


'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
}







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							'mobilevitv2'


     def __init__(      self ,    lowerCamelCase=3 ,    lowerCamelCase=256 ,    lowerCamelCase=2 ,    lowerCamelCase=2.0 ,    lowerCamelCase="swish" ,    lowerCamelCase=3 ,    lowerCamelCase=32 ,    lowerCamelCase=0.1 ,    lowerCamelCase=0.02 ,    lowerCamelCase=1E-5 ,    lowerCamelCase=512 ,    lowerCamelCase=[6, 12, 18] ,    lowerCamelCase=0.1 ,    lowerCamelCase=255 ,    lowerCamelCase=[2, 4, 3] ,    lowerCamelCase=[128, 192, 256] ,    lowerCamelCase=1.0 ,    lowerCamelCase=2 ,    lowerCamelCase=0.0 ,    lowerCamelCase=0.0 ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)

          __A  :					str						     =       num_channels
          __A  :					Dict						     =       image_size
          __A  :					Tuple						     =       patch_size
          __A  :					Union[str, Any]						     =       expand_ratio
          __A  :					Dict						     =       hidden_act
          __A  :					Union[str, Any]						     =       conv_kernel_size
          __A  :					Tuple						     =       output_stride
          __A  :					Optional[int]						     =       initializer_range
          __A  :					Dict						     =       layer_norm_eps
          __A  :					Dict						     =       n_attn_blocks
          __A  :					Any						     =       base_attn_unit_dims
          __A  :					str						     =       width_multiplier
          __A  :					List[str]						     =       ffn_multiplier
          __A  :					int						     =       ffn_dropout
          __A  :					List[str]						     =       attn_dropout
          __A  :					Union[str, Any]						     =       classifier_dropout_prob

          # decode head attributes for semantic segmentation
          __A  :					Dict						     =       aspp_out_channels
          __A  :					int						     =       atrous_rates
          __A  :					int						     =       aspp_dropout_prob
          __A  :					Optional[int]						     =       semantic_loss_ignore_index







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							version.parse('1.11'   )


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          if self.task == "image-classification":
               return OrderedDict([("logits", {0: "batch"})]		)
          else:
               return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return 1E-4





'''simple docstring'''


from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase          =     """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


_UpperCamelCase          =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_UpperCamelCase          =     """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""







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

     """simple docstring"""






     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return datasets.MetricInfo(
              description=_DESCRIPTION ,    citation=_CITATION ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
                  {
                      "predictions": datasets.Sequence(datasets.Value("int32"		)		),
                      "references": datasets.Sequence(datasets.Value("int32"		)		),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "predictions": datasets.Value("int32"		),
                      "references": datasets.Value("int32"		),
                  }		) ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"] ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=True ,    lowerCamelCase=None		):


          '''simple docstring'''





          return {
              "accuracy": float(
                  accuracy_score(lowerCamelCase ,    lowerCamelCase ,    normalize=lowerCamelCase ,    sample_weight=lowerCamelCase		)		)
          }

