
"""simple docstring"""



from sklearn.metrics import mean_squared_error

import datasets


_snake_case          =						'''\
@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}
}
'''

_snake_case          =						'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


_snake_case          =						'''
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        _SCREAMING_SNAKE_CASE  (  datasets.Metric							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			str  )		->  Dict:







											"""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 				__lowerCamelCase	(					self	:			Any  )		->  Dict:







											"""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 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			str=None				,     UpperCAmelCase_	:			Union[str, Any]="uniform_average"				,     UpperCAmelCase_	:			Union[str, Any]=True  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			mean_squared_error(
											    UpperCAmelCase_				,     UpperCAmelCase_				,     sample_weight=UpperCAmelCase_				,     multioutput=UpperCAmelCase_				,     squared=UpperCAmelCase_  )

											return {"mse": mse}








"""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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""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	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






