


'''simple docstring'''






from sklearn.metrics import mean_squared_error

import datasets


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

a_   :      str				    =      "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


a_   :      Union[str, Any]				    =      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,			_KWARGS_DESCRIPTION    )
class 	a       (       datasets.Metric    ):
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      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 							__UpperCAmelCase						(	self  )   ->							List[str]:
      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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__=None ,			__magic_name__="uniform_average" ,			__magic_name__=True  )   ->							Any:
      _a      =							mean_squared_error(
          lowerCAmelCase__ ,			lowerCAmelCase__ ,			sample_weight=lowerCAmelCase__ ,			multioutput=lowerCAmelCase__ ,			squared=lowerCAmelCase__  )

      return {"mse": mse}








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

