAKE_CASE_							)
                            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                            total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                     return incorrect / total

              return {"wer": compute_score(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      '''wer'''							), "cer": compute_score(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      '''cer'''							)}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,  _KWARGS_DESCRIPTION)
class  UpperCAmelCase_						(				datasets.Metric):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              if self.config_name not in _CONFIG_NAMES:
                     raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""    )

              UpperCamelCase     :						Optional[Any] 						=		'''int64''' if self.config_name in ['''fleurs-lang_id''', '''minds14'''] else '''string'''

              return datasets.MetricInfo(
                  description=_DESCRIPTION			,       citation=_CITATION			,       inputs_description=_KWARGS_DESCRIPTION			,       features=datasets.Features(
                      {'''predictions''': datasets.Value(__SCREAMING_SNAKE_CASE    ), '''references''': datasets.Value(__SCREAMING_SNAKE_CASE    )}    )			,       codebase_urls=[]			,       reference_urls=[]			,       format='''numpy'''			,       )






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		bleu_kwargs if bleu_kwargs is not None else {}
              UpperCamelCase     :						Dict 						=		wer_kwargs if wer_kwargs is not None else {}

              if self.config_name == "fleurs-lang_id":
                     return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )}
              elif self.config_name == "minds14":
                     return fa_and_simple_accuracy(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )
              elif self.config_name == "covost2":
                     UpperCamelCase     :						List[str] 						=		bleu_kwargs.pop('''smooth_method'''			,       '''exp'''    )
                     UpperCamelCase     :						Tuple 						=		bleu_kwargs.pop('''smooth_value'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		bleu_kwargs.pop('''force'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Union[str, Any] 						=		bleu_kwargs.pop('''lowercase'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[Any] 						=		bleu_kwargs.pop('''tokenize'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		bleu_kwargs.pop('''use_effective_order'''			,       __SCREAMING_SNAKE_CASE    )
                     return bleu(
                         preds=__SCREAMING_SNAKE_CASE			,       labels=__SCREAMING_SNAKE_CASE			,       smooth_method=__SCREAMING_SNAKE_CASE			,       smooth_value=__SCREAMING_SNAKE_CASE			,       force=__SCREAMING_SNAKE_CASE			,       lowercase=__SCREAMING_SNAKE_CASE			,       tokenize=__SCREAMING_SNAKE_CASE			,       use_effective_order=__SCREAMING_SNAKE_CASE			,       )
              elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
                     UpperCamelCase     :						List[Any] 						=		wer_kwargs.pop('''concatenate_texts'''			,       __SCREAMING_SNAKE_CASE    )
                     return wer_and_cer(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       self.config_name    )
              else:
                     raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""    )

