"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        a				(		lowerCAmelCase_	,							lowerCAmelCase_   ):
    _snake_case     :  Union[str, Any]     							=							'nat'

    _snake_case     :  List[str]     							=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  int=4   ,				__lowerCAmelCase	:  Union[str, Any]=3   ,				__lowerCAmelCase	:  Dict=64   ,				__lowerCAmelCase	:  int=[3, 4, 6, 5]   ,				__lowerCAmelCase	:  List[str]=[2, 4, 8, 16]   ,				__lowerCAmelCase	:  Tuple=7   ,				__lowerCAmelCase	:  List[str]=3.0   ,				__lowerCAmelCase	:  int=True   ,				__lowerCAmelCase	:  int=0.0   ,				__lowerCAmelCase	:  Tuple=0.0   ,				__lowerCAmelCase	:  Any=0.1   ,				__lowerCAmelCase	:  List[str]="gelu"   ,				__lowerCAmelCase	:  List[Any]=0.02   ,				__lowerCAmelCase	:  Union[str, Any]=1e-5   ,				__lowerCAmelCase	:  List[Any]=0.0   ,				__lowerCAmelCase	:  Tuple=None   ,				__lowerCAmelCase	:  Optional[int]=None   ,				**__lowerCAmelCase	:  Optional[Any]   ,				):
      super().__init__(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      patch_size
      _UpperCAmelCase						 =      num_channels
      _UpperCAmelCase						 =      embed_dim
      _UpperCAmelCase						 =      depths
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      _UpperCAmelCase						 =      num_heads
      _UpperCAmelCase						 =      kernel_size
      _UpperCAmelCase						 =      mlp_ratio
      _UpperCAmelCase						 =      qkv_bias
      _UpperCAmelCase						 =      hidden_dropout_prob
      _UpperCAmelCase						 =      attention_probs_dropout_prob
      _UpperCAmelCase						 =      drop_path_rate
      _UpperCAmelCase						 =      hidden_act
      _UpperCAmelCase						 =      layer_norm_eps
      _UpperCAmelCase						 =      initializer_range
      # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _UpperCAmelCase						 =      int(embed_dim * 2 ** (len(__lowerCAmelCase					) - 1)					)
      _UpperCAmelCase						 =      layer_scale_init_value
      _UpperCAmelCase						 =      ["""stem"""] + [f'''stage{idx}''' for idx in range(1   ,				len(__lowerCAmelCase					) + 1					)]
      _UpperCAmelCase ,				_UpperCAmelCase						 =      get_aligned_output_features_output_indices(
          out_features=__lowerCAmelCase   ,				out_indices=__lowerCAmelCase   ,				stage_names=self.stage_names					)

"""simple docstring"""


import datasets


UpperCAmelCase__  		=						"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

UpperCAmelCase__  		=						"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

UpperCAmelCase__  		=						"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  return (preds == labels).mean()


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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      return datasets.MetricInfo(
          description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
              {
                  """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""					),
                  """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""					),
              }					)   ,				codebase_urls=[]   ,				reference_urls=[]   ,				format="""numpy"""   ,				)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Any					):
      return {"accuracy": simple_accuracy(__lowerCAmelCase   ,				__lowerCAmelCase					)}
