





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__							:      Dict         =    {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    str  		=					"""poolformer"""





   def __init__(			self       ,      snake_case=3       ,      snake_case=16       ,      snake_case=16       ,      snake_case=3       ,      snake_case=4.0       ,      snake_case=[2, 2, 6, 2]       ,      snake_case=[64, 128, 320, 512]       ,      snake_case=[7, 3, 3, 3]       ,      snake_case=[4, 2, 2, 2]       ,      snake_case=[2, 1, 1, 1]       ,      snake_case=4       ,      snake_case=0.0       ,      snake_case="gelu"       ,      snake_case=True       ,      snake_case=1E-5       ,      snake_case=0.02       ,      **snake_case       ,      )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Tuple   		=	num_channels
      a__						:    Optional[Any]   		=	patch_size
      a__						:    List[str]   		=	stride
      a__						:    Union[str, Any]   		=	padding
      a__						:    Any   		=	pool_size
      a__						:    Optional[int]   		=	hidden_sizes
      a__						:    Optional[int]   		=	mlp_ratio
      a__						:    Optional[Any]   		=	depths
      a__						:    Tuple   		=	patch_sizes
      a__						:    List[Any]   		=	strides
      a__						:    List[Any]   		=	num_encoder_blocks
      a__						:    int   		=	drop_path_rate
      a__						:    Optional[int]   		=	hidden_act
      a__						:    str   		=	use_layer_scale
      a__						:    Tuple   		=	layer_scale_init_value
      a__						:    Tuple   		=	initializer_range
      super().__init__(**snake_case   )







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Optional[int]  		=					version.parse("""1.11"""	)





   @property
   def 						_snake_case			(			self   )					->	Mapping[str, Mapping[int, str]]:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	float:
      """simple docstring"""





      return 2E-3








from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE__							:      List[str]         =    """\
@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}
}
"""

SCREAMING_SNAKE_CASE__							:      Tuple         =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







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





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(self._get_feature_types()   )       ,      reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]       ,      )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"   )   ),
             "references": datasets.Sequence(datasets.Value("float"   )   ),
         }
      else:
         return {
             "predictions": datasets.Value("float"   ),
             "references": datasets.Value("float"   ),
         }





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case="uniform_average"       ,      snake_case=True   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    List[str]   		=	mean_squared_error(
          snake_case       ,      snake_case       ,      sample_weight=snake_case       ,      multioutput=snake_case       ,      squared=snake_case   )

      return {"mse": mse}

