

"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


__A	:       Any        =						'\\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	:       int        =						'\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


__A	:       Tuple        =						'\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\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'





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


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


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							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    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						str=None	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]="uniform_average"):
							A     =   mean_absolute_error(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	sample_weight=__SCREAMING_SNAKE_CASE	,	multioutput=__SCREAMING_SNAKE_CASE)

							return {"mae": mae_score}













'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import EfficientNetImageProcessor
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def __init__( self				:    List[str]			,   lowercase				:    Optional[Any]			,   lowercase				:    int=1_3			,   lowercase				:    List[Any]=3			,   lowercase				:    int=1_8			,   lowercase				:    List[Any]=3_0			,   lowercase				:    Optional[int]=4_0_0			,   lowercase				:    Any=True			,   lowercase				:    int=None			,   lowercase				:    Any=True			,   lowercase				:    Optional[int]=[0.5, 0.5, 0.5]			,   lowercase				:    List[Any]=[0.5, 0.5, 0.5]			,   )				->     List[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			size if size is not None else {"""height""": 1_8, """width""": 1_8}
									UpperCamelCase__       =			parent
									UpperCamelCase__       =			batch_size
									UpperCamelCase__       =			num_channels
									UpperCamelCase__       =			image_size
									UpperCamelCase__       =			min_resolution
									UpperCamelCase__       =			max_resolution
									UpperCamelCase__       =			do_resize
									UpperCamelCase__       =			size
									UpperCamelCase__       =			do_normalize
									UpperCamelCase__       =			image_mean
									UpperCamelCase__       =			image_std







			def 					A   ( self				:    Optional[Any]      )				->     Any:

									'''simple docstring'''







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}
@require_torch
@require_vision
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE							,unittest.TestCase						):


			'''simple docstring'''





			__a				:				List[Any]         =       EfficientNetImageProcessor if is_vision_available() else None






			def 					A   ( self				:    Optional[Any]      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			EfficientNetImageProcessorTester(self      )






			@property
			def 					A   ( self				:    Optional[int]      )				->     Optional[Any]:

									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()






			def 					A   ( self				:    Dict      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowercase			,   """image_mean"""      )      )
									self.assertTrue(hasattr(lowercase			,   """image_std"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_normalize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """do_resize"""      )      )
									self.assertTrue(hasattr(lowercase			,   """size"""      )      )






			def 					A   ( self				:    List[str]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,   {"""height""": 1_8, """width""": 1_8}      )

									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict			,   size=4_2      )
									self.assertEqual(image_processor.size			,   {"""height""": 4_2, """width""": 4_2}      )






			def 					A   ( self				:    Union[str, Any]      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   Image.Image      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )






			def 					A   ( self				:    Dict      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   numpify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   np.ndarray      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )







			def 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   torchify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   torch.Tensor      )

									# Test not batched input
									UpperCamelCase__       =			image_processing(image_inputs[0]			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   return_tensors="""pt"""      ).pixel_values
									self.assertEqual(
									    encoded_images.shape			,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )
