

"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCAmelCase			=					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')







@dataclass
class   __lowercase      :
  snake_case_												=							field(
      default="""cifar10"""						,      metadata={"""help""": """Name of a dataset from the datasets package"""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """The column name of the images in the files."""}       )
  snake_case_												=							field(default=__lowerCamelCase						,      metadata={"""help""": """A folder containing the training data."""}       )
  snake_case_												=							field(default=__lowerCamelCase						,      metadata={"""help""": """A folder containing the validation data."""}       )
  snake_case_												=							field(
      default=0.15						,      metadata={"""help""": """Percent to split off of train for validation."""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }						,      )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }						,      )

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       {}
   if self.train_dir is not None:
    UpperCAmelCase__       :	str        =       self.train_dir
   if self.validation_dir is not None:
    UpperCAmelCase__       :	int        =       self.validation_dir
   UpperCAmelCase__       :	int        =       data_files if data_files else None







@dataclass
class   __lowercase      :
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }						,      )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={
          """help""": (
              """Override some existing default config settings when a model is trained from scratch. Example: """
              """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
          )
      }						,      )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}       )
  snake_case_												=							field(
      default="""main"""						,      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,      )
  snake_case_												=							field(default=__lowerCamelCase						,      metadata={"""help""": """Name or path of preprocessor config."""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }						,      )
  snake_case_												=							field(
      default=0.75						,      metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}       )
  snake_case_												=							field(
      default=__lowerCamelCase						,      metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}       )







@dataclass
class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							field(
      default=1e-3						,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}       )







def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	Union[str, Any]        =       torch.stack([example["""pixel_values"""] for example in examples]   )
 return {"pixel_values": pixel_values}
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[Any]        =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
 if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Dict        =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
 else:
  UpperCAmelCase__ ,   UpperCAmelCase__ ,   UpperCAmelCase__       :	Any        =       parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("""run_mae"""				,     __UpperCamelCase				,     __UpperCamelCase   )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,     datefmt="""%m/%d/%Y %H:%M:%S"""				,     handlers=[logging.StreamHandler(sys.stdout   )]				,     )

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 UpperCAmelCase__       :	List[Any]        =       training_args.get_process_log_level()
 logger.setLevel(__UpperCamelCase   )
 transformers.utils.logging.set_verbosity(__UpperCamelCase   )
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
     + F"distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"   )
 logger.info(F"Training/evaluation parameters {training_args}"   )

 # Detecting last checkpoint.
 UpperCAmelCase__       :	str        =       None
 if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCAmelCase__       :	Dict        =       get_last_checkpoint(training_args.output_dir   )
  if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
   raise ValueError(
       F"Output directory ({training_args.output_dir}) already exists and is not empty. "
       """Use --overwrite_output_dir to overcome."""   )
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
       """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Initialize our dataset.
 UpperCAmelCase__       :	int        =       load_dataset(
     data_args.dataset_name				,     data_args.dataset_config_name				,     data_files=data_args.data_files				,     cache_dir=model_args.cache_dir				,     use_auth_token=True if model_args.use_auth_token else None				,     )

 # If we don't have a validation split, split off a percentage of train as validation.
 UpperCAmelCase__       :	List[Any]        =       None if """validation""" in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split				,     __UpperCamelCase   ) and data_args.train_val_split > 0.0:
  UpperCAmelCase__       :	List[Any]        =       ds["""train"""].train_test_split(data_args.train_val_split   )
  UpperCAmelCase__       :	Optional[Any]        =       split["""train"""]
  UpperCAmelCase__       :	Tuple        =       split["""test"""]

 # Load pretrained model and image processor
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 UpperCAmelCase__       :	Optional[Any]        =       {
     """cache_dir""": model_args.cache_dir,
     """revision""": model_args.model_revision,
     """use_auth_token""": True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  UpperCAmelCase__       :	int        =       ViTMAEConfig.from_pretrained(model_args.config_name				,     **__UpperCamelCase   )
 elif model_args.model_name_or_path:
  UpperCAmelCase__       :	List[str]        =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,     **__UpperCamelCase   )
 else:
  UpperCAmelCase__       :	List[str]        =       ViTMAEConfig()
  logger.warning("""You are instantiating a new config instance from scratch."""   )
  if model_args.config_overrides is not None:
   logger.info(F"Overriding config: {model_args.config_overrides}"   )
   config.update_from_string(model_args.config_overrides   )
   logger.info(F"New config: {config}"   )

