'''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


__SCREAMING_SNAKE_CASE	:					Any					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					str					=  {
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  Any      =						"swiftformer"




      def __init__(	self						:    List[str]						, A						:    str=3						, A						:    str=[3, 3, 6, 4]						, A						:    str=[48, 56, 112, 220]						, A						:    Any=4						, A						:    Optional[int]=[True, True, True, True]						, A						:    Optional[int]="gelu"						, A						:    int=3						, A						:    str=2						, A						:    List[str]=1						, A						:    Union[str, Any]=0.0						, A						:    Dict=True						, A						:    Tuple=1E-5						, A						:    Tuple=1E-5						, **A						:    Union[str, Any]						, ):
             super().__init__(**A	)
             _UpperCAmelCase					:  List[str]											=   num_channels
             _UpperCAmelCase					:  List[Any]											=   depths
             _UpperCAmelCase					:  List[Any]											=   embed_dims
             _UpperCAmelCase					:  int											=   mlp_ratio
             _UpperCAmelCase					:  Any											=   downsamples
             _UpperCAmelCase					:  List[Any]											=   hidden_act
             _UpperCAmelCase					:  Optional[int]											=   down_patch_size
             _UpperCAmelCase					:  Tuple											=   down_stride
             _UpperCAmelCase					:  Dict											=   down_pad
             _UpperCAmelCase					:  List[str]											=   drop_path_rate
             _UpperCAmelCase					:  int											=   use_layer_scale
             _UpperCAmelCase					:  Union[str, Any]											=   layer_scale_init_value
             _UpperCAmelCase					:  List[str]											=   batch_norm_eps

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]      =						version.parse("1.11"   )




      @property
      def 					_A     (	self						:    List[Any]	):
             return OrderedDict(
                 [
                     ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                 ]	)





      @property
      def 					_A     (	self						:    int	):
             return 1E-4


'''simple docstring'''




from sklearn.metrics import accuracy_score

import datasets


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
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
"""


__SCREAMING_SNAKE_CASE	:					Any					=  """
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}
"""


__SCREAMING_SNAKE_CASE	:					Optional[int]					=  """
@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      lowerCamelCase_     (datasets.Metric   ):
      '''simple docstring'''

      def 					_A     (	self						:    int	):
             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 					_A     (	self						:    str						, A						:    List[str]						, A						:    int						, A						:    List[Any]=True						, A						:    List[str]=None	):
             return {
                 "accuracy": float(
                     accuracy_score(A						, A						, normalize=A						, sample_weight=A	)	)
             }

