





from sklearn.metrics import mean_absolute_error

import datasets


UpperCAmelCase__  :	Tuple     ='''\
@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}
}
'''

UpperCAmelCase__  :	Optional[Any]     ='''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


UpperCAmelCase__  :	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.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

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

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''

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


				def _snake_case				(						self							):
								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_absolute_error.html"""
								    ]      ,       )


				def _snake_case				(						self							):
								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      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="uniform_average"							):
								lowerCamelCase		       =mean_absolute_error(UpperCAmelCase_      ,       UpperCAmelCase_      ,       sample_weight=UpperCAmelCase_      ,       multioutput=UpperCAmelCase_							)

								return {"mae": mae_score}









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_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =hidden_sizes
										_SCREAMING_SNAKE_CASE	     =neck_hidden_sizes
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =mlp_ratio
										_SCREAMING_SNAKE_CASE	     =expand_ratio
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =conv_kernel_size
										_SCREAMING_SNAKE_CASE	     =output_stride
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =classifier_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias

										# decode head attributes for semantic segmentation
										_SCREAMING_SNAKE_CASE	     =aspp_out_channels
										_SCREAMING_SNAKE_CASE	     =atrous_rates
										_SCREAMING_SNAKE_CASE	     =aspp_dropout_prob
										_SCREAMING_SNAKE_CASE	     =semantic_loss_ignore_index

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




						@property
						def 							__UpperCamelCase (				self    :					Union[str, Any]     )							->   Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase (				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 							__UpperCamelCase (				self    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4