    # adapt config
 config.update(
     {
         """mask_ratio""": model_args.mask_ratio,
         """norm_pix_loss""": model_args.norm_pix_loss,
     }   )

 # create image processor
 if model_args.image_processor_name:
  UpperCAmelCase__       :	Any        =       ViTImageProcessor.from_pretrained(model_args.image_processor_name				,     **__UpperCamelCase   )
 elif model_args.model_name_or_path:
  UpperCAmelCase__       :	Dict        =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,     **__UpperCamelCase   )
 else:
  UpperCAmelCase__       :	Dict        =       ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  UpperCAmelCase__       :	Tuple        =       ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path				,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )				,     config=__UpperCamelCase				,     cache_dir=model_args.cache_dir				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     )
 else:
  logger.info("""Training new model from scratch"""   )
  UpperCAmelCase__       :	Any        =       ViTMAEForPreTraining(__UpperCamelCase   )

 if training_args.do_train:
  UpperCAmelCase__       :	int        =       ds["""train"""].column_names
 else:
  UpperCAmelCase__       :	int        =       ds["""validation"""].column_names

 if data_args.image_column_name is not None:
  UpperCAmelCase__       :	Union[str, Any]        =       data_args.image_column_name
 elif "image" in column_names:
  UpperCAmelCase__       :	Optional[Any]        =       """image"""
 elif "img" in column_names:
  UpperCAmelCase__       :	Dict        =       """img"""
 else:
  UpperCAmelCase__       :	Dict        =       column_names[0]

 # transformations as done in original MAE paper
 # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
 if "shortest_edge" in image_processor.size:
  UpperCAmelCase__       :	Optional[int]        =       image_processor.size["""shortest_edge"""]
 else:
  UpperCAmelCase__       :	List[str]        =       (image_processor.size["""height"""], image_processor.size["""width"""])
 UpperCAmelCase__       :	Dict        =       Compose(
     [
         Lambda(lambda __UpperCamelCase   : img.convert("""RGB"""   ) if img.mode != "RGB" else img   ),
         RandomResizedCrop(__UpperCamelCase				,     scale=(0.2, 1.0)				,     interpolation=InterpolationMode.BICUBIC   ),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean				,     std=image_processor.image_std   ),
     ]   )

 def preprocess_images(__UpperCamelCase   ):
  UpperCAmelCase__       :	Optional[int]        =       [transforms(__UpperCamelCase   ) for image in examples[image_column_name]]
  return examples

 if training_args.do_train:
  if "train" not in ds:
   raise ValueError("""--do_train requires a train dataset"""   )
  if data_args.max_train_samples is not None:
   UpperCAmelCase__       :	Dict        =       ds["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
  # Set the training transforms
  ds["train"].set_transform(__UpperCamelCase   )

 if training_args.do_eval:
  if "validation" not in ds:
   raise ValueError("""--do_eval requires a validation dataset"""   )
  if data_args.max_eval_samples is not None:
   UpperCAmelCase__       :	str        =       (
       ds["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
   )
  # Set the validation transforms
  ds["validation"].set_transform(__UpperCamelCase   )

 # Compute absolute learning rate
 UpperCAmelCase__       :	List[Any]        =       (
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  UpperCAmelCase__       :	str        =       training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 UpperCAmelCase__       :	int        =       Trainer(
     model=__UpperCamelCase				,     args=__UpperCamelCase				,     train_dataset=ds["""train"""] if training_args.do_train else None				,     eval_dataset=ds["""validation"""] if training_args.do_eval else None				,     tokenizer=__UpperCamelCase				,     data_collator=__UpperCamelCase				,     )

 # Training
 if training_args.do_train:
  UpperCAmelCase__       :	List[str]        =       None
  if training_args.resume_from_checkpoint is not None:
   UpperCAmelCase__       :	Optional[int]        =       training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   UpperCAmelCase__       :	Union[str, Any]        =       last_checkpoint
  UpperCAmelCase__       :	Dict        =       trainer.train(resume_from_checkpoint=__UpperCamelCase   )
  trainer.save_model()
  trainer.log_metrics("""train"""				,     train_result.metrics   )
  trainer.save_metrics("""train"""				,     train_result.metrics   )
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  UpperCAmelCase__       :	Any        =       trainer.evaluate()
  trainer.log_metrics("""eval"""				,     __UpperCamelCase   )
  trainer.save_metrics("""eval"""				,     __UpperCamelCase   )

 # Write model card and (optionally) push to hub
 UpperCAmelCase__       :	Union[str, Any]        =       {
     """tasks""": """masked-auto-encoding""",
     """dataset""": data_args.dataset_name,
     """tags""": ["""masked-auto-encoding"""],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**__UpperCamelCase   )
 else:
  trainer.create_model_card(**__UpperCamelCase   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 main()


if __name__ == "__main__":
      main()











'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCamelCase_		:     Tuple          =		'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

UpperCamelCase_		:     Optional[Any]          =		'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

UpperCamelCase_		:     Dict          =		'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''





def       __a      (  _UpperCamelCase:       Tuple   ,		_UpperCamelCase:       int       )		->					Optional[Any]:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       int="binary"       )		->					Any:

     """simple docstring"""




     _snake_case				= simple_accuracy(_UpperCamelCase   ,		_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average=_UpperCamelCase       )       )
     return {
         "accuracy": acc,
         "f1": fa,
     }





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       Any       )		->					Tuple:

     """simple docstring"""




     _snake_case				= {}
     for id_pred, label in zip(_UpperCamelCase   ,		_UpperCamelCase       ):
          _snake_case				= F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
          _snake_case				= id_pred["prediction"]
          if question_id in question_map:
               question_map[question_id].append((pred, label)       )
          else:
               _snake_case				= [(pred, label)]
     _snake_case				,   _snake_case				= [], []
     for question, preds_labels in question_map.items():
          _snake_case				,   _snake_case				= zip(*_UpperCamelCase       )
          _snake_case				= fa_score(y_true=_UpperCamelCase   ,		y_pred=_UpperCamelCase   ,		average="macro"       )
          fas.append(_UpperCamelCase       )
          _snake_case				= int(sum(pred == label for pred, label in preds_labels       ) == len(_UpperCamelCase       )       )
          ems.append(_UpperCamelCase       )
     _snake_case				= float(sum(_UpperCamelCase       ) / len(_UpperCamelCase       )       )
     _snake_case				= sum(_UpperCamelCase       ) / len(_UpperCamelCase       )
     _snake_case				= float(fa_score(y_true=_UpperCamelCase   ,		y_pred=[id_pred["prediction"] for id_pred in ids_preds]       )       )
     return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}





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






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )
          return datasets.MetricInfo(
              description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(self._get_feature_types()       )   ,codebase_urls=[]   ,reference_urls=[]   ,format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None   ,)






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          if self.config_name == "record":
               return {
                   "predictions": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "prediction_text": datasets.Value("string"       ),
                   },
                   "references": {
                       "idx": {
                           "passage": datasets.Value("int64"       ),
                           "query": datasets.Value("int64"       ),
                       },
                       "answers": datasets.Sequence(datasets.Value("string"       )       ),
                   },
               }
          elif self.config_name == "multirc":
               return {
                   "predictions": {
                       "idx": {
                           "answer": datasets.Value("int64"       ),
                           "paragraph": datasets.Value("int64"       ),
                           "question": datasets.Value("int64"       ),
                       },
                       "prediction": datasets.Value("int64"       ),
                   },
                   "references": datasets.Value("int64"       ),
               }
          else:
               return {
                   "predictions": datasets.Value("int64"       ),
                   "references": datasets.Value("int64"       ),
               }






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          if self.config_name == "axb":
               return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          elif self.config_name == "cb":
               return acc_and_fa(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,fa_avg="macro"       )
          elif self.config_name == "record":
               _snake_case				= [
                   {
                       "qas": [
                           {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                           for ref in references
                       ]
                   }
               ]
               _snake_case				= {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
               return evaluate_record(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )[0]
          elif self.config_name == "multirc":
               return evaluate_multirc(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
               return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )}
          else:
               raise KeyError(
                   "You should supply a configuration name selected in "
                   "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"       )